Global Financial Stability Report, October 2008 
World Economic and Financial Surveys

Global Financial Stability Report

Financial Stress and Deleveraging

Macrofinancial Implications and Policy

October 2008

International Monetary Fund

Washington DC
© 2008 International Monetary Fund

Production: IMF Multimedia Services Division

Cover: Jorge Salazar

Figures: Theodore F. Peters, Jr., 

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Global financial stability report — Washington, DC : 

International Monetary Fund, 2002–

v. ; cm. — (World economic and financial surveys)

Semiannual

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2. International finance — Periodicals. 3. Economic 

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iii

Preface ix

Executive Summary xi

Chapter 1. Assessing Risks to Global Financial Stability 1

Global Financial Stability Map 2

The Default Cycle 10

Financial System Deleveraging 18

Systemic Implications 32

Emerging Market Resilience Is Being Tested 44

Financial Stability Policies 49

Annex 1.1. Global Financial Stability Map: Construction and Methodology 56

Annex 1.2. Financial Investment in Commodities Markets 62

Annex 1.3. Loss Estimates on U.S. Credit Instruments 66

Annex 1.4. Factors Infl uencing the Pace and Level of Bank Capital Rebuilding 69

References 71

Chapter 2. Stress in Bank Funding Markets and Implications for Monetary Policy 73

The Microstructure of Bank Funding Markets 74

The Causes of Elevated Interbank Spreads 78

Implications for the Interest Rate Transmission Mechanism of Monetary Policy 85

Policy Recommendations 96

Conclusions 100

Annex 2.1. Empirical Framework: The Causes of High Interbank Spreads 100

Annex 2.2. Empirical Framework: Monetary Transmission 103

References 105

Chapter 3. Fair Value Accounting and Procyclicality 109

Fair Value Accounting Through the Business Cycle 110

Modeling Fair Value Accounting Through the Business Cycle Using Simulations 117

Conclusions and Policy Recommendations 127

Annex 3.1. Data and Modeling Assumptions 131

References 133

Chapter 4. Spillovers to Emerging Equity Markets 135

Performance of Emerging Market Equity Markets 136

Cross-Country Equity Price Correlations 138

Determinants of Emerging Market Equity Prices 140

Spillovers and Their Impact 144

The Role of Local Institutional Investors 145

CONTENTS
CONTENTS

iv

Key Results and Conclusions 145

Annex 4.1. Panel Estimation Specifi cation and Results 152

Annex 4.2. Vector Autoregression Model Results 155

References 155

Glossary 157

Annex: Summing Up by the Acting Chair 163

Statistical Appendix 167

Boxes

 1.1 Recent Financial Market Developments 6

 1.2 Measuring Capital Adequacy 21

 1.3 Global Bank Writedowns and Capital-Raising 23

 1.4 U.S. Government-Sponsored Enterprises and Housing Reform Developments 34

 1.5 Impact of Credit Market Turmoil on Hedge Funds 41

 1.6 Forecasting Loan Charge-Off Rates 68

 2.1 Pricing and Hedging Role of Interbank Deposit-Related Derivatives 75

 2.2 Is the LIBOR Fix Broken? 77

 2.3 The Federal Reserve’s Term Auction Facility 82

 2.4 Breakdown of the Financial Sector for Monetary Transmission Analysis 88

 3.1 Off-Balance-Sheet Entities and Procyclicality 111

 3.2 Disclosures Recommended by the Financial Stability Forum 113

 3.3 Dealing with Procyclicality in the Basel II Framework 116

 3.4 Options Surrounding the Application of Fair Value Accounting to 

Mitigate Procyclicality 119

 4.1 Is There a Stock Market Wealth Effect in Emerging Markets? 146

 4.2 The Role of Emerging Market Institutional Investors in Emerging Market Equities 148

Tables

 1.1 Estimates of Financial Sector Potential Writedowns 15

 1.2 Estimates of Potential Losses on Loans 17

 1.3 European and U.S. Public Securitization 29

 1.4 Sensitivity of Deleveraging to Public Sector Support 32

 1.5 Macro and Financial Indicators in Selected Emerging Market Countries 46

 1.6 Changes in Risks and Conditions Since the April 2008 Global Financial 

Stability Report 57

 1.7 Asset Class Characteristics 63

 1.8 Test for Causality Between Commodities Prices and Financial Positions 66

 1.9 Comparison of Financial Sector Loss Estimates, October 2008 67

1.10 Deleveraging Illustration: Key Assumptions 70

 2.1 List of Restrictions Used in the Structural Vector Autoregression for 

Each LIBOR and Euribor Spread 84

 2.2 Static Vector Error Correction Mechanism (2, 3) Estimation with 

Variable Controls: United States 92
CONTENTS

v

 2.3 Static Vector Error Correction Mechanism (2, 3) Estimation with Variable 

Controls: Euro Area 93

 2.4 List of Variables Used in the Vector Autoregressions 102

 3.1 Balance Sheet of Representative U.S. and European Financial Institutions 118

 3.2 Parameter Values for Each Simulation 120

 3.3 Equity-to-Assets Ratio Through the Business Cycle 122

 3.4 Application of Fair Value by U.S. and European Banks, 2007 123

 4.1 Emerging Equity Market Peaks and Troughs: Current and Previous Episodes 139

 4.2 Fixed-Effects Panel Least-Squares Estimation of the Determinants of Emerging 

Market Equity Prices—Monthly Observations (January 2001–May 2008), 

30 Countries, First Specifi cation 142

 4.3 Fixed-Effects Panel Least-Squares Estimation of the Determinants of Emerging 

Market Equity Prices—Monthly Observations (January 2001–May 2008), 

30 Countries, Second Specifi cation 143

 4.4 Unit Root Tests 153

 4.5 Pedroni Heterogeneous Panel Cointegration Tests 153

Figures

 1.1 Global Financial Stability Map 2

 1.2 Heat Map: Developments in Systemic Asset Classes 3

 1.3 Systemic Bank Default Risk 4

 1.4 Asset Price Volatility and Funding and Market Liquidity 5

 1.5 Allocation to Global and Emerging Market Risk Assets 5

 1.6 U.S. Loan Charge-Off Rates 11

 1.7 U.S. Households’ Balance Sheets: Net Worth 11

 1.8 U.S. Mortgage Delinquencies by Vintage Year 12

 1.9 Prices of U.S. Mortgage-Related Securities 13

1.10 U.S. Residential Real Estate Loan Charge-Off Rates 13

1.11 U.S. Consumer Loan Charge-Off Rates 14

1.12 Macroeconomic and Corporate Indicators and Default Rates 14

1.13 Comparison of Financial Crises 16

1.14 Financial Sector Losses 17

1.15 Ratio of Household Debt to Gross Disposable Income 18

1.16 U.K. Mortgage Foreclosures 19

1.17 Net Acquisition of Financial Assets by U.S. Financial Firms 20

1.18 Bank Ratios 20

1.19 U.S. Banks’ Price-to-Book Ratios and Risk Exposures 26

1.20 European Banks’ Price-to-Book Ratios and European Real Estate Prices 27

1.21 U.K. Banks’ Price-to-Book Ratios 28

1.22 AA Rated Bank Bond Index Spreads Relative to Government Bonds 28

1.23 European Banks’ Cross-Border Liabilities, end-2007 29

1.24 Net Cross-Border U.S. Dollar Claims of European Banks 29

1.25 Cross-Currency Swaps with U.S. Dollar 30

1.26 Private Sector Credit Growth 30

1.27 Euro Area Financial Institution Lending for House Purchase 31

1.28 Market Capitalization and Equity Book Values of Select Financial Institutions 33
CONTENTS

vi

1.29 Probability of Default Based on Equity Option Prices 38

1.30 U.S. Equity Index Performance 39

1.31 Total Assets on Federal Reserve’s Balance Sheet 44

1.32 Potential U.S. Commitments and Mortgage Markets 44

1.33 Sovereign Credit Default Swap Spreads 45

1.34 Net Foreign Equity Investment in Emerging Economies 45

1.35 Emerging Market External and U.S. High-Grade Corporate Spreads 47

1.36 Onshore Emerging Market Dollar Interest Rates 47

1.37 Credit Default Swap Spreads on Selected Emerging Market Banks, 

January 2007–Early October 2008 48

1.38 Real Policy Rates: Latest Levels and Changes from end-2008 48

1.39 Break-Even Infl ation Rates 49

1.40 Baltic States: Real Bank Loan Growth to Nonfi nancial Private Sector 49

1.41 Global Financial Stability Map: Monetary and Financial Conditions 58

1.42 Global Financial Stability Map: Risk Appetite 59

1.43 Global Financial Stability Map: Macroeconomic Risks 60

1.44 Global Financial Stability Map: Emerging Market Risks 61

1.45 Global Financial Stability Map: Credit Risks 62

1.46 Global Financial Stability Map: Market and Liquidity Risks 63

1.47 Commodity Futures Prices and Financial Positions 65

 2.1 Unsecured European Bank Borrowing Volumes 76

 2.2 Spread Between Three-Month Uncollateralized Interbank Rates and 

Overnight Index Swaps 79

 2.3 Joint Probability of Distress: Selected Financial Institutions Participating in 

Interbank Panels 80

 2.4 Three-Month Forex Swap Spreads 81

 2.5 Structured VAR Model: Variance Decomposition of LIBOR/Euribor Minus 

Overnight Index Swap (OIS) Spread 85

 2.6 Selected Countries: Size of Financial Assets 87

 2.7 United States: Structural Changes in Financial Sector Liabilities 87

 2.8 United States: Selected Interest Rate Spreads 89

 2.9 Euro Area: Selected Interest Rates Spreads 90

2.10 Dynamic Vector Error Correction Mechanism (VECM) (2,3) Estimation of 

the U.S. Fed Funds Rate and Market Rates—United States 94

2.11 Dynamic Vector Error Correction Mechanism (VECM) (2,3) Estimation of 

the EONIA Rate and Market Rates—Euro Area 95

2.12 Summary Chart: Accuracy of Forecasts—U.S. Model, 1996–2008 96

2.13 Summary Chart: Accuracy of Forecasts—U.S. Model, 2006–08 97

2.14 Summary Chart: Accuracy of Forecasts—Euro Area Model, 2006–08 98

2.15 Decomposition of Spread Between Three-Month U.S. Dollar LIBOR and 

Overnight Index Swaps 103

 3.1 Selected U.S.-Based Financial Institutions: Change in Level 1, 2, and 3 Assets 114

 3.2 Aggregate Fair Value Hierarchy, end-2007 115

 3.3 Simulation of Full Fair Value 121

 3.4 Simulation of Full Fair Value: Changes in Funding Conditions and Financial 

Market Distress 123
CONTENTS

vii

The following symbols have been used throughout this volume:

. . . to indicate that data are not available;

— to indicate that the fi gure is zero or less than half the fi nal digit shown, or that 

the item does not exist;

– between years or months (for example, 1997–99 or January–June) to indicate the 

years or months covered, including the beginning and ending years or months;

/ between years (for example, 1998/99) to indicate a fi scal or fi nancial year.

“Billion” means a thousand million; “trillion” means a thousand billion.

“Basis points” refer to hundredths of 1 percentage point (for example, 25 basis points 

are equivalent to !/4 of 1 percentage point).

“n.a.” means not applicable.

Minor discrepancies between constituent fi gures and totals are due to rounding.

As used in this volume the term “country” does not in all cases refer to a territorial 

entity that is a state as understood by international law and practice. As used here, the 

term also covers some territorial entities that are not states but for which statistical 

data are maintained on a separate and independent basis.

 3.5 Simulation of Full Fair Value: International versus Retail-Oriented Banks 124

 3.6 Simulation of Partial Fair Value 125

 3.7 Simulation of Smoothing Techniques 126

 3.8 Yield Curves and Business Cycles 127

 3.9 Simulation of Full Fair Value with Upward Sloping Yield Curve 128

 4.1 Selected Equity Market Indices 136

 4.2 Emerging Market Economies: Composition of Capital Infl ows 136

 4.3 Current Account Balances and Capital Flows from a Global Perspective 137

 4.4 Stock Market Capitalization 137

 4.5 Total Equity Market Returns 137

 4.6 Price/Earnings Ratios, July 31, 2008 137

 4.7 Total Foreign Holdings of Equity 138

 4.8 Emerging Markets Equity Indices and Foreign Investor Presence 138

 4.9 Implied Correlations from Dynamic Conditional Correlation GARCH Specifi cation 140

4.10 Emerging Market Countries: Implied Correlations from Dynamic 

Conditional Correlation GARCH Specifi cation 141

4.11 Equity Price Response to Global Excess Liquidity Increase 154

4.12 Equity Price Response to Credit Risk Premium Increase 154

4.13 Equity Price Response to Market Risk Premium Increase 154

ix

The Global Financial Stability Report (GFSR) assesses key issues in global fi nancial market developments with a view to identifying systemic vulnerabilities. By calling attention to fault lines in the global 

fi nancial system, the report generally seeks to play a role in preventing crises and, when they occur, 

helping to mitigate their effects and offer policy advice, thereby contributing to global fi nancial stability and to sustained economic growth of the IMF’s member countries.

The analysis in this report has been coordinated in the Monetary and Capital Markets (MCM) 

Department under the general direction of Jaime Caruana, Counsellor and Director. The project 

has been directed by MCM staff Jan Brockmeijer, Deputy Director; Peter Dattels and Laura Kodres, 

Division Chiefs; and Brenda González-Hermosillo and L. Effi e Psalida, Deputy Division Chiefs. It has 

benefi ted from comments and suggestions from Jonathan Fiechter and Christopher Towe, both Deputy 

Directors, and Mahmood Pradhan, Assistant Director.

Primary contributors to this report also include Sergei Antoshin, Elie Canetti, Sean Craig, Phil de 

Imus, Kristian Hartelius, Heiko Hesse, Andy Jobst, John Kiff, Rebecca McCaughrin, Paul Mills, Ken 

Miyajima, Christopher Morris, Alicia Novoa, Mustafa Saiyid, Jodi Scarlata, Miguel Segoviano, Seiichi 

Shimizu, Juan Solé, Mark Stone, Tao Sun, Rupert Thorne, and Christopher Walker. Other contributors include Ana Carvajal, Antonio Garcia-Pascual, Geoff Heenan, Xiongtao Huang, Michael Moore, 

Aditya Narain, Silvia Ramirez, and André Santos. Kenneth Sullivan provided consultancy support and 

Vance Martin provided empirical support. Martin Edmonds, Oksana Khadarina, Yoon Sook Kim, Alin 

Miresteam, Jean Salvati, Xiaobo Shao, Narayan Suryakumar, and Kalin Tintchev provided analytical 

support. Caroline Bagworth, Shannon Bui, Christy Gray, and Aster Teklemariam were responsible for 

word processing. David Einhorn of the External Relations Department edited the manuscript and 

coordinated production of the publication.

This particular issue draws, in part, on a series of discussions with accountancies, banks, securities 

fi rms, asset management companies, hedge funds, auditors, standard setters, fi nancial consultants, and 

academic researchers, as well as regulatory and other public authorities in major fi nancial centers and 

countries. The report refl ects information available up to September 25, 2008.

The report benefi ted from comments and suggestions from staff in other IMF departments, as well 

as from Executive Directors following their discussion of the Global Financial Stability Report on September 15, 2008. However, the analysis and policy considerations are those of the contributing staff and 

should not be attributed to the Executive Directors, their national authorities, or the IMF.

PREFACE

xi

Confi dence in global fi nancial institutions 

and markets has been badly shaken. 

Threats to systemic stability became 

manifest in September with the collapse or 

near-collapse of several key institutions. The 

October 2008 World Economic Outlook notes that 

the strains affl icting the global fi nancial system 

are expected to deepen the downturn in global 

growth and restrain the recovery. Moreover, 

the risk of a more severe adverse feedback loop 

between the fi nancial system and the broader 

economy represents a critical threat. The 

combination of mounting losses, falling asset 

prices, and a deepening economic downturn, 

has caused serious doubts about the viability 

of a widening swath of the fi nancial system. 

The ongoing deleveraging process outlined 

in the April 2008 GFSR has accelerated and 

become disorderly—marked by a rapid decline 

in fi nancial institutions’ share prices, higher 

costs of funding and credit default protection, 

and depressed asset prices. One result has 

been sudden failures of institutions as markets 

have become unwilling (or unable) to provide 

capital and funding or absorb assets. Piecemeal 

interventions to address the attendant liquidity 

strains and resolve the troubled institutions did 

not succeed in restoring market confi dence, as 

they have not addressed the widespread nature 

of the underlying problems. The intensifying 

worries about counterparty risks have created a 

near lock-up of global money markets. Chapter 

1 provides the basis for a more comprehensive 

policy approach—as is now being considered in 

some countries. It evaluates how far the deleveraging process has progressed and how much lies 

ahead. It also suggests a comprehensive set of 

measures that could arrest the currently destructive process. 

Restoration of fi nancial stability would now 

benefi t from a publicly-stated collective commitment by the authorities of the affected countries 

to address the issue in a consistent and coherent 

manner. While the precise measures will inevitably differ across countries, experience from 

earlier crises indicates that fi ve principles could 

serve to guide the scope and design of measures 

that could form the basis for a restoration of 

confi dence in these exceptional circumstances. 

These include:

(1) Employ measures that are comprehensive, 

timely, and clearly communicated. They should 

encompass the principal challenges arising from 

the strains of deleveraging: namely, improving 

funding availability, cost, and maturity to stabilize 

balance sheets; injecting capital to support viable 

institutions with sound underpinnings that are 

currently unable to provide adequate credit; and 

EXECUTIVE SUMMARY

With fi nancial markets worldwide facing growing turmoil, internationally 

coherent and decisive policy measures will be required to restore confi dence in 

the global fi nancial system. Failure to do so could usher in a period in which 

the ongoing deleveraging process becomes increasingly disorderly and costly for 

the real economy. In any case, the process of restoring an orderly system will be 

challenging, as a signifi cant deleveraging is both necessary and inevitable. It is 

against this challenging and still evolving backdrop that the Global Financial 

Stability Report (GFSR) frames the recent events to suggest potential policy 

measures that could be helpful in the current circumstances. 
EXECUTIVE SUMMARY

xii

buttressing troubled assets by using public sector 

balance sheets to promote orderly deleveraging. In applying existing or new regulations, 

authorities should avoid exacerbating procyclical 

effects. The objectives of the measures should be 

clear and operational procedures transparent.

(2) Aim for a consistent and coherent set 

of policies across countries to stabilize the 

global fi nancial system in order to maximize 

impact while avoiding adverse effects on other 

countries.

(3) Ensure rapid response on the basis of 

early detection of strains. This requires a high 

degree of coordination within each country, and 

in many cases across borders, and a framework 

that allows for decisive action by potentially different sets of authorities.

(4) Assure that emergency government interventions are temporary and taxpayer interests 

are protected. Accountability of government 

actions is important for all stakeholders and the 

conditions for support should include private 

participation in downside risks and taxpayer 

participation in upside benefi ts. Intervention 

mechanisms should minimize moral hazard, 

while recognizing the exigency of the situation 

and the evident need for public support. 

(5) Pursue the medium-term objective of a 

more sound, competitive, and effi cient fi nancial 

system. Achieving this objective requires both 

an orderly resolution of nonviable fi nancial 

institutions and a strengthening of the international macrofi nancial stability framework to 

help improve supervision and regulation at the 

domestic and global levels, as well as mechanisms to improve the effectiveness of market 

discipline. Funding and securitization markets 

critical to pricing and intermediating credit 

should be strengthened, including by reducing 

counterparty risks through centralized clearing 

organizations.

While satisfying these guiding principles, 

concrete actions are needed to tackle three 

interrelated areas associated with deleveraging: 

insuffi cient capital, falling and uncertain asset 

valuations, and dysfunctional funding markets. 

Arresting the spiraling interaction between these 

three elements is essential if there is to be a 

more orderly deleveraging process. 

Capital. To keep private sector credit growing, 

even modestly, while strengthening bank capital 

ratios, the GFSR estimates some $675 billion in 

capital would be needed by the major global 

banks over the next several years. Several measures could be considered:

• With many financial institutions finding it 

much more difficult to raise private capital 

at the present time, the authorities may need 

to inject capital into viable institutions. While 

there are many ways to accomplish this, it 

is preferable that the scheme provide some 

upside for the taxpayer, coupled with incentives for existing and new private shareholders 

to provide new capital.

• Though politically difficult, orderly resolution 

of nonviable banks would demonstrate a commitment to a competitive and well-capitalized 

banking system. 

Assets. As private sector balance sheets shed 

assets to reduce leverage, the use of public 

sector balance sheets can help prevent “fi resale” liquidations that threaten to reduce bank 

capital.

• Countries whose banks have large exposures to securitized or problem assets could 

consider mechanisms for the government 

to purchase or provide long-term funding 

for such assets. This should create greater 

certainty about balance sheet health. Setting 

up an asset management company provides a 

framework of legal clarity and accountability 

for the process. 

• Allowing for a greater degree of judgment in 

the application of mark-to-market rules may 

avoid accelerating capital needs by reducing 

the pressure to value securities at low “firesale” prices. Such judgment would require 

close supervision and should be accompanied 

by appropriate disclosure in order to avoid 

undermining confidence in balance sheets of 

existing institutions.

Funding. Financial institutions that rely on 

wholesale funding, especially in cross-border 

markets, have faced severe and mounting refi -
EXECUTIVE SUMMARY

xiii

nance risks. Central banks therefore are exploring more ways to extend term fi nancing to meet 

funding needs of institutions. The measures 

described above to boost capital and underpin 

asset valuations, as well as those already undertaken to provide liquidity, should provide essential support for the markets to function properly 

and confi dence to be reestablished. Continued 

progress on reducing counterparty risks, including centralized clearing and settlement arrangements, will also help. But experience in past 

crises indicates that in some circumstances additional measures may be needed. Under extreme 

circumstances:

• Deposit insurance of individual retail accounts 

could be expanded beyond normal limits. 

However, expansion of deposit insurance 

limits or, if conditions deteriorate further, use 

of a blanket guarantee should only be undertaken as a temporary, emergency measure and 

is best undertaken in a coordinated fashion 

across countries.

• Guarantees could cover senior and subordinated debt liabilities for a temporary period 

of time. Ideally, these types of guarantees 

should include some cost to the institutions 

receiving coverage, such as a usage fee, fitness 

test, or other criteria. 

While these measures represent a broad 

approach, some of the specifi cs have already 

been put in place by various authorities, and 

there are encouraging signs that more are 

being considered. Other positive developments 

include the resolve and determination of the 

authorities to act decisively; the signifi cant 

balance sheet adjustments already under way; 

and an openness to revisit the global regulatory 

framework. This opens a window of opportunity 

to better align regulation and incentives in various jurisdictions in the medium run. For now, 

however, the principal focus will remain that of 

containing existing disruptive forces.

Chapter 1

Against this backdrop, Chapter 1 of the GFSR

assesses the extent of further losses faced by 

global institutions. It measures the reduction 

in leverage needed in the fi nancial system, 

estimates the amount of assets that need to be 

shed, and calculates the amount of capital to 

be raised. This analysis concludes that public 

resources will be needed to ensure a return to 

fi nancial stability and a more orderly deleveraging process that avoids a severe credit crunch. 

The most signifi cant risk remains the intensifi cation of the adverse feedback loop between the 

fi nancial system and the real economy.

Because the United States remains the 

epicenter of the fi nancial crisis, Chapter 1 

examines U.S. prospects in some detail. The 

continuing decline in the U.S. housing market 

and wider economic slowdown is contributing 

to new loan deterioration—delinquencies on 

prime mortgages and commercial real estate 

as well as corporate and consumer loans are 

increasing. With default rates yet to peak and 

the recent heightened market distress, declared 

losses on U.S. loans and securitized assets are 

likely to increase further to about $1.4 trillion, signifi cantly higher than the estimate 

in the April 2008 GFSR. With the economic 

slowdown spreading, fi nancial institutions will 

increasingly face losses on non-U.S. assets as 

well. In some European countries, too, these 

diffi culties are being accentuated by weakening 

local housing markets.

Financial institutions had been raising capital 

to bolster their balance sheets and these efforts 

were initially successful, but now the prospects 

for further issuance are more limited and more 

expensive, refl ecting weaker confi dence in the 

underlying viability of institutions. As a result, 

Chapter 1 suggests that the deleveraging in the 

banking sector will take place along multiple 

dimensions: requiring asset sales, slower new 

asset growth, and radical changes to banks’ 

business models as many previous sources of 

revenue have nearly disappeared. A similar deleveraging process is under way for many nonbanks, such as hedge funds, where the ability 

to use margin fi nancing and private repurchase 

(repo) markets to take leveraged positions has 

been severely curtailed. Strains in funding 
EXECUTIVE SUMMARY

xiv

markets have increased redemptions in money 

market mutual funds and exacerbated rollover 

risks for corporate borrowers. The far-reaching nature of the events that are unfolding is 

illustrated by the fact that within a period of 

one week, large stand-alone investment banks 

disappeared from the U.S. fi nancial landscape. 

While the long-run implications are not certain, 

fi nancial sectors are likely to consolidate, new 

business models will need to be found, and 

fi rms will operate with less leverage in the foreseeable future.

The ongoing uncertainty surrounding valuation of what were once thought to be low-risk 

assets has led to diffi culties in judging capital 

adequacy. Chapter 1 observes that most market 

participants, rating agencies, and regulators 

agree that capital buffers will need to be higher 

than previously thought. Moreover, they should 

be based on a forward-looking analysis of risk, 

rather than a mechanical application of regulatory ratios. To the extent that the move to 

permanently higher capital ratios is mandated, 

the ratios should be phased in so that their 

attainment does not amplify the existing cyclical 

downturn. Though achieving higher levels will 

further slow the restoration of normal credit 

conditions, the process should be under way by 

late 2009 to put fi nancial institutions in a better 

position to support the recovery. 

Whereas emerging markets overall had initially remained fairly resilient to global fi nancial turmoil, they have recently come under 

increasing pressure. The cost and availability 

of fi nancing have become more diffi cult and 

equity markets have corrected sharply, albeit 

from elevated levels. Capital outfl ows have 

intensifi ed, leading to tighter international and, 

in some cases, domestic liquidity conditions. 

Borrowers and fi nancial institutions in emerging 

markets will be confronted with a more trying 

macroeconomic environment. Policymakers, 

too, face challenges as global growth slows and 

the lagged pass-through of domestic infl ationary pressures continues—and all this against the 

backdrop of lower confi dence and the reversal 

of earlier fl ows into these markets. There is an 

important risk that such a confl uence of circumstances could accelerate a downturn in the 

domestic credit cycle in some emerging market 

economies. 

Chapter 1 also lays out some more specifi c 

policy implications for public authorities 

than those presented above, building on the 

analysis in the chapter and conclusions of 

previous GFSRs. Although the focus has been 

on what the public sector should do, private 

sector fi nancial institutions continue to play 

a crucial role in identifying and rectifying 

defi ciencies in order to place fi nancial intermediation on a more sound footing. The key 

elements, which will need to be reinforced 

through support from regulators and supervisors, are:

• Maintain an orderly deleveraging process. 

Financial institutions should, first, focus on 

strengthening their balance sheets—preferably by attracting new capital rather than 

selling assets; and second, ensure adequate 

funding sources consistent with their business 

model.

• Strengthen risk management systems. As part 

of overall risk management improvements, 

firms should endeavor to better align compensation packages to reward returns on 

a risk-adjusted basis using more robust 

risk management practices, with greater 

emphasis on the long-term component of 

compensation. 

• Improve valuation techniques and reporting.

Implementation of new Financial Stability 

Forum (FSF) disclosure guidelines and frequent asset valuations and timely disclosures 

will reduce uncertainty and are important 

steps that can help provide information about 

the health of counterparties.

• Develop better clearing and settlement mechanisms 

for over-the-counter products. Private sector 

efforts to build clearing and settlement 

facilities to lower counterparty risks should 

continue apace, particularly for the credit 

default swaps market, where settlement issues 

need to be addressed urgently. Higher capital 

charges for counterparty exposures would 
EXECUTIVE SUMMARY

xv

help and are being considered by various 

regulators.

Chapter 2

The combination of liquidity and solvency 

risks has led to a period of elevated short-term 

interest rate spreads and substantially reduced 

transaction volumes, with funding markets 

remaining stressed for an unprecedented 

period. Chapter 2 delves into the ongoing 

inability of the bank funding markets to perform their role in distributing liquidity across 

banks and near-banks and the consequences 

for the interest rate channel of monetary policy 

transmission. 

The chapter fi rst notes that the short-term 

rates-setting procedures, including for London Interbank Offer Rates (LIBOR) and the 

Euribor rate, are not broken, but improvements are desirable, since the LIBOR rates are 

estimated to underpin some $400 trillion of 

fi nancial derivatives contracts. Although most 

of the analysis in the chapter preceded the 

most recent steep rise in LIBOR rates, the basic 

recommendations remain intact. In examining 

the reasons for the elevated spreads between 

the LIBOR and the overnight index swap (OIS) 

market, the chapter confi rms that default 

concerns became the overriding component of 

the U.S. dollar LIBOR-OIS spread starting in 

early 2008. In addition, foreign currency swap 

spreads explain the Euribor-OIS and sterling 

LIBOR-OIS spreads, signifying that U.S. dollar 

liquidity pressures are spilling over into these 

other currencies. 

The chapter also examines how the interest 

rate channel of monetary policy transmission 

has been affected by the crisis, in light of three 

longer-term trends: increased growth of activity 

in near-banks, more extensive use of wholesale 

funding markets, and a movement away from 

a stable deposit base to a larger proportion of 

funding obtained with short-term maturities. 

Although these trends have generally made 

interest rate transmission more stable, over the 

last year the smooth relationships between the 

policy rate and lending rates that had been 

established changed dramatically, particularly 

for the United States. From mid-2007 until June 

2008, the reliability of forecasting lending rates 

for both the United States and for the euro area 

has deteriorated, but more so for the United 

States. 

The chapter recommends:

• Improving infrastructure in funding markets.

Specifically, for the calculation of the LIBOR, 

a larger sample of banks and quotes that also 

includes nonbank sources of unsecured term 

funding, as well as publishing aggregate volume data, would engender greater confidence 

in these benchmark rates.

• More attention to both credit and liquidity risks by 

the authorities. Since wide interbank spreads 

were driven primarily by bank distress risks 

(encompassing both credit and liquidity 

risks), it is unlikely that ever-easier access to 

emergency liquidity from central banks will 

relieve the continued stress in interbank funding. Public authorities will need to continue 

to address counterparty risks, since private 

institutions are finding it increasingly difficult 

to do so.

• Limited indirect support to money markets. Central 

bank lending facilities aimed at restoring the 

functioning of interbank markets to transmit 

monetary policy need to be designed carefully. They should provide incentives for 

market participants to start dealing among 

themselves and thus to allow for an orderly 

exit by the central bank once more extreme 

strains have eased. The European Central 

Bank’s alterations to its collateral policies 

beginning next year is a step in this direction.

• Encouraging central bank cooperation and communication. Recent experience has highlighted 

the importance of properly functioning forex 

swap markets in addition to local money markets. In particular, the latest round of liquidity distress was countered by the cooperative 

actions of major central banks to address 

foreign currency funding needs. Regular 

communication by central banks about their 

actions and reasons for them can reduce 
EXECUTIVE SUMMARY

xiv

uncertainties. Continued convergence of their 

operational procedures would also aid in 

achieving this goal. 

Chapter 3

Since the crisis began, the role of fair value 

accounting (FVA) practices has been under 

close scrutiny. Chapter 3 examines the potential procyclical role that the application of FVA 

methods may have played in the development 

and outcome of the current credit cycle. 

Using actual accounting data from fi ve representative types of fi nancial institutions, this chapter simulates the balance sheet effects of several 

shocks calibrated to recent events. The analysis 

confi rms that, depending on the types of assets 

and liabilities present on the balance sheet, 

these shocks amplify cyclical fl uctuations of valuations. The simulations are also used to examine 

potential adjustments surrounding FVA methods, showing such adjustments act as expected 

to smooth the cyclical variation, but by doing 

so the assigned valuations do not represent fair 

values. It is worth recognizing, however, that in 

some cases, such as in highly illiquid markets or 

in buoyant or dire circumstances, FVA can also 

produce valuations that do not refl ect longerterm fundamentals and the cash fl ows and risks 

under consideration. 

Overall, the chapter concludes that the application of FVA is still the way forward, but that 

further enhancements of FVA methodologies 

are needed to help mitigate the exaggerated 

effects of some valuation techniques. A key challenge will be to enrich the FVA framework so 

that it can contribute to better market discipline 

and fi nancial stability. The various accounting, 

prudential, and risk management approaches to 

valuation should be reconciled so that they work 

together to promote a more stable fi nancial 

system. Importantly, this will require adjustments 

on the part of all three disciplines to ensure 

consistency. 

The policy recommendations are:

• Selectively add information on valuation. Accounting valuations themselves need to be supplemented with additional information, such as 

the expected variation of fair value valuations, 

modeling techniques, and assumptions, so 

that the user can appropriately assess the risks 

of the institution. 

• Raise capital buffers and provisions. Higher 

capital buffers and the use of forward-looking 

provisioning would help protect against the 

downturn in the cycle. If protection against 

the full magnitude of the downward cycle 

is desired, then the simulations suggest that 

building up a capital cushion of some 30–40 

percent above normal levels in good times 

would be required to absorb the most severe 

shocks. 

• Provide targeted risk disclosures. Firms could 

contemplate providing more focused reporting that is meant to satisfy different needs of 

users. Shorter reports at higher frequencies 

may be better than longer reports and lower 

frequencies, depending on the intended 

audience.

Chapter 4

Emerging market (EM) countries have 

not been at the forefront of the crisis, but 

their vulnerability to knock-on effects should 

not be underestimated. Chapter 4 examines 

equity markets in EM countries to assess the 

extent to which external/global and domestic/ 

fundamental factors drive equity market 

valuations. It confi rms that global factors are 

important in explaining the movement in EM 

equity prices, as are domestic fundamentals. 

Using various measures of correlation, Chapter 4 also fi nds that the scope for spillovers to 

emerging equity markets has risen, suggesting 

a growing transmission channel for equity price 

movements. This can, in turn, affect consumption and investment in emerging markets, 

although such macrofi nancial linkages are 

found to be small and they tend to play out 

gradually. Nevertheless, it suggests that policymakers need to remain engaged over the longer 

run in building resilience in their local fi nancial markets.
EXECUTIVE SUMMARY

xvii

Specifi cally, the standard policies that could 

help to make markets more resilient in the 

medium run are well-known and typically 

include:

• Fostering a broader and more diversified investor base. Encourage a diversity of investors, 

including institutional investors, such as 

pension funds and insurance companies, 

which tend to have long-term investment 

horizons.

• Aiding price discovery. Remove impediments to 

price discovery by avoiding artificial delays in 

revealing prices or limiting price movements.

• Supporting infrastructure development. Adopt 

legal, regulatory, and prudential rules that are 

consistent with international best practice. 

• Ensuring stock exchanges are well run. A robust 

trading environment and supporting infrastructure for trading equities and new financial instruments can also help develop capital 

markets, although enhancements and innovation need to be properly sequenced. 

* * *

A number of the policy lessons arising from 

the crisis are now beginning to be implemented 

and many more will need to be formulated and 

evaluated before coming into effect. The IMF 

has been active in the debates on a number 

of items, some of which have been covered 

in this latest GFSR. While not all the policy 

recommendations in the April 2008 GFSR are 

repeated here, they remain relevant. The IMF 

will continue to cooperate with the FSF, monitor progress, and assist its member countries 

through its bilateral surveillance, including the 

Financial Sector Assessment Program, and technical assistance to make their fi nancial systems 

healthier and more resilient to global fi nancial 

sector risks.

1

CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

The global fi nancial system has undergone a period of unprecedented 

turmoil. Market confi dence dwindled 

and has remained fragile, leading to 

the collapse or near-collapse of large, and in 

some cases systemically important, fi nancial 

institutions, and calling forth public intervention in the fi nancial system on a scale not 

seen for decades. The fi nancial system has 

been severely weakened by mounting losses 

on impaired and illiquid assets, uncertainty 

regarding the availability and cost of funding, 

and further deterioration of loan portfolios as 

global economic growth slows. Finding a purely 

private sector resolution of fi nancial market 

strains has become increasingly diffi cult, while 

case-by-case intervention by authorities has not 

alleviated market concerns. In response, more 

comprehensive approaches are now being considered or implemented to bring about a more 

orderly process of deleveraging and to break 

the adverse feedback loop between the fi nancial 

system and the global economy. Such a comprehensive approach—if well coordinated among 

countries—should be suffi cient to restore confi dence and the proper functioning of markets 

and avert a more protracted downturn in the 

global economy.

As anticipated in the April 2008 Global 

Financial Stability Report (GFSR), credit quality 

concerns are broadening. In the United States, 

credit deterioration has spread to higher quality residential mortgages and to consumer and 

corporate loans as the economy slows. Pressures 

are now emerging in Europe, as house prices in 

some countries decline, economic growth falters, and lending conditions tighten. Although 

fi nancial fi rms have recognized much of the 

subprime-related losses, further potential creditrelated writedowns are placing additional strains 

on balance sheets.

A more resilient fi nancial system will ultimately emerge from restructuring and deleveraging, but market forces are in the meantime 

resulting in a disorderly, accelerated adjustment 

process, requiring the use of public balance 

sheets to restore order. In this environment, 

fi nancial fi rms face enormous challenges in 

raising capital to cover losses, while efforts to 

shed assets are keeping downward pressures on 

prices. In addition, doubts about the soundness 

of some banks and their business models have 

led to severe impairment of the funding markets 

and sudden and at times unruly consolidation 

in the sector. Government initiatives aim to 

support a more orderly deleveraging process, 

but its diffi cult and protracted nature is likely to 

curtail credit availability, placing a further drag 

on the economic recovery. The most signifi cant 

risk remains a worsening of an adverse feedback 

loop between the fi nancial system and the real 

economy.

Emerging markets had been fairly resilient to 

the global credit turmoil, but now face greater 

risks. The pronounced reduction in investors’ risk 

appetite has resulted in a retrenchment in shortterm capital fl ows to emerging markets, exerting 

pressure on local markets, and sharply raising 

costs of credit. Together with slowing global 

growth, this results in a very challenging environment for some countries.

Policies will need to continue to consider 

carefully the balance of risks to the fi nancial 

system and to the broader economy and are 

likely to require further initiatives to restore 

confi dence. Effective and coordinated impleNote: This chapter was written by a team led by Peter 

Dattels and comprised of Sergei Antoshin, Elie Canetti, 

Ana Carvajal, Sean Craig, Antonio Garcia-Pascual, 

Kristian Hartelius, Geoff Heenan, Xiongtao Huang, Phil 

de Imus, Rebecca McCaughrin, Ken Miyajima, Michael 

Moore, Chris Morris, Silvia Ramirez, Mustafa Saiyid, 

Andre Santos, Narayan Suryakumar, Rupert Thorne, and 

Chris Walker.
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

2

mentation should stabilize market sentiment 

and protect against downside economic risks, 

and allow for a more orderly and smooth deleveraging. Such measures could help asset prices 

rebound, and with them, the willingness of 

investors to again provide a now more consolidated banking sector with fresh capital. This 

would allow fi nancial intermediation and credit 

markets to normalize more quickly and at less 

economic cost. 

Against this backdrop, Chapter 1 fi rst outlines 

the key risks that have materialized since the 

April 2008 GFSR. Second, it examines the depth 

of the default cycle and potential losses. The 

third and fourth sections evaluate the challenges 

posed by the deleveraging of the fi nancial 

system in mature economies and the broader 

systemic implications. The fi fth section assesses 

the vulnerability of emerging markets to global 

stress. Finally, the last section considers nearterm policy priorities aimed at rebuilding confi -

dence and improving the functioning of global 

markets, along with medium-term policies to 

strengthen the international fi nancial architecture and reduce systemic risks.

Global Financial Stability Map

Since the April 2008 GFSR, monetary and 

fi nancial conditions have tightened further, risk 

Credit

risks

Market and

liquidity risks

Risk

appetite

Monetary and

financial

Macroeconomic

risks

Emerging market

risks

Conditions

Risks

Figure 1.1. Global Financial Stability Map

April 2008 GFSR

Source: IMF staff estimates.

Note: Closer to center signifies less risk, tighter monetary and financial 

conditions, or reduced risk appetite.

October 2008 GFSR
3

appetite has continued to contract, and global 

macroeconomic, credit, market and liquidity, and emerging market risks have increased 

(Figure 1.1).

As envisaged in the last GFSR, an adverse 

feedback loop between the banking system and 

the global economy appears to be unfolding, 

as weakening economic conditions reinforce 

the credit deterioration and stress in mortgage, 

credit, and funding markets, with risks also 

rising in certain emerging markets that had 

shown considerable resilience until recently 

(Figure 1.2). 

Macroeconomic risks continue to rise . . .

Global economic activity is decelerating as growth in advanced economies slows 

and expansions in emerging economies lose 

momentum. Despite better-than-expected 

performance early this year, rising fi nancial 

turmoil has led to a downgrade in the IMF’s 

baseline forecast for global economic growth 

in 2008-09, and global growth is expected to 

moderate as the forces that weigh on activity 

remain fi rmly in place.1 In particular, the supply of credit is expected to contract markedly, 

placing a drag on economic growth⎯not just 

in the United States, but in other advanced 

and emerging economies. Global infl ation risks 

have moderated on the back of sharp declines 

in commodity prices from mid-year highs. 

However, the volatility of infl ation expectations, 

particularly in emerging markets, is challenging monetary authorities in an environment of 

slowing growth, and may hamper their abil1See the October 2008 World Economic Outlook (WEO) 

(IMF, 2008d). Both the WEO and GFSR provide 

assessments of macroeconomic risks, but in the former 

report, these metrics are viewed in the context of risks 

around a baseline projection for global growth. The 

GFSR incorporates these metrics, as well as infl ation risks, economic confi dence, and other factors, 

all viewed from the perspective of fi nancial stability. 

Hence, the overall portrayals of macroeconomic risks 

in the WEO and GFSR, while closely related, are not 

directly comparable. See Annex 1.1 for details of the 

specifi c metrics.

Figure 1.2. Heat Map: Developments in Systemic Asset 

Classes

Emerging markets

Corporate credit

Prime RMBS

Commercial MBS

Money markets

Financial institutions

Subprime RMBS

Jan-07

Apr-07

Jul-07

Oct-07

Jan-08

Apr-08

Jul-08

Source: IMF staff estimates.

Note: The heat map measures both the level and 1-month volatility of the spreads, prices, and 

total returns of each asset class relative to the average during 2004–06 (i.e., wider spreads, lower 

prices and total returns, and higher volatility). That deviation is expressed in terms of standard 

deviations. Green signifies a standard deviation under 1, yellow signifies 1 to 4 standard 

deviations, and red signifies greater than 4 standard deviations. MBS = mortgage-backed security; 

RMBS = residential mortgage-backed security.

GLOBAL FINANCIAL STABILITY MAP
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

4

ity to respond to potential fi nancial stability 

concerns.2

. . . as credit market stress and spillovers have led to a 

further tightening of fi nancial conditions.

The effects of easing monetary conditions on 

fi rms’ fi nancing costs in the United States and 

United Kingdom have been more than offset by 

equity price declines and wider credit spreads. 

As fi nancial institutions attempt to delever and 

reduce risks, their willingness and ability to 

continue extending credit has been curtailed, 

resulting in a tightening of monetary and fi nancial conditions. The pressure to reduce leverage 

and risk has also had a pronounced impact on 

nonbank fi nancial institutions, including hedge 

funds and other leveraged entities, leading to 

the demise of the independent broker-dealer 

model. Exacerbated by the adverse feedback 

loop between the fi nancial system and the real 

economy, credit supply constraints could persist 

for a prolonged period.

Systemic risks have risen as credit deterioration 

broadens, further straining fi nancial institutions.

Credit risks have risen, refl ecting continued 

pressures on bank balance sheets and weakness 

in broader credit markets as well as plunging 

equity prices that make further capital-raising 

efforts diffi cult (Figure 1.3). Financial institutions in the United States and Europe continue 

to face enormous strains as a result of past credit 

indiscipline, market demands for larger capital 

cushions, and the likelihood of assets being 

brought back onto balance sheets. Uncertainty 

as to the treatment of systemically connected 

institutions under stress, in particular in the 

wake of the bankruptcy of a major U.S. brokerdealer, has raised the perception of counterparty 

credit risk to fi nancial institutions around the 

world, most visibly in the United States and 

Europe. As such, the global fi nancial system has 

2The earlier run-up in commodity prices was accompanied by increased investment fl ows to commodity index 

funds, but our analysis fails to fi nd meaningful causal 

relationships between fi nancial positions and prices of 

major commodities (see Annex 1.2). 

2

3

4

5

6

7

0

2

4

6

8

10

12

14

2006 07 08

Sources: Bloomberg L.P.; and IMF staff estimates.

1Among 15 selected large and complex financial institutions (LCFIs).

2Measures the largest probability of default among the sampled LCFIs each day.

Figure 1.3. Systemic Bank Default Risk

Expected number of bank defaults 

given at least one bank default1

(number, left scale)

Largest probability of default 2

(percent, right scale)
5

entered a new phase of the crisis where solvency 

concerns have increased to the point where 

further public resources have had to be committed to contain systemic risks and the economic 

fallout.

Despite extraordinary measures by central banks to 

contain systemic risks, market and liquidity risks have 

risen . . .

Coordinated central bank actions have 

continued to aim at reducing risks to systemically important fi nancial institutions. However, 

funding and liquidity strains remain high, as 

refl ected in persistently wide interbank spreads 

and liquidity premia (see Figure 1.4 and Chapter 2), and have recently risen even further. 

Funding in interbank and commercial paper 

markets have locked up with mostly overnight 

rolls and little to no term activity, refl ecting 

persistent and increasing concerns about counterparty credit risk and future liquidity needs 

(Box 1.1). Furthermore, the pressure of asset 

sales from fi nancial institutions as they seek to, 

or are forced to, delever under highly illiquid 

and uncertain conditions has pushed market and 

liquidity risks to the same heightened level as 

credit risks.

. . . and risk appetite has continued to evaporate.

More fragile market sentiment, the loss of 

market liquidity, and elevated macroeconomic 

risks have, in turn, suppressed risk appetite to 

very low levels. A number of indicators show 

fund managers have become even more risk 

averse, increasing cash allocations and scaling 

back positions in risky assets (Figure 1.5). While 

at times some investors cautiously sought value 

in distressed assets at current prices, at other 

times of market stress, the fl ight to safety has 

been extreme and broad-based. Going forward, 

a bottoming in prices of distressed assets is 

needed to help the fi nancial sector to delever 

through asset sales and reduced writedowns.

Overall risks to emerging markets have deepened.

Emerging market risks have risen as global 

fi nancial deleveraging and derisking weigh on 

–2

–1

0

1

2

3

4

1996 98 2000 02 04 06 08

0

100

200

300

400

500

Sources: Bloomberg L.P.; and IMF staff estimates.

Note: Asset price volatility index uses implied volatility derived from options from 

stock market indices, interest, and exchange rates. Funding and market liquidity index 

uses the spread between yields on government securities and interbank rates, spread 

between term and overnight interbank rates, currency bid-ask spreads, and daily 

return-to-volume ratios of equity markets. A higher value indicates tighter market 

liquidity conditions. LTCM = Long-Term Capital Management; Y2K = Year 2000.

Figure 1.4. Asset Price Volatility and Funding and

Market Liquidity

Funding and market 

liquidity index

(January 1996 = 100; left scale)

Asset price volatility index

(in standard deviations

from the period average;

right scale)

Russia/LTCM Y2K 9-11

Internet

bubble

Credit crisis

begins

–0.6

–0.4

–0.2

0

0.2

0.4

0.6

0.8

1.0

–50

–40

–30

–20

–10

0

10

20

30

2003 04 05 06 07 08

Fed starts 

tightening

Net inflows to emerging 

market bond and equity 

funds (percent share 

of assets under 

managment, left scale)

Global fund managers 

degree of risk taking 

(net percent reporting 

higher than benchmark, 

right scale)

May/June

2006

Summer

2007

Sources: Emerging Portfolio Fund Research, Inc.; Merrill Lynch; and IMF 

staff estimates.

Figure 1.5. Allocation to Global and Emerging 

Market Risk Assets

GLOBAL FINANCIAL STABILITY MAP
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

6

This box describes recent dramatic market developments, including the responses by markets and policymakers, and assesses remaining uncertainties. 

The fi nancial crisis entered a new phase in 

September as the rise of systemic risks led to a 

sweeping government response and an unprecedented restructuring of the fi nancial system. 

First, Lehman Brothers Holdings, Inc. declared 

bankruptcy, prompting the three largest remaining U.S. investment banks to sell themselves to, 

or become, depository institutions. Leh man’s 

bankruptcy also spread default risk and removed 

an important fi nancial counterparty, sharply 

reducing liquidity in derivatives markets. Second, 

the insurance conglomerate AIG nearly collapsed, raising broader concerns about fi nancial 

product insurance and instigating a public sector 

rescue. Third, prime money market funds experienced massive withdrawals and some closures, 

forcing asset liquidations and capital hoarding, 

and bringing into question the viability of fi nancial institutions dependent on wholesale funding. 

The market response was swift, intense, and 

broad-based (see fi rst fi gure). Risky assets sold 

off, overnight interbank rates surged, implied 

dollar funding costs increased, interest rate 

swap spreads widened, and, as default probabilities increased, credit default swap (CDS) 

spreads soared. Emerging markets, which had 

been relatively insulated from the crisis, came 

under pressure as global fi nancing conditions 

deteriorated. 

Liquidity became increasingly scarce and 

funding shifted almost exclusively to overnight 

markets. Demand for dollar funding grew acute, 

driving rates sharply higher in unsecured funding markets, while bid-ask spreads widened in 

foreign currency swap markets. As a fl ight-toquality intensifi ed, yields on some U.S. treasury 

bills temporarily became negative and marketmaking declined sharply. CDS markets became 

illiquid as counterparty risk concerns rose, making it diffi cult for investors to hedge positions.

The beginnings of a run on money market 

funds led many to limit their investments to 

very short-term, safe collateral. Prime money 

market funds, which invest partly in corporate 

debt and asset-backed securities, suffered some 

$320 billion of redemptions in one week, threatening especially those with no external support 

Box 1.1. Recent Financial Market Developments

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

200

220

240

260

280

300

320

9/12 9/15–9/19 9/22–9/26 9/28–10/3

AIG

receives

loan from Fed

Lehman

bankruptcy Ireland

guarantees

bank deposits

S&P 500

financial index 

(left scale) Joint

central

bank

actions

U.S.

government

rescue plan

proposal

Equity

shortselling

ban

Primary 

Fund 

fails

U.S. 

money

market

guarantee

U.S. 

Congress

passes 

EESA

U.S. 

House

votes down

EESA

Dexia

rescue

Bradford

& Bingley

nationalized

Fortis

rescue

Three-month treasury yield 

(percent, right scale)

Sources: Bloomberg L.P.; and IMF staff estimates.

Note: EESA = Emergency Economic Stabilization Act.

Timeline of Recent Market Developments

0

200

400

600

800

1000

1200

1400

2007 2008

Sources: iMoneyNet; JPMorgan Chase & Co.; and IMF staff 

estimates.

U.S. Money Market Fund Assets Under 

Management

(In billions of U.S. dollars)

Government retail

Prime retail

Prime institutional

Government institutional

Note: The main author of this box is Rebecca 

McCaughrin.
7

Recent Central Bank and Government Actions

United States Euro Area1 United Kingdom1 Other1

9/14/2008 Federal Reserve expands 

eligible collateral for Primary 

Dealer Credit Facility and Term 

Securities Lending Facility 

(TSLF), increases frequency 

and size of schedule 2 TSLF 

auctions, and eases restrictions 

on transactions between banks 

and broker-dealers

9/15/2008 $70 billion overnight repos €30 billion overnight 

repos

£5 billion 2-day repos Other central banks provide 

liquidity, including Japan 

(¥1.5 trillion) and Australia 

(A$2.1 billion), among 

others

9/16/2008 Federal Reserve extends $85 

billion 2-year credit line to AIG; 

$50 billion overnight and $20 

billion 28-day repos

€70 billion overnight 

repos

£20 billion 2-day and £5 

billion 3-month repos

Other central banks provide 

liquidity, including Japan 

(¥2.5 trillion), Switzerland 

(SF726.4 million) and 

Australia (A$1.7 billion), 

among others

9/17/2008 Treasury announces 

supplemental fi nancing program 

for Federal Reserve, and 

auctions $40 billion special cash 

management bills

€150 billion 7-day repos Bank of England (BoE) 

extends Special Liquidity 

Scheme

Other central banks provide 

liquidity, including Japan 

(¥3 trillion), and Australia 

(A$4.3 billion), among 

others

9/18/2008 Federal Reserve expands its 

temporary reciprocal currency 

arrangements by $180 billion 

with major central banks, and 

conducts $5 billion 14-day and 

$100 billion overnight repos; 

Treasury auctions $60 billion for 

supplemental fi nancing program

€25 billion overnight 

and $40 billion overnight 

repos

$14 billion overnight 

and £66 billion 7-day 

repos

Other central banks provide 

liquidity, including Japan 

(¥2.5 trillion), Switzerland 

($10 billion), and Australia 

(A$2.8 billion), among 

others

9/19/2008 Federal Reserve announces 

plan to loan banks funds to 

buy asset-backed commercial 

paper (ABCP) and buy agency 

discount notes (DN) outright; 

Federlal Reserve purchases 

$8 billion agency DNs and 

conducts $20 billion in 3-day 

repos; Treasury proposes $700 

billion troubled asset resolution 

program, announces guaranty 

program for money market 

funds, and auctions $60 billion 

for supplemental fi nancing 

program; Securities and 

Exchange Commission (SEC) 

tightens restrictions on net short 

positions on fi nancial stocks

$40 billion in 3-day repos Financial Services 

Authority tightens 

restrictions on net short 

positions on fi nancial 

stocks; BoE conducts 

$21 billion in 3-day 

repos

Other central banks provide 

liquidity, including Japan 

(¥3 trillion), Switzerland 

($10 billion), and Australia 

(A$1.9 billion), among 

others; several regulatory 

institutions impose 

restrictions on equity short 

sales

9/22/2008 Federal Reserve conducts $20 

billion in overnight repos

European Central Bank 

(ECB) conducts $25 

billion 28-day repos

BoE conducts $26 

billion repos 

GLOBAL FINANCIAL STABILITY MAP
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

8

United States Euro Area1 United Kingdom1 Other1

9/23/2008 Federal Reserve conducts $20 

billion in 28-day repos and 

purchases $2 billion in agency 

DNs

BoE conducts $30 

billion repos 

9/24/2008 Federal Reserve expands its 

temporary reciprocal currency 

arrangements to Australian and 

Scandinavian central banks; 

conducts $25 billion in overnight 

reverse repos

€50 billion 84-day repos BoE conducts $30 

billion repos 

9/25/2008 Federal Reserve conducts $22 

billion in overnight reverse repos

BoE conducts $35 

billion repos 

9/26/2008 Federal Reserve conducts $26 

billion in 3-day reverse repos; 

purchases $4.5 billion agency 

DNs

BoE conducts $10 

billion overnight repos 

and $30 billion 7-day 

repos

9/28/2008 Fortis partly taken over 

by governments of 

Belgium, Netherlands, and 

Luxembourg via €11.2 

billion bailout package 

for 49 percent ownership 

stake; Germany organizes 

a €35 billion credit line 

for Hypo Real Estate

Bradford & Bingley 

(B&B) nationalized; 

Santander to pay £612 

million for B&B’s 

branches and deposits

9/29/2008 Federal Reserve increases swap 

lines to foreign central banks 

from $290 billion to $620 billion, 

increases the size of the 84-day 

Term Auction Facility (TAF) 

auctions from $25 billion to $75 

billion, introduces forward TAF 

auctions

ECB conducts €120 

billion 38-day repos

BoE conducts $10 

billion repos 

Iceland’s government takes 

75 percent stake in Glitnir 

Bank

9/30/2008 Federal Reserve conducts $20 

billion 28-day repos

Irish government 

guarantees all deposits, 

covered bonds, senior 

and dated subordinated 

debt (until September 

2010); Dexia receives 

€6 billion infusion from 

Belgian and French 

governments and main 

shareholders; ECB 

conducts €190 billion 

7-day repos

BoE conducts $10 

billion repos 

10/1/2008 Federal Reserve conducts $20 

billion overnight reverse repos

BoE conducts $7.5 

billion overnight repos 

and $13.4 billion 7-day 

repos

10/2/2008 Federal Reserve conducts $25 

billion overnight reverse repos

Greek government 

guarantees all bank 

deposits

BoE conducts $8.9 

billion repos

Brazilian central bank eases 

reserve requirements 

Recent Central Bank and Government Actions (continued)

Box 1.1 (continued)
9

from a parent bank (see second fi gure). As a 

result, they were unable to provide the nearly 

$2 trillion of credit they typically extend daily, 

leading to diffi culties for fi nancial institutions 

dependent on wholesale funding and nonfi nancial corporations needing refi nancing. 

Global central banks moved rapidly to 

provide liquidity, including to prime money 

market funds (see table).1 Liquidity support 

was accompanied by other forms of government 

support and regulatory action. The Federal 

Reserve extended an $85 billion, two-year loan 

to AIG when no private rescue materialized, 

facilitated by the U.S. Treasury’s establishment 

of a supplementary fi nancing facility. In addition, the government announced a guaranty 

program for money market funds, protecting 

investors from loss. Regulators in a number 

of countries limited equity short sales in an 

effort to stem precipitous declines in fi nancial 

institutions’ share prices. Last, the U.S. govern1In addition, a consortium of fi nancial fi rms set up 

a pooled fund to provide collateralized borrowing to 

each other, with the intention of accepting a broader 

range of collateral for longer durations than central 

banks.

ment proposed a Troubled Asset Relief Program 

(TARP) to purchase distressed assets from 

fi nancial institutions in order to reduce balance 

sheet pressures. 

Market conditions remained mixed in the 

wake of these initiatives. Liquidity support 

helped reduce overnight borrowing costs, but 

effective overnight policy rates experienced 

signifi cant intraday volatility as a result of the 

large liquidity injections, increased demand for 

dollar funding by non-U.S.-market participants, 

and delays by money managers in completing 

funding. Term funding costs are still elevated 

and tiering has become noticeable, refl ecting 

concerns about counterparty risk and future 

liquidity needs. Conditions in other markets, 

including, for instance, major equity indices, 

CDS spreads on key fi nancial companies, shortterm dollar lending rates in overseas markets, 

and emerging market assets, eased from extreme 

levels, though they remained under stress. 

Signifi cant uncertainties remain, resulting in 

fragile market confi dence. First, the scope of 

government programs to help fi nancial institutions dispose of troubled assets remains uncertain. Second, the simultaneous occurrence of 

several large credit events is testing the CDS 

United States Euro Area1 United Kingdom1 Other1

10/3/2008 Congress approves $700 billion 

rescue package: Treasury 

authorized to purchase distressed 

assets; FDIC temporarily allowed 

to borrow unlimited funds from 

the Treasury; FDIC deposit 

insurance temporarily increased 

from $100,000 to $250,000; 

Federal Reserve granted the 

ability to pay interest on reserves; 

SEC authorized to suspend 

mark-to-market accounting rules; 

Federal Reserve conducts $25 

billion 3-day reverse repos

ECB to allow more 

banks to participate 

in unscheduled cash 

auctions; Netherlands 

government purchases 

Dutch operations of 

Fortis for €16.8 billion; 

ECB auctions $50 billion 

overnight repos and a 

€194 billion liquidityabsorbing quick tender

BoE extends eligible 

collateral for its 

weekly long-term repo 

operations to include 

AAA-rated ABS and 

highly rated ABCP; 

conducts $8.2 billion 

overnight repos and $30 

billion 7-day repos

Russian central bank 

extends unsecured loans 

to qualifi ed banks for up to 

six months and introduces 

other measures

10/5/2008 €35 billion rescue 

package promised to 

Germany’s Hypo Real 

Estate Group withdrawn

Sources: JPMorgan Chase & Co.; and national authorities.

1U.S. dollar operations are an extension of the Federal Reserve TAF.

Recent Central Bank and Government Actions (concluded)

GLOBAL FINANCIAL STABILITY MAP
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

10

the appetite for emerging market assets and 

exacerbate vulnerabilities. Emerging market 

equities and corporate bonds have followed a 

similar downward trajectory as mature credit 

markets, and default probabilities have risen on 

sovereign and corporate debt. Capital outfl ows 

have intensifi ed, leading to tighter international and in some cases internal liquidity 

conditions. Vulnerabilities vary across different 

economies, but those economies with greater 

reliance on short-term fl ows or with leveraged 

banking systems funded internationally are 

particularly vulnerable. In addition, slowing 

global growth could accelerate a downturn in 

domestic credit cycles, raising defaults. Though 

infl ation concerns have eased over the past few 

months, sharp increases in infl ation volatility 

could induce fi nancial instability in some local 

markets, should infl ation expectations become 

entrenched, and reduce policy fl exibility amid 

heightened global risks. Nonetheless, sizable 

reserve cushions and favorable external balances in many emerging economies and sound 

policies continue to provide resilience to global 

stress. 

The Default Cycle

The depth and breadth of the credit default 

cycle will be a key determinant of pressures on 

the fi nancial system going forward. This section assesses recent performance of key U.S. 

and European credit markets, and estimates the 

trajectory of the U.S. default cycle for a variety 

of loans. The base case suggests that charge-off 

rates on U.S. residential mortgages, already at 

historic highs, will climb further, while consumer loans exceed record levels and corporate 

and commercial real estate (CRE) loans reach 

multi-year highs (Figure 1.6).3 The results 

show that further losses lie ahead for fi nancial 

institutions, rising well beyond the estimates of 

nearly $1 trillion in the April 2008 GFSR. Under 

a more stressed economic scenario, entailing 

a deeper and more protracted U.S. recession, 

larger declines in house prices, and a longer 

period of tight lending standards, charge-off 

rates on CRE and corporate loans could climb 

close to historical peaks, exacerbating losses.4

3A charge-off is a loan that is removed from a bank’s 

books and charged against loan loss reserves. Loans that 

are removed are those that are no longer collectible, due 

either to bankruptcy or default. Charge-off rates are the 

ratio of gross charge-offs minus recoveries to the average 

level of loans outstanding during a quarter, annualized. 4See Box 1.6 in Annex 1.3 for details on the econometric 

results. In addition to the assumptions in Table 1.1, bank 

lending standards are expected to be at their tightest in 

Q4 2008 in the base case. Our stress case assumes lending 

standards remain tight for a longer period. This scenario 

analysis was applied only to whole loans (not securities). 

settlement infrastructure. Market participants 

are still assessing their counterparty exposures 

in markets that are neither well-automated nor 

transparent. Many will face logistical risks identifying, closing, offsetting, and reestablishing positions, while others may face debilitating losses on 

their credit exposure. Third, markets will remain 

subject to the potential for disorderly asset 

sales as current (and likely future) bankruptcy 

proceedings ensue. Fourth, while the government actions may help accelerate the deleveraging process, this will not eliminate the need 

for banks to continue to delever and replenish 

capital over the coming years. Fifth, while the 

viability of the business models of major independent broker-dealers has now been resolved 

(in the negative), uncertainties remain about 

other fi nancial business models, including, for 

instance, fi nancial insurers, nondiversifi ed mortgage originators and servicers, and certain types 

of money market funds. Finally, markets remain 

uncertain about how policy authorities will balance the competing claims of trying to minimize 

moral hazard while protecting against systemic 

risk, thus complicating policymakers’ abilities to 

send clear signals about their intentions.

Box 1.1 (concluded)
11

Highly levered U.S. households are under pressure from 

falling net worth and tighter credit conditions.

After amassing record amounts of mortgage debt and housing assets in recent years, 

household balance sheets and real disposable 

incomes have come under pressure owing to 

falling house prices, a deteriorating employment backdrop, and rising oil and food prices. 

In the fi rst half of 2008, U.S. household net 

worth fell on a year-on-year basis for the fi rst 

time since 2003, driven primarily by the halt 

in both real estate and fi nancial asset growth 

(Figure 1.7).5

Falling house prices and a slowing economy threaten to 

weaken higher-quality mortgages.

U.S. residential mortgages are experiencing 

unprecedented credit deterioration. Since the 

last GFSR, delinquencies on U.S. subprime and 

Alt-A mortgages have risen further and home 

foreclosures have reached new highs, especially in regions where home prices have fallen 

the most (Figure 1.8). Refl ecting this credit 

deterioration, bank charge-offs have risen, and 

prices on nonagency mortgage-related securities 

(especially Alt-A and senior subprime tranches) 

have resumed their declines (Figure 1.9). At the 

same time, nonconforming prime mortgages 

(“jumbo”) are facing tighter lending standards, 

higher mortgage rates, and more limited 

securitization potential, making them harder 

to refi nance. The increases in the conforming 

loan limits of the government-sponsored enterprises (GSEs) and the Federal Housing Authority (FHA) have yet to alleviate pressure in that 

sector. The conforming mortgage market has 

benefi ted from GSE securitization (and a more 

explicit government guarantee), but faces many 

of the same cyclical pressures as the broader 

mortgage market, which have led to a rise in 

prime mortgage defaults.

5By contrast, during the early 1990s downturn, the 

growth in household net worth slowed but did not 

decline, despite the savings and loan crisis. In the downturn beginning in 2000, net worth fell, primarily due to 

falling equity prices. 

Figure 1.7. U.S. Households’ Balance Sheets: 

Net Worth

(Percent yearly contributions to net worth growth) 

Sources: Federal Reserve; and IMF staff estimates.

1Year-on-year percent change in net worth.

1985 90 95 2000 05

–15

–10

–5

0

5

10

15

20

Total liabilities

Financial assets

Tangible assets

Net worth1

Figure 1.6. U.S. Loan Charge-Off Rates

(In percent of loans outstanding; annualized rate) 

Sources: Federal Reserve; and IMF staff estimates.

Note: Dotted lines are stress case estimates.

–0.5

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5 Estimate

Commercial and industrial 

Commercial real estate

Consumer

Residential real estate

1991 93 95 97 99 2001 03 05 07 09

THE DEFAULT CYCLE
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

12

While the U.S. housing sector may fi nally 

trough at some point in 2009, continued 

declines in house prices and sluggish growth are 

likely to deepen and broaden the default cycle.

The combination of tighter lending standards, 

falling home prices, and lower recovery values 

would lead to a rise in charge-off rates on residential mortgages from the current 1.1 percent 

rate to a peak of 1.9 percent by mid-2009, and 

they could remain elevated throughout 2010 

(Figure 1.10).

Pressures on household balance sheets presage 

deterioration in consumer loans.

Charge-off rates on U.S. consumer loans 

have risen. In addition, there are rising signs of 

stress as consumers tap credit lines to support 

consumption amid higher mortgage and other 

costs. Additionally, the ability to pay down 

higher-interest credit card debt with cheaper 

home equity loans has diminished, suggesting 

that some consumers are being forced to shift 

from secured mortgage debt to higher-cost, 

unsecured credit card debt.6 However, with 

tighter lending standards, the availability of 

this type of credit may fall. Our analysis shows 

that tighter bank lending standards and slowing growth are likely to lead to consumer loan 

charge-off rates of about 3.9 percent by early 

2009, slightly above the peak levels of 2002, 

before falling to more normal levels by 2010 

(Figure 1.11). Under a stress scenario, chargeoff rates climb to over 4 percent.

Stresses on U.S. consumers are also leading to credit 

weakening in commercial real estate loans.

Charge-off rates on U.S. CRE loans have 

already reached decade-high levels, as weaker 

consumer fundamentals weigh on the retail 

and condominium sectors. As with other loan 

categories, credit deterioration has been more 

pronounced on recently originated (2006–07) 

loans, which had weaker underwriting standards 

6Banks have accommodated this increase so far, partly 

because credit card securitization has remained relatively 

robust over the last year. 

0

2

4

6

8

10

12

14

16

18

0

5

10

15

20

25

30

35

40

0

0.5

1.0

1.5

2.0

2.5

3.0

Sources: Merrill Lynch; and LoanPerformance.

Figure 1.8. U.S. Mortgage Delinquencies by 

Vintage Year

(60+ day delinquencies, in percent of original balance)

2005

2005

2004

2004

2003

2003

2001

2001

2002

2002

2000

2000

2007

2007

2006

2006

2005

2000

2001

2002

2004

2003

Months after origination

Alt-A

Subprime

2007

2006

Prime

0 10 30 20 40 50 60

0 10 30 20 40 50 60

0 10 30 20 40 50 60
13

(e.g., higher loan-to-value and debt service 

coverage ratios). Econometric analysis indicates 

that private consumption strongly affects the 

level of CRE charge-off rates. Charge-offs may 

rise to a 17-year high of about 1.7 percent by 

the end of 2009, or to 1.9 percent under our 

stress scenario, remaining elevated for some 

time, though still below the levels reached in the 

early 1990s.

Tighter access to credit is pressuring leveraged 

companies and small and mid-sized enterprises, while 

nonfi nancial investment-grade fi rms’ access remains 

relatively robust.

A weakening economic environment is 

already leading to corporate credit deterioration, especially for fi rms closely tied to the 

consumer. Credit quality has deteriorated on 

leveraged buyout deals in the last few years, as 

shown by the rising ratio of rating downgrades 

to upgrades in this sector.7 Secondary market 

liquidity for leveraged loans remains low and 

banks and managers of collateralized loan 

obligations are selling loans at signifi cant losses. 

Some of these sales have been to private equity 

fi rms, partly encouraged by lower prices and 

seller-provided fi nancing for the purchases. 

Consequently, the leveraged loan pipeline has 

declined to $70 billion from a peak of $304 

billion in mid-2007, relieving one source of 

potential stress on asset prices.

High-yield corporate bond issuance has 

slowed considerably, and fi rms are facing 

reduced access, higher rates, and shorter durations on their commercial paper obligations. 

As the cycle has begun to turn, default rates 

have started to increase, rising to 2.5 percent. 

Through mid-September of this year, globally, 

57 corporate issuers have defaulted, compared 

with just 22 issuers in all of 2007.8 The cur7A more pronounced deterioration in recent leveraged 

loans may ultimately materialize where “covenant-lite” 

agreements may have hindered early intervention by 

lenders. 8In the United States, the ratio of rating agency 

upgrades to downgrades on high-yield bonds is at its lowest level in four years. 

Figure 1.9. Prices of U.S. Mortgage-Related 

Securities

(In U.S. dollars)

Jumbo MBS

Agency MBS

ABX BBB

ABX AAA

Alt-A

Sources: JPMorgan Chase & Co.; and Lehman Brothers.

Note: ABX = an index of credit default swaps on mortgage-related 

asset-backed security; MBS = mortgage-backed security.

0

20

40

60

80

100

120

2006 07 08

Sources: Federal Reserve; S&P Case-Shiller; and IMF staff estimates.

1As a percent of loans outstanding; annualized rate. 

2Series standardized over the period from 1991:Q1 to 2010:Q4.

Figure 1.10. U.S. Residential Real Estate Loan 

Charge-Off Rates

(In percent)

Charge-off rate1

Estimate

Tighter lending standards;

lower home prices

Home price appreciation (inverted, right scale) 2

Residential real estate lending standards

(right scale) 2

–2

–1

0

1

2

3

4

–0.2

0.2

0.6

1.0

1.4

1.8

2.2

1991 93 95 97 99 2001 03 05 07 09

THE DEFAULT CYCLE
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

14

rent trend is broadly in line with our baseline 

forecast (a 4 to 6 percent U.S. high-yield default 

rate) (Figure 1.12).9 Moreover, tighter bank 

lending standards are set to squeeze small and 

medium-sized fi rms, given their greater reliance on direct bank borrowing than on capital 

market fi nancing. Despite continued strong balance sheets for investment-grade nonfi nancial 

corporations, charge-off rates on commercial 

and industrial loans have already increased 

to their highest level since 2004. Our analysis 

suggests that slowing GDP growth and tighter 

lending standards could raise charge-off rates 

from 0.7 to 1.7 percent by the second quarter 

of 2009—still slightly below the level reached 

during the 1990–91 and 2001 downturns. They 

would match the previous peak only under the 

stress scenario.

Signifi cant writedowns have already been realized, but 

more may lie ahead. . .

Our estimate of aggregate writedowns based 

on global holdings of U.S.-originated and securitized mortgage, consumer, and corporate debt 

has risen to $1.4 trillion (versus $945 billion 

in April), largely due to higher-than-expected 

losses on prime mortgage loans and corporate 

debt (Table 1.1) and wider spreads on related 

securities.10

The scale of the current credit crisis is likely 

to be higher in dollar terms compared with 

fi nancial crises over the past two decades, and 

could be sizable relative to GDP, though costs 

are more broadly spread across different countries and institutions. The ultimate fi scal cost 

is highly uncertain at this stage and is policy 

dependent (Figure 1.13). 

Increased writedowns owe to a further deterioration in the corporate debt and prime residential mortgage markets, as the crisis originally 

centered in subprime mortgages has spilled over 

9See Box 1.1 of the April 2008 GFSR for details (IMF, 

2008a). 10The methodology for estimating losses and charge-off 

rates is discussed in greater detail in Annex 1.3. Losses on 

loans and securities in other regions are not included in 

these estimates. 

–2

–1

0

1

2

3

4

Sources: Federal Reserve; Bureau of Economic Analysis; and IMF staff 

estimates. 1As a percent of loans outstanding; annualized rate.

2Series standardized using data from 1996:Q1 to 2010:Q4.

Figure 1.11. U.S. Consumer Loan Charge-Off Rates

(In percent)

Charge-off rate1 Estimate

GDP (inverted; right scale) 2

Consumer loan lending standards (right scale) 2

1996 98 2000 02 04 06 08 10

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Tighter lending 

standards; 

slower growth

1998 99 2000 01 02 03 04 05 06 07 08

0

2

4

6

8

10

–3.5 12

–3.0

–2.5

–2.0

–1.5

–1.0

–0.5

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Slower growth; 

tighter lending

standards

Recessions

Industrial production1

Bank lending standards2

S&P 500 earnings per share1

High-yield default rate 

(right scale)3

Faster growth; 

looser lending

standards

Sources: Bureau of Economic Analysis; Federal Reserve; JPMorgan Chase & Co.; 

Merrill Lynch; Moody’s; National Bureau of Economic Research; and IMF staff 

estimates. 1Year-on-year changes; standardized; inverted scale.

2Net survey balances; standardized.

3Issuer-weighted.

Figure 1.12. Macroeconomic and Corporate 

Indicators and Default Rates

(In percent)
15

to adversely affect economic prospects more 

broadly. Both high- and low-grade corporate 

debt have been signifi cantly weakened by developments in the fi nancial sector, while non-fi nancial sectors, such as industrials and utilities, are 

also starting to weaken.11 The prime residential 

mortgage market has been affected by a combination of factors, including especially rising 

unemployment and falling U.S. house prices. 

The impact of these factors had previously been 

felt mostly by less creditworthy borrowers of 

mortgage loans. 

11Potential losses due to the bankruptcy of Lehman 

Brothers are included in our estimates for losses on 

corporate debt. 

While writedowns have mushroomed over 

the last year, there is still a signifi cant gap 

between reported and estimated writedowns. 

Reported writedowns reached $760 billion by 

end- September, $580 billion of which were 

incurred by global banks (Figure 1.14).12 As 

expected, losses have been mostly mortgage12Writedowns for individual banks have been somewhat higher than expected. This appears to be mainly 

due to one or more of the following factors: (1) earlier 

incomplete disclosure of exposure to problem loans or 

securities; (2) higher-than-expected loss provisions for 

loans held to maturity; (3) losses on restructurings and 

sales of subsidiaries with credit market exposure; and (4) 

losses on trading and execution, possibly due to leveraged 

exposure. 

Table 1.1. Estimates of Financial Sector Potential Writedowns

(In billions of U.S. dollars) 

Base Case Estimates of Writedowns 

on U.S. Loans Writedowns on U.S. Loans

Outstandings

April

estimated

losses

October 

estimated

losses Banks Insurance

Pensions/ 

Savings

GSEs and 

government

Other

(hedge 

funds, etc.)

Subprime 300 45 50 35–40 0–5 0–5 — 10–15 

Alt-A 600 30 35 20–25 0–5 0–5 — 5–10 

Prime 3,800 40 85 25–30 0–5 0–5 45–55 0–5 

Commercial real estate 2,400 30 90 60–65 5–10 0–5 — 10–20 

Consumer loans 1,400 20 45 30–35 0–5 0–5 — 10–15 

Corporate loans 3,700 50 110 80–85 0–5 0–5 — 25–30 

Leveraged loans 170 10 10 5–10 0–5 0–5 — 0–5 

Total for loans 12,370 225 425 255–290 5–40 0–35 45–55 60–100 

Base Case Estimates of Mark-to-Market Losses 

on Related Securities Losses on Securities

Outstandings

April

estimated 

mark-to-market 

losses

October 

estimated

mark-to-market 

losses Banks Insurance

Pensions/ 

Savings

GSEs and 

government

Other

(hedge 

funds, etc.)

ABS 1,100 210 210 100–110 40–45 35–55 10–15 10–25

ABS CDOs 400 240 290 145–160 55–75 30–45 15–20 15–30

Prime MBS 3,800 0 80 20–25 10–15 10–20 20–25 0–5

CMBS 940 210 160 80–90 20–25 15–35 10–20 15–20

Consumer ABS 650 0 0 — — — — —

High-grade corporate debt 3,000 0 130 65–75 20–30 20–35 — 5–20

High-yield corporate debt 600 30 80 45–50 10–15 15–20 — 5–15

CLOs 350 30 30 15–20 0–5 0–5 — 5–10

Total for securities 10,840 720 980 470–530 155–210 125–215 55–80 55–125

Total for loans and securities 23,210 945 1,405 725–820 160–250 125–250 100–135 115–225

Sources: Goldman Sachs; JPMorgan Chase & Co.; Lehman Brothers; Markit.com; Merrill Lynch; and IMF staff estimates. 

Note: The prime residential loans category includes a portion of GSE-backed mortgage securities. ABS = asset-backed security; CDO = collateralized debt obligation; CLO = collateralized loan obligation; GSE = government-sponsored enterprise; CMBS = commercial mortgage-backed 

security; MBS = mortgage-backed security. 

THE DEFAULT CYCLE
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

16

related, and have been primarily shouldered by 

U.S. and European banks, with limited losses in 

Asia. At the same time, provisioning for future 

losses on corporate and leveraged loans has 

increased, and further writedowns have been 

taken on trading activities and exposures to 

monolines.

Nonbank institutions have shouldered 

at least $180 billion of losses to date. Some 

$100 billion of credit-related losses have been 

reported by insurance companies thus far 

(of which $20 billion is by monolines). Writedowns taken by GSEs have been about $20 

billion but are expected to climb further by 

up to $115 billion over the full credit cycle. 

Hedge funds and other market participants 

are estimated to have incurred $60 billion in 

losses. Data on losses by pension and savings 

institutions are unavailable. Accordingly, at 

least 55 percent of known potential losses (in 

our base case) have already been recognized by 

fi nancial institutions.

. . . and could rise further under a scenario of greater 

stress.

Higher losses could materialize across most 

loan categories under the stress scenario 

(Table 1.2).13 Peak charge-off rates (which are 

30 to 50 basis points higher than in our baseline 

scenario) would translate into losses on bank 

loans that are about 20 percent ($80 billion) 

higher. Should markets for securitized debt 

price in a more negative scenario, losses could 

be of a greater magnitude.

In Europe, high leverage and falling house 

prices portend worsening credit quality in some 

mortgage markets.

Global losses could be higher should credit 

quality worsen and writedowns mount on nonU.S. loans. Already, fundamentals are deteriorating in some European economies, where house 

price appreciation has slowed considerably or 

turned negative, lending standards have tightened, and mortgage rates have risen. Delinquen13See Annex 1.3 for details on the scenario analysis. 

0

200

400

600

800

1000

1200

1400

1600

U.S. 

subprime crisis 

(2007–present)

Asia 

banking crisis 

(1998–99)

Japan 

banking crisis 

(1990–99)

U.S. savings 

and loan crisis

(1986–95)

0

5

10

15

20

25

30

35

40

Sources: World Bank; and IMF staff estimates.

Note: U.S. subprime costs represent staff estimates of losses on banks and other 

financial institutions from Table 1.1. All costs are in real 2007 dollars. Asia includes 

Indonesia, Malaysia, Korea, the Philippines, and Thailand.

Figure 1.13. Comparison of Financial Crises

Banking losses (in billions of U.S. dollars, left scale)

Percent of GDP (right scale)

Other financials 

(in billions of U.S. dollars, left scale) 
17

cies have begun to rise on mortgage-related and 

other asset-backed securities, though they vary 

by sector, vintage, and collateral type. Collateral 

performance has been weakest on U.K. mortgage-related assets, for which primary markets 

are inactive, secondary market liquidity is thin 

(with the exception of AAA-rated securities), 

and spreads on related securities continue to 

widen.

As in the United States, the U.K. household 

sector is highly leveraged and is now undergoing a similar deleveraging-cum-housing-defl ation 

cycle (Figure 1.15). So far, mortgage arrears 

have picked up moderately from low historical levels, and bank charge-offs on mortgages 

remain very low.14 However, with house prices 

falling rapidly, arrears and losses are likely to 

rise several times over. Nevertheless, our analysis 

suggests that U.K. defaults are unlikely to breach 

their historical peak, reached in the early 1990s, 

with mortgage loss rates likely to be considerably lower than those observed in the United 

States (Figure 1.16).15 Moreover, the effects of 

14These data may understate the actual level, since 

they exclude many of the lenders that specialize in 

the nonconforming market, several of which have 

already experienced diffi culties and scaled back their 

operations. 15These estimates assume that house prices decline 

15.5 percent year-on-year, GDP growth troughs at 0.6 percent year-on-year, and the unemployment rate remains 

fairly stable at 5.8 percent in 2009. 

Table 1.2. Estimates of Potential Losses 

on Loans

(In billions of U.S. dollars; 2007:Q2 through August 2008) 

Outstanding

Base

Case

Stress

Case Difference

All residential 4,700 170 210 40

Commercial real estate 2,400 90 100 10

Consumer loans 1,400 45 50 5

Corporate loans 3,700 110 130 20

Leveraged loans 170 10 15 5

Total for loans 12,370 425 505 80

Source: IMF staff estimates.

Note: The analysis applies the specific lending standards index 

for each loan class, and the assumptions for them are discussed in 

Box 1.6 in Annex 1.3.

0

100

200

300

400

500

600

700

Americas 800 Europe Asia

Bank writedowns by region

0

100

200

300

400

500

600

700

Mortgages 800

Loans/leveraged finance

Other

SIVs/conduits

Trading

Monolines

Bank writedowns by type

0

100

200

300

400

500

600

700

800

Banks 900

Hedge funds/other

Insurers

GSEs

Financial sector writedowns

Sources: Bloomberg L.P.; and IMF staff estimates.

Note: SIVs = structured investment vehicles; GSEs = government-sponsored 

enterprises.

Figure 1.14. Financial Sector Losses

(In billions of U.S. dollars; 2007:Q2 through August 2008)

THE DEFAULT CYCLE
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

18

nonprime losses should be less pronounced, 

given the small market share of U.K. nonprime 

loans.

The Spanish household sector has also 

become signifi cantly leveraged in recent years, 

with the ratio of household debt to disposable 

income exceeding the average ratio for the euro 

area, and approaching that of the United States. 

Doubtful loans are increasing, although from a 

historically low level.16 However, as in housing 

markets with similar appreciations elsewhere in 

Europe, banks have become more cautious in 

their lending, with year-on-year euro area mortgage lending falling.

Financial System Deleveraging

This section examines the diffi cult and 

protracted nature of the deleveraging process 

in the fi nancial system and its implications for 

the real economy. There has been an epochal 

restructuring of the fi nancial system, triggered 

and accelerated to a large extent by market 

pressures. Financial institutions have been 

forced to make signifi cant adjustments over 

the past six months, with the process at times 

disorderly and exacerbating the systemic aftershocks. Each of the major U.S. broker-dealers 

no longer exists in its previous form, whether 

due to bankruptcy or by either becoming, or 

being absorbed by, a deposit-taking bank. In 

addition, substantial amounts of capital have 

been raised. Banks have widened their sources 

of funding to compensate for dysfunctional 

securitization and interbank funding markets. Some banks have sold liquid assets and 

absorbed off-balance-sheet structured investment vehicles and conduits, while attempting to 

reduce balance sheet risk and strengthen liquidity buffers. Others are allowing illiquid assets to 

16Nonperforming loans at large Spanish banks have 

risen from 0.6 percent of total loans at the end of 2007 

to 1.1 percent as of June 2008. The nonperforming-loan 

ratio is based on an unweighted average of the fi ve largest Spanish banks. For an assessment of global housing 

market developments, see Box 1.2 of the October 2008 

WEO (IMF, 2008d). 

1999 2000 01 02 03 04 05

40

60

80

100

120

140

160

Sources: Banco de España; Datastream; Eurostat; and IMF staff estimates.

Figure 1.15. Ratio of Household Debt to Gross 

Disposable Income

(In percent)

United States

Germany

France

United Kingdom

Spain

Euro area
19

run off at maturity, but this takes time, as the 

impaired assets have average maturities of four 

to fi ve years.

The deleveraging process may continue past 

the end of the decade.17 Bank balance sheets 

are under pressure to expand, as certain types of 

near-bank entities contract, fold, or are bought, 

and credit is reintermediated, and as fi rms draw 

down prenegotiated credit lines.18 Confi dence 

in securitization markets remains impaired, and 

regulators, credit rating agencies, and markets 

are reevaluating whether and how banks should 

continue to be restructured to cope with the 

risks revealed during the crisis. The pace of 

deleveraging will depend on the depth of the 

economic and housing downturns, the scope 

for banks to restructure activities and rebuild 

profi ts, and the willingness of investors to 

provide banks with fresh capital. Should conditions improve faster than expected, deleveraging 

will be smoother and the supply of credit less 

constrained.

Deleveraging extends beyond the banking 

system to other leveraged fi nancial institutions, 

such as hedge funds and other near-bank entities through the unwinding of structures that 

were highly leveraged, thinly capitalized, and/or 

17Deleveraging, in this context, covers a range of strategies. On the liabilities side of bank balance sheets, these 

strategies entail raising fresh capital, as well as ensuring diversifi ed, longer-maturity, and durable sources of 

funding. On the assets side, the strategies are to avoid 

concentrated exposures to illiquid or risky assets, dispose 

of noncore assets, and adopt hedging strategies that accurately mirror exposures. 18“Near-bank entities” typically intermediate credit (or 

hold securities of those loans) traditionally originated by 

banks, primarily rely on capital market fi nancing, have 

not generally been eligible for regular central bank funding (though access has been expanding), and in some 

cases are only loosely regulated. They include the specialpurpose entities that issue ABS, mortgage-backed securities (MBS), CDOs, and asset-backed commercial paper 

(ABCP), and fi rms such as real estate investment trusts, 

global funds, the GSEs, and, until recently, the fi ve major 

U.S. investment banks. These entities also intermediate 

some securities, such as auction rate securities, tender 

option bonds, and variable-rate demand obligations that 

transform the long-term liabilities of U.S. municipalities, student loan originators, and others into short-term 

liabilities. 

–3

–2

–1

0

1

2

3 0

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

Sources: Bank of England; HBOS; U.K. Office for National Statistics; and IMF 

staff estimates. 1

As a percent of all loans.

Figure 1.16. U.K. Mortgage Foreclosures

(In percent)

Changes in household 

consumption (standardized)

Unemployment rate 

(inverse, standardized)

House price appreciation 

(standardized)

Foreclosure rate (right scale)1

1985 90 95 2000 05 10

Estimate

FINANCIAL SYSTEM DELEVERAGING
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

20

heavily reliant on short-term fi nancing to fund 

long-term assets.19 Deleveraging can be observed 

in the curtailment in asset acquisition by U.S. 

ABS issuers and the major broker-dealers since 

mid-2007 (Figure 1.17).

Capital will need to rise in relation to credit and balance 

sheet size, but to what standard?

Regulators, rating agencies, and investors 

use different metrics for assessing bank capital 

adequacy, and these measures have infl uenced 

the amount and form of capital raised by banks. 

The Basel II regime puts primary emphasis 

on the ratio of Tier 1 capital to risk-weighted 

assets. Rating agencies, too, continue to prefer 

risk-weighted asset measures, although they 

favor different measures of capital. However, 

investors have placed increasing emphasis on 

simple measures, after losing confi dence in 

the valuation and risk assessment of structured 

fi nance products and other illiquid assets. The 

leverage ratio (i.e., the ratio of Tier 1 capital to 

total assets) is a simple measure that is used as 

an additional capital fl oor by U.S. regulators, 

and recently has been promoted as a complementary measure by Swiss regulators (though as 

noted below, it is not, by itself, precise enough 

to be the primary measure of solvency risk) 

(Box 1.2). The following exercise uses both the 

ratio of common equity to risk-weighted assets 

and the leverage ratio to project one possible 

profi le and path of adjustment for U.S. and 

European banks (Figure 1.18), consistent with 

the credit growth scenario outlined above.20

These leverage ratios are reduced over time by 

rebuilding capital cushions in relation to assets. 

19As of early 2007, near-bank entities had an estimated 

$15 trillion of assets, compared with the $10 trillion 

and $40 trillion in assets of U.S. and European banks, 

respectively. 20This is not the only possible path, and should not be 

interpreted as suggesting that the new levels of capital 

are the “correct” ones. They are merely capital ratios that 

market analysts have suggested as appropriate mediumterm goals for banking systems as a whole. Capital ratios 

for individual banks will, and should, vary, depending on 

their circumstances. 

0

400

800

1200

1600

2003 04 05 06 07 08

Sources: Federal Reserve; and IMF staff estimates.

Note: ABS = asset-backed security; GSE = government-sponsored enterprises.

Figure 1.17. Net Acquisition of Financial Assets by 

U.S. Financial Firms

(Annual rate in billions of U.S. dollars, four-quarter moving average)

Commercial banks and GSEs

ABS issuers and broker-dealers

2

3

4

5

6

7

2008 09 10 11 12 13 14

5

6

7

8

9

10

2008 09 10 11 12 13 14

Sources: Bankscope; Bloomberg L.P.; Merrill Lynch; and IMF staff estimates.

Note: Dotted lines represent base case scenario for new standards of capital 

adequacy.

Figure 1.18. Bank Ratios

(In percent)

United States

United Kingdom

Tier 1 Capital-to-Total Assets (Leverage)

Common Equity to Risk-Weighted Assets

Europe excluding

United Kingdom

United States

United Kingdom

Europe excluding

United Kingdom
21

The factors infl uencing the path of adjustment 

are discussed below.

There are important differences between the 

drivers of deleveraging in the United States and 

those in Europe. In the United States, the pressures derive to a greater extent from the need 

to cover losses, which have depleted capital, 

while in Europe, the deleveraging process is also 

driven by the need to reduce leverage multiples 

closer to those in the United States and to avoid 

the earnings volatility that comes with having a 

large marked-to-market balance sheet. 

The current global crisis has greatly increased 

market uncertainty about the appropriate measures that should be used for measuring banks’ 

capital adequacy.

Banking regulators and market practitioners 

point out that the build-up of excessive exposures occurred while banks were still largely 

operating under the Basel I capital framework, 

and that Basel II will more appropriately align 

capital requirements with risk, but would not 

have prevented the current outcome. Regulators 

will now take additional measures to improve 

the measurement of risks relating to structured 

instruments, off-balance-sheet items, and contingent liquidity risks. These will improve both 

the minimum capital requirements that Pillar 

1 sets out and the supervisory review of banks’ 

risk management practices under Pillar 2.1 The 

net result will be more robust regulatory capital 

requirements going forward.

But many market participants and other 

observers are reacting to market valuation 

uncertainties by monitoring readily calculable 

measures of capital adequacy, including the 

leverage ratio—the ratio of equity to assets. 

Additionally, market observers and rating 

agencies are placing a particular premium on 

loss-bearing capital, in the form of common 

equity, as opposed to hybrid capital. Under current circumstances, the leverage ratio is a useful 

but simple measure that is not, by itself, precise 

enough to be the primary measure of solvency 

Note: The main author of this box is Rupert 

Thorne. 1Basel II is arranged into three pillars: Pillar 1 on 

minimum capital requirements; Pillar 2 on supervisory review of bank practices; and Pillar 3 on market 

disclosure.

risk or to ensure a suffi cient buffer against 

losses on risky assets.

A key lesson is that the risks to solvency cannot be adequately analyzed using only a singledimensioned statistic. Risk-based capital ratios 

are, in principle, superior measures of capital 

adequacy, but their accuracy relies heavily on a 

proper risk valuation of assets. Under current 

circumstances, given the uncertainty about valuations of assets, the simple leverage ratio may be 

a useful complementary measure. Monitoring of 

multiple measures of capital and liquidity ratios 

(whether or not formal limits are established 

for them), together with rigorous stress testing, 

can help to ensure that fi rms remain robust to a 

variety of shocks.

Recent events have also highlighted a dilemma 

over capital adequacy; in principle, capital 

exists as a buffer to protect fi rms under diffi cult 

market conditions. But minimum requirements 

(whether set by regulators, by rating agencies, 

or implicitly by markets) can become hard limits 

and in some cases become more demanding 

during periods of market stress if risk measures 

rise as market volatility increases. In this regard, 

some have recommended allowing capital to be 

drawn down during such times, so that it acts as 

a true buffer. But such a policy probably implies 

that there should be signifi cantly higher average 

capital ratios over the cycle than at present, and 

even if supervisors may be willing to tolerate 

buffers dipping during downturns, markets may 

require further convincing that this is appropriate as they make their own assessments of 

solvency risks.2

2Chapter 3 provides some rough guidelines given 

the propensity of fair value accounting techniques to 

operate procyclically.

Box 1.2. Measuring Capital Adequacy

FINANCIAL SYSTEM DELEVERAGING
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

22

Capital-raising by banks has been signifi cant, but has 

become more diffi cult.

Global banks raised some $430 billion of 

capital from the second half of 2007 through 

September 2008.21 However, raising capital has 

become extremely diffi cult in recent months. 

First, as growth weakened and house prices continued to fall, investors’ hopes that the turmoil 

would be short-lived proved false. Second, equity 

holders in distressed institutions have incurred 

heavy losses. Third, bank share prices more 

generally have fallen substantially and could fall 

further, reducing investors’ incentives to provide 

fresh capital.22 Fourth, some rights issues in 

Europe have been poorly received. Issuing banks 

received the capital they sought, but substantial 

amounts were left with the underwriters, creating an overhang of shares that then depressed 

prices further.23 In response to concerns that 

short selling was seriously frustrating efforts by 

fi nancial fi rms to raise capital, and also concerns 

that it was aggravating the effects of false reports 

and unfounded rumors in the marketplace, 

regulators in several mature and emerging market economies adopted temporary bans on short 

sales of certain stocks, and permanent measures 

to broadly discourage “naked” short selling and 

raise disclosure requirements for short selling.24

21Box 1.3 provides a fuller analysis of reported bank 

losses and capital-raising by type and source. 22In some cases, options granted to strategic investors 

when banks raised capital at the start of the crisis are 

pushing up the cost of raising additional capital, as the 

earlier investors have to be compensated for the paper 

losses they have suffered before new capital can be raised. 23Unlike in the United States, European companies 

are required to offer new shares to existing shareholders 

fi rst to protect them from dilution (“preemption rights”), 

making issuance time-consuming. Under unstable conditions, there can be increased volatility in the price 

of the shares, which in turn can affect the success of a 

rights issue if the market price falls below the issue price. 

Streamlining the rights issue process while ensuring 

existing shareholders have other mechanisms to protect 

against dilution would help alleviate these problems. 24Countries that adopted such measures included 

Australia, Belgium, France, Germany, the Netherlands, 

Russia, Taiwan Province of China, the United Kingdom, 

and the United States. The U.S. Securities and Exchange 

Commission (SEC) issued new rules to curtail “naked” 

short sales—which have been prohibited since 1938—by 

Under a scenario of still weak housing markets, in which price declines only start to slow 

in mid-2009, public markets may not be hospitable to raising public capital. Faced with these 

circumstances, banks would need to rely more 

on raising capital through retained earnings and 

from private sources of capital, while slowing the 

pace of asset growth to reduce leverage. However, in view of further losses ahead, prospects 

for building internally generated capital are 

likely to remain poor through 2009.25

With the global economy starting to recover 

later in 2009, consistent with the WEO scenario, 

and house prices showing early signs of stabilizing, bank earnings rise and prospects for raising 

capital improve.26 Just ahead of these developments, the market for raising bank capital is 

expected to re-open in 2009, allowing banks to 

raise $675 billion in additional capital globally 

over the next few years. Nevertheless, sizable 

adjustments would be needed on the asset side 

of bank balance sheets, in addition to capitalraising, in order to boost capital ratios and 

achieve the desired restructuring of business 

lines, as illustrated below.

Deleveraging through asset sales and run-off is proving 

to be challenging given current market conditions.

Deleveraging by reducing assets has also 

proved problematic for banks. Selling assets in 

illiquid market conditions crystallizes losses that 

deplete capital and therefore push up leverage 

multiples. Distressed sale prices can establish 

a fresh benchmark price to which remaining 

assets are marked, potentially affecting large 

tightening prior possession requirements and raising 

penalties for delivery failure. This ban was lifted in early 

October. The U.K. Financial Services Authority also 

banned short selling in securities of fi nancial fi rms and 

added disclosure requirements for substantial short interest positions in securities undertaking rights issues. 25Under our stress scenario, peak defaults would be 

about 20 to 25 percent higher than in our base case, and 

they could persist for longer than in our base case. This 

would considerably aggravate the challenge of acquiring 

more bank capital. 26Although only an illustration, the paths fi t well with 

other approaches. See, for instance, IMF (2008b, 2008d); 

and Claessens, Kose, and Terrones (forthcoming). 
23

This box provides greater detail on bank writedowns 

and capital-raising efforts, as well as the changing 

nature of banks’ investor base.

Since the turmoil started in mid-2007, global 

writedowns at banking institutions have totaled 

roughly $580 billion through September 2008. 

They have been concentrated in a few banks, 

with the three largest losers accounting for 

around 30 percent, and the 20 largest around 

three-quarters, of the total. About 95 percent of 

the writedowns were reported by North American and European banks, with only a small 

amount reported by Asian fi rms. Over the same 

period, capital raised has totaled $430 billion 

and has been similarly concentrated in a small 

number of institutions in the United States and 

Europe (see fi rst fi gure).

The form of capital that was raised changed 

during this period (see second fi gure). At 

fi rst, a substantial portion was in the form of 

hybrid securities, which combine elements 

of debt and equity.1 These were attractive to 

issuers, as they are tax effi cient, do not dilute 

common—shareholders, and partly count 

toward— regulatory capital. They were seen as 

signaling to the market that the bank was in a 

strong position (in contrast to common equity 

issuance), and offered investors the security of 

a bond, with some element of upside potential. However, hybrid capital has become less 

attractive in recent months as regulators, 

rating agencies, and investors have grown less 

comfortable with its high share within total 

capital.2 Recently issued hybrid instruments 

1These include preferred and preference shares, 

trust preferred securities, deferrable coupon securities, and various convertible securities. 2Beginning in 2009, U.S. banks will be limited to 

a maximum of 25 percent trust preferred capital to 

Tier 1 capital (and 15 percent for internationally 

active banks).

0

100

200

300

400

500

600

Writedowns Capital

Source: Bloomberg L.P.

2007:Q3

0

50

100

150

200

Europe

Asia

Americas

Writedowns

Capital

Writedowns

Capital

Writedowns

Capital

Writedowns

Capital

Writedowns

Capital

2007:Q4 2008:Q1 2008:Q2 2008:Q3

2007:Q3–2008:Q3

Bank Writedowns and Capital Raised

(In billions of U.S. dollars)

Note: The authors of this box are Ana Carvajal, 

Antonio Garcia Pascual, and Xiongtao Huang.

Box 1.3. Global Bank Writedowns and Capital-Raising

FINANCIAL SYSTEM DELEVERAGING
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

24

carry considerably higher spreads than those 

issued before the crisis. For example, deeply 

subordinated bonds issued by some of the 

affected institutions were paying yields of 7.5 

to 8.5 percent (spreads of 300 to 400 basis 

points over U.S. treasuries), compared with 

around 6 percent (or about 100 basis points 

over treasuries) before the crisis.

Box 1.3 (concluded)

Preferred 

shares

42%

Perpetual bonds

4% Deeply 

subordinated 

bonds

10%

Convertible 

bonds

13%

Asset sales

6%

Stock offers

27%

Rights issue

38%

Other

13% Preferred 

shares

3%

Perpetual bonds

7%

Deeply 

subordinated bonds

10%

Convertible bonds

12%

Asset sales

2%

Stock offers

15%

Capital Raised by Type of Instrument

(In percent)

Sources: Bloomberg L.P.; and IMF staff estimates.

North American Banks

($178 billion)

European Banks

($153 billion)

Sovereign 

wealth funds

7%

Public investors

69%

Other institutional 

investors

24% Sovereign 

wealth funds

60%

Public investors

12%

Other institutional 

investors

28%

Capital Raised by Source

(In percent)

Sources: Bloomberg L.P.; and IMF staff estimates.

January 2008–July 2008

($300 billion)

July 2007–December 2007

($56 billion)
25

parts of the banking system and providing a further negative feedback loop.27 Deleveraging has 

also been hampered as off-balance-sheet vehicles 

are absorbed and customers tap credit lines. 

Banks are renewing such lines sparingly when 

they expire, but most are reluctant to reduce or 

withdraw lines before expiry for fear of alienating good customers. While some banks have 

failed or been bought outright, others are pursuing the sale, or winding down, of businesses less 

viable under current funding conditions, but 

this takes time. In the scenario above, we assume 

that banks are able to sell some $2.4 trillion of 

assets to nonbanks, while some $7.6 trillion of 

bank assets run off bank balance sheets during 

2008–13, reducing credit growth. In total, U.S. 

and European banks shed some $10 trillion of 

assets, equivalent to around 14.5 percent of the 

stock of bank credit in those regions.28

Markets are pressuring banks to fundamentally change 

business models, and the deleveraging process is 

forcing industry consolidation.

The fi nancial crisis has prompted a broad 

reassessment of fi nancial sector business models, 

27For instance, Merrill Lynch’s $6.7 billion sale of ABS 

CDOs to an affi liate of Lone Star Funds in August 2008 

at a price equivalent to 22 cents on the dollar was seen 

as establishing a new mark for such assets that all banks 

would then have to adopt. 28We assume that heavier borrowing in securities 

markets offsets only a small part of the slowdown in asset 

growth. 

and in some cases investors are making swift 

judgments regarding which institutions are likely 

to survive or thrive. This has exerted pressure 

on bank equity valuations, pushing a number 

of bell-weather institutions into consolidation, 

and ultimately resulted in the demise of the 

stand-alone broker-dealer model. Many market 

participants concluded that the business model 

they followed made them vulnerable, especially 

during periods of prolonged market illiquidity. In particular, broker-dealers generally had 

leverage of around 30 times, with around half of 

their assets funded in the repurchase (“repo”) 

market. Repo markets are subject to sudden 

pullbacks by cash lenders, especially when markets are illiquid, since a default may leave the 

cash lender with collateral that may be diffi cult 

to sell. The short maturity of most repo transactions means margins and “haircuts” can swiftly 

be used to exclude a borrower from the market. If the broker-dealer tries to pass on higher 

fi nancing costs to its clients, those clients will 

take their business elsewhere, and may ask for 

their cash to be segregated, potentially leading 

to a run on cash at the broker-dealer. After this 

happened to Bear Stearns, markets were fi nely 

tuned to the risk of another occurrence. It was 

this factor that helped push Lehman Brothers to 

fi le for Chapter 11 bankruptcy protection, which 

created the conditions that were conducive to 

the merger of Bank of America and Merrill 

Lynch, and which made the remaining investThere has been a marked difference between 

the forms of capital raised by U.S. and European banks. U.S. banks have issued more hybrid 

capital, while European banks relied more on 

new stock issues. The difference refl ects in part 

the larger share of hybrid instruments in Tier 1 

capital for European versus U.S. fi rms already, 

and the signifi cantly larger use of discounted 

rights issues by European institutions. Rights 

issues allow fi rms to mitigate the “dilution” of 

existing shareholders that arises from discounted sales that target a narrow group of 

investors.

The profi le of those investing in banks has 

also changed (see third fi gure). In the second 

half of 2007, some 88 percent of fresh capital 

came from institutional investors and sovereign 

wealth funds (SWFs), with the latter investing 

in just a handful of institutions. Since January 

2008, in contrast, 69 percent of the funds raised 

came from public investors and only 31 percent 

from institutional investors and SWFs.

FINANCIAL SYSTEM DELEVERAGING
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

26

ment banks targets for speculative pressure. 

Subsequently, both Morgan Stanley and Goldman Sachs were granted approval to transform 

into bank holding companies, effectively ending 

the era of a distinction between investment and 

commercial banking created by the 1933 GlassSteagall Act.

Markets are also discriminating between 

different commercial bank business models, as 

suggested by the relationship between U.S. bank 

business lines, price-to-book values (P/B), and 

capital positions (Figure 1.19). First, after having 

an average P/B of almost 2.0 prior to the crisis, 

most banks now have ratios below 1.0 as share 

prices have fallen. Those banks also have Tier 

1 regulatory capital ratios that are lower than 

the average of 8.9 percent for banks with P/B 

values over 1.0, suggesting that markets may still 

view these banks as undercapitalized. Second, 

a majority of banks that have a measure of 

business risk greater than the median (15) are 

trading below 75 percent of their book values. 

This could refl ect investors’ lack of confi dence 

in the banks’ ability to manage future credit 

losses. Third, most of the fi rms with a Tier 1 

ratio that is more than 1 percentage point below 

8.9 percent have especially low P/B ratios. Most 

of the banks in the bottom right quadrant of 

Figure 1.19 are U.S. regional banks or thrifts. In 

sum, market participants are penalizing banks 

with signifi cant exposure to weaker business 

lines and lower capital adequacy ratios, suggesting that these banks need to enhance capital 

buffers, sell assets, or be acquired by more diversifi ed and better-capitalized competitors.

In Europe, banks exposed to falling real 

estate values have lower relative valuations. For 

example, the P/B ratios of banks in Denmark, 

Ireland, and the United Kingdom have fallen 

signifi cantly since early 2007 (to below 1.0 in the 

case of Ireland). These countries have experienced the steepest deceleration in real estate 

values in the region over the last couple of years 

(Figure 1.20). In contrast, the P/B ratios of 

banks in Germany and the Netherlands have 

been steadier over the crisis period, as have 

their corresponding real estate values.

Figure 1.19. U.S. Banks’ Price-to-Book Ratios and 

Risk Exposures

Sources: SNL Financial; and IMF staff estimates.

Note: Represents the top 30 publicly-traded U.S. banks and thrifts by assets. The 

size of the circles represents a bank’s percentage point deviation from an 8.9 

percent Tier 1 regulatory capital ratio. Yellow circles represent negative deviations; 

green circles represent positive deviations. The risk exposure is a composite ranking 

of a firm’s exposure to real estate loans, to regions that have experienced the largest 

declines in home prices, and that have the largest share of nonperforming assets.

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0

3.5

2.9

0.6

(0.3)

(1.2)

(1.8)

(1.7)

(1.6)

(1.5)

(1.3)

0.6 (0.7)

(0.9)

(0.1) (0.2) 0.1

(0.6) (0.7)

(1.4)

(0.6)

(1.4) (1.2) (1.5)

(1.2)

(1.3)

1.9

(2.3)

Risk exposure ranking (30 is the highest exposure)

Price-to-book ratio
27

Within countries, investors are discriminating 

between banks with high real estate exposures 

and those with diversifi ed businesses. In the 

United Kingdom, mortgage banks are currently 

trading signifi cantly below their book values, 

while those with more diversifi ed revenue 

streams (e.g., global exposure, several business 

lines) are trading above (Figure 1.21). As in the 

United States, markets are also penalizing U.K. 

banks that rely disproportionately on wholesale 

funding. In order to reduce funding risk, banks 

are competing aggressively for retail deposits, 

with some mortgage specialists offering retail 

accounts at above-wholesale-market interest 

rates. However, markets appear to doubt the 

longer-term viability of these mortgage banks 

as stand-alone businesses, leading to speculation that they could be consolidated with more 

diversifi ed fi rms.

Other factors besides real estate exposure 

are also affecting bank valuations. For example, 

overall, Spanish banks have maintained P/B 

ratios of 1.6, above those in much of Europe. 

This may refl ect their lower leverage levels 

compared with other European banks, greater 

reliance on more stable deposit funding, and a 

better regulatory environment.

In sum, the market is sending clear signals to 

bank management regarding unfavored business 

models; this has already resulted in the effective end of independent U.S. investment banks 

as viable entities. More broadly, this is likely 

to result in further consolidation, including 

through the exit of further banks and nonbank 

intermediaries. 

Certain aspects of bank funding models—including overreliance on cross-border funding—have contributed to 

vulnerabilities and exacerbated deleveraging pressures.

With securitization and wholesale funding 

markets adversely affected by the credit crisis, 

many banks have sought alternative sources of 

funds, including by increasing debt issuance 

(especially covered bonds, private placements, 

registered bonds, and offshore issues), aggressively bidding for customer deposits, and drawing on central bank and other facilities. Events 

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

2007 2008

2002 03 04 05 06 07 08

–15

–10

–5

0

5

10

15

20

25

Sources: Bloomberg L.P.; and IMF staff estimates.

Note: Price-to-book values less than 1.0 may indicate the market’s lack of 

confidence in the ability of banks in the respective countries to manage 

potential losses from current exposures.

Figure 1.20. European Banks’ Price-to-Book Ratios 

and European Real Estate Prices

France

Denmark

Germany

Ireland

Netherlands

Spain

United Kingdom

France

Denmark

Germany

Ireland

Netherlands

Spain

United Kingdom

Bank Price-to-Book Ratios

Change in Real Estate Prices

(Percent)

FINANCIAL SYSTEM DELEVERAGING
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

28

have shown that over-reliance on wholesale 

funding can be a critical vulnerability, especially 

when the quality of the assets funded is called 

into question.29

Simultaneously, refi nancing risk and costs 

have increased (Figure 1.22), as longer-term 

wholesale fi nancing has become less available, 

leading to greater bank reliance upon shortterm funding (overnight and weekly). Given 

the shortening in maturity of previous debt 

and loan issues (Figure 1.23), over the next 15 

months the top 15 banks face funding needs 

of over $700 billion. More creditworthy banks 

have responded by expanding their issuance of 

longer-dated paper to secure their funding over 

the medium term, leading to “barbell-shaped” 

maturity profi les for their debt.30

Some banks with high exposure to less hospitable wholesale funding markets have responded 

by aggressively competing for deposits. Euroarea banks hit by wholesale funding strains, 

for example, were able to expand their retail 

deposit base and reduce their funding gap.31

Despite these efforts to diversify funding, however, wholesale borrowings remain their largest 

funding source with large refi nancing requirements in 2009 and 2010. In general, highly 

leveraged and less creditworthy banks that 

aggressively bid for customer deposits and rely 

heavily on short-term debt have experienced 

relatively large increases in their funding costs, 

reducing their profi tability and ability to raise 

additional capital. The almost complete shutdown of securitization markets in Europe has 

made deleveraging more diffi cult (Table 1.3). In 

contrast, in the United States, the securitization 

market, though impaired, is still allowing banks 

29See Chapter 2 for further details. 

30For example, major Australian banks sharply 

expanded their bond issuance during the fi rst quarter of 

2008, over two-thirds of which was issued in longer-dated 

offshore tenors, mostly in dollars and euros. As a result, 

these Australian banks are generally ahead of their funding plans, albeit at a higher cost. 31The funding gap was reduced from 1,540 billion 

euros in September 2007 to 1,410 billion euros in March 

2008 (ECB, 2008, p. 110). 

2003 04 05 06 07 08

0

40

80

120

160

200

240 EUR 1 year

USD 1 year

GBP 1 year

EUR 10 year

USD 10 year

GBP 10 year

Source: Bloomberg L.P.

Note: Banks’ bond spreads are estimated as the difference in the Bloomberg 

corporate bond bank AA indices in the United States, United Kingdom, and 

European Union. Indices covered domestic currency bond issued by the 

banking industry in corresponding region. The government bond benchmarks 

are chosen to match the relevant maturities of the bank bond issues.

Figure 1.22. AA Rated Bank Bond Index Spreads

Relative to Government Bonds

(In basis points)

Housing-oriented Diversifed

–2.0

–1.5

–1.0

–0.5

0

0.5

1.0

1.5

Figure 1.21. U.K. Banks’ Price-to-Book Ratios

Current

Change since end-2006

Source: Bloomberg L.P.
29

to move some assets they originate off balance 

sheets.32

Cross-border fi nancing of banking systems has 

also emerged as a source of systemic liquidity 

risk. Banks heavily dependent on international 

funding and swap markets experienced signifi -

cant stress as these markets came under pressure. Indeed, those that have experienced the 

most severe stress where governments had to 

inject capital, notably Iceland (Glitnir Bank) and 

Belgium (Fortis and Dexia), are large relative to 

their home country fi nancial systems and therefore had to rely more on wholesale cross-border 

fi nancing to achieve the high leverage necessary 

to boost returns. European banks with large 

holdings of dollar assets were especially exposed. 

These assets were fi nanced in the wholesale 

market, including from U.S. banks, with much 

of this short-term borrowing from interbank 

markets, as refl ected in the rise in borrowings 

from banks shown in Figure 1.24.33 This became 

apparent when the crisis hit and European 

banks responded by raising additional funds 

in Japanese yen, euros, and British pounds, 

and swapping them into dollars using foreign 

exchange and cross-currency swaps. The U.S. 

dollar foreign exchange swap and the cross-currency swap basis widened sharply against major 

currencies, as swap markets tended to become 

32Almost all new securitization in Europe is now 

retained (mostly for use as collateral with the ECB), as 

compared to before the crisis when it could be distributed to capital markets. 33See McGuire and von Peter (2008) for more details 

on the structure of this fi nancing. 

Table 1.3. European and U.S. Public 

Securitization

(In billions of U.S. dollars)

2008

annualized 2007 2006

European (RMBS and CMBS) 0 250 308

United States (HEL, CMBS, credit 

card, and student loan) 180 614 790

Sources: Citibank; and IMF staff estimates.

Note: CMBS = commercial mortgage-backed security; HEL = 

home equity loan; RMBS = residential mortgage-backed security.

FINANCIAL SYSTEM DELEVERAGING

Swit

Sweden

zerland

Spain

Netherlands

Ireland

Germany

France

Denmark

Austria

Belgium

0

50

100

150

200

250

300

350

400

Sources: Bank for International Settlements; IMF, World Economic Outlook 

database.

Figure 1.23. European Banks’ Cross-Border

Liabilities, end-2007

(As a percentage of GDP)

Switzerland

1,057%

–600

–400

–200

0

200

400

600

800

1997 99 2001 03 05 07

Source: Bank for International Settlements.

Figure 1.24. Net Cross-Border U.S. Dollar Claims

of European Banks

(In billions of U.S. dollars)

Borrowing from foreign branches and affiliates

Borrowing from unaffiliated banks

Borrowing from monetary authorities

Lending to the nonbanking sector
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

30

one-sided and illiquid (Figure 1.25),34 pushing European and other banks to rely on their 

branches and subsidiaries in the United States 

to raise dollar funding. The swap basis widened 

again around end-March as stresses on global 

markets increased, raising cross-border funding 

costs and encouraging European banks to draw 

on the Federal Reserve’s new Term Auction 

Facility (TAF). In September, it reached record 

wides in the wake of the Lehman Brothers bankruptcy, and narrowed only when the Fed added 

an overnight TAF and sharply increased dollars available through swaps with major central 

banks, effectively substituting for the illiquid 

swap market. Demand for the European Central 

Bank (ECB) TAF has been especially strong, as 

demonstrated by high participation and bid/

cover ratios (see Box 2.3 in Chapter 2), particularly by smaller European banks with limited 

U.S. operations.

However, offi cial support should be used only 

in the short-term, and many banks that have 

relied heavily on potentially risky cross-currency 

funding will need to delever before funding 

market conditions can return to normal. 

The deleveraging process will take a toll on credit 

growth to the private sector.

The complexity of the deleveraging process 

is likely to hamper the availability, and raise the 

cost, of credit for a prolonged period. Building 

upon earlier analysis (IMF, 2008c), we estimate 

the impact of bank balance sheet adjustment 

on the global supply of credit to the private 

sector.35 The supply of credit is driven by several 

factors, including the pace and depth of credit 

deterioration, capital market sentiment, and 

the degree of balance sheet adjustment needed 

34See Baba, Packer, and Nagano (2008) for a more 

detailed description of the link between swap markets 

and bank funding. 35The April 2008 GFSR outlined two scenarios for 

private sector credit growth for the United States. This 

analysis draws upon more recent data and broadens the 

analysis to include the euro area and the United Kingdom. The scenario uses WEO growth assumptions as the 

basis for generating an implied path for the demand for 

credit. 

2007 2008

–140

–120

–100

–80

–60

–40

–20

0

20

Japanese yen

British pound

Euro

Source: Bloomberg L.P.

Figure 1.25. Cross-Currency Swaps with U.S. Dollar

(In basis points, one-year tenors)

–5

0

5

10

15

20

25

1990 95 2000 05 10

Sources: Federal Reserve; official sources; and IMF staff estimates.

Figure 1.26. Private Sector Credit Growth

(Borrowing as a percentage of debt outstanding, quarter-on-quarter

annualized, seasonally adjusted)

United States

United Kingdom

Estimate

Euro area
31

to restore capital ratios. The scenario shows 

that credit growth is likely to fall sharply in the 

period ahead to levels that are consistent with 

the “credit crunch” scenario sketched in the 

April 2008 GFSR (Figure 1.26). These constrained conditions for credit are likely to persist 

at least through next year, and perhaps longer.36

This pattern is consistent with developments 

so far. U.S. credit growth has started to slow 

to rates last seen just after the 2001 downturn, 

broadly in line with the predictions in the last 

GFSR. Household borrowing has slowed markedly. The growth in corporate lending is likely 

to abate once existing loan commitments have 

matured or been drawn down. In Europe, 

household credit growth is also slowing, driven 

almost exclusively by mortgage lending (Figure 1.27). Corporate loan growth has begun to 

slow in Ireland, Spain, and the United Kingdom 

as earnings prospects have dimmed along with 

weaker economic growth.

If the expected private sector fresh capitalraising were to fail to materialize, and in the 

absence of public sector asset purchases, private 

sector credit growth could fall as low as –7.3 

percent quarter-on-quarter annualized in the 

United States, and would be slightly less negative in Europe (Table 1.4).37 The private sector 

bank capital purchases alone would limit that 

drop, but credit growth would still turn slightly 

negative quarter-on-quarter in all three regions 

before rebounding.38 To the extent the private 

sector bank capital purchases do not materialize, then a public sector alternative would need 

to be substituted. Finally, purchases of troubled 

36There is, of course, considerable uncertainty surrounding this scenario, and changes in the environment 

can rapidly alter the outcome. For instance, a more 

determined effort by banks to shrink their balance sheets 

through the sale, rather than run-off, of assets may alter 

the trajectory. Similarly, a greater-than-expected willingness among investors to subscribe to fresh capital for 

banks might allow more assets to be rolled over rather 

than to mature, and keep credit growth from dipping. 37So far, only the United States has announced a major 

publicly funded asset management initiative. 38As a point of reference, U.S. credit growth to the 

private sector has never been negative in the 55 years for 

which records exist. 

FINANCIAL SYSTEM DELEVERAGING

–15

–10

–5

0

5

10

15

20

25

30

35

40

2001 02 03 04 05 06 07 08

Source: European Central Bank.

Figure 1.27. Euro Area Financial Institution Lending 

for House Purchase

(In billions of euros)
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

32

assets of $1 trillion in the United States39

along with support measures for banks taken 

and expected from Europe, sees credit growth 

troughing at just above zero quarter-on-quarter 

annualized in the base case. In sum, in a disorderly deleveraging scenario where the private 

sector is unwilling to provide fresh capital to 

banks or purchase troubled assets, credit growth 

would become sharply negative, having a profoundly negative impact on the real economy. 

Government intervention to inject capital and 

remove troubled assets would be needed to prevent such an occurrence. 

Systemic Implications

The global fi nancial system has entered a 

new phase of the crisis, where the threat to 

solvency of some institutions has led to persistent, widespread counterparty risk concerns and 

required the commitment of public resources 

to contain systemic risks and the economic 

fallout. The burden of providing liquidity and 

supporting markets is stretching the existing 

39Including $700 billion under the U.S. Treasury 

authority to purchase troubled assets, and some $300 

billion assumed to result from the earlier commitment to 

purchase mortgage-backed securities. 

capacities of monetary and other authorities. 

This section addresses specifi c areas of the U.S. 

and other fi nancial systems that could undergo 

further stress on a systemic scale. In addition, 

some wider ramifi cations of deleveraging are 

highlighted.

Raising capital from the private sector has become very 

challenging and segments of the fi nancial system have 

become undercapitalized.

The extreme downward pressure on equity 

prices of fi nancials can be likened to a “run on 

bank capital,” or rather on the capitalization 

value of banks. Much as depositors in a conventional run on a bank might rush to withdraw 

their funds before others do so, now investors 

have been rushing to sell equities of fi nancial 

institutions. As a consequence, many banks that 

were mainstays of their economies until recently 

have seen their market capitalizations crushed, 

and are trading at close to common equity and 

less than book values (Figure 1.28). As a temporary measure to short circuit a vicious downward 

spiral, the authorities have resorted to temporary 

bans on short selling. Nevertheless, falling equity 

prices and the diffi culties fi nancial institutions 

face in raising equity from public markets illustrate a challenge for the authorities in restoring 

market confi dence. The authorities can give reassurance to depositors and, in some exceptional 

circumstances, other creditors by assurances of 

prompt action to resolve problem institutions 

and to prevent failures that could cause systemic 

problems. But such assurances provide little 

comfort to equity investors who believe that 

their investments could be largely wiped out in 

a public resolution. Indeed, the likelihood of 

offi cial intervention in less viable banks may, in 

some cases, have accelerated the downward pressure on equity prices of other banks struggling 

to delever or absorb the economic downturn. 

Government involvement in the resolutions of 

Northern Rock, Bear Stearns, Fannie Mae, Freddie Mac, AIG, Fortis, Dexia, and other institutions have illustrated this, as in each case the 

announced resolutions failed to support equity 

prices of other fi nancial institutions. 

Table 1.4. Sensitivity of Deleveraging to Public 

Sector Support

(In percent, quarter-on-quarter)

Trough in Private Sector 

Credit Growth

United

States

United 

Kingdom

Europe 

excluding 

the United 

Kingdom

With $2 trillion public sector 

purchases 0.1 0.0 0.1

With private sector bank 

recapitalization, but no 

public sector purchases –2.7 –2.2 –1.3

No public purchases 

and no private bank 

recapitalization –7.3 –6.3 –4.5

Source: IMF staff estimates.

Note: Public sector purchases (50 percent, United States; 

 10 percent, United Kingdom; and 40 percent, Europe excluding the 

United Kingdom).
33

The government-sponsored enterprises were unable to 

withstand sizable losses or provide extensive support to 

the U.S. mortgage and housing markets without support 

from the government.

The GSEs’ capital positions—which were 

already thinner than for most other fi nancial 

institutions—came under pressure as mortgage 

credit deterioration broadened, posing vulnerabilities for broader markets. Given the massive 

size of the GSEs’ assets and liabilities and global 

investor base, the broader global markets were 

vulnerable to further losses (Box 1.4), while further weakness in their capital positions limited 

their ability to facilitate new mortgage originations. Losses incurred by the GSEs have been 

relatively limited compared with their outstanding mortgage exposure. However, our estimates 

suggest that over the next few years, the GSEs 

are likely to incur a total of $100 billion to $135 

billion in gross losses (excluding the effects of 

hedging and mortgage insurance) (Table 1.1). 

Although excess capital remained above the 

surcharge on the minimum regulatory capital 

requirements for both GSEs, it would have 

been insuffi cient had losses breached the upper 

range of loss estimates.40 Furthermore, given 

their public policy mandate, the GSEs were also 

under pressure to help stabilize mortgage markets, which would have required further capital40To be classifi ed as adequately capitalized, the GSEs 

needed to meet the minimum and risk-based capital 

(RBC) standards. The minimum capital requirement for 

the retained portfolio of mortgages was set at 2.5 percent 

of assets plus 0.45 percent of adjusted off-balance-sheet 

obligations and 0.5 percent for the guarantee business 

that provides mortgage insurance. As of 2004–05, an 

additional 30 percent surcharge was applied to the GSEs’ 

minimum capital requirements, though this was reduced 

to 20 percent in March 2008 (and then to 15 percent in 

May 2008 in the case of Fannie Mae). The RBC requirement is equal to the amount of capital that each GSE 

must hold to absorb projected losses and management 

and operations risk, and is based on interest rate stress test 

scenarios. The new statutory regulator—the Federal Housing Finance Agency (FHFA)—is formulating new capital 

requirements for Fannie Mae and Freddie Mac. However, 

following the placement of the GSEs into conservatorship 

and the announcement of an enhanced credit line and 

capital injection from the U.S. Treasury, capital support is 

essentially being provided by U.S. taxpayers. 

0

200

400

600

800

1000

U.S. broker U.K. banks Euro area banks

dealers

U.S. commercial 

banks

Source: Bloomberg L.P.

Note: U.S. broker dealers include Lehman Brothers, Morgan Stanley, Goldman 

Sachs and Merrill Lynch. The other three categories, namely, U.S., U.K. and euro 

area banks, include institutions that have retail banking businesses in their 

respective regions.

Figure 1.28. Market Capitalization and Equity Book 

Values of Select Financial Institutions

(In billions of U.S. dollars)

Market capitalization end-2006

Total common equity end-2007

Current market capitalization

SYSTEMIC IMPLICATIONS
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

34

This box discusses the role of the mortgage-related 

U.S. government-sponsored enterprises, and assesses 

government actions taken to restore confi dence, reduce 

systemic risks of a more pronounced liquidity crisis, 

and stabilize the secondary mortgage market. 

The two largest housing-related U.S. government-sponsored enterprises (GSEs), Fannie 

Mae and Freddie Mac, were established with the 

intent of providing liquidity to the residential 

mortgage market, thereby promoting home 

ownership, particularly among low- and middleincome households. They fulfi ll their mission by 

purchasing mortgages from primary mortgage 

originators, packaging them into securities, 

enhanced with credit guarantees, and then selling the guaranteed securities in the secondary 

market (see fi rst fi gure). In addition, the GSEs 

purchase mortgage-related securities, loans, and 

other types of assets for their investment portfolios; this business line has been the subject of 

controversy owing to their funding advantage 

and the lack of a clear public purpose. 

The GSEs are systemically important institutions, affecting a wide range of market participants and breadth of assets. Fannie Mae and 

Freddie Mac have a combined $5.3 trillion 

in mortgage risk, based on mortgages they 

securitize ($3.7 trillion) or directly hold in their 

portfolios ($1.6 trillion). Taking into account 

the debt issued to fund their activities, the GSEs 

thus contribute roughly one-quarter of the 

$31 trillion outstanding U.S. bond market debt 

(see second fi gure). The GSEs’ activities also 

have important implications for broader fi xedincome asset prices and volatility, since they 

hedge mortgage convexity risk associated with 

the prepayments of mortgages with treasuries, 

interest rate swaps, swaptions, treasury options, 

and other instruments. 

Banks, as large originators of conforming 

mortgages and investors in agency debt and 

MBS, have signifi cant ties to the GSEs. Money 

Box 1.4. U.S. Government-Sponsored Enterprises and Housing Reform Developments

Note: The main author of this box is Rebecca 

McCaughrin.

Source: IMF staff.

Note: MBS = mortgage-backed security; GSE = government-sponsored enterprise.

GSEs securitize and sell 

MBS to investors, while retaining 

a portion for the 

investment portfolio

Investors 

(banks, foreign investors, 

investment funds)

Fannie Mae/

Freddie Mac

MBS $$$

Negative convexity hedged with

treasuries, interest rate swaps,

swaptions, other derivatives

$$$

Banks

Bank/Lender

Bank/lender originates 

mortgage loans

Borrower

Central banks,

investment/ 

pension funds, 

banks, money

market funds

GSEs issue debt securities 

to investors to finance 

loan purchases

Lender sells mortgages to 

the GSEs that meet their 

underwriting standards

Mortgages

$$$

Mortgages $$$

Debt securities

$$$

Basic Structure of Government-Sponsored Enterprises’ (GSEs) Business Practices
35

SYSTEMIC IMPLICATIONS

market funds are also dependent on the shortterm discount notes issued by the GSEs, particularly as other forms of short-term investment 

(e.g., asset-backed commercial paper, auction 

rate securities, etc.) have declined or become 

more risky. Foreign institutions—central banks 

in particular—also have signifi cant exposures to 

debt issued and guaranteed by the GSEs. 

The GSEs are important participants in the 

mortgage market, both as investors and providers of mortgage fi nancing. As the credit turmoil 

deepened, traditional investors scaled back their 

demand for mortgage products, as did providers 

of mortgage fi nancing, shifting the burden more 

heavily to the GSEs. Despite the deterioration in 

the housing market, the GSEs have continued to 

(modestly) grow their investment portfolios and 

to guarantee mortgages that conform to their 

requirements. Together with Ginnie Mae, they 

stepped up their provision of liquidity to the secondary mortgage market, accounting for over 90 

percent of new securitizations in recent months, 

as liquidity provided by private securitizers dried 

up (see third fi gure). 

Corporate

19.3%

Asset-backed

8.1%

Money markets

13.5%

Agency

debentures

9.8%

Marketable 

treasuries

16.4%

Municipal

8.7% Mortgage-related

24.2%

Banks/credit unions

16.8%

Central banks

33.6%

Investment 

managers

35.8%

Corporates

1.1%

Retail 1.9%

Government 3.5%

Insurance/pension funds

7.2%

Sources: Bloomberg L.P.; Fannie Mae; Freddie Mac; Inside MBS & ABS; Securities Industry and Financial Markets Association; U.S. Treasury; 

Federal Reserve; Thomson Financial.

U.S. Fixed-Income Market

(In percent)

Composition of Outstanding U.S. Bond Market

($30.5 trillion, as of 2008:Q1)

Primary Investors in Fannie Mae/Freddie Mac Debt

($1.7 trillion as of mid-2008)

Primary Investors in Mortgage-Related Securities

($6.6 trillion, as of mid-2007)

Insurance/

pension funds

14.5%

Mutual funds

9.9%

REITs

1.3%

Foreign investors

18.4% Banks

18.6%

GSEs

17.6%

Other

19.7%
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

36

As capital needs intensifi ed owing to rising 

losses and limited alternative sources of mortgage fi nancing, government efforts sought to 

reduce the probability of a liquidity-driven event 

at the GSEs and to stabilize mortgage markets. 

The government initially implemented a series 

of measures, including (1) a temporary increase 

in the line of credit with the U.S. Treasury;1

(2) temporary authority for the U.S. Treasury to 

purchase unlimited equity in the GSEs at terms 

and conditions it sets; and (3) a temporary consultative role for the Federal Reserve to regulate 

the GSEs. In the interim, the Federal Reserve 

also temporarily provided an unconstrained 

1The line of credit had not been increased since 

it was set at $2.25 billion in 1957, and was generally 

viewed as insuffi cient given the growth in the GSEs 

since then.

liquidity backstop to the GSEs through collateralized loans at the primary discount rate. 

However, as risks to the overall safety and 

soundness of the enterprises and to broader 

fi nancial markets continued to increase, the 

government sought a more direct and broad 

intervention, placing Fannie Mae and Freddie 

Mac into conservatorship under the direction 

of the newly created Federal Housing Finance 

Agency (FHFA). The U.S. Treasury injected 

capital through the purchase of $1 billion 

of senior preferred equity in each company 

(plus warrants representing 79.9 percent of 

the common stock) and was given authority to 

inject a maximum of $100 billion of capital into 

each entity to ensure their net worth remains 

positive. Dividends on existing common and 

preferred stock were immediately suspended, in 

effect drawing a distinction between debt and 

equity holders. Under the new structure, Fannie Mae and Freddie Mac are able to securitize 

GSE-eligible mortgages without limit, while their 

investment portfolios are permitted to expand 

moderately (to $850 billion each) through end2009. Beyond that period, they will be required 

to shrink their investment portfolios 10 percent 

per annum until each reaches $250 billion. In 

addition, the U.S. Treasury was granted temporary authority to purchase new agency-backed 

MBS through a designated asset manager. 

Finally, a short-term secured credit facility was 

established for the housing GSEs, including the 

Federal Home Loan Banks (FHLBs). 

The government’s actions achieved three 

goals in the short term. First, by appointing 

FHFA as the conservator, the U.S. Treasury 

avoided full nationalization and instead 

became a stakeholder, thus limiting the potential fi scal impact. Second, the plan ensured 

the GSEs will maintain positive net worth (up 

to a limit), in turn restoring confi dence in the 

agency debt market, while the reduced risk 

of a portfolio reduction by the GSEs and the 

U.S. Treasury’s authority to purchase agencybacked MBS supports that market. Third, the 

secured lending facility, which is intended to 

serve as a last resort liquidity backstop, reduced 

Box 1.4 (concluded)

Other

Government (Ginnie Mae)

Subprime/Alt-A

Nonconforming prime jumbo

Conforming (Fannie Mae/Freddie Mac)

2008

Annualized

2001 2002 2003 2004 2005 2006 2007

0

500

1000

1500

2000

2500

3000

Sources: Inside MBS & ABS; and IMF staff estimates.

Mortgage Securitizations

(In billions of U.S. dollars)
37

raising efforts, over and above those entailed by 

absorption of credit losses. 

In July, the U.S. authorities put in place the 

legal apparatus and authority for more direct 

and explicit support to the GSEs, but hopes that 

this would be suffi cient to restore market confi dence and encourage further private capital 

provision subsequently diminished. Those pressures were refl ected in market-based indicators. 

For instance, equity option prices implied, at the 

time, that the probability of equity values falling 

to zero was higher than for investment banks 

SYSTEMIC IMPLICATIONS

the potential for liquidity problems among the 

GSEs in the future.

The longer-term role of the GSEs, though, 

must still be resolved. Ultimately, the government will need to determine the GSEs’ status 

and role in the housing markets. The U.S. 

Treasury has explicitly noted that their business model is inherently fl awed.2 This suggests 

that the hybrid nature of the companies is not 

sustainable and that they will eventually need 

to be converted either into fully private or fully 

public companies or operate under a stronger 

regulatory framework. There are a number of 

outstanding questions that remain beyond 2009, 

once the U.S. Treasury’s extraordinary support 

expires and the portfolios of the GSEs begin to 

shrink over the coming decade. 

Reinforced government support has helped 

to bolster confi dence in the GSEs’ debt and 

MBS. While the major rating agencies cut the 

ratings of the preferred stock issued by the 

GSEs in light of the suspension of dividends and 

the dilutive impact of the government’s capital 

injection, they also upgraded their outlook 

on the GSEs’ subordinated debt, owing to the 

reduced risk of a deferral of interest payments. 

Refl ecting a more explicit government guarantee, senior and subordinated agency debt and 

agency-backed MBS debt spreads tightened relative to both treasuries and interest rate swaps 

and default risk fell (see fourth fi gure). The 

risk premia on the GSEs’ regular short-term 

discount note and longer-term debt auctions 

declined, thus enabling the GSEs to continue to 

2Statement by U.S. Treasury Secretary Henry M. 

Paulson, Jr. on Treasury and Federal Housing Finance 

Agency Action to Protect Financial Markets and Taxpayers, September 7, 2008.

guarantee new mortgages. The spread between 

MBS issued by public (e.g. Ginnie Mae) and 

conventional (e.g., Fannie Mae, Freddie Mac) 

entities also narrowed. Debt issued by the 

FHLBs, which had been trading in line with 

agency debt, continued to do so under the new 

structure, since all three entities have access to 

the short-term credit facility. With the existing 

shareholders diluted and dividends suspended, 

the GSEs’ common and preferred equity prices 

plunged. Placing the GSEs into conservatorship 

introduced an additional operational complication for the credit default swap market, since 

such an action constitutes a credit default-triggering event.

Senior debt

Subordinated debt

0

50

100

150

200

250

300

350

400

2004 05 06 08 07

Source: Datastream.

Credit Default Swap Spreads on GovernmentSponsored Enterprise Debt

(In basis points, five-year tenors)
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

38

and that the GSEs needed to increase their 

capital base in the next few quarters in order to 

delever their portfolios and adjust their balance 

sheets (Figure 1.29).41

The loss of confi dence in the GSEs and risks 

to the global fi nancial system prompted the government to intervene in early September, placing the two largest GSEs into conservatorship. 

The authorities’ intervention has reduced the 

risk of portfolio reduction by the GSEs in the 

near term, removed uncertainty regarding their 

capital adequacy, potentially freed up scarce 

market capital, reinforced confi dence in their 

debt and mortgage guarantees, and in general 

helped to improve mortgage securitization. The 

GSEs now have greater ability to support the 

mortgage market through a (modest) expansion in their investment portfolios through 

end-2009, supplemented by the U.S. Treasury’s 

intended purchases. The initial market reaction 

was a tightening in agency debt and agencybacked MBS spreads, while GSE equity prices 

fell sharply. Tighter agency-backed MBS spreads 

and lower guarantee fees should, in turn, help 

to reduce mortgage rates and increase the 

availability of mortgage credit for GSE-eligible 

borrowers. However, the government’s plan has 

only limited benefi cial impact on the moribund 

primary and secondary nonagency mortgage 

market. More generally, the deleveraging trend 

remains in place, as do pressures on the housing 

market and household balance sheets.

Mounting credit losses could result in further bank 

solvency issues, potentially stretching deposit insurance 

resources.

In view of their signifi cant exposure to real 

estate assets, a number of U.S. regional banks 

have come under signifi cant pressures (Figure 1.30). The dilution of existing GSE equity 

and the elimination of dividends appears manageable for most banks, but a few have signifi -

41The higher probability of default based on equity 

option prices may also have refl ected the risk that equity 

holders may not be prioritized in the event of a government recapitalization. 

7/19/2008 8/16/2008 9/20/2008 1/17/2009 1/16/2010

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

Sources: Bloomberg L.P.; IMF staff estimates; see Capuano (2008) for 

further details.

Figure 1.29. Probability of Default Based on Equity

Option Prices

(In percent)

Fannie Mae

Freddie Mac

Major U.S. investment banks
39

cant exposure relative to their tangible capital. 

In addition, the banking sector will likely continue to face a challenging environment until 

U.S. house prices stabilize. This raises concerns 

about the adequacy of Federal Deposit Insurance Corporation (FDIC) reserves to cover the 

insurance of deposits, especially given a possible 

increase in the size of covered accounts.

Were further big depository institutions to 

fail, this could put a substantial strain on government depository insurance funds. There have 

been few failures in deposit-taking institutions in 

the United States so far—especially in comparison with the savings and loan crisis—though the 

failure of IndyMac, one of the large mortgage 

lenders (with assets of about $30 billion) has 

raised concerns about the adequacy of funds 

for multiple large-scale bank resolutions. The 

Emergency Economic Stabilization Act (ESSA) 

has provided for additional resources in such 

an event.42 Other countries also face challenges 

with respect to deposit insurance. A number 

of authorities are taking steps to boost market 

confi dence by temporarily expanding the coverage provided by their deposit insurance regimes. 

Some are also considering steps to make more 

permanent improvements in the design of 

schemes. For instance, the U.K. authorities 

are considering reforming their deposit insurance regime following the deposit run on, and 

subsequent nationalization of, Northern Rock, 

including improving the clarity and funding of 

the arrangements. Deposit guarantee arrangements in a wide range of other countries are 

being reassessed, and lessons need to be shared 

between countries.

42As of the second quarter of 2008, FDIC data showed 

that large banks ($50 billion and larger) are all well-capitalized. However, the number and combined assets of banks 

on the FDIC’s regulatory watch list rose to 117 and $78 

billion, respectively. FDIC reserves have fallen to $45.2 billion (representing just 1.01 percent of all insured deposits, 

which is considered historically low), owing to costs of 

absorbing IndyMac and other bank closures. If funds are 

drained further, the FDIC may raise insurance premiums 

to replenish its reserves, borrow up to $40 billion from the 

Federal Financing Bank, and, with the passage of EESA, 

request an unlimited line of credit from the U.S. Treasury. 

SYSTEMIC IMPLICATIONS

0

20

40

60

80

100

120

2007 2008

Sources: Bloomberg L.P.; and IMF staff estimates.

Figure 1.30. U.S. Equity Index Performance

(January 2007 = 100)

S&P 500

S&P financials

S&P regional banks
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

40

Operational risks in credit derivative and repurchase 

markets pose risks, already evident during the 

bankruptcy of Lehman Brothers, spreading and 

intensifying concerns of counterparty risk.

Despite actions taken by the major market 

participants, encouraged by the Federal Reserve 

Bank of New York and other authorities to 

reduce the risk of unconfi rmed trades and 

settlement problems, the settlement of CDSs 

referencing Fannie Mae, Freddie Mac, Lehman 

Brothers Holdings Inc., and other systemically 

important fi nancial institutions could cause 

a disruptive chain reaction in the event of a 

failure of a major CDS protection counterparty. 

The problem lies in the large overhang of 

redundant bilateral contracts, as counterparties 

often establish offsetting contracts rather than 

close out existing contracts, thereby increasing counterparty risks. These risks have been 

partly mitigated by increased efforts to terminate offsetting contracts, more electronically 

processed transactions, and the creation of a 

central clearinghouse (although it will not be 

fully operational until late 2009).43 Somewhat 

similar operational challenges can occur in the 

settlement of repurchase transactions, including those via triparty arrangements, and other 

over-the-counter derivative trades. Moreover, 

these challenges can be exacerbated by the 

potentially complex nature of the bankruptcy 

of a large counterparty. The Lehman Brothers 

Holdings Inc. bankruptcy demonstrated how 

such operational risks can have systemic implications because it was one of the 10 market makers 

standing behind about 90 percent of outstanding contracts. The logistics of closing out trades 

in which Lehman Brothers was a counterparty 

are daunting. All surviving counterparties will 

have to replace, offset, or close their outstanding 

derivative trades (not just CDS trades) against 

Lehman Brothers. These operational challenges 

are exacerbated by elevated market volatility, 

43For any given participant, all transactions on the 

same underlying entity will be netted to a single position, 

and a single margin account maintained on its whole 

portfolio of CDS. 

reduced liquidity, and concerns about the creditworthiness of other prospective counterparties.

Deleveraging and funding pressures are having wider 

repercussions on liquidity in core markets . . .

Global deleveraging, fi nancial sector consolidation, the reduced number of leveraged 

investors and market makers, and heightened 

uncertainty have reduced trading liquidity in 

various core markets and have reduced the 

ability of market participants to adjust their 

positions quickly to market developments.44

This, in turn, further contributes to reducing 

liquidity and increasing idiosyncratic and basis 

risks. Such risks are evident in sovereign swap 

and bond markets where hedging activities 

associated with synthetic structures have led 

to high volatility, resulting in sizable losses for 

dealers. Reduced liquidity extends to emerging markets where market-making costs have 

increased signifi cantly as use of bank balance 

sheets has been circumscribed, for example, in 

offering total return swaps. With counterparty 

concerns at elevated levels, some prime brokers 

have experienced a sharp drop in liquidity as 

hedge funds have shifted funds into segregated 

accounts or into trust vehicles, and many are 

aiming to reduce their concentration in a single 

prime broker after the Lehman Brothers bankruptcy. In turn, prime brokers are tightening 

lending standards or, in some cases, have ended 

their relationships with hedge funds, reinforcing the broader deleveraging of the fi nancial 

system (Box 1.5).45

. . . posing signifi cant threats to fi nancial intermediaries.

Persistent strains on term funding markets 

have escalated to a point where most credit is 

44For example, in Japan, foreign relative-value hedge 

funds have largely departed the Japanese market after 

many suffered large losses in March. Typically, government bond market makers hedge their cash positions in 

the futures market. However, the volume of open futures 

positions has fallen dramatically, and correlations with 

cash market movements have declined. 45Since banks that have large prime brokerage units 

cannot use segregated funds to help fi nance their assets, 

this therefore adds to deleveraging pressures. 
41

This box discusses the channels by which the 

credit market deterioration has affected hedge fund 

performance. 

A number of hedge funds with large creditrelated exposure have been negatively affected 

by the current market turmoil, particularly 

those with U.S. mortgage-related asset-backed 

securities and collateralized debt obligation 

exposure. Equity funds have also suffered 

losses, especially those with net long exposure 

to fi nancials and consumer cyclical companies. 

The more volatile trading environment also 

appears to have impaired the performance of 

many hedge funds, including macro and convertible arbitrage strategies (see fi rst fi gure).1

Finally, the credit market crisis has resulted 

in tighter fi nancing conditions specifi cally for 

fi xed-income-oriented hedge funds, reducing 

their ability to lever returns. 

Typically, hedge funds seeking direct (or 

explicit) leverage can obtain funding either 

through margin fi nancing from a prime broker 

or through private repo markets. Margin fi nancing from prime brokers has been cut, and haircuts and fees on repo fi nancing have increased 

(see fi rst table). The combination of these factors has caused average hedge fund leverage to 

fall to 1.4 times capital (from 1.7 times last year) 

according to market estimates.2 Hedge funds 

Note: The main author of this box is Mustafa Saiyid.

1Theoretically, higher market volatility should have 

increased profi t opportunities for strategies such as 

macro and convertible arbitrage, allowing macro 

managers to take advantage of wider swings in the 

performance of various asset classes, and convertible 

arbitrage managers to go long or short on the more 

highly-valued convertibility option relative to the 

underlying stock. 2Changes in leverage are calculated over the whole 

universe of strategies, heavily weighted by equity longshort and merger arbitrage strategies, which typically 

carry low leverage of 1.5 to 2.0 times (equity) capital. 

Other strategies typically operate with much higher 

levels of leverage, although it is diffi cult to make a 

direct comparison between equity and fi xed-income 

leverage. For example, leverage is 4 times capital for 

tactical/macro funds; 5 to 9 times capital for convertible arbitrage funds; and as much as 10 times capital 

have also increased cash balances. In the United 

States, cash balances have doubled to 16 percent 

of portfolios over the past year. Globally, average cash balances of hedge funds have risen to 

22 percent (up from 14 percent one year ago) 

(see second fi gure).

Hedge funds are reportedly receiving largesize redemption requests from investors seeking 

to withdraw capital before others. Only a certain 

amount of capital is allowed to leave the fund 

through a “gate” at each quarter. The fi rst 

ones to redeem come out relatively whole as 

the fund’s most liquid assets are sold to service 

their requests, compared with those that seek to 

redeem later and are left holding more illiquid assets. Ninety-fi ve percent of hedge funds 

have “gates” in their offering memoranda, 

which allow redemptions of up to 10 percent 

of fund assets. Redemption requests are usually 

allocated on a pro-rata basis, a procedure that 

for relative value/fi xed-income arbitrage funds. Since 

fi xed-income arbitrage strategies rely more heavily on 

leverage to generate returns, changes in fi nancing 

conditions affect the performance of these strategies 

more than others.

Box 1.5. Impact of Credit Market Turmoil on Hedge Funds

Sources: Bloomberg L.P.; and IMF staff estimates.

Note: VIX = S&P 500 volatility index.

Performance of Hedge Fund Strategies and 

Risk Appetite

80

90

100

110

120

130

140

0

10

20

30

40

50

2006 2008 2007

VIX

(percent, left scale)

Macro

(index level, right scale)

Convertible arbitrage

(index level, right scale)

SYSTEMIC IMPLICATIONS
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

42

results in investors seeking larger redemptions 

than they need, as this increases the likelihood 

of being allocated redemption amounts more in 

line with their actual needs. 

The combination of falling asset values, 

higher volatility, rising collateral haircuts, 

and investor redemptions have resulted in an 

increasing frequency of hedge fund failures in 

recent months, especially for those with exposure to structured credit.3 Fixed-income hedge 

funds that have failed since June 2007 managed 

$97 billion in assets (see second table). Losses 

of investors in these hedge funds may already 

be as high as $60 billion over the course of the 

past year. 

In response, hedge funds are seeking to 

restrict redemptions, but in return are having 

to cut their fees. Some are seeking to lengthen 

3Even relatively small declines in performance—

5 to 10 percent, for instance—can force funds to 

liquidate large amounts of assets to meet margin calls 

or redemption requests. See Table 1.3 in the October 

2007 GFSR (IMF, 2007).

“lock-ups” of investor capital for as long as three 

years, while others have increasingly invoked 

“gates.” Moreover, the average annual base fee 

has declined by as much as 50 basis points from 

2 percent last year. 

New restrictions on short selling could add 

further pressure to business models of hedge 

funds. Equity long-short strategies, which make 

up almost half of the $2 trillion hedge fund 

universe, are likely to suffer from reduced 

opportunities to make money from short positions. Some hedge funds report that they are 

avoiding the fi nancial sector altogether, as 

they are unable to hedge long exposures with 

Box 1.5 (concluded)

Typical “Haircut” or Initial Margin

(In percent)

April 2007 August 2008

U.S. treasuries 0.25 3

Investment-grade bonds 0–3 8–12

High-yield bonds 10–15 25–40

Equities 15 20

Investment grade corporate CDS 1 5

Senior leveraged loans 10–12 15–20

Mezzanine leveraged loans 18–25 35+

ABS CDOs: AAA 2–4 951

AA 4–7 951

A 8–15 951

BBB 10–20 951

Equity 50 1001

AAA CLO 4 10–20

Prime MBS 2–4 10–20

ABS 3–5 50–60

Sources: Citigroup; Morgan Stanley Prime Brokerage; and 

IMF staff estimates.

Note: ABS = asset-backed security; CDO = collateralized 

debt obligation; CDS = credit default swap; CLO = collateralized 

loan obligation; MBS = mortgage-backed security; RMBS = 

residential mortgage-backed security. 1Theoretical haircuts as CDOs are no longer accepted as 

collateral.

Source: Morgan Stanley Prime Brokerage.

Note: Leverage defined as assets divided by equity capital cash 

balances as a percent of total assets.

Leverage and Cash Balances of Global Hedge 

Funds

(In percent)

0

5

10

15

20

25

30

30

40

50

60

70

80

2007 2008

 Cash balances (left scale)

Net leverage (right scale)

Large Hedge Fund Failures

(June 2007–August 2008)

Strategy Number Assets1

AssetWeighted 

Leverage2

Fixed-income 31 97 16

Structured products 21 79 17

Sovereign/Macro 4 8 14

Other fixed-income 6 10 10

Sources: Bloomberg L.P.; and IMF staff estimates.

Note: Includes hedge fund failures exceeding $100 million. 

1In billions of U.S. dollars

2Leverage is defined as the ratio of assets to equity capital.
43

now provided primarily on overnight terms. As 

a result, a number of nonbank institutions face 

possible failure, require offi cial sector support, 

or must sell assets into illiquid markets to meet 

redemption pressures. Prime money market 

mutual funds (MMFs) have already experienced escalating redemptions, forcing failures 

in some cases, and a shortening in duration 

and reallocation to safe haven assets in other 

cases (Box 1.1). Instead of their traditional role 

of supplying liquidity to banks, they are now 

competing with banks for overnight funds and 

with fi nancial fi rms for safer assets. This has 

exacerbated interbank funding pressures and 

increased rollover risks. The conservative stance 

of prime MMFs has also reduced the availability 

and raised the cost of commercial paper fi nancing to nonfi nancial corporations. To break this 

spiral, the U.S. authorities introduced a temporary guarantee on MMF investments in ABCP. 

Importantly, hedge funds are also facing tighter 

funding conditions, exacerbating redemption 

pressures due to weak performance, reduced 

investor risk appetite, and the impact of equity 

short selling restrictions. There are risks of a 

forced unwinding of their asset positions in the 

months ahead and a disorderly exit from hedge 

funds, with wider ramifi cations for market 

liquidity and volatility (Box 1.5).

Rising public commitments could put pressure on 

perceived risks of sovereign credits.

Since balance sheet stresses in both the 

bank and near-bank sectors have severely 

compromised their ability to provide credit to 

the broader economy, the offi cial sector has 

had to play a more active role in alleviating 

stresses. Government efforts to bolster market 

confi dence and support broader fi nancial and 

nonfi nancial sectors should eventually assist in 

an orderly deleveraging by providing support to 

private balance sheets (Figures 1.31 and 1.32). 

However, increasing government commitments 

could further raise concerns about sovereign 

risk as risk is transferred from the private to the 

public sector. For instance, there are signifi cant 

uncertainties about the budgetary impact of 

the U.S. government’s GSE rescue operations, 

supplemental fi nancing support to the Federal 

Reserve, support to the FDIC and other government agencies, and the $700 billion troubled 

asset purchase program. There is similar fi scal 

uncertainty related to government bilateral commitments introduced in Europe to support troubled institutions.46 Refl ecting concerns about a 

deterioration in fi scal positions and uncertainty 

46There are several channels through which fi scal 

costs could rise: (1) a decline in net worth in the GSEs, 

thus requiring further capital infusions; (2) a deterioration in U.S. secondary mortgage market, requiring additional purchases of agency-backed MBS; (3) funding 

diffi culties among the GSEs, leading to the extension 

of funds through the secured lending credit facility; (4) 

losses due to price declines on troubled assets that the 

U.S. Treasury might purchase from fi nancial institutions; (5) increased demand for temporary insurance 

for money funds; and (6) a depletion in FDIC reserves, 

requiring a line of credit or other funds. At this stage, 

it is diffi cult to quantify the outlays with any degree of 

confi dence. 

shorts in this sector. Prime brokers comment 

that there are few alternatives to short sales, 

as synthetic shorts through options markets 

remain expensive and the credit default swap 

market provides imperfect hedges for long 

stock exposures. 

From a systemic point of view, mounting 

strains on hedge funds could force rapid and 

disorderly unwinding of positions in various 

assets with wider ramifi cations for market liquidity. This could have potential knock-on effects 

for other market participants, for example, 

through counterparty exposures in derivatives 

markets. Institutional investors, including some 

pension funds and endowments, could suffer 

losses on exposures to fi xed-income and equity 

long-short hedge funds, as such allocations had 

risen signifi cantly in recent years.

SYSTEMIC IMPLICATIONS
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

44

regarding the effectiveness of the government 

actions, mature market sovereign CDS spreads 

have widened (Figure 1.33).

Emerging Market Resilience Is 

Being Tested 

Global stress spreads to emerging markets . . .

As the global fi nancial turmoil has intensifi ed, 

emerging market countries that once appeared 

relatively immune to the fi nancial and economic 

shocks emanating from mature markets have 

increasingly been tested. Deleveraging by global 

fi nancial institutions has raised the cost and 

reduced the availability of external fi nancing, 

and investor risk appetite has decreased, reducing the demand for emerging market assets. 

Hopes for “decoupling” of emerging market 

countries from mature markets have diminished,47 and emerging market policymakers are 

coping with a global growth slowdown, the risk 

of capital outfl ows, and infl ation risks on the 

back of earlier commodity price increases.

. . . and vulnerabilities are broadening.

Vulnerabilities have risen in a number of 

emerging markets, some of which are highlighted in Table 1.5. Emerging Asia has suffered 

a substantial increase in vulnerability over the 

last six months, with infl ation and terms-of-trade 

shocks hitting particularly hard, accompanied by 

concerns over the region’s gearing to weakening global growth. Latin America has generally 

benefi ted from a positive terms-of-trade effect 

from higher commodity prices, while monetary 

policy has been more aggressive in containing infl ation risk, but recent commodity price 

declines have raised concerns about the region’s 

continued ability to resist a global slowdown. As 

highlighted in earlier GFSRs, domestic credit 

and infl ation have grown rapidly in emerging 

Europe, and now that the domestic credit cycle 

47Chapter 4 examines whether increasing fi nancial 

integration has potentially raised emerging markets’ 

vulnerability to external global shocks, focusing on the 

channel of equity markets. 

0

1000

2000

3000

4000

Potential Treasury 

funding commitments

GSE equity injection

Treasury MBS purchase

Asset purchase (EESA)

Investment bank (mortgages)

Commercial bank

(commercial mortgages)

Commercial bank

(residential mortgages)

U.S. bank 

mortgage assets

Prime

Subprime

Alt-A

25%

20%

15%

10%

5%

Sources: Federal Reserve, Flow of Funds; JPMorgan Chase & Co.; and IMF 

staff estimates.

Note: GSE = government-sponsored enterprise; MBS = mortgage-backed 

security; EESA = Emergency Economic Recovery Act.

Figure 1.32. Potential U.S. Commitments and 

Mortgage Markets

(In percent of GDP; and billions of U.S. dollars)

ABCP Credit Facility Other

Maiden Lane Portfolio Holdings Estimated forex swap lines

PDCF AIG loan

TAF Discount Window Borrowing

Permanent Portfolio Repo

0

200

400

600

800

1000

1200

1400

1600

1800

2007 2008

Sources: Federal Reserve; and Morgan Stanley.

Note: ABCP = asset-backed commercial paper; AIG = American International 

Group; PDCF = Primary Dealer Credit Facility; TAF = Term Auction Facility.

Figure 1.31. Total Assets on Federal Reserve’s 

Balance Sheet

(In billions of U.S. dollars)
45

is turning in some economies, the risk of a 

hard landing or regional fi nancial crunch has 

increased. 

Against the backdrop of rising emerging market risks, 

institutional investors have reduced positions, especially 

in equities.

Mutual and pension funds have scaled back 

emerging market exposure in response to 

rising emerging market vulnerabilities. Flows 

into emerging equity markets have slowed or 

reversed since the beginning of the year, amid 

investor concerns about emerging market infl ation and exposure to a slowing global business 

cycle (see Chapter 4 and Figure 1.34).48 This 

has been more pronounced in Asia, with especially heavy outfl ows from Korea and Thailand, 

bringing net sales of Asian equities to $56 billion 

in the year through September. Latin America 

and Emerging Europe, the Middle East, and 

Africa have also experienced net equity portfolio 

outfl ows in recent months. As a consequence, 

share prices have dropped sharply.

Tightening external and internal conditions in emerging 

markets could result in a downturn in the domestic 

credit cycle.

Emerging economies are faced with more 

costly and less available external fi nancing, as 

strained global banks restrict funding in the 

face of the credit crunch. Spreads on emerging 

market sovereigns and corporates have widened 

substantially (Figure 1.35). Issuance of emerging 

market external corporate debt contracted from 

$88 billion in the fi rst three quarters of 2007 

to $40 billion during the same period in 2008. 

Leveraged investors—such as hedge funds—that 

depend on funding from prime brokers or 

other fi nancial institutions have been forced to 

scale back emerging market investments. The 

slowdown or reversal of funding infl ows has 

contributed to sharp increases in onshore dollar 

funding costs⎯as implied by currency forwards or cross-currency swaps⎯in economies as 

48Chapter 4 provides a longer-term view of vulnerabilities to equity market changes. 

0

10

20

30

40

50

60

70

80

90

Greece Portugal

Italy Spain

Germany Netherlands

United States United Kingdom

Source: Bloomberg L.P.

Figure 1.33. Sovereign Credit Default Swap Spreads

(In basis points, ten-year tenors)

2007 2008

–40

–30

–20

–10

0

10

20

30

EMEA Latin

America

Asia India Taiwan

Province

of China

Korea Other

Sources: Bloomberg L.P.; and IMF staff estimates.

Note: “Other” includes Indonesia, Philippines, Thailand, and Vietnam. 

EMEA = Emerging Europe, the Middle East, and Africa.

Figure 1.34. Net Foreign Equity Investment in 

Emerging Economies

(In billions of U.S. dollars)

2005

2006

2007

2008 to date

Net Flows to Emerging 

Market Equity Funds

Net Foreign Investment in Local 

Equities

EMERGING MARKET RESILIENCE IS BEING TESTED 
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

46

Table 1.5. Macro and Financial Indicators in Selected Emerging Market Countries1

Commodity 

Price 

Sensitivity2

Current 

Account 

Balance3

Gross 

Reserves 

to Short-Term 

External Debt4

Net External 

Position vis-à-vis 

BIS-Reporting 

Banks5

Growth in 

Credit to the 

Private Sector6

(In percent, Inflation7

Real

Policy Rate8

(Ratio) (In percent of GDP) (Ratio) (In percent of GDP) year-on-year) (In percent) (In percent)

Europe

Bulgaria 1.6 –21.9 1.1 –29.0 54.5 14.5 –9.4

Croatia 1.1 –9.0 0.9 –59.7 11.6 8.4 . . .

Estonia 1.2 –11.2 0.2 –78.7 21.5 11.1 . . .

Hungary 0.6 –5.5 0.9 –54.1 18.0 6.7 1.8

Iceland9 3.9 –8.0 . . . –267.9 . . . 13.6 1.9

Kazakhstan 4.5 –1.7 0.6 –8.0 22.8 20.0 –9.5

Latvia 1.9 –15.0 0.3 –72.5 22.2 16.7 –10.7

Lithuania 1.2 –10.5 0.9 –45.6 36.4 12.2 –7.0

Poland 0.7 –5.0 0.8 –17.1 29.5 4.8 1.2

Romania 0.6 –14.5 0.9 –36.4 62.0 9.0 1.2

Russia 4.1 5.8 2.9 2.2 51.4 14.7 –3.7

Serbia . . . –16.1 2.8 –15.1 37.0 14.3 . . .

Turkey 0.3 –6.7 0.9 –12.2 32.9 12.1 4.7

Ukraine 0.6 –7.6 1.0 –9.5 63.9 26.8 –14.8

Gulf States

Kuwait 13.6 45.2 . . . 8.9 35.5 11.4 –5.7

Saudi Arabia 7.6 31.3 . . . 27.9 28.5 10.6 . . .

United Arab Emirates 5.3 27.5 . . . –1.3 45.3 . . . . . .

Africa

Egypt 2.1 0.8 7.5 16.0 12.6 22.2 –11.2

Ghana 2.7 –9.8 . . . –8.0 . . . 15.3 . . .

Nigeria 4.5 6.5 . . . 15.5 96.5 9.7 . . .

South Africa 1.2 –7.7 1.6 4.7 15.2 11.6 0.4

Uganda 2.8 –7.7 . . . 13.4 41.2 8.7 . . .

Asia

China 0.3 9.8 6.9 1.1 17.5 6.3 1.2

India 0.5 –3.1 5.9 –8.9 24.1 12.0 –3.0

Indonesia 1.2 1.8 2.1 –8.5 31.4 11.9 –2.9

Korea 0.2 –1.0 1.3 –17.5 16.0 5.9 –0.7

Malaysia 1.4 11.7 6.0 –10.1 10.3 7.7 –4.2

Pakistan 0.6 –6.9 12.1 4.8 21.6 24.3 –11.3

Philippines 0.3 2.1 2.2 –2.2 5.2 12.2 –6.5

Thailand 0.6 3.4 3.1 3.2 7.0 9.2 –5.7

Vietnam 1.9 –13.6 14.5 –10.2 63.9 27.0 –13.0

Latin America

Argentina10 6.7 0.4 1.1 2.8 37.6 9.1 –0.2

Brazil 1.8 –0.7 1.7 –8.1 31.0 6.4 6.6

Chile 2.5 –0.5 1.1 –7.9 17.8 9.5 –1.8

Colombia 4.8 –4.9 1.7 0.4 21.0 7.5 2.5

Mexico 1.4 –1.0 1.4 –2.3 11.5 5.4 2.9

Peru 2.7 –0.2 3.0 2.6 31.7 5.8 0.2

Venezuela 8.3 7.2 2.2 24.1 51.8 33.7 –8.9

Sources: Bloomberg L.P; Bank for International Settlements (BIS); IMF, Direction of Trade Statistics, International Financial Statistics (IFS), 

and World Economic Outlook (WEO); and IMF staff estimates. 1The shaded boxes of the table point to areas of potential concern. Cut-off values are as follows: measure of commodity price sensitivity 

of less than 1; current account balance below –5 percent of GDP; ratio of reserves to short-term debt below 1; net external liabilities to BISreporting banks less than –10 percent of GDP; growth of credit to the private sector greater than 20 percent year-on-year; inflation greater than 

10 percent year-on-year; real policy rates below zero. 2The ratio of exports of primary commodities to total exports divided by the ratio of imports of primary commodities to total imports 

estimated by IMF staff. Average of 2002–04. 3Projections of the current account balance and GDP for 2008 in dollar terms from the WEO.

4Short-term debt is measured at remaining maturity. End–2007 estimated by IMF staff.

5Data on external positions of reporting banks vis-à-vis individual countries and all sectors from the BIS.

6The latest observations ranging from February 2007/08 to June 2007/08 from the IFS.

7Year-on-year inflation in July 2008 or latest observations.

8Policy rates in mid-August 2008 are deflated by inflation shown in the previous column.

9Though it is classified as a mature market, Iceland is included in this table becuase of its relatively high levels on some indicators included in 

this table. 10Analysts believe and various indicators suggest that actual inflation is considerably higher than the official data.
47

diverse as Brazil, Korea, and India (Figure 1.36). 

Smaller corporates and fi nancials have been 

especially squeezed and in some cases shut off 

from dollar funding. 

A combination of global credit tightening, 

rising domestic interest rates based on infl ation 

concerns, and a global growth slowdown could 

accelerate a downturn in domestic credit, which, 

following a lending boom, is likely to lead to 

rising defaults and deterioration in asset quality. 

Credit growth in several emerging markets has 

begun to slow, forcing a downturn in real estate 

prices in some cases.49 Most emerging market 

banking systems had been insulated from the 

global credit turmoil (Figure 1.37),50 but some 

are facing increasing external fi nancing pressures. In those dominated by foreign-owned 

banks dependent on parent bank fi nancing, 

the deterioration in asset quality as well as a 

downturn in parent banks’ home markets, could 

slow external funding, as seems evident in the 

Baltics. In countries more reliant on portfolio 

fl ows, banks have come under stress as outfl ows 

have tightened money market liquidity, raising concerns about access to funding. These 

pressures are most evident in Russia, where 

they aggravated concerns about counterparty 

risk and led to illiquid interbank markets and 

substantial emergency public support to avert a 

systemic crisis. 

High infl ation rates have complicated policymaking, 

sending real interest rates below zero.

Infl ation-targeting regimes are being tested as 

infl ation exceeds central bank targets in many 

emerging markets. In response, many central 

banks have tightened monetary policy. However, 

policy rate increases have often been insuffi -

cient to prevent real interest rates from falling, 

often into negative territory, as monetary policy 

49See Figure 1.40 in Annex 1.1 for private sector credit 

growth among emerging market economies. 50Emerging market economies have largely avoided 

direct exposure to mortgage-related structured products, 

refl ecting in part that attractive domestic investment 

opportunities obviate the need for complex products that 

enhance yield. 

0

200

400

600

800

1000

1200

2007 2008

Sources: J.P. Morgan Chase & Co.; and Merrill Lynch.

Figure 1.35. Emerging Market External and U.S.

High-Grade Corporate Spreads

(In basis points)

Emerging market 

Asia corporates

Emerging market 

Europe corporates

Emerging market 

Latin corporates

U.S. high-grade

0

2

4

6

8

10

12

14

2006 2008 2007

Sources: Bloomberg L.P.; and IMF staff estimates.

Figure 1.36. Onshore Emerging Market Dollar 

Interest Rates

(In percent; 10-day moving averages)

Brazil 3-month onshore

dollar interest rate

India implied

3-month dollar rate

Korea implied 

3-month dollar rate

U.S. 3-month 

overnight index swap

EMERGING MARKET RESILIENCE IS BEING TESTED 
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

48

authorities viewed commodity price shocks as 

either exogenous or transitory (Figure 1.38).51

Compounding the problem, policy settings in 

many economies were relatively loose coming 

into the credit crisis, partly owing to accommodative monetary conditions in mature economies.

More recently, with global growth prospects 

weakening further, commodity prices have 

fallen, leading to a moderation in market-based 

infl ation expectations (Figure 1.39). However, 

the gradual removal of distortionary subsidies, 

while welcome, and the potential for another 

run-up in commodity prices once the global 

economy stabilizes, continue to pose risks to the 

infl ation outlook. Moreover, signs of secondround effects are showing up on the back of 

relatively robust domestic demand, as core infl ation and wages have risen.

The risks of a hard landing are highest in 

Eastern Europe.

House prices in eastern Europe have soared 

in tandem with domestic credit growth, and the 

credit portfolios of banks in emerging Europe 

have become increasingly exposed to the real 

estate sector. Banks have not experienced a 

signifi cant increase in loan losses so far, but have 

increased provisions for bad loans. Internal risk 

controls could force a sharp reduction in credit 

growth to protect bank capital if asset quality 

deteriorates sharply. The risk of such a scenario 

has risen, for instance, in the Baltics, where 

house price appreciation has slowed or prices 

have fallen, real credit growth is falling sharply 

(Figure 1.40), real GDP growth has decelerated sharply or turned negative, and infl ation 

remains elevated. Elsewhere in eastern Europe, 

specifi cally in Bulgaria, Romania, and Ukraine, 

house prices and domestic credit are still growing, but credit spreads have risen as well, signaling an increase in risks.

Domestic banks in central and eastern Europe 

have also built up large negative net foreign 

51See Chapter 3 of the October 2008 WEO (IMF, 

2008d) for a discussion on the linkage between infl ation 

and commodity prices. 

Sources: Bloomberg L.P.; and IMF staff estimates.

Figure 1.37. Credit Default Swap Spreads on 

Selected Emerging Market Banks, January 2007—

Early October 2008

(In basis points)

–1000

0

1000

2000

3000

4000

Early October 2008 5000

January 2007

Glitnir (Iceland)

Landsbanki (Iceland)

Kaupthing (Iceland)

Halyk Bank (Kazakhstan)

Kazkommerts (Kazakhstan)

TuranAlem (Kazakhstan)

Bank of Moscow

VTB (Russia) Rosselkhozbank (Russia)

ICICI (India)

IDBI (India) China Development Bank Bank of China 

EXIM BoC(China) Malayan Banking

Hana (Korea)

OCB (Singapore)

EXIM (India)

EXIM (Korea)

State Bank of India

EM sovereign CDS Industrial Bank of Korea

Woori (Korea)

Kookmin (Korea)

Sberbank (Russia)

Argentina

HungaryColombiaSlovakiaRomaniaKoreaLatin AmericaPeruChileIndiaEMEAIsrael

AsiaSouth Africa

LithuaniaKuwaitVenezuela

Ukraine

Pakistan

Mexico

China

Poland

Turkey

Brazil

Bulgaria

Taiwan PoC

Czech Republic

Iceland

Singapore

Malaysia

Hong Kong SAR

Russia

Latvia

Kazakhstan

Philippines

Egypt –21

–15

–12

–9

–6

–3

0

3

6

9

Sources: Bloomberg L.P.; and IMF staff estimates.

Note: Policy rates or similar indicators used by market analysts and 

year-on-year inflation in December 2006 and September 2008 or latest. 

Analysts believe and various indicators suggest that actual inflation is 

considerably higher than the official data in Argentina. EMEA = Emerging 

Europe, the Middle East, and Africa.

Figure 1.38. Real Policy Rates: Latest Levels and 

Changes from end-2006

(In percent)

Latest

Change from end-2006
49

positions vis-à-vis international lenders. In these 

countries, the maintenance of credit growth 

hinges crucially on cross-border lending by 

foreign parent banks to local subsidiaries.52 Most 

of those parent banks remain vulnerable to a 

downturn in market sentiment as they obtain a 

substantial part of their funding from international wholesale markets, and many⎯including 

parent banks in Sweden, Austria, and Italy⎯have 

come under increasing stress from the global 

credit shocks in September. While most are committed to a long-term presence in the region, if 

external fi nancing conditions deteriorate further 

and force parent banks to contract credit to the 

region, a soft landing in the Baltics and southeast Europe could be jeopardized. Indeed, IMF 

analysis fi nds that, under a stress scenario, shocks 

emanating from common western European 

lenders could have widespread spillover effects 

across emerging Europe, provoking or contributing to contagion in the region (Àrvai, Driessen, 

and Ötker-Robe, forthcoming).

Global spillovers and rising vulnerabilities could test the 

resilience of emerging markets 

A continuation of heavy capital outfl ows from 

emerging markets would pose challenges for 

countries that rely heavily on external fi nancing 

and with lower reserve ratios. Should diffi cult 

external credit conditions persist or even intensify, economies that are more leveraged, or those 

where domestic credit growth has been particularly rapid, are likely to see a buildup of pressures on domestic banking systems. Under such 

conditions, the premium on the maintenance of 

a sound macroeconomic framework is increased.

Financial Stability Policies

The analysis in this report sets out the sizable 

adjustments needed as part of the deleveraging process and highlights the pressures that 

52Net foreign liabilities (external positions vis-à-vis Bank 

for International Settlements (BIS) reporting banks) have 

risen as a consequence of sustained large current account 

defi cits and rapid growth in domestic credit (Table 1.5). 

Sources: Barclays Capital; Bloomberg L.P.; and IMF staff estimates.

Figure 1.39. Break-Even Inflation Rates

(In basis points)

Mexico Colombia

Brazil

Turkey

Korea

Poland

South Africa

0

200

400

600

800

1000

1200

2007 2008

0

10

20

30

40

50

60

70

2005 2008 2006 2007

Sources: Bloomberg L.P.; European Central Bank; and IMF staff estimates.

Figure 1.40. Baltic States: Real Bank Loan Growth to

Nonfinancial Private Sector

(In percent, real terms, year-on-year change)

Estonia

Lithuania

Latvia

FINANCIAL STABILITY POLICIES
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

50

systemically important institutions are facing as 

part of that adjustment. Even before the events 

of recent weeks, fi nding a purely private sector resolution of fi nancial market strains had 

become increasingly diffi cult in an environment 

marked by declining asset values, exacerbated 

by procyclical forces such as ratings downgrades, 

and the challenges of distinguishing good 

from bad assets and strong from weak institutions. Now, the global disappearance of trust in 

counterparties and widespread cash hoarding 

that has surfaced recently has made it inevitable 

that, if a resolution plan is to achieve an orderly 

deleveraging process that limits damage to the 

fi nancial system and the economy, the authorities will need to play a major role in it.

The ultimate goal should be to mitigate the 

adverse feedback loop between the fi nancial 

system and the economy. To achieve this, measures must focus on rebuilding confi dence in 

institutions and markets and on reducing the 

pressures on banks to cut back the provision of 

new credit as part of their deleveraging. The 

public sector must signal as clearly as possible 

the principles that guide its approach. In recent 

months, volatility and illiquidity have been 

exacerbated by market uncertainty about how 

authorities will balance the competing claims of 

minimizing moral hazard and protecting against 

systemic risk.

Measures must be comprehensive, timely, and 

clearly communicated, addressing the underlying causes of uncertainty and the areas under 

strain from deleveraging pressures. IMF experience in previous fi nancial crises indicates that 

early and decisive action is needed in order 

to normalize markets and stem the spread of 

fi nancial and economic distress. To halt—and 

begin to reverse—the negative spiral in markets 

and the economy, the policy strategy needs to 

address three key, interrelated, sources of stress: 

fi rst funding markets need to be restarted; continuing uncertainties about problem assets need 

to be reduced; and fi rms’ capital positions need 

to be improved. 

Actions to stabilize the global fi nancial system 

should be coordinated across countries, and 

in particular across the major fi nancial centers. While the specifi c measures adopted may 

vary from country to country, depending on 

their individual areas of weaknesses and relative strengths, coordination of early action to 

address problems would send a strong signal 

to boost market confi dence, and will also help 

avoid adverse effects that one country’s measures may have on others or perverse incentives 

in international markets. Authorities need to 

ensure that they can rapidly respond to further 

emerging pressures, based on a mechanism for 

the early detection of strains, to contain systemic 

repercussions. This may require an enabling 

framework that allows for decisive action when 

needed.

Private sector solutions are preferred but, if 

needed, emergency government interventions 

should be temporary and taxpayer interests 

protected. The objectives of intervention should 

be clear and operating procedures transparent. Accountability of government actions to 

all stakeholders is important and conditionality 

for support of institutions should include steps 

to restructure weak but viable institutions so 

as to place them on a sounder footing. Mechanisms should limit moral hazard and taxpayer 

costs as much as possible, while recognizing the 

exigency of the situation and the clear need 

for public support. Those measures that have a 

distorting effect on markets should be removed 

once confi dence is restored.

Lastly, measures taken must further the 

medium-term objective of a restructured 

fi nancial system that is sound, competitive, and 

effi cient. Achieving this requires an orderly 

resolution of unviable banks and the repairing 

of market discipline. Funding and securitization 

markets critical to pricing and intermediating 

credit should be strengthened, including by 

reducing over time counterparty risks through 

centralized clearing. This period of change 

provides an opportunity to reexamine the 

international macrofi nancial stability framework 

governing the regulation of the fi nancial sector 

and strengthen the hand of supervisors and 

regulators. Events have highlighted the need to 
51

focus regulation and supervision on the types 

of fi nancial activities, rather than the type of 

intermediary—bank, insurance, company, or 

investment fund. Mechanisms for closer and 

more effective cross-border coordination and 

collaboration among supervisors, regulators, and 

central banks are needed.

In the near term, a comprehensive and global approach 

is needed to stem crisis risks and address their 

underlying causes.

The worsening in confi dence and market dysfunction in September led the U.S. authorities 

to supplement their case-by-case approach that 

addresses points of distress as they arise with 

a more comprehensive and systemic response. 

The new approach encompasses a wide-ranging 

set of measures, including liquidity support for 

banks and near-bank institutions such as brokerdealers and money market funds; asset purchases to free up bank balance sheets; support 

for the housing market; extending deposit insurance; and restricting short selling. The actions 

taken by the U.S. authorities are intended to 

relieve pressures on fi nancial balance sheets 

and to restore confi dence. These measures are 

positive, comprehensive, and necessary; their 

goal is to provide a catalyst for private markets 

to support asset prices, open up funding, and 

allow the rebuilding of capital cushions. It is too 

early to assess their impact, but, given the obvious continuing uncertainties, these measures 

have provided some reassurance to markets that 

the authorities stand ready to take the necessary 

measures to avoid more disruptions.

While the epicenter of the crisis is the United 

States, the fi nancial strains caused by a disorderly deleveraging and a rapid retrenchment 

of risk positions is global, and thus many other 

countries around the world have undertaken 

policy responses. Actions have been the most 

wide-ranging in the United States and Europe, 

while some Asian countries have expanded their 

liquidity support to markets.

 In Europe, measures to improve funding 

have been coordinated within the euro area 

through the ECB’s operations, and internationally, dollar liquidity needs have been alleviated 

through swap facilities between European 

central banks and the Federal Reserve. EU-wide 

action is also under way to improve supervisory capital requirements and other aspects of 

market structure. However, near-term measures 

to address capital, shortages, and problem assets 

have tended to be undertaken more on a caseby-case and on a national basis.

Policy actions to date have varied, both 

between Europe and the United States, and 

between different countries within Europe, 

partly refl ecting different circumstances. In 

the case of the United States, there is a greater 

need to address the resolution of problem loans 

themselves—notably subprime mortgages—than 

in Europe, where problems over the loan quality of domestically generated assets have been 

less severe to date and housing market conditions vary widely from country to country, but 

fi nancial institutions have faced funding and 

asset quality problems in both U.S. dollars and 

domestic currency.

However, the common cross-border problems that fi nancial institutions, markets, and 

real economies face argue for more globally 

consistent policy approaches than have so far 

been the case. Without such coordination, the 

adjustment process is likely to be more painful 

and protracted, steps by individual jurisdictions 

to defuse their own market pressures may spill 

over to other jurisdictions, and concerns about 

inequitable burden-sharing may prevent necessary but costly measures from being taken. This 

may reduce the benefi t of policies to restore 

confi dence to the global fi nancial system and 

increase the costs. 

Measures to address problem assets.

 As private sector balance sheets seek to shed 

assets in order to delever, the use of public sector balance sheets can help prevent “fi re sale” 

liquidations that threaten to reduce bank capital. Countries where banks have large exposures 

to securitized or other problem assets could 

consider mechanisms for the government to 

purchase or provide long-term funding to assets. 

FINANCIAL STABILITY POLICIES
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

52

This should create greater certainty about balance sheet health. Setting up an asset management company provides a framework of legal 

clarity and accountability for the process. 

The Troubled Asset Relief Program 

(TARP)envisaged under the recently enacted 

U.S. Emergency Economic Stabilization Act 

presents both opportunities and challenges. The 

program represents a systematic approach to 

purchase troubled assets from fi nancial institutions and is consistent with international experience that removing unsound assets from banks’ 

balance sheets is a vital step in the resolution of 

crises. However, its operational design has yet 

to be established and international experience 

suggests that it is these operational details that 

will be crucial to its success. In order to reduce 

public costs, it is important that the objectives 

behind the repurchase program be clearly set 

out from the beginning, that asset purchases 

help to reestablish market prices, and that 

appropriate expertise and incentives be put in 

place to ensure reasonable returns for public 

sector capital. Management of the program 

needs to be independent of political considerations, but still be subject to a high degree of 

accountability. 

A major challenge will be to avoid adverse 

selection in the troubled assets that the government buys. The structured assets to be bought 

under the program are inherently heterogeneous and the diffi culty of accurately estimating their value and risks lies at the heart of the 

crisis. Care will therefore be needed to ensure 

that the purchase process results in prices that 

adequately refl ect the difference in quality of 

the assets bought. In the absence of such differentiation there is the risk that those parties that 

own more inferior assets benefi t at the expense 

of those that showed more credit discipline during the boom and therefore offer higher-quality 

assets under the program. 

Although the program should improve banks’ 

liquidity and free up space on their balance 

sheets, its impact on banks’ capital positions will 

likely be uneven. Indeed, banks’ sale of assets 

may crystallize their losses, potentially accelerating the need to raise new capital. The program 

has enough fl exibility to be able to focus on the 

capitalization needs. In addition, the asset purchases may need to be combined with a wider 

capital-raising strategy, or with a plan to resolve 

banks that may become unviable. Moreover, in 

balancing both objectives, experience shows that 

capitalization should be the fi rst priority. In the 

meantime, relief from strict application of markto-market prices for regulatory capital purposes 

or some other form of regulatory forbearance 

may avoid accelerating capital needs while capital-raising remains very diffi cult.

The major budgetary implications of an asset 

purchase program mean that any decision in 

Europe to set up such a program will inevitably 

need to take place at the individual country 

level, but further coordination and a common 

approach is needed, even if the implementation has to be tailored to the specifi c circumstances of each country. It is also an opportunity 

to resolve the diversity of deposit insurance 

regimes, one of the most important reforms 

needed to strengthen the fi nancial stability 

framework in Europe. Governments in countries 

where market confi dence in their fi nancial institutions is being hindered by large exposures to 

structured, securitized assets trapped on balance 

sheets due to illiquid markets, or by other problem assets (whether U.S. or domestic), should 

similarly consider putting in place mechanisms 

for government purchase or funding of problem assets. Authorities should ensure that these 

mechanisms are consistent with each other in 

their design and underlying principles, to avoid 

adding to uncertainty over valuations of assets 

and balance sheets.

Measures to improve capital positions.

To keep credit growing while strengthening 

capital ratios, this chapter suggests that an estimated $675 billion of additional capital needs 

to be raised from public markets. With capital 

markets at present almost shut, governments 

will likely, in some cases, need to be involved in 

recapitalization of fi nancial institutions where 

they are viable and important to the fi nancial 
53

system. Even if recapitalization plans are government-led, they should ensure that incentives 

remain for private sector capital-raising, including from existing shareholders, and they should 

also be tied to measures to restructure fi rms and 

deal with troubled assets so as to ensure future 

balance sheet health. In order to protect public 

interests, any new capital provided by governments should have preferred status. Unviable 

banks should be closed in orderly fashion. 

Careful consideration of takeovers of troubled 

institutions is needed to ensure that the consolidated entity is not unduly weakened nor that it 

is of a size or results in a level of fi nancial sector 

concentration that would imply higher systemic 

risks in the future. 

Although restrictions on short sales of shares 

of fi nancial institutions may provide some temporary support to fi nancial institutions’ market 

capitalization in an environment of uncertainty, 

such measures do not fundamentally address 

underlying balance sheet weaknesses. Moreover, 

their impact may also be vitiated by the ability 

of market participants to take short positions 

through other instruments, such as derivatives. 

They may also have unintended and unhelpful 

consequences, including on market liquidity. 

Such restrictions should therefore be temporary 

and limited in scope to the measures needed to 

prevent systemic instability under exceptional 

circumstances while broader measures to restore 

confi dence are being introduced. 

Measures to restart funding and improve liquidity 

management. 

Financial institutions that rely on wholesale 

funding, including in cross-border markets, have 

been facing severe and mounting refi nancing 

problems and concerns about counterparty risk 

have risen sharply. Stabilizing institutions’ access 

to funding is essential while progress in improving capitalization and asset quality is made. 

For the time being, therefore, central banks 

will need to continue to coordinate to supply 

liquidity in suffi cient scale and with long enough 

maturities to provide confi dence in the stability 

of banks’ funding.

However, if systemic circumstances deteriorate 

further to a point where the loss of confi dence 

in fi nancial institutions puts their access to suffi cient liquidity and capital market funding in 

doubt, offi cial guarantees may be unavoidable as 

a temporary measure until confi dence returns. 

Furthermore, a guarantee for the senior and 

subordinated debt liabilities of fi nancial institutions need not be blanket to all institutions (e.g., 

guarantees of wholesale market liabilities may 

not be needed for those institutions that do not 

rely on such funding). Provision of such guarantees should include safeguards (fees, recourse 

to the balance sheet of the guaranteed bank, 

etc.). Alternatively, caps on deposit insurance 

of retail accounts could be increased beyond 

normal limits, as a number of countries have 

already done or are considering. The capacity 

of the government balance sheet to absorb the 

extra cost needs to be carefully considered when 

deciding whether and how to expand guarantees. Actions should be coordinated across countries and should include measures to prevent 

banks from using the expanded guarantee to 

gain international market share, so as to avoid 

transferring pressure to other countries. The 

U.S. government’s actions to temporarily provide guarantees to money market mutual funds 

are a helpful step to restore investor confi dence 

in that sector. In some countries, support for 

short-term collateralized funding between banks 

through triparty repurchase agreements or for 

money market funds could be provided through 

a backstop guarantee, while longer-term solutions to reduce counterparty risks in the broader 

markets are addressed, such as centralized clearing and settlement arrangements.

Cross-border vulnerabilities have been exposed by 

the crisis.

Many banks have faced persistent diffi culty 

in obtaining cross-border funding of suffi ciently 

long maturity and with suffi cient reliability. All 

banks with signifi cant cross-border activities need 

to reassess the adequacy and robustness of their 

cross-border funding plans. In addition, national 

authorities need plans in place to deal with 

FINANCIAL STABILITY POLICIES
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

54

banking crises that involve large cross-border 

funding needs. The Federal Reserve’s Term Auction Facility, accessible in several other countries 

through swap operations, is a useful example of 

cross-border cooperation, albeit on a temporary 

basis. Central banks should seek to regularize the 

procedures for cooperation going forward.

Authorities continue to work on cross-border 

cooperation and contingency planning for crisis 

management, but more progress is needed. 

Further international work is needed to address 

the diffi culties of dealing with cross-border fi rms 

under existing bankruptcy laws and insolvency 

regimes. This includes the need to address 

national legislation, such as requirements to 

ring-fence assets, where it acts as an obstacle to 

internationally cooperative solutions. Authorities should also clarify international arrangements for coordinating the deposit insurance of 

cross-border institutions. In the meantime, more 

robust information-sharing arrangements and 

mechanisms for rapid cooperation need to be 

put in place. Countries should start by addressing potential vulnerabilities and exposures in 

fi nancial relationships between particular pairs 

or small groups of countries where they are 

systemically important.

Emerging markets should also address risks spread 

through fi nancial channels.

Financial institutions in emerging markets 

have been less affected than those in mature 

markets, in part because the use of structured 

credit products was largely restricted to the latter. But spillovers have been increasing in recent 

weeks, sharply in some cases. Many of the policy 

lessons from the crisis for mature markets are 

similarly applicable to emerging market authorities in areas such as crisis management, central 

bank liquidity operations, capital adequacy, 

supervision of liquidity management, deposit 

insurance, and the clarity of authorities’ roles 

and responsibilities. 

Authorities need to ensure that they have the 

fl exibility in their market operations to address 

liquidity and other market strains that may suddenly arise. The large foreign exchange reserves 

buildup in many emerging market countries in 

recent years means that many have the fi nancial 

resources to provide foreign currency liquidity or 

to otherwise lend to their systems if needed; they 

must also make sure that they have the operational capacity and contingency plans to do so.

With regard to emerging Europe, recent 

assessments of countries under the IMF’s Financial Sector Assessment Program (FSAP) have 

stressed the need for close cooperation between 

home and host supervisors. This should, at a 

minimum, include coordinated inspections of 

internationally active banks, joint risk assessments, and “war games” to handle stress situations. Cooperative arrangements for the joint 

management of a major bank failure also need 

to be further developed. Going forward, FSAPs 

will continue to stress the need for authorities to 

improve their contingency plans and take better 

account of the risk of spillovers across institutions, markets, and regions. Where fi nancial 

systems are exposed to heightened liquidity risks 

and loss of market confi dence, the IMF can play 

a role in sharing information and experiences 

on best practices for policy responses, providing technical assistance on instrument design, 

strengthening surveillance and, if needed, providing program support.

Alongside short-term measures to stabilize markets, 

more robust foundations for the global fi nancial system 

are needed.

Events of the last few weeks have dramatically changed the fi nancial landscape. There 

can be little doubt that some of these changes 

in market behavior and functioning will prove 

to be lasting. This period of change provides 

an opportunity to rethink the fi nancial architecture with fewer constraints about the need 

to preserve existing market practices than in 

the past. Events have shown that problems of 

measuring solvency, liquidity, and risk are in 

many cases common across sectoral and national 

boundaries. Regulation and supervision should 

be designed according to the type of fi nancial 

activities being performed by regulated institutions, and less by the type of intermediary—
55

bank, insurance company, or investment fund. 

There is an opportunity and a need to move 

toward a macroprudential and regulatory framework that is more integrated in its approach and 

uniform in its standards, and that involves closer 

and more effective cross-border coordination 

and collaboration among supervisors, regulators, 

and central banks.

Clarity is needed regarding authorities’ roles and 

responsibilities.

The market turmoil has illustrated how fl uid 

the distinction between liquidity and solvency 

support becomes during systemic fi nancial crises 

and has raised questions about the costs of intervention. It is important that decisions to address 

the turmoil are not hampered by lack of clarity 

over the roles and responsibilities of authorities. The following actions can help avoid such 

potential confusion:

• The respective roles of central banks, regulators, supervisors, and fiscal authorities 

regarding financial stability should be clarified. Central banks should focus on systemic 

liquidity needs and the lender-of-last-resort 

function. They should play a central role in 

maintaining financial stability and should 

have access to the information on individual 

financial institutions necessary to perform 

this task. Regulators and supervisors (whether 

inside or outside central banks) should focus 

on prudential issues at individual firms while 

taking full account of overall financial stability 

conditions. Fiscal authorities should decide 

on and meet the costs of resolving solvency 

problems. There need to be enhanced procedures for these authorities to communicate 

and cooperate.

• Where costs to the public sector arise from 

support for problem firms, or where funds or 

guarantees are provided to address solvency 

issues, these costs should be reflected directly 

on the fiscal authorities’ balance sheet to 

provide political accountability.

• Regulatory and prudential regimes should 

be updated to provide comprehensive financial oversight, allow for prompt responses to 

risks, and remove adverse incentives and conflicts of interest. In the United Kingdom, following lessons from Northern Rock, reforms 

are being undertaken to enhance supervision; 

this would be an opportunity to take a more 

transparent, rules-based approach. In the 

United States, the Treasury blueprint for a 

modernized financial regulatory structure, 

emphasizing regulatory consolidation, and 

the recent changes to regulation of GSEs, are 

useful starting points for reform, but further 

steps are needed. For instance, the business 

models of the GSEs need to be clarified, differentiating their public and private sector 

activities. Their commercial activities should 

be regulated and capitalized in the same 

way as fully private-sector institutions, facilitating a level playing field and fostering market 

discipline. It will be important to ensure that 

regulatory changes in individual jurisdictions 

are well aligned in order to avoid regulatory 

arbitrage.

Monitoring of multiple measures of capital and liquidity 

by regulators, rating agencies, and markets should be 

accompanied by transparent risk disclosure.

Going forward, changes to the monitoring 

of capital adequacy and broader balance sheet 

health are needed. Risk-based capital measures 

continue to be the right approach for the 

regulation of capital, but they require a good 

risk assessment of the assets. The shortcomings 

that have been exposed in the ability of even 

the most sophisticated market participants to 

value and measure the risks underlying structured products have led to uncertainty about the 

appropriate capital targets for banks to pursue. 

Monitoring of multiple measures of capital and 

liquidity ratios, together with rigorous stress 

testing, can help in assessing fi rms’ ability to 

withstand a variety of shocks. The third Corrigan Report (Counterparty Risk Management 

Policy Group, 2008) provides a number of useful 

guidelines for fi nancial institutions to improve 

their management of economic capital. Regulators need to closely examine the lessons from 

the current crisis to ensure that risk measureFINANCIAL STABILITY POLICIES
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

56

ment takes a suffi ciently long-term perspective 

and to avoid procyclical elements that allowed 

capital requirements to be reduced during 

periods of market calm, but have aggravated the 

capital shortages during the current downturn. 

Finally, any changes to capital requirements 

should be phased in to avoid aggravating the 

impact of deleveraging.

Disclosure of the risks on (and off) banks’ 

balance sheets needs to be transparent and 

consistent both across institutions and over time. 

Supervisors need to examine fi rms’ progress in 

meeting the recommendations of the Financial 

Stability Forum on standardized risk disclosures 

as part of, or alongside, their fi nancial reports. 

But these recommendations are highly specifi c 

to the problem assets that triggered the current 

turmoil; it is even more important for supervisors, accounting bodies, and markets to search 

for more timeless standards for general risk 

disclosures that are consistent across fi rms and 

borders.

Globally, differences in regulatory and 

accounting measures that obscure comparability 

between institutions’ risk, solvency, and liquidity 

measures need to be eliminated where possible. 

In the United States, regulatory consolidation would help to achieve this. Global moves 

to make regulatory practices, measures, and 

published data more consistent across countries 

would also be very helpful.

Policies are needed to improve the robustness of 

liquidity management.

Temporary measures to shore up fi nancial 

institutions’ liquidity need to be backed up by 

actions to improve the robustness of their liquidity management going forward. This will require 

a three-pronged approach:

• Banks and securities firms need to improve 

their liquidity management practices, raising 

holdings of liquid assets and limiting reliance 

on central bank term financing as a liquidity 

backstop.

• Regulators need to devise more rigorous 

standards for firms’ liquidity plans, especially 

given the potential for major markets to 

remain illiquid for much longer periods than 

had previously been considered. The Basel 

Committee on Banking Supervision’s draft 

revised guidelines on liquidity are a welcome 

step forward, including the recommendation that national supervisors enforce closer 

compliance than in the past. Much remains 

to be done on the specifics to translate these 

principles into practice.

• At the same time, it is not realistic to expect 

every firm to be able to hold sufficient liquidity to cope with all possible market-wide problems, without a central bank backstop that can 

be applied quickly and flexibly in the event of 

system-wide pressures. 

Chapter 2 discusses some specifi c proposals to 

enhance liquidity management. In addition, 

all authorities should review their national 

deposit insurance schemes and, where needed, 

strengthen the schemes’ funding and ensure 

that they are appropriately supported by prudential regimes and bank resolution procedures. 

Authorities should agree on a set of international principles for deposit insurance systems 

that sets out a common core of objectives while 

recognizing that there may be different system 

designs that can achieve them.53

Annex 1.1. Global Financial Stability 

Map: Construction and Methodology54

This annex outlines our choice of indicators for each of 

the broad risks and conditions in the global fi nancial 

stability map (Figure 1.1). To complete the map, these 

indicators are supplemented by market intelligence and 

judgment that cannot be adequately represented with 

available indicators.

To begin construction of the stability map, 

we determine the percentile rank of the current 

53The International Association of Deposit Insurers has 

developed a set of core principles for effective deposit 

insurance systems that could provide a possible basis. At 

the same time, work has been taking place for several 

years on revising the European Union Directive on 

deposit insurance to achieve greater harmonization and 

clearer resolution of cross-border issues. 54The main author of this annex is Ken Miyajima. 
57

level of each indicator relative to its history to 

guide our assessment of current conditions, 

relative both to the April 2008 GFSR and over a 

longer horizon. Where possible, we have therefore favored indicators with a reasonable time 

series history. However, the fi nal choice of positioning on the map is not mechanical and represents the best judgment of IMF staff. Table 1.6 

shows how each indicator has changed since 

the last GFSR and our overall assessment of the 

movement in each risk and condition.

Monetary and Financial Conditions

The availability and cost of funding linked to 

global monetary and fi nancial conditions (Figure 1.41). To capture movements in general 

monetary conditions in mature markets, we 

begin by examining the cost of short-term 

liquidity, measured as the average level of real 

short rates across the G-7. From there, we take 

a broad measure of excess liquidity, defi ned as 

the difference between broad money growth 

and estimates for money demand. Realizing 

that the channels through which the setting 

of monetary policy is transmitted to fi nancial 

markets are complex, some researchers have 

found that including capital market measures 

more fully captures the effect of fi nancial 

prices and wealth on the economy. We therefore also use a fi nancial conditions index that 

incorporates movements in real exchange rates, 

real short- and long-term interest rates, credit 

spreads, equity returns, and market capitalization. Rapid increases in offi cial reserves held by 

the central bank create central bank liquidity 

in the domestic currency and in global markets. 

In particular, the recycling of dollar reserves in 

the United States contributes to looser liquidity conditions. To measure this, we look at the 

growth of offi cial international reserves held 

at the Federal Reserve. While the above measures capture the price effects of monetary and 

-fi nancial conditions, to examine the quantity 

effects we incorporate changes in lending conditions, based on senior loan offi cer surveys in 

mature markets.

Risk Appetite

The willingness of investors to take on additional risk by increasing exposure to riskier asset 

classes, and the consequent potential for increased 

losses (Figure 1.42). We aim to measure the 

Table 1.6. Changes in Risks and Conditions 

Since the April 2008 Global Financial 

Stability Report

Conditions and Risks

Changes since 

April 2008 

GFSR

Monetary and Financial Conditions ↓

G-7 real short rates ↔

G-3 excess liquidity ↓

Financial conditions index ↓

Growth in official reserves ↑

G-3 lending conditions ↓

Risk Appetite ↓

Investor survey of risk appetite ↓

Investor confidence index ↔

Emerging market fund flows ↓

Risk aversion index ↓

Macroeconomic Risks ↑

World Economic Outlook global growth risks ↔

G-3 confidence indices ↑

Economic surprise index ↓

OECD leading indicator ↑

Implied global trade growth ↑

Global break-even inflation rates ↓

Emerging Market Risks ↑

Fundamental EMBIG spread ↔

Sovereign credit quality ↑

Credit growth ↓

Median inflation volatility ↑

Corporate spreads ↔

Credit Risks ↑

Global corporate bond index spread ↑

Credit quality composition of corporate bond 

index ↑

Speculative-grade corporate default rate forecast ↑

Banking stability index ↔

Loan delinquencies ↑

Market and Liquidity Risks ↑↑

Hedge fund estimated leverage ↓

Net noncommercial positions in futures markets ↔

Common component of asset returns ↑

World implied equity risk premia ↓

Composite volatility measure ↑

Financial market liquidity index ↑

Source: IMF staff estimates.

Note: Changes are defined for each risk/condition such that ↑

signifies higher risk, easier monetary and financial conditions, or 

greater risk appetite, and ↓ signifies the converse; ↔ indicates 

no appreciable change. The number of arrows for the six overall 

conditions and risks correspond to moves on the global financial 

stability map.

ANNEX 1.1. GLOBAL FINANCIAL STABILITY MAP: CONSTRUCTION AND METHODOLOGY
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

58

extent to which investors are actively taking on 

more risk. A direct approach to this exploits 

survey data. The Merrill Lynch Fund Manager 

Survey asks an estimated 200 fund managers 

what level of risk they are currently taking relative to their benchmark. We then track the net 

percentage of investors reporting higher-thanbenchmark risk-taking. An alternative approach 

is to examine institutional holdings and fl ows 

into risky assets. The State Street Investor Confi dence Index uses changes in equity holdings 

by large international institutional investors 

relative to domestic investors to measure relative risk tolerance.55 The index extracts relative risk tolerance by netting out wealth effects 

and assuming that changes in fundamentals 

symmetrically affect all kinds of investors. 

We also take account of fl ows into emerging 

market bond and equity funds, as these represent another risky asset class. Risk appetite 

may also be inferred indirectly by examining 

price or return data. As an example of this 

approach, the Goldman Sachs Risk Aversion 

Index measures investors’ willingness to invest 

in risky assets as opposed to risk-free securities, 

building on the premises of the capital asset 

pricing model.56 By comparing returns between 

government debt and equities, the model allows 

the level of risk aversion to move over time. 

Taken together, these measures provide a broad 

indicator of risk appetite.

Macroeconomic Risks

Macroeconomic shocks with the potential to 

trigger a sharp market correction, given existing 

conditions in capital markets (Figure 1.43). Our 

principal assessment of the macroeconomic 

risks is based on the analysis contained in the 

55The estimated changes in relative risk tolerance of 

institutional investors from Froot and O’Connell (2003) 

are aggregated using a moving average. The index is 

scaled and rebased so that 100 corresponds to the year 

2000. 56The index represents the value of the coeffi cient 

of risk aversion, constrained to values between 0 

and 10. 

-2

0

2

4

6

8

10

12

–6

–4

–2

0

2

4

6

8

10

–20

–10

0

10

20

30

40

–20

–10

0

10

20

30

40

50

60

70

80

–90

–80

–70

–60

–50

–40

–30

–20

–10

0

91

93

95

97

99

101

103

105

107

Figure 1.41. Global Financial Stability Map: Monetary 

and Financial Conditions

G-7 Real Short-Term

Interest Rate1

(GDP-weighted average, in percent)

1991

1990

1991 94 97 2000 03 06

93 96 99 2002 05 08 1997 2000 03 06

94 97 2000 03 06 08 1990 93 96 99 2002 05 08

Euro area

Euro area

Euro area

Euro area

United States

Composite

United

States

United States

United States

Japan

Japan

Japan

(right scale)

China

Japan

Composite

Composite

Composite

G-3 Excess Household and

Corporate Liquidity

(In percent)

Composite Goldman Sachs

Global Financial Conditions

Index2 (January 1991 = 100)

G-3 Bank Lending Conditions3

(Net percentage of domestic respondents 

tightening standards for loans)

Custodial Reserve Holdings at

the Federal Reserve

Bank of New York

(In percent, 12-month growth)

Sources: Bloomberg L.P.; Goldman Sachs; OECD; lending surveys by Bank of Japan, 

European Central Bank, and Federal Reserve Board for households and corporates; and IMF 

staff estimates.

Note: Dashed lines are period averages. Vertical lines represent data as of the April 2008 

GFSR. 1Only G-3 subindicators are shown.

2A GDP-weighted average of China, euro area, Japan, and the United States. Each country 

index represents a weighted average of variables, including interest rates, credit spreads, 

exchange rates, and financial wealth. 3

Monthly interpolated GDP-weighted average. Euro area 1999:Q1 to 2002:Q4 based on 

values implied by credit growth. 
59

WEO and is consistent with the overall conclusion reached in that report on the outlook 

and risks for global growth. We complement 

that analysis by examining various economic 

confi dence measures. The fi rst of these is 

a GDP-weighted sum of confi dence indices 

across the major mature markets to determine 

whether businesses and consumers are optimistic or pessimistic about the economic outlook. 

A second component is a “surprise” index that 

shows whether data releases are consistently 

surprising fi nancial markets on the upside or 

downside. The aim is to capture the extent to 

which informed participants are likely to have 

to revise their outlook for economic growth. 

Third, recognizing the importance of turning 

points between expansions and slowdowns of 

economic activity, we incorporate changes in 

the Organization for Economic Cooperation 

and Development’s composite leading indicator. Fourth, in order to gauge infl ection points 

in global trade, we include global trade growth 

estimates implied by the Baltic Dry Index, a 

high-frequency indicator based on the freight 

rates of bulk raw materials that is commonly 

used as a leading indicator for global trade. 

Finally, market-implied break-even infl ation 

rates, based on estimates of intermediate-dated 

yield differentials between nominal and infl ation-linked domestic bonds, proxies expectations of infl ation.

Emerging Market Risks

Underlying fundamentals in emerging markets 

and vulnerabilities to external risks (Figure 1.44). 

These risks are conceptually separate from, 

though closely linked to, macroeconomic risks, 

except insofar as they focus only on emerging 

markets. Using an econometric model of 

emerging market sovereign spreads, we identify 

the movement in Emerging Market Bond 

Index Global (EMBIG) spreads accounted for 

by changes in fundamentals, as opposed to the 

movement in spreads attributable to other factors. Included in the fundamental factors are 

changes in economic, political, and fi nancial 

–50

–40

–30

–20

–10

0

10

20

60

70

80

90

100

110

120

–0.6

–0.4

–0.2

0

0.2

0.4

0.6

0.8

1.0

0

2

4

6

8

10

12

Figure 1.42. Global Financial Stability Map: Risk Appetite 

Merrill Lynch Fund Manager Survey

(Net percent of investors

reporting higher risk-taking

than benchmark)

State Street Investor

Confidence Index1

2001 1999 2001 03 05 07 03 05 07

2001 1990 93 96 99 05 03 05 07 2002

Total Net Inflows into Emerging

Market Bond and Equity Funds

(In percent of assets

under management,

13-week moving average)

Goldman Sachs Risk Aversion

Index

Sources: Emerging Portfolio Fund Research, Inc.; Goldman Sachs; Merrill Lynch; State Street 

Global Markets; and IMF staff estimates.

Note: Dashed lines are period averages. Vertical lines represent data as of the April 2008 GFSR.

1The estimated changes in relative risk tolerance of institutional investors from Froot and 

O’Connell (2003) are integrated to a level, scaled, and rebased so that 100 corresponds to the 

average level of the index in the year 2000.

Increased

risk-taking

Increased

risk-taking

Greater

risk aversion

ANNEX 1.1. GLOBAL FINANCIAL STABILITY MAP: CONSTRUCTION AND METHODOLOGY
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

60

risks within the country.57 This is complemented with a measure of the trend in actions 

by sovereign rating agencies such as Moody’s 

and Standard & Poor’s, to gauge changes in the 

macroeconomic environment and progress in 

reducing vulnerabilities arising from external 

fi nancing needs. We also measure fundamental conditions in emerging market countries 

that are separate from those related to sovereign debt, particularly given the reduced need 

for such fi nancing in many emerging market 

countries, by including an indicator of growth 

in private sector credit. Other components of 

the subindex include a measure of the volatility 

of infl ation rates, and a measure of corporate 

credit spreads relative to sovereign counterparts.

Credit Risks

Changes in, and perceptions of, credit quality 

that have the potential for creating losses resulting 

in stress to systemically important fi nancial institutions (Figure 1.45). Spreads on a global corporate bond index provide a market-price-based 

measure of investors’ assessment of corporate 

credit risk. We also examine the credit-quality 

composition of the high-yield index to identify 

whether it is increasingly made up of higher- or 

lower-quality issues, calculating the percentage of the index comprised of CCC or lower 

rated issues. We also incorporate forecasts of 

the global speculative default rate produced by 

Moody’s. Another component of the subindex 

is a Banking Stability Index, which represents 

the expected number of defaults among large 

57The model uses three fundamental variables to fi t 

EMBIG spreads: economic, fi nancial, and political risk 

ratings. The economic risk rating is the sum of risk points 

for annual infl ation, real GDP growth, the government 

budget balance as a percentage of GDP, the current 

account balance as a percentage of GDP, and GDP per 

capita as a percentage of the world average GDP per 

capita. The fi nancial risk rating includes foreign debt as a 

percentage of GDP, debt service as a percentage of GDP, 

net international reserves as months of import cover, 

exports of goods and services as a percentage of GDP, 

and exchange rate depreciation over the last year. The 

political risk rating is calculated using 12 indicators representing government stability and social conditions. 

0

2

4

6

8

1970 80 90 2000

–60

–40

–20

0

20

40

60

–15

–10

– 5

0

5

10

15

0.94

0.96

0.98

1.00

1.02

1.04

1.06

0

40

80

120

160

200

Sources: IMF, World Economic Outlook; Bloomberg L.P.; Dresdner Kleinwort; OECD; The 

Baltic Exchange; Barclays Capital; and IMF staff estimates.

Note: Dashed lines are period averages. Vertical lines represent data as of the April GFSR, 

except for “Global Real GDP Growth”. 12008 and 2009 data points account for skewness in the distribution of risks to the baseline 

forecast. 2Amplitude adjustment is carried out by adjusting mean to unity and the amplitude of the raw 

index to agree with that of the reference series by means of a scaling factor. 3The Baltic Dry Index is a shipping and trade index measuring changes in the cost of 

transporting raw materials such as metals, grains, and fuels by sea. 4Tracking GDP-weighted basis point-changes of estimated longer-term breakevens for 

Australia, Brazil, Canada, Colombia, France, Germany, Greece, Italy, Japan, Korea, Mexico, 

Poland, South Africa, Sweden, Turkey, the United Kingdom, and the United States.

Figure 1.43. Global Financial Stability Map: 

Macroeconomic Risks

2000 02 04 06

1990 2000 02 04 06 08 95 2000 05

1980 85 90 95 05 2000

1991 97 06 94 2000 03

Global Real GDP Growth1

(In percent)

G-3 Confidence Indicator

(GDP-weighted average of 

deviations from the average)

Dresdner Kleinwort Global 

Economic Activity Surprise Index

(Net number of positive less

negative data surprises, on a 

rolling 6-month cumulative basis)

Implied Global Trade Growth3

(In percent, year-on-year, 

implied by the Baltic Dry index)

Global Break-Even Rate Index4

(January 2000 = 100)

OECD Leading Indicator2

(Amplitude adjusted series for the

29 OECD member countries)

October 2008 WEO

Germany

Composite

Japan

United 

States

April 2008 WEO

0

5

10

15

20

25

30

35
61

complex fi nancial institutions (LCFIs), given at 

least one LCFI default (see, for example, Goodhart and Segoviano, forthcoming). This index 

is intended to highlight market perceptions of 

systemic default risk in the fi nancial sector. To 

capture broader credit risks, we also include 

delinquency rates on a wide range of noncorporate credit, including residential and commercial mortgages and credit card loans.

Market and Liquidity Risks

The potential for instability in pricing risks that 

could result in broader spillovers and/or mark-tomarket losses (Figure 1.46). An indicator attempting to capture the extent of market sensitivity 

of hedge fund returns provides an indirect 

measure of institutional susceptibility to price 

changes. The subindex also includes a speculative 

positions index, constructed from the noncommercial average absolute net positions relative 

to open interest of a range of futures contracts 

as reported to the Commodity Futures Trading 

Commission. These typically rise when speculators are taking relatively large positional bets on 

futures markets, relative to commercial traders. 

Also included is an estimation of the proportion 

of return variance across a range of asset classes 

that can be explained by a common factor. The 

higher the size of a common factor across assetclass returns, the greater the risk of a disorderly 

correction in the face of a shock. An additional 

indicator is an estimate of equity risk premia in 

mature markets using a three-stage dividend discount model. Low ex ante equity risk premia may 

suggest that investors are underestimating the risk 

attached to equity holdings, thereby increasing 

potential market risks. There is also a measure of 

implied volatility across a range of assets. Finally, 

to capture perceptions of funding, secondary market trading, and counterparty risks, we 

incorporate the spread between major mature 

market government securities yields and interbank rates, the spread between interbank rates 

and expected overnight interest rates, bid-ask 

spreads on major mature market currencies, and 

daily return-to-volume ratios of equity markets.

100

300

500

700

900

1100

1300

1500

–80

–60

–40

–20

0

20

40

60

80

5

10

15

20

25

30

35

0.5

1.0

1.5

2.0

2.5

0

200

400

600

800

1000

1200

Sources: Bloomberg L.P.; JPMorgan Chase & Co.; The PRS Group; IMF, International Financial 

Statistics; Credit Suisse; and IMF staff estimates.

Note: Dashed lines are period averages. Vertical lines represent data as of the April 2008 GFSR.

1EMBIG = Emerging Market Bond Index Global. The model excludes Argentina because of breaks in 

the data series related to debt restructuring. Owing to the short data series, the model also excludes 

Indonesia and several smaller countries. The analysis thus includes 32 countries. 2Net actions of upgrades (+1 for each notch), downgrades (–1 for each notch), changes in outlooks 

(+/– 0.25), reviews and creditwatches (+/–0.5). 344 countries.

4Average of 12-month rolling standard deviations of consumer price changes in 36 emerging 

markets.

Figure 1.44. Global Financial Stability Map: 

Emerging Market Risks

Actual adjusted spreads

Sovereign 

(right scale)

Corporate 

(right scale)

Corporate-tosovereign ratio 

(left scale)

Fundamentals 

model spreads

1998

1996 98 02 04 06 08 2000

2002 03 04 05 06 07 08

1996 98 02 04 06 08 2000

2000 2000 02 95 05 08 04 06 08 1991

Adjusted EMBIG Spreads: 

Actual and Fundamentals 

Model Estimates1

(In basis points)

Emerging Market Private Sector 

Credit Growth3

(GDP-weighted average, in percent)

Emerging Market Corporate 

Credit Spreads

(In basis points)

Median Volatility of Inflation Across 

Emerging Market Countries4

(In percent)

Emerging Market Sovereign Credit 

Quality: Net Credit Ratings Changes2

(12-month rolling sum of net ratings 

upgrades less downgrades)

0.4

0.6

0.8

1.0

1.2

1.4

1.6

ANNEX 1.1. GLOBAL FINANCIAL STABILITY MAP: CONSTRUCTION AND METHODOLOGY
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

62

Annex 1.2. Financial Investment in 

Commodities Markets58

This annex addresses the possible causal relationships between increased fi nancial market participation and commodity prices. Using investor 

positioning data, the fi ndings suggest it is diffi cult to establish a causal relationship for the six 

c ommodities studied, though numerous caveats 

should lead to caution in interpreting the econometric results.

Commodity Investing

Commodities have attracted increasing 

fi nancial interest in recent years, owing to low 

or negative correlations with other major asset 

classes and hedging properties against infl ation 

(Table 1.7).59 The case for commodities investment has been buttressed by strong returns, 

with the Goldman Sachs Commodity Index 

(GSCI) returning an annual average of 9 percent in U.S. dollar terms from 1990 through 

July 2008, and more than 40 percent since early 

2007 (Table 1.7). While diffi cult to verify, private sector estimates suggest that commodities 

assets under management totaled $270 billion 

in the second quarter of 2008, $175 billion of 

which were institutional investor fl ows linked 

to commodity indices (Cooper, Norrish, and 

Sen, 2008). These fi gures do not fully capture 

investments from more specialized asset managers. Commodity-trading advisers (CTAs) may 

58The main authors of this annex are Sergei Antoshin, 

Elie Canetti, and Ken Miyajima. 59Views are mixed on the effectiveness of commodities as a hedge against U.S. dollar depreciation. IMF 

(2008c, Box 1.4) and other studies have found that 

commodity prices in dollar terms tend to increase as 

the dollar depreciates. However, measured in a currency 

basket, commodity prices are generally less correlated 

with the dollar and the sign is reversed (Table 1.7), 

suggesting negative correlations between the prices of 

dollar-denominated commodities and the dollar may 

partly refl ect changes in the value of the dollar against 

other currencies. Also, commodity prices have been 

signifi cantly more volatile than the dollar, prompting some to argue that commodities are a poor dollar 

hedge. 

50

150

250

350

450

10

15

20

25

30

0

2

4

6

8

10

12

3

4

5

6

7

2.5

3.5

4.5

5.5

6.5

Figure 1.45. Global Financial Stability Map: Credit Risks

Forecast 

default rate

Actual 

default rate

1998 2000

1998

1991 94 97 2000 03 06

2000 02 2004 05 06 07 08 04 06 08

02 1999 2001 03 05 07 04 06 08

Merrill Lynch Global Corporate 

Bond Index Spread

(In basis points)

Moody’s Speculative Grade Default 

Rates: Actual and 12-Month Forecast

(In percent)

Share of CCC or Lower-Rated 

Corporate Securities in Merrill 

Lynch Global HIgh-Yield Index

(In percent)

Expected Number of Bank Defaults 

Given at Least One Bank Default

(Among 15 selected banks)

Delinquency Rate on Consumer 

and Mortgage Loans2

(In percent)

Sources: Merrill Lynch; Moody’s; Bloomberg L.P.; Mortgage Bankers Association; 

Federal Reserve; and IMF staff estimates.

Note: Dashed lines are period averages. Vertical lines represent data as of the April 

2008 GFSR. 1Measuring the largest probability of default among the sampled 15 banks each day.

230-, 60-, and 90-day delinquencies for residential and commercial mortgages, and 

credit card loans in the United States.
63

have as much as $40 billion to $60 billion in 

assets under management, while hedge funds 

may have as much as $80 billion in commodity 

investments, much of it, however, in “spread” 

trades that do not impart a directional bias to 

prices.60

Commodity-indexed funds have attracted 

attention because of their aggregate size and 

rapid growth in recent years, and because, 

unlike many other investments, they represent 

a long-only investment in commodity futures. 

The two largest indices are the GSCI and the 

Dow Jones’ AIG Commodity Index, which 

together account for well over three-quarters 

of total indexed investments. The funds generally gain exposure to commodities through 

over-the-counter (OTC) total return swaps 

(primarily from major broker-dealers) that 

replicate the performance of the key commodity indices. The dealers, in turn, hedge their 

exposure, in part through exchange-traded 

futures.

At least two arguments are commonly 

advanced suggesting that fi nancial invest60See Tesar (2008). CTAs use a wide variety of trading 

models, including simple technical trading rules that can 

amount to trend-following strategies that are independent of fundamentals. 

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

6

10

14

18

22

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

1

2

3

4

5

6

7

8

0

100

200

300

400

500

600

–2.0

–1.5

–1.0

–0.5

0

0.5

1.0

1.5

2.0

2.5

3.0

Sources: Credit Suisse Tremont Index LLC; Bloomberg L.P.; JPMorgan Chase & Co; IBES; 

Morgan Stanley Capital International; and IMF staff estimates.

Note: Dashed lines are period averages. Vertical lines represent data as of the April 2008 GFSR.

1

36-month rolling regressions of hedge fund performance versus real asset returns.

2

Data represent the absolute value of the net position taken by noncommercial traders in 

17 selected U.S. futures markets. High values are indicative of heavy speculative positioning across 

markets, either net-long or net-short. 3

Represents an average z-score of the implied volatility derived from options from stock market 

indices, interest, and exchange rates. A value of 0 indicates the average implied volatility across 

asset classes is in line with the period average (from 12/31/98 where data are available). Values of 

+/–1 indicate average implied volatility is one standard deviation above or below the period average. 4

Based on the spread between yields on government securities and interbank rates, spread 

between term and overnight interbank rates, currency bid-ask spreads, and daily return-to-volume 

ratios of equity markets. A higher value indicates tighter market liquidity conditions.

Figure 1.46. Global Financial Stability Map: Market 

and Liquidity Risks

1994 2000 02 04 06 08 96 98

1999 1996 98 2000 02 04 06 2001 03 05 07

1993 95 97 99 03 05 07 2001

1997 99 03 05 07 1997 2001 07 2001 99 03 05

Hedge Fund Estimated Leverage1

(Sum of betas across asset 

classes)

Absolute Value of Net Noncommercial 

Positions in U.S. Futures Markets2

(In percent of open-interest 

across select futures markets, 

30-day moving average)

Estimated Common Component 

in Asset Class Returns

(Share of the variation in returns, 

90-day moving average)

Composite Volatility Index3

(In standard deviations from 

the period average)

Funding and Market Liquidity Index4

(January 1996 = 100)

World Implied Equity Risk Premia

(In percent)

Table 1.7. Asset Class Characteristics

(Based on monthly change during January 1990–July 2008)

GSCI 

TR

GSCI TR 

in SDRs

Global 

Equities

EM 

Equities

US$ 

NEER

U.S. 

CPI

(Correlations)

GSCI TR 1.00

GSCI TR in SDRs 0.97 1.00

Global Equities –0.03 –0.06 1.00

EM Equities 0.06 0.07 0.71 1.00

US$ NEER –0.11 0.06 –0.18 –0.08 1.00

U.S. CPI 0.14 0.11 –0.17 –0.09 –0.16 1.00

(Annualized average change and volatility, in percent)

Average change 9.0 7.8 5.1 8.7 –1.4 3.0

Volatility 19.7 19.7 13.9 23.2 5.6 0.8

Sources: Bloomberg L.P.; and IMF staff estimates.

Note: All assets are in dollar terms, unless otherwise specified. GSCI TR 

signifies the S&P Goldman Sachs Commodity Total Return Index, which 

reflects spot, roll, and cash yields; SDRs signifies special drawing rights; 

NEER signifies the nominal effective exchange rate, where a higher value 

signifies the dollar’s appreciation; EM = emerging markets; CPI = consumer 

price index.

ANNEX 1.2. FINANCIAL INVESTMENT IN COMMODITIES MARKETS
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

64

ment in commodities, particularly in index 

funds, does not have a material impact on 

commodity prices. First, index investors do 

not take physical delivery, a fact supported by 

the lack of evidence from inventory data of 

commodity hoarding, though the quality and 

coverage of such data are questionable. Many 

observers have questioned whether fi nancial 

investors can infl uence commodity prices in 

the absence of physical demand. The second 

justifi cation argues that some commodities 

without signifi cant fi nancial market participation have exhibited price rises and volatility 

equal to or greater than commodities with 

liquid futures markets, suggesting a limited 

role for fi nancial investors.61 Other observers, however, argue that large increases in 

fi nancial investment in commodities futures 

and the fact that near futures prices and spot 

prices generally converge provide a prima facie 

case that increased fi nancial investment may 

infl uence commodity prices, at least in the 

short run.

Causality Study

Whatever the merits of these arguments, 

if fi nancial market participation infl uences 

commodity prices, increases in investment 

should precede price increases. This annex 

examines such temporal causality between 

investor positions and prices of oil, copper, 

wheat, corn, soybeans, and rice.62 Positioning 

data comes from the publicly available Commitment of Traders Report from the U.S. Commodities and Futures Trading Commission 

(CFTC). Investors are classifi ed as “commercial” 

if they are hedging an existing exposure and 

61The WEO (IMF, 2008d) studies the relationship 

between commodities that are heavily traded in fi nancial markets and those that are not. It fi nds that while 

fi nancialization may have led to increased co-movements 

between some commodities, no apparent connection 

is found to either price volatility or price changes (see 

Box 3.1 in Chapter 3). 62The CFTC does not publish index positions on rice, 

another agricultural commodity of interest. 

“noncommercial” if they are not. Swaps dealers are classifi ed as commercial investors, since 

they use futures markets to offset their OTC 

positions. However, since those OTC positions 

sometimes have as their counterparties the 

commodity index funds, which generally invest 

with a return motive (rather than to hedge), the 

CFTC started to publish the positions of commodity index traders (CITs) separately from 

2006.63,64

While there clearly are periods and commodities where positions and prices move 

together, there are other times when positions were not rising during periods of rapid 

price appreciation (Figure 1.47). For example, 

wheat index positions were fairly fl at while 

noncommercial positions were declining, even 

as prices rose rapidly from mid-2007 through 

the fi rst quarter of 2008. Corn index positions were the same at the end of the second 

quarter of 2008 as two years earlier, during 

which time the spot price more than tripled. 

Non commercial positions in corn and soybeans 

peaked in February 2008, while prices kept 

rising through the end of the second quarter. 

Non commercial positions in oil were quite 

volatile, even as oil prices rose almost continuously from the beginning of 2007 through 

the second quarter of 2008, by which time 

net oil positions had dropped roughly to zero. 

Noncommercial copper positions were declining through the period of the sharpest price 

increases, roughly from the beginning of 2004 

through mid-2006.

Granger causality tests can evaluate whether 

changes in investor positions precede price 

changes. Earlier work has generally failed to fi nd 

63In addition to swaps with indexed funds, dealers also 

tailor swaps to individual investors or commercial entities, 

involving both long and short positions, especially in oil 

markets. Thus, swap dealers generally hedge only net 

positions, which may be much smaller than their aggregate gross positions. 64Prices are as reported by U.S. commodities 

exchanges. The spot price is defi ned as the price of 

the futures contract closest to expiration, while the 

futures price is the price of the contract expiring in 

12 months. 
65

such a temporal relationship, concluding that 

causality runs from prices to positions.65 This 

annex extends those earlier studies to encompass the most recent period during which commodity prices rose particularly sharply, and also 

tests whether the new CIT positions data can 

explain prices.

However, there are a few shortcomings that 

limit the power of the statistical tests and that 

require that caution be taken in interpreting 

these results. First, the CFTC only publishes 

traders’ positions aggregated across maturities. 

It would be preferable to use only the positions 

in the maturity of the contract for which prices 

are being tested. Second, the data are weekly, 

which may hamper the identifi cation of very 

short-run effects, given that transmission from 

positions to prices may happen at a higher 

frequency. Indeed, some market participants 

anecdotally suggest there are short-run effects 

that may last only a matter of days.66 Third, as 

the CFTC acknowledges, traders sometimes 

may be misclassifi ed between commercial and 

noncommercial positions, and some traders 

classifi ed as commercial may have speculative 

motives.67

For tests on fi nancial positions, we used 

data over the entire period for which CIT 

positions are available (since January 2006). 

For corn, soybeans, and wheat, the hypothesis that CIT positions and noncommercial 

(excluding CIT) positions lead prices is tested. 

For rice, crude oil, and copper, for which CIT 

positions data are not available, noncommercial 

positions are used to test whether they lead (or 

65Box 5.1 in the September 2006 WEO did not fi nd 

strong evidence of the infl uence of speculative positions 

as a driver of commodity prices (IMF, 2006). See also 

Haigh, Hranaiova, and Overdahl (2007) and Interagency 

Task Force on Commodity Markets (2008). 66However, using formal statistical tests, the report 

by the Interagency Task Force on Commodity Markets 

(2008) failed to fi nd any signifi cant causality from 

position changes to price changes using nonpublic 

daily data. 67The CFTC reviews, and occasionally revises, the 

classifi cations of futures market traders on an ongoing 

basis. 

Sources: Bloomberg L.P.; and the U.S. Commodities Futures Trading 

Commission, Commitment of Traders Report.

Note: CIT = commodity index traders.

Figure 1.47. Commodity Futures Prices and Financial 

Positions

20

40

60

80

100

120

140

-150

-100

-50

0

50

100

150

2004 05 06 07 08 2004 05 06 07 08

2004 05 06 07 08 2004 05 06 07 08

2004 05 06 07 08 2004 05 06 07 08

100

150

200

250

300

350

400

–40

–30

–20

–10

0

10

20

30

40 Copper

6

8

10

12

14

16

18

20

22

–9

–6

–3

0

3

6

9

Rice

200

300

400

500

600

700

800

–100

0

100

200

300

400

500

Corn

Crude Oil

200

400

600

800

1000

1200

–80

–40

0

40

80

120

160

200

240

Wheat

500

700

900

1100

1300

1500

1700

–100

0

100

200

300

400

500 Soybeans

Commodity futures prices, U.S. dollars (left scale)

Net long noncommercial positions (right scale)

Net long CIT positions (right scale)

ANNEX 1.2. FINANCIAL INVESTMENT IN COMMODITIES MARKETS
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

66

Granger-cause) price movements. Time series 

vector models and Granger causality tests are 

used to address causality between positions 

and prices.

The results of our study are summarized in 

Table 1.8. In four of six commodities, there 

is a strong correlation between fi nancial positions and commodities prices. However, tests for 

causality yield much weaker results. The second 

column demonstrates that fi nancial positions 

lead prices only in the case of copper. Moreover, 

the signifi cance of this fi nding is limited by the 

fact that net long noncommercial positions are 

negative most of the time (Figure 1.47). Causality from prices to positions is established only 

for rice.

Overall, there is correlation between prices 

and positions in some commodities markets. 

However, we are unable to detect causality from 

fi nancial positions to prices for major commodities used in the study.

Annex 1.3. Loss Estimates on U.S. 

Credit Instruments68

This annex updates the methodology for estimating 

losses on U.S. credit instruments, and highlights the 

main revisions.

In light of further developments in delinquencies and charge-offs as well as a repricing in 

securitized debt, we have updated the loss estimates laid out in the April 2008 GFSR as shown 

in Table 1.9. Our estimate of total near-term 

global losses on U.S. credit-related debt has 

been raised to $1.4 trillion (from $945 billion). 

The upward revision mostly refl ects increased 

loss estimates on corporate debt and prime 

residential mortgages.69 Our loss estimates for 

corporate debt, including loans and securities, 

have risen signifi cantly to refl ect the deterioration in the debt of fi nancial institutions that has 

taken place since April. Higher loss estimates for 

the prime mortgage market refl ect a more negative base case home price scenario.

For the corporate sector, our estimate of 

losses on securities debt has risen to $210 billion, while that on loans has increased to 

$110 billion, refl ecting a more negative base 

case assumption about the credit cycle over 

the next few years (Box 1.6). Loss estimates 

for collateralized loan obligations (CLOs) are 

unchanged.70

68The main authors of this annex are Mustafa Saiyid 

and Sergei Antoshin. 69Loss estimates have also been published by the Bank 

of England (BoE) and the Organization for Economic 

Cooperation and Development (OECD), but they are not 

fully comparable to those of the IMF due to differences 

in the size and variety of asset classes considered. The 

IMF’s April loss estimate of $945 billion related to U.S. 

residential and commercial mortgages, consumer credit, 

and corporate debt. In contrast, the BoE loss estimates 

of $317 billion to $380 billion applied to U.S. subprime 

residential mortgage securities only; while the OECD’s 

loss estimate of $422 billion was only for U.S. residential 

mortgage-related securities. 70Like the CMBX, the LCDX (the leveraged loan credit 

default swap index) was reportedly shorted extensively 

by speculators seeking to profi t from deterioration of the 

leveraged loan market, and index pricing used earlier 

may have exaggerated loss estimates. 

Table 1.8. Test for Causality Between 

Commodities Prices and Financial Positions

Correlation Causality Causality

between 

Prices and

from 

Positions

from 

Prices to

Positions1 to Prices2 Positions3

Crude oil 

Noncommercial traders No No No

Copper 

Noncommercial traders No Yes No

Corn 

Index traders Yes No No

Noncommercial traders Yes No No

Soybeans 

Index traders Yes No No

Noncommercial traders No No No

Rice 

Noncommercial traders Yes No Yes

Wheat 

Index traders No No No

Noncommercial traders Yes No No

Sources: Commodities Futures Trading Commission, 

Commitment of Traders Report; and IMF staff estimates.

1“Yes” means cointegration exists between prices and positions 

at 5 percent significance. “No” means no cointegration.

2“Yes” means the short-run coefficient on the first lag of 

positions in the price equation is significant and positive. “No” 

means the coefficient is insignificant or negative.

3“Yes” means the short-run coefficient on the first lag of prices 

in the positions equation is significant and positive. “No” means the 

coefficient is insignificant or negative.
67

Losses on prime residential mortgage loans 

has increased to $85 billion, and we are now 

estimating mark-to-market losses on prime 

securities of $80 billion. The increase for loans 

refl ects higher-than-expected delinquencies 

on prime loans and losses for governmentsponsored enterprises (GSEs) on loans pooled 

into guaranteed securities.71 The increase 

for prime securities is mostly coming from 

mark-to-market losses on prime nonconform71Losses on loans pooled into guaranteed securities 

are expected to accrue only to the GSEs, since the GSEs 

guarantee timely principal and interest payments. 

ing (“jumbo”) mortgage loans packaged into 

securities.

Another contribution to the change in loss 

estimates since April is due to a $50 billion 

increase in the valuation of subprime mortgage-related CDOs. The TABX (tranched 

ABX index) used as a benchmark for these 

securities now shows no distinction in pricing 

between senior and junior tranches, which 

are all marked at around 3 to 4 cents on the 

dollar, refl ecting the erosion of any protection 

from relative subordination of securities in the 

capital structure. Estimated losses on subprime 

mortgage-related asset-backed securities (ABS) 

are little changed since April, with realized 

delinquencies on 2006–07 subprime vintages 

higher than projected, but those on the 2004–

05 vintages lower than projected.72 As a result, 

prices of 2006–07 vintage subprime ABS have 

continued to fall, but those of 2004–05 vintages 

have risen.

Our earlier estimate of the distribution 

of losses between various types of market 

participant has been modifi ed to refl ect the 

impact of credit derivatives in transferring 

risk from one type of market participant to 

another. We continue to estimate that some 50 

to 60 percent of losses will be borne by banks, 

10 to 20 percent by insurance companies, 

10 to 15 percent by pension funds and savings institutions, 5 to 10 percent by the GSEs, 

and the remainder by hedge funds and other 

participants.

Looking ahead, the market pricing of various U.S. securities, including prime mortgage 

securities, consumer ABS, and corporate debt, 

could deteriorate further if realized cash fl ow 

losses come out higher than market expectations. Over the past year, wider spreads on these 

types of securities have been partially, if not 

completely, offset by falling U.S. Treasury yields 

on a total return basis. This may not be the case 

in the future.

72This may refl ect the fact that the earlier vintages are 

more seasoned, or that the relaxation of underwriting 

standards intensifi ed after 2005. 

Table 1.9. Comparison of Financial Sector Loss 

Estimates, October 2008

(In billions of U.S. dollars)

Base-Case Estimates of Losses on U.S. Loans

Outstanding

Estimated loss

April 2008 GFSR

Estimated 

loss

October 

2008

Subprime residential 300 45 50

Alt-A residential 600 30 35

Prime residential 3,800 40 85

Commercial real estate 2,400 30 90

Consumer loans 1,400 20 45

Corporate loans 3,700 50 110

Leveraged loans 170 10 10

Total for loans 12,370 225 425

Base-Case Estimates of Mark-to-Market Losses on Related Securities

 Outstanding

Estimated 

mark-to-market 

loss

April 2008 GFSR

Estimated 

mark-tomarket 

loss

October 

2008

ABS 1,100 210 210

ABS CDOs 400 240 290

Prime MBS 3,800 0 80

CMBS 940 210 160

Consumer ABS 650 0 0

High-grade corporate debt 3,000 0 130

High-yield corporate debt 600 30 80

CLOs 350 30 30

Total for securities 10,840 720 980

Total for loans and securities 23,210 945 1,405 

Sources: Goldman Sachs; JPMorgan Chase & Co.; Lehman Brothers; 

Markit.com; Merrill Lynch; and IMF staff estimates.

Note: ABS = asset-backed securities; CDO = collateralized debt obligation; 

CLO = collateralized loan obligation; CMBS = commercial mortgage-backed 

security; MBS = mortgage-backed security.

ANNEX 1.3. LOSS ESTIMATES ON U.S. CREDIT INSTRUMENTS
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

68

This box provides technical details on the baseline and 

stress scenario for bank charge-off rates on various 

types of loans.

To forecast bank charge-off rates for each 

bank loan type, a distributed lag model was 

used to accommodate the highly autocorrelated 

time series. The following potential explanatory variables for individual and joint signifi -

cance were tested: bank lending conditions; 

fi nancial and real estate assets, liabilities, and 

net worth from both household and corporate 

balance sheets; drivers of households’ net worth 

(housing prices and equity prices); measures 

of households’ debt obligations (the mortgage 

obligation ratio, bank loans); measures and 

drivers of income (disposable personal income, 

corporate profi ts, personal consumption); 

and business-cycle variables (GDP, industrial 

production, the purchasing managers index, 

employment, the unemployment rate). The 

sample was comprised of quarterly data from 

1991 to 2008 so as to incorporate the last two 

recessions. 

Corporate Loans

In estimating charge-offs on commercial 

and industrial (C&I) loans, bank lending 

conditions and business-cycle variables were 

strongly signifi cant. After running various specifi cations we adopted the following 

representation: 

C_CI(t) = 0.292 + 0.589*C_CI(t – 1) 

+ 0.194*C_CI(t – 2) + 0.004*L_CIL(t) 

– 0.059*GDP(t), 

where C_CI(t) is the charge-off rate for C&I 

loans at time t, L_CIL(t) is bank lending conditions for C&I loans, and GDP(t) is gross domestic product.

The baseline scenario relied on WEO estimates for GDP, where GDP growth troughs at 

–0.33 percent in the second quarter of 2009 

before rising to 2.81 percent in 2010. Lending 

standards are assumed to peak at 70 percent 

in the fourth quarter of 2008 and revert to 

their long-run equilibrium level by end-2010. 

Under these assumptions, the charge-off rate 

rises from the current 0.82 percent to a high of 

1.69 percent in the third quarter of 2009, before 

leveling off at 1.27 percent in 2010. In the stress 

scenario, GDP declines 1.35 percent in the third 

quarter of 2009 and then recovers to 2.51 percent by the end of 2010. Lending standards 

remain at 75 percent for two quarters and then 

take 1.5 years to normalize. Under this scenario, 

the charge-off rate reaches 2.06 percent in the 

fourth quarter of 2009.

Commercial Real Estate (CRE) Loans

Forecasting CRE charge-off rates is complicated by structural breaks during the estimation 

period and the variation within the sector.1

Several variables were signifi cant, including 

retail sales, consumption, employment, and 

bank lending standards, resulting in the fi nal 

estimated model specifi cation of: 

C_CRE(t) = 0.200 + 0.917*C_CRE(t – 1) 

– 0.054*C(t), 

where C_CRE(t) is the charge-off rate for CRE 

loans and C(t) is private consumption.

Under the baseline scenario for this model, 

we assumed that private consumption growth 

troughs at –1.62 percent in the second quarter 

of 2009 before picking up to 2.83 percent 

in 2010, consistent with the WEO. The 

charge-off rate rises from the current 0.93 percent to 1.71 percent by end-2009, and then 

declines to 1.51 percent in 2010. In the stress 

scenario, consumption contracts by 2.59 percent in the second quarter of 2009 but recovers to grow at 2.65 percent by end-2010. The 

charge-off rate peaks at 1.90 percent by the 

end of 2009.

Residential Real Estate (RRE) Loans 

We estimated delinquency rates instead of 

charge-offs, using bank lending standards and 

1Since the nature of the current cycle is somewhat 

similar to the 1990–91 recession, in the sense that 

the banking sector comes under signifi cant pressure, we ran the estimation over the entire period of 

1991–2008.

Box 1.6. Forecasting Loan Charge-Off Rates
69

Annex 1.4. Factors Infl uencing the Pace 

and Level of Bank Capital Rebuilding73

This annex describes the approach taken in preparing an illustration of how global banks may delever 

to raise capital ratios, and outlines some of the factors 

that have to be taken into account in such an exercise. 

The task of assessing how much capital banks need 

is made more complex by different objectives and time 

scales over which they are to be achieved.

73The main author of this annex is Christopher Morris. 

The simulation in this annex endeavors to 

derive both the demand and supply of credit 

for 2008–14 (Table 1.10). The demand for 

credit is driven by the nominal GDP growth 

forecasts projected by the WEO (IMF, 2008d). 

The supply of credit is driven by the various 

factors that will lead banks’ balance sheets to 

expand or contract, and by bank profi tability. 

Underlying this adjustment is a need to achieve 

higher capital adequacy for investors, regulators, and policymakers.

home prices as explanatory variables.2 The 

model specifi cation is: 

D_RRE(t) = 0.366 + 0.851*D_RRE(t – 1) 

+ 0.008*L_RRE(t) 

– 0.008*HP_RRE(t), 

where D_RRE(t) is the delinquency rate for RRE 

loans, L_RRE(t) is bank lending standards for 

RRE loans, and HP_RRE(t) is the Case-Shiller 

10 house price index.

Under the baseline scenario, the tightening 

in bank lending standards peaks at 90 percent in the fourth quarter of 2008 and then 

declines relatively quickly in 2009–10. House 

prices based on historical and future data 

have troughed at –17 percent in 2008:Q2 and 

will continue to decline through 2010. Under 

these assumptions, residential real estate loan 

charge-offs are expected to rise from the cur2Due to the nature of the historical charge-offs 

series (which show little variability and thus cannot 

be used for forecasting), we instead used delinquency 

rates (which show a greater variability). In addition, 

since the episode of severe house price deterioration 

is unique in the United States, the effect of house 

price depreciation could be nonlinear, pushing delinquencies higher and depressing recovery rates. Using 

delinquencies instead of charge-offs and an assumption about future recovery rates help model a nonlinear effect of house price appreciation. Forecasted 

delinquencies are then converted into charge-offs 

assuming that 23 percent (30 percent) of delinquent 

loans will be charged off under the baseline (stress) 

case scenario. The assumptions on the default and 

recovery rates are consistent with dealers’ estimates.

rent 1.13 percent to a peak of 1.89 percent in 

the second quarter of 2009, but then decline 

to 1.32 percent by the end of 2010. The stress 

scenario assumes that bank lending standards 

remain at 90 percent for two quarters and then 

take 2.5 years to normalize, house prices decline 

by 22 percent by the end of 2008, and recovery 

rates remain at 61 percent, on average, through 

2010. In this case, charge-off rates peak at 2.32 

percent in end-2009.

Consumer Loans

We combined data on credit cards and 

other consumer credit into a single category 

for “consumer loans.”3 The fi nal model was 

estimated as: 

C_CL(t) = 1.187 + 0.608*C_CL(t – 1) 

+ 0.007*L_CL(t) – 0.072*GDP(t), 

where CL_L(t) denotes the charge-off rate for 

consumer loans. L_CL(t) is lending standards 

for consumer loans, and GDP(t) is gross domestic product.

Under the baseline scenario, charge-off rates 

rise from the current 3.37 percent to 3.92 percent in the second quarter of 2009 and then 

decline to 2.83 percent by end-2010. Under the 

stress scenario, charge-offs climb to 4.16 percent 

in the second quarter of 2009.

3Credit card charge-offs exhibit clear cyclical 

behavior, whereas other consumer credit (mostly auto 

loans) tends to be highly autocorrelated and has a low 

sensitivity to its key driver, GDP.

ANNEX 1.4. FACTORS INFLUENCING THE PACE AND LEVEL OF BANK CAPITAL REBUILDING
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

70

First, it is necessary to make some judgment 

on what proportion of the more than $7.6 trillion or so of committed corporate credit lines 

globally will be drawn down (King, 2008; Malhotra and Henriques, 2008). These credit lines 

were negotiated when liquidity risk was seriously 

underpriced, and are therefore at highly attractive rates for the borrower. However, some customers do not need the funds, and may be wary 

of increasing their debt at this point. The committed credit lines will expire in a year or so. For 

these reasons, we assume that only around $2.4 

trillion of these credit lines will be drawn down.

Strains in the securitization market also 

complicate the calculations. The securitization 

market has been seriously impaired for over a 

year now, and there are few signs of any imminent rebound. For the purposes of these calculations, we surmise that the securitization market 

will remain closed until well into 2009, and that, 

as a result, some $3 trillion of assets that would 

otherwise be securitized will remain on bank 

balance sheets.74 We assume that the securitiza74U.S. securitization net issuance in the fi rst half of 

2008 was around half the $2 trillion issued a year earlier, 

refl ecting the sharp fall in issuance of collateralized debt 

obligations and asset-backed securities. A similar decline 

can be seen in the European market. 

tion market will revive gradually starting at the 

end of 2008.

The introduction of an accounting rule in the 

United States—FAS 140—also complicates the 

picture. Having already been delayed by a year, 

this new rule is scheduled to come into effect in 

2010, and will likely require a signifi cant amount 

of assets that were previously off-balance-sheet 

by U.S. banks to be brought onto the balance 

sheet. This is a pure accounting change that will 

have no direct economic or fi nancial impact, 

but it will cause the capital and leverage ratios 

of U.S. banks to change, perhaps with some indirect fi nancial impact (King and others, 2008). 

We assume FAS 140 is introduced on schedule, 

but in a milder form, and with some phasing in. 

As a result, some $2 trillion of assets will transfer onto U.S. bank balance sheets during 2010 

through 2012.

We also provide a projection of the new levels 

of capital that authorities, regulators, ratings 

agencies and investors will demand, and how 

patient they are prepared to be. Our base case 

is that the new standards of capital adequacy are 

8 percent common equity to risk weighted assets 

ratios, and 4.5 percent Tier 1 capital to total 

assets ratios (equivalent to 22 times leverage) 

(King, Samuels, and Harrison, 2008; RamsTable 1.10. Deleveraging Illustration: Key Assumptions

Asset growth Driven by October 2008 WEO growth forecasts.

Bank income Driven by returns on assets that dip as growth is weak to 2009, but rebounds to historical norms 

as growth returns to trend.

Bank charge-offs For the United States, driven by our model for defaults; for Europe, by charge-offs as estimated 

by Merrill Lynch Research. 

Taxes At rate relevant for the country, tax losses reclaimed immediately.

Dividends Drop rapidly to below historical norm payout ratios and stay there until 2011 before returning to 

historical norms.

New capital-raising None until 2009:Q1, then a total of $675 billion spread evenly over the next eight quarters. 

Drawdown of committed credit lines $2.5 trillion in 2008–09, trailing off toward the end.

Lack of securitization $3 trillion of assets build up on bank balance sheets until securitization market gradually reopens 

in 2010.

Financial Accounting Standard 140 Brings $3 trillion of U.S. bank QSPEs onto balance sheets during 2010–12.

Asset maturities $7.6 trillion during 2008–13, front loaded tailing off at the end.

Asset sales $2.4 trillion during 2008–12.

Source: IMF staff estimates.

Note: GSE = government-sponsored enterprise; QSPE = qualifying special-purpose entity.
71

den and others, 2008; and Steenis, Helby, and 

Hayne, 2008). We assume that regulators will be 

tolerant in giving banks time to achieve these 

new levels.

As mentioned in the main text, we assume 

that banks shed some $10 trillion of assets from 

their balance sheets compared with those they 

would have otherwise retained if there were no 

need to delever.75 This is assumed to be split 

into $7.6 trillion of assets that are simply allowed 

to mature and not be replaced, and $2.4 trillion 

of assets that are sold. The former are assumed 

to reduce credit growth; the latter are assumed 

not to do so.

It is assumed that banks are unable to raise 

fresh capital for the remainder of 2008, but in 

2009 some appetite for bank capital is assumed 

to return, enabling banks in the United States, 

United Kingdom, and the rest of Europe to raise 

roughly $675 billion collectively over the next 

few years.

Bank revenues are assumed to dip to below 

historical norms as growth weakens into 2009, 

but then to pick up to historical norms as 

growth rebounds. Bank charge-offs for U.S. 

banks are in line with the estimates described 

in Annex 1.3. For European banks they refl ect a 

combination of a joint exercise undertaken with 

Merrill Lynch’s research department, and our 

own estimates described in Annex 1.3.

Assets brought on-balance-sheet as a result 

of committed credit lines or impaired securitization markets are assumed to come on with 

40 percent risk weightings. Taxes are charged at 

the corporate income tax rate for the country, 

and any tax losses are assumed to be reclaimed 

immediately. Dividends are assumed to move 

swiftly down from the current high payout ratios 

to historical norms by the end of 2008 and 

then fall below historical norms until March 

2010, after which they gradually return to 

historical norms.

75This is mathematically the amount banks need to 

remove from their balance sheets to achieve the new 

ratios given the other assumptions. See also Graham 

(2008). 

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forthcoming, “Regional Financial Interlinkages 

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user/article/summary?view=89&category=

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(August 6). Available via the Internet: www.crmpolicygroup.org.

European Central Bank (ECB), 2008, “Funding 

Liquidity Risks,” Financial Stability Review (June), 

pp. 106–10.

Froot, Kenneth, and Paul G.J. O’Connell, 2003, “The 

Risk Tolerance of International Investors,” NBER 

Working Paper No. 10157 (Cambridge, Massachusetts: National Bureau of Economic Research).

Goodhart, Charles, and Miguel Segoviano, forthcoming, “Banking Stability Measure,” IMF Working 

Paper (Washington: International Monetary Fund).

Graham, Stuart, 2008, “European Banking Advisor: 

Opportunities as Well as Threats,” Merrill Lynch 

Research (June 4). Available via the Internet: 

http://www.ml.com/independentresearch.

Haigh, Michael S., Jana Hranaiova, and James Overdahl, 2007, “Hedge Funds, Volatility, and LiquidREFERENCES
CHAPTER 1 ASSESSING RISKS TO GLOBAL FINANCIAL STABILITY

72

ity Provision in Energy Futures Markets,” Journal 

of Alternative Investments, Vol. 9, No. 4 (Spring), 

pp. 10–38. 

Interagency Task Force on Commodity Markets, 2008, 

“Interim Report on Crude Oil,” July 22 (Washington: Commodity Futures Trading Commission). 

International Monetary Fund (IMF), 2006, World 

Economic Outlook, World Economic and Financial 

Surveys (Washington, September).

———, 2007, Global Financial Stability Report, World 

Economic and Financial Surveys (Washington, 

October).

———, 2008a, Global Financial Stability Report, World 

Economic and Financial Surveys (Washington, 

April).

———, 2008b, United States: 2008 Article IV Consultation, IMF Country Staff Report No. 08/255 (July 

30) (Washington: International Monetary Fund).

———, 2008c, World Economic Outlook, World Economic and Financial Surveys (Washington, April).

———, 2008d, World Economic Outlook, World Economic 

and Financial Surveys (Washington, October). 

King, Matt, 2008, “Why the Banks Aren’t Lending,” 

Citigroup Credit Products Strategy (July 23). Available via the Internet (by subscription): www.fi direct.

citigroup.com.

———, Simon Samuels, and Mike Harrison, 2008, 

“There’s a Hole in My Bucket: Further Deterioration in European Banks’ Capital Ratios,” Citigroup 

Equity Research, Europe (June 12). Available 

via the Internet (by subscription): www.fi direct.

citigroup.com.

King, Matt, Bret Dooley, Stav Gaon, Keith Horowitz, 

and Brad Ball, 2008, “The $5 Trillion Question: 

Assessing FAS 140/FIN46R,” Citigroup Credit Products Strategy (June 23). Available via the Internet 

(by subscription): www.fi direct.citigroup.com.

Lowe, Philip, 2008, speech by Reserve Bank of 

Australia Assistant Governor Philip Lowe on 

“The Financial Cycle and Developments in 

the Australian Financial System” at the Sixth 

Annual Retail Financial Services Forum, Sydney, 

August 13. 

Malhotra, Priyanka, and Roberto Henriques, 2008, 

“A Question of Commitment: European Banks 

and Contingent Credit Facilities,” JPMorgan European Credit Research (July 8). Available via the 

Internet (by subscription): www.mm.jpmorgan.com.

McGuire, Patrick, and Goetz von Peter, 2008, “International Banking Activity Amidst the Turmoil,” BIS 

Quarterly Review (June) (Basel: Bank for International Settlements), pp. 31–43.

Ramsden, Richard, Brian Foran, Louise Pitt, and 

Quan Mai, 2008, “Timing the Turnaround: 

Credit, Capital, Consensus, and Curve,” Goldman Sachs Credit Research (June 17). Available via the Internet (by subscription): www.

gs.com/research. 

Steenis, Huw van, Michael Helby, and Steven Hayne, 

2008, “Focus on European and US Banks’ Capital 

Ratios,” Morgan Stanley Research, Europe (July 

14). Available via the Internet: www.morganstanley.

com/institutional/research/.

Tesar, Robert, 2008, “The Impact of Managed Money 

on Futures Markets,” presentation at the conference on “Spikes and Calls: Commodity Markets in 

2008,” Merrill Lynch Global Markets and Investment Banking Group, Chicago, June 17.
73

CHAPTER 2

In the current crisis, bank funding markets have struggled to provide liquidity 

across the banking sector and thus to act as a conduit for monetary policy in 

mature economies. This chapter examines the reasons for the recent difficulties in 

these markets and the effects on the interest rate channel of monetary policy transmission. It finds that elevated interbank spreads are not just the product of how 

interbank rates are calculated and that the recent pressures have been principally 

driven by concerns about bank distress risk, with U.S. dollar liquidity strains 

playing a significant role in the European money markets. Empirical work shows 

that aspects of the transmission of policy rate changes are far less reliable, particularly in the United States. Policy interventions to further broaden access to 

emergency liquidity may be necessary to constrain systemic risks but are unlikely 

to resolve bank funding stresses until broader policy measures, including those 

aimed at the underlying counterparty credit concerns, are implemented.

The persistence of high interbank rates 

in the main advanced economies since 

the current crisis began in the summer of 2007 is startling, even as central 

banks have taken unprecedented steps to ease 

conditions. After a period of some moderation of pressures following the rescue of Bear 

Stearns in mid-March, ongoing concerns about 

the health of fi nancial institutions and the 

reemergence of fi nancial distress in September 

brought back renewed pressures to interbank 

rates. Uncollateralized interbank rates serve 

as a benchmark for a signifi cant proportion of 

fi nancial derivative instruments, and therefore 

have important knock-on effects for other fi nancial markets as well as borrowers with interest 

rates indexed to interbank rates. In addition to 

having adverse consequences for banks’ cost of 

funding, elevated interbank rate spreads may 

also have serious effects on the transmission 

of monetary policy. A change in the central 

bank policy rate (typically an overnight rate) is 

designed to be transmitted through interbank 

and money market interest rates, ultimately 

infl uencing consumer and business lending 

rates and therefore domestic demand. However, 

the interconnections between money and other 

credit markets that have developed over the 

past two decades mean that disruptions to the 

money and funding markets can have adverse 

macroeconomic consequences.

This chapter examines the current stress 

in interbank markets from three perspectives. 

First, the basic microstructure of how interbank 

markets operate is examined. The discussion 

includes an assessment of whether the quoted 

London Interbank Offered Rate (LIBOR) and 

euro counterpart (the Euro Interbank Offered 

Rate, or Euribor) are currently distorted.1

1The LIBOR, calculated by the British Bankers’ Association, and the Euribor, calculated by the European 

Banking Federation, are benchmarks based on expected 

marginal unsecured funding costs of a creditworthy bank 

in the interbank market at various maturities out to one 

year. In contrast, the Euro Overnight Index Average 

Note: This chapter was written by a team led by 

Brenda González-Hermosillo and Mark Stone, and comprised of Andreas Jobst, John Kiff, Paul Mills, Miguel 

Segoviano, and Seiichi Shimizu. Vance Martin (University of Melbourne), Alin Mirestean, and Jean Salvati 

assisted in the empirical work, and Yoon Sook Kim 

provided research assistance.

STRESS IN BANK FUNDING MARKETS AND 

IMPLICATIONS FOR MONETARY POLICY
CHAPTER 2 STRESS IN BANK FUNDING MARKETS AND IMPLICATIONS FOR MONETARY POLICY

74

Second, uncollateralized interbank rates are 

analyzed empirically to determine what has 

driven interbank spreads higher. In particular, 

the question examined is whether the persistent, 

wide spreads over policy rates that have been 

observed refl ect credit risk or other factors, and 

whether the underlying causes have changed 

over time. Third, structural changes infl uencing 

monetary transmission and the recent impact 

of elevated interbank spreads are described and 

empirically analyzed to test whether the interest rate channel of monetary policy has recently 

become less dependable. The fi nal section 

identifi es policy proposals to redress current 

interbank market pressures. In particular, a wide 

array of policies should aim at cross-market 

credit and liquidity issues, which have become 

intertwined during the current period of stress. 

This will entail central banks and other government entities rethinking their policy options.

The Microstructure of Bank Funding 

Markets

Term LIBOR and Euribor rates remain 

worthwhile as measures of a typical creditworthy 

bank’s marginal cost of unsecured term funds in 

the wholesale money market, although volumes 

of unsecured term interbank lending have been 

shrinking as a proportion of short-term funding activity for many years. Nevertheless, given 

the huge outstanding amounts of derivative 

contracts and other fi nancial instruments linked 

to term LIBOR and Euribor, these benchmark 

rates need to be maintained. Although the survey methodologies have been effective at eliminating most biases at the individual contribution 

level, proposals by the British Bankers’ Association (BBA) to increase the number of sampled 

banks and introduce more aggressive scrutiny 

of individual bank contributions are welcome. 

However, the defi nition of LIBOR and Euribor 

(EONIA) and Sterling Overnight Index Average (SONIA) 

are weighted-average rates of actual unsecured overnight 

rates by all banks surveyed for unsecured loans in euros 

and sterling, respectively.

in the calculation process should be amended 

to refl ect bank unsecured term funding costs in 

wholesale money markets more generally, rather 

than just in the interbank market, to ensure that 

the indices remain representative of actual unsecured wholesale bank funding costs. Collection 

and publication of aggregate volume data would 

also help users to assess the reliability of term 

interbank rate fi xings.

Interbank markets are integral to the functioning of many other fi nancial markets, and so 

understanding the reasons for their disruption 

is important for relieving strains elsewhere in 

the fi nancial system. They are especially tightly 

linked to interest rate derivatives (forwards, 

futures, and swaps), of which an estimated 

$400 trillion principal outstanding are LIBORrelated interest rate swaps (BBA, 2008a). In 

addition, interbank rates play key roles in capital 

markets more generally. For example, “credit” 

spreads on most fi xed-income instruments are 

calculated from LIBOR- and Euribor-based interest rate swap curves to facilitate cross-market 

and cross-currency comparison given that issuers 

and investors use LIBOR-based derivatives to 

hedge and transform interest rate and currency 

risks (see Box 2.1).

Conceptually, large commercial banks fund 

their balance sheets in layers, starting with a 

capital base comprised of equity, subordinated 

debt, and hybrids of the two, plus medium- and 

long-term senior debt. The next layer consists 

of customer deposits—assumed to be “sticky” 

in most circumstances even though callable 

at little or no notice.2 The fi nal funding layer 

comprises various shorter-term liabilities such as 

commercial paper, certifi cates of deposit, repurchase agreements, swapped foreign exchange 

liabilities, and wholesale deposits. This layer is 

managed on a dynamic basis as its composition 

and maturity can change rapidly with cash fl ow 

needs and market conditions. Within this layer, 

2The embedded call option at par in a bank deposit 

makes banks vulnerable to deposit “runs” whereby depositors rush to withdraw deposits to avoid principal loss or 

being denied access through the bank’s bankruptcy. 
75

unsecured interbank funding has become a relatively small component of a typical large bank’s 

short-term funding mix due to the regulatory 

capital charges associated with unsecured lending to other banks.3

For some time, the majority of the limited 

amount of unsecured interbank lending that 

3Under Basel I, unsecured interbank deposits maturing within one year attracted a 20 percent risk-weighting 

for capital requirement purposes. Basel II uses ratings 

to determine risk weights. While the minimum weighting remains 20 percent, it can be higher for deposits of 

greater than three months’ maturity. As a result, banks 

are encouraged to collateralize their exposures with bank 

counterparties or, if lending is unsecured, to do so at 

maturities of three months or less.

does take place has been at maturities of one 

week or less, and predominantly overnight. 

This refl ects general trends in money market 

activity. The European Central Bank euro 

money market survey (ECB, 2007) showed that 

in recent years the vast majority of transactions 

(about 70 percent) are overnight, while maturities of one month or less account for nearly 

all of them (about 95 percent) (Figure 2.1). 

Most bank short-term funding now comes from 

nonbank sources such as money market funds, 

securities lending reinvestment portfolios, 

and central bank foreign exchange reserves. 

However, these sources are increasingly switching to secured lending and derivatives-based 

structures—for example, lending overnight and 

Until recently, marketable government 

securities were the pricing benchmarks for 

fi xed-income transactions, primarily because 

of their large outstanding amounts and market 

liquidity. It was not until the early 1990s that 

potential benchmarks based on derivatives, 

such as interest rate swaps and futures, were 

liquid enough to be considered as viable 

alternatives.

There are several reasons why government 

bonds no longer serve as very useful benchmarks for anything other than the most commonly traded government securities: 

• Government securities often enjoy a “fl ight 

to quality” during episodes of market stress 

(Fleming, 2000; McCauley, 2001). 

• Many governments do not issue enough securities at the right maturities to be adequate 

for hedging purposes (Remolona and 

Wooldridge, 2003; Fleming, 2000).

• Even individual government securities issued 

by the same issuer, differing only by maturity 

date or even subtle contractual terms, are subject to idiosyncratic pricing (Duffee, 1996).

As a result, interbank deposit-rate-based 

derivatives have become the preferred fi xedincome benchmarking and hedging instruments, 

with U.S. corporate bonds being the exception. 

Not only are they more liquid than the alternatives, but long and short positions in these derivative markets are unrestricted, so the kinds of 

idiosyncrasies seen in securities markets are rare.

For some purposes, overnight index swap 

(OIS) rates may be more appropriate benchmarks, because they are more representative of 

risk-free rates, and they better refl ect changes in 

policy rates (Goldman Sachs, 2008). However, 

shifting a legacy of over $400 trillion of instruments based on the London Interbank Offered 

Rate (LIBOR) would be a daunting operational 

task, and OIS benchmarks have their own downsides. For example, the overnight rates on which 

they are based can be subject to signifi cant 

volatility, and benchmarks based on actual bank 

funding costs are the most appropriate when 

the marginal cost of bank funding is the relevant comparator. A likely outcome is that, as a 

greater credit and liquidity risk component has 

become evident in LIBOR and Euribor rates, 

parties to interest rate derivatives will more carefully consider various benchmark rates.

Box 2.1. Pricing and Hedging Role of Interbank Deposit-Related Derivatives

Note: John Kiff prepared this box.

THE MICROSTRUCTURE OF BANK FUNDING MARKETS
CHAPTER 2 STRESS IN BANK FUNDING MARKETS AND IMPLICATIONS FOR MONETARY POLICY

76

positioning for monetary policy actions with 

interest rate derivatives such as overnight index 

swaps (OIS).4

The relative paucity of interbank term lending means that LIBOR and Euribor “fi xings” 

beyond a week or month’s maturity may not 

represent actual transactions but rely instead on 

banks’ assessments of their notional ability to 

borrow at those rates.5 Although the integrity of 

the U.S. dollar LIBOR fi xing process has been 

questioned by some market participants and 

the fi nancial press, it appears that U.S. dollar 

LIBOR remains an accurate measure of a typical 

creditworthy bank’s marginal cost of unsecured 

U.S. dollar term funding (Box 2.2). A BBA 

proposal to introduce more aggressive scrutiny 

of individual bank contributions is still welcome, 

as it should improve the accuracy of the LIBOR 

calculation by, potentially, expanding the panel 

of contributing banks, and increasing incentives 

to submit accurate funding rates while maintaining transparency (BBA, 2008a, 2008b).

In addition, consideration should be given 

formally to expanding the scope of the LIBOR 

and Euribor so that they represent unsecured 

term funding rates available to banks in wholesale money markets. This would ensure that they 

remain indicative of marginal unsecured bank 

funding costs, and would better refl ect the range 

of funding sources that contributing banks actually consider when estimating their “interbank” 

funding costs. Broadening the meaning of these 

rates should not undermine the contractual 

4An OIS exchanges the average realized overnight 

unsecured rate with a fi xed interest rate over a specifi ed 

term. Hence, the three-month OIS swap rate embodies 

the market’s expectation of overnight rates over the next 

three months. 5For example, each day, just before 11:00 a.m. London 

time, each member bank of the BBA’s LIBOR contributor panels for 10 different currencies submits the rate at 

which it could borrow funds in reasonable market size at 

various maturities (from overnight out to 12 months) in 

the interbank market. For each currency and maturity, 

the highest and lowest quartiles of rates are excluded and 

the rate is fi xed at the simple average of the remaining 

contributions. Rate contributions are nonbinding, in 

that the banks are not obliged to prove that they did, or 

could, trade at the submitted levels.

2000 01 02 03 04 05 06 07

0

20

40

60

80

100

120

140

160

 Source: European Central Bank.

 Note: Tom/Next is a transaction for settlement tomorrow and maturity the 

next day.

Figure 2.1. Unsecured European Bank Borrowing

Volumes

(In euros; indexed to 2002:Q2 = 100)

Overnight

3 months to 1 year

More than 1 year

1 month to 3 months

Tom/Next to 1 month
77

Box 2.2. Is the LIBOR Fix Broken?

Market observers have been expressing 

concerns that some LIBOR contributors 

submit rates that are too low, particularly 

when they are facing liquidity constraints 

(Mollenkamp and Whitehouse, 2008). This is 

said to be driven by the requirement of the 

British Bankers’ Association (BBA) that all rate 

submissions be published, and by the fact that 

banks facing liquidity strains may not want to 

reveal the higher market rates they are actually 

being offered.

For example, between January and April 

2008, the range of contributor bank shortterm credit default swap (CDS) spreads far 

exceeded the range of three-month U.S. 

dollar LIBOR contributions as a spread over 

the three-month U.S. dollar overnight index 

swap (OIS) rate (a proxy for the effective 

“risk free” rate). Prior to August 2007, the 

two ranges fl uctuated very closely together, 

but since then, and particularly since January 

2008, the range of CDS spreads has been far 

wider than that of the LIBOR-OIS contribution spreads (see the fi rst fi gure). However, 

contributing banks say that CDS spreads play 

little to no role in day-to-day short-term lending decisions per se. 

In practice, outlier rate contributions have 

little impact on LIBOR fi xings, because the lowest and highest are trimmed from the averaging calculation. However, if a downward bias 

in reported rates were to involve more banks, 

the median rate would provide a more accurate 

fi xing in some situations. It has also been suggested that the LIBOR panel be expanded to 

minimize the impact of outliers and of contributors that may not be representative of banks that 

actually need funds in the relevant currency 

at the time of fi xing. For example, European 

banks that are active in the London interbank 

market, but that book transactions at their 

continental head offi ces, could be included. 

Although JPMorgan (2008) suggests that the 

impact of such an expansion is likely to be 

marginal,1 it is a welcome development that the 

BBA’s committee overseeing the LIBOR setting 

process is to consider expanding the LIBOR 

contributor panels.

Even though volumes of unsecured term 

interbank lending have been shrinking for 

some years, the BBA has argued that the LIBOR 

remains refl ective of the rate at which the 

panel banks could raise unsecured cash in the 

interbank lending markets. Also, LIBOR rates 

remain worthwhile as a measure of a typical 

creditworthy bank’s marginal cost of unsecured 

funds, irrespective of source. For example, the 

second fi gure shows that various potential U.S. 

dollar three-month bank unsecured funding 

rates trade very closely to each other. 

 1JPMorgan (2008) compared three-month euro 

LIBOR to Euribor fi xings, and found the difference to 

be statistically insignifi cant. The Euribor contributor 

panel is much broader than the BBA LIBOR panel 

(43 versus 16), and proportionally fewer observations 

are trimmed before averaging (the Euribor averaging 

calculation covers the middle 70 percent of contributions versus LIBOR’s 50 percent).

0

50

100

150

200

250

2007 2008

Sources: Bloomberg L.P.; and British Bankers’ Association.

Note: CDS = credit default swap.

Spread Between Highest and Lowest

Three-Month U.S. Dollar LIBOR

Contributions and Highest and Lowest

Short-Term Contributor CDS Spreads

(In basis points)

CDS

LIBOR

THE MICROSTRUCTURE OF BANK FUNDING MARKETS

Note: John Kiff prepared this box. 
CHAPTER 2 STRESS IN BANK FUNDING MARKETS AND IMPLICATIONS FOR MONETARY POLICY

78

integrity of the trillions of LIBOR- and Euriborindexed fi nancial contracts, since this would better align the formal defi nition and procedures 

with how the rates are actually estimated.

The Causes of Elevated Interbank Spreads

The empirical results presented below provide 

evidence that the sustained strains in U.S. and 

European unsecured interbank markets have 

been driven by concerns about the distress risk of 

fi nancial institutions (accounting for as much as 

30 to 45 percent of the total variance). In the case 

of European banks, U.S. dollar liquidity pressures 

have also been important (representing up to an 

additional 30 to 35 percent of the variance). Once 

these factors are incorporated, other sources of 

stress in the markets are found to be relatively 

unimportant.

Although credit risk is theoretically distinguishable from liquidity risk, in practice these risks 

are intertwined, particularly during periods of 

stress. The empirical analysis suggests that high 

interbank rate spreads are due to banking sector 

distress risk, a term used here to represent both 

banks’ credit and liquidity risks. Consequently, 

the results suggest that policies aimed at only 

In addition, the money market broker ICAP 

has recently introduced a more broadly defi ned 

measure of one- and three-month bank funding 

costs, and their fi xings have differed little from 

U.S. dollar LIBOR fi xings (see third fi gure).2

2ICAP asks banks to contribute their estimates of the 

rates at which prime banks would likely obtain funding 

(as opposed to what they themselves are funding at) 

as of 9:15 a.m. New York time. Any unsecured funding 

sources are covered, including interbank deposits, 

certifi cates of deposit, and commercial paper.

Box 2.2 (concluded)

–2

–1

0

1

2

3

4

5

June 2008 July 2008

 Sources: British Bankers’ Association (BBA); and ICAP plc.

ICAP NYFR and BBA LIBOR Spread

(In basis points)

1-month

3-month

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

07 08

Sources: Board of Governors of the Federal Reserve System; 

British Bankers’ Association; and JPMorgan Chase & Co.

Note: OIS = overnight index swaps.

Selected Three-Month Spreads

(In percent)

U.S. dollar LIBOR

and OIS

Euro/U.S. dollar

forex swap

and OIS

U.S.

commercial

paper and

OIS

Eurodollar deposit 

(London) and OIS

2006
79

addressing market liquidity pressures may be ineffective, unless credit concerns are also addressed.

Interbank money markets have continued to 

show unabated signs of stress since the summer 

of 2007 (Figure 2.2). Not only the level, but 

also the volatility of these spreads has remained 

elevated. What has been driving elevated 

LIBOR-OIS spreads in recent months? Are these 

spreads driven primarily by liquidity factors 

or credit concerns in the interbank market?6

Answers to these questions should allow central 

banks and other authorities to better calibrate 

their response to the ongoing crisis.

In principle, term LIBOR rates refl ect the 

expected path of monetary policy, as well as 

a risk premium associated with credit, liquidity, and other risks.7 However, the OIS rate 

embodies the market’s estimation of the path of 

unsecured overnight rates, and so of policy rates 

in U.S. dollars, euros, and sterling. Hence, the 

LIBOR-OIS spread should strip out the effects 

of policy rate expectations, leaving a measure of 

interbank rate stress and credit concerns.

Several recent studies have attempted to 

separate the credit and noncredit components 

of LIBOR-OIS spreads.8 However, they essentially 

focus on the role of the credit component measured by an aggregation of credit default swap 

(CDS) spreads among a collection of banks that 

are assumed to be independent of each other. 

The remaining noncredit component is typically assumed fully to represent liquidity risk. The 

6Earlier work on the transmission of liquidity shocks 

was presented in IMF (2008b) and further documented 

in Frank, González-Hermosillo, and Hesse (2008, 2008b). 7Although, for simplicity, most analyses assume that 

credit default swap (CDS) spreads primarily refl ect credit 

risk, liquidity risks are also embedded in these spreads 

and it is diffi cult to disentangle them. There are two 

reasons for this. First, CDS are traded instruments so 

their prices contain a risk premium that refl ects current 

market conditions, such as market volatility and investors’ 

risk appetite. Second, banks’ funding liquidity risk—the 

ease with which banks can raise funds—are likely to be 

also factored into their CDS. 8See Bank of England (2007); Taylor and Williams 

(2008a); Michaud and Upper (2008); Baba, Packer, and 

Nagano (2008); ECB (2008a); and Imakubo, Kimura, and 

Nagano (2008).

–20

0

20

40

60

80

100

120

05 06 07 08

Source: Bloomberg L.P.

Figure 2.2. Spread Between Three-Month 

Uncollateralized Interbank Rates and Overnight 

Index Swaps

(In basis points)

U.S. dollar LIBOR 

Sterling LIBOR

Euribor

2004

THE CAUSES OF ELEVATED INTERBANK SPREADS
CHAPTER 2 STRESS IN BANK FUNDING MARKETS AND IMPLICATIONS FOR MONETARY POLICY

80

recent literature also fi nds confl icting evidence 

of the role of credit and liquidity risks in driving interbank lending spreads. For example, 

Michaud and Upper (2008) fi nd that their measure of credit risk has little explanatory power for 

the day-to-day fl uctuations in the premium. The 

Bank of England (2007) fi nds that while in the 

early stages of the crisis the spreads were more 

refl ective of liquidity factors, a larger part of the 

move could be attributed to an increase in credit 

risk premia during the last months of 2007. Taylor and Williams (2008a) argue that counterparty 

risk between banks was largely responsible for 

the rise in the LIBOR-OIS spreads. In contrast, 

the ECB (2008a) assigns about 50 percent of the 

recent rise in interbank spreads to credit risk and 

assigns the remainder to liquidity risk.

This section attempts to refi ne these initial 

estimates for the U.S. dollar, Euribor, and sterling LIBOR rates less the OIS.9 First, the joint 

probability of distress of the banking system, as 

a measure of systemic bank credit risk, is introduced (Figure 2.3). In addition, the remaining 

noncredit component is not assumed to be equivalent to a generic type of “liquidity” risk. Instead, a 

number of variables are used to proxy for different types of liquidity and volatility risk. Third, the 

estimation technique explicitly takes into account 

the observed time-varying variances in the variables that proxy for the risks, resulting in more 

precise estimates of the various components.

Turning fi rst to the choice of variables:

• Joint probability of distress. The measure of systemic distress risk used is represented by the 

joint probability of distress (JPoD) of a group 

of systemically important banks corresponding 

to the group of banks that are contributors to 

each of the interbank rate fixings. The JPoD 

represents the probability of distress of all the 

banks in that group and, therefore, embeds 

banks’ distress dependence.10 Since the JPoD 

9The euro LIBOR and Euribor results are similar and, 

given the higher volumes of transactions in Euribor, only 

the Euribor results are reported. 10For further details, see IMF (2008b); Segoviano 

(2008); Segoviano, Goodhart, and Hoffmann (2006); and 

Segoviano and Goodhart (2008).

0

0.001

0.002

0.003

0.004

0.005

05 06 07 08

Sources: Bloomberg L.P.; and IMF staff estimates.

Figure 2.3. Joint Probability of Distress: Selected

Financial Institutions Participating in Interbank

Panels

Sterling LIBOR

U.S. dollar LIBOR

Euribor

2004
81

is constructed from individual banks’ probabilities of distress, which are extracted from 

CDS spreads, JPoDs necessarily embody both 

credit and liquidity risks.

• Collateralized bank funding market (repo spread).

Reflecting the fact that banks face a trade-off as 

they largely fund themselves in secured interbank markets, the degree of stress in the collateralized bank funding market is proxied by the 

spread between U.S. government-sponsored 

enterprises (GSE) debt three-month repo rates, 

and U.S. government collateralized repo rates. 

This spread is used as a proxy for global stress 

in secured interbank lending markets.

• Market liquidity. Market liquidity risk, measured by the spread between five-year onthe-run and off-the-run U.S. treasury notes, 

captures the flight to liquid assets. U.S. treasury notes are viewed by the markets as free 

of credit risk. However, periods of stress are 

often characterized by strong demand for the 

most liquid (on-the-run) U.S. treasury notes.

• Market volatility. Volatility risk in financial 

markets is proxied by the implied volatility in 

the S&P 500 (VIX), a measure often used to 

reflect investors’ risk appetite.11

• Interest rate volatility. Another measure of 

volatility risk is uncertainty about the future 

path of interest rates, proxied by the implied 

volatility of swaptions (options to enter into 

an interest rate swap) with maturities between 

one and six months.

• Forex swap. The role of U.S. dollar liquidity 

pressures is also examined, as many European 

banks with U.S. dollar assets have faced difficulties funding these positions (see Box 2.3). 

U.S. dollar liquidity pressures are proxied by 

forex swap spreads, or the spread between 

the three-month pound sterling (or euro) 

and U.S. dollar forward rates, and the threemonth U.S. OIS rate (Figure 2.4).12

11González-Hermosillo (2008) discusses the variables 

typically used to measure investors’ risk appetite. 12A forex swap is a bilateral contract where different 

currencies are exchanged by combining foreign currency 

spot and forward contracts. Financial institutions with a 

need for foreign currency funds face a choice between 

–20

0

20

40

60

80

100

120

140

2004 05 06 07 08

Sources: Bloomberg L.P.; JPMorgan Chase & Co.; and IMF staff estimates.

1Spread between sterling/U.S. dollar forex swap and 3-month U.S. dollar 

overnight index swaps. 2Spread between euro/U.S. dollar forex swap and 3-month U.S. dollar 

overnight index swaps.

Figure 2.4. Three-Month Forex Swap Spreads

(In basis points)

Sterling/U.S. dollar1 Euro/U.S. dollar2

THE CAUSES OF ELEVATED INTERBANK SPREADS
CHAPTER 2 STRESS IN BANK FUNDING MARKETS AND IMPLICATIONS FOR MONETARY POLICY

82

In December 2007, the Federal Reserve 

announced a temporary Term Auction Facility 

(TAF) that enabled U.S. banks to borrow for 

four weeks against the wider range of collateral 

permissible at the discount window.1 This direct 

provision of term funding using an open auction 

process with a minimum interest rate removed 

the stigma associated with discount window 

access while preserving the anonymity of users.2

By the end of June 2008, the amount of outstanding borrowing through the TAF was $150 billion 

(versus $14 billion at the discount window). U.S. 

dollars were also made available to European 

banks through foreign currency swap arrangements between the Fed and the European Central Bank (ECB) and Swiss National Bank.3

The initial TAF auctions were seemingly 

successful in reducing the spread between the 

three-month U.S. dollar LIBOR and overnight 

index swap rates (see fi rst fi gure). However, 

uncollateralized term funding conditions deteriorated again in March, coinciding with problems 

at Bear Stearns, and despite some improvement 

have remained strained since then.

Despite an increase in the amounts allotted to the ECB’s TAF auctions in May, auction 

demand for U.S. dollars from European banks 

has increased more rapidly (see second fi gure). 

Note: This box was prepared by Brenda GonzálezHermosillo and John Kiff. 1The outstanding TAF balance was originally 

effectively capped at $40 billion, but it was increased 

in several steps to $150 billion in May 2008. On July 

30, the Fed also announced that it would extend the 

maturity of some TAF auctions from the original four 

weeks to 12. 2In August, the Fed narrowed the spread between 

the discount and target Fed Funds rates from 100 to 

50 basis points, and extended the term of the primary 

credit program to up to 30 days (from overnight). 

However, despite encouragement from Fed offi cials, 

banks remained reluctant to tap the facility, although 

its usage did increase somewhat after the Fed reduced 

the spread to 25 basis points in March 2008. 3The outstanding ECB and Swiss National Bank 

swap facilities were originally capped at $20 billion 

and $4 billion, respectively, but the caps were also 

increased in steps, and stood at $55 billion and $12 

billion in August 2008.

Box 2.3. The Federal Reserve’s Term Auction Facility

Impact of Term Auction Facility (TAF) on

U.S. Dollar LIBOR Minus Overnight Index

Swap Spread

(In basis points)

2007 2008

Fed announces TAF

(12/12/07)

(1/4/08)

(3/7/08)

(5/2/08)

(7/30/08)

Fed increases size of TAF

 Sources: Bloomberg L.P.; and Board of Governors of the 

Federal Reserve System.

0

20

40

60

80

100

120

European Central Bank: U.S. Dollar Term

Auction Facility (TAF)

(In billions of U.S. dollars; 4-week tender)

Dec

2007 2008

Tender dates

Feb Apr Jun

 Source: European Central Bank.

0

20

40

60

80

100

120

140 Total bid amount

Total amount allotted
83

However, banks are also likely to be facing 

other liquidity pressures that are diffi cult to 

measure directly. These include uncertainty 

relating to their own funding liquidity needs 

as they move off-balance-sheet positions onto 

their balance sheets, as well as uncertainty about 

asset valuation, particularly if others unwind 

similar positions at distressed prices. Banks may 

also hoard liquidity during periods of market 

stress as a mechanism to avoid potential signaling effects that they may be the ones in need of 

liquidity. These and other factors are captured 

by the unexplained component of the variation of interbank spreads, which is refl ected in 

the residual of the model discussed below and 

described in Annex 2.1.

To examine these questions, a multivariate 

vector autoregression (VAR) and a structural 

borrowing directly in the uncollateralized cash market for 

the foreign currency, or borrowing in another (typically 

domestic) currency’s uncollateralized cash market and 

then converting the proceeds into a foreign currency 

obligation through a forex swap. For example, when an 

institution raises U.S. dollars via a forex swap using the 

euro as the funding currency, it exchanges euros for dollars at the spot rate, while contracting to exchange in the 

reverse direction at maturity at the forward rate.

VAR (SVAR) model are used based on a sample 

of daily data from January 1, 2004, through May 

28, 2008. In using the VAR and SVAR frameworks, and in contrast with other approaches 

in the empirical literature that have examined 

interbank lending spreads, the model captures 

the observed time-varying volatility in the 

spreads highlighted by the data. The adoption 

of a time-varying volatility structure is consistent 

with the observation that the factors explaining 

the spread are not constant over the sample, but 

can change at each point in time.13

Empirical Results

In a standard VAR model, the variables are 

ordered to refl ect the econometricians’ views 

about the relative sequential infl uence of each 

of the variables. By contrast, the SVAR requires 

a more direct set of assumptions about the 

relationships among the variables. The benefi ts 

of this approach are that the assumptions are 

transparent, and that the restrictions about the 

13Further technical details of the model specifi cation 

are provided in Annex 2.1.

This growing excess demand, and widening 

foreign currency (forex) swap spread (see 

Figure 2.4) suggests that the facility is serving 

a useful function but is yet to satisfy the strong 

demand for dollars arising from European 

banks bringing onto their balance sheets U.S. 

dollar-denominated assets that were previously 

in off-balance-sheet vehicles funded by assetbacked commercial paper and other means.

The effectiveness of the TAF in reducing 

interbank funding market strains has been 

intensely debated. There is, as yet, no consensus in the literature. Some fi nd that the TAF 

has been effective in reducing spreads and 

dampening volatility (see Michaud and Upper, 

2008; McAndrews, Sarkar, and Wang, 2008; 

Frank, Hesse, and Klueh, forthcoming; and Wu, 

2008), whereas others fi nd little impact (Taylor 

and Williams, 2008a, 2008b). As shown in this 

chapter, volatility in uncollateralized interbank 

funding rates refl ects not only liquidity pressures, but also default risks and other factors. 

Moreover, identifying and untangling potential drivers of interbank liquidity conditions is 

empirically challenging, so it is not surprising 

that there is disagreement about the causes of 

elevated interbank rates. In particular, the TAF, 

with its currency-swap facility, was not designed 

to address uncertainty about the size of the 

banks’ assets coming onto their balance sheets 

or potential counterparty risks, which is why the 

TAF has not been fully successful in eliminating 

interbank funding market strains. 

THE CAUSES OF ELEVATED INTERBANK SPREADS
CHAPTER 2 STRESS IN BANK FUNDING MARKETS AND IMPLICATIONS FOR MONETARY POLICY

84

interactions among the variables can be made 

explicit and consistent with stylized facts.14

Thus, for each of the interbank lending markets, the associated SVAR model makes several 

assumptions about the relationships among the 

variables of interest (Table 2.1). For instance, 

the market volatility factor is assumed to affect 

the implied volatility from equity markets represented by the VIX and the implied volatility 

from swaption contracts. The volatility of the 

interest rate factor is applied to the implied 

volatility from the swaption and so on. Finally, 

the residual factor affects only LIBOR/Euribor, 

as it is assumed to be unrelated to the other variables, perhaps capturing bank-specifi c risks. All 

factors are designed to infl uence the unsecured 

interbank rates.

The empirical results suggest that the 

dominant infl uence on the movements in the 

variance of all three interbank rates arises 

from the proxy for systemic distress risk. For 

example, in the case of the three-month U.S. 

dollar LIBOR-OIS spread, the JPoD vari14The variance decomposition reported is based on 

a one-day lag in order to reduce problems arising from 

time differences between U.S. and European markets. 

For robustness, various lags were examined. Additionally, two alternative SVAR specifi cations were modeled, as 

well as an unconstrained VAR model with a time- varying 

variance-covariance matrix, all of which gave similar 

results. Finally, various tests of over-identifi cation of the 

restrictions were undertaken. Due to space constraints, 

only the results of the SVAR are presented here. See 

González-Hermosillo, Martin, and Segoviano (forthcoming) for details. 

able (the measure of systemic distress risk) 

reaches around 45 percent of total variance on 

April 2, 2008, shortly after the Bear Stearns’ 

collapse.15 In the past six months or so, until 

very recently, the systemic distress risk variable has accounted for the majority of the 

explained portion of the variance in the spread 

(Figure 2.5). In addition, the fi rst hump in the 

JPoD contribution occurred much earlier, in 

late July 2007, when the markets fi rst showed 

signs of signifi cant stress. The role of the other 

variables is relatively small, but notably the 

repo spread began to show signs of stress in 

2005 when the U.S. housing market began its 

recent downturn. After being important during 

much of 2005–06, the effects from forex swaps 

(dollar/euro) have been relatively small since 

mid-2007.

Similar results are found for the Euribor 

panel of banks, with systemic bank distress risk 

dominating the variance decomposition during 

the period around the Bear Stearns collapse. 

Importantly, the forex swap variable accounted 

for over 30 percent of the total variance at an 

earlier point of the crisis. U.S. dollar liquidity 

pressures have also become more important 

in recent months. The relative contribution of 

15The panels in Figure 2.5 are designed to show the 

percentage of the variance decomposition represented 

by each variable, adding up to 100 percent. The period 

before mid-2007 had a much smaller variance than during the crisis.

Table 2.1. List of Restrictions Used in the Structural Vector Autoregression for Each LIBOR and Euribor 

Spread

Market

Volatility

Interest 

Rate

Volatility

Market

Liquidity 

U.S. Dollar

Liquidity Credit Distress Residual

S&P 500 volatility index X

Swaption X X

On/Off-the-run X X

Currency/U.S. dollar swap X X X

Repurchase agreement X X

Joint probability of distress X X

LIBOR/Euribor X X X X X X X
85

other factors is relatively small in comparison 

(Figure 2.5).

For the sterling LIBOR-OIS spread, the dominant variable is the forex swap spread, amounting to close to half the total LIBOR-OIS variance 

at times. The movements of the forex swap and 

systemic risk variables show collinearity as they 

are very similar. When the forex swap variable is 

omitted, the JPoD for the banks on the sterling 

LIBOR panel represents as much as 35 percent 

of the total variance during March 2008. The 

contributions from other variables are relatively 

small (Figure 2.5).

Implications for the Interest Rate 

Transmission Mechanism of 

Monetary Policy16

Structural changes in the fi nancial sector 

appear not to have undermined monetary policy 

transmission over the past 25 years, but did 

set the stage for the alterations in the interest 

rate transmission mechanism beginning in the 

summer of 2007. The most germane structural 

changes were the emergence of near-banks, the 

shift of banks toward market fi nancing, and 

the shortening of the term of market liabilities 

through the late-1990s.

Most importantly, empirical work indicates 

that the normal relations governing the passthrough of policy rates into the markets for 

short-term bank fi nancing and for short- and 

long-term near-bank fi nancing has become 

less reliable over the past year, particularly in 

the United States. The early stages of transmission have been impeded by (1) banks’ higher 

dependence on short-term market fi nancing 

and the dislocation of these markets, and (2) 

the increased importance of near-banks in 

the fi nancial sector coupled with disruption 

to their fi nancing. The alterations in the early 

stages of the transmission process mean that 

16This section was developed in part based on discussions at the conference on “Challenges for Monetary 

Policy from Financial Innovation and Globalization” in 

January 2008 in Paris. See IMF (2008a).

0

10

20

30

40

50

60

0

10

20

30

40

50

60

0

10

20

30

40

50

60

0

10

20

30

40

50

60

Figure 2.5. Structured VAR Model: Variance 

Decomposition of LIBOR/Euribor Minus Overnight 

Index Swap (OIS) Spread

(In percent)

Market volatility1

Interest rate volatility 2

Market liquidity 3

Forex swap4

Repo spread 5

Joint probability of distress 6

3-Month U.S. Dollar LIBOR and 3-Month U.S. Dollar OIS

3-Month Sterling LIBOR and 3-Month Sterling OIS

(With forex swap)

3-Month Sterling LIBOR and 3-Month Sterling OIS

(Without forex swap)

3-Month Euribor and 3-Month Euribor OIS

Source: IMF staff estimates.

1Implied volatility from S&P 500 equity index.

2Lehman Brothers swaption volatility index. Implied volatility of interest rate 

swaption with maturities ranging from 1 month to 6 months. 3Five-year on-the-run/off-the-run U.S. treasury note spread.

4Spread between 3-month euro/U.S. dollar and sterling/U.S. dollar forex and 

3-month OIS. 5Spread between the yields on 3-month U.S. agency repo and 3-month U.S. 

treasury repo. 6Joint probability of distress of selected banks participating in U.S. dollar LIBOR, 

sterling LIBOR, or Euribor fixing.

2004 05 07 06 08

2004 05 07 06 08

2004 05 07 06 08

2004 05 07 06 08

IMPLICATIONS FOR THE INTEREST RATE TRANSMISSION MECHANISM OF MONETARY POLICY
CHAPTER 2 STRESS IN BANK FUNDING MARKETS AND IMPLICATIONS FOR MONETARY POLICY

86

changes in the policy interest rate are less likely 

to be reliably passed on to the middle and late 

stages. The results for borrower fi nancing rates, 

which have thus far been less affected, need to 

be interpreted alongside other evidence (such 

as tighter lending standards and slower credit 

growth) that suggests that banks are reducing 

lending by cutting back loan originations rather 

than raising interest rates. The current reintermediation process, whereby loans shift from 

lightly capitalized, market-funded “near-banks” 

back to more heavily capitalized banks, will add 

to the capital needs of the system and act as a 

drag on credit creation, possibly exaggerating 

the credit cycle.17

Structural Changes to the Financial System

This section examines the impact of structural 

changes in the fi nancial sector and the current 

fi nancial turmoil on interest rate transmission of 

monetary policy.18 A change in the policy interest rate is transmitted in three stages:

(1) To the interest rates in money and other 

lender fi nancing markets (early linkages);

(2) From lender fi nancing interest rates to 

the funding costs and lending rates for household and business borrowers (middle linkages);19 and

(3) From household and business fi nancing 

costs to the ultimate policy objectives of price 

17See IMF (2008e, Chapter 4) for further analysis of 

procyclical lending behavior. 18The main channels of monetary transmission are 

overlapping and shifting in line with changes in the fi nancial system. (For a recent review of monetary transmission 

see Kuttner and Mosser, 2002.) Interest rate channel transmission begins with a change in the short-term policy 

rate that infl uences market interest rates, and, after some 

price stickiness, will raise the real rate of interest and the 

user cost of capital, thereby affecting aggregate demand, 

and so economic output and infl ation. The other main 

channels are the bank-lending channel (Bernanke and 

Blinder, 1988), the balance-sheet/financial accelerator channel

(Bernanke, Gertler, and Gilchrist, 1999), and the expectations channel. The interruption of monetary transmission 

during the recent fi nancial turmoil is addressed in IMF 

(2008a) and Adrian and Shin (2008). 19Business borrowers in this section refers to nonfi nancial businesses.

and output stability (fi nal linkages) (IMF, 2006, 

2008b; and Bhatia, 2007).

This section focuses on the early and middle 

linkages of interest rate transmission because 

they are easier to model empirically and insuffi -

cient time has passed to assess the impact of the 

current turmoil on the fi nal linkages.

The structural changes in the fi nancial sector over the past 25 years, which are described 

in the next section, may be altering monetary 

transmission by reshaping the traditional 

maturity transformation function of banks. And, 

at this crucial juncture in the business cycle, 

policymakers are acutely concerned about the 

implications of these structural changes for 

interest rate transmission and how they may 

have complicated the early linkages of transmission in the turmoil.

Against this backdrop, this section is concerned with two questions: How have the early 

and middle linkages of interest rate transmission been affected by fi nancial sector structural 

changes over the past 25 years? And how is 

transmission being altered by the fi nancial turmoil that began in the summer of 2007?

The analysis is primarily concerned with the 

United States, where structural changes have 

been most evident and monetary transmission 

seems to have been the most disrupted. The 

euro area and, to a lesser extent, Japan and the 

United Kingdom are also examined.

Implications of Structural Changes for Interest 

Rate Transmission

The main structural changes over the past 25 

years are outlined below.

“Near-bank” financial institutions have gained 

a large share of financial intermediation. In the 

United States, near-banks (issuers of assetbacked securities [ABS] and other structured 

products, GSEs, fi nance companies, securities 

brokers and dealers, and funding corporations) 

now account for a large share of the fi nancial 

sector (Figure 2.6). Banks’ share of the fi nancial 

sector declined through the 1980s and 1990s 

and leveled off thereafter. Meanwhile, the long-
87

term growth of GSEs, ABS issuers, and broker 

dealers accelerated around 2000 (Box 2.4). 

Then, around 2003, GSE and bank shares of 

the mortgage market shrunk rapidly as other 

near-bank entities, market-fi nanced ABS issuers, and fi nance companies grew. In the euro 

area, the shift from traditional banks to other 

intermediaries has been more moderate than in 

the United States and the United Kingdom. The 

majority of “other intermediaries” in the euro 

area are mutual funds that function as investment vehicles for households and insurance 

corporations, holding shares and other securities. Corporate fi nancing through debt securities 

is relatively limited, and the recent increase in 

home mortgages appears to have been mainly 

supplied by commercial banks.20

Banks have been shifting away from deposits to 

less reliable market financing. “Core deposits” 

dominated U.S. banks’ liabilities in the past, but 

have been gradually replaced by other “managed liabilities” (Figure 2.7).21 At the same 

time, near-banks—which are entirely market 

fi nanced—have grown sharply. This is related 

to the “originate-to-distribute” fi nancing model 

that relies heavily on sound short-term market 

liquidity management. Euro area and U.K. 

banks also rely more on market fi nancing than 

in the past, as in the United States. Similarly, 

the share of deposits by households (defi ned 

roughly the same as U.S. core deposits22) has 

been gradually declining over time, while deposits held by nonfi nancial corporations, other 

fi nancial intermediaries, and nonresidents have 

20ECB (2008b) suggests that statistical differences 

in the share of monetary fi nancial institutions’ (MFI) 

fi nancing derived from households among the euro area, 

the United Kingdom, and the United States are partly the 

result of respective accounting and statistical treatments. 

For example, European accounting rules have largely prevented MFIs from removing securitized loans from their 

balance sheets, thereby making MFIs’ assets larger relative 

to U.S. counterparts. 21U.S. “core deposits” are defi ned as the sum of 

checkable deposits and low value time and saving 

deposits, which includes some (checkable) deposits from 

businesses. 22The euro area and U.K. equivalents of core deposits 

are specifi ed as deposits by households only.

1987 1997 2007 1987 1997 2007 1999 2007

0

1

2

3

4

5

6

7

8

9

10

 Sources: U.S. Board of Governors of the Federal Reserve System; U.K. Office 

of National Statistics; European Central Bank; and IMF staff estimates.

Figure 2.6. Selected Countries: Size of Financial 

Assets

(In multiples of GDP)

Depository banks

United States United Kingdom Euro area

Insurance and pension funds

Others

Sources: U.S. Board of Governors of the Federal Reserve System; and IMF 

staff estimates. 1Core deposits: checkable deposits, and small time and savings deposits.

2Managed liabilities: total liabilities less core deposits.

3Ratio of banks’ and near-banks’ funding through Fed Funds, security repo 

agreements, and commercial paper, vis-à-vis total liabilities.

Figure 2.7. United States: Structural Changes in 

Financial Sector Liabilities

(In percent of total liabilities)

0

10

20

30

40

50

60

70

80

Bank: Core deposits1

(left scale)

Bank and near-bank: 

Short-term funding ratio3

(right scale)

Bank: Managed liabilities2

(left scale)

1980 84 88 2000 04 08 92 96

0

2

4

6

8

10

12

14

16

IMPLICATIONS FOR THE INTEREST RATE TRANSMISSION MECHANISM OF MONETARY POLICY
CHAPTER 2 STRESS IN BANK FUNDING MARKETS AND IMPLICATIONS FOR MONETARY POLICY

88

steadily increased.23 In addition to these “managed deposits,” fi nancing through repurchase 

agreements and issuance of debt securities, 

23For example, deposits by nonresidents recently 

accounted for 46 percent of U.K. bank liabilities.

both in domestic and foreign markets, have 

expanded, indicating that European banks are 

also increasingly exposed to developments in 

money markets. At the same time, the share of 

household deposits for Japanese banks has been 

stable and even increasing over time. This may 

For purposes of this analysis of monetary 

transmission, the fi nancial sector can be divided 

into banks and near-banks (see fi gure).

Banks. Traditionally collected short-term 

deposits and transformed into long-term loans, 

but more recently have moved to greater reliance on market fi nancing.

Near-banks. Similar to banks on the asset side 

but dissimilar in fi nancing. In the U.S. fl ow of 

funds tables, near-banks comprise the following 

entities:

• Asset-back security (ABS) issuers, which typically 

are private bank-controlled conduits that 

securitize mortgages and consumer credits, 

and are fi nanced by ABS and asset-backed 

commercial paper (ABCP), which in turn are 

held by a variety of investors including banks, 

life insurance companies, mutual funds, and 

foreign entities. ABS issuers are very similar 

in terms of assets but mainly issue bonds and 

differ from banks on the liability side.

• Government-sponsored enterprises (GSEs) and 

GSE pools, which fi nance home mortgages 

through issuing agency- and GSE-backed 

securities that banks, foreign investors, and 

many other sectors purchase. These entities 

largely benefi t from the information collection specialization of banks and hold similar 

assets to banks, but are fi nanced mainly by 

long-term bonds.

• Finance companies, which are similar to banks 

on the asset side but are market-fi nanced and 

relatively small.

• Securities brokers and dealers, which are largely 

investment banks that fi nance their traded 

assets by security repo agreements and other 

types of credit that are advanced again by 

banks, money market mutual funds, and, 

especially in recent years, foreign entities. The 

information collection skills of these brokers 

and dealers are different from those of banks 

and their role in monetary transmission is 

related to security transactions and their role 

as market-makers. This group also includes 

funding corporations, which are funding 

subsidiaries of foreign fi nancial institutions, 

nonbank fi nancial holding companies, and 

custodial accounts associated with security 

lending. Their funding is obtained mainly 

from commercial paper markets.

Box 2.4. Breakdown of the Financial Sector for Monetary Transmission Analysis

0

10

20

30

40

50

60

70

80

90

100

1980 85 90 95 05 2000

United States: Breakdown of Financial Sector

(In percent of total assets)

 Source: U.S. Board of Governors of the Federal Reserve System.

 Note: Government-sponsored enterprises (GSEs) include 

agency- and GSE-backed mortgage pools. Broker dealers include 

funding corporations. ABS = asset-backed security.

Depository banks

Insurance and

pension funds

ABS issuers

Finance companies

GSEs

Broker dealers

Others

Note: Mark Stone and Seiichi Shimizu prepared 

this box.
89

partly refl ect the prolonged low interest environment since the late-1990s.24

Until recently, bank liability maturities had 

shortened and become more volatile. The short-term 

markets became more important for banks and 

near-banks through the mid-1990s as a more 

fl exible way to manage their asset and liability 

structures. An “aggregate short-term funding 

ratio” for the United States comprising commercial paper, Fed Funds, and security repo 

agreements of banks and near-banks shows an 

upward trend through 2000, with a subsequent 

large swing (Figure 2.7). Banks in the United 

Kingdom have also tapped an increased share 

of funding through repo agreements through 

2004.25 However, short-term market fi nancing costs are more volatile than the traditional 

main fi nancing source of core deposits. The 

interest rate on checkable deposits is relatively 

stable compared with rates on federal funds 

and repurchase agreements and time deposits, 

and the liability share of checkable deposits 

has fallen.

Implications of the Current Financial Turmoil for 

Interest Rate Transmission

The dramatic alteration in the interest rate 

transmission mechanism brought on by the 

market turbulence that erupted in July 2007 

can be seen in the changing costs and composition of bank and near-bank fi nancing 

(IMF, 2008c, Chapter 3). In the United States, 

interest rate spreads and the volatility of banks’ 

short-term fi nancing rose to levels exceeding 

24In addition, limited subprime exposure has sheltered 

Japanese banks from the balance sheet and funding diffi culties experienced by U.S. and European banks (IMF, 

2008d). 25Data show a fairly low and stable share of repo funding in the euro area. This is mostly because noneuro 

repo funding is apparently not included. According to 

the latest European repo market survey conducted by the 

International Capital Market Association, the outstanding volume of repo in Europe increased from 924 billion euros as of June 2001 to 3,153 billion euros as of 

end-2007, equivalent to 7 percent and 14 percent of MFI 

liabilities, respectively (see International Capital Market 

Association, 2008).

Sources: Bloomberg L.P.; and Merrill Lynch.

Note: ABCP = asset-backed commercial paper; ABS = asset-backed 

security; CD = certificate of deposit; MBS = mortgage-backed security. 1Spread over treasury securities of comparable maturity.

2Merrill Lynch fixed ABS index.

3Credit card (accounts assessed) interest rate.

Figure 2.8. United States: Selected Interest Rate

Spreads1

(In percent)

–1

0

1

2

3

4

5

1988 92 94 96 98 90 2000 02 04 08 06

Bank Financing

–1

0

1

2

3

4

5

1988 92 94 96 98 90 2000 02 04 08 06

Near-Bank Financing

0

3

6

9

12

15

1988 92 94 96 98 90 2000 02 04 08 06

Borrower Financing

3-month LIBOR

Bank bond (investment grade)

3-month CD

3-month ABCP

Agency MBS

ABS2

30-year mortgage Corporate bond (high-yield)

Corporate bond (A rated) Consumer credit 3

IMPLICATIONS FOR THE INTEREST RATE TRANSMISSION MECHANISM OF MONETARY POLICY
CHAPTER 2 STRESS IN BANK FUNDING MARKETS AND IMPLICATIONS FOR MONETARY POLICY

90

those of previous downturns (Figure 2.8, top 

panel), immediately raising marginal fi nancing 

costs and probably, in effect, cutting off some 

banks from the markets. The overall shortterm funding ratio for banks and near-banks 

declined sharply beginning in the second half 

of 2007 (Figure 2.7). Banks’ fi nancing from 

the Fed Funds and repo markets declined from 

the third quarter of 2007. Similarly, security 

brokers and dealers reduced funding from repo 

markets in line with attempts to deleverage, 

while customer lending associated with security 

transactions increased and the issuance of assetbacked commercial paper (ABCP) contracted 

signifi cantly.

Banks and near-banks were compelled to tap 

longer-term fi nancing, notwithstanding the 

higher costs. The spreads over treasury securities of comparable maturities of long-term 

bank fi nancing instruments shot up to levels 

far above previous cyclical highs (Figure 2.8, 

middle panel). In the United States, Federal 

Home Loan Bank advances were another 

important source of mortgage-related fi nancing 

for banks.

Funding stresses exerted downward pressure 

on bank lending even as lenders faced increasing demand for commercial and industrial 

loans, as nonfi nancial corporations drew down 

previously established credit lines (Federal 

Reserve Board, 2008). According to the Federal 

Reserve’s Senior Loan Offi cer Opinion Survey, 

banks signifi cantly and quickly tightened lending standards for most categories of loans (see 

Chapter 1). However, neither bank lending rate 

spreads over treasury securities nor corporate 

bond yield spreads have risen to date above 

levels previously experienced during economic 

downturns, suggesting that credit tightening is 

in the form of quantity rather than price adjustment (Figure 2.8, bottom panel).

Thus far, the upshot of the market turmoil 

has been an expanding role for banks at the 

expense of near-banks. Near-banks are more 

vulnerable to funding illiquidity and have 

stopped gaining market share in relation to the 

banks. This is partly explained by the contracSource: Eurpean Central Bank.

Note: ABCP = asset-backed commercial paper; ABS = asset-backed 

security. 1Spread over government securities of comparable maturity.

2Consumer credit (over 1 year and up to 5 years) rate as reported in the 

ECB Monthly Bulletin.

Figure 2.9. Euro Area: Selected Interest Rate

Spreads1

(In percent)

–1.0

–0.5

0

0.5

1.0

1.5

2.0

2.5

1999 2000 02 04 08 01 03 07 05 06

Bank Financing

Near-Bank Financing

Borrower Financing

3-month Euribor

Bank bond index

1-year household deposits

–0.6

–0.4

–0.2

0

0.2

0.4

0.6

0.8

1.0

1.2

1999 2000 02 04 08 01 03 07 05 06

–2

0

2

4

6

8

10

12

14

16

18

1999 2000 02 04 08 01 03 07 05 06

Consumer loans2

Corporate bond (high-yield)

Housing loan

Corporate bond (A-rated)

ABCP

Pfandbriefe (10+ years)

ABS
91

tion of ABS and ABCP issuance, refl ecting banks 

bringing the associated assets onto their balance 

sheets (IMF, 2008c, Chapter 2).

In the euro area over the past year, interest 

rate spreads for both lender and borrowing 

fi nancing display patterns similar to the United 

States, indicating that other monetary systems 

may have suffered a similar alteration to the 

normal interest rate transmission process (Figure 2.9). Banks have tried to secure more stable fi nancing through deposits with an agreed 

maturity as well as debt securities, and nearbank fi nancing spreads have widened, although 

they account for a smaller share of the fi nancial 

system. Household mortgage loan growth has 

abated in line with the ongoing trend since 

2006, but credit growth to nonfi nancial corporations has remained robust.

Empirical Analysis

The empirical analysis below aims to gauge 

the impact of structural changes in the fi nancial 

sector on interest rate transmission in the past 

and during the current time of stress. The specifi c questions examined are:

(1) Have structural changes in the fi nancial 

sector over the past 25 years undermined or 

enhanced interest rate transmission?26

(2) How has the recent market turmoil 

affected the markets crucial for interest rate 

transmission?

Interest rate transmission in the United 

States and the euro area is modeled here in a 

system of simultaneous regression equations 

comprising the effective policy rate and a 

market interest rate or yield.27 The policy rates 

are the actual Fed Funds rate for the United 

States and the Euro Overnight Index Average 

(EONIA) for the euro area, and the market 

interest rates or yields comprise (1) lender 

26The empirical literature has generally concluded 

that over the last several decades the interest rate 

pass-through has probably strengthened while other 

transmission channels may have weakened (Kuttner and 

Mosser, 2002). 27The technical details are presented in Annex 2.2.

fi nancing interest rates and yields (three-month 

LIBOR, the investment-grade bank bond yield, 

the ABS yield, and the mortgage agency bond 

yield); and (2) borrower fi nancing rates and 

yields (the mortgage rate and high-yield corporate bond yields).

The regressions are estimated using monthly 

data from the initial date of data availability 

to end-June 2008, using an approach aimed 

at distinguishing between the short- and longterm effects of changes in monetary policy 

rates on market rates (in terms of direction, 

timeliness, and magnitude).28 The estimated 

equations model the contemporaneous monthly 

change in the market rate as dependent on 

its previous changes and on past changes in 

the overnight unsecured rate, and any deviation from the long-run equilibrium (with the 

core deposit ratio of the banking sector as an 

unreported control variable). The impact of the 

policy rate on each market rate is the focus, as 

opposed to direct estimation of the potential 

variables that explain the market rate itself.29

This approach facilitates the analysis of shifts in 

interest rate transmission as well as cross-market 

comparisons.

The results suggest that interest rate 

transmission from the Fed Funds or EONIA 

to market rates has operated broadly as 

expected over the past 25 years (Tables 2.2 

and 2.3).30 The long-term pass-through is 

28Specifi cally, a two-dimensional Vector Error Correction Mechanism (VECM) model with a three-period 

lag structure is estimated. The cointegration vector 

represents a possible linear combination of each interest 

rate pair, which establishes a long-term relation towards 

which convergence occurs over time (Banerjee and others, 1993; Granger, 1986; Hendry and Juselius, 2000). 29For instance, with elevated commodity prices, measures of infl ation expectations may be expected to infl uence market rates. However, market implied infl ation 

expectations in advanced economies have, until recently, 

remained relatively stable (compared with emerging 

economies), indicating that their infl uence on monetary 

policy transmission has been limited. 30Kok Sørensen and Werner (2006) found that, in the 

euro area, rates on mortgage loans and time deposits 

adjust more effi ciently than rates on consumer loans and 

checking deposits.

IMPLICATIONS FOR THE INTEREST RATE TRANSMISSION MECHANISM OF MONETARY POLICY
CHAPTER 2 STRESS IN BANK FUNDING MARKETS AND IMPLICATIONS FOR MONETARY POLICY

92

measured by the cointegration factor, which 

measures the speed with which deviations 

from long-run relationships return to normal. These factors are mostly stable over time 

and are close to unity for all market interest 

rates (with the exception of high-yield bonds). 

This indicates a smooth long-run transmission of policy rate changes to market interest 

rates. Further, over the long run, the threemonth U.S. dollar LIBOR and Euribor rates 

have a more stable and reliably estimated 

relation with the policy rate compared with 

other lender fi nancing rates (Figures 2.10 

and 2.11).

Table 2.2. Static Vector Error Correction Mechanism (2, 3) Estimation with Variable Controls: 

United States

 Short-Term Pass-Through Direction of Long-Term Pass-Through Fed Funds Causality

Cointegration factor At 1-month At 2-month Modified GG

Fed Funds Rate vis-à-vis: coefficient coefficient coefficient score Adj. R2

Lender Financing Rates (Banks) Three-month LIBOR rate

Entire sample: 1/1985–6/2008 –1.48*** 0.06*** –0.05* –1.001 0.05

Period I: 1/1985–12/1991 –1.13*** 0.34 0.05 –1.001 0.05

Period II: 12/1991–9/2000 –0.11 0.42 0.15 0.721 0.43

Period III: 9/2000–12/2004 –6.30*** 0.04 –0.30 –0.661 0.19

Period IV: 12/2004–6/2008 –1.17*** 1.27*** –0.11 –1.001 0.56

Bank bond yield

Entire sample: 12/1987–6/2008 –1.25*** 0.06 –0.05 –1.001 0.07

Period I: 12/1987–12/1991 –0.50** 0.14 –0.02 –0.711 0.23

Period II: 12/1991–9/2000 4.27*** 0.17 –0.04 1.001 0.15

Period III: 9/2000–12/2004 –0.42 0.05 –0.27* –0.011 0.12

Period IV: 12/2004–6/2008 6.26 –0.46** 0.17 1.00 0.27

Lender Financing Rates (Near-Banks) Asset-backed security (ABS) yield

Entire sample: 11/1987–6/2008 –1.45*** 0.06 –0.07 –1.001 0.10

Period I: 11/1987–12/1991 –3.33*** –0.17 –0.91 –1.001 0.83

Period II: 12/1991–9/2000 –1.23*** 0.06 0.03 –1.001 0.07

Period III: 9/2000–12/2004 –1.43*** 0.18 –0.28 –1.001 0.25

Period IV: 12/2004–6/2008 8.43* –0.16 –0.30 1.001 0.25

Agency mortgage-backed security (MBS) yield

Entire sample: 1/1985–6/2008 –1.66*** 0.04 0.00 –1.002 0.09

Period I: 1/1985–12/1991 –2.46*** –0.01 0.00 –1.001 0.16

Period II: 12/1991–9/2000 –1.14*** 0.09 –0.01 –1.001 0.11

Period III: 9/2000–12/2004 –0.98*** 0.09 –0.08 –1.001 0.10

Period IV: 12/2004–6/2008 –2.47*** 0.03 –0.06 –1.001 0.31

Borrower Financing Rates 30-year mortgage loan rate (fixed)

Entire sample: 1/1985–6/2008 –2.35 *** 0.02 –0.05 –1.001 0.23

Period I: 1/1985–12/1991 –1.16 *** 0.03 –0.12 –1.001 0.31

Period II: 12/1991–9/2000 –1.66*** –0.12 0.09 –1.001 0.16

Period III: 9/2000–12/2004 –0.24 –0.12 –0.18 –0.032 0.37

Period IV: 12/2004–6/2008 –6.42*** 0.05 0.07 –1.001 0.20

Corporate bond (high-yield)

Entire sample: 12/1987–6/2008 0.43* –0.09 0.01 –1.001 0.11

Period I: 12/1987–12/1991 0.48** –0.04 0.01 –0.221 0.26

Period II: 12/1991–9/2000 0.25 0.23 0.70 0.232 0.15

Period III: 9/2000–12/2004 0.11 0.65 –0.44 –1.001 0.14

Period IV: 12/2004–6/2008 1.14* –0.13 0.70 ** 0.771 0.49

Note: The modified GG score is between –1 and 1, where –1 means that the Fed Funds rate leads the market rate perfectly whereas

+1 means the opposite (Jobst, 2006; Gonzalo and Granger, 1995). The superscript “1” indicates that the parameter associated with 

causality in the GG score is statistically significant. A superscript “2” indicates both the parameter associated with causality and the one 

associated with the adjustment to the long-run equilibrium are statistically significant. *,**, and *** indicate statistical significance at the 

10, 5, and 1 percent level.
93

The results for the short-run pass-through 

coeffi cients are more mixed. The one- and twomonth pass-through estimates vary widely, and 

in most cases are not statistically signifi cant, indicating that the initial impact of changes in the 

policy rate takes more than two or three months 

to take effect.

Another method to assess whether the 

overnight rate is transmitted to other market 

rates is to look at lead-lag relations. The application of the modifi ed Gonzalo-Granger (GG) 

(1995) score of adjustment coeffi cients gauges 

the direction of causality between the various 

interest rate pairs.31 The generally negative 

31The modifi ed GG-test measure of the error correction coeffi cients on the fi rst and second lags in a regression gauges how much each interest rate contributes 

individually to the differential price discovery and how 

quickly deviations from their long-run equilibrium will be 

eliminated. A negative GG score means that an increase 

in the interest rate differential will initiate an adjustment 

in the market interest rate.

Table 2.3. Static Vector Error Correction Mechanism (2, 3) Estimation with Variable Controls: Euro Area

Short-Term Pass-Through Direction of

Long-Term Pass-Through EONIA Causality

Cointegration factor At 1-month At 2-month Modified GG

EONIA Rate vis-à-vis: coefficient coefficient coefficient score Adj. R2

Lender Financing Rates (Banks) 3-month Euribor rate

Entire sample: 12/1998–6/2008 –0.97*** 0.21*** 0.20*** –0.531 0.22

Period II: 12/1998–9/2000 –1.13*** 0.00 –0.01 –1.001 0.54

Period III: 9/2000–12/2004 –1.03*** 0.16 –0.01 –0.941 0.22

Period IV: 12/2004–6/2008 –3.29*** 0.02 0.23* 0.102 0.53

Bank bond yield

Entire sample: 12/1998–6/2008 –1.14*** 0.08 0.06** –0.971 0.08

Period II: 12/1998–9/2000 3.64*** 0.12 0.10 1.002 0.60

Period III: 9/2000–12/2004 –1.43 0.00 –0.01 –1.001 0.10

Period IV: 12/2004–6/2008 –0.20 –0.07 –0.15 –0.251 0.28

Lender Financing Rates (Near-Banks) Asset-backed security (ABS) yield

Entire sample: 12/1998–6/2008 –1.17*** 0.06 0.10 –1.001 0.05

Period II: 12/1998–9/2000 3.35*** 0.16 0.11 1.002 0.66

Period III: 9/2000–12/2004 –1.40*** –0.03 –0.03 –1.001 0.08

Period IV: 12/2004–6/2008 –0.82*** 0.00 0.06 –1.001 0.15

Pfandbriefe (German covered bond) yield

Entire sample: 12/1998–6/2008 –1.44*** 0.07 –0.06 –1.001 0.08

Period II: 12/1998–9/2000 2.01*** 0.16 –0.17 1.001 0.69

Period III: 9/2000–12/2004 –2.09*** –0.02 –0.01 –1.001 0.12

Period IV: 12/2004–6/2008 –1.45*** 0.02 –0.04 –1.001 0.28

Borrower Financing Rates Housing loan rate (ECB)

Entire sample: 2/2003–6/2008 –0.64*** 0.10** 0.04 –1.002 0.41

Period II: n.a. n.a. n.a. n.a. n.a. n.a.

Period III: 2/2003–12/2004 0.36 0.18* 0.29*** –0.811 0.63

Period IV: 12/2004–6/2008 –1.37*** –0.06 –0.12** 0.051 0.63

Corporate bond (high-yield)

Entire sample: 12/1998–6/2008 –0.24*** –0.51 –0.32 –0.931 0.15

Period II: 12/1998–9/2000 –0.90*** –0.15 –0.36 –1.001 0.59

Period III: 9/2000–12/2004 –0.24*** –0.57 –0.38 –0.961 0.19

Period IV: 12/2004–6/2008 0.13 –1.45** –0.62 –1.001 0.35

Note: EONIA = Euro Overnight Index Average; Euribor = Euro interbank offered rate; ECB = European Central Bank. The modified GG score is 

between –1 and 1, where –1 means that the effective European Central Bank policy rate (EOCNIA) leads the market rate perfectly whereas +1 

means the opposite (Jobst, 2006; Gonzalo and Granger, 1995). The superscript “1” indicates that the parameter associated with causality in the 

GG score is statistically significant. A superscript “2” indicates both the parameter associated with causality and the one associated with the 

adjustment to the long-run equilibrium are statistically significant. *,**, and *** indicate statistical significance at the 10, 5, and 1 percent level.

IMPLICATIONS FOR THE INTEREST RATE TRANSMISSION MECHANISM OF MONETARY POLICY
CHAPTER 2 STRESS IN BANK FUNDING MARKETS AND IMPLICATIONS FOR MONETARY POLICY

94

GG scores (close to unity) suggest that (in this 

model specifi cation) the policy rate almost 

always leads the market rate—a good indication 

of normal transmission processes.

Impact of Structural Changes in the Financial 

Sector on Interest Rate Transmission

Rolling window estimates of long-run passthrough (gauged by the cointegration factors) 

between market rates and policy rates show 

generally stable interest rate transmission for the 

three-month LIBOR and Euribor rates up to the 

summer of 2007 (Figures 2.10 and 2.11).32 The 

absolute value of the cointegration coeffi cient 

is rarely less than one and varied considerably 

during the 1990s, but stabilized beginning in 

the early 2000s in both economies at the same 

time as these markets deepened. The estimated 

long-term interest rate pass-through for the 

near-bank U.S. agency mortgage-backed security 

(MBS) yield and the Pfandbriefe yield has stabilized beginning only in the early 2000s.

Not surprisingly, borrower fi nancing rates 

generally have a less stable relationship with 

the policy rate. Mortgage rates have a cointegration factor lower than –1 for much of the 

period in both the United States and euro area, 

perhaps because banks feel the need to increase 

their lending rates more than the Fed Funds 

or EONIA rates to compensate for attracting 

a riskier class of borrowers. As expected, the 

long-term impact of policy rate changes is the 

weakest and least stable for high-yield corporate 

bonds.

Impact of Recent Market Disruption on Interest 

Rate Transmission

Market disruptions since the summer of 2007 

appear to have been more severe for the United 

States compared with the euro area. In the 

United States, rolling window forecasts of the 

market rates based on the historical trend over 

the past 15 years are fairly accurate prior to the 

32Rolling window regressions move the estimation 

period ahead by one month, allowing assessment of the 

stability of the estimated relations.

–7

–5

–3

–1

1

3

30-year mortgage rate

Corporate bond 

 (high yield)

Borrower Financing Rates

1993 95 97 99 03 05 07 2001

Source: IMF staff estimates.

Note: Sample time period: 1/31/1985–6/30/2008. VECM (2,3) eight-year rolling 

window estimation results of the cointegration coefficients (with 90 percent 

confidence band) denoting the long-term equilibrium relation between level 

changes in the effective U.S. Federal Funds rate and selected market rates of 

lenders and borrowers (with seasonal control). A coefficient value of “–1” 

indicates a stable long-term equilibrium relation of the policy rate and the selected 

market rate, whereas deviations from this value indicate a breakdown in the 

relation. The dates in the charts refer to the end dates of the rolling window. ABS = 

asset-backed security; MBS = mortgage-backed security. 1The error bands for the 3-month LIBOR rate are very tight (between 1.6 

percent and 7.6 percent of the coefficient value) and have thus been suppressed 

from the figure for clarity.

–5

–4

–3

–2

–1

0

1

Figure 2.10. Dynamic Vector Error Correction

Mechanism (VECM) (2,3) Estimation of the

U.S. Fed Funds Rate and Market Rates—United 

States

(Coefficient value)

3-month LIBOR1

ABS yield

Bank bond yield

Agency MBS yield

Lender Financing Rates (Banks and Near-Banks)

1993 95 97 99 03 05 07 2001
95

summer of 2007 (Figure 2.12).33 However, from 

mid-2007 the forecast errors for the three-month 

LIBOR jumped substantially at the same time 

as the extraordinary increase in money market 

spreads and the collapse of the structured credit 

market in response to subprime mortgage market distress (Figure 2.13).34 The larger forecast 

errors for the near-bank fi nancing rates (ABS 

and U.S. agency MBS yields) and a widening of 

forecast confi dence intervals after the summer 

of 2007 is evidence of a dramatic alteration in 

the predictability of interest rate transmission. 

These results suggest that the early linkages of 

interest rate transmission in the United States 

have been impeded by the fi nancial turmoil. 

At the same time, forecasts of borrower fi nancing rates have not been signifi cantly altered to 

the same degree as lender fi nancing markets 

thus far, but forecast accuracy appears to have 

decreased after the crisis.

Monetary transmission in the euro area 

appears to have suffered from a similar—but 

smaller—degree of uncertainty in the passthrough of policy rates to short-term lender 

fi nancing rates (Figure 2.14). Similar to the 

United States, longer-term fi nancing rates continue to show unstable forecasts, and deviations 

from actual rates reveal that policy rates have 

become disconnected over the past six months. 

The gradual movement of credit creation from 

near-banks back to banks does not seem to have, 

as yet, translated into signifi cantly higher retail 

borrowing rates, perhaps refl ecting the smaller 

role of near-banks in the euro area. The relatively reliable forecasts of mortgage rates may be 

due to euro area banks’ lower dependence on 

money markets (as well as alternative sources of 

33These forecasts are derived as out-of-sample estimates 

of the market rate in the next month based on the VECM 

model parameters over an eight-year (six-year) rolling 

window of preceding observations of U.S. (euro area) 

data. 34Note that the large over-prediction in January 

2008 for the bank and near-bank fi nancing rates in the 

United States is a result of the dramatic cut of the Fed 

Funds (target) rate from 4.25 to 3 percent and should 

be excluded from our general assessment of forecast 

accuracy.

–3

–2

–1

0

1

2

Borrower Financing Rates

30-year mortgage rate

Corporate bond (high-yield)

2000 03 04 05 07 08 01 02 06

Source: IMF staff estimates.

Note: EONIA = Euro Overnight Index Average. Sample time period: 1/31/1985–

6/30/2008. VECM (2,3) six-year rolling window estimation results of the 

cointegration coefficients (with 90 percent confidence band) denoting the 

long-term equilibrium relation between level changes in the effective European 

Central Bank policy rate (EONIA) and selected market rates of lenders and 

borrowers (with seasonal control). A coefficient value of “–1” indicates a stable 

long-term equilibrium relation of the policy rate and the selected market rate, 

whereas deviations from this value indicate a breakdown in the relation. The dates 

in the charts refer to the end dates of the rolling window. ABS = asset-backed 

security. 1The data series of bank of bank bond yields starts in June 1992.

–4

–3

–2

–1

0

1

2

3

Figure 2.11. Dynamic Vector Error Correction

Mechanism (VECM) (2,3) Estimation of the

EONIA Rate and Market Rates—Euro Area

(Coefficient value)

3-month Euribor

ABS yield

Bank bond yield1

Pfandbriefe yield (Germany)

Lender Financing Rates (Banks and Near-Banks)

2000 03 04 05 07 08 01 02 06

IMPLICATIONS FOR THE INTEREST RATE TRANSMISSION MECHANISM OF MONETARY POLICY
CHAPTER 2 STRESS IN BANK FUNDING MARKETS AND IMPLICATIONS FOR MONETARY POLICY

96

funds via the covered mortgage bond market). 

Corporate bond yields are mostly underpredicted since the summer of 2007.

The relatively stable relationship between 

borrower fi nancing rates and the Fed Funds 

rate should not be seen as evidence of unimpeded monetary transmission. First, separate 

regressions suggest that the sensitivity over the 

past 25 years of the mortgage rate to changes in 

the Fed Funds rates is largely explained by the 

indirect effect of policy rate changes operating through the LIBOR rate rather than by the 

direct effect of the policy rate on the mortgage 

rate.35 Thus, the early linkages of interest rate 

transmission from the Fed Funds rate to the 

mortgage rate seem to be historically strong. By 

contrast, in the euro area, the indirect effect 

of the EONIA on the mortgage rate is much 

less apparent. Second, stricter overall lending 

standards as well as shrinking issuance in highyield and structured credit markets indicate 

that lenders are tightening credit availability by 

adjusting quantities rather than prices. In this 

light, overall monetary transmission—including 

through channels other than interest rates—may 

be constrained.

Policy Recommendations

This chapter has shown that the persistence of 

disturbances in money and related fi nancial markets that began in the summer of 2007 appears 

to be impeding interest rate transmission. The 

increased complexity, depth, and interconnectedness of these markets means that measures to 

help restore normal market conditions necessarily cover a wide policy spectrum. Below are 

recommendations aimed at alleviating the strains 

in U.S. and European interbank markets.

The following recommendations concern 

interbank rate setting and use:

35A three-equation model (not reported) of the 

mortgage interest rates was estimated including bank 

fi nancing rates (LIBOR and the three-month Euribor, in 

addition to the policy rates).

Source: IMF staff estimates.

Note: Forecast error represents deviation (in percent) of dynamic 

(out-of-sample) Vector Error Correction Mechanism (2,3) forecast estimate from 

actual market rates (based on eight-year rolling window, starting in January 1988). 

The estimation algorithm includes the change of core deposits of commercial 

banks as seasonal control variable. ABS = asset-backed security; MBS = 

mortgage-backed security.

Figure 2.12. Summary Chart: Accuracy of 

Forecasts—U.S. Model, 1996–2008 

(In percent)

–50

–40

–30

–20

–10

0

10

20

30

40

50

–50

–40

–30

–20

–10

0

10

20

30

40

50

1996 98 2000 02 04 06 08

1996 98 2000 02 04 06 08

3-month LIBOR

ABS yield

Bank bond yield

Agency MBS yield

30-year mortgage rate

Corporate bond (high-yield)

Under-estimation

Deviation from actual value

Over-estimation Under-estimation

Deviation from actual value

Over-estimation

Starting date of 

subprime crisis

Starting date of 

subprime crisis

Borrower Financing Rates

Lender Financing Rates

(Banks and Near-Banks)
97

• Improve infrastructure in funding markets. 

Although transaction volumes in the London 

and euro area term interbank markets on 

which LIBOR and Euribor calculations are 

based have shrunk to negligible levels, they 

remain worthwhile measures of banks’ marginal funding costs and there is no systematic 

evidence that published rates are biased. 

However, greater confidence in the representativeness of the calculated rates would be 

achieved by expanding their scope to encompass banks’ unsecured term funding costs 

in wholesale money markets more generally, 

rather than just in the interbank market, and 

by publishing aggregate volume data.36

• Allow markets to choose own benchmark. Official 

policies to encourage a switch to the use of 

OIS rates, rather than LIBOR or Euribor 

rates, are not warranted. Although OIS rates 

are more representative of credit risk-free 

rates and the expected path of policy rates, 

shifting an estimated legacy of over $400 

trillion notional outstanding of LIBOR-based 

instruments would be a daunting operational 

task. In addition, LIBOR remains the appropriate benchmark rate for contracts needing 

to reflect marginal bank funding costs. Hence, 

counterparties to interest rate derivatives 

should use whichever benchmark (LIBOR/

Euribor or OIS) is most appropriate to their 

needs. While an active OIS market provides 

useful information about market expectations 

to policymakers, no formal policy action is 

justified.

The following recommendations are based 

on the empirical analysis of the factors driving 

interbank spreads:

• Attention to both credit and liquidity strains.

Wide interbank spreads appear to have been 

primarily driven by systemic distress risk—a 

combination of credit and liquidity risk repre36The BBA has announced that it will be seeking to 

expand the number of banks it surveys for its rate fi xings. 

The need for such expansion has been underscored since 

mid-September, when the spreads between various unsecured funding rates widened signifi cantly.

2006 07 08

Starting date of 

subprime crisis 

Source: IMF staff estimates.

Note: Forecast error represents deviation (in percent) of dynamic 

(out-of-sample) Vector Error Correction Mechanism (2,3) forecast estimate from 

actual market rates (based on eight-year rolling window, starting in May 1988). 

The estimation algorithm includes the change of core deposits of commercial 

banks as seasonal control variable. ABS = asset-backed security; ABCP = 

asset-backed commercial paper.

Figure 2.13. Summary Chart: Accuracy of 

Forecasts—U.S. Model, 2006–08 

(In percent)

–50

–40

–30

–20

–10

0

10

20

30

40

50

–50

–40

–30

–20

–10

0

10

20

30

40

50

3-month LIBOR

ABS yield

Bank bond yield

ABCP yield

30-year mortgage rate

Corporate bond 

(high-yield)

2006 07 08

Starting date of 

subprime crisis 

Effective Fed Funds 

Rate versus Lender 

Financing Rates

(Banks and Near-Banks)

Effective Fed Funds 

Rate versus Borrower 

Financing Rates

Under-estimation

Deviation from actual value (in percent)

Over-estimation Under-estimation

Deviation from actual value (in percent)

Over-estimation

POLICY RECOMMENDATIONS
CHAPTER 2 STRESS IN BANK FUNDING MARKETS AND IMPLICATIONS FOR MONETARY POLICY

98

sented in an interdependent measure derived 

from CDS spreads of major banks. Hence, to 

relieve interbank funding stresses, policies 

should aim at jointly addressing credit and 

liquidity issues.

• More transparency to remove uncertainty. To this 

end, regulators and supervisors can facilitate the reduction in uncertainty surrounding the assessment of credit risk by market 

participants. For example, they could move 

to standardize and improve the disclosure of 

off-balance-sheet items; increase the transparency of the valuation of collateral; and require 

better disclosure of the maturity structure 

of liabilities and of the liquidity management practices of major financial institutions 

(IMF, 2008c). Although such steps would not 

remove counterparty credit concerns, they 

would help address some of the uncertainty 

about the health of some banks’ balance 

sheets.

The severe alteration in the reliability of 

interest rate transmission during the past year, 

as evidenced by the empirical results for bank 

and near-bank fi nancing rates, demonstrates the 

interconnections between the various fi nancial 

market rates that serve as the early and middle 

linkages of monetary transmission. Policies to 

restore these linkages are complex and interconnected and encompass central bank liquidity 

management, fi nancial institution and market 

oversight, systemic crisis management, the monetary policy framework, and even fi scal policy. 

In this light, the aims and instruments of policy 

measures should be specifi ed as transparently as 

possible to foster effectiveness, accountability, 

and credibility.37 Such policy measures include:

• Indirect money market support. The disturbances 

of markets in securities used as collateral—

such as the GSE securities and ABS markets—

can spread to the repo markets for which 

they serve as collateral. Thus, central banks 

should have a broad mandate and strategy 

to take measures to restore the functioning 

37This discussion draws on Chailloux and others (2008), 

and IMF (2008e).

Source: IMF staff estimates.

Note: Forecast error represents deviation (in percent) of dynamic 

(out-of-sample) Vector Error Correction Mechanism (2,3) forecast estimate from 

actual market rates (based on six-year rolling window, starting in May 1999). The 

estimation algorithm includes the change of core deposits of commercial banks as 

seasonal control variable. ABS = asset-backed security.

Figure 2.14. Summary Chart: Accuracy of 

Forecasts—Euro Area Model, 2006–08 

(In percent)

–50

–40

–30

–20

–10

0

10

20

30

40

50

–50

–40

–30

–20

–10

0

10

20

30

40

50

3-month Euribor

ABS yield

Bank bond yield

Pfandbriefe yield

(Germany)

Housing loan rate

Corporate bond 

(high-yield)

Under-estimation

Deviation from actual value

Over-estimation Under-estimation

Deviation from actual value

Over-estimation

2006 07 08

Starting date of 

subprime crisis 

2006 07 08

Starting date of 

subprime crisis 

Lender Financing Rates (Banks and Near-Banks)

Borrower Financing Rates
99

of markets indirectly necessary for smooth 

monetary transmission. In the United States, 

the Federal Reserve introduced the TAF (as 

well as other facilities, including the Term 

Securities Lending Facility [TSLF] and the 

Primary Dealer Credit Facility [PDCF]), which 

indirectly undertook significant market risk 

from $29 billion of securities owned by Bear 

Stearns, and was given the capacity to lend 

against collateral to the largest GSEs. The 

Special Liquidity Scheme announced by the 

Bank of England in April 2008 also serves as 

indirect money market support by exchanging 

treasury bills for other types of securities used 

as collateral.38 Circumstances permitting, the 

design of such interventions should be considered ahead of time and incorporated into 

central bank contingency planning and crisis 

simulations. Clear exit criteria for extraordinary interventions should be in place to help 

address moral hazard and limit the degree 

to which intervention substitutes for regular 

market functioning in the long term.

• Oversight of bank liquidity management. The latest disruptions to interbank funding markets, 

and the resulting increased dependence on 

overnight and short-term liquidity, justify an 

escalation in the oversight of bank liquidity 

management. In particular, supervisors and 

central banks should ensure that stress testing 

is rigorous and includes scenarios involving 

sustained market-wide shocks and possible 

spillovers; that current elevated operational 

risks are being addressed; and that financial 

institutions’ liquidity buffers and contingency 

plans are robust and comprehensive.39 Access 

38The TSLF, introduced in March 2008, involves the 

Federal Reserve swapping government securities with primary dealers for illiquid collateral-serving securities for 

extended periods. The PDCF, also announced in March 

2008, gives primary dealers access to Fed discount window 

liquidity against a wide pool of collateral. The Special 

Liquidity Scheme announced by the Bank of England 

in April 2008 plays a role similar to that of the TSLF in 

exchanging risky collateral for government securities, but 

for terms of up to three years. 39Good practices for liquidity risk management are 

outlined in the Principles of Liquidity Risk Management 

to emergency central bank liquidity facilities 

should be granted only in extreme scenarios 

to ensure that banks do not become overly 

dependent on central bank support during 

periods of liquidity stress.

• Oversight of near-banks. Actual and potential 

liquidity support to near-banks (primary dealers, GSEs) justifies stronger oversight of them 

by the central banks providing such liquidity. 

Efforts in this direction are under way, though 

in some cases assuring compliance with supervisory recommendations may entail legislative 

change. The U.S. Securities and Exchange 

Commission and the Federal Reserve recently 

agreed to a memorandum of understanding 

under which they will freely share information 

and analysis pertaining to the financial conditions of U.S. investment banks. Further, the 

Federal Reserve has been given a consultative 

role in the supervision of the housing GSEs. 

The U.K. Treasury proposed broadening the 

mandate of the Bank of England over systemic 

financial stability, including the establishment 

of a financial stability committee.

• Central bank cooperation. The importance of U.S. 

dollar liquidity pressures for Euribor spreads, 

suggested by the empirical results, highlights 

the global integration of funding markets 

and the importance for central banks of the 

spillover of shocks from one county to another. 

More frequent cooperation and communication between central banks, including information sharing, becomes ever more important in 

a crisis (see IMF, 2008c, Chapter 3).

• Fiscal costs. Central bank losses that could result 

from their absorption of credit risk in liquidity crises should ultimately be borne by the 

government to protect the balance sheet of the 

central bank, so reinforcing its independence 

while fostering transparency and political 

accountability for taxpayers’ interests.40 Crisis 

published by the IIF (2007) and the draft of the Principles 

for Sound Liquidity Risk Management and Supervision of the 

Basel Committee on Banking Supervision (2008). 40For instance, there should be a mechanism to transfer the substantial credit risk incurred by the Federal 

POLICY RECOMMENDATIONS
CHAPTER 2 STRESS IN BANK FUNDING MARKETS AND IMPLICATIONS FOR MONETARY POLICY

100

management can also be facilitated through 

close cooperation between the government’s 

debt manager and the central bank if additional government collateral is required.

• Communication. During a period of stress, 

central banks need to provide more information about market conditions than in 

normal times, including details on monetary 

operations, to maintain functioning markets 

and to facilitate transmission. In particular, information may need to be delivered 

more frequently, including between regular 

meetings of the monetary policy committee. 

Separating measures aimed at price stability from those in support of market liquidity 

can be especially challenging when money 

market conditions are stressed but inflation 

is on the rise.41 Further, there should be a 

more explicit discussion of the uncertainties pertaining to the impact of financial 

stress on monetary transmission in order to 

prepare the public and markets for unforeseen changes in the policy stance necessitated 

by systemic developments. Indeed, monetary 

policymakers should consider whether the 

market turbulence, and the resulting rise in 

the cost of bank capital, changes their estimation of the neutral policy rate.

Conclusions

Short-term funding markets in mature economies have been under stress for an extended 

period despite extraordinary policy interventions by central banks to widen the availability 

of secured liquidity. Although interbank lending 

is no longer the principal source of bank term 

funding, wide spreads are not simply arising 

from the method for calculating interbank 

rates and are principally driven by concerns 

about banks being in signifi cant distress, with 

U.S. dollar liquidity strains also representing 

Reserve to support the Bear Stearns takeover to the U.S. 

Treasury. 41For the ECB, this challenge is discussed in GonzálezPáramo (2007).

a signifi cant factor in the euro money market. 

Further, evidence of disruptions to bank and 

near-bank fi nancing markets indicates that the 

transmission of policy interest rate changes are 

less certain and reliable. Policy interventions to 

further broaden access to emergency liquidity 

may continue to contain systemic risks but are 

unlikely to resolve the crisis until broader policy 

measures are implemented.

Annex 2.1. Empirical Framework: The 

Causes of High Interbank Spreads42

This annex explains the variables used in the Structural Vector Autoregression (SVAR) model, which is 

used to estimate the components of the various LIBOR 

spreads, and the technical rationale for undertaking 

this modeling technique. The variable used in the 

SVAR model to proxy bank distress risk is relatively 

new and has been adapted to assess the joint risk of 

distress in a number of the banks included in the 

LIBOR and Euribor panels. The construction of this 

variable is described first, followed by the SVAR model.

Joint Probability of Distress

The measure of systemic distress risk used 

here is represented by the joint probability of 

distress (JPoD) of a group—portfolio—of systemically important banks. The JPoD represents 

the probability that all the banks in the group 

experience distress, and embeds banks’ distress 

dependence. This is based on the fact that banks 

are usually connected—either directly, through 

the interbank deposit market and joint participation in syndicated loans, or indirectly, through 

lending to common sectors or engaging in 

similar proprietary trades. Banks’ distress dependence tends to rise in times of stress, since the 

fortunes of banks decline concurrently through 

either spillovers and contagion after idiosyncratic shocks (direct links) or through negative 

systemic shocks (indirect links). Therefore, in 

42See González-Hermosillo, Martin, and Segoviano 

(forthcoming) for a more detailed presentation of this 

framework.
101

such periods, the banking system’s JPoD may 

experience larger and nonlinear increases than 

those experienced by the probabilities of distress 

(PoDs) of individual banks. Consequently, it 

becomes essential for the proper measurement 

of systemic distress risk to incorporate banks’ 

distress dependence.

In modeling the JPoD, we follow Segoviano 

and Goodhart (2008). Thus, fi rst, we conceptualize the banking system as a portfolio

of banks. Then, we infer from credit default 

swap (CDS) spreads the PoDs of the individual 

banks comprising the portfolio. Subsequently, 

using such PoDs as inputs (exogenous variables), and employing a novel nonparametric 

methodology—the Consistent Information 

Multivariate Density Optimizing (CIMDO) 

methodology—we derive the banking system’s 

multivariate density from which the JPoD is 

estimated.43

The banks’ distress dependence embedded 

in the JPoD captures the linear (correlation) 

and nonlinear dependencies among the banks 

in the portfolio, and allows for these to change 

throughout the economic cycle. These are key 

advantages over traditional risk models, the 

majority of which incorporate only correlations, 

and assume them to be constant throughout the 

economic cycle.44

The Vector Autoregression Framework

A vector autoregression (VAR) framework was 

specifi ed to decompose the variance of LIBOR 

spreads into the contributions of various factors 

43The CIMDO methodology is a nonparametric 

approach to model densities based on cross-entropy 

(Segoviano, 2006). 44The distress dependence embedded in the JPoD is 

characterized by the CIMDO-copula (Segoviano, 2008). 

The structure of linear and nonlinear dependencies 

among the assets in a portfolio can be represented by 

copula functions. Our approach infers copulas directly 

from the joint movement of individual banks’ PoDs. This 

is in comparison with traditional approaches, in which 

parametric copulas have to be chosen and calibrated 

explicitly—usually a diffi cult task, especially with limited 

available data.

characterizing the LIBOR market. In specifying 

the VAR, the factors are characterized to have 

General Autoregressive Conditional Heteroscedasticity (GARCH) volatility specifi cations to 

capture the empirically observed volatility in 

the spreads.45 In identifying the structural 

shocks, two types of specifi cations are adopted: 

(1) a recursive identifi cation; and (2) a structural VAR.

Model Specifi cation

As previously discussed, the factors driving the movements in LIBOR spreads broadly 

encompass volatility, credit, and different types 

of liquidity risks, as well as idiosyncratic risk. 

Systemic distress risk is measured by the JPoD 

of the various groups of banks— effectively, 

portfolios of banks—participating in the 

setting of the LIBOR and Euribor rates.46

Finally, the contribution of idiosyncratic shocks 

represents the residual part of the variance 

that is not explained by the other measures 

of risk.

In specifying the VAR, separate models are 

adopted for each of the three LIBOR spreads 

and Euribor spreads. The full set of seven 

variables, denoted by yt below and associated 

with the LIBOR and Euribor spreads, is given in 

Table 2.4.

Consider the following dynamic structural 

model of yt

:

B0yt

 = B1yt–1 +B2yt–2 + ... + Bkyt–k + ut, (1)

where the Bi

 (i = 0,1,...,k) are matrices of structural parameters with B0 having coeffi cients of 1 

down the main diagonal to represent the usual 

normalization, k represents the order of the 

45The adoption of a time-varying volatility structure 

means that the variance decompositions are no longer 

constant over the sample, but can change at each point 

in time as a result of changes in the conditional variance. 46The 16 banks participating in the British Bankers’ 

Association’s LIBOR fi xings are listed at www.bba.org.

uk, and the 43 banks in the Euribor panel are listed at 

www.euribor.org. For the Euribor panel, only 15 banks 

were considered for the construction of the JPoD due to 

constraints in the availability of CDS data.

ANNEX 2.1. EMPIRICAL FRAMEWORK: THE CAUSES OF HIGH INTERBANK SPREADS
CHAPTER 2 STRESS IN BANK FUNDING MARKETS AND IMPLICATIONS FOR MONETARY POLICY

102

lags, and ut is a vector of independent structural 

disturbances with the property

E[ut

] = 0, E[utut′] = Ht, E[utus′] = 0, t ≠ s. (2)

The matrix Ht

 is a time-varying diagonal 

matrix where the diagonal terms have univariate 

GARCH (1,1) representations:

hi,t = δi

 + αiu2

i,t–1 + βi

hi,t–1. (3)

When embedded into the VAR, the GARCH 

variable framework implies that the variancecovariance matrix of the VAR disturbances is 

time-varying, but unlike the structural disturbances in equation (2), this matrix is not necessarily diagonal, in which case the volatilities of 

all factors have an effect on all variables in the 

VAR.

The model is estimated using maximum likelihood methods, by maximizing the conditional 

log-likelihood with respect to the unknown 

parameters {B0, B1,...,Bk, δ, α, β}.

The dimension of the model is represented by 

N, where N = 7.

Recursive Identifi cation

In identifying the structural shocks, fi rst, a 

recursive identifi cation is adopted. This is standard in the VAR literature. Let B0 in equation 

(1) be lower triangular, in which case its inverse 

is also lower triangular and is given by:

L = B0

–1. (4)

For the N = 7 variate model, L is represented as:

1 0 0 0 0 0 0

l

2,1 1 0 0 0 0 0

l

3,1 l3,2 1 0 0 0 0

L = [l

4,1 l4,2 l4,3 1 0 0 0 ] (5)

l

5,1 l5,2 l5,3 l5,4 1 0 0

l

6,1 l6,2 l6,3 l6,4 l6,5 1 0

l

7,1 l7,2 l7,3 l7,4 l7,5 l7,6 1 

In choosing the ordering of the variables, the 

LIBOR is chosen last so that all factors have an 

instantaneous effect on the LIBOR, as given by 

the last rows in equation (5).

The ordering of the recursive identifi cation is 

to a certain extent arbitrary.

Structural VAR

The full structural VAR is given by specifying 

the following restrictions given by the L matrix 

below.47

1 0 0 0 0 0 0

a2,1 1 0 0 0 0 0

0 0 1 0 0 a3,6 0

L = [ 0 0 a4,3 1 0 a4,6 0 ] (6)

 0 0 0 0 1 a5,6 0

 0 0 0 0 a6,5 1 0

a7,1 a7,2 a7,3 a7,4 a7,5a7,6 1 

47See Table 2.1 for the associated list of restrictions 

used in the structural VAR for each LIBOR and Euribor 

spread.

Table 2.4. List of Variables Used in the Vector Autoregressions

Variable U.S. Dollar LIBOR Euro LIBOR Sterling LIBOR Euribor

Volatility_Market VIX VIX VIX VIX

Volatility_Interest Rates Implied volatility from 

swaption

Implied volatility from 

swaption

Implied volatility from 

swaption

Implied volatility from 

swaption

Liquidity_Market On/Off–the-run On/Off-the-run On/Off-the-run On/Off-the-run

Liquidity_U.S. dollar Euro/U.S. dollar forex swap Euro/U.S. dollar forex swap Pound sterling/

U.S. dollar forex swap

Euro/U.S. dollar forex swap

Interbank_Secured Repo Repo Repo Repo

Systemic Distress JPoD (portfolio of banks 

setting the U.S. LIBOR)

JPoD (portfolio of banks 

setting the euro LIBOR)

JPoD (portfolio of banks 

setting the U.K. LIBOR)

JPoD (portfolio of banks 

setting the Euribor)

Interbank_Unsecured LIBOR (U.S.) LIBOR (Euro) LIBOR (U.K.) Euribor

Note: VIX = S&P 500 volatility index; JPoD = joint probability of distress; Euribor = Euro Interbank Offered Rate.
103

This suggests the following interpretations for 

the structural factors following Table 2.4. The 

volatility factor is assumed to affect VIX and 

Swaption. The volatility of interest rates factor 

hits the Swaption. The market liquidity factor 

affects the on-the-run/off-the-run treasury note 

spread and the forex swap. The U.S. dollar 

liquidity factor affects the forex swap. The credit 

factor impacts the Repo and JPoD variables. 

The distress factor affects the credit variables 

(Repo and JPoD) and the liquidity variables 

(on-the-run/off-the-run and forex swap). Finally, 

the idiosyncratic or residual factor only hits the 

LIBOR/Euribor and represents the effects not 

captured by the factors described above. Notice 

that all factors are designed to impact the 

LIBOR/Euribor, as given by the last row in the 

L matrix.

For example, the structural VAR results for 

the U.S. dollar LIBOR-overnight index swap 

(OIS) spread are depicted in more detail in 

Figure 2.15. The results suggest that the JPoD 

has been the dominant factor explained by 

the model since the onset of the current crisis, 

peaking at around 45 of the total variance in the 

spring of 2008. Interestingly, the fi rst hump in 

the JPoD contribution occurred much earlier, in 

July 2007, when it contributed to more than 30 

percent of the LIBOR-OIS variance. The role of 

the other variables has been relatively small during the crisis period. Notably, however, the repo 

spread began to show signs of stress in 2005 

when the U.S. housing market began its recent 

downturn. In contrast with the Euribor fi xing, 

the effect from the dollar/euro forex swap has 

been relatively small during the current crisis.

Annex 2.2. Empirical Framework: 

Monetary Transmission

As one measure of monetary policy transmission (and interest rate elasticity) over time, we 

adopt a restricted VAR framework in the form 

of a simple Vector Error Correction Mechanism (VECM). In general, the VECM specifi cation defi nes the long-term consistency of 

joint dynamics of endogenous variables within 

Source: Bloomberg L.P.; and IMF staff estimates.

1Implied volatility from S&P 500 equity index.

2Lehman Brothers swaption volatility index. Implied volatility of interest rate 

swaption with maturities ranging from 1 month to 6 months. 3Five-year on-the-run/off-the-run U.S. treasury note spread.

4Spread between 3-month euro/U.S. dollar forex swap and 3-month U.S. 

overnight index swaps. 5Spread between the yields on 3-month U.S. agency repo and 3-month U.S. 

treasury repo. 6Joint probability of distress of selected banks participating in U.S. dollar LIBOR 

fixing.

Figure 2.15. Decomposition of Spread Between

Three-Month U.S. Dollar LIBOR and Overnight Index 

Swaps

(In percent)

0

0.5

1.0

1.5

2.0

2.5

3.0

2004 05 07 06 08

0

3

6

9

12

15

2004 05 07 06 08

0

0.2

0.4

0.6

2004 05 07 06 08

0

10

20

30

40

50

2004 05 07 06 08

0

5

10

15

2004 05 07 06 08

0

10

20

30

40

50

2004 05 07 06 08

Market Volatility1

Interest Rate Volatility2

Market Liquidity3

Forex Swap4

Repo Spread5

Joint Probability of Distress6

ANNEX 2.2. EMPIRICAL FRAMEWORK: MONETARY TRANSMISSION
CHAPTER 2 STRESS IN BANK FUNDING MARKETS AND IMPLICATIONS FOR MONETARY POLICY

104

a linear system of simultaneous equations. If 

the variables share at least one cointegration 

vector—which means that there is one long-term 

stable relation between them—we restrict their 

long-run behavior to converge to their cointegrating relationship while allowing the model 

to accommodate a wide range of short-term 

random disturbances (Johansen, 1991; Johansen 

and Juselius, 1990). The cointegration restriction shows the scale and direction of short-term 

adjustments needed to restore the long-term 

equilibrium relation.

The degree of cointegration is refl ected in 

the specifi cation of the error correction term, 

which is defi ned by past deviations from the 

long-run equilibrium through a series of partial 

short-run adjustments of level changes over the 

sample time period. These adjustments represent intertemporal corrections that indicate the 

short-term lead-lag relation of the endogenous 

variables.48

The VECM model used for this chapter 

defi nes the long-run intertemporal relation 

between the policy rate and selected market rates 

of lenders (banks and near-banks) and borrowers (households and nonfi nancial corporates) in 

both the United States and the euro area. The 

model estimates show the direction of causality 

(and its signifi cance) between changes in the 

policy rate and market rates over the short run 

and the nature of their long-term relation in 

response to unanticipated interest rate shocks.

For the United States, we pair the monthly 

average effective Fed Funds rate (as a proxy 

for the offi cial policy rate, or “target rate”) 

with several fi nancing rates (e.g., three-month 

LIBOR rate for unsecured interbank lending, 

asset-backed commercial paper and certifi cate 

of deposit rates, and yields of either bank-issued 

bonds or asset-backed securities) and borrower 

rates (e.g., corporate bond yield, 30-year mortgage rate, and consumer loan rates). Analogously, for the euro area, the effective policy 

48For instance, an integrated variable, I(1), is typically 

one exhibiting trending behavior, with a differenced 

series showing mean-reverting behavior.

rate of the European Central Bank (ECB), the 

Euro Overnight Index Average (EONIA), is 

matched with market rates for both fi nanciers 

and borrowers.49

The two-dimensional VECM model,

Xt

, = C + Λ(i

t

 – α – β2rt)

 CE

 + ∑p

j=1ΦXt–j + Ξt

Zt + Et, (7)

is specifi ed with the endogenous data vector Xt

 = (Δi

t

,Δrt)′ consisting of the effective 

Fed Funds rate (or the EONIA rate for the 

euro area), i

t

, and the selected end-of-month 

market interest rate,rt

, at fi rst differences. All 

endogenous variables are cointegrated at the 

same order and are stationary in differences.50

The endogenous variables have one cointegration equation at a statistical signifi cance level 

below 5 percent according to the Unrestricted 

Cointegration Rank Test (MacKinnon, Haug, 

and Michelis, 1999). The model has a uniquely 

defi ned cointegration equation ordered such 

that the Fed Funds rate coeffi cient is set to one. 

The estimated parameter coeffi cients of shortterm dynamics are represented by the (2 x 2) 

matrix Φ of jointly dependent past Xt

 values.51

C is a (2 x 1) vector of constants c1 and c2. Ξ is 

the (2 x 1) parameter coeffi cient vector of the 

core deposit rate52 as contemporaneous seasonal 

control variable. Et

 is the (2 x 1) vector of non49Instead of using the ECB interest rate on the main 

refi nancing operations, which changes only infrequently, 

as the effective policy rate in the euro area, we used the 

EONIA, which is the interest rate the ECB tries to align 

with the rate of open market operations. 50The classical Augmented Dickey-Fuller (Dickey and 

Fuller, 1979, 1981) and Phillips-Perron (1988) unit root 

tests suggest that all endogenous variables are stochastic 

with a constant forecast value and time-varying autocovariance. Although the cointegration restriction of VECM 

does not require level stationarity of the constituent time 

series (unlike VAR), it implies difference stationarity of 

each time series regardless of the individual degree of 

integration. 51The simple lag structure has been optimized based 

on the Akaike criterion over all iterative estimation steps. 52The core deposit ratio is defi ned as the sum of checkable deposits and savings deposits held by banks in the 

United States/euro area as a share of total liabilities each 

month.

}
105

autoregressive and heteroscedastic normal i.i.d. 

residuals.

The cointegration equation (with rank order 

of one and constant drift) restricts the long-run 

behavior of the two level series of vector Xt

 to 

converge to a common, long-term trend subject 

to the short-term impact of interest rate shocks. 

The short-term adjustment factors λ1 and λ2 of 

(2 x 1) vector Λ correct these deviations against 

the long-term trend and indicate the short-term 

lead-lag relation of the endogenous variables. We compute the modifi ed Gonzalo and 

Granger (GG) score (λ1 – λ2)/(|λ1| + |λ2|)(Jobst, 

2006) as an indication of the direction of causality, with –1 implying that the Fed Funds rate 

(or the EONIA rate for the euro area) leads 

the market rate perfectly and +1 implying the 

reverse.

In the chapter, we focus primarily on the 

market rate equation of our VECM specifi cation, where the contemporaneous monthly 

change of the selected market rate, rt

, is modeled as dependent on its previous changes, past 

changes of the policy rate, i

t

, and any deviation 

from the long-run relation. The model is estimated both statically for nonoverlapping sample 

periods and dynamically over an eight-year 

rolling window with monthly updates based on 

a heteroscedasticity-consistent coeffi cient covariance matrix (White, 1980). There is a particular 

focus on the evolution of the cointegration 

coeffi cients. Over a relatively long span of data, 

Monte Carlo simulation of possible interest 

rate paths confi rms that these coeffi cients can 

reasonably be interpreted as “long-term elasticities” of selected market rates to changes in the 

policy rate.53

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Intermediaries, Financial Stability and Monetary 

Policy,” paper prepared for the Federal Reserve 

53We recognize that for short samples it is possible for 

cointegration analysis to indicate that a long-run relation can be found when in fact there is only a cyclical 

relation. 

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———, 2008b, “Transmission of Liquidity Shocks: 

Evidence from the 2007 Subprime Crisis,” IMF 

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address at the IMF European Offi ce and Monetary 

and Capital Markets Department, Bank of England, and Banque de France Conference, Paris, 

January 29.

———, 2008b, “Financial Stress and Economic Downturns,” World Economic Outlook, World Economic and 

Financial Surveys (Washington, October).

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———, 2008d, Japan: Selected Issues, IMF Staff Country 

Report No. 08/254 (Washington, July).

———, 2008e, United States: Staff Report for the 2008 

Article IV Consultation, IMF Staff Country Report 

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REFERENCES

CHAPTER 3 FAIR VALUE ACCOUNTING AND PROCYCLICALITY

S

ince the 2007 market turmoil surrounding complex structured credit products, 

fair value accounting (FVA) and its 

application through the business cycle 

has been a topic of considerable debate. As the 

illiquidity of certain products became more 

severe, fi nancial institutions turned increasingly to model-based valuations that, despite 

increased disclosure requirements, were nevertheless accompanied by growing opacity in 

the classifi cation of products across the fair 

value (FV) spectrum. Moreover, under stressed 

liquidity conditions, fi nancial institutions made 

wider use of unobservable inputs in their valuations, increasing uncertainty among fi nancial 

institutions, supervisors, and investors regarding 

the valuation of fi nancial products under such 

conditions.

It has been during this period that the procyclical impact of FVA on bank balance sheets 

and, more specifi cally, the valuation of complex fi nancial instruments in illiquid markets 

came to the fore, raising questions on the use 

of market prices below “theoretical valuation” 

and the validity of “distressed sales.” Financial 

products were fair valued despite concerns 

that the current market prices were not an 

accurate refl ection of the product’s underlying 

cash fl ows or of the price at which the instrument might eventually be sold. Sales decisions 

based on fair value pricing in a weak market 

with already falling prices resulted in further 

declines in market prices, refl ecting a market 

illiquidity premium. Additionally, falling prices 

can, and did, activate margin calls and sale triggers that are components of risk management 

criteria, contributing further to the downward 

trend. As bank net worth is positively correlated with the business cycle, and as fair market 

values for collateral values fall, losses have been 

passed through to banks’ capital (Kashyap, 

2005). The weakening of bank balance sheets 

and regulatory requirements for prudential 

capital replenishment has served to heighten 

concerns as to the future course of some mar109

In light of the uncertainties about valuation highlighted by the 2007–08 market turbulence, this chapter provides an empirical examination of the potential 

procyclicality that fair value accounting (FVA) methods could introduce in bank 

balance sheets. The chapter finds that, while weaknesses in the FVA methodology may introduce unintended volatility and procyclicality, thus requiring 

some enhancements, it is still the preferred accounting framework for financial 

institutions. It concludes that capital buffers, forward-looking provisioning, 

and more refined disclosures can help to mitigate the procyclicality of FVA. The 

analysis presented does not preclude that there are other dimensions to FVA 

that are relevant and that, after further scrutiny, may indicate the need for 

additional refinements to the FVA methodology. Going forward, the valuation 

approaches for accounting, prudential measures, and risk management need to 

be reconciled and will require adjustments on the part of all parties.

Note: This chapter was written by a team led by 

Jodi Scarlata and comprised of Alicia Novoa and Juan 

Solé. Kenneth Sullivan provided consultancy support. 

Yoon Sook Kim provided research support, and Xiaobo 

Shao provided technical support.
CHAPTER 3 FAIR VALUE ACCOUNTING AND PROCYCLICALITY

110

kets, the health of banks, and, more broadly, 

the fi nancial system.

This chapter reviews the principles and 

application of fair value accounting and the 

implications of its features and how these impact 

bank balance sheets. Using a simple model, 

it provides empirical support for the public 

discussions regarding the procyclicality of FVA 

on bank balance sheets. Utilizing representative 

bank balance sheets from a sample of actual 

institutions, the chapter examines the application of FVA to banks’ balance sheets during the 

course of a normal business cycle, as well as 

during extreme shocks, such as have recently 

occurred, to distill in what manner FVA may 

contribute to procyclicality. The chapter examines the results obtained, discusses actual and 

proposed alternatives to FVA, and elaborates on 

policy implications going forward.

The chapter addresses one angle of the FVA 

debate, focusing on the relationship between 

procyclicality, FVA, and its impact on banks’ 

balance sheets. This chapter does not intend 

to provide a defi nitive assessment of FVA and 

recognizes that—beyond its cyclical aspects—

there may be additional elements that deserve 

further scrutiny, such as regulatory and risk 

management considerations, and the need for 

further enhancements to the FVA methodology. 

In its specifi c analysis of FVA and procyclicality, 

the chapter fi nds that, while the application of 

FVA methodology introduces unwanted volatility across time, for the purposes of obtaining a 

point estimate at a specifi c date of a bank’s current fi nancial condition, FVA ensures the most 

accurate assessment—alternative techniques 

have their own shortcomings. Yet diffi culties 

exist not only in determining the fair values 

of assets in downturns and illiquid markets, 

but also during boom times in active markets 

when prices can overshoot and incorporate risk 

premia that infl ate profi ts. Under such circumstances, market prices may not accurately refl ect 

risks and can result in exaggerated profi ts that 

distort incentives (e.g., management compensation) and amplify the cyclical upturn. In 

rapidly evolving fi nancial markets, inaccurate 

valuations may quickly alter the implications for 

solvency and, more broadly, fi nancial stability.

The chapter emphasizes that FVA should be 

structured so that it contributes to good risk 

management and ensures that fi nancial statements include adequate disclosure of valuations, 

methodologies, and volatilities such that inherent uncertainties are well understood. While the 

volatility of estimation errors in valuation techniques should be reduced as much as possible, 

genuine economic volatility should be faithfully 

refl ected in fi nancial statements and preserved 

by regulators and supervisors (Barth, 2004; Borio 

and Tsatsaronis, 2005). The chapter concludes 

by providing some quantitative insight for regulators and supervisors to better assess the implications of FVA on bank balance sheets and capital, 

and puts forward proposals for dealing with 

issues of the volatility of FVA and FV classifi cation. Importantly, it stresses the need for resolving the tensions between valuation approaches 

across risk managers, accountants, and prudential supervisors and regulators, so as to ensure 

that accounting frameworks do not unduly contribute to potential fi nancial instability.

Fair Value Accounting Through the 

Business Cycle

The Current Accounting Framework

Both U.S. Generally Accepted Accounting 

Principles (U.S. GAAP) and International 

Financial Reporting Standards (IFRS) use 

a mixed attributes model in which different valuation criteria are applied to different 

types of assets and liabilities, depending on 

their characteristics and on management’s 

intentions in holding them. In essence, both 

frameworks require FV valuation for fi nancial 

assets and liabilities held for trading purposes 

and available-for-sale assets, and all derivatives. Held-to-maturity (HTM) investments,1

1Nonderivative fi nancial assets with fi xed or determinable payments and fi xed maturity that an entity has the 

intention and ability to hold to maturity.
111

loans, and liabilities not fair valued are valued 

at amortized cost. Both frameworks provide a 

carefully specifi ed option to fair value (FVO) 

certain fi nancial assets and liabilities2 that 

would normally be valued at amortized cost.

The mixed attributes model is intended to 

be as neutral as possible—without emphasizing one accounting principle over another. But 

its uneven application to balance sheets produces accounting volatility and may not fully 

capture the effects of economic events in all 

instruments included in the banks’ fi nancial 

statements.

What Is Fair Value?

IFRS and U.S. GAAP similarly defi ne FV 

as the amount for which an asset could be 

exchanged, and a liability settled, between 

knowledgeable, willing parties, in an arm’slength, orderly transaction. U.S. GAAP (Financial Accounting Standard (FAS) 157) are more 

2Namely, when they are risk-managed on a FV basis, 

though differences remain between FAS 159 and IAS 39.

prescriptive than IFRS because they consider 

that FV is an “exit” or “selling” price.3 Both 

accounting frameworks prescribe a hierarchy 

of FV methodologies that start with observable prices in active markets (Level 1), using 

prices for similar instruments in active or 

not active markets or valuation models using 

observable inputs (Level 2), and moving to a 

mark-to-model methodology with unobservable 

inputs and model assumptions (Level 3).4 The 

absence of market prices, trading activity, or 

comparable instruments’ prices and inputs is a 

prominent feature of complex structured credit 

products, many of which are held off-balancesheet (Box 3.1). Consequently, both frameworks 

require extensive disclosures of information on 

3Nevertheless, differences will disappear given the 

international convergence to IFRS currently under way, 

led by both the U.S. Financial Accounting Standards 

Board and the International Accounting Standards 

Board, which will help achieve a single set of high-quality 

accounting standards. 4This language is U.S. GAAP-specifi c and not IFRS, 

but it is used extensively in the banking industry and in 

fi nancial statements of IFRS users as well.

Recent market turmoil has heightened public 

awareness of the extensive use of off-balancesheet entities (OBSEs) by fi nancial institutions. 

With variations, both the International Financial 

Reporting Standards and the U.S. Generally 

Accepted Accounting Principles (U.S. GAAP) 

have specifi c criteria to determine when instruments transferred to OBSEs should be consolidated on-balance-sheet. Any retained interest in 

securitized fi nancial assets should be on-balancesheet and accounted for at fair value, usually in 

the trading book.

Mandatory disclosures on OBSEs are not prevalent. Their absence may have added to market 

confusion and contributed to procyclical behavior by helping to create a market perception that 

the banks were standing behind their OBSEs. 

Both the International Accounting Standards 

Board (IASB) and the U.S. Financial Accounting 

Standards Board (FASB) have projects under 

way to improve OBSE disclosures and enhance 

the criteria for derecognition and consolidation 

of OBSEs. Examples are the IASB’s consolidation and derecognition projects, and the FASB’s 

changes to FAS 140 and Interpretation 46(R). 

The FASB’s recently revised standard, FAS 140, 

would go into effect for fi scal years beginning 

after November 15, 2009.

Regardless, OBSEs require fi nancial supervisors to revisit prudential reporting so that the 

integrity of banks’ risk exposures can be better 

captured and explained, as well as adequately 

buffered (i.e., capital) to the satisfaction of 

supervisors.

Box 3.1. Off-Balance-Sheet Entities and Procyclicality

Note: Alicia Novoa prepared this box.

FAIR VALUE ACCOUNTING THROUGH THE BUSINESS CYCLE
CHAPTER 3 FAIR VALUE ACCOUNTING AND PROCYCLICALITY

112

the FV methodologies used, specifi c assumptions, risk exposures, sensitivities, etc.

Thus defi ned, FV does not require the presence of deep and liquid markets to be applied. 

FV can be estimated when a market does not 

exist, as FV valuation models comprise the 

expected, risk-discounted cash fl ows that market participants could obtain from a fi nancial 

instrument at a certain point in time. While FV 

incorporates forward-looking assessments, it 

must also refl ect current market conditions and 

measures of risk-return factors,5 and incorporate all factors that market participants consider 

relevant, with fi rm-specifi c risk preferences or 

inputs kept to a minimum. Under this defi nition, two key issues underlying the FV methodology present a challenge—what constitutes an 

active market, and what can be considered an 

observable price or input.

Forced or “fi re” sales would not be valid 

determinants of market prices, because the 

accounting frameworks presume that a reporting entity is a going concern that does not need 

or intend to liquidate its assets, or materially 

curtail the scale of its operations. Yet, accounting standard setters have decided to leave to 

the judgment of management, supervisors, and 

auditors how to determine “regularly occurring” or “distressed” sales, and when sales in 

thin markets, at heavy discounts, could be used 

for balance sheets’ FVA.6 Consequently, market 

participants and supervisors would expect to 

5IFRS do not explicitly mention some risk factors 

(e.g., counterparty credit risk, liquidity risk), which may 

have added confusion to fi nancial statement preparers 

during the 2007–08 turmoil. An International Accounting Standards Board Expert Advisory Group is currently 

working on this and other FV issues. The U.S. Financial 

Accounting Standards Board is reevaluating some disclosure requirements (e.g., credit derivatives) and has issued 

new standards (e.g., FAS 161 on derivatives and hedging). 

Both boards are examining requirements for off-balancesheet entities. 6White papers prepared by the six largest international 

audit fi rms and other audit fi rms summarize guidance 

on what constitutes an active market, FV measurement in 

illiquid markets, and forced sales. See Center for Audit 

Quality (2007) and Global Public Policy Committee 

(2007). 

see banks’ external auditors use a very cautious 

approach to examining the prices and inputs 

used to FV fi nancial instruments in order to 

minimize late write-downs or write-offs and 

opportunities for management to “cherrypick” the accounting treatment of fi nancial 

instruments.7

Disclosures of Fair Value Accounting

Both IFRS and U.S. GAAP mandate various disclosures, particularly when information 

other than market inputs is used to estimate FV. 

For example, IFRS 7 requires disclosure (1) if 

the transaction price of a fi nancial instrument 

differs from its FV when it is fi rst recorded 

in the balance sheet; and (2) of the implications of using “reasonably possible alternative 

assumptions” to refl ect the sensitivities of FV 

measurement.8 IFRS 7 also contains reporting requirements that include the publication 

of sensitivity tests for individual items of the 

fi nancial statements. Similarly, FAS 157 requires 

banks’ balance sheets to be suffi ciently clear 

and transparent so as to fully explain to market 

participants, through quantitative and qualitative notes to the fi nancial statements, the nature 

of the changes and the methodologies used, to 

name a few items.9

Although some U.S. and European Union 

(EU) fi nancial institutions voluntarily provide 

such disclosures, neither IFRS nor U.S. GAAP 

require disclosure on the governance and management control processes10 surrounding FV 

7The International Auditing and Assurance Standards 

Board has issued ISA 540, “Auditing Accounting Estimates, Including Fair Value Accounting Estimates, and 

Related Disclosures,” providing criteria on how auditors 

must assess accounting estimates, and might issue further 

guidance on the audit of FV estimates some time in 2009, 

if warranted. 8IFRS 7, “Financial Instruments: Disclosures,” became 

effective on January 1, 2007. 9For those fi nancial assets measured at amortized cost, 

the entity must also disclose the FV in the notes to the 

statements. 10Including audit-related programs.
113

valuation.11 Enhancement of disclosures in this 

direction could increase confi dence in banks’ 

balance sheets and lower investors’ aversion to 

transact in instruments whose valuations may not 

be well understood (Box 3.2).12 This would not 

necessarily indicate a need for more disclosures, 

but for a more appropriate composition, medium 

(e.g., websites), and frequency of disclosures.

Volatility and Procyclicality of Fair Value 

Accounting

Barth (2004) argues that there are three 

potential channels through which FV may introduce volatility into fi nancial statements. The fi rst 

is the volatility associated with changes in the 

underlying economic parameters. The second is 

11The Financial Stability Forum recommends disclosures about price verifi cation processes to enhance 

governance and controls over valuations and related 

disclosures (Box 3.2). Disclosures regarding risk management governance structures and controls would also be 

welcome. 12An example is the U.S. Securities and Exchange Commission letter of March 2007 to major fi nancial institutions outlining the nature of recommended disclosures.

the volatility produced by measurement errors 

and/or changing views regarding economic 

prospects throughout the business cycle. As to 

the third, volatility may be introduced by relying on the mixed attributes model that applies 

FVA to certain instruments and amortized cost 

to others, reducing the netting effect that full 

fair valuation of assets and liabilities would 

produce.13 Each of these sources of volatility is 

either explicitly or implicitly present in the simulation exercises examined later in the chapter.

The mixed attributes model adopted by IFRS 

and U.S. GAAP has embedded volatility and procyclicality aspects.14 On the one hand, historical 

cost accounting, applicable to HTM investments 

13Barth (2004) argues that mixed attributes models 

impair the relevance and reliability of fi nancial statements and that this constitutes one of the primary reasons behind hedge accounting. IAS 39 aimed to alleviate 

mismatches in assets and liabilities valuations due to the 

mixed attributes model and the complexities of hedge 

accounting. 14It should be noted that procyclicality of accounting and reporting standards existed prior to the recent 

attention to FVA. It has long been recognized that as the 

business cycle and market sentiment change, so too will 

valuations of assets and liabilities.

At the request of the Financial Stability 

Forum (FSF), a Senior Supervisors Group 

conducted a survey of disclosure practices for 

selected fi nancial exposures such as special 

purpose entities (SPE) and collateralized debt 

obligations, among others. The group issued 

a report concluding that disclosure practices 

currently observed can be enhanced without amending existing accounting disclosure 

requirements.1 The FSF is encouraging fi nancial 

institutions to use these disclosure practices for 

their mid-year 2008 fi nancial reports and urging 

Note: Alicia Novoa prepared this box.

1“Leading-Practice Disclosures for Selected Exposures,” April 11, 2008. Twenty large, internationally 

oriented fi nancial fi rms were surveyed (15 banks and 

fi ve securities fi rms) as of end-2007.

supervisors to improve risk disclosure requirements in Pillar 3 of Basel II.

A preliminary reading of fi nancial reports 

prepared for mid-2008 by some U.S., European 

Union and Canadian banks shows the inclusion by U.S. banks of more quantitative notes 

in their fi nancial statements, as compared with 

their end-2007 reporting.2 Typical information 

includes fi nancial assets securitized, cash fl ows 

received on SPE-retained interests, assets in nonconsolidated variable-interest entities (VIEs), 

and maximum exposures to loss in consolidated 

and nonconsolidated VIEs, with details broken 

down by instrument. 

2Canada has postponed adoption of the full International Financial Reporting Standards until 2011.

Box 3.2. Disclosures Recommended by the Financial Stability Forum

FAIR VALUE ACCOUNTING THROUGH THE BUSINESS CYCLE
CHAPTER 3 FAIR VALUE ACCOUNTING AND PROCYCLICALITY

114

and loans, is less volatile and backward-looking. 

When such an investment or loan is correctly 

priced at origination, its FV equals its face value. 

Over the life of the asset and until maturity, its 

reported stream of profi ts is stable and its carrying value is based on its value at origination. 

But if market conditions negatively affect these 

portfolios and there is evidence of a credit loss 

event and asset impairment, then the reporting values must be reassessed and provisions for 

losses must be accrued or write-offs recorded. 

The latter is often a late recognition of excess 

risk taken earlier, in good times. In this sense, 

historical costs are subject to a backward-looking 

assessment of value (e.g., signs of loan distress) 

combined with procyclical provisioning, which 

often coincide with a downturn of an economic 

cycle, adding to stresses.

On the other hand, FVA introduces more 

volatility in earnings and capital during the 

life of an asset or liability than historical cost 

accounting and incorporates forward-looking 

assessments.15 Gains and losses in fair valued 

instruments generally affect the income statement, and this increased volatility of FVA and 

resulting procyclical effects may create incentives for banks to restructure their balance 

sheets (e.g., lower loan originations, higher/

lower securitizations, introduce hedging, etc.).16

Nevertheless, higher FV volatility, per se, would 

not necessarily be a problem if market participants are well informed and could correctly 

interpret the information provided in the fi nancial statements. In this sense, increased volatility 

may be thought of as part of the process of fair 

valuing fi nancial instruments, and a refl ection of 

genuine economic volatility, not as a cause itself 

of procyclicality.

However, in some cases, the symmetrical treatment within FVA can produce seemingly misleading results. For example, the use of FVA on 

15IFRS and U.S. GAAP accounting standards—and 

FVA is no exception—are applicable to reporting entities 

irrespective of their size or systemic importance. 16One intention of the FVO in both accounting frameworks is to enable entities to reduce accounting mismatches by applying FV on matching assets and liabilities.

–20

0

20

40

60

80

Goldman Sachs

Bear Stearns

Bank of America

Morgan Stanley

JPMorgan Chase & Co.

Citigroup

Lehman Brothers

Merrill Lynch

–80

–60

–40

–20

0

20

40

60

80

Goldman Sachs

Bear Stearns

Bank of America

Morgan Stanley

JPMorgan Chase & Co.

Citigroup

Lehman Brothers

Merrill Lynch

–80

–60

–40

–20

0

20

40

60

80

Goldman Sachs

Bear Stearns

Bank of America

Morgan Stanley

JPMorgan Chase & Co.

Citigroup

Lehman Brothers

Merrill Lynch

Level 3 Assets

Level 1 Assets

Level 2 Assets

Source: Quarterly reports.

Note: Level 1 assets use quoted prices for identical assets. Level 2 assets use a 

more illiquid market where prices are observable for similar products, or for the 

same product but at different dates. Level 3 defines the absence of a material 

observable input used in the item’s valuation. Citigroup, JPMorgan Chase & Co., 

Lehman Brothers, and Merrill Lynch reported fourth quarter at end-December, while 

the other banks concluded their fourth quarter at end-November.

Figure 3.1. Selected U.S.-Based Financial 

Institutions: Change in Level 1, 2, and 3 Assets

(Percent change; 2007:Q1–2008:Q1)

Percent change between Q1 and Q2

Percent change between Q3 and Q4

Percent change between Q2 and Q3

Percent change between 2007:Q4 and 2008:Q1
115

a bank’s own debt, where the price of the bank’s 

bonds and notes falls due to a decline in its own 

creditworthiness, will result in a gain that must 

be recognized in the bank’s fi nancial statements, 

equal to the difference between the original 

value of the debt and its market price. As counter intuitive as this situation may be, it is still a 

faithful representation of FV and is a signal to 

supervisors or other users of fi nancial statements to have appropriate tools (e.g., prudential 

fi lters)17 for understanding the implications of 

FVA and the impact on regulatory capital.

As valuation moves from market prices to 

mark-to-model valuation, FVA poses reliability 

challenges to which markets, particularly under 

distress, are sensitive.18 These “subjective” 

aspects of FVA may compound market illiquidity or price spirals if they increase uncertainty 

around valuations. In both the United States 

and European Union, fi nancial institutions’ 

balance sheets are heavily represented in 

Level 2 instruments, a possible indication that 

fi nancial institutions are biased toward using 

Level 2 methods due to their fl exibility, as well 

as a desire to avoid “obscure” Level 3 assets 

and liabilities (Figures 3.1 and 3.2). Falling 

valuations can activate certain management 

decision rules that trigger the liquidation of 

certain assets or portfolios, adding additional 

stress. Hence, there is a need for good risk 

management practices to be consistent with FV 

mark-to-model valuations. Clear and transparent quantitative and qualitative notes to the 

fi nancial statements regarding the nature of 

the changes and methodologies could enhance 

reliability of mark-to-model valuations.

17Bank supervisors use prudential fi lters as a tool to 

adjust changes in the (accounting) equity of a bank due 

to the application of the accounting framework, so that 

the quality of regulatory capital may be properly assessed. 

For example, when the gains that result from a deterioration in a bank’s own creditworthiness (fair valued 

liability) are included in a bank’s prudential own funds, 

they must be “fi ltered out” by the supervisor in order to 

determine the true amount of regulatory own funds. 18In principle, valuations are thus better aligned with 

the prevailing mark-to-model techniques used in risk 

management.

European financial Total

institutions

U.S. financial 

institutions

0

10

20

30

40

50

60

70

80

90

100

Source: Fitch Ratings.

1Aggregates asset and liability positions.

Figure 3.2. Aggregate Fair Value Hierarchy, 

end-20071

(In percent)

Level 1 valuations

Level 2 valuations

Level 3 valuations

22 28 25

72 67 69

6 56

FAIR VALUE ACCOUNTING THROUGH THE BUSINESS CYCLE
CHAPTER 3 FAIR VALUE ACCOUNTING AND PROCYCLICALITY

116

Although more volatile, FVA could play a 

role by partially mitigating the turbulence if 

warning signals are heeded, thereby helping 

markets to recover earlier before damaging 

self-fulfi lling downturns worsen. FVA that 

captures and refl ects current market condiA key improvement in the Basel II framework 

is its enhanced risk sensitivity. Yet this very 

feature is associated with the unintended effect 

of heightening its procyclical propensity. Basel II 

recognizes possible business cycle effects and 

how they should be addressed in both Pillar 1 

(minimum capital requirements) and Pillar 2 

(supervisory review process) of the framework. 

If Basel II is properly implemented, then greater 

risk sensitivity can lead banks to restore capital 

earlier in a cyclical downturn, thus preventing 

a buildup of required capital when it could 

amplify the cycle.

Under Basel II’s Standardized Approach, risk 

weights are based on external ratings constructed to see through the cycle, so that cyclical 

effects are muted. It is in the internal-ratingsbased (IRB) approaches that deterioration in 

credit risk feeds more directly into the capital 

requirements. The three main risk components in the IRB approaches (e.g., probability 

of default, loss given default, and exposure at 

default) are themselves infl uenced by cyclical movements and may give rise to a cyclical 

impact on banks’ capital requirements.

Basel II includes mitigating measures to 

address these concerns. Although Pillar 1 does 

not mandate the use of through-the-cycle models, it promotes estimates of risk components 

based on observations that “ideally cover at least 

one economic cycle,” and whose validation must 

be based on data histories covering one or more 

complete business cycles. It also requires the 

use of the so-called downturn loss given default 

which factors in the risk that recoveries will fall 

as defaults increase in downturns. Sound stress 

testing processes must be in place that involve 

scenarios based on economic or industry downturns and include specifi c credit risk stress tests 

that take into account a mild recession to assess 

the effects on the bank’s risk parameters.

Pillar 2 places the onus on both banks and 

supervisors to assess business cycle risk and take 

appropriate measures to deal with it. Banks are 

required to be “mindful of the stage of the business cycle in which they are operating” in their 

internal assessment of capital adequacy, perform 

forward-looking stress tests, address capital volatility in their capital allocation, and defi ne strategic plans for raising capital. In turn, encouraging 

forward-looking credit risk assessments or higher 

provisioning for loan losses (that consider losses 

over the loans’ whole life) is left to national 

supervisors.1 Thus, where Pillar 1 does not 

adequately capture business cycle effects, supervisors should take remedial action under Pillar 2, 

including through additional capital buffers. 

The capital disclosures required by Pillar 3 

may assist markets and stakeholders in exercising pressure on the banks to maintain their 

capital levels throughout the full business cycle.

In its recent report, “Enhancing Market and 

Institutional Resilience,” the Financial Stability Forum called for the Basel Committee to 

develop Pillar 2 guidance on stress testing 

practices and their use in assessing capital 

adequacy through the cycle; examine the balance between risk sensitivity and cyclicality; and 

update the risk parameters and the calibration 

of the framework, if needed (Financial Stability Forum, 2008). In response, the committee 

is establishing a data collection framework to 

monitor Basel II’s impact on the level and cyclicality of prudential capital requirements over 

time across member countries. The committee 

is expected to use these results to further calibrate the capital adequacy framework. 

1The U.S. Financial Accounting Standards Board 

has a project under way to address provisioning and 

related credit risk disclosures.

Box 3.3. Dealing with Procyclicality in the Basel II Framework

Note: Aditya Narain and Alicia Novoa prepared this 

box.
117

tions on a timely basis could lead to a better 

identifi cation of a bank’s risk profi le, if better 

information is provided. An earlier warning 

that can prompt corrective action by shareholders, management, and supervisors allows for 

a timelier assessment of the impact of banks’ 

risky actions on regulatory capital and fi nancial 

stability. Moreover, since FVA should lead to 

earlier recognition of bank losses, it could have 

a less protracted impact on the economy than, 

for example, loan portfolios whose provisions 

for losses are usually made when the economy 

is already weak. Raising new capital at an earlier stage might enable banks to retain writtendown assets or other assets originally not for 

sale on their balance sheets and, thus, to avoid 

asset price spirals.

On the prudential front, the negative impact 

of vastly lower valuations stemming from 

recent market conditions raises questions as 

to whether increases in regulatory capital may 

be needed for complex structured products, 

off-balance-sheet entities (OBSEs), or other 

risks. Guidance from Pillar 2 of Basel II could 

encourage banks to put greater attention into 

FV during periods of falling or rising asset 

prices, so that they may better control for the 

procyclical aspects of FVA (Box 3.3). Pillar 3 

disclosures could improve the transparency of 

valuations, methodologies, and uncertainties. 

Nevertheless, FVA can serve as an early warning system for supervisors to pursue closer 

scrutiny of a bank’s risk profi le, risk-bearing 

capacity, and risk management practices.

Modeling Fair Value Accounting Through 

the Business Cycle Using Simulations

Using model simulations, this section 

assesses the effects that changes in fi nancial 

instruments’ fair value have on the balance 

sheet of three types of large, internationally 

active fi nancial institutions—U.S. commercial 

banks, U.S. investment banks, and European 

banks—as well as more retail-oriented U.S. 

and EU banks. The balance sheets of a sample 

of representative institutions were taken as 

of end-2006 to construct prototypical institutions (Table 3.1). The simulations illustrate the 

impact of changes in valuations and, ultimately, 

on these representative banks’ equity capital. 

The section also explores possible alternatives 

related to FVA and its current application—full 

fair value, smoothing techniques, circuit breakers, and reclassifi cations—that aim to reduce 

its volatility on balance sheets (Box 3.4).

The fi rst simulation serves as the baseline 

for subsequent scenarios and consists of tracking the evolution of the banks’ balance sheets 

throughout a normal business cycle.19 Four 

scenarios are applied to the normal cycle with 

the goal of gauging the degree to which fair 

valuations amplify fl uctuations in balance sheet 

components, and more notably, on accounting 

capital.20 The sources of increased cyclicality 

are (1) a bust-boom cycle in equity valuations; 

(2) a bust-boom cycle in the housing market; 

(3) a widening and then contraction of banks’ 

funding spreads; and (4) a bust-boom cycle 

in debt securities’ valuations, all of which are 

calibrated using the most current cyclical movements (Table 3.2). As noted by Fitch Ratings 

(2008a, 2008b), among others, the sensitivities 

of FV measurements to changes in signifi cant 

assumptions are particularly important when 

valuations are model-based and/or markets 

become highly illiquid. Specifi cally, the method 

by which an institution chooses to value 

components of its balance sheet constitutes 

one of the three main transmission channels 

through which FVA introduces volatility into 

the balance sheet (Barth, 2004). The simulations help underscore this point and provide 

a sense of the magnitude of these effects. In 

addition, the simulations illustrate how a sudden tightening in banks’ funding conditions, 

or changes in the liquidity conditions in securi19Annex 3.1 discusses the data and underlying assumptions for the simulations. 20Enria and others (2004) examine the impact of 

several one-off shocks on the balance sheet of a representative European bank under alternative accounting 

frameworks.

MODELING FAIR VALUE ACCOUNTING THROUGH THE BUSINESS CYCLE USING SIMULATIONS
CHAPTER 3 FAIR VALUE ACCOUNTING AND PROCYCLICALITY

118

ties markets, exacerbate cyclical fl uctuations in 

balance sheets.

It is worth noting that from a cash fl ow perspective, the changes in assumptions underlying 

valuations (such as those made in the simulations below) may not necessarily be of future 

consequence to the reporting institution, as 

those gains and losses have not been realized 

and may never be. In this sense, the ensuing 

changes in regulatory capital produced by the 

updated valuations are somewhat artifi cial. 

With these considerations in mind, the simulation results should be interpreted as a simple 

exercise to gauge how changes in the underlying valuation parameters in the presence of 

FVA may lead to substantial fl uctuations in 

banks’ equity.

Simulation Results

The simulations highlight three key points 

regarding FVA and its potential regulatory 

and fi nancial stability implications: (1) strong 

capital buffers are crucial to withstand business cycle fl uctuations in balance sheet components, especially when FV is applied more 

Table 3.1. Balance Sheet of Representative U.S. and European Financial Institutions

(In percent of total assets, as of December 31, 2006) 

U.S. 

Commercial 

Banks

U.S. 

Investment 

Banks

European 

Banks

U.S. 

Retail-Oriented 

Banks

European 

Retail-Oriented 

Banks

Financial assets 

Securities 

Debt securities 21.82 27.85 15.71 14.96 17.72

Trading book FV1 21.82 27.85 14.98 5.09 16.59

Banking book2 — — 0.73 9.87 1.13

Shares 6.73 7.50 6.55 0.64 2.96

Trading book FV1 6.73 7.50 6.32 0.47 2.96

Banking book2 — — 0.23 0.17 —

Derivatives (trading) 2.67 5.28 14.71 1.19 4.44

Interest rate swaps 1.48 1.87 7.76 . . . . . .

Other derivatives 1.20 3.41 6.96 . . . . . .

Loans 

Corporate/consumer 10.11 5.63 23.77 23.00 25.84

Short-term (fixed rate) <1 year FV1 4.72 2.82 11.88 6.84 12.92

Medium-term (>1 year and <5 year) 3.66 2.82 3.57 10.97 3.88

Fixed rate FV1 0.72 1.41 1.78 1.71 1.94

Variable rate FV1 2.94 1.41 1.78 9.26 1.94

Long-term (>5 year) 1.73 n.a. 8.32 5.19 9.04

Fixed rate FV1 0.46 n.a. 4.16 2.03 4.52

Variable rate FV1 1.27 n.a. 4.16 3.16 4.52

Mortgages 16.51 n.a. 6.54 37.44 26.43

Fixed rate FV1 12.83 n.a. 1.40 29.09 10.78

Variable rate FV1 3.68 n.a. 5.14 8.35 15.65

Other assets 28.60 43.27 20.93 17.34 5.41

Financial liabilities 

Debt securities/equity (trading) FV1 4.68 8.68 12.77 0.01 12.71

Derivatives (trading) 3.20 5.49 15.34 0.96 3.47

Interest rate swaps 2.09 1.73 7.84 . . . . . .

Other derivatives 1.10 3.76 7.49 . . . . . .

Short- and long-term financial liabilities/bonds FV1 18.25 27.21 10.35 19.56 18.97

Other liabilities 65.26 51.52 56.23 69.72 61.16

Of which: deposits and interbank borrowing 42.44 3.72 24.88 60.12 56.72

Net equity3 7.65 3.71 2.86 9.75 4.36

Sources: Annual reports; the U.S. Securities and Exchange Commission’s 10-K filings; and IMF staff estimates.

Note: Columns may not add to 100 percent as some balance sheet items are not displayed in the table.

1Valued at fair value.

2Annual statements showed negligible or zero holdings for the sampled banks. 

3Net equity in percent of total (non-risk-weighted) assets.
119

MODELING FAIR VALUE ACCOUNTING THROUGH THE BUSINESS CYCLE USING SIMULATIONS

The procyclicality of fair value accounting has 

prompted the search for options that allow fi nancial 

institutions to cope with situations of market turmoil. 

Alternatives range from considering a wider selection 

of “observable” prices or inputs to a change in the 

accounting treatment of fi nancial instruments.

Consensus Pricing Services

Consensus pricing services, often independent 

brokers and agencies, can provide price quotes for 

complex or illiquid fi nancial instruments, often 

using prices based on their own sales of relevant 

instruments that allow them to observe price 

behavior and market-test their estimates. Through 

this approach, illiquid products could obtain a 

Level 2 price, potentially limiting valuation uncertainty and underpricing in downturns. However, 

diffi culties may remain if there is a wide dispersion 

of values or if banks contend that values do not 

refl ect market conditions.

Valuation Adjustments

Banks could estimate the “uncertainty” surrounding the price of certain assets and make a valuation 

adjustment to the carrying value of an instrument 

disclosed in the fi nancial statements. Valuation 

adjustments would allow banks to work with less 

perfect prices that are corrected to refl ect current 

market conditions. These estimates of “uncertainty” 

might incorporate the liquidity of inputs, counterparty risk, or any market reaction likely to occur 

when the bank’s position is realized. Valuation 

adjustments could improve fair value measurements 

and discipline in reporting, yet they need close 

monitoring to ensure that this practice does not 

evolve into management “cherry picking,” providing a means to evade a certain accounting fair value 

level classifi cation, or improving the balance sheet.

Reclassifi cations

The transfer of assets from available-for-sale or 

trading to the held-to-maturity category could 

avoid the volatility resulting from valuation changes 

amid a downward spiral. From an accounting 

perspective, reclassifi cations could be penalized 

by not allowing banks to revert to the trading 

book when markets rebound. From a prudential 

standpoint, deteriorated held-to-maturity assets 

would require higher regulatory capital. Allowing reclassifi cations—particularly if not fully 

disclosed—may postpone the weaknesses of the balance sheets, and promote cherry-picking elements 

of the accounting framework. 

Full Fair Value Accounting

Recognizing the signifi cant challenges full fair 

value accounting would pose, a longer-term alternative would be to adopt a full fair value model 

for all fi nancial assets and liabilities, irrespective of 

an entity’s intention of holding them. One single 

fair value principle, with some limited exceptions, 

would reduce the complexity of fi nancial instruments reporting, balance sheet window dressing, 

and cherry picking, and allow for more transparent representations of the fi nancial condition of an 

entity. It could improve the comparability of fi nancial information across balance sheets and enhance 

market discipline, but it would pose challenges for 

implementation, modeling capabilities, and auditing 

estimates.

Internal Decision Rules

Regulators could require banks to have internal 

decision rules based on fair value that require careful review of all the implications of changing fair 

value and the specifi c occasions when such changes 

could trigger management decisions, so that these 

decisions do not adversely affect regulatory capital 

or accentuate downward price spirals. 

Smoothing Techniques and Circuit Breakers

Smoothing asset prices and circuit breakers 

could be used as price adjusters to fair value 

accounting to reduce excessive price volatility in 

the balance sheet. However, both reduce the information content of fi nancial statements by suspending equity at an artifi cially higher-than-fair-value 

calculated level. 

The simulation exercises examine the following 

alternatives: reclassifi cations, full fair value accounting, smoothing techniques, and circuit breakers.

Box 3.4. Options Surrounding the Application of Fair Value Accounting to Mitigate Procyclicality

Note: Alicia Novoa and Jodi Scarlata prepared this box.
CHAPTER 3 FAIR VALUE ACCOUNTING AND PROCYCLICALITY

120

extensively to assets than liabilities; (2) fair 

valuing an expanded set of liabilities acts to 

dampen the overall procyclicality of the balance 

sheet; and (3) when combined with additional 

liquidity shortages in fi nancial markets, the 

FVA framework magnifi es the cyclical volatility 

of capital.

The Effects of Economic Shocks Under Full 

Fair Value

In the normal cycle, fair valuing both sides 

of the balance sheet produces fl uctuations that 

are mild compared with the bust-boom scenarios seen in Figure 3.3, an intuitive result.21

However, it is worth noting that, in the case of 

the representative U.S. investment bank, equity 

21The results are presented in terms of the evolution 

of banks’ normalized equity through the cycle—that is, at 

each point in the cycle, banks’ equity is divided by their 

initial level of equity (i.e., at end-2006). 

behaves in a countercyclical manner due to 

the strong effect of fair valuing the liabilities.22

Under full fair value (FFV), the value of the 

bank’s liabilities declines as economic activity 

weakens and probabilities of default (PDs) rise, 

mitigating the decline in equity. This effect 

arises because of the asset/liability structure of 

the investment banks’ balance sheet, which consists of a large proportion of fi nancial liabilities 

that are fair valued. Liabilities at FFV, as is done 

by some U.S. investment banks, can introduce 

an element of countercyclicality by serving as 

an implicit counterbalancing hedge to the fair 

22Chapter 4 of the October 2008 World Economic Outlook

(IMF, 2008a) examines the procyclicality of leverage 

ratios of U.S. investment banks, fi nding their extreme 

variation across the cycle. Note that this is consistent with 

the scenario conducted later in this chapter where funding spreads vary through the cycle, producing the same 

procyclicality found in IMF (2008a).

Table 3.2. Parameter Values for Each Simulation

(In percent)

Business Cycle 

Trend Points

Business Cycle 

Trough Points

Business Cycle 

Peak Points

Normal cycle PD for all loans and securities 1.18 1.40 0.73

LGD for mortgages 20.30 20.30 20.30

LGD for loans1 and securities 46.20 46.20 46.20

Stock market index 100.00 100.00 100.00

Stock market cycle PD for all loans and securities 1.18 1.40 0.73

LGD for mortgages 20.30 20.30 20.30

LGD for loans1 and securities 46.20 46.20 46.20

Stock market index 100.00 80.00 120.00

Real estate market cycle PD for mortgages 1.18 5.29 0.73

PD for loans1 and securities 1.18 1.40 0.73

LGD for mortgages 20.30 30.50 20.30

LGD for loans1 and securities 46.20 46.20 46.20

Stock market index 100.00 100.00 100.00

Funding spreads cycle PD for all loans and securities 1.18 1.40 0.73

LGD for mortgages 20.30 20.30 20.30

LGD for loans1 and securities 46.20 46.20 46.20

Stock market index 100.00 100.00 100.00

Change in spreads (in basis points) 0.00 58.66 –58.66

Debt securities valuation cycle PD for all loans and securities 1.18 1.40 0.73

LGD for mortgages 20.30 20.30 20.30

LGD for loans1 46.20 46.20 46.20

Stock market index 100.00 100.00 100.00

LGD for debt securities 46.20 67.30 25.10

Sources: IMF staff estimates; Nickell and others (2000); and BCBS (2006a).

Note: PD = probability of default; LGD = loss given default.

1 Loans excluding mortgages.
121

valuation of assets.23 This phenomenon has 

raised related concerns by some market observers who regard with unease a bank’s ability to 

record revaluation gains as its own creditworthiness weakens and the price of its own debt 

declines.24 The presence of gains that are a 

construct of the particular technique chosen for 

valuation signals the need for clear disclosure 

of underlying assumptions to avoid misrepresentation of fi nancial statements.

In the bust-boom cycles in equity valuations 

and in the housing market, the European banks 

exhibit the largest deviations from trend. For 

the equity price shock, despite roughly comparable magnitudes of equity shares across the 

portfolios of the three groups of banks shown 

in Table 3.1, a combination of two effects are at 

work. First, there is the countercyclical effect of 

the relatively greater proportion of FV liabilities 

for U.S. investment banks. Second, European 

banks have a lower capital base and thus the 

relative size of valuation changes to normalized 

equity capital is larger. In the housing market 

scenario, the European banks exhibit wider fl uctuations, despite the fact that the U.S. commercial banks hold a much larger fraction—about 

two-and-half times greater—of their loan portfolio in mortgages. In both scenarios, the lower 

capital base of the European banks vis-à-vis the 

U.S. commercial banks is a key element. Similar 

results in terms of capital-to-assets ratios are presented in Table 3.3, but refl ect a less dramatic 

impact on European banks.25 More generally, a 

bank’s balance sheet would evolve through the 

cycle—contracting in downturns and expanding 

in upturns—such that it would restore a bank’s 

23Note, however, that this result refl ects only one 

element of countercyclical forces, as “other liabilities” 

represent about 50 percent of the balance sheet and can 

potentially introduce additional countercyclicality. 24See Guerrera and White (2008). Additionally, Barth, 

Hodder, and Stubben (2008) suggest that these counterintuitive effects are attributable primarily to incomplete 

recognition of contemporaneous changes in asset values. 25Some portion of the lower equity position in European banks may stem from differences in IFRS versus 

U.S. GAAP accounting treatments (Citigroup, 2008; and 

Financial Times, 2008).

MODELING FAIR VALUE ACCOUNTING THROUGH THE BUSINESS CYCLE USING SIMULATIONS

94

96

98

100

102

104

106

Business 

cycle trend

Normal Cycle

Business 

cycle trend

Business 

cycle trend

Business

cycle trough

Business 

cycle peak

40

60

80

100

120

140

160

Business 

cycle trend

Bust-Boom Cycle in Share Prices

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

40

60

80

100

120

140

160

Business 

cycle trend

Bust-Boom Cycle in Real Estate

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

105.1

96.9

101.5

97.6

149.3

121.4

105.1

96.9

66.7

101.5

53.5

80.7

Source: IMF staff estimates.

Figure 3.3. Simulation of Full Fair Value

U.S. commercial banks

U.S. investment banks

European banks
CHAPTER 3 FAIR VALUE ACCOUNTING AND PROCYCLICALITY

122

capital adequacy ratio, a result that is not easily 

testable in this simple framework.

The recent events have raised two interesting scenarios regarding increased funding 

costs and a downward spiral in the valuation 

of debt securities. Sudden changes in a bank’s 

ability to obtain funding largely exacerbate 

the fl uctuations in balance sheets (Figure 3.4). 

This exercise underscores the signifi cance of 

general liquidity conditions in driving balance 

sheet fl uctuations and how the FVA framework 

recognizes these changes promptly. Interestingly, the countercyclical behavior observed in 

the U.S. investment banks’ equity disappears. 

In fact, the U.S. investment banks are hardest 

hit by both the tightening of funding conditions and the distress in securities markets. This 

should not be surprising given that, contrary 

to the U.S. commercial banks and European 

banks, the U.S. investment banks do not rely on 

deposits—which are not fair valued—to fund 

their activities. Note, too, that these simulations 

do not account for structured credit products or 

the OBSEs that were so central to much of the 

2007–08 turbulence and would likely increase 

the procyclicality of the balance sheets. Such 

a deterioration of banks’ balance sheets could 

affect market confi dence and overall share 

prices, which in turn could generate additional 

volatility in banks’ balance sheets.

The results presented thus far have focused 

on the balance sheets of large internationally active institutions. Comparatively, the 

more retail-oriented banks tend to have larger 

loan and mortgage portfolios and rely more 

extensively on deposits for their funding.26

To illustrate the effects of these two structural characteristics, simulations comprising 

the cycle in funding spreads and the bustboom cycle in real estate were conducted for 

all banks, excluding the representative U.S. 

investment banks. The results corroborate 

the supposition that the more retail-oriented 

institutions are less vulnerable to changes in 

funding conditions than their internationally 

active counterparts (Figure 3.5). Conversely, 

the retail-oriented banks are harder hit by a 

bust in the housing market than the internationally active banks.

The Effects of Mixed Attributes Models

Using two versions of the mixed attributes 

model, this exercise shows how the degree 

to which fi nancial institutions apply FV to 

their assets and liabilities affects the extent to 

which there can be offsetting volatility effects. 

26Note, however, that retail-oriented European banks 

also have a larger fraction of debt securities and fi nancial 

liabilities than the larger European banks.

Table 3.3. Equity-to-Assets Ratio Through the Business Cycle

(In percent)

Baseline Period 1 Period 2 Period 3 Period 4

Business Cycle 

Trend

Business Cycle 

Trough

Business Cycle 

Trend

Business Cycle 

Peak

Business Cycle 

Trend

U.S. Commercial Banks

Normal cycle 7.6 7.5 7.6 7.9 7.6

Bust-boom cycle in share prices 7.6 6.3 7.3 9.1 7.6

Bust-boom cycle in real estate 7.6 5.4 7.6 7.9 7.6

U.S. Investment Banks

Normal cycle 3.7 3.8 3.7 3.6 3.7

Bust-boom cycle in share prices 3.7 2.3 3.4 5.0 3.7

Bust-boom cycle in real estate 3.7 3.8 3.7 3.6 3.7

European Banks

Normal cycle 2.9 2.8 2.9 3.0 2.9

Bust-boom cycle in share prices 2.9 1.6 2.6 4.2 2.9

Bust-boom cycle in real estate 2.9 1.9 2.9 3.0 2.9

Source: IMF staff estimates.
123

Table 3.4 shows that fi nancial institutions apply 

FV differentially. But what is not shown in the 

table is the extent to which the vast majority of 

banks continue to use amortized cost to value 

their loan portfolio. Thus, for the purposes of 

the simulations, two variations of the model are 

considered: (1) “fi nancial liabilities and bonds” 

are valued at amortized cost throughout the 

cycle; and (2) “loans” and “mortgages” are also 

valued at amortized cost.27

Figure 3.6 underscores the idea that the 

asymmetric application of a mixed attributes 

model, where FV is applied more extensively to 

assets than liabilities, has the effect of mechanically increasing the procyclical behavior of the 

balance sheet. In other words, the fl uctuations 

in equity—for all types of institutions and for 

all the scenarios considered—are larger when 

a smaller fraction of liabilities are fair valued 

(compare with Figure 3.3., the results under 

FFV). Thus, the benefi ts intended by the introduction of the FVO, which were to reduce the 

accounting volatility of the mixed attributes 

27In effect, valuing these instruments at amortized cost 

would produce comparable results to being classifi ed as 

HTM.

MODELING FAIR VALUE ACCOUNTING THROUGH THE BUSINESS CYCLE USING SIMULATIONS

Table 3.4. Application of Fair Value by U.S. and 

European Banks, 2007

(In percent of total balance sheet )

Financial Institutions

Assets at 

Fair Value 

on a Recurring

Basis

Liabilities

at Fair Value 

on a Recurring

Basis

Return 

on 

Equity

JPMorgan Chase & Co. 41 16 12.86

Citigroup 39 22 3.08

Bank of America 27 6 10.77

Goldman Sachs 64 43 31.52

Lehman Brothers 42 22 20.89

Merrill Lynch 44 33 –25.37

Morgan Stanley 44 27 9.75

Credit Suisse 64 39 17.88

Société Générale 46 32 3.36

Royal Bank of Scotland 45 31 15.13

BNP Paribas 65 55 16.98

Deutsche Bank 75 48 18.55

UBS 54 35 –10.28

HSBC 40 25 16.18

Barclays 52 39 20.50

Crédit Agricole 44 24 10.67

Sources: Fitch Ratings (2006b); and Bloomberg L.P. 55

65

75

85

95

105

115

125

135

145

Business 

cycle trend

U.S. Commercial Banks

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

55

65

75

85

95

105

115

125

135

145

Business 

cycle trend

U.S. Investment Banks

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

55

65

75

85

95

105

115

125

135

145

Business 

cycle trend

European Banks

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

117.4

103.8

85.0

98.3

140.9

96.9

58.8

101.5

62.0

97.6

141.9

105.1

Source: IMF staff estimates.

Note: LGD = loss given default.

Figure 3.4. Simulation of Full Fair Value: Changes in 

Funding Conditions and Financial Market Distress

Normal cycle

Cycle in funding spreads

Cycle in debt securities’ LGDs
CHAPTER 3 FAIR VALUE ACCOUNTING AND PROCYCLICALITY

124

methods and the need for FV hedge accounting 

techniques, are lessened. This could be interpreted as supporting an expanded application 

of FV, rather than a reduced application, 

however, this conclusion should be interpreted 

cautiously. Bear in mind that the application of 

FV to banks’ own debt may produce revaluation 

gains as the value of liabilities declines on their 

balance sheets. While this logically has the effect 

of reducing procyclicality, the lower value of liabilities should lead a user of fi nancial statements 

to consider the reason for the lower valuation 

and thus the future viability of the bank, as this 

information cannot be considered a positive factor even though procyclicality is reduced. Thus, 

the reasons for variability in own-debt valuations 

should be properly disclosed.

This simulation highlights that the greater the 

imbalance of the mixed attributes application to 

assets and liabilities, the greater is the accounting volatility. When fi nancial instruments are 

valued at a historical cost that does not represent the current market conditions, an accurate 

picture of a bank’s equity becomes blurred and 

the informational content of the accounting 

statement weakens. Historical costs have low 

information content for investors who rely on 

current fi nancial fi gures as a basis for investment decisions. For a regulator, making an 

accurate assessment of the health of a bank, 

and formulating the appropriate regulatory 

response, becomes increasingly diffi cult.

The second simulation (not shown), where 

fi nancial liabilities plus loans and mortgages are 

all valued at amortized cost, showed that the 

range of fl uctuations diminished further than 

in the above simulation. Thus, although the 

wider application of the mixed attributes model 

can reduce fl uctuations in the balance sheet, 

the cost comes in the form of a further reduction in up-to-date information.

Smoothing Techniques and Circuit Breakers on 

Reporting Prices

Simulations using proposed alternatives 

to smooth balance sheet volatility show that 

a smoothing/averaging technique for falling 

94

96

98

100

102

104

106

108

110

Business 

cycle trend

Normal Cycle

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

40

60

80

100

120

140

160

Business 

cycle trend

Cycle in Funding Spreads

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

10

40

70

100

130

160

Business 

cycle trend

Bust-Boom Cycle in Real Estate

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

Source: IMF staff estimates.

Figure 3.5. Simulation of Full Fair Value: 

International versus Retail-Oriented Banks

U.S. commercial banks

Retail-oriented U.S. banks

European banks

Retail-oriented European banks

105.1

103.8

96.5

98.3

62.0

88.9

141.9

114.0

107.8

103.8

14.4

69.1
125

asset prices blurs the bank’s capital position in 

magnitudes varying by the amount and period 

over which the averages are calculated. Smoothing 

techniques and other impediments to allowing 

valuations to adjust—so-called “circuit breakers”—make it harder for regulators and investors to accurately assess the fi nancial position of 

a bank, as they hide the economic volatility that 

should be accounted for in the balance sheet.

To illustrate, two smoothing simulations were 

conducted, each averaging share prices over 

different lengths. The fi rst simulation uses a 

two-period average, whereas the second simulation is extended to three periods. As shown 

in Figure 3.7, the longer the averaging length, 

not surprisingly, the smoother is the path of 

the balance sheet. Notably, the application of a 

smoothing technique might reduce the occasion 

for “forced” sales, as it could avoid sale triggers 

in some cases. Accordingly, this could lessen a 

downward price spiral in the market for a fi nancial product by avoiding forced sales, but comes 

at the expense of a reduction in the informational content of fi nancial statements and potentially lengthening the resolution period.

Similarly, concepts such as a circuit breaker, 

whereby rules stem the recognition of a fall in 

asset prices, mask the underlying equity position 

by suspending equity at an artifi cially higher 

level than under FV and, more generally, may 

hamper price discovery. However, in this case, 

the cycle may be extenuated even longer than 

with a smoothing technique because the circuit 

breaker can maintain the same value for a given 

period, while the smoothing is a rolling average 

that is updated during each period of the cycle. 

Additionally, this measure is asymmetrically 

applied, as the circuit breaker has generally 

been proposed for when valuations are falling. 

Even though not a preferred technique, for symmetry, one could apply circuit breakers during 

“bubble” periods to stop the artifi cial infl ation 

of equity. If not, asymmetric treatment of valuations may create perverse risk-taking incentives 

for managers as long as fi nancial institutions 

are able to benefi t from the upside in valuation 

while the downside would remain capped.

MODELING FAIR VALUE ACCOUNTING THROUGH THE BUSINESS CYCLE USING SIMULATIONS

90

95

100

105

110

115

Business 

cycle trend

Normal Cycle

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

40

60

80

100

120

140

160

Business 

cycle trend

Bust-Boom Cycle in Share Prices

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

40

60

80

100

120

140

Business 

cycle trend

Bust-Boom Cycle in Real Estate

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

Source: IMF staff estimates.

1Includes short- and long-term financial liabilities valued at amortized cost.

Figure 3.6. Simulation of Partial Fair Value1

U.S. commercial banks

U.S. investment banks

European banks

111.3

107.9

94.7

96.3

155.5

125.4

50.5

78.7

63.8

95.5

111.3

109.4
CHAPTER 3 FAIR VALUE ACCOUNTING AND PROCYCLICALITY

126

The Effects of a Changing Yield Curve

Yield curve effects are introduced to the 

baseline scenario to evaluate how the change in 

interest rates over the cycle affects the balance 

sheet.28 This chapter follows Keen (1989) and 

assumes the following stylized facts regarding 

the cyclical behavior of yield curves:29 (1) both 

short- and long-term rates tend to decline during business cycle downturns and rise during 

expansions; and (2) short-term rates tend to rise 

more relative to long-term rates during expansions (i.e., the yield curve fl attens) and fall more 

relative to long-term rates during recessions 

(i.e., the yield curve steepens) (Figure 3.8).30

The infl uence of interest rates tends to dominate the effect of the change in PDs, such that 

the interest rate effect dampens the magnitude 

of procyclical equity fl uctuations for the European banks, and even becomes countercyclical 

for the U.S. commercial banks (Figure 3.9). For 

the U.S. investment banks, the change in interest 

rates renders the evolution of equity procyclical, 

rather than countercyclical, as in the baseline 

simulation. This reversal in behavior is due to 

the fact that the U.S. investment banks have a 

slightly larger share of FV liabilities than assets 

being revalued when interest rates change.31 But 

this also highlights the European banks as an 

intermediate structure between the investment 

bank and retail bank characteristics. Regardless of the balance sheet structure, changes to 

28Although this simulation is subject to the Lucas critique in that bank behavior is assumed not to change in 

response to policy adjustments, it provides some insights 

into the interaction between FVA and interest rates. 29See also Piazzesi and Schneider (2006).

30Interestingly, the addition of changes in the yield 

curve counteracts the effect of the evolution of PDs. The 

drop in the yield curve in the downturn results in higher 

valuations and thus counterbalances the downward effect 

of the PDs, while the positive effect on valuations stemming from lower PDs is counterbalanced by a higher 

yield curve in the upturn. 31This simulation abstracts from the effect of revalu ing 

interest rate swaps. Unfortunately, it was not possi ble to 

obtain a suffi ciently complete and consistent data set 

on these instruments to include them in the simulation. 

Nevertheless, preliminary results using available data on 

interest rate swaps showed similar qualitative results.

70

80

90

100

110

120

130

Business 

cycle trend

U.S. Commercial Banks

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

40

60

80

100

120

140

160

Business 

cycle trend

U.S. Investment Banks

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

40

60

80

100

120

140

160

Business 

cycle trend

European Banks

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

Source: IMF staff estimates.

Figure 3.7. Simulation of Smoothing Techniques

Bust-boom cycle in share prices

Three-period average

Two-period average

Circuit breaker

121.4

102.3

80.7

89.5

61.0

81.2

137.3

93.6

149.3

101.5

53.5

75.5
127

interest rates and other monetary policy tools 

can dampen procyclical infl uences, suggesting 

countercyclical monetary policy could have the 

benefi cial outcome of also helping to counteract 

the effects of the asset valuation cycles on banks’ 

equity. Note, however, that these simulations do 

not allow the fi nancial institutions to respond 

to policy changes, and thus these results, while 

informative, should be taken with caution.

Conclusions and Policy 

Recommendations

The fi nancial turmoil that started in July 2007 

unveiled weaknesses in the application of some 

accounting standards32 and with the valuation 

and reporting of certain structured products. 

While these weaknesses may have contributed to 

the recent events, they also provide an opportunity to better understand them.

The chapter fi nds that, despite concerns 

about volatility and measurement diffi culties, FVA is the appropriate direction forward 

and can provide a measure that best refl ects a 

fi nancial institution’s current fi nancial condition, though various enhancements are needed 

to allow FVA to reinforce good risk management techniques and improved prudential 

norms. Nevertheless, the application of FVA 

makes more transparent the effects of economic 

volatility on balance sheets that, under certain 

risk management frameworks, could exacerbate cyclical movements in asset and liability 

values. Exaggerated profi ts in good times create 

the wrong incentives. Conversely, more uncertainty surrounding valuation in downturns may 

translate into overly tight credit conditions, and 

negatively affect growth at a time when credit 

expansion is most needed. This is not to say that 

alternative accounting frameworks such as historical cost accounting avoid such fl uctuations, 

but rather that FVA recognizes them as they 

develop. Regardless, accounting frameworks 

are not meant to address the market-wide or 

32Although the weaknesses are related more to issues of 

OBSEs, consolidation, and derecognition, than to FV.

CONCLUSIONS AND POLICY RECOMMENDATIONS

1 2 3 4 5 7 10 20 30

3.25

3.75

4.25

4.75

5.25

5.75

6.25

6.75

Figure 3.8. Yield Curves and Business Cycles

(In percent)

Trend

Trough

Peak

Maturities (in years)

Source: IMF staff calculations.
CHAPTER 3 FAIR VALUE ACCOUNTING AND PROCYCLICALITY

128

systemic outcomes of their application, as they 

are applied only to individual institutions. Nevertheless, much of the controversy surrounding 

FV stems more from the risk management and 

investment decision rules using FV outcomes, 

rather than the framework itself. The interaction 

of FV estimates with other decision rules should 

be delinked from specifi c covenants such as sales 

triggers, margin calls or additional collateral 

requirements during downturns, or compensation tied to short-term profi ts during upturns.

Overall, the simulations confi rm a number of issues in the ongoing FVA debate and 

underscore three key points regarding FVA 

and its potential regulatory and fi nancial 

stability implications. First, strong capital 

buffers and provisions make an important 

contribution to withstanding business cycle fl uctuations in balance sheets, especially when FVA 

is applied more extensively to assets than liabilities. Second, when combined with additional 

liquidity shortages in fi nancial markets, the FVA 

framework magnifi es the cyclical volatility of 

capital. Third, fair valuing an expanded set of 

liabilities acts to dampen the overall procyclicality of the balance sheet. However, the latter may 

also give rise to the counterintuitive outcome of 

producing gains when the valuation of liabilities 

worsens. This is of particular concern when a 

deterioration in a bank’s own creditworthiness, 

and the subsequent decline in value of own 

debt, results in profi ts and a false sense of 

improvement in the bank’s equity position.

Proposals for alternative accounting methods, 

such as historical cost or simplistic mechanisms 

to smooth valuation effects on bank balance 

sheets, reduce the transparency of a fi nancial 

institution’s health by blurring the underlying capital position. While these techniques 

may avoid sale triggers incorporated in risk 

management covenants and limit downward 

price spirals, the measurement variance that 

they introduce can increase uncertainties 

regarding valuations. The loss of transparency 

makes it more diffi cult for all users of fi nancial 

statements—for example, for supervisors to 

conduct adequate oversight of fi nancial institu90

95

100

105

110

Business 

cycle trend

Normal Cycle

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

40

60

80

100

120

140

160

Business 

cycle trend

Bust-Boom Cycle in Share Prices

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

40

60

80

100

120

140

160

Business 

cycle trend

Bust-Boom Cycle in Real Estate

Business 

cycle trend

Business 

cycle trend

Business 

cycle trough

Business 

cycle peak

Source: IMF staff estimates.

Figure 3.9. Simulation of Full Fair Value with Upward 

Sloping Yield Curve

U.S. commercial banks

European banks

U.S. investment banks

103.9

98.4

95.8

103.0

148.1

116.0

54.2

85.4

103.9

98.4

70.3

95.8
129

tions and recommend appropriate regulatory 

measures to deal with prudential concerns, and 

for investors who will demand increased risk 

premia in the face of uncertainty.

Policy Proposals

Most proposals should aim to deal with the 

use of FV estimates to lessen the volatility that 

FVA can introduce to the balance sheet. Assessments of provisioning and capital adequacy 

should take better account of the business cycle. 

Improved transparency can be achieved not 

necessarily by more disclosures, but better disclosures. The proposal this year by the Financial 

Stability Forum (2008) on disclosure practices in 

fi nancial reports provides recommendations in 

this direction.

The simulations support the relevance of 

establishing a capital buffer that looks through 

the cycle, augmenting the capital position during boom cycles to withstand the burden on 

capital that stems from economic downturns. 

Although a partial analysis, the simulations show 

that FVA can introduce fi nancial statement volatility and provide a fi rst indication that buffers 

of around 2 to 4 percent of additional capital 

would help banks weather normal cyclical downturns, whereas higher buffers—on the order of 

30 to 40 percent extra capital—would be needed 

to offset more severe shocks. Recognizing 

that these estimates do not refl ect concurrent 

changes in risk-weighted assets, they nevertheless 

provide an initial estimate of the magnitude of 

the needed capital buffer, as well as the direction for further analysis. Note that these are not 

adjustments to FV calculation, per se, but are 

adjustments meant to help mitigate the impact 

on bank balance sheets. Consideration to making other changes to the accounting framework 

so that the FV calculations themselves obviate 

the need for these other adjustments would be 

useful at this juncture.

Broadening the current narrow concept of 

provisions to incorporate additional methods 

of retaining income in upswings could provide 

a way of better offsetting balance sheets’ procyclical effects. It is generally agreed that provisions protect against expected losses and capital 

protects against unexpected losses. A buildup of 

provisions better linked to the expected volatility, 

higher risks, and potentially larger losses of 

an asset could better anticipate the potential 

negative effects on the balance sheet that would 

be refl ected through the cycle, as long as the 

buildup does not provide a way for smoothing or 

manipulating earnings. Coordination between 

accounting standard setters and supervisors 

would be needed to effect such changes.

Similarly, the use of forward-looking provisioning,33 combined with a supervisor’s experienced 

credit judgment in assessing the probability of 

default, loss given default, and loan loss provisioning,34 could mitigate the procyclical forces 

on the balance sheet. The recognition of credit 

losses in the loan portfolio earlier in a downward 

cycle would lessen an accompanying decline 

in bank profi ts and the potential for a squeeze 

in credit extension that could contribute to a 

further downward economic trend. Similarly, on 

the upside, dividend distributions should only 

come from realized earnings that are not biased 

by upward cyclical moves.

From an oversight perspective, the simulations 

underscore the importance of understanding 

the cyclical implications of FVA. An enhanced 

role for prudential supervisors will be needed to 

ensure close inspection of a bank’s risk pro33Forward-looking provisioning denotes provisions 

based on the likelihood of default over the lifetime of the 

loan, refl ecting any changes in the probability of default 

(after taking into account recovery rates). Dynamic (or 

statistical) provisioning can be considered an extension 

of forward-looking provisions with reliance on historical 

data on losses for provisioning calculations. Conceptually, 

dynamic provisioning would entail that during the upside 

of the cycle, specifi c provisions are low and the statistical 

provision builds up, generating a fund; during the downturn, the growth in specifi c provisions can be met using 

the statistical fund instead of the profi t and loss account. 

See Enria and others (2004) and Bank of Spain (2007). 

For further discussion of this topic and the transparency 

of the Spanish provisioning method, see Fernández de 

Lis, Pagés, and Saurina (2000) and Jiménez and Saurina 

(2006). 34Basel Committee on Banking Supervision (2006b) 

and IAS 39.

CONCLUSIONS AND POLICY RECOMMENDATIONS
CHAPTER 3 FAIR VALUE ACCOUNTING AND PROCYCLICALITY

130

fi le and risk management practices, and make 

appropriate recommendations for augmented 

capital buffers and provisions, as needed. A comprehensive bank supervisory framework should 

include stress tests of FV positions through 

the business cycle. Similarly, auditors will have 

a critical role to play in ensuring credibility, 

consistency, and neutrality in the application of 

FVA, and overall in supporting market confi -

dence rather than appearing to augment procyclicality by encouraging lower valuations during 

a downturn. A closer collaborative framework 

among audit and accounting standard setters 

and supervisors would be highly benefi cial for 

markets and fi nancial stability.

In light of the different dynamics through 

the fi nancial cycle and the doubts that can 

surround valuations, FV estimates should be 

supplemented by information on a fi nancial 

instrument’s price history, the variance around 

the FV calculations, and management’s forwardlooking view of asset-price progression and how 

it will impact the institution’s balance sheet. 

Reporting a range within which the FV price 

could fall would help users of fi nancial statements to better understand and utilize the volatilities with which they are dealing. FV estimates 

should be supplemented with detailed notes on 

the assumptions underlying the valuations and 

sensitivity analyses so that investors can conduct 

their own scenario analyses and determine 

whether the FV price is representative of market 

conditions.

More refi ned disclosures could meet the 

expanding needs of various users, including 

investors, supervisors, and depositors, in a common framework of disclosure. For example, a 

series of shorter reports that would be available 

on websites,35 issued more frequently (e.g., 

quarterly),36 and cater to a narrower group of 

users’ needs could highlight the most relevant 

35The Financial Accounting Standards Board’s XRBL 

project for fi nancial institutions would provide data 

online in about three years, as discussed in the April 

2008 edition of the Global Financial Stability Report (IMF, 

2008b). 36This would be separate from U.S. SEC 10-Q fi lings.

information, with a particular emphasis on risk 

developments. Further, the volatility associated 

with an FV balance sheet may mean that the balance sheet is no longer the primary medium for 

evaluating bank capital. Market participants and 

supervisors may increasingly turn to cash fl ow 

statements, income and equity statements, and 

risk measures to provide enhanced information, 

and these statements must evolve in response to 

users’ needs.

Albeit of a simple structure and subject to 

isolated shock scenarios, the simulations point 

to the fact that the application of FV to both 

sides of the balance sheet would introduce a 

countercyclical component that may cushion 

some of the fi nancial shocks that can result in 

large swings in bank equity. This result, however, arises in the shock scenarios, in part, from 

a deterioration in the own-debt values as risk 

premia rise on the liability side of the balance 

sheet. This logically compensates for the deterioration of the asset side during a downturn. 

From the viewpoint of assessing the riskiness of 

the fi nancial institution or its future prospects, 

the result can be viewed as paradoxical, as it can 

hardly be regarded as a positive factor for the 

fi nancial institution to have its own-debt values 

deteriorate. The simulations also illustrate how 

a bank’s response to a particular shock varies 

substantially depending on the specifi c balance sheet structure and thus there is a need 

to discern the source of the cyclicality through 

additional disclosures.

A key challenge going forward will be to 

enrich the FVA framework so that market participants and supervisors are better informed 

in order to promote market discipline and 

fi nancial stability. The fragmented solution 

that currently exists between the accounting, 

prudential, and risk management approaches to 

valuation is insuffi cient and must be reconciled. 

Importantly, this will require adjustments on 

the part of all three disciplines to resolve these 

tensions. As the 2007–08 fi nancial turmoil 

continues to unfold, FVA alternatives and disclosures will continue to be tested and provide 

insights for improvement.
131

Annex 3.1. Data and Modeling 

Assumptions37

This annex presents the construction of the simulation 

exercises and reviews the assumptions underlying the 

various scenarios.

Banks’ Balance Sheets

To accurately refl ect the balance sheets of a 

representative large U.S. commercial bank, a 

large U.S. investment bank, a large European 

bank, and retail-oriented U.S. and European 

banks, the fi nancial statements at end-2006 for 

these fi ve banking groups were compiled from 

the institutions’ annual reports and the U.S. 

Securities and Exchange Commission’s 10-K fi lings.38 Individual bank balance sheets were then 

used to construct a weighted average for each 

type of institution, and the resulting representative balance sheets (Table 3.1).

Table 3.1 indicates the line items that were 

fair valued in the simulations.39,40 Not all the 

items in the balance sheet were fair valued in 

the simulations: items that are typically not 

available for sale (e.g., securities in the banking book) and items that fall under the “other” 

categories were held constant.41

37Juan Solé prepared this annex. 

38December 2006 was selected as the fi ling period in 

order to obtain balance sheets that are relatively recent, 

while at the same time not refl ecting too closely banks’ 

balance sheet structure in the run-up to or fall-out from 

the 2007–08 U.S. subprime meltdown. 39For simulation purposes, all banks were assumed to 

be newly established, so that all balance sheet items are at 

fair value at the start of the simulations. Thus, the shocks 

applied to the baseline refl ect only the pure impact of 

the shocks, and not a combination of the imposed shock 

plus any initial deviations from fair value. 40IAS 39 prevents the valuation of demand deposits at 

less than face value, even if a signifi cant portion of these 

display economic characteristics of a term deposit. Consequently, deposits remain at face value in the exercise. 41Despite credit derivative exposures being a central 

element in the 2007–08 turmoil, an explicit breakdown 

of them was unavailable in the 2006 reports. Some 

mortgage-backed securities were included in the debt 

securities category.

Valuation of Assets and Liabilities Under 

Fair Value

Loans and debt securities are valued at their 

expected net present value (NPV), which takes 

into account the probability of default (PD) and 

the loss given default (LGD) of each instrument. 

In other words, the value of a given security (or 

loan) with a maturity of T years is given by the 

expression

T E(CFt

) NPV = Σ ————, t =1 (1 + δt

)t

where δt is the discount rate for year t, and 

E(CFt

) is the expected cash fl ow for year t factoring in the possibility that the security (or loan) 

defaults, that is,

E(CFt

) = [PDt⋅(1 + rt)⋅N⋅(1 – LGDt)]

 + [(1 – PDt

)⋅rt⋅N] for all t < T, 

and

E(CFT) = [PDT⋅(1 + rT)⋅N⋅(1 – LGDT)]

 + [(1 – PDt

)⋅(1 + rT)⋅N ],

where PDt stands for probability of default,42 rt

is the interest rate on the loan, N is the notional 

amount of the loan, and LGDt

 is the loss given 

default.

Under fair value (FV), traded shares are 

valued at their market price. Since the detailed 

composition of the shares portfolio of banks 

was not available, it was assumed that banks 

hold a generic type of share that represents 

the Standard & Poor’s 500 Stock Market Index. 

Therefore, the number of shares for each type 

of bank was obtained by dividing the value of 

their shares portfolio at end-2006 by the value of 

the S&P 500 index at the same date.

Characterization of the Business Cycles

To simplify the analysis, the chapter considers a stylized business cycle consisting of four 

periods representing different points in a typical 

42Strictly speaking, PDt

 is the conditional probability 

of default at time t. That is, the probability that, conditional on not having defaulted before, a loan defaults in 

period t.

ANNEX 3.1. DATA AND MODELING ASSUMPTIONS
CHAPTER 3 FAIR VALUE ACCOUNTING AND PROCYCLICALITY

132

business cycle: trend, trough, peak, and back to 

trend. Each point in the business cycle is characterized by a different probability of default on 

securities and loans. To construct the normal 

business cycle, the PDs on loans and debt securities were assumed to change with the pulse of 

the cycle, increasing during economic downturns and decreasing during upswings. To isolate 

the effect of the evolving PDs on valuations, 

the baseline simulation abstracts from changes 

in interest rates during the cycle and initially 

assumes a fl at yield curve.

In principle, different classes of securities 

and loans may have different PDs and evolve 

differently throughout the cycle. For simplicity, 

however, this chapter assumes that all securities 

and loans have the same PD and display the 

same cyclical behavior, except for the scenario 

of the bust-boom cycle in real estate, where a 

different PD for mortgages is assumed. In addition, loans are assumed to be bullet instruments 

whose principal is repaid in full upon maturity. 

The specifi c values for these PDs were derived 

from Nickell, Perraudin, and Varotto (2000), 

who investigated the dependence of securitiesrating transition probabilities on the state of 

the economy.43 The PDs at different stages of 

the business cycle were computed using their 

estimated transition matrices at different points 

in the cycle (Table 3.2).44

To compute the NPV of loans and securities, 

it is also necessary to have a measure of losses 

in the event of default. Thus, LGD rates were 

taken from the Basel Committee on Banking 

Supervision’s (2006a) Fifth Quantitative Impact 

Study (QIS-5), and equal 20.3 percent for mortgage loans and 46.2 percent for corporate loans. 

To isolate the effect of the evolving PDs, the 

LGD rates were held constant through the cycle 

(except in the bust-boom cycle in the housing 

43See also Pederzoli and Torricelli (2005), Bangia and 

others (2002), and Altman and others (2005). 44It should be noted that the Basel Committee on 

Banking Supervision’s Fifth Quantitative Impact Study 

estimated the PD for a group of G-10 (ex-U.S.) banks’ 

retail mortgage portfolio at 1.17 percent, very close to the 

estimate of 1.18 percent for the trend period used here.

market and in the downward price spiral for 

debt securities).45

Characterization of the Economic Shocks

The fi rst scenario considered is a bust-boom 

cycle in stock market valuations where, concurrent with a normal cycle, share prices initially 

plummet by 20 percent during the downturn 

of the economic cycle and then surge to a 

level that is 20 percent above the original 

level, to ultimately return to their trend value 

(Table 3.3).46

The second scenario is a bust-boom cycle in 

the housing market, in which mortgage default 

rates and LGD rates dramatically increase during the downturn, and then rebound during 

the recovery. In this scenario, PDs of mortgage 

loans increase to 5.29 percent in the trough of 

the cycle—a magnitude that is commensurate 

with the recent meltdown in the U.S. housing 

market.47 Additionally, the reduction in house 

values—and thus the expected decline in 

recoveries—was factored in through a 50 percent increase in the LGD rate over the average 

values reported in the QIS-5 (i.e., from 20.3 to 

30.5 percent).

To simulate the cycle in funding conditions, 

the chapter assumes that during the business 

cycle trough, banks’ cost of funding increases 

by 58.7 basis points. This increase in spreads 

was obtained by computing the average rise in 

LIBOR overnight index swap spreads for U.S. 

and European banks during the summer of 

2007 (see Figure 2.2 in Chapter 2). Conversely, 

45Although this may be a less realistic assumption than 

allowing LGDs to evolve through the cycle, the qualitative 

results of the simulations would not be altered. 46The initial price of the representative stock held 

by banks was normalized to the value of the S&P 500 

index at end-2006, which closed at 1,418 on December 

29, 2006. 47To estimate the PDs during the 2007–08 U.S. housing 

crisis, it was assumed that 100 percent of foreclosures 

and 70 percent of delinquencies beyond 90 days end up 

in default. These percentages are then combined with 

the respective PDs to yield an overall estimated PD of 

5.29 percent for all mortgages. See UBS (2007). The data 

source is Merrill Lynch, April 2008.
133

to analyze the effects of ample liquidity conditions, the simulation assumes that banks’ funding costs decrease by the same amount during 

the cycle peak.

To construct the scenario of distressed 

securities markets and then recovery, it was 

assumed that the LGD rates for debt securities 

sharply increase during troughs and decrease 

by the same amount during peaks.48 During the 

cycle trough, the LGD rate for debt securities 

increases to 67.3 percent49 from its initial base 

of 46.2 percent. Subsequently, the simulation 

applies the same shock magnitude (but reversed 

sign) to the LGD during the cycle peak—that is, 

the LGD decreases to 25.1 percent.

References

Acharya, V.V., S.T. Bharath, and A. Srinivasan, 2007, 

“Does Industry-wide Distress Affect Defaulted 

Firms?—Evidence from Creditor Recoveries,” 

Journal of Financial Economics, Vol. 85, No. 3, 

pp. 787–821.

Altman, Edward, B. Brady, A. Resti, and A. Sironi, 

2005, “The Link between Default and Recovery Rates: Theory, Empirical Evidence, and 

Implications,” Journal of Business, Vol. 78, No. 6, 

pp. 2203–27.

Bangia, Anil, Francis Diebold, André Kronimus, 

Christian Schagen, and Til Schuermann, 2002, 

“Ratings Migration and the Business Cycle, with 

Application to Credit Portfolio Stress Testing,” 

Journal of Banking and Finance, Vol. 26, No. 2–3, 

pp. 445–74.

Bank of Spain, 2007, “Dynamic Provisioning in 

Spain: Impacts of the New Spanish Statistical Provision in the Second Half of 2000.” Available via 

the Internet: www.bde.es/provesta/impactoe.htm.

48The rationale behind this characterization of 

distressed markets follows Altman and others (2005) in 

that, during times of distress, the demand for securities 

declines, hence reducing both the market price and the 

recovery rate (i.e., the inverse of LGD) of securities. See 

Acharya, Bharath, and Srinivasan (2007), Altman and 

others (2005), and Bruche and González-Aguado (2008) 

for discussions of the link between distressed markets and 

increases in LGD rates. 49Derived from Bruche and González-Aguado (2008).

Barth, Mary, 2004, “Fair Values and Financial Statement Volatility,” in The Market Discipline Across 

Countries and Industries, ed. by Claudio Borio, 

William Curt Hunter, George G Kaufman, and 

Kostas Tsatsaronis (Cambridge, Massachusetts: 

MIT Press).

———, Leslie D. Hodder, and Stephen R. Stubben, 

2008, “Fair Value Accounting for Liabilities and 

Own Credit Risk,” The Accounting Review, Vol. 83, 

No. 3, pp. 629–64.

Basel Committee on Banking Supervision (BCBS), 

2006a, Results of the Fifth Quantitative Impact Study 

(QIS-5) (Basel: Bank for International Settlements).

———, 2006b, Sound Credit Risk Assessment and Valuation 

for Loans (Basel: Bank for International Settlements).

Borio, Claudio, and Kostas Tsatsaronis, 2005, 

“Accounting, Prudential Regulations and Financial 

Stability: Elements of a Synthesis,” BIS Working 

Paper No. 180 (Basel: Bank for International 

Settlements).

Bruche, Max, and Carlos González-Aguado, 2008, 

“Recovery Rates, Default Probabilities, and 

the Credit Cycle,” CEMFI Working Paper No. 

0612 (Madrid: Centro de Estudios Monetarios y 

Financieros).

Center for Audit Quality, 2007, “Measurements of 

Fair Value in Illiquid (or Less Liquid) Markets,” 

Center for Audit Quality White Paper (October).

Citigroup, 2008, “There’s a Hole in My Bucket: 

Further Deterioration in European Banks’ Capital 

Ratios,” Industry Focus, Citigroup Global Markets: 

Equity Research, June 16.

Enria, Andrea, Lorenzo Cappiello, Frank Dierick, 

Sergio Grittini, Andrew Haralambous, Angela 

Maddaloni, Philippe A.M. Molitor, Fatima Pires, 

and Paolo Poloni, 2004, “Fair Value Accounting 

and Financial Stability,” ECB Occasional Paper 

No. 13 (Frankfurt: European Central Bank).

Fernández de Lis, Santiago, Jorge Martínez Pagés, 

and Jesús Saurina, 2000, “Credit Growth, Problem 

Loans and Credit Risk Provisioning in Spain,” 

Banco de España Working Paper No. 0018. Available via the Internet: www.bde.es/informes/be/

docs/dt0018e.pdf.

Financial Stability Forum, 2008, “Report of the 

Financial Stability Forum on Enhancing Market 

and Institutional Resilience,” paper presented to 

the meeting of the G-7 Ministers and Governors, 

Washington, April 7.

Financial Times, 2008, “Banks According to GAAP,” 

July 29. Available via the Internet: www.ft.com.

REFERENCES
CHAPTER 3 FAIR VALUE ACCOUNTING AND PROCYCLICALITY

134

Fitch Ratings, 2008a, “Fair Value Accounting: Is It 

Helpful in Illiquid Markets?” Credit Policy Special 

Report (April 28). Available via the Internet (by 

subscription): www.fitchratings.com.

———, 2008b, “Fair Value Disclosures: A Reality 

Check,” Credit Policy Special Report, (June 26). 

Available via the Internet (by subscription): www.

fitchratings.com.

Global Public Policy Committee, 2007, “Determining 

Fair Value of Financial Instruments under IFRS 

in Current Market Conditions.” Available via the 

Internet: www.pwc.com.

Guerrera, F., and B. White, 2008, “Banks Find Way to 

Cushion Losses,” Financial Times, July 8. Available 

via the Internet: www.ft.com.

International Accounting Standards Board (IASB), 

2001, “Financial Instruments: Recognition and 

Measurement,” IAS 39. Available via the Internet: 

www.iasb.org.

———, 2007, “Disclosure of Financial Instruments,” 

IFRS 7. Available via the Internet: www.iasb.org.

International Monetary Fund (IMF), 2008a, “Financial 

Stress and Economic Downturns,” Chapter 4, World 

Economic Outlook, World Economic and Financial 

Surveys (Washington, October).

———, 2008b, Global Financial Stability Report, World Economic and Financial Surveys (Washington, April).

Jiménez, Gabriel, and Jésus Saurina, 2006, “Credit 

Cycles, Credit Risk, and Prudential Regulation,” 

International Journal of Central Banking, Vol. 2, No. 2 

(June), pp. 65–98.

Kashyap, Anil, 2005, “Financial System Procyclicality,” paper presented at the “Joint Conference of 

the IDB and Federal Reserve Bank of Atlanta’s 

Americas Center: Toward Better Banking in Latin 

America,” Washington, January 10.

Keen, Howard, 1989, “The Yield Curve as a Predictor of Business Cycle Turning Points,” Business 

Economics, (October). Available via the Internet: 

http://findarticles.com/p/articles/mi_m1094/

is_n4_v24/ai_7987167.

Nickell, Pamela, William Perraudin, and Simone 

Varotto, 2000, “Stability of Rating Transitions,” 

Journal of Banking and Finance, Vol. 24, No. 1–2, 

pp. 203–27.

Pederzoli, Chiara, and Costanza Torricelli, 2005, 

“Capital Requirements and Business Cycle 

Regimes: Forward-Looking Modeling of Default 

Probabilities,” Journal of Banking and Finance, 

Vol. 29, No. 12 (December), pp. 3121–140.

Piazzesi, Monica, and Martin Schneider, 2006, 

“Equilibrium Yield Curves,” NBER Working Paper 

No. 12609 (Cambridge, Massachusetts: National 

Bureau of Economic Research).

Union Bank of Switzerland (UBS), 2007, “Subprime 

Loss Protection—A Do-It-Yourself Kit,” Mortgage 

Strategist, June 26.
135

CHAPTER 4 SPILLOVERS TO EMERGING EQUITY MARKETS

After months of relative resilience, 

emerging market (EM) equities 

have now experienced the effects of 

the fi nancial turmoil that began in 

advanced economies in the summer of 2007. 

This chapter examines whether increasing 

fi nancial integration has potentially raised EMs’ 

vulnerability to external global shocks, focusing 

on the channel of equity markets. This question 

remains relevant because many EM economies have experienced a long run-up in equity 

prices, despite the partial reversal of recent 

months. Moreover, EM resilience will likely continue to be tested if the global fi nancial turmoil 

remains protracted and the global economic 

slowdown continues.

The chapter addresses three key questions:

• How vulnerable are EMs to changing external conditions? In tackling this question, the chapter 

explores the external and domestic determinants of EM equity market valuations and 

analyzes whether the external determinants 

are economically important. It finds that, 

although closer links with foreign markets 

are important drivers of equity prices, to 

date, the more open EM economies or those 

with higher levels of foreign investor participation have not been affected disproportionately by the global financial turbulence.

• How extensive are macro-financial linkages? The 

chapter considers whether wealth effects 

are important and whether they make EM 

consumption and investment growth vulnerable to equity market declines. It finds the 

effect on private consumption and investment 

to be statistically significant but small. Moreover, wealth effects tend to play out gradually 

as opposed to financial-to-financial spillover 

channels.

• What can EM countries do to minimize their vulnerability to spillovers? The chapter stresses the 

importance of building and sustaining resilient 

capital markets, particularly equity markets. 

This can be achieved not only by fostering 

deeper capital markets, but by introducing 

legal, regulatory, and accounting reforms that 

conform to international best practice and 

developing a well- functioning securities market 

with supporting infrastructure.

The chapter fi rst traces developments in the 

equity prices of EMs during the long run-up and 

correction, and compares this cycle to the previous peak and trough for a selected number of 

countries, for which the experiences from the 

two cycles have been quite different. An overall 

increase in equity market correlations across 

countries is evident. The chapter then develops an empirical framework for assessing what 

drives EM equity prices, and fi nds that domestic/fundamental factors, such as growth and 

exchange rate expectations, and global/external 

conditions, such as excess liquidity and credit 

This chapter shows that emerging market equity prices are influenced by both 

global and domestic factors, and therefore global developments constitute a 

significant channel for spillovers when the international economic environment changes. This can, in turn, affect domestic consumption and investment. 

Strengthening their resilience to equity price declines remains an important goal 

for emerging market countries.

Note: This chapter was written by a team led by L. 

Effi e Psalida and comprising Heiko Hesse and Tao Sun. 

Oksana Khadarina provided research support.
CHAPTER 4 SPILLOVERS TO EMERGING EQUITY MARKETS

136

and market risk premia, both play a role. The 

chapter then provides additional results showing 

potential spillovers to EM equities and discusses 

their effects on consumption and investment. It 

fi nishes with a summary of the key results, draws 

some policy conclusions, and points to measures 

that can help make equity markets more resilient when equity prices decline.

Performance of Emerging Market 

Equity Markets

After a period of lackluster growth, EM 

equity market prices rose signifi cantly beginning in 2003 (Figure 4.1). This development was 

associated with a concomitant rise of EM capital 

infl ows (Figure 4.2), which in net terms often 

masked the high level of gross capital infl ows 

because of the growing role of EM cross-border 

outward investments (Figure 4.3).

In a number of the more mature emerging 

markets, the stock market capitalization-to-GDP 

ratio is now approaching that of advanced economies, although it is not certain that the ratio is 

sustainable in all cases (Figure 4.4). It is noteworthy that in many EM economies, total equity 

market returns have increased at a much faster 

pace than in advanced economies (Figure 4.5), 

although, on the whole, the price-earning ratios 

are comparable (Figure 4.6).

Foreign holdings of EM equity have in creased 

overall since 2003, although not necessarily as 

a proportion of the total value of equities in all 

cases (Figure 4.7). In addition to push factors 

in this period, such as abundant global liquidity and a search for yield, growing nonresident 

holdings can be, at least partially, attributed to 

the diversifi cation of the international investor base (IMF, 2007a) and the opening up 

and maturation of emerging fi nancial markets 

(IMF, 2007b, Chapter 3). Although in principle 

a higher proportion of foreign equity holdings 

can increase the sensitivity of EM equity prices 

to changes in the global environment, the presence of foreign investors does not seem to be 

associated with larger equity losses since the 

October 2007 peak (Figure 4.8).

0

50

100

150

200

250

300

350

400

450

500

0

100

200

300

400

500

600

700

800

900

United States Japan

Latin America

EMEA

Asia

Germany

Emerging

markets

Emerging

markets

United

Kingdom

Sources: Bloomberg L.P.; and Datastream.

Note: EMEA = Emerging Europe, Middle East, and Africa.

Figure 4.1. Selected Equity Market Indices

(January 1, 2003 = 100; in U.S. dollars)

2003 04 05 06 07 08

2003 04 05 06 07 08

Source: IMF, International Financial Statistics database.

Figure 4.2. Emerging Market Economies: 

Composition of Capital Inflows

(In billions of U.S. dollars)

0

100

200

300

400

500

600

700

800

900

1000 Bank loans

Portfolio investment: Bonds

Portfolio investment: Equity

Direct investment

2003 04 05 06
137

1990 92 94 96 98 2000 02 04 06

–8

–6

–4

–2

0

2

4

6

8

10

12

Net capital flows

Gross capital inflows

Current account

1990 92 94 96 98 2000 02 04 06

–8

–6

–4

–2

0

2

4

6

8

10

12

1990 92 94 96 98 2000 02 04 06

–8

–6

–4

–2

0

2

4

6

8

10

12

Source: IMF, World Economic Outlook database.

Figure 4.3. Current Account Balances and Capital

Flows from a Global Perspective

(In percent of GDP)

Latin America

Developing Asia

Central and Eastern Europe

Sources: World Federation of Exchanges; Bloomberg L.P.; and IMF, World 

Economic Outlook database. 1In percent of GDP for 2007.

Figure 4.4. Stock Market Capitalization

(In percent of GDP)

Vietnam

Turkey

Thailand

South Africa 

Saudi Arabia

Russia

Mexico

Korea

Jordan

Chi

India

na

Chile

Brazil

Argentina

0

40

80

120

160

200

240

280

320

360 2003

Peak (October 2007)

Current (July 2008)1

Sources: S&P Emerging Markets Database; and Datastream.

Figure 4.5. Total Equity Market Returns

(January 2003–July 2008; percent change)

United States

Japan

United Kingdom

Th

Kor

ailand

ea

Germany

South Africa 

Chile

China

Mexico

India

Turkey

Indonesia 

Russia

Argentina

Bulgaria 

Brazil

0

200

400

600

800

1000

1200

Sources: S&P Emerging Markets Database; Datastream; and Bloomberg L.P.

Figure 4.6. Price/Earnings Ratios, July 31, 2008

0

5

10

15

20

25

30

35

Jordan

China

Indonesia

Chile

United States

India

Turkey

Saudi Arabia

South Africa

Japan

Brazil

Argentina

Mexico

Korea

Russia

Hong Kong

Germany

United Kingdom

Thailand

PERFORMANCE OF EMERGING MARKET EQUITY MARKETS
CHAPTER 4 SPILLOVERS TO EMERGING EQUITY MARKETS

138

Table 4.1 compares the current partial 

reversal of equity prices to the previous equity 

cycle for eight EM economies that had experienced high price rises and subsequent abrupt 

declines in the context of more generalized 

crises in the 1990s. These eight EM countries 

and events are also compared to four previous 

stock market events in advanced economies as 

well as their price decline through end-August 

2008. A few observations are noteworthy. Unlike 

the previous large and, in many cases, disorderly 

corrections, which emanated from generalized 

crises in EM countries, downward equity price 

adjustments to date have been shallower when 

compared to the high levels reached at the peak 

of the cycle. Clearly, however, the downward 

phase may not be over. Current equity price 

corrections in advanced economies are also shallower and more gradual than past events, and, 

in some cases, they follow a more modest stock 

market rise than in the past—for example, when 

compared to Japan’s bubble of the late 1980s 

and the dot-com bubble in the United States. 

During the upturn of the current cycle, stock 

market increases in advanced economies have 

also been modest relative to increases in EMs.

The fact that corrections to date have been 

only partial and more differentiated across EM 

countries points to a number of contrasts when 

compared to the previous cycle, including stronger and more differentiated country- specifi c 

fundamentals and deeper fi nancial markets in 

EM economies, a substantial growth of “SouthSouth” investment fl ows, and signifi cant petrodollar recycling.

Cross-Country Equity Price Correlations

In principle more fi nancial integration can 

increase EM equity price sensitivity to global 

events. Indeed, stock market correlations of 

EM economies with the United States have 

increased in recent years, and a simple pair-wise 

analysis indicates that on average the correlation 

between equity prices in a number of EMs’ main 

stock index and equity prices in the S&P 500 

increased from 0.17 during the period January 

Sources: IMF, Balance of Payments and World Economic Outlook databases.

Note: For China, data refer to 2004 and 2006.

Figure 4.7. Total Foreign Holdings of Equity

2003

2006

In percent of total equity

2003

2006

In percent of GDP

0

10

20

30

40

50

Jordan

China

ChileIndia

Turkey

South Africa

Brazil

Argentina

Mexico

Korea

Russia

Thailand

0

10

20

30

40

50

Jordan

Ind

Chile

China

ia

Turkey

South Africa

Brazil

Argentina

Mexico

Korea

Russia

Thailand

Sources: IMF, World Economic Outlook and International Financial Statistics 

databases.

Note: Green squares indicate countries with foreign holdings of equities 

exceeding 5 percent of GDP.

Figure 4.8. Emerging Markets Equity Indices and

Foreign Investor Presence

(Percent change)

Change in equity indices 1/1/2003–10/31/2007

Change in equity indices 10/31/2007–7/31/2008

–50

–40

–30

–20

–10

0

10

20

30

40

0 200 400 600 800 1000 1200
139

1998– December 2002 to 0.91 during the period 

January 2003–May 2008.1

1Correlations are known for being biased estimates 

of potential linkages as they are overestimated in times 

of high volatility and underestimated in tranquil times. 

A more robust test, which allows for analyzing the co-movement of the stock markets by 

Appropriate corrections, however, require assumptions 

about the reason for the bias. 

Table 4.1. Emerging Equity Market Peaks and Troughs: Current and Previous Episodes

Current Episode (October 2007–August 2008)

Equity Price Index 

(percent change) Equity Market

Rise to Peak to Capitalization/GDP Price/Earnings

peak current At peak At current Difference At peak At current Difference

Emerging markets

Argentina 1,006 –14 24 20 4 15.8 13.5 2.4

Brazil 1,364 –14 107 93 14 16.7 7.6 9.1

Hong Kong SAR 226 –31 1,435 946 489 22.9 13.3 9.6

Indonesia 882 –18 47 43 4 30.2 27.1 3.2

Korea 590 –40 129 78 52 17.6 9.5 8.1

Mexico 417 –15 42 37 5 19.8 11.6 8.2

South Africa 467 –26 333 245 87 19.7 17.7 2.0

Thailand 402 –26 84 65 19 12.2 9.1 3.0

Memorandum item:

Advanced economies

Germany 339 –19 64 50 14 13.9 13.3 0.6

Japan 111 –18 111 92 19 21.4 16.0 5.4

United Kingdom 162 –27 152 111 42 13.1 12.2 0.9

United States 84 –17 152 127 25 18.3 24.7 –6.5

Past Episode

Equity Price Index

(percent change) Equity Market

Capitalization/GDP Price/Earnings

Peak to trough

Rise to Peak to

peak trough At peak At trough Difference At peak At trough Difference

Emerging markets

Argentina Feb. 2000–June 2002 86 –85 23 13 10 43.6 –10.2 53.8

Brazil July 1997–Jan. 1999 134 –69 74 35 39 18.6 8.4 10.2

Hong Kong SAR July 1997–Aug. 1998 99 –60 337 152 186 19.7 9.0 10.7

Indonesia Jan. 1997–Sep. 1998 134 –92 42 10 32 24.0 –23.6 47.6

Korea Apr. 1996–Aug. 1998 100 –83 37 14 23 16.5 –13.7 30.2

Mexico1 Nov. 1994–Feb. 1995 71 –64 28 25 3 17.9 16.4 1.6

South Africa Jan. 1996–Aug. 1998 188 –64 214 106 109 20.3 7.8 12.6

Thailand May 1996–Aug. 1998 174 –93 80 16 65 21.7 –1.9 23.6

Memorandum item:

Advanced economies

Germany Feb. 2000–Sep. 2002 74 –63 81 31 51 24.7 9.6 15.1

Japan2 Dec. 1989–July 1992 928 –52 140 55 85 . . . . . . . . .

United Kingdom Dec. 1999–Mar. 2003 122 –47 200 88 112 28.6 16.5 12.1

United States Aug. 2000–Sep. 2002 235 –46 180 98 82 28.7 31.4 –2.7

Sources: Datastream; S&P Emerging Markets Database; World Federation of Exchanges; and IMF, World Economic Outlook database.

Note: All peaks/troughs refer to equity price peaks prior to the onset of fi nancial crisis and the last troughs associated with the crisis. Difference denotes subtraction. For the past episode, “rise to peak” describes index price appreciation experienced since the previous trough, while 

“peak to trough” measures price declines from the peak to the following trough. In the current episode, we take end-October 2007 (when the 

equity prices in the U.S. and many EM economies reached peaks) as the peak time. “Peak to current” in the current episode demonstrates price 

performance for equity indices from the market peak to end-August 2008. 1For peak in 1994, stock market capitalization refers to December 1994.

2Equity market capitalization of TOPIX stock index.

CROSS-COUNTRY EQUITY PRICE CORRELATIONS
CHAPTER 4 SPILLOVERS TO EMERGING EQUITY MARKETS

140

inferring their correlation changes over time, 

also indicates varied but overall increasing 

correlation levels during the past fi ve years.2

Specifi cally, Latin American equity price indices 

are generally highly correlated with U.S. equities, peaking during the February 2007 shortlived turbulence (Figure 4.9).3 Correlations 

between mature European bourses, proxied 

by the Euronext 300 index, Emerging Europe, 

Middle East, and Africa (EMEA), and Asia have 

increased, albeit from a relatively low level. Similarly, correlations between EM equity markets 

have risen both within and across geographic 

regions (Figure 4.10).

Determinants of Emerging Market 

Equity Prices

To examine whether EMs are more susceptible to the global distress, we examine fi rst 

what drives equity prices. There is an extensive 

literature on the driving forces of equity prices 

ranging from the Gordon (1962) model, which 

uses the expected real dividend growth and real 

discount rates as primary determinants, to more 

elaborate analyses by Campbell Harvey and 

Geert Bekaert, which include liquidity and risk 

premia measures.4 Expectations about the future 

path of dividend growth and discount rates can 

be infl uenced by global fi nancial conditions 

such as the abundant liquidity experienced 

2The Dynamic Conditional Correlation (DCC) 

Generalized Autoregressive Conditional Heteroskedasticity (GARCH) specifi cation by Engle (2002) is used, 

estimated in a three-stage procedure. First, univariate 

GARCH models are fi tted for each of the variables in 

the specifi cation. Second, the intercept parameters are 

obtained from the transformed variables, and, fi nally, the 

coeffi cients governing the dynamics of the conditional 

correlations are estimated. See also Frank, GonzálezHermosillo, and Hesse (2008) for an application to the 

2007 subprime crisis. 3In comparison, correlations between the United States 

and mature Europe and those between the United States 

and Japan have remained high and low, respectively, and 

generally stable throughout the fi ve-year period. 4For more information on the work of Campbell 

Harvey and Geert Bekaert, see their respective websites 

at www.duke.edu/~charvey/curvit.htm and www.gsb. 

columbia.edu/faculty/gbekaert/.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

United States - Brazil

United States - Chile

United States vs. Emerging Market Countries

Europe vs. Emerging Market Countries

United States - Korea

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Europe - Turkey

Europe - South Africa

Europe - India

Sources: Datastream; S&P Emerging Markets Database; and IMF staff estimates.

Note: GARCH = Generalized Autoregressive Conditional Heteroskedasticity.

Figure 4.9. Implied Correlations from Dynamic

Conditional Correlation GARCH Specification

2003 04 05 06 07 08

2003 04 05 06 07 08
141

during the run-up to the 1997–98 turbulence 

and 2003–07 asset price increases (IMF, 2000, 

2007c) or irrational exuberance (Shiller, 2000, 

pp. xxi, 296), in addition to domestic micro and 

macro determinants. Empirical studies on the 

topic have been quite diverse in terms of model 

specifi cation that embeds different hypotheses 

or explanations. However, despite the broad 

fi eld of study in this area, the approaches focus 

primarily on two sets of factors as determinants 

of equity prices: domestic/fundamental and 

global/fi nancial (IMF, 1998, 2000).

This section develops an empirical framework for assessing the determinants of EM 

equity prices. The framework employs fi xedeffects panel data specifi cations for monthly 

observations—January 2001 to May 2008—

covering 30 EM economies (see Annex 4.1 for a 

detailed presentation of the estimation specifi -

cations and results). The model utilizes two sets 

of explanatory variables:

• Domestic or fundamental factors include (1) economic growth; (2) the differential between 

domestic and global interest rates; (3) the 

forward exchange rate; (4) the inflation 

differential; and (5) equity market capitalization (measured as a ratio to GDP), which, in 

addition to price effects, captures increases 

in the volume of shares—such as new shares 

issued by listed companies and initial public 

offerings—and provides a proxy for equity 

market depth.5

• Global factors include proxies for (1) global 

excess liquidity (the difference between 

broad money growth and estimates for money 

demand in the euro area, Japan, and the 

United States; (2) credit risk premium (the 

level of the 10-year U.S. dollar swap spread); 

and (3) market risk premium (the implied 

volatility of the S&P 500 index [VIX]).

The estimation results for the full 30- country 

sample over the 89-month period suggest that, 

5Although, at fi rst view, the log change in equity prices 

and the stock market capitalization-to-GDP ratio may 

seem highly correlated, the correlation between the series 

is only 0.19 for the full country and period sample. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Brazil - Mexico

Korea - Singapore

Russia - Turkey

Intra-Regional Correlations

Inter-Regional Correlations

China - India

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Brazil - Turkey

Korea - Russia

India - South Africa

Sources: Datastream; S&P Emerging Markets Database; and IMF staff estimates.

Note: GARCH = Generalized Autoregressive Conditional Heteroskedasticity.

Figure 4.10. Emerging Market Countries: Implied 

Correlations from Dynamic Conditional Correlation 

GARCH Specification

2003 04 05 06 07 08

2003 04 05 06 07 08

DETERMINANTS OF EMERGING MARKET EQUITY PRICES
CHAPTER 4 SPILLOVERS TO EMERGING EQUITY MARKETS

142

for a given economy, equity prices are positively associated with GDP growth, an expected 

exchange rate appreciation, and an increase 

of market capitalization as a ratio to GDP 

(Table 4.2). As regards the global factors, all 

three are statistically signifi cant, with global 

excess liquidity being positively related and 

credit and market risk premia having a negative 

relation to equity prices as expected.6 A comparison of the three main geographical regions 

6A fourth external factor, portfolio equity infl ows, is 

statistically insignifi cant; this result is consistent with 

previous studies indicating no statistically apparent effect 

of foreign infl ows on domestic equity prices (see, for 

example, IMF, 2007b, Box 1.3). 

of Latin America, Asia, and EMEA indicates 

stronger spillover effects for Latin America as 

the three global factors remain signifi cant and 

with higher negative coeffi cients than in the 

full country sample for credit and market risk. 

Equity prices in EMEA are being driven strongly 

by exchange rate expectations, while in Asia they 

have the closest positive association with rises in 

the market- capitalization-to-GDP ratio.

In response to the intuition that equity 

market capitalization may be serially correlated 

with equity prices, an alternative model specifi cation is used, which replaces equity market 

capitalization with private sector credit growth 

as a proxy for domestic fi nancial deepenTable 4.2. Fixed-Effects Panel Least-Squares Estimation of the Determinants of Emerging Market Equity 

Prices—Monthly Observations (January 2001–May 2008), 30 Countries, First Specifi cation 

Economies 30 Countries Asia EMEA Latin America

Domestic factors

GDP growth 0.5916 0.6191 0.4114 0.3869

(0.0004)*** (0.0193)** (0.1771) (0.0752)*

Interest rate differential –0.0893 –0.0923 0.0338 –0.0391

(<0.0001)*** (0.0772)* (0.6143) (0.0616)*

Forward exchange rate 0.0260 0.0061 0.3615 0.2266

(0.0002)*** (0.2091) (<0.0001)*** (<0.0001)***

Inflation rate differential –0.0564 0.0033 –0.0310 –0.1900

(0.0155)** (0.9497) (0.3786) (0.0002)***

Market capitalization/GDP ratio 0.5172 0.7572 0.3608 0.6450

(<0.0001)*** (<0.0001)*** (<0.0001)*** (<0.0001)***

External factors

Global excess liquidity 1.0842 0.4366 0.9136 1.0303

(<0.0001)*** (0.0109)** (0.0024)*** (<0.0001)***

Credit risk premium –6.0922 –2.8994 –4.8127 –6.7605

(<0.0001)*** (0.0119)** (0.0233)** (<0.0001)***

Market risk premium –0.2247 –0.1381 –0.2542 –0.2839

(<0.0001)*** (<0.0001)*** (<0.0001)*** (<0.0001)***

Foreign equity inflow –0.0001 0.0000 0.0001 0.0000

(0.1984) (0.8676) (0.4444) (0.0473)

Error correction term –0.0472 –0.0443 –0.0408 –0.0724

(<0.0001)*** (<0.0001)*** (<0.0001)*** (<0.0001)***

Other factors

Constant –0.4429 –0.6888 –0.2933 –0.5407

(<0.0001)*** (<0.0001)*** (<0.0001)*** (<0.0001)***

Adjusted R2 0.5663 0.7634 0.4671 0.7456

Time-series sample (monthly) Jan. 2001–May 2008 Jan. 2001–May 2008 Jan. 2001–May 2008 Jan. 2001–May 2008

No. of cross-section countries 30 12 12 6

No. of observations 2,294 892 875 527

Sources: IMF, World Economic Outlook and International Financial Statistics databases; World Bank, World Development Indicators database; 

S&P Emerging Market Database; Bloomberg L.P.; and Datastream. 

Note: Probability values are in parentheses (***significant at 1 percent level; **significant at 5 percent level; *significant at 10 percent level). 

EMEA = Emerging Europe, Middle East, and Africa.
143

ing, and includes the Morgan Stanley Capital 

International world index (MSCI)—a measure 

of prices from advanced stock markets—as an 

additional global push factor. The results are 

along the same lines as in the previous model 

and, in this case, credit growth and the MSCI 

are signifi cant and positively related to equity 

price increases (Table 4.3). Along regional 

lines, under this specifi cation as well, the results 

indicate global factors are strong in both Latin 

America and Asia, with global excess liquidity having a strong positive relation and global 

market and credit risk being signifi cantly negative, although the MSCI shows no signifi cant 

effect in Latin America and EMEA. Exchange 

rate expectations are strongest in EMEA and 

Latin America, and domestic credit is signifi -

cant in all three regions.

Two “what if” scenarios were performed to 

further analyze the impact of global factors on 

equity prices.7 The fi rst scenario is a 10 percent 

decline in global excess liquidity—from its 

May 2008 level of 4.5 percentage points—and 

7The results are specifi c to this model’s estimated 

coeffi cients, and a different analysis using a different 

methodology, country, and period coverage would likely 

come with slightly different results.

Table 4.3. Fixed-Effects Panel Least-Squares Estimation of the Determinants of Emerging Market Equity 

Prices—Monthly Observations (January 2001–May 2008), 30 Countries, Second Specifi cation

Economies 30 Countries Asia EMEA Latin America

Domestic factors

Credit growth 0.7124 0.6777 0.4711 0.3456

(<0.0001)*** (<0.0001)*** (<0.0001)*** (<0.0001)***

GDP growth 0.3777 0.6727 0.1577 –0.1014

(0.0893)* –0.1785 –0.6549 –0.7599

Forward exchange rate 0.0361 0.0162 0.3816 0.6384

(0.0002)*** (0.0774)* (<0.0001)*** (<0.0001)***

Interest rate differential 0.0210 –0.2244 0.1084 0.0993

(0.4125) (0.0228)** (0.1884) (0.0026)***

Inflation rate differential 0.0507 –0.0720 0.0532 0.0188

(0.1104) (0.4711) (0.2212) (0.8076)

External factors

Global excess liquidity 0.9203 1.5011 0.3663 0.8726

(<0.0001)*** (<0.0001)*** (0.3334) (0.0235)**

Market risk premium –0.2746 –0.2699 –0.3177 –0.3514

(<0.0001)*** (<0.0001)*** (<0.0001)*** (<0.0001)***

Credit risk premium –6.7535 –7.3195 –4.1517 –7.0568

(<0.0001)*** (0.0011)*** (0.1277) (0.0082)***

MSCI 0.1141 0.1744 0.0474 0.1224

(0.0187)** (0.0168)** (0.5823) (0.1587)

Foreign equity inflow 0.0001 –0.0002 0.0004 0.0000

(0.2157) (0.7282) (0.0169)** (0.2741)

Error correction term –0.0358 –0.0222 –0.0305 –0.0660

(<0.0001)*** (0.1930) (0.0003)*** (0.0002)***

Other factors

Constant 0.0613 0.0513 0.0711 0.0900

(<0.0001)*** (<0.0001)*** (<0.0001)*** (<0.0001)***

Adjusted R2 0.1842 0.1564 0.1825 0.4017

Time-series sample (monthly) Jan. 2001–May 2008 Jan. 2001–May 2008 Jan. 2001–May 2008 Jan. 2001–May 2008

No. of cross-section countries 30 12 12 6

No. of observations 2,301 892 882 527

Sources: IMF, World Economic Outlook and International Financial Statistics databases; World Bank, World Development Indicators database; 

S&P Emerging Market Database; Bloomberg L.P.; Datastream.

Note: Probability values are in brackets (***significant at 1 percent level; **significant at 5 percent level; *significant at 10 percent level). 

EMEA = Emerging Europe, Middle East, and Africa; MSCI = Morgan Stanley Capital International world index.

DETERMINANTS OF EMERGING MARKET EQUITY PRICES
CHAPTER 4 SPILLOVERS TO EMERGING EQUITY MARKETS

144

a 10 percent increase in both the credit and 

market risk premia. The results indicate that EM 

equity price growth would decline by 1.25 percentage points when all three global indicators 

worsen, with the liquidity indicator having the 

largest effect when applied individually. The second scenario, which is a much larger shock of 

zero excess liquidity and a sharp increase in risk 

premia, points to an equity price growth rate 

that could be as much as 16 percentage points 

lower than the base case.8

Spillovers and Their Impact

The results discussed in the previous section 

point to the strong infl uence of external factors on EM equity prices, thus indicating that 

spillovers play a potentially important role. This 

section considers an additional approach—

vector autoregression (VAR)—in order to 

further test the role of co-movements in EM 

stock valuations. It then discusses the effect that 

such spillovers could have on consumption and 

investment when transmitted through the equities channel.9

A More Dynamic Analysis—Vector 

Autoregressions

A key limitation of the cross-economy panel 

regression approach used in the previous section is that it only allows for relatively simple 

interactions across economies. An analysis using 

a cross-economy set of VAR models allows for 

more precise disentangling of the separate 

spillover effects of unexpected changes in 

equity prices. Specifi cally, using the same sets 

of indicators that are statistically signifi cant as 

8The risk premia in the second scenario increase to 

their high levels of May 2000 for credit risk, which was at 

1.39 versus 0.59 in May 2008, and August 1998 for market 

risk, which was at 44 versus 18 in May 2008. Psalida and 

Sun (forthcoming) contains an elaboration of these 

scenarios. 9This is a modest approach, as the data requirements 

and model specifi cation do not lend themselves to testing 

directly for cross-border spillovers and potential contagion across a large number of EMs.

in the data panel, we estimate an eight-variable 

structural VAR model for seven economies—

Argentina, Chile, China, Romania, Russia, 

Singapore, and South Africa.10 The VAR is partitioned into an exogenous foreign block and a 

country-specifi c block of variables. The foreign 

block includes global excess liquidity and credit 

and market risk premia in the United States, 

while the economy-specifi c block includes 

(economy- specifi c) GDP growth, the forward 

exchange rate, the interest rate and infl ation 

rate differentials, and the market-capitalization-to-GDP ratio.11 The data have monthly 

frequency, typically available for January 2001 

to May 2008.12

Overall, the results of the dynamic VAR 

analysis are in line with those in the panel 

regressions. (Figures 4.11–4.13 in Annex 4.2 

present some of the impulse responses for the 

seven countries examined.) Three observations can be made: (1) most individual equity 

price responses to shocks in the global indicators are as expected and in the same direction 

as for the panel; (2) the equity price response 

tapers off after three months in most cases; 

and (3) smaller countries have slightly larger 

responses overall.

10The seven economies are selected using the criterion of the most and the least open in their respective 

geographic region among the overall 30-country sample 

based on the Chinn-Ito Financial Openness Indicator. 

The least open economies are Argentina, China, and Russia, while the most open are Chile, Romania, Singapore, 

and South Africa. 11The lag length is selected using Schwarz’s Bayesian 

information criterion, which points to one lag except for 

Romania (two lags). 12Granger Causality/Block Exogeneity Wald Tests were 

carried out to identify the exogeneity of variables. Wald 

statistics show that global excess liquidity, and credit and 

market risk premia, are generally not infl uenced by other 

variables in the model and are therefore treated as exogenous. Moreover, the Cholesky decomposition method 

(degrees-of-freedom adjusted) is used to transform the 

impulses. Several different orderings were performed 

keeping the most exogenous variable fi rst and the most 

endogenous last. Changes in the ordering of the variables 

do not have a material effect on the statistical signifi cance 

of the spillover effects.
145

Impact of Emerging Market Equity Valuation on 

Consumption and Investment

Does the signifi cant impact of stock market changes on the consumption of advanced 

economies carry over to emerging markets? 

Research has shown that while results vary 

depending on the methodology or sample 

period used, estimates in the United States 

regarding changes in consumption are 0.3 to 

0.7 percent for every 10 percent fl uctuation 

in the real returns in the stock market. Similarly, wealth effects are estimated to be 0.15 

to 0.3 percent in Japan and 0.1 to 0.3 percent 

across various European countries.13

The relationship between equity fl uctuations 

and consumption patterns is also robust across 

emerging market countries, but is often of 

smaller magnitude, in most cases probably due 

to the lower and more concentrated domestic 

participation rate in the equity market and the 

relatively recent signifi cant increases in equity 

valuations relative to GDP. Estimates for 22 EM 

economies indicate a statistically signifi cant but 

small real wealth effect of about 0.15 percent 

for a 10 percent change in the stock market 

between 1985 and 2007 (see Box 4.1 for more 

details).

The effects of stock market valuation changes 

are also relevant for a number of other macroeconomic variables such as government 

revenues and private investment. Results from 

estimating a simple model for private investment suggest that a 10 percent change in stock 

prices would lead to about a 1 percent change 

in investment, which is a substantially stronger 

effect than on private consumption.14

13For more information, see IMF (2000, 2002), 

Ludwig and Sløk (2004), and Slacalek (2006), among 

others. 14It should also be noted that, when comparing the 

investment of publicly listed fi rms to aggregate private 

investment, in a number of EMs, unlisted companies 

may represent a larger share of economic activity. 

Nonetheless, their dynamics seem to mirror those of the 

aggregate economy. 

The Role of Local Institutional Investors

It has been argued that a broad and diverse 

domestic investor base can cushion the domestic capital market from abrupt changes in 

international investor sentiment. The growing 

role of EM institutional investors—as funded 

pension schemes and insurance sectors grow 

across EM economies—contributes to broadening and diversifying the pool of investment 

into EM equities, both domestically and across 

EM borders. (Box 4.2 discusses aspects of this 

issue.)

The long-term horizon of institutional investors, such as pension and mutual funds and 

insurance companies, can play a stabilizing 

role in domestic equity markets. At the same 

time, the rise of more active and short-termoriented local investors, such as hedge funds 

and private equity funds, albeit rather small at 

present, diversifi es and broadens the investor 

base, although herding behavior among some 

more active participants potentially could also 

exacerbate market volatility during a downturn 

or fi nancial turmoil.

Key Results and Conclusions

The key results from the empirical analysis above suggest that both global forces and 

domestic economic fundamentals contribute to 

emerging equity prices. More specifi cally, the 

panel estimations and the vector autoregression 

analysis presented in the previous sections can 

be summarized as follows:

• There is evidence of spillovers to emerging 

markets through the equity market channel 

as shown by the significant negative relationship of global credit and market risk premia, 

and the positive relationship of global excess 

liquidity to EM equity prices, indicating that 

emerging equity markets are integrated with 

advanced economies.15

• Strong domestic economic growth and indicators of financial deepening such as credit 

15These results are consistent with studies on Asian and 

Latin American economies (IMF, 2007c, 2008a, 2008b).

KEY RESULTS AND CONCLUSIONS
CHAPTER 4 SPILLOVERS TO EMERGING EQUITY MARKETS

146

This box examines whether stock market valuation 

changes in emerging markets would affect consumption and fi nds that there is such an effect. With large 

increases in emerging equity prices in recent years and 

a fairly sharp recent correction, the magnitude of the 

impact of stock market wealth on household consumption becomes of interest. 

Although there is a large body of literature 

about the effect of asset price changes on private 

consumption in advanced economies, such studies are scarce for emerging market (EM) economies.1 To shed more light on the relationship 

between stock market valuation changes and 

private consumption, a simple two-step panel 

model following Bayoumi and Edison (2003) is 

estimated, covering 1985–2007 for 22 EMs in the 

Morgan Stanley Capital International EM equity 

index (MSCI).2 The two-step procedure allows 

for differentiation between the long- and the 

short-run stock market wealth effects.3

At the fi rst stage, the following levels-equation 

is specifi ed:

Cit = αBMit–1 + βGDPit–1 + γSMit–1 + εit, (1)

where 

 Cit = log real private consumption expenditure per capita in country i and year t,

BMit–1 = log real broad money per capita and a 

proxy for money wealth 

Note: Heiko Hesse prepared this box.

1Estimates of stock market wealth effects in the 

United States range from 0.3 to 0.7 percent for a 

10 percent change in equity prices and vary across 

other advanced economies (see, for example, Bayoumi and Edison, 2003; Slacalek, 2006; and IMF, 

2000, 2002). Funke (2004) presents evidence of a 

small but statistically signifi cant stock market wealth 

effect in 16 EMs over 1985–2000 ranging from 0.2 to 

0.4 percent. 2The countries are Argentina, Brazil, Chile, China, 

Colombia, Czech Republic, Hungary, India, Indonesia, Israel, Korea, Malaysia, Mexico, Morocco, Pakistan, Peru, Philippines, Poland, Russia, South Africa, 

Thailand, and Turkey. 3The long-run impact from changes in the stock 

market index on private consumption expenditure 

per capita refers to the estimated sample from 1985–

2007 (and to 1997–2007 for one specifi cation).

GDPit–1 = log real GDP per capita capturing 

household income, and 

 SMit–1 = log real stock market index. 

At the second stage, differences are taken 

of the variables in equation (1) and an error 

correction mechanism (ECM), taken as the 

residual from (1), is introduced as well as the 

infl ation rate.

Cit = b1ΔBMit–1 + b2ΔGDPit–1 + b3ΔSMit–1

 + b4ECMit + b5infl ationit–1 + εit, (2)

where b3 is the short-run marginal propensity to 

consume out of equity wealth, proxied by the 

change in stock market returns, and b4 is the 

rate at which the system converges to deviations 

from long-run equilibrium.

The table presents estimated stock market 

wealth effects under different model specifi cations, distinguishing between the long- and 

short-run relationship. A 10 percentage increase 

in the stock market valuation would on average lead to an increase of private per capita 

consumption of 0.12 percent in the short 

run and 0.15 percent in the long run. These 

Box 4.1. Is There a Stock Market Wealth Effect in Emerging Markets?

Stock Market Wealth Effect, 1985–2007

(Percent change in private consumption expenditure from a 

10 percent change in stock market returns)

Model Specification Short-Run Long-Run 

Baseline 0.12 0.15

(0.029)** (0.002)***

Threshold of +/– 20 percent 0.12 0.14

(0.092)* (0.057)*

Threshold of +/– 30 percent 0.07 0.36

(0.381) (0.001)***

1997–2007 0.12 0.21

(0.094)* (0.005)***

Sources: IMF, World Economic Outlook and International 

Financial Statistics databases; World Bank, World Development 

Indicators database; S&P Emerging Markets Database; 

Bloomberg L.P.; Datastream; and IMF staff estimates.

Note: The results are based on a two-step procedure 

with estimates from a short- and long-run relationship. The 

threshold of +/– 20 percent includes only observations where 

the stock market increased/decreased by more than 20 percent 

during any given year. The models are estimated with country 

fixed effects and include year dummy variables as well as 

robust standard errors. Nonstationarity and cointegration tests 

were conducted. P-values are in parentheses: *** significant at 

1 percent level; ** significant at 5 percent level; and *significant 

at 10 percent level.
147

growth and higher market capitalization are 

key driving forces for equity prices, which 

supports the view that high EM equity prices 

are driven, at least partially, by underlying 

domestic fundamentals.

• Exchange rate expectations have been playing an important role in determining EM 

equity prices. The effective appreciation of 

EM currencies during the U.S. dollar’s recent 

depreciation has contributed to EM equity 

price rises.

These results are reinforced by the results of 

the VAR models and the increased correlations 

during recent years between EM stock market 

indices and indices in Europe and the United 

States as well as rising correlations between EMs 

themselves.

The empirical analysis also suggests a relatively small but signifi cant impact of changes 

in EM stock market valuations on EM consumption and investment. In general, there is 

no one-size-fi ts-all approach for dealing with 

the stock market wealth effect. The approach 

should be country-specifi c, depending on 

domestic factors such as the monetary policy 

framework, fi nancial regulation, the degree of 

consumer leverage, especially for retail investors, and the level of stock market participation 

in the economy.

The empirical results point to a number of 

macroeconomic policy challenges facing the 

authorities in emerging markets. The protracted global fi nancial crisis and dual infl ation 

and growth threats are presenting a more serious test for emerging markets: 

• As the experience of the past year has shown, 

emerging market authorities need to be 

alert to the negative impact of slowing global 

growth and its effect, among other factors, on 

EM asset prices.

results are of the same order of magnitude as 

Funke (2004). Restricting the sample period to 

1997–2007, when stock market valuations exhibited large increases as percent of GDP, shows a 

slightly higher stock market wealth effect. 

Additional specifi cations consider the effect 

of large valuation changes, where the equity 

market increased/decreased by more than 

20 and 30 percent, respectively, in any given 

year. These fi ndings suggest a slightly more 

pronounced wealth effect for the 30 percent 

threshold.4

In addition to private consumption, the 

wealth effects of stock market valuation 

changes are also relevant for a number of other 

key macroeconomic variables, notably govern4The model presented here does not take into 

account other factors affecting household wealth, 

such as increases in real estate values, structural differences across EM fi nancial markets, such as depth 

and volatility, and the relatively low degrees in EMs of 

consumer leverage and stock market participation. 

ment revenues and private investment. A simple 

fi xed-effects model (with year dummy variables) of real private investment regressed on 

contemporaneous and lagged real stock market 

returns for 19 EM countries over the period 

1985–2007 suggests that a 10 percent change 

in stock prices would lead to about 1 percent 

change in investment. This is in line with the 

results in Henry (2000), who utilizes the same 

methodology.

Overall, the fi ndings suggest that there is a 

stock market wealth effect in EM countries, 

albeit smaller than in advanced economies. 

What are the possible implications for policymakers? The signifi cant effect of stock market 

fl uctuation on private consumption and therefore demand is something that policymakers 

need to be aware of, especially since large buildups of asset prices are often followed by busts. 

Such considerations become even more relevant 

as—with continuing fi nancial integration—

domestic asset prices are increasingly infl uenced 

by regional and global factors.

KEY RESULTS AND CONCLUSIONS
CHAPTER 4 SPILLOVERS TO EMERGING EQUITY MARKETS

148

This box examines the evolving role of emerging 

market institutional investors in emerging stock 

markets and notes illustrative examples from a few 

selected countries. Emerging institutional investors 

with a long-term horizon can play a stabilizing role 

in emerging equity markets, while herding behavior 

among more active and short-term-oriented institutional investors could potentially exacerbate market 

volatility during a downturn or fi nancial turmoil. 

Financial globalization has been associated 

with increasing emerging market (EM) investor 

diversifi cation (IMF, 2007a, 2007b, Chapter 1). 

In recent years, the EM institutional investor (EMII) base, including pension funds, 

insurance companies, and mutual funds, has 

grown substantially in many EM countries. 

Specifi cally, total assets of EM pension funds 

have risen by more than 140 percent since 

2000, driven by both rising asset prices and 

the growth of domestic pension systems (fi rst 

fi gure). The strong growth of the EM mutual 

fund industry corresponds with rapidly increasing equity valuations, fast income growth, and 

the emergence of a growing middle class channeling some of its savings away from traditional 

bank deposits. 

In recent years, countries such as Brazil, 

Korea, Malaysia, and Mexico have adopted legislation to build up their insurance and mutual 

fund sectors as well as domestic pension systems, 

while some have also eased domestic as well as 

outward investment limits by EMIIs. In addition, 

some EM sovereigns have set up new types of 

investment funds—often to complement their 

sovereign wealth funds—that actively invest 

in foreign assets in both advanced and other 

emerging markets, including equities.1 

Despite its growth in recent years, the level 

of the EMII asset base remains relatively small 

1Sovereign wealth funds typically invest most of 

their assets beyond their national borders, with a 

few exceptions where they are also active players in 

domestic fi nancial markets.

Box 4.2. The Role of Emerging Market Institutional Investors in Emerging Market Equities

EMEA

Asia

Latin America

1996 97 98 99 2000 01 02 03 04 05 06

0

200

400

600

800

1000

1200

Source: JPMorgan Chase & Co.

Note: EMEA = Emerging Europe, Middle East, and Africa.

Emerging Market Economies: Pension Fund 

Assets Under Management

(In billions of U.S. dollars)

EMEA

Asia

Latin America

2000 01 02 03 04 05 06 07

0

500

1000

1500

2000

2500

3000

Source: Investment Company Institute.

Note: EMEA = Emerging Europe, Middle East, and Africa.

Emerging Market Economies: Total Assets 

Under Management of Mutual Funds

(In billions of U.S. dollars)

Note: Heiko Hesse is the primary author of this 

box.
149

compared to that of advanced economies, and 

it varies across countries and regions (second 

fi gure). For example, Turkey’s mutual fund 

industry and private pension funds are very 

small compared to some of their middle-income 

peers, with 70 percent of the free fl oat on the 

stock market held by nonresidents.2 In contrast, 

South Africa has a thriving and large EMII 

base with the state-owned pension fund (Public 

Investment Corporation—PIC)—one of the 

largest in the world—being the biggest domestic 

equity holder, and with the insurance sector 

having the highest penetration (in terms of premia to GDP) among EMs. Similarly in Brazil, a 

large and diverse EMII base has contributed to 

the deepening of the fi nancial market. However, 

compared to advanced economies, the asset 

allocation of pension funds and insurance com2The relatively undeveloped state of Turkey’s 

nonbank fi nancial sector may be partly due to past 

periods of macroeconomic volatility and high infl ation. Recent legislation with regard to private pension 

funds and insurance led to some rapid growth of the 

local EMII asset base, albeit from a low level.

panies in many EMs tends to include a higher 

proportion of government securities, in part 

due to government regulation.

Amplifi ers or Not?

In principle, a diverse investor base—with 

regard to investment horizons and risk 

appetite—can contribute to fi nancial stability, especially by spreading risks more widely. 

In practice, however, whether EMIIs are a 

stabilizing factor moderating boom-and-bust 

cycles in equity markets depends on their asset 

allocation behavior, which in turn is driven by 

their risk profi le, investment horizon, liability 

profi le, and constraints imposed by their governance and regulation.

The stable investment horizon and typically buy-and-hold behavior of pension funds 

and insurance companies can contribute to 

a fi nancially stable base for domestic stock 

markets.3 These EMIIs are able to keep their 

3See Roldos (2007) for a discussion of instances 

where pension funds can contribute to asset price 

distortions.

Israel

Kazakhstan

Korea

Chile

Bolivia

El Salvador

Peru

Brazil

Colombia

Argentina

Uruguay

Mexico

Costa Rica

Dominican Republic

Honduras

Poland

Hungary

Czech Republic

Croatia

Sloveni

Bulgaria

a

Slovak Republic

Russia

Turkey

Macedon

Ukr

ia, FYR

aine

0

10

20

30

40

50

60

70

Sources: International Federation of Pension Funds Administrators; OECD; IMF, World Economic Outlook database; and IMF staff estimates.

Pension Fund Assets in Selected Countries, 2006

(In percent of GDP)

KEY RESULTS AND CONCLUSIONS
CHAPTER 4 SPILLOVERS TO EMERGING EQUITY MARKETS

150

• Changes in exchange rate expectations and a 

perception that monetary policy is “behind the 

curve” could be a possible trigger for further 

EM equity price declines. Therefore, greater 

exchange rate flexibility may help reduce pressures related to one-way exchange rate bets, 

while prompt and transparent monetary policy 

may help stabilize investor risk appetite.

asset allocations unchanged during market 

downturns or even go against market trends 

and may enhance the depth and breadth of 

equity markets. In addition, guided by their 

mandate, they pursue portfolio reallocations 

gradually, which can limit abrupt price movements. For example, in Korea, institutional 

investors’ stable funding to the market can 

act as a buffer against the reversal of foreign 

equity infl ows, especially since they are highly 

domestically oriented in their portfolios. 

However, the regulatory changes of recent 

years are reducing home bias, as indicated 

by the increasing foreign allocations of the 

Korean National Pension Fund as well as retail 

investors. 

In some larger EM countries, foreign investors, including hedge funds, can contribute 

to equity price volatility. For example, foreign 

investor sentiment dominates developments 

in the Turkish stock market since free fl oat 

holdings of local EMIIs are small compared to 

those of nonresidents. In the Brazilian equity 

market, one-third of the trading volume is by 

foreign investors, and nonresidents were the 

main players in the initial public offerings 

market until this segment signifi cantly slowed 

following the onset of the international fi nancial turbulence. 

Overall, the EMII base is expected to continue to grow, benefi ting from high GDP and 

export earnings growth and further enabling 

reforms and regulation, especially in the 

domestic pension systems and asset allocation 

liberalization, leading to a larger investment 

share of EMIIs in emerging equities.

Box 4.2 (concluded)

South Africa

Brazil

Chile

Mexico

Costa Rica

Argentina

Slovenia

Poland

Hungary

Slovak Republic

Czech Republic

Russia

Turkey

Romania

Korea

Taiwan

India

China

Pakistan

Philippines

0

5

10

15

20

25

30

35

40

45

50

Sources: Investment Company Institute; European Fund and Asset Management Association; and other national mutual fund associations.

Mutual Fund Assets in Selected Countries, 2007

(In percent of GDP)
151

The empirical results discussed above and the 

policy challenges associated with them point to 

the need to enhance the structural resilience of 

emerging equity markets. Although not directly 

stemming from the empirical work presented, 

there are a number of actions that facilitate the 

development of emerging capital markets more 

broadly and enhance their resilience:

• Fostering deeper and more liquid capital 

markets with diverse institutional investors, 

including domestic and foreign as well as both 

buy-and-hold and active participants, helps 

improve the resilience of a national financial 

market to withstand shocks.

• Establishing funded pension schemes and a 

domestic insurance sector broadens the local 

institutional investor base and creates demand 

for long-term financial instruments.

• Increasing the demand for long-term instruments may in turn facilitate the development 

of more diverse local financial products. This 

may entail extending the yield curve on sovereign and corporate fixed-income securities as 

well as equities, which would help to deepen 

and diversify domestic financial markets and, 

therefore, help to mitigate sensitivity to external shocks.16

• The benefits of discretionary interference by 

the authorities in a structured and formally 

regulated market—for example, to artificially 

delay or limit the magnitude of price declines 

during times of financial stress—need to be 

counterbalanced against possible reputational 

costs that can derail capital market development over the medium term.

• When reforms are adopted in the legal, 

regulatory and prudential, and accounting 

systems, they need to be consistent with international standards.

• At the firm level, the governance and transparency of performance and decision-making 

structures need improvement, and greater 

16For example, in Korea the strong growth of local 

savings instruments such as mutual funds, of which over 

50 percent are invested in equities, has increased the 

household sector’s stake in equity markets substantially.

emphasis needs to be put on strengthening 

risk management.

As regards the development of EM equity 

markets more concretely, robust securities 

market infrastructure and institutions, including in the securities exchange and clearing 

systems, are necessary for developing a sound 

capital market.17 Specifi cally, a well-functioning securities market and supporting 

infrastructure—such as repo markets, margin 

trading, securities lending, and derivatives markets—can reduce transaction costs and foster 

liquidity. A well-structured stock exchange also 

spreads risks through loss-sharing arrangements with members. Systemic risk is reduced 

when trading occurs in a formally regulated 

exchange that engages in market surveillance, 

undertakes adequate disclosure, and imposes 

appropriate margin requirements and position 

limits. Careful implementation is important at 

each stage:

• Enhancements to the securities market 

infrastructure and the introduction of new 

financial instruments in particular need to 

be properly sequenced, and with appropriate oversight in place, so as to reap the full 

benefits of innovation, while at the same time 

managing with due care the risks to financial 

stability and ensuring the proper functioning 

of markets.

• Derivatives markets in particular need to be 

developed within an appropriate framework 

of solid product design, regulation, and 

sound market infrastructure and oversight.

• A prerequisite for a proficient short selling 

mechanism is a well-functioning stock-lending system, which can develop with a sufficiently large participation of stock lenders to 

reduce the costs of covering short positions 

and, overall, to minimize the occurrence of 

a short squeeze. It should also be noted that 

17For further elaboration on these topics, see Purfi eld 

and others (2006), Fratzscher (2006), and Shah and others (forthcoming). See Ghosh and Revilla (2007) for a 

discussion on East Asia in particular. 

KEY RESULTS AND CONCLUSIONS
CHAPTER 4 SPILLOVERS TO EMERGING EQUITY MARKETS

152

introducing shorting in a bearish equity market could exacerbate the downturn.

In sum, while EM economies have so far 

remained relatively unaffected by the global 

turbulence, this chapter shows that they are not 

immune. Active steps to enhance their resilience are still needed in most EM countries.

Annex 4.1. Panel Estimation 

Specifi cation and Results18

Two panel fi xed-effects data models are 

employed to examine the factors driving equity 

valuations. In addition, the analysis employs 

nonstationary panel techniques to deal explicitly 

with the nonstationarities that are present in 

some individual time series that constitute the 

members of the panel. The error correction 

terms from the panel cointegration are taken as 

inputs to the driving factors panel specifi cation. 

Therefore, this combination of conventional 

and nonstationary panel techniques allows us 

to focus explicitly on the stochastic and nonstochastic long-run trend features of the data and 

fi lter out the effects of short-run transitional 

dynamics.

Two steps are taken in each group of models.

Step 1. Unit root tests are performed for 

all variables and then cointegration tests are 

executed for nonstationary variables. The regressions of the price indices and nonstationary 

explanatory variables are then run to obtain 

error correction terms.19

Step 2. The driving factor model is run by 

incorporating the error correction terms.

Data Panel with Equity Market Capitalization

The fi rst panel uses a monthly sample of 30 

economies from January 2001 to May 2008.20

The dependent variable—equity price growth—

18Tao Sun prepared this annex.

19See Psalida and Sun (forthcoming) for more details 

on panel cointegration tests performed on these data. 20The period since 2001 is chosen for two reasons. 

First, 2001 marked the beginning of a long upward trend 

for EM equities starting from a low point. Second, data 

is modeled as a function of fi ve domestic indicators, four global or external indicators, and an 

error correction term. The coeffi cients for these 

variables provide a measure of the magnitude of 

spillovers.

The panel regressions are run on a sample of 

the following 30 economies:

Asia: China, Hong Kong SAR, India, Indonesia, Korea, Malaysia, Pakistan, Philippines, Singapore, Sri Lanka, Thailand, and Vietnam.

Europe, Middle East, and Africa (EMEA): 

Bulgaria, Croatia, Czech Republic, Estonia, 

Hungary, Latvia, Lithuania, Nigeria, Romania, 

Russia, South Africa, and Turkey.

Latin America: Argentina, Brazil, Chile, 

Colombia, Mexico, and Peru.

The dependent variable in the panel regression is the log change in the U.S. dollar equity 

price indices, while the panel cointegration utilizes the log level in the U.S. dollar equity price 

indices. The independent variables are as follows:

• Domestic Factors

(a) GDP growth: The change of the monthly 

consensus forecast for annual GDP growth 

rate in the Consensus Forecast Database, in 

local currency, as a proxy for macroeconomic 

fundamentals; 21

(b) Interest rate differential: The spread 

between the one-year domestic and the sixmonth U.S. treasury rates; 22

(c) Exchange rate expectation: The log change 

in forward exchange rates (including nondeliverable forward rates in 18 economies for which 

data were available); the log level in forward 

exchange rates in the panel cointegration;

(d) Infl ation rate differential: The difference 

between the domestic and the U.S monthly 

infl ation rates;

availability, especially portfolio equity infl ows, was much 

improved from that date. 21Forecasted GDP growth is chosen as a proxy for fundamentals rather than corporate profi ts, dividend yield, 

or taxes for reasons of data availability for this country 

sample. 22We use the six-month U.S. treasury rate because we 

do not have the one-year U.S. treasury rate for the full 

period sample.
153

(e) Equity market capitalization: Equity 

market capitalization-to-GDP ratio, calculated 

as (1 + growth of market capitalization)/

(1+ GDP growth) in the panel regressions, as 

a proxy of capital market size; the log level 

of equity market capitalization in the panel 

cointegration.

• Global or External Factors

(a) Global excess liquidity: The difference 

between broad money growth and estimates 

for money demand in the euro area, Japan, 

and the United States;

(b) Market volatility premium: The implied 

volatility of the S&P 500 index (VIX) as a 

proxy for market volatility;

(c) Credit risk premium: The level of the 

10-year U.S. dollar swap spread, which is the 

difference between the 10-year U.S. dollar 

swap rate and the 10-year U.S. treasury note, 

as a proxy for aggregate default risk; 23

(d) Portfolio equity infl ows: The growth of 

fl ows to emerging markets’ (Asia, EMEA, and 

Latin America) equity funds as a proxy for 

portfolio equity infl ows.

Error Correction Term

By employing nonstationary panel techniques, we obtain error correction terms from 

the panel cointegrations among log equity 

price index, log market capitalization, and 

the log forward exchange rate. The economic 

rationale for this cointegration is as follows: 

equity prices are related to market capitalization; an expected exchange rate appreciation 

could promote capital infl ows and encourage 

domestic capital to remain in domestic equity 

markets, thus driving up equity prices. The 

results indicate that a 1 percent reduction in 

the error correction term is associated with 

a 0.05 percent increase in equity price growth, 

refl ecting the adjustment over time for closing 

the gap with respect to the long-run relationship between these variables (see Table 4.2 for 

the results).

23For similar approaches see Hartelius, Kashiwase, and 

Kodres (2008), and Gonzalez-Hermosillo (2008).

Unit Root Tests and Panel Cointegration Test

Unit root tests show that the indicators 

used in panel cointegration tests—the log 

equity price index, log market capitalization, and log forward exchange rate—are 

nonstationary, while all other variables 

used in the panel regressions are stationary 

(Table 4.4). According to the Pedroni panel 

cointegration tests performed on the log equity price index, log market capitalization, and 

log forward exchange rate, the majority of statistics point to the conclusion that the variables 

are cointegrated (Table 4.5) (Pedroni, 1999).

Table 4.4. Unit Root Tests 

Log Equity 

Price 

Indices

Log Forward

Exchange Rate

Log Market 

Capitalization

Levin-Lin rho-stat 4.13 1.99 4.76

Levin-Lin t-rho-stat 5.39 3.73 6.81

Levin-Lin ADF-stat 3.57 3.87 4.57

IPS ADF-stat 4.30 1.14 5.22

Sources: IMF, International Financial Statistics database; S&P 

Emerging Markets Database; Bloomberg L.P.; and Datastream. 

Note: The critical values are –1.28 (10 percent) and –1.64 

(5 percent). 

ANNEX 4.1. PANEL ESTIMATION SPECIFICATION AND RESULTS

Table 4.5. Pedroni Heterogeneous Panel 

Cointegration Tests

Log Equity Price Index, 

Log Forward Exchange Rate, 

Log Market Capitalization

Panel v–stat 3.47**

Panel rho–stat –3.67**

Panel pp–stat –2.16**

Panel adf–stat 1.34

Group rho–stat –4.28**

Group pp–stat –3.69**

Group adf–stat 1.49

Sources: IMF, International Financial Statistics database; S&P 

Emerging Markets Database; Bloomberg L.P.; and Datastream. 

Note: The fi rst four tests are pooled within-dimension tests 

and the last three tests are group mean between-dimension tests. 

Specifi cally, the fi rst three statistics correct for serial correlation, 

the fourth parametric test similar to the ADF-type test allows the 

number of lags in the model to be estimated directly. The last 

three statistics treat the parameter of interest as varying across the 

members of the panel. The critical values for the variance statistic 

(v-stat) are 1.28 (10 percent) and 1.64 (5 percent), and those for all 

others are –1.28 (10 percent) and –1.64 (5 percent).
CHAPTER 4 SPILLOVERS TO EMERGING EQUITY MARKETS

154

Figure 4.13. Equity Price Response to Market Risk Premium Increase

(Percentage points)

1 2 3 4 5 6 7 8 9 10 11 12

–0.08

–0.06

–0.04

–0.02

0

0.02

0.04

0.06

0.08 Chile

1 2 3 4 5 6 7 8 9 10 11 12

–0.08

–0.06

–0.04

–0.02

0

0.02

0.04

0.06

Russia 0.08

1 2 3 4 5 6 7 8 9 10 11 12

–0.08

–0.06

–0.04

–0.02

0

0.02

0.04

0.06

0.08 South Africa

Sources: S&P Emerging Markets Database; Datastream; and IMF staff estimates.

Note: The solid line represents impulse response; the dotted lines are two-standard-error confidence interval.

Sources: S&P Emerging Markets Database; Datastream; and IMF staff estimates.

Note: The solid line represents impulse response; the dotted lines are two-standard-error confidence interval.

Figure 4.11. Equity Price Response to Global Excess Liquidity Increase

(Percentage points)

1 2 3 4 5 6 7 8 9 10 11 12

–0.08

–0.06

–0.04

–0.02

0

0.02

0.04

0.06

0.08 Russia

1 2 3 4 5 6 7 8 9 10 11 12

–0.08

–0.06

–0.04

–0.02

0

0.02

0.04

0.06

0.08 Romania

1 2 3 4 5 6 7 8 9 10 11 12

–0.08

–0.06

–0.04

–0.02

0

0.02

0.04

0.06

0.08 Singapore

Sources: S&P Emerging Markets Database; Datastream; and IMF staff estimates.

Note: The solid line represents impulse response; the dotted lines are two-standard-error confidence interval.

Figure 4.12. Equity Price Response to Credit Risk Premium Increase

(Percentage points)

1 2 3 4 5 6 7 8 9 10 11 12

–0.08

–0.06

–0.04

–0.02

0

0.02

0.04

0.06

0.08 Chile

1 2 3 4 5 6 7 8 9 10 11 12

–0.08

–0.06

–0.04

–0.02

0

0.02

0.04

0.06

0.08 Argentina

1 2 3 4 5 6 7 8 9 10 11 12

–0.08

–0.06

–0.04

–0.02

0

0.02

0.04

0.06

0.08 China
155

Data Panel with Domestic Credit and the MSCI

The second panel employs the same countries, frequency of data, and explanatory 

variables, the only difference being that equity 

market capitalization is replaced by private 

credit in the domestic factors and the MSCI is 

added to the global factors. Specifi cally:

• Domestic credit: The log change in the credit 

to the private sector, as a proxy for financial 

deepening; and

• MSCI: The log change in the Morgan Stanley 

Capital International world index of 23 major 

stock markets.24

See Table 4.3 for the results.

Annex 4.2. Vector Autoregression 

Model Results

Figures 4.11 to 4.13 in this annex present 

some of the equity price impulse responses for 

the seven countries examined.

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157

GLOSSARY

Asset-backed commercial Commercial paper collateralized by a pool of loans, leases, receivables, 

paper (ABCP) or structured credit products.

Asset-backed security (ABS) A security that is collateralized by the cash fl ows from a pool of underlying assets, such as loans, leases, or receivables. Often, when the cash 

fl ows are collateralized by real estate, an ABS is called a mortgagebacked security. 

Asset-backed securities index An index of credit default swaps referencing 20 bonds collateralized by 

(ABX) subprime mortgages or home equity loans.

Auction rate security Long-term debt or preferred stock for which the coupon or dividend is 

regularly reset via Dutch auction.

Basel II An accord providing a comprehensive revision of the Basel capital 

adequacy standards issued by the Basel Committee on Banking Supervision. Pillar I of the accord covers the minimum capital adequacy standards for banks, Pillar II focuses on enhancing the supervisory review 

process, and Pillar III encourages market discipline through increased 

disclosure of banks’ fi nancial condition.

Call (put) option A fi nancial contract that gives the buyer the right, but not the obligation, to buy (sell) a fi nancial instrument at a set price on or before a 

given date.

Carry trade A leveraged transaction in which borrowed funds are used to take 

a position in which the expected interest return exceeds the cost of 

the borrowed funds. The “cost of carry” or “carry” is the difference 

between the interest yield on the investment and the fi nancing cost 

(e.g., in a “positive carry” the yield exceeds the fi nancing cost).

Collateralized debt obligation A structured credit security backed by a pool of securities, loans, or 

(CDO) credit default swaps, where interests in the security are divided into 

tranches with differing repayment and interest earning streams. The 

pool can be either managed within preset parameters or static. If the 

CDO is backed by other structured credit securities, it is called a structured-fi nance CDO, and if it is backed solely by other CDOs, it is called 

a CDO-squared.

Collateralized loan obligation A collateralized debt obligation backed by whole commercial loans, 

(CLO) revolving credit facilities, or letters of credit.

Commercial paper A private unsecured promissory note with a short maturity. U.S. issues 

need not be registered with the Securities and Exchange Commission 

provided the maturity is within 270 days; typically new issues refi nance 

maturing ones.
GLOSSARY

158

Conduit A legal entity whose assets consist of various types of loans, receivables, 

and structured credit products. A conduit’s liabilities usually consist of 

short-term commercial paper and are supported by a liquidity facility 

with 100 percent coverage.

Convertible arbitrage strategy A strategy entailing a long position on a convertible security and a 

short position on the underlying stock into which it converts.

Covered bond A debt obligation on which the investor has recourse fi rst to a pool of 

assets that secures the bond. Unlike asset-backed securities, covered 

bonds remain on the issuer’s consolidated balance sheet and thus 

provide creditors with a second level of protection through recourse to 

other assets of the borrower.

Credit default swap (CDS) A default-triggered credit derivative. Most CDS default settlements are 

“physical,” whereby the protection seller buys a defaulted reference 

asset from the protection buyer at its face value. “Cash” settlement 

involves a net payment to the protection buyer equal to the difference 

between the reference asset face value and the price of the defaulted 

asset.

Credit derivative A fi nancial contract under which an agent buys or sells risk protection against the credit risk associated with a specifi c reference entity 

(or entities). For a periodic fee, the protection seller agrees to make a 

contingent payment to the buyer on the occurrence of a credit event 

(default in the case of a credit default swap).

Credit spread The spread between benchmark securities and other debt securities 

that are comparable in all respects except for credit quality (e.g., the 

difference between yields on U.S. Treasuries and those on single Arated corporate bonds of a certain term to maturity). 

Derivative A fi nancial contract whose value derives from underlying securities 

prices, interest rates, foreign exchange rates, commodity prices, or market and other indices.

EMBIG JPMorgan’s Emerging Market Bond Index Global, which tracks the 

total returns for traded external debt instruments in 34 emerging market economies with weights roughly proportional to the market supply 

of debt.

Emerging markets (EMs) Developing countries’ fi nancial markets that are less than fully developed, but are nonetheless broadly accessible to foreign investors.

Government-sponsored A fi nancial institution that provides credit to specifi c groups or areas 

enterprise (GSE) of the economy, such as farmers or housing. Most GSEs maintain legal 

and/or fi nancial ties to the government.

Fixed-effects panel data An econometric panel data technique that accounts for possible timemodel invariant unobserved characteristics in the underlying data.
GLOSSARY

159

GARCH model The Generalized Autoregressive Conditional Heteroskedasticity 

(GARCH) framework allows for the modeling of the volatility—second 

moments—of the variables of interest. In the Dynamic Conditional Correlation (DCC) GARCH model, correlations are time-varying.

Hedge fund Investment pool, typically organized as a private partnership and often 

resident offshore for tax and regulatory purposes. These funds face 

few restrictions on their portfolios and transactions. Consequently, they 

are free to use a variety of investment techniques—including short 

positions, transactions in derivatives, and leverage—to attempt to raise 

returns and risk.

Hedging Offsetting an existing risk exposure by taking an opposite position in 

the same or similar risk—for example, in related derivatives contracts.

Home equity loan/home Loans or lines of credit drawn against the equity in a home, calculated 

equity line of credit as the current market value less the value of the fi rst mortgage. When 

(HEL/HELOC) originating an HEL or HELOC, the lending institution generally secures 

a second lien on the home, i.e., a claim that is subordinate to the fi rst 

mortgage (if it exists). 

Hybrid security A broad group of securities that combine the elements of both debt 

and equity. They pay a fi xed or fl oating rate coupon or dividend until 

a certain date, at which point the holder has a number of options 

including converting the securities into the underlying share. Therefore, unlike equity, the holder has a predetermined cash fl ow, and, 

unlike a fi xed-income security, the holder has the option to gain when 

the issuer’s equity price rises. It is typically subordinate to other debt 

obligations in the capital structure of the fi rm.

Implied volatility The expected volatility of a security’s price as implied by the price of 

options or swaptions (options to enter into swaps) traded on that security. Implied volatility is computed as the expected standard deviation 

that must be imputed by investors to satisfy risk neutral arbitrage conditions, and is calculated with the use of an options pricing model such 

as Black-Scholes. 

Impulse response function An econometric technique typically used for vector autoregressions that 

traces the impact to the variable in question over time from a shock to 

another variable.

Institutional investor A bank, insurance company, pension fund, mutual fund, hedge fund, 

brokerage, or other fi nancial group that takes investments from clients 

or invests on its own behalf.

Interest rate swap An agreement between counterparties to exchange periodic interest 

payments on some predetermined principal amount. For example, one 

party will make fi xed-rate, and receive variable-rate, interest payments.
GLOSSARY

160

Intermediation The process of transferring funds from the ultimate source to the ultimate 

user. A fi nancial institution, such as a bank, intermediates when it obtains 

money from depositors or other lenders and onlends to borrowers.

Internal-ratings-based (IRB) A methodology of the Basel Capital Accord that enables banks to use 

approach their internal models to generate estimates of risk parameters that are 

inputs into the calculation of their risk-based capital requirements.

Investment-grade obligation A bond or loan is considered investment grade if it is assigned a credit 

rating in the top four categories. S&P and Fitch classify investmentgrade obligations as BBB- or higher, and Moody’s classifi es investmentgrade obligations as Baa3 or higher. 

Leverage The proportion of debt to equity (also assets to equity and assets to 

capital). Leverage can be built up by borrowing (on-balance-sheet leverage, commonly measured by debt-to-equity ratios) or by using off-balance-sheet transactions.

Leveraged buyout (LBO) The acquisition of a company using a signifi cant level of borrowing 

(through bonds or loans) to meet the cost of acquisition. Usually, the 

assets of the company being acquired are used as collateral for the 

loans.

Leveraged loan A bank loan that is rated below investment grade (BB+ and lower by 

S&P or Fitch, and Baa1 and lower by Moody’s) to fi rms with a sizable 

debt-to-EBITDA (earnings before interest, taxes, depreciation, and 

amortization) ratio, or trade at wide spreads over LIBOR (e.g., more 

than 150 basis points).

LIBOR The London Interbank Offered Rate is an index of the interest rates 

at which banks offer to lend unsecured funds to other banks in the 

London wholesale money market. 

Loss given default (LGD) The fraction of a loan or security’s nominal value that would not be 

recovered following default.

Mark-to-market The valuation of a position or portfolio by reference to the most recent 

price at which a fi nancial instrument can be bought or sold in normal 

volumes. 

Mark-to-model Pricing of a position or portfolio based on a set of assumptions and 

fi nancial models.

Mortgage-backed security A security that derives its cash fl ows from principal and interest 

(MBS) payments on pooled mortgage loans. MBSs can be backed by residential mortgage loans or loans on commercial properties.

Originate-to-distribute model A business model of fi nancial intermediation, under which fi nancial 

institutions originate loans such as mortgages, repackage them into 

securitized products, and then sell them to investors.
GLOSSARY

161

Overnight index swap (OIS) An interest rate swap whereby the compounded overnight rate in the 

specifi ed currency is exchanged for some fi xed interest rate over a 

specifi ed term.

Pfandbriefe The German word (literally “letter of pledge”) for covered bonds. They 

are mainly used to refi nance mortgages or public projects, and issued 

only by specially authorized banks.

Prime brokerage A bundled package of services provided by banks or investment banks 

to hedge funds, including global custody, securities lending, margin 

fi nancing, portfolio reporting and accounting, and other operational 

support.

Private equity Shares in privately held companies that are not listed on a public stock 

exchange.

Private equity fund A pool of capital invested by a private equity partnership, typically involving the purchase of majority stakes in companies and/or entire business 

units to restructure their capital, management, and organization.

Probability of default (PD) The likelihood that a loan or security will not be repaid and will fall 

into default.

Regulatory arbitrage The process of taking advantage of differences in regulatory treatment 

across countries or different fi nancial sectors, as well as differences 

between the real (economic) risks and the regulatory risk, to reduce 

regulatory capital requirements.

Repurchase agreement (repo) An agreement whereby the seller of securities agrees to buy them back 

at a specifi ed time and price. The transaction is a means of borrowing 

cash collateralized by the securities “repo-ed” at an interest rate implied 

by the forward repurchase price.

Risk aversion The degree to which an investor who, when faced with two investments 

with the same expected return but different risk, prefers the one with 

the lower risk. That is, it measures an investor’s aversion to uncertain 

outcomes or payoffs.

Risk premium The extra expected return on an asset that investors demand in 

exchange for accepting the higher risk associated with the asset.

Securitization The creation of securities from a pool of preexisting assets and receivables 

that are placed under the legal control of investors through a special 

intermediary created for this purpose (a “special purpose vehicle” [SPV] 

or “special purpose entity” [SPE]). In the case of “synthetic” securitizations, the securities are created from a portfolio of derivative instruments.

Sovereign wealth fund (SWF) A special investment fund created/owned by a government to hold 

assets for long-term purposes; it is typically funded from reserves or 

other foreign currency sources, including commodity export revenues, 

and predominantly owns, or has signifi cant ownership of, foreign currency claims on nonresidents.
GLOSSARY

162

Spread See “credit spread” above. Other defi nitions include (1) the gap 

between the bid and ask price of a fi nancial instrument; and (2) the 

difference between the price at which an underwriter buys an issue 

from the issuer and the price at which the underwriter sells it to the 

public.

Stock market wealth effect The impact from changes in stock values on macroeconomic variables, 

for instance, consumption or investment.

Structured credit product An instrument that pools and tranches credit risk exposure, including 

mortgage-backed securities and collateralized debt obligations. 

Structured investment vehicle A legal entity whose assets consist of asset-backed securities and various 

(SIV) types of loans and receivables. An SIV’s funding liabilities are usually 

tranched and include short- and medium-term debt; the solvency of 

the SIV is put at risk if the value of the assets of the SIV falls below the 

value of the maturing liabilities.

Subprime mortgage A mortgage to borrowers with impaired or limited credit histories, who 

typically have low credit scores.

Swap An agreement between counterparties to exchange periodic interest 

payments based on different reference fi nancial instruments on a predetermined notional amount. 

Tender option bond A debt obligation that grants the debt holder the right to require the 

issuer, or a third-party agent of the issuer, to purchase the debt, typically at par value.

Value-at-risk (VaR) An estimate of the loss, over a given horizon, that is statistically unlikely 

to be exceeded at a given probability level.

Variable rate demand A fl oating rate, long-term debt instrument on which the coupon is 

obligation reset periodically, typically daily or weekly, and where the investor has 

the option to sell the instrument back to the issuer or issuer’s agent.

Vector autoregression (VAR) An econometric time series technique that models the dynamic interaction among the variables of interest.

Yield curve The relation between the interest rates (or yields) and time to maturity 

for debt securities of equivalent credit risk.
SUMMING UP BY THE ACTING CHAIR

Executive Directors had a broad-ranging 

discussion of recent fi nancial market developments, the prospects for global fi nancial stability, and the linkages between fi nancial markets 

and the macroeconomy. Since the issuance of 

the April 2008 Global Financial Stability Report

(GFSR), the global fi nancial system has come 

under increasing stress, which is unlikely to 

dissipate quickly. Directors welcomed the steps 

taken by governments in recent weeks to support the housing and mortgage fi nance markets 

and ensure systemic liquidity support. Private 

fi nancial institutions, for their part, have taken 

notable remedial actions, by attempting to 

adjust their balance sheets and business models 

through revealing losses, raising capital, securing stable funding sources, and improving their 

risk management systems. Directors agreed that 

these efforts should assist eventual recovery in 

the fi nancial system. However, they observed 

that near-term liquidity pressures, deleveraging, 

and banking consolidation still pose signifi cant 

challenges, even while varying in their implications for banks across the advanced economies—in particular, between U.S. and euro area 

banks, and indeed, even within the European 

region. In any case, as the events of recent days 

demonstrate, the adjustment process has not 

yet run its full course, and fi nancial institution 

failures and other market disturbances may still 

lie ahead.

Directors stressed that the preeminent policy 

challenge in the present context of uncertainty 

and turmoil is to mitigate the risks of an adverse 

feedback loop between the fi nancial system 

and the economy in the near term, while establishing a clear road to lasting recovery for the 

fi nancial system. Directors underscored the 

growing ramifi cations of the fi nancial crisis for 

economic growth in the advanced economies 

and the greater risk of spillovers to emerging 

markets. The considerable uncertainty regarding the depth and breadth of the credit default 

cycle is likely to continue to curtail credit availability and place a further drag on economic 

recovery. Directors observed that fi nancial 

market stresses—while emanating from and 

concentrated in advanced countries—have an 

evident potential to threaten emerging markets, 

where policy frameworks are being tested. In 

addition, more recent fi nancial market developments suggest that national authorities will need 

to remain resolute in the face of pressures for 

further public resources to contain systemic risks 

and the economic fallout of threats to the solvency of some institutions. This will be challenging against the backdrop of the deterioration in 

the global economic outlook, with a deepening 

housing downturn, tighter credit conditions, 

and heightened infl ation risks, which has—in 

turn—increased fi nancial stresses. Against this 

background, Directors called for continued vigilance with respect to macrofi nancial linkages, 

and welcomed the further work in the GFSR in 

this area. 

Directors agreed with the staff’s recommendation that national authorities will need 

to stand ready for further policy actions, as 

needed, in the event of a weaker-than-expected 

global economic recovery in 2009. Specifi cally, 

Directors were mindful that central banks and 

fi scal authorities will need to work together 

to address problems in the fi nancial system 

as well as the prospects for slower growth and 

The following remarks by the Acting Chair were made at the conclusion of the Executive Board’s discussion of the 

Global Financial Stability Report on September 15, 2008.

163

ANNEX
ANNEX SUMMING UP BY THE ACTING CHAIR

164

potentially higher infl ation. At the same time, 

various authorities will need to make clear to 

the public their respective roles in the event 

of broader threats to fi nancial institutions’ 

solvency. Directors generally agreed that central banks should primarily deal with liquidity 

issues, and that the fi scal authorities should 

take the lead on major institutions’ solvency 

issues. They pointed out that liquidity and 

solvency issues are increasingly diffi cult to 

separate, but that transparency and clear communication about how various problems are to 

be addressed will be critical to their successful 

resolution. Directors stressed that public support for troubled fi nancial institutions in the 

advanced economies should take account of 

moral hazard concerns, as well as its mediumterm implications for the public sector balance 

sheet, and be backed up where possible by concrete restructuring plans. They also called for 

close monitoring of the performance of fi nancial institutions and for continued coordination 

among national supervisors. 

Directors underlined the importance of continuing to build the infrastructure necessary to 

handle effectively the liquidation of fi nancial 

institutions in diffi culty. Several Directors recognized that new product growth, such as credit 

default swaps, could raise important issues about 

resolution, as the newest bankruptcy of Lehman 

Brothers Holdings demonstrates. 

Directors discussed the recent conservatorship 

of the U.S. government-sponsored enterprises 

(GSEs), Fannie Mae and Freddie Mac. They 

noted that the U.S. Treasury and the Federal 

Housing Finance Agency (FHFA) have acted 

promptly to avert fallout of potentially global 

systemic dimensions, and that the new plan 

should help support the U.S. housing and mortgage fi nance markets. Although a large tail risk 

event was avoided, Directors acknowledged that 

the broad stresses in the fi nancial system will 

still be present for some time to come. Directors 

saw the recent intervention as an opportunity 

to develop and implement concrete plans for a 

broad restructuring of the GSEs that would aim 

at ensuring market discipline and facilitating 

competition, while minimizing fi scal costs and 

containing systemic risks. 

Directors agreed that the deleveraging process 

raises several near-term issues. The private sector 

should take the lead on corrective actions—with 

regulators and supervisors providing guidance 

to regulated fi nancial institutions to promote 

steady improvement. The environment for raising capital has become more diffi cult, and sustained efforts by private fi nancial institutions will 

be required to raise capital and reduce assets. 

Directors acknowledged that reductions in assets 

would naturally limit credit extension. They 

also saw scope for reassessing funding models 

and mechanisms, and restructuring balance 

sheets and business models. Directors noted the 

updated estimate of potential future losses to 

fi nancial institutions of their U.S.-based loans 

and securities, suggesting that the updated values could also be linked with information about 

progress on fi nancial institutions’ loss recognition and capital-raising activities. 

Directors noted that emerging market 

countries have shown resilience to the global 

fi nancial crisis thus far, refl ecting policy 

improvements, strengthened reserves, and high 

growth in recent years, but are now experiencing the spillovers from the stresses in advanced 

economies. External corporate and bank borrowing is becoming more costly and less available, with falling domestic credit growth and 

slowing housing markets. Lower risk appetite 

of institutional investors has led to some easing 

in capital fl ows to emerging markets. Directors 

emphasized the considerable differences in the 

experience of emerging markets, and noted that 

the countries that are most vulnerable to spillovers are likely to be those with large current 

account defi cits, recent rapid credit growth, and 

adverse terms of trade shocks. 

Directors discussed various aspects of the 

fi nancial crisis and its implications, as presented 

in Chapters 2, 3, and 4 of the GFSR. They welcomed the analysis in Chapter 2 of the stress in 

bank funding markets and its potential effect 

on monetary policy transmission. They observed 

that much of banks’ funding liquidity diffi cul-
SUMMING UP BY THE ACTING CHAIR

165

ties, as proxied by the difference between the 

3-month London Interbank Offer Rate (LIBOR) 

and the overnight index swap rate, have been 

related to distress risk—a combination of credit 

and liquidity risks experienced by fi nancial institutions. In the cases of the sterling LIBOR and 

the Euribor, an additional factor infl uencing 

those spreads was the inability to obtain dollar 

funding. Directors agreed that central banks 

would need to provide liquidity support, attempt 

to lower distress risks, and address foreign 

exchange funding issues.

Directors welcomed the analysis in Chapter 2 

of the interest rate transmission mechanism for 

monetary policy in the United States and in the 

euro area. The empirical results in the chapter 

suggest that the interest rate transmission channel became less predictable during the crisis—

more so in the United States than in the euro 

area, and more so for the transmission between 

the effective policy rates and intermediate interest rates than for fi nal consumer and business 

borrowing rates. Several Directors saw this experience as pointing to the importance of resolving 

the underlying counterparty credit risk issues.

Directors noted that the crisis has raised issues 

about how fi nancial fi rms arrive at valuations 

of assets and liabilities, particularly in illiquid 

markets. This issue was examined in Chapter 3 

by looking at the relationship between fair value 

accounting (FVA) and procyclicality. Directors 

noted that, under different circumstances, FVA 

could either add to or mitigate the procyclicality of banks’ balance sheets. In some cases, the 

procyclical effects could be dampened by certain 

enhancements to FVA methodologies, but it was 

noted that there are also other ways of accomplishing this through higher capital buffers and 

provisions. On balance, Directors generally considered that FVA is still the way forward. They also 

noted that some alternative accounting methods 

may reduce transparency and blur the assessment 

of fi nancial institutions’ capital positions. Directors supported the staff recommendation that 

accounting, prudential, and risk management 

practices will need to work more coherently to 

safeguard fi nancial stability in the future. 

Directors welcomed the analysis in Chapter 4 on the impact of the advanced country 

subprime crisis on equity markets in emerging 

market countries. Directors noted that global, 

external factors (including global liquidity, 

market and credit risks) have been found to 

be important infl uences on the evolution of 

domestic equity prices, and that, over time, 

the integration of equity markets has made the 

transmission of shocks more likely to occur. 

Directors welcomed the staff’s additional work 

to gauge the possible effect of equity price 

changes on real domestic private consumption 

and investment, which shows that, although 

there is a statistically signifi cant consumer 

wealth effect from equities, it is relatively small 

compared with advanced economies, and subject 

to a time lag. Even though the real economic 

effects are likely limited, Directors believed it is 

still important for emerging market countries to 

move forward to strengthen the infrastructure 

underpinning their equity markets, and to make 

them transparent, well-functioning parts of their 

fi nancial systems so as to limit potential negative 

effects on the real economy. 

In conclusion, while global fi nancial stability 

continues to be impaired and systemic risks are 

still elevated, most Directors viewed the ongoing 

adjustment process as one that could lead, ultimately, to a stronger, more robust global fi nancial 

system. In particular, Directors noted that regulators and supervisors have a responsibility to ensure 

comprehensive fi nancial industry oversight that 

addresses past problems of lax supervision and 

regulatory gaps. They noted that, while national 

authorities in individual countries move forward 

to stem the effects of the fi nancial market crisis 

and its macroeconomic consequences, the IMF 

should, in coordination with national bodies and 

other international groups such as the Financial 

Stability Forum, sustain and intensify its efforts to 

assess developments and infl uence policy. They 

agreed that the IMF is uniquely placed for adding 

a multilateral perspective to policy responses to the 

crisis, including through the GFSR and the World 

Economic Outlook, and country Financial Sector 

Assessment Programs (FSAPs).
166

* * *

Following the Executive Board meeting on the 

GFSR on September 15, the Executive Board was 

briefed informally on September 22 on the latest fi nancial market developments and the exceptional policy 

responses contemplated by the United States and other 

governments. The following paragraphs provide an 

overview of this briefi ng. 

Directors noted that acute strains in fi nancial 

markets had been observed in mid-September 

and acknowledged that these threatened a 

severe disruption of the fi nancial system as it 

became apparent that, without public intervention, market adjustment would be disorderly 

and more costly for the real economy. A host 

of authorities accordingly moved to provide 

a needed rapid and concerted response. 

Although the specifi cs of the measures are still 

being worked out, Directors emphasized that 

the recent events will likely have far-reaching 

consequences for the structure of the fi nancial 

sector and for the macroprudential framework 

required to support fi nancial stability. The staff 

observed that global fi nancial strains will likely 

slow the growth recovery, as anticipated in the 

October 2008 World Economic Outlook. Moreover, 

the risk of a stronger adverse feedback loop 

between the fi nancial system and the broader 

economy has been heightened and still represents the principal threat to the projected 

upturn. The effects of this distress are also 

spilling over to many other parts of the world, 

including emerging market countries. 

Directors noted that addressing the attendant liquidity strains and resolving the troubled 

institutions on a case-by-case basis had failed to 

support market confi dence, requiring the U.S. 

authorities to shift to a more comprehensive 

approach. Uncertainty about which institutions 

might be rescued and which would not intensifi ed systemic concerns and obliged the Federal 

Reserve to extend a large collateralized emergency loan to insurance company AIG. It also 

required the commitment of a potentially large 

amount of public resources to facilitate the functioning of markets. Directors were broadly supportive of this strategy, and welcomed the U.S. 

authorities’ intention to design the specifi cs of 

the proposal with due regard to minimizing the 

risk to the U.S. budget. 

ANNEX SUMMING UP BY THE ACTING CHAIR
167

STATISTICAL APPENDIX

This statistical appendix presents data 

on fi nancial developments in key 

fi nancial centers and emerging markets. It is designed to complement the 

analysis in the text by providing additional data 

that describe key aspects of fi nancial market 

developments. These data are derived from a 

number of sources external to the IMF, including banks, commercial data providers, and 

offi cial sources, and are presented for information purposes only; the IMF does not, however, 

guarantee the accuracy of the data from external sources. 

Presenting fi nancial market data in one 

location and in a fi xed set of tables and 

charts, in this and future issues of the GFSR, 

is intended to give the reader an overview of 

developments in global fi nancial markets. 

Unless otherwise noted, the statistical appendix refl ects information available up to July 31, 

2008.

Mirroring the structure of the chapters of the 

report, the appendix presents data separately 

for key fi nancial centers and emerging market 

countries. Specifi cally, it is organized into three 

sections: 

• Figures 1–14 and Tables 1–9 contain information on market developments in key financial 

centers. This includes data on global capital 

flows, and on markets for foreign exchange, 

bonds, equities, and derivatives as well as sectoral balance sheet data for the United States, 

Japan, and Europe.

• Figures 15 and 16, and Tables 10–21 present 

information on financial developments in 

emerging markets, including data on equity, 

foreign exchange, and bond markets, as well 

as data on emerging market financing flows.

• Tables 22–27 report key financial soundness 

indicators for selected countries, including 

bank profitability, asset quality, and capital 

adequacy.
STATISTICAL APPENDIX

168

List of Tables and Figures 

Key Financial Centers 

Figures 

 1. Major Net Exporters and Importers of Capital in 2007 169

 2. Exchange Rates: Selected Major Industrial Countries 170

 3. United States: Yields on Corporate and Treasury Bonds 171

 4. Selected Spreads 172

 5. Nonfi nancial Corporate Credit Spreads 173

 6. Equity Markets: Price Indices 174

 7. Implied and Historical Volatility in Equity Markets 175 

 8. Historical Volatility of Government Bond Yields and Bond Returns for Selected Countries 176

 9. Twelve-Month Forward Price/Earnings Ratios 177 

10. Flows into U.S.-Based Equity Funds 177

11. United States: Corporate Bond Market 178

12. Europe: Corporate Bond Market 179

13. United States: Commercial Paper Market 180

14. United States: Asset-Backed Securities 181

Tables 

 1. Global Capital Flows: Infl ows and Outfl ows 182

 2. Global Capital Flows: Amounts Outstanding and Net Issues of International Debt 

Securities by Currency of Issue and Announced International Syndicated Credit 

Facilities by Nationality of Borrower 184 

 3. Selected Indicators on the Size of the Capital Markets, 2007 185 

 4. Global Over-the-Counter Derivatives Markets: Notional Amounts and Gross Market 

Values of Outstanding Contracts 186 

 5. Global Over-the-Counter Derivatives Markets: Notional Amounts and Gross Market 

Values of Outstanding Contracts by Counterparty, Remaining Maturity, and Currency 187 

 6. Exchange-Traded Derivative Financial Instruments: Notional Principal Amounts 

Outstanding and Annual Turnover 188 

 7. United States: Sectoral Balance Sheets 190 

 8. Japan: Sectoral Balance Sheets 191 

 9. Europe: Sectoral Balance Sheets 192 

Emerging Markets 

Figures 

15. Emerging Market Volatility Measures 193 

16. Emerging Market Debt Cross-Correlation Measures 194

Tables

10. Equity Market Indices 195

11. Foreign Exchange Rates 198

12. Emerging Market Bond Index: EMBI Global Total Returns Index 200

13. Emerging Market Bond Index: EMBI Global Yield Spreads 202 

14. Emerging Market External Financing: Total Bonds, Equities, and Loans 204 

15. Emerging Market External Financing: Bond Issuance 206

16. Emerging Market External Financing: Equity Issuance 208 

17. Emerging Market External Financing: Loan Syndication 209 

18. Equity Valuation Measures: Dividend-Yield Ratios 211 

19. Equity Valuation Measures: Price-to-Book Ratios 212 

20. Equity Valuation Measures: Price/Earnings Ratios 213 

21. Emerging Markets: Mutual Fund Flows 214 

Financial Soundness Indicators 

22. Bank Regulatory Capital to Risk-Weighted Assets 215 

23. Bank Capital to Assets 217 

24. Bank Nonperforming Loans to Total Loans 219 

25. Bank Provisions to Nonperforming Loans 222 

26. Bank Return on Assets 224 

27. Bank Return on Equity 226 
KEY FINANCIAL CENTERS

169

Countries That Export Capital1

Countries That Import Capital3

Figure 1. Major Net Exporters and Importers of Capital in 2007

Source: IMF, World Economic Outlook database as of September 25, 2008.

1As measured by countries’ current account surplus (assuming errors and omissions are part of the 

capital and financial accounts). 2Other countries include all countries with shares of total surplus less than 1.9 percent.

3As measured by countries’ current account deficit (assuming errors and omissions are part of the 

capital and financial accounts). 4Other countries include all countries with shares of total deficit less than 2.5 percent.

Other countries 2

20.3%

Taiwan Province of China 1.9%

Sweden 2.2%

United Arab Emirates 2.2%

Singapore 2.2%

Kuwait 2.8%

Netherlands 3.0%

Norway 3.4%

Switzerland

4.1% Russia

4.4% Saudi Arabia

5.5%

Japan

12.1%

Germany

14.5%

China

21.3%

Other countries 4

20.2%

United States

49.2%

Spain 9.8%

United Kingdom

8.0%

Australia 3.8%

Italy 3.5%

Greece 3.0%

Turkey 2.5%
80

85

90

95

100

105

110

75

80

85

90

95

100

105

70

80

90

100

110

120

130

140

150

90

95

100

105

110

95

100

105

110

115

120

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2.0

2.1 1.0

1.2

1.4

1.6

1.8

2.0

United States

2000 02 04 06 08

2000 02 04 06 08

2000 02 04 06 08

2000 02 04 06 08

2000 02 04 06 08

Figure 2. Exchange Rates: Selected Major Industrial Countries

(Weekly data)

Bilateral exchange rate (left scale)1

Nominal effective exchange rate (right scale)2

Japan

United Kingdom

Euro Area

Switzerland

Sources: Bloomberg L.P.; and the IMF Global Data System.

Note: In each panel, the effective and bilateral exchange rates are scaled so that an upward movement implies an appreciation of the respective local currency. 1Local currency units per U.S. dollar except for the euro area and the United Kingdom, for which data are shown as U.S. dollars per local currency. 22000 = 100; constructed using 1999–2001 trade weights.

0.8

1.0

1.2

1.4

90 1.6

100

110

120

130

140

170

STATISTICAL APPENDIX
KEY FINANCIAL CENTERS

171

0

2

4

6

8

10

12

14

16

18

20

–200

0

200

400

600

800

1000

1200

Merrill Lynch high-yield bond index

Merrill Lynch high-yield bond index

10-year treasury bond

Aaa

Aaa

Baa

Baa

Yields

(In percent)

Figure 3. United States: Yields on Corporate and Treasury Bonds

(Monthly data)

Yield Differentials with 10-Year U.S. Treasury Bond

(In basis points)

1978 80 82 84 86 88 90 92 94 96 98 2000 02 04 06 08

1978 80 82 84 86 88 90 92 94 96 98 2000 02 04 06 08

Sources: Bloomberg L.P.; and Merrill Lynch.
STATISTICAL APPENDIX

172

–40

0

40

80

120

160

–40

0

40

80

120

160

–40

0

40

80

120

160

Figure 4. Selected Spreads

(In basis points; monthly data)

1998 99 2000 01 02 03 04 05 06 07 08

1998 99 2000 01 02 03 04 05 06 07 08

1998 99 2000 01 02 03 04 05 06 07 08

United States

Euro area

Japan

Repo Spread1

Commercial Paper Spread2

Swap Spreads3

Sources: Bloomberg L.P.; and Merrill Lynch.

1Spread between yields on three-month U.S. treasury repo and on three-month U.S. treasury bill.

2Spread between yields on 90-day investment-grade commercial paper and on three-month U.S. treasury bill.

3Spread over 10-year government bond.
KEY FINANCIAL CENTERS

173

0

20

40

60

80

100

120

140

0

40

80

120

160

200

240

0

50

100

150

200

250

300

350

1998 99 2000 01 02 03 04 05 06 07 08

1998 99 2000 01 02 03 04 05 06 07 08

1998 99 2000 01 02 03 04 05 06 07 08

United States

United States

United States

Euro area

Euro area

Euro area

Japan

Japan

Japan

AA Rated

Figure 5. Nonfinancial Corporate Credit Spreads

(In basis points; monthly data)

A Rated

BBB Rated

Source: Merrill Lynch.
STATISTICAL APPENDIX

174

0

200

400

600

800

1000

1200

0

100

200

300

400

500

0

20

40

60

80

100

120

Figure 6. Equity Markets: Price Indices

(January 1, 1990 = 100; weekly data)

Nasdaq

Wilshire 5000

S&P 500

United States

Europe

Japan

Germany (DAX)

Europe

(FTSE Eurotop 300)

United Kingdom

(FTSE All-Share)

Source: Bloomberg L.P.

Topix

Nikkei 225

1990 92 94 96 98 2000 02 04 06 08

1990 92 94 96 98 2000 02 04 06 08

1990 92 94 96 98 2000 02 04 06 08
KEY FINANCIAL CENTERS

175

0

10

20

30

40

50

60

70

0

10

20

30

40

50

60

70

0

10

20

30

40

50

60

70

0

10

20

30

40

50

60

70

1998 2000 02 04 06 08 1998 2000 02 04 06 08

1998 2000 02 04 06 08 1998 2000 02 04 06 08

S&P 500 Nikkei 225

DAX

Figure 7. Implied and Historical Volatility in Equity Markets

(Weekly data)

FTSE 100

VIX1

Implied volatility Historical volatility

Sources: Bloomberg L.P.; and IMF staff estimates.

Note: Implied volatility is a measure of the equity price variability implied by the market prices of call options on equity futures. Historical volatility is 

calculated as a rolling 100-day annualized standard deviation of equity price changes. Volatilities are expressed in percent rate of change. 1VIX is the Chicago Board Options Exchange volatility index. This index is calculated by taking a weighted average of implied volatility for the eight S&P 500 

calls and puts.
STATISTICAL APPENDIX

176

0

5

10

15

20

25

30

35

40

0

5

10

15

20

25

30

0

20

40

60

80

100

120

0

5

10

15

20

25

30

0

5

10

15

20

25

30

35

40

0

5

10

15

20

25

30

0

5

10

15

20

25

30

35

40

0

5

10

15

20

25

30

Yield volatility

(left scale)

Yield volatility

(left scale)

Yield volatility

(left scale)

Yield volatility

(left scale)

Return volatility

(right scale)

Return volatility

(right scale)

Return volatility

(right scale)

Return volatility

(right scale)

1998 02 04 06 08 2000 2000 1998 02 04 06 08

1998 02 04 06 08 2000 2000 1998 02 04 06 08

United States

Germany

Japan

United Kingdom

Figure 8. Historical Volatility of Government Bond Yields and Bond Returns for Selected Countries1

(Weekly data)

Sources: Bloomberg L.P.; and Datastream.

1Volatility calculated as a rolling 100-day annualized standard deviation of changes in yield and returns on 10-year government bonds. Returns are based on 

10-plus-year government bond indices.
KEY FINANCIAL CENTERS

177

0

5

10

15

20

25

30

35

–5

5

15

25

35

45

55

65

75

1990 92 94 96 98 2000 02 04 06 08

Japan

(right scale)

United States

(left scale)

Germany

(left scale)

Source: I/B/E/S.

Figure 9. Twelve-Month Forward Price/Earnings Ratios

–60

–40

–20

0

20

40

60

80

0

200

400

600

800

1000

1200

1400

1600

1800

Sources: Investment Company Institute; and Datastream.

1In billions of U.S. dollars.

Figure 10. Flows into U.S.-Based Equity Funds

Net flows into

U.S.-based equity funds1

(left scale)

S&P 500

(right scale)

1990 94 96 98 2000 02 04 06 08 92
STATISTICAL APPENDIX

178

0

5

10

15

20

25

0

10

20

30

40

50

0

2

4

6

8

10

12

Investment Grade

Figure 11. United States: Corporate Bond Market

0

0.5

1.0

1.5

2.0

2.5

3.0

High-Yield

Aaa Moody’s spread1

(in percent; right scale)

Merrill Lynch high-yield spread1

(in percent; right scale)

Issuance

(in billions of U.S. dollars;

left scale)

Issuance

(in billions of U.S. dollars;

left scale)

1990 92 94 96 98 2000 02 04 08 06

1990 92 94 96 98 2000 02 04 08 06

Sources: Board of Governors of the Federal Reserve System; and Bloomberg L.P.

1Spread against yield on 10-year U.S. government bonds.
KEY FINANCIAL CENTERS

179

0

200

400

600

800

1000

1200

1400

1600

1800

0

5

10

15

20

25

30

35

40

High-Yield Spread2

(In basis points)

European Corporate Bond Issuance

(In billions of U.S. dollars)

Sources: DCM Analytics; and Datastream.

1Nonfinancial corporate bonds.

2Spread between yields on a Merrill Lynch High-Yield European Issuers Index bond and a 10-year German government

benchmark bond.

Figure 12. Europe: Corporate Bond Market1

1998 2000 01 02 03 04 05 06 07 08 99

1989 93 95 97 99 2001 03 05 07 91
STATISTICAL APPENDIX

180

0

20

40

60

80

100

120

140

160

0

50

100

150

200

250

300

350

400

1997

1991 93 95 97 99 2001 03 05 07

98 99 2000 01 02 03 04 05 06 07 08

Discount Rate Spread2

(In basis points; weekly data)

Amount Outstanding

(In billions of U.S. dollars; monthly data)

Source: Board of Governors of the Federal Reserve System.

1Nonfinancial commercial paper.

2Difference between 30-day A2/P2 and AA commercial paper.

Figure 13. United States: Commercial Paper Market1
KEY FINANCIAL CENTERS

181

0

50

100

150

200

250

300

350

0

500

1000

1500

2000

2500

3000

Automobile

Credit card

Home equity

Student loan

CBO/CDO2

Other

ABS Spread1

(In basis points)

Figure 14. United States: Asset-Backed Securities

1998

1995 97 99 2001 03 05 07 2008

Q1

2000 02 04 06 08

Sources: Merrill Lynch; Datastream; and the Securities Industry and Financial Markets Association.

1Merrill Lynch AAA Asset-Backed Master Index (fixed rate) option-adjusted spread.

2Collateralized bond/debt obligations; for 2007 and 2008 Q1, CBO/CDO amount outstanding is included in Other.

Total Amount Outstanding

(In billions of U.S. dollars)
STATISTICAL APPENDIX

Table 1. Global Capital Flows: Infl ows and Outfl ows1

(In billions of U.S. dollars)

Inflows

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

United States

Direct investment 105.6 179.0 289.4 321.3 167.0 84.4 63.8 146.0 112.6 242.0 237.5

Portfolio investment 333.1 187.6 285.6 436.6 428.3 427.6 550.2 867.3 832.0 1,126.9 1,145.1

Other investment 265.7 54.2 167.2 280.4 187.5 283.2 244.4 519.9 302.7 692.3 675.0

Reserve assets n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.

Total capital flows 704.4 420.8 742.2 1,038.2 782.9 795.2 858.3 1,533.2 1,247.3 2,061.1 2,057.7

Canada

Direct investment 11.5 22.7 24.8 66.1 27.7 22.1 7.2 –0.7 27.2 62.8 111.8

Portfolio investment 11.7 16.6 2.7 10.3 24.2 11.9 14.1 41.8 7.8 27.9 –32.5

Other investment 28.0 5.4 –10.8 0.8 7.8 5.1 12.3 –3.9 29.8 30.8 56.8

Reserve assets n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.

Total capital flows 51.2 44.8 16.6 77.2 59.7 39.0 33.6 37.1 64.8 121.5 136.0

Japan

Direct investment 3.2 3.3 12.3 8.2 6.2 9.1 6.2 7.8 3.2 –6.8 22.2

Portfolio investment 79.2 56.1 126.9 47.4 60.5 –20.0 81.2 196.7 183.1 198.6 196.6

Other investment 68.0 –93.3 –265.1 –10.2 –17.6 26.6 34.1 68.3 45.9 –89.1 48.9

Reserve assets n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.

Total capital flows 150.4 –34.0 –125.9 45.4 49.1 15.7 121.5 272.8 232.3 102.6 267.7

United Kingdom

Direct investment 37.5 74.7 89.3 122.2 53.8 25.5 27.6 77.9 195.6 146.1 224.9

Portfolio investment 43.7 35.2 183.9 255.6 69.6 76.2 155.6 159.9 240.3 292.4 420.9

Other investment 322.2 110.5 90.0 414.6 327.0 109.1 396.7 741.2 936.2 862.2 1,554.2

Reserve assets n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.

Total capital flows 403.4 220.3 363.3 792.4 450.5 210.8 579.9 979.0 1,372.1 1,300.7 2,200.0

Euro area

Direct investment . . . . . . 216.3 416.3 199.8 185.0 153.2 121.4 189.2 258.7 391.0

Portfolio investment . . . . . . 305.1 268.1 318.3 298.4 383.3 520.0 682.4 1,008.8 891.7

Other investment . . . . . . 198.4 340.3 238.1 59.9 198.0 355.8 798.7 881.8 1,255.8

Reserve assets n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.

Total capital flows . . . . . . 719.8 1,024.7 756.3 543.2 734.5 997.1 1,670.3 2,149.3 2,538.5

Emerging Markets and 

Developing Countries2

Direct investment 191.4 186.7 212.0 212.0 227.9 190.1 203.8 276.4 374.2 464.0 532.5

Portfolio investment 146.3 37.9 103.9 94.9 13.7 –13.9 84.8 133.3 201.3 336.6 441.8

Other investment 142.9 –117.9 –83.7 –7.3 –59.0 3.4 126.4 211.1 184.6 383.8 955.1

Reserve assets n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a.

Total capital flows 480.7 106.8 232.2 299.6 182.7 179.6 415.0 620.9 760.0 1,184.5 1,929.4

Sources: IMF, International Financial Statistics and World Economic Outlook databases as of September 25, 2008.

1The total net capital flows are the sum of direct investment, portfolio investment, other investment flows, and reserve assets. “Other 

investment” includes bank loans and deposits. 2This aggregate comprises the group of Other Emerging Market and Developing Countries defined in the World Economic Outlook, together 

with Hong Kong SAR, Israel, Korea, Singapore, and Taiwan Province of China.

182
KEY FINANCIAL CENTERS

Outflows

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

–104.8 –142.6 –224.9 –159.2 –142.4 –154.5 –149.6 –316.2 –36.2 –241.2 –333.3

–116.9 –130.2 –122.2 –127.9 –90.6 –48.6 –123.1 –177.4 –257.5 –499.0 –294.6

–262.8 –74.2 –165.6 –273.1 –144.7 –87.9 –54.3 –510.1 –267.0 –513.9 –661.9

–1.0 –6.7 8.7 –0.3 –4.9 –3.7 1.5 2.8 14.1 2.4 –0.1

–485.5 –353.8 –504.1 –560.5 –382.6 –294.7 –325.4 –1,000.9 –546.6 –1,251.7 –1,289.9

–23.1 –34.1 –17.3 –44.5 –36.2 –26.8 –23.6 –42.6 –29.7 –39.3 –54.0

–8.6 –15.1 –15.6 –43.0 –24.4 –18.6 –13.8 –18.9 –44.1 –69.2 –42.8

–16.2 9.4 10.2 –4.2 –10.7 –7.9 –14.2 –7.1 –16.6 –31.0 –55.1

2.4 –5.0 –5.9 –3.7 –2.2 0.2 3.3 2.8 –1.3 –0.8 –3.9

–45.4 –44.8 –28.5 –95.4 –73.4 –53.2 –48.4 –65.8 –91.7 –140.3 –155.8

–26.1 –24.6 –22.3 –31.5 –38.5 –32.0 –28.8 –31.0 –45.4 –50.2 –73.5

–47.1 –95.2 –154.4 –83.4 –106.8 –85.9 –176.3 –173.8 –196.4 –71.0 –123.5

–192.0 37.9 266.3 –4.1 46.6 36.4 149.9 –48.0 –106.6 –86.2 –260.6

–6.6 6.2 –76.3 –49.0 –40.5 –46.1 –187.2 –160.9 –22.3 –32.0 –36.5

–271.6 –75.8 13.4 –168.0 –139.2 –127.7 –242.3 –413.6 –370.8 –239.4 –494.1

–60.9 –122.8 –202.5 –246.3 –61.8 –50.3 –65.6 –98.2 –91.7 –89.5 –269.1

–85.0 –53.2 –34.3 –97.2 –124.7 1.2 –58.4 –259.2 –291.5 –366.9 –255.6

–277.8 –22.9 –97.1 –426.8 –255.5 –151.0 –415.6 –596.9 –931.6 –776.7 –1,520.0

3.9 0.3 1.0 –5.3 4.5 0.6 2.6 –0.4 –1.7 1.3 –2.6

–419.8 –198.6 –332.9 –775.6 –437.6 –199.5 –537.1 –954.7 –1,316.5 –1,231.8 –2,047.2

. . . . . . –348.8 –413.7 –298.0 –163.8 –165.4 –205.1 –459.7 –448.0 –552.0

. . . . . . –341.7 –385.3 –255.0 –163.2 –318.3 –428.1 –512.4 –667.8 –585.1

. . . . . . –30.1 –165.9 –243.6 –220.8 –284.0 –392.6 –689.8 –906.4 –1,152.9

. . . . . . 11.6 16.2 16.4 –3.0 32.8 15.6 22.9 –2.6 –6.0

. . . . . . –709.1 –948.8 –780.1 –550.8 –735.0 –1,010.2 –1,639.0 –2,024.8 –2,296.0

–41.7 –27.2 –35.3 –41.7 –41.4 –32.9 –37.5 –86.7 –115.1 –213.7 –245.6

–110.2 –9.4 –43.9 –103.9 –105.7 –88.3 –131.6 –161.2 –265.7 –525.3 –497.0

–128.9 35.3 –61.6 –124.2 42.9 20.0 –131.3 –203.4 –261.1 –409.4 –788.0

–91.4 –29.8 –102.0 –138.0 –124.7 –195.1 –364.0 –508.5 –595.9 –754.5 –1,256.3

–372.2 –31.2 –242.9 –407.8 –228.9 –296.4 –664.3 –959.7 –1,237.8 –1,902.9 –2,786.9

183
184

STATISTICAL APPENDIX

Table 2. Global Capital Flows: Amounts Outstanding and Net Issues of International Debt Securities by 

Currency of Issue and Announced International Syndicated Credit Facilities by Nationality of Borrower

(In billions of U.S. dollars)

 2008

2002 2003 2004 2005 2006 2007 Q1

Amounts outstanding of international 

debt securities by currency of issue

U.S. dollar 4,123.9 4,536.9 4,905.3 5,380.4 6,399.4 7,553.3 7,733.6

Japanese yen 433.2 487.7 530.1 471.7 486.9 577.7 668.5

Pound sterling 618.2 776.3 980.6 1,062.2 1,449.1 1,708.5 1,723.4

Canadian dollar 51.6 79.3 112.4 146.6 177.9 266.2 265.0

Swedish krona 11.1 15.8 20.9 23.2 34.3 46.7 50.8

Swiss franc 159.2 195.6 227.9 208.6 253.7 301.7 343.3

Euro 3,283.1 4,824.7 6,209.1 6,306.2 8,301.7 10,535.8 11,429.2

Other 151.9 216.6 285.0 354.5 454.4 609.5 648.7

Total 8,832.1 11,133.0 13,271.3 13,953.4 17,557.4 21,599.4 22,862.5

Net issues of international debt 

securities by currency of issue

U.S. dollar 423.5 413.1 368.4 475.1 1,018.9 1,154.0 180.3

Japanese yen –17.5 3.3 26.9 3.8 19.4 67.2 6.3

Pound sterling 52.4 84.4 132.8 197.6 222.7 228.0 28.8

Canadian dollar 3.6 15.5 25.5 29.4 32.1 51.1 8.9

Swedish krona 1.1 2.0 3.4 6.2 7.0 9.4 0.4

Swiss franc 8.0 15.8 12.7 13.1 28.1 24.5 1.7

Euro 491.9 777.5 917.2 986.0 1,201.1 1,153.0 105.1

Other 30.6 38.0 52.2 86.9 79.2 105.2 28.5

Total 993.7 1,349.6 1,539.0 1,798.1 2,608.7 2,792.4 360.1

Announced international syndicated credit 

facilities by nationality of borrower

All countries 1,296.9 1,241.4 1,806.7 2,232.3 2,121.8 2,134.1 344.4

Industrial countries 1,199.8 1,131.2 1,637.7 1,991.0 1,824.3 1,676.3 278.9

Of which:

United States 739.2 606.4 897.2 977.9 848.9 726.4 99.5

Japan 19.5 18.2 27.5 19.3 42.8 53.1 14.6

Germany 84.4 97.6 116.3 131.6 170.8 94.2 9.5

France 64.2 65.2 151.1 170.9 118.0 126.6 30.2

Italy 22.8 46.1 22.8 73.6 26.0 27.4 0.4

United Kingdom 109.9 103.9 151.4 180.6 137.0 166.4 59.2

Canada 34.9 30.2 38.7 71.3 72.7 76.5 13.4

Source: Bank for International Settlements.
185

KEY FINANCIAL CENTERS

Table 3. Selected Indicators on the Size of the Capital Markets, 2007

(In billions of U.S. dollars unless noted otherwise)

Total Reserves Stock Market Debt Securities Bank

Bonds, 

Equities, and

Bonds, Equities, 

and Bank Assets2

GDP Minus Gold1 Capitalization Public Private Total Assets Bank Assets2 (In percent of GDP)

World 54,545.1 6,448.0 65,105.6 28,632.1 51,189.8 79,821.9 84,784.5 229,712.0 421.1

European Union 15,688.8 279.7 14,730.9 8,788.0 19,433.3 28,221.3 43,146.3 86,098.5 548.8

Euro area 12,202.6 172.1 10,040.1 7,612.7 15,411.2 23,023.8 30,137.1 63,461.4 520.1

North America 15,243.6 100.5 22,108.8 7,415.8 24,049.2 31,465.0 13,776.4 67,350.3 441.8

Canada 1,436.1 41.0 2,186.6 821.8 764.0 1,585.7 2,582.3 6,354.6 442.5

United States 13,807.6 59.5 19,922.3 6,594.0 23,285.2 29,879.3 11,194.1 60,995.7 441.8

Japan 4,381.6 952.8 4,663.8 7,147.7 2,069.8 9,217.5 7,839.4 21,720.6 495.7

Memorandum items:

EU countries

Austria 371.2 10.7 236.4 217.3 438.7 655.9 598.1 1,490.5 401.5

Belgium 454.3 10.4 404.4 515.6 540.3 1,055.9 2,278.8 3,739.1 823.1

Denmark 312.0 32.5 290.9 93.3 598.2 691.5 1,075.2 2,057.6 659.4

Finland 246.3 7.1 359.1 130.1 122.0 252.1 256.4 867.5 352.2

France 2,593.8 45.7 2,737.1 1,447.2 2,919.6 4,366.8 8,685.2 15,789.1 608.7

Germany 3,320.9 44.3 2,105.2 1,700.3 3,905.8 5,606.1 6,492.7 14,204.0 427.7

Greece 313.8 0.6 265.0 453.8 134.3 588.1 503.0 1,356.0 432.1

Ireland 261.2 0.8 143.9 57.3 507.2 564.5 1,481.9 2,190.3 838.4

Italy 2,104.7 28.4 1,072.5 2,019.0 2,201.1 4,220.1 3,746.5 9,039.1 429.5

Luxembourg 49.5 0.1 166.1 0.0 104.7 104.7 774.6 1,045.4 2,110.6

Netherlands 777.2 10.3 574.5 315.6 1,705.6 2,021.2 2,347.6 4,943.4 636.0

Portugal 223.4 1.3 147.2 176.5 270.2 446.7 257.1 850.9 380.8

Spain 1,440.0 11.5 1,799.8 580.0 2,561.6 3,141.7 2,915.6 7,857.1 545.6

Sweden 454.8 27.0 576.9 168.6 492.7 661.3 681.2 1,919.4 422.0

United Kingdom 2,765.4 49.0 3,851.7 913.5 2,931.2 3,844.7 11,052.5 18,748.9 678.0

Emerging market countries3 17,281.7 4,910.1 20,950.2 4,908.4 2,911.7 7,820.1 15,003.8 43,774.1 253.3

Of which: 

Asia 7,482.4 2,988.2 13,782.7 2,556.4 1,948.5 4,505.0 9,382.3 27,670.0 369.8

Latin America 3,608.5 445.2 2,292.2 1,456.5 628.4 2,084.9 1,988.7 6,365.8 176.4

Middle East 1,563.8 341.1 1,275.9 39.5 84.3 123.8 1,166.3 2,566.0 164.1

Africa 1,099.8 289.8 1,181.7 89.2 77.0 166.2 646.3 1,994.3 181.3

Europe 3,527.2 845.9 2,417.6 766.7 173.5 940.2 1,820.2 5,178.0 146.8

Sources: World Federation of Exchanges; Bank for International Settlements; IMF, International Financial Statistics (IFS) and World Economic Outlook databases as of 

September 25, 2008; ©2003 Bureau van Dijk Electronic Publishing-Bankscope; and Standard & Poor’s Emerging Markets Database. 1Data are from IFS.

2Sum of the stock market capitalization, debt securities, and bank assets.

3This aggregate comprises the group of Other Emerging Market and Developing Countries defined in the World Economic Outlook, together with Hong Kong SAR, Israel, 

Korea, Singapore, and Taiwan Province of China.
STATISTICAL APPENDIX

186

Table 4. Global Over-the-Counter Derivatives Markets: Notional Amounts and Gross Market Values of Outstanding Contracts1

(In billions of U.S. dollars)

Notional Amounts Gross Market Values 

End-Dec. End-June End-Dec. End-June End-Dec. End-Dec. End-June End-Dec. End-June End-Dec.

2005 2006 2006 2007 2007 2005 2006 2006 2007 2007

Total 297,666 370,178 414,845 516,407 596,004 9,748 9,949 9,691 11,140 14,522

Foreign exchange 31,360 38,127 40,271 48,645 56,238 997 1,136 1,266 1,345 1,807

Forwards and forex swaps 15,873 19,407 19,882 24,530 29,144 406 436 469 492 675

Currency swaps 8,504 9,696 10,792 12,312 14,347 453 535 601 619 817

Options 6,984 9,024 9,597 11,804 12,748 138 165 196 235 315

Interest rate2 211,970 262,526 291,582 347,312 393,138 5,397 5,445 4,826 6,063 7,177

Forward rate agreements 14,269 18,117 18,668 22,809 26,599 22 25 32 43 41

Swaps 169,106 207,588 229,693 272,216 309,588 4,778 4,840 4,163 5,321 6,183

Options 28,596 36,821 43,221 52,288 56,951 597 580 631 700 953

Equity-linked 5,793 6,782 7,488 8,590 8,509 582 671 853 1,116 1,142

Forwards and swaps 1,177 1,430 1,767 2,470 2,233 112 147 166 240 239

Options 4,617 5,351 5,720 6,119 6,276 470 523 686 876 903

Commodity3 5,434 6,394 7,115 7,567 9,000 871 718 667 636 753

Gold 334 456 640 426 595 51 77 56 47 70

Other 5,100 5,938 6,475 7,141 8,405 820 641 611 589 683

Forwards and swaps 1,909 2,188 2,813 3,447 5,629 . . . . . . . . . . . . . . .

Options 3,191 3,750 3,663 3,694 2,776 . . . . . . . . . . . . . . .

Credit default swaps 13,908 20,352 28,650 42,580 57,894 243 294 470 721 2,002

Single-name instruments 10,432 13,873 17,879 24,239 32,246 171 186 278 406 1,143

Multi-name instruments 3,476 6,479 10,771 18,341 25,648 71 109 192 315 859

Unallocated 29,199 35,997 39,740 61,713 71,225 1,659 1,685 1,609 1,259 1,642

Memorandum items:

Gross credit exposure4 n.a. n.a. n.a. n.a. n.a. 1,900 2,032 2,036 2,672 3,256

Exchange-traded derivatives 31,360 38,127 40,271 48,645 56,238 . . . . . . . . . . . . . . .

Source: Bank for International Settlements.

1All figures are adjusted for double-counting. Notional amounts outstanding have been adjusted by halving positions vis-à-vis other reporting dealers. Gross market values 

have been calculated as the sum of the total gross positive market value of contracts and the absolute value of the gross negative market value of contracts with nonreporting 

counterparties. 2Single-currency contracts only.

3Adjustments for double-counting are estimated.

4Gross market values after taking into account legally enforceable bilateral netting agreements.
KEY FINANCIAL CENTERS

Table 5. Global Over-the-Counter Derivatives Markets: Notional Amounts and Gross Market Values of Outstanding Contracts by 

Counterparty, Remaining Maturity, and Currency1

(In billions of U.S. dollars)

Notional Amounts Gross Market Values

End-Dec. End-June End-Dec. End-June End-Dec. End-Dec. End-June End-Dec. End-June End-Dec.

2005 2006 2006 2007 2007 2005 2006 2006 2007 2007

Total 297,666 370,178 414,845 516,407 596,004 9,748 9,949 9,691 11,140 14,522

Foreign exchange 31,360 38,127 40,271 48,645 56,238 997 1,136 1,266 1,345 1,807

By counterparty

With other reporting dealers 12,165 15,306 15,532 19,173 21,334 323 368 438 455 594

With other financial institutions 12,721 15,123 16,023 19,144 24,154 412 471 521 557 801

With nonfinancial customers 6,474 7,698 8,716 10,329 10,751 261 297 307 333 412

By remaining maturity

Up to one year2 23,907 29,579 30,270 36,950 40,316 . . . . . . . . . . . . . . .

One to five years2 5,164 5,851 6,702 8,090 8,553 . . . . . . . . . . . . . . .

Over five years2 2,289 2,697 3,299 3,606 7,370 . . . . . . . . . . . . . . .

By major currency

U.S. dollar3 26,295 31,791 33,755 40,513 46,947 867 969 1,069 1,112 1,471

Euro3 12,857 15,344 16,037 18,280 21,806 397 472 509 455 790

Japanese yen3 7,575 9,536 9,490 10,602 12,857 255 243 325 389 371

Pound sterling3 4,424 5,217 6,135 7,770 7,979 121 148 197 174 260

Other3 11,571 14,366 15,124 20,125 22,888 354 439 431 561 723

Interest rate4 211,970 262,526 291,582 347,312 393,138 5,397 5,445 4,826 6,063 7,177

By counterparty

With other reporting dealers 91,541 114,826 127,432 148,555 157,245 2,096 2,221 1,973 2,375 2,774

With other financial institutions 95,320 114,930 125,708 153,370 184,396 2,625 2,516 2,223 2,946 3,523

With nonfinancial customers 25,109 32,770 38,441 45,387 51,497 676 708 630 742 879

By remaining maturity 

Up to one year2 69,378 90,755 104,098 132,402 127,601 . . . . . . . . . . . . . . .

One to five years2 86,550 101,909 110,314 125,700 134,713 . . . . . . . . . . . . . . .

Over five years2 56,042 69,861 77,170 89,210 130,824 . . . . . . . . . . . . . . .

By major currency

U.S. dollar 74,441 88,115 97,430 114,371 129,756 1,515 2,120 1,661 1,851 3,219

Euro 81,442 103,461 111,791 127,648 146,082 2,965 2,299 2,300 2,846 2,688

Japanese yen 25,605 32,581 38,113 48,035 53,099 294 463 297 364 401

Pound sterling 15,060 19,071 22,238 27,676 28,390 344 291 311 627 430

Other 15,422 19,298 22,009 29,581 35,811 279 273 257 375 439

Equity-linked 5,793 6,782 7,488 8,590 8,509 582 671 853 1,116 1,142

Commodity5 5,434 6,394 7,115 7,567 9,000 871 718 667 636 753

Credit default swaps 13,908 20,352 28,650 42,580 57,894 243 294 470 721 2,002

Unallocated 29,199 35,997 39,740 61,713 71,225 1,659 1,685 1,609 1,259 1,642

Source: Bank for International Settlements.

1All figures are adjusted for double-counting. Notional amounts outstanding have been adjusted by halving positions vis-à-vis other reporting dealers. Gross market values 

have been calculated as the sum of the total gross positive market value of contracts and the absolute value of the gross negative market value of contracts with nonreporting 

counterparties. 2Residual maturity.

3Counting both currency sides of each foreign exchange transaction means that the currency breakdown sums to twice the aggregate. 4Single-currency contracts only.

5Adjustments for double-counting are estimated.

187
STATISTICAL APPENDIX

188

Table 6. Exchange-Traded Derivative Financial Instruments: Notional Principal Amounts Outstanding 

and Annual Turnover

 

 1995 1996 1997 1998 1999 2000

(In billions of U.S. dollars)

Notional principal amounts outstanding

Interest rate futures 5,876.2 5,979.0 7,586.7 8,031.4 7,924.9 7,907.8

Interest rate options 2,741.8 3,277.8 3,639.9 4,623.5 3,755.5 4,734.2

Currency futures 33.8 37.7 42.3 31.7 36.7 74.4

Currency options 120.4 133.4 118.6 49.2 22.4 21.4

Stock market index futures 172.2 195.9 210.9 291.6 346.9 377.5

Stock market index options 337.7 394.5 808.7 947.4 1,510.6 1,149.2

Total 9,282.0 10,018.2 12,407.1 13,974.8 13,597.0 14,264.6

North America 4,852.3 4,841.2 6,347.9 7,395.1 6,930.6 8,168.6

Europe 2,241.2 2,828.0 3,587.3 4,397.1 4,008.9 4,198.0

Asia-Pacific 1,990.1 2,154.0 2,235.7 1,882.5 2,407.8 1,611.8

Other 198.4 195.0 236.2 300.1 249.7 286.2

(In millions of contracts traded)

Annual turnover

Interest rate futures 561.0 612.2 701.6 760.0 672.7 781.2

Interest rate options 225.5 151.1 116.8 129.7 118.0 107.7

Currency futures 99.6 73.7 73.6 54.5 37.1 43.5

Currency options 23.3 26.3 21.1 12.1 6.8 7.0

Stock market index futures 114.8 93.8 115.9 178.0 204.9 225.2

Stock market index options 187.3 172.3 178.2 195.0 322.5 481.5

Total 1,211.5 1,129.4 1,207.1 1,329.3 1,362.0 1,646.0

North America 455.0 428.3 463.5 530.0 462.8 461.3

Europe 354.8 391.7 482.8 525.9 604.7 718.6

Asia-Pacific 126.4 115.9 126.9 170.9 207.7 331.3

Other 275.5 193.4 134.0 102.5 86.8 134.9

Source: Bank for International Settlements.
189

KEY FINANCIAL CENTERS

 2008

2001 2002 2003 2004 2005 2006 2007 Q1

(In billions of U.S. dollars)

9,269.5 9,955.6 13,123.7 18,164.9 20,708.8 24,476.2 26,769.6 26,794.8

12,492.8 11,759.5 20,793.7 24,604.1 31,588.3 38,116.5 44,281.7 45,391.2

65.6 47.0 79.9 103.5 107.6 161.4 158.5 164.2

27.4 27.4 37.9 60.7 66.1 78.6 132.7 193.6

344.2 365.7 549.4 635.3 784.1 1,045.4 1,131.9 1,160.9

1,575.4 1,701.7 2,203.4 3,025.1 4,533.9 6,565.5 8,106.8 7,745.1

23,775.0 23,856.9 36,787.9 46,593.7 57,788.7 70,443.7 80,581.3 81,449.7

16,203.5 13,720.2 19,504.4 27,608.9 36,385.2 42,551.4 43,991.3 38,894.2

6,141.8 8,801.6 15,407.1 16,308.6 17,973.5 23,217.1 30,568.1 36,682.9

1,318.4 1,206.0 1,659.9 2,426.9 3,004.5 4,049.6 4,971.0 4,790.5

111.2 129.1 216.5 249.3 425.5 625.6 1,050.9 1,082.0

(In millions of contracts traded)

1,057.5 1,152.1 1,576.8 1,902.6 2,110.4 2,621.2 3,076.6 820.9

199.6 240.3 302.3 361.0 430.8 566.7 663.3 198.6

49.0 42.6 58.8 83.7 143.0 231.1 353.1 102.1

10.5 16.1 14.3 13.0 19.4 24.3 46.4 17.4

337.1 530.6 725.8 804.5 918.7 1,233.7 1,930.2 629.6

1,148.2 2,235.5 3,233.9 2,980.1 3,139.8 3,177.5 3,815.6 851.9

2,801.9 4,217.2 5,911.8 6,144.9 6,762.1 7,854.5 9,885.2 2,620.5

675.6 912.2 1,279.8 1,633.6 1,926.8 2,541.8 3,146.5 881.1

957.7 1,075.1 1,346.5 1,412.7 1,592.9 1,947.4 2,560.2 816.1

985.1 2,073.1 3,111.6 2,847.6 2,932.4 2,957.1 3,592.5 772.3

183.4 156.7 174.0 251.0 310.0 408.2 586.0 150.9
STATISTICAL APPENDIX

190

Table 7. United States: Sectoral Balance Sheets

(In percent)

2002 2003 2004 2005 2006 2007

Corporate sector

Debt/net worth 49.7 47.7 44.4 42.0 40.7 40.2

Short-term debt/total debt 29.9 26.7 26.5 25.2 25.1 25.9

Interest burden1 14.4 11.8 8.6 6.8 6.3 6.4

Household sector

Net worth/assets 81.5 81.7 81.3 80.9 80.5 80.0

Equity/total assets 21.0 24.3 24.4 23.9 24.5 23.4

Equity/financial assets 34.1 38.5 39.0 38.6 39.2 37.1

Net worth/disposable personal income 498.8 540.0 553.6 568.8 578.2 556.2

Home mortgage debt/total assets 12.6 12.8 13.3 13.9 14.3 14.6

Consumer credit/total assets 4.2 3.9 3.8 3.6 3.5 3.5

Total debt/financial assets 29.9 29.0 29.8 30.9 31.2 31.7

Debt-service burden2 13.4 13.6 13.6 14.0 14.3 14.3

Banking sector3

Credit quality

Nonperforming loans4/total loans 1.5 1.2 0.9 0.8 0.8 1.0

Net loan losses/average total loans 1.1 0.9 0.7 0.6 0.4 0.5

Loan-loss reserve/total loans 1.9 1.8 1.5 1.3 1.2 1.2

Net charge-offs/total loans 1.1 0.9 0.6 0.6 0.4 0.6

Capital ratios

Total risk-based capital 12.8 12.8 12.6 12.3 12.4 12.2

Tier 1 risk-based capital 10.0 10.1 10.0 9.9 9.8 9.5

Equity capital/total assets 9.2 9.2 10.1 10.3 10.2 10.3

Core capital (leverage ratio) 7.8 7.9 7.8 7.9 7.9 7.6

Profitability measures

Return on average assets (ROA) 1.3 1.4 1.3 1.3 1.3 0.9

Return on average equity (ROE) 14.5 15.3 13.7 12.9 13.0 9.1

Net interest margin 4.1 3.8 3.6 3.6 3.4 3.4

Efficiency ratio5 55.8 56.5 58.0 57.2 56.3 59.1

Sources: Board of Governors of the Federal Reserve System, Flow of Funds; Department of Commerce, Bureau of Economic Analysis; Federal 

Deposit Insurance Corporation; and Federal Reserve Bank of St. Louis. 1Ratio of net interest payments to pre-tax income.

2Ratio of debt payments to disposable personal income. 

3FDIC-insured commercial banks.

4Loans past due 90+ days and nonaccrual.

5Noninterest expense less amortization of intangible assets as a percent of net interest income plus noninterest income.
191

KEY FINANCIAL CENTERS

Table 8. Japan: Sectoral Balance Sheets1

(In percent)

FY2002 FY2003 FY2004 FY2005 FY2006 FY2007

Corporate sector

Debt/shareholders’ equity (book value) 146.1 121.3 121.5 101.7 98.2 97.1

Short-term debt/total debt 39.0 37.8 36.8 36.4 35.3 34.1

Interest burden2 27.8 22.0 18.4 15.6 15.2 16.2

Debt/operating profits 1,370.0 1,079.2 965.9 839.9 820.4 798.6

Memorandum item:

Total debt/GDP3 100.9 90.9 96.4 85.6 89.6 83.4

Household sector

Net worth/assets 84.4 84.5 84.6 84.9 85.0 . . .

Equity 3.5 4.9 5.7 8.7 8.8 . . .

Real estate 34.6 32.9 31.4 29.9 29.6 . . .

Net worth/net disposable income 725.2 728.5 723.0 739.0 744.1 . . .

Interest burden4 5.1 4.9 4.8 4.6 4.7 . . .

Memorandum items:

Debt/equity 448.2 317.6 268.4 174.5 170.1 . . .

Debt/real estate 45.1 47.0 49.0 50.6 50.6 . . .

Debt/net disposable income 134.2 133.2 131.5 131.6 130.9 . . .

Debt/net worth 18.5 18.3 18.2 17.8 17.6 . . .

Equity/net worth 4.1 5.8 6.8 10.2 10.3 . . .

Real estate/net worth 41.0 38.9 37.1 35.2 34.8 . . .

Total debt/GDP3 79.4 77.5 76.1 76.2 75.2 . . .

Banking sector

Credit quality

Nonperforming loans5/total loans 7.4 5.8 4.0 2.9 2.5 2.4

Capital ratio

Stockholders’ equity/assets 3.3 3.9 4.2 4.9 5.3 4.5

Profitability measures

Return on equity (ROE)6 –19.5 –2.7 4.1 11.3 8.5 6.1

Sources: Ministry of Finance, Financial Statements of Corporations by Industries; Cabinet Office, Economic and Social Research Institute, 

Annual Report on National Accounts; Japanese Bankers Association, Financial Statements of All Banks; and Financial Services Agency, The 

Status of Nonperforming Loans. 1Data are fiscal year beginning April 1. Stock data on households are only available through FY2006.

2Interest payments as a percent of operating profits.

3Revised due to the change in GDP figures.

4Interest payments as a percent of disposable income.

5Nonperforming loans are based on figures reported under the Financial Reconstruction Law.

6Net income as a percentage of stockholders’ equity (no adjustment for preferred stocks, etc.).
STATISTICAL APPENDIX

192

Table 9. Europe: Sectoral Balance Sheets1

(In percent)

2002 2003 2004 2005 2006 2007

Corporate sector

Debt/equity2 76.0 73.2 71.1 72.7 75.7 . . .

Short-term debt/total debt 35.3 35.2 35.0 37.1 39.1 . . .

Interest burden3 18.4 17.1 17.4 18.1 19.8 . . .

Debt/operating profits 339.3 328.5 326.4 348.4 381.5 . . .

Memorandum items:

Financial assets/equity 1.4 1.5 1.5 1.5 1.6 . . .

Liquid assets/short-term debt 77.3 84.6 92.7 96.5 94.2 . . .

Household sector

Net worth/assets 83.6 83.7 83.9 84.5 84.8 . . .

Equity/net worth 11.0 11.6 11.5 12.2 12.1 . . .

Equity/net financial assets 32.7 34.1 33.9 34.7 34.3 . . .

Interest burden4 6.1 6.0 5.7 5.7 5.8 . . .

Memorandum items:

Nonfinancial assets/net worth 65.9 65.8 66.0 64.8 64.8 . . .

Debt/net financial assets 54.8 53.0 52.7 48.4 47.8 . . .

Debt/income 98.2 100.7 105.5 106.6 110.5 . . .

Banking sector5

Credit quality

Nonperforming loans/total loans 2.5 2.3 2.4 2.4 2.3 1.9 

Loan-loss reserve/nonperforming loans 81.5 73.0 68.1 57.0 58.7 78.5

Loan-loss reserve/total loans 2.4 2.4 2.4 1.4 1.3 1.4

Capital ratios

Equity capital/total assets 3.1 2.9 3.8 4.4 4.4 4.5

Capital funds/liabilities 5.4 5.0 5.7 6.7 6.6 6.7

Profitability measures

Return on assets, or ROA (after tax) 0.4 0.5 0.5 0.6 0.6 0.6

Return on equity, or ROE (after tax) 9.0 11.3 13.5 14.2 14.7 12.5

Net interest margin 1.6 1.5 1.1 1.3 1.2 1.2

Efficiency ratio6 69.0 73.1 64.8 59.4 58.0 60.7

Sources: ©2003 Bureau van Dijk Electronic Publishing-Bankscope; and IMF staff estimates.

1GDP-weighted average for France, Germany, and the United Kingdom, unless otherwise noted.

2Corporate equity adjusted for changes in asset valuation.

3Interest payments as a percent of gross operating profits.

4Interest payments as percent of disposable income.

5Fifty largest European banks. Data availability may restrict coverage to fewer than 50 banks for specific indicators.

6Cost-to-income ratio.
193

EMERGING MARKETS

0

10

20

30

40

50

0

10

20

30

40

50

60

Figure 15. Emerging Market Volatility Measures

MSCI Emerging Markets index1

Emerging Market Equity Volatility

(In percent)

EMBI Global index2

Emerging Market Debt Volatility

(In percent)

Sources: For “Emerging Market Equity Volatility,” Morgan Stanley Capital International (MSCI); and IMF staff estimates. For “Emerging 

Market Debt Volatility,” JPMorgan Chase & Co.; and IMF staff estimates. 1Data utilize the MSCI Emerging Markets index in U.S. dollars to calculate 30-day rolling volatilities.

2Data utilize the EMBI Global total return index in U.S. dollars to calculate 30-day rolling volatilities. 

1998 99 2000 01 02 03 04 05 06 07 08

1998 99 2000 01 02 03 04 05 06 07 08
194

STATISTICAL APPENDIX

0

0.1

0.2

0.3

0.4

0.5

0.6

0

0.1

0.2

0.3

0.4

0.5

0.6

0

0.1

0.2

0.3

0.4

0.5

0.6

0

0.1

0.2

0.3

0.4

0.5

0.6

Figure 16. Emerging Market Debt Cross-Correlation Measures

2002 03 04 05 06 07 08

2002 03 04 05 06 07 08

2006 2007 08

2006 2007 08

Average Cross-Correlations, 2002–081

Average Regional Cross-Correlations, 2002–082

Overall 

Latin America

Latin America

Asia

Asia

Emerging Europe, Middle East, and Africa

Overall Emerging Europe, Middle East, and Africa

Average Cross-Correlations, 2006–081

Average Regional Cross-Correlations, 2006–082

Sources: JPMorgan Chase & Co.; and IMF staff estimates.

1Thirty-day moving simple average across all pair-wise return correlations of 20 constituents included in the EMBI Global.

2Simple average of all pair-wise correlations of all markets in a given region with all other bond markets, regardless of region.
195

EMERGING MARKETS

Table 10. Equity Market Indices

2008 End of Period 2007 End of Period End of Period 12-

Month 

High 

12-

Month 

Low 

AllTime 

High1

AllTime 

Q1 Q2 Q3 Q4 2003 2004 2005 2006 2007 Low1

World 1,437.4 1,402.1 1,633.6 1,588.8 1,036.3 1,169.3 1,257.8 1,483.6 1,588.8 1,682.4 1,378.6 1,682.4 423.1

Emerging Markets 1,104.6 1,087.1 1,204.9 1,245.6 442.8 542.2 706.5 912.7 1,245.6 1,338.5 956.9 1,338.5 175.3

Latin America 4,316.1 4,751.5 4,139.6 4,400.4 1,100.9 1,483.6 2,150.0 2,995.7 4,400.4 5,195.4 3,143.7 5,195.4 185.6

Argentina 3,120.7 4,187.7 3,284.1 2,918.8 933.6 1,163.0 1,857.1 3,084.1 2,918.8 4,187.7 2,396.5 4,187.7 152.6

Brazil 3,648.3 4,292.5 3,430.6 3,867.2 802.0 1,046.6 1,569.4 2,205.4 3,867.2 4,727.6 2,354.3 4,727.6 84.1

Chile 1,972.2 1,714.7 1,840.5 1,802.8 800.6 997.3 1,180.7 1,492.4 1,802.8 2,057.9 1,478.6 2,057.9 183.0

Colombia 590.0 607.1 598.1 619.3 108.6 245.0 495.7 549.8 619.3 734.0 498.2 734.0 41.2

Mexico 6,288.2 5,947.3 6,192.4 5,992.1 1,873.1 2,715.6 3,943.6 5,483.3 5,992.1 6,775.7 5,021.7 6,775.7 308.9

Peru 1,306.7 1,320.9 1,320.0 1,248.7 344.1 343.4 441.3 671.4 1,248.7 1,488.3 968.6 1,488.3 73.5

Venezuela 163.4 163.4 161.6 163.4 103.8 151.0 107.4 174.1 163.4 180.2 156.9 278.4 56.1

Asia 439.0 396.7 513.4 513.7 206.4 231.6 286.2 371.5 513.7 571.9 395.2 571.9 104.1

China 65.1 62.2 88.6 85.5 25.5 25.3 29.3 52.3 85.5 104.2 57.6 136.9 12.9

India 635.0 544.6 701.9 855.1 246.2 273.1 382.9 560.8 855.1 884.8 544.6 884.8 77.7

Indonesia 3,536.1 3,338.1 3,197.4 3,857.1 831.1 1,324.0 1,579.8 2,449.0 3,857.1 4,053.5 2,544.3 4,053.5 280.0

Korea 469.8 458.0 529.8 516.9 246.0 256.4 386.3 395.2 516.9 562.7 428.1 562.7 59.5

Malaysia 472.4 435.2 501.4 542.4 300.4 335.9 329.0 408.8 542.4 573.5 435.2 573.5 88.3

Pakistan 500.5 397.4 432.6 447.6 188.2 211.7 333.3 333.7 447.6 516.4 348.4 516.4 54.4

Philippines 594.8 478.5 733.0 721.0 303.7 381.1 431.9 620.2 721.0 803.1 478.5 917.3 132.6

Taiwan Province of China 329.4 293.7 363.9 334.0 259.1 257.7 275.8 318.3 334.0 384.5 288.4 483.5 103.9

Thailand 344.9 319.4 347.9 361.0 280.5 263.9 292.0 274.9 361.0 385.7 301.1 669.4 72.0

Europe, Middle East, 

& Africa 403.4 423.8 421.6 458.2 163.9 222.7 300.3 364.4 458.2 473.8 354.6 473.8 80.8

Czech Republic 470.2 491.8 499.1 539.5 152.9 234.8 371.5 408.3 539.5 552.3 421.8 552.3 62.8

Egypt 2,211.9 1,925.6 1,698.0 2,077.9 234.6 505.3 1,215.7 1,389.3 2,077.9 2,309.7 1,484.5 2,309.7 89.9

Hungary 1,423.6 1,325.1 1,892.5 1,738.1 646.9 1,057.0 1,447.0 1,690.0 1,738.1 2,050.8 1,321.0 2,050.8 77.1

Israel 249.9 277.6 250.9 264.0 141.4 167.4 209.3 194.4 264.0 284.4 225.4 284.4 67.6

Jordan 518.5 600.7 427.8 531.4 238.3 379.2 650.6 439.6 531.4 636.0 410.1 760.7 103.1

Morocco 567.2 545.7 455.3 453.9 171.4 189.1 231.3 342.9 453.9 576.9 412.1 576.9 99.6

Poland 2,030.7 1,757.9 2,480.6 2,341.6 1,118.3 1,419.3 1,867.4 2,253.2 2,341.6 2,690.3 1,757.9 2,690.3 99.6

Russia 1,359.5 1,492.8 1,310.1 1,536.4 461.1 479.9 813.4 1,250.3 1,536.4 1,641.5 1,127.0 1,641.5 30.6

South Africa 717.1 716.3 714.3 713.1 296.8 352.4 492.0 641.3 713.1 808.8 631.1 808.8 99.7

Turkey 605,039.3 531,671.2 844,484.4 864,616.4 319,807.8 425,008.5 645,739.1 614,409.1 864,616.4 916,239.3 531,671.2 916,239.3 425.8

Sectors

Energy 985.1 1,141.6 958.7 1,154.2 287.4 349.0 548.6 760.0 1,154.2 1,255.4 739.5 1,255.4 81.7

Materials 645.9 654.1 687.8 657.9 250.1 265.0 325.4 442.1 657.9 750.5 515.2 750.5 98.5

Industrials 290.9 246.0 353.0 351.1 98.9 128.0 156.1 210.7 351.1 403.8 246.0 403.8 52.6

Consumer discretionary 439.4 403.5 498.1 490.9 233.8 292.3 381.1 422.6 490.9 527.8 403.5 527.8 74.1

Consumer staples 313.3 307.3 317.6 330.2 118.6 147.0 197.0 266.2 330.2 343.1 261.8 343.1 80.4

Health care 437.0 442.6 435.3 458.8 272.5 290.8 393.3 356.3 458.8 476.4 396.2 476.4 83.3

Financials 351.0 326.7 419.7 424.0 138.8 187.9 240.6 328.8 424.0 473.0 326.7 473.0 74.6

Information technology 220.8 204.5 248.6 231.5 149.6 161.5 209.1 231.8 231.5 264.4 192.1 300.0 73.1

Telecommunications 295.6 272.7 299.9 328.0 100.8 131.6 158.9 218.0 328.0 343.2 235.0 343.2 62.9

Utilities 330.2 333.3 363.4 379.2 127.2 149.8 197.0 282.1 379.2 389.1 300.5 389.1 63.1
STATISTICAL APPENDIX

196

Table 10 (continued)

Period on Period Percent Change

2008 End of period 2007 End of period End of period

Q1 Q2 Q3 Q4 2003 2004 2005 2006 2007

World –9.5 –2.5 1.9 –2.7 30.8 12.8 7.6 18.0 7.1

Emerging Markets –11.3 –1.6 13.7 3.4 51.6 22.4 30.3 29.2 36.5

Latin America –1.9 10.1 10.3 6.3 67.1 34.8 44.9 39.3 46.9

Argentina 6.9 34.2 3.0 –11.1 98.5 24.6 59.7 66.1 –5.4

Brazil –5.7 17.7 20.1 12.7 102.9 30.5 50.0 40.5 75.3

Chile 9.4 –13.1 –2.7 –2.0 79.7 24.6 18.4 26.4 20.8

Colombia –4.7 2.9 –1.0 3.5 59.0 125.7 102.3 10.9 12.6

Mexico 4.9 –5.4 –4.7 –3.2 29.8 45.0 45.2 39.0 9.3

Peru 4.6 1.1 21.2 –5.4 88.4 –0.2 28.5 52.1 86.0

Venezuela 0.0 0.0 –9.5 1.1 33.6 45.4 –28.9 62.2 –6.2

Asia –14.5 –9.6 17.8 0.1 47.1 12.2 23.5 29.8 38.3

China –23.8 –4.6 40.2 –3.4 80.3 –0.7 15.6 78.7 63.5

India –25.7 –14.2 17.3 21.8 65.5 11.0 40.2 46.5 52.5

Indonesia –8.3 –5.6 16.0 20.6 60.0 59.3 19.3 55.0 57.5

Korea –9.1 –2.5 12.8 –2.4 33.2 4.2 50.6 2.3 30.8

Malaysia –12.9 –7.9 –1.3 8.2 23.1 11.8 –2.1 24.2 32.7

Pakistan 11.8 –20.6 –6.5 3.5 28.9 12.5 57.5 0.1 34.1

Philippines –17.5 –19.6 –3.8 –1.6 44.5 25.5 13.3 43.6 16.3

Taiwan Province of China –1.4 –10.8 3.3 –8.2 36.7 –0.6 7.0 15.4 4.9

Thailand –4.5 –7.4 11.2 3.7 115.4 –5.9 10.6 –5.9 31.3

Europe, Middle East, 

& Africa –12.0 5.1 7.7 8.7 51.2 35.8 34.9 21.3 25.8

Czech Republic –12.9 4.6 1.8 8.1 31.6 53.6 58.2 9.9 32.1

Egypt 6.5 –12.9 9.3 22.4 140.8 115.4 140.6 14.3 49.6

Hungary –18.1 –6.9 –3.7 –8.2 20.8 63.4 36.9 16.8 2.8

Israel –5.3 11.1 7.8 5.2 55.7 18.4 25.0 –7.1 35.8

Jordan –2.4 15.8 –4.0 24.2 55.3 59.1 71.6 –32.4 20.9

Morocco 25.0 –3.8 8.8 –0.3 23.8 10.4 22.3 48.3 32.4

Poland –13.3 –13.4 –4.4 –5.6 29.9 26.9 31.6 20.7 3.9

Russia –11.5 9.8 9.0 17.3 70.3 4.1 69.5 53.7 22.9

South Africa 0.6 –0.1 2.7 –0.2 8.8 18.7 39.6 30.3 11.2

Turkey –30.0 –12.1 15.4 2.4 88.2 32.9 51.9 –4.9 40.7

Sectors

Energy –14.6 15.9 18.8 20.4 76.2 21.4 57.2 38.5 51.9

Materials –1.8 1.3 22.9 –4.4 36.8 6.0 22.8 35.9 48.8

Industrials –17.1 –15.4 19.9 –0.5 60.1 29.5 22.0 35.0 66.6

Consumer discretionary –10.5 –8.2 3.8 –1.4 68.4 25.0 30.4 10.9 16.2

Consumer staples –5.1 –1.9 5.5 4.0 34.4 24.0 34.0 35.1 24.1

Health care –4.8 1.3 3.4 5.4 60.5 6.7 35.2 –9.4 28.8

Financials –17.2 –6.9 12.7 1.0 40.7 35.4 28.1 36.7 28.9

Information technology –4.6 –7.4 2.1 –6.9 43.9 8.0 29.5 10.9 –0.1

Telecommunications –9.9 –7.8 15.4 9.4 38.7 30.5 20.8 37.2 50.4

Utilities –12.9 1.0 6.0 4.3 75.7 17.8 31.5 43.2 34.4
197

EMERGING MARKETS

Table 10 (concluded)

2008 End of Period 2007 End of Period End of Period 12-

Month 

High

12-

Month 

Low

AllTime 

High1

AllTime

Q1 Q2 Q3 Q4 2003 2004 2005 2006 2007 Low1

Developed Markets

Australia 1,071.1 1,053.8 1,325.4 1,273.7 655.5 797.9 959.6 1,135.1 1,273.7 1,374.4 1,017.1 1,374.4 250.2

Austria 239.0 249.4 295.0 287.7 118.0 185.3 262.7 316.6 287.7 342.2 225.1 348.9 79.7

Belgium 86.3 68.0 105.6 96.5 60.1 77.9 94.8 113.0 96.5 117.7 68.0 121.0 35.4

Canada 1,712.1 1,870.6 1,784.1 1,761.7 1,019.7 1,139.3 1,406.8 1,628.3 1,761.7 1,952.7 1,549.0 1,952.7 338.3

Denmark 3,759.3 3,733.5 4,214.0 4,104.8 1,772.7 2,115.9 2,994.0 3,662.6 4,104.8 4,353.7 3,377.5 4,353.7 556.5

Finland 150.2 127.8 192.9 183.5 97.4 93.9 123.4 140.3 183.5 200.2 126.7 383.1 22.9

France 124.4 117.6 150.5 147.1 93.2 100.6 124.9 147.1 147.1 161.2 116.7 178.6 42.9

Germany 113.5 108.8 136.7 139.7 74.6 79.2 98.2 116.9 139.7 140.8 107.4 163.6 41.4

Greece 115.2 98.9 144.2 148.4 63.6 83.3 108.1 127.3 148.4 150.5 98.0 197.2 38.2

Hong Kong SAR 11,288.0 10,725.6 13,076.5 13,994.0 6,341.3 7,668.5 8,016.2 10,152.8 13,994.0 14,780.4 10,032.2 14,780.4 1,995.5

Ireland 76.5 62.7 98.7 84.8 65.9 85.2 93.5 120.3 84.8 121.2 62.7 126.8 40.5

Italy 91.6 84.3 115.7 112.4 78.1 93.2 106.0 121.4 112.4 123.1 83.9 132.1 39.5

Japan 765.4 834.5 1,031.5 940.1 637.3 699.1 999.3 1,060.2 940.1 1,135.4 726.5 1,655.3 462.1

Netherlands 92.2 82.9 112.5 107.3 68.4 69.3 88.3 101.3 107.3 117.5 82.7 134.9 38.5

New Zealand 108.1 95.0 140.4 131.7 107.6 127.0 130.0 138.2 131.7 145.8 95.0 145.8 56.7

Norway 2,768.7 3,075.8 3,327.3 3,305.9 1,240.9 1,690.3 2,267.7 2,951.8 3,305.9 3,501.4 2,530.4 3,501.4 455.9

Portugal 92.3 77.2 110.4 115.1 66.1 74.7 82.2 105.5 115.1 125.7 77.2 128.0 35.2

Singapore 1,745.7 1,676.6 2,118.9 1,971.8 1,005.1 1,148.1 1,295.4 1,696.1 1,971.8 2,216.4 1,613.5 2,216.4 508.2

Spain 149.0 135.5 164.5 172.2 89.6 104.3 122.1 158.2 172.2 180.3 135.5 180.3 27.4

Sweden 7,463.1 6,585.5 9,669.8 8,429.2 4,675.2 5,785.4 7,489.8 9,047.5 8,429.2 10,459.4 6,585.5 12,250.4 787.2

Switzerland 950.3 915.0 1,175.3 1,117.0 714.3 747.1 994.6 1,159.5 1,117.0 1,228.5 892.9 1,256.8 158.1

United Kingdom 1,698.5 1,665.8 1,934.2 1,920.8 1,348.7 1,453.0 1,685.3 1,865.6 1,920.8 2,011.3 1,609.8 2,016.6 585.4

United States 1,254.8 1,222.8 1,443.6 1,390.9 1,045.4 1,137.4 1,180.6 1,336.3 1,390.9 1,480.2 1,206.6 1,493.0 273.7

Period on Period Percent Change

Developed Markets

Australia –15.9 –1.6 5.4 –4.1 8.5 21.7 20.3 18.3 12.2 . . . . . . . . . . . .

Austria –16.9 4.3 –15.2 –2.5 28.5 57.0 41.7 20.5 –9.1 . . . . . . . . . . . .

Belgium –10.6 –21.2 –11.2 –9.4 8.7 29.5 21.7 19.2 –14.6 . . . . . . . . . . . .

Canada –2.8 9.3 1.9 –1.3 24.6 11.7 23.5 15.7 8.2 . . . . . . . . . . . .

Denmark –8.4 –0.7 3.5 –2.7 22.4 19.4 41.5 22.3 12.1 . . . . . . . . . . . .

Finland –18.2 –14.9 11.5 –5.1 –2.9 –3.6 31.4 13.7 30.8 . . . . . . . . . . . .

France –15.5 –5.5 –5.9 –2.3 14.6 7.9 24.2 17.8 0.0 . . . . . . . . . . . .

Germany –18.8 –4.1 –1.6 2.2 33.2 6.1 24.1 19.0 19.5 . . . . . . . . . . . .

Greece –22.3 –14.2 5.2 2.8 35.8 31.1 29.8 17.7 16.6 . . . . . . . . . . . .

Hong Kong SAR –19.3 –5.0 18.3 6.6 31.9 20.9 4.5 26.7 37.8 . . . . . . . . . . . .

Ireland –9.8 –18.0 –20.6 –16.4 16.0 29.2 9.8 28.7 –29.5 . . . . . . . . . . . .

Italy –18.5 –7.9 –5.0 –2.9 12.2 19.3 13.8 14.6 –7.4 . . . . . . . . . . . .

Japan –18.6 9.0 –8.9 –9.7 21.6 9.7 42.9 6.1 –11.3 . . . . . . . . . . . .

Netherlands –14.1 –10.1 –0.9 –4.8 3.6 1.3 27.5 14.7 6.0 . . . . . . . . . . . .

New Zealand –17.9 –12.1 –0.5 –6.6 19.6 18.0 2.4 6.3 –4.7 . . . . . . . . . . . .

Norway –16.3 11.1 –1.2 –0.6 38.1 36.2 34.2 30.2 12.0 . . . . . . . . . . . .

Portugal –19.9 –16.3 –12.2 4.1 15.9 13.1 10.0 28.3 9.2 . . . . . . . . . . . .

Singapore –11.5 –4.0 4.0 –7.5 31.4 14.2 12.8 30.9 16.3 . . . . . . . . . . . .

Spain –13.4 –9.1 –1.0 4.4 28.3 16.4 17.0 29.5 8.8 . . . . . . . . . . . .

Sweden –11.5 –11.8 –3.5 –14.7 32.9 23.7 29.5 20.8 –6.8 . . . . . . . . . . . .

Switzerland –14.9 –3.7 –3.4 –5.2 18.4 4.6 33.1 16.6 –3.7 . . . . . . . . . . . .

United Kingdom –11.6 –1.9 –2.3 –0.7 14.4 7.7 16.0 10.7 3.0 . . . . . . . . . . . .

United States –9.8 –2.5 1.6 –3.8 26.8 8.8 3.8 13.2 4.1 . . . . . . . . . . . .

Source: Data are provided by Morgan Stanley Capital International. Regional and sectoral compositions conform to Morgan Stanley Capital International definitions. 

1From 1990 or initiation of the index.
STATISTICAL APPENDIX

198

Table 11. Foreign Exchange Rates

(Units per U.S. dollar)

2008 End of Period 2007 End of Period End of Period 12-

Month 

High

12-

Month 

Low

AllTime 

High1

AllTime 

Q1 Q2 Q3 Q4 2003 2004 2005 2006 2007 Low1

Emerging Markets

Latin America

Argentina 3.17 3.03 3.15 3.15 2.93 2.97 3.03 3.06 3.15 3.02 3.18 0.98 3.86

Brazil 1.75 1.60 1.83 1.78 2.89 2.66 2.34 2.14 1.78 1.59 2.09 0.00 3.95

Chile 435.24 527.89 510.47 497.95 592.75 555.75 512.00 533.38 497.95 429.55 528.02 295.18 759.75

Colombia 1,831.30 1,913.50 2,024.50 2,018.00 2,780.00 2,354.75 2,286.50 2,240.00 2,018.00 1,655.03 2,202.50 689.21 2,980.00

Mexico 10.64 10.31 10.94 10.91 11.23 11.15 10.63 10.82 10.91 10.27 11.20 2.68 11.67

Peru 2.75 2.96 3.08 3.00 3.46 3.28 3.42 3.20 3.00 2.69 3.17 1.28 3.65

Venezuela 2,147.30 2,147.30 2,147.30 2,147.30 1,598.00 1,918.00 2,147.30 2,147.30 2,147.30 2,147.30 2,147.30 45.00 2,147.50

Asia

China 7.01 6.85 7.51 7.30 8.28 8.28 8.07 7.81 7.30 6.85 7.62 4.73 8.73 

India 40.12 43.04 39.77 39.42 45.63 43.46 45.05 44.26 39.42 39.27 43.04 16.92 49.05 

Indonesia 9,229.00 9,228.00 9,105.00 9,400.00 8,420.00 9,270.00 9,830.00 8,994.00 9,400.00 9,000.00 9,480.00 1,977.00 16,650.00 

Korea 990.30 1,046.05 915.25 936.05 1,192.10 1,035.10 1,010.00 930.00 936.05 900.75 1,049.49 683.50 1,962.50 

Malaysia 3.19 3.27 3.41 3.31 3.80 3.80 3.78 3.53 3.31 3.13 3.52 2.44 4.71 

Pakistan 62.70 68.40 60.71 61.63 57.25 59.43 59.79 60.88 61.63 60.35 69.70 21.18 64.35 

Philippines 41.74 44.96 44.95 41.23 55.54 56.23 53.09 49.01 41.23 40.27 47.13 23.10 56.46 

Taiwan Province of China 30.38 30.35 32.67 32.43 33.96 31.74 32.83 32.59 32.43 30.00 33.13 24.48 35.19 

Thailand 31.44 33.44 31.88 29.80 39.62 38.92 41.03 35.45 29.80 29.18 33.59 23.15 55.50 

Europe, Middle East, 

& Africa

Czech Republic 15.98 15.16 19.32 18.20 25.71 22.42 24.55 20.83 18.20 15.10 21.22 17.71 42.17 

Egypt 5.45 5.34 5.59 5.53 6.17 6.09 5.74 5.71 5.53 5.33 5.70 3.29 6.25 

Hungary 165.14 149.41 175.93 173.42 208.70 181.02 212.97 190.29 173.42 149.41 194.47 90.20 317.56 

Israel 3.56 3.35 4.02 3.86 4.39 4.32 4.61 4.22 3.86 3.24 4.33 1.96 5.01 

Jordan 0.71 0.71 0.71 0.71 0.71 0.71 0.71 0.71 0.71 0.71 0.71 0.64 0.72 

Morocco 10.13 10.08 10.61 10.43 10.08 11.09 11.94 11.70 10.43 9.75 10.85 7.75 12.06 

Poland 2.22 2.13 2.64 2.47 3.73 3.01 3.25 2.90 2.47 2.13 2.86 1.72 4.71 

Russia 23.49 23.44 24.86 24.63 29.24 27.72 28.74 26.33 24.63 23.32 25.89 0.98 31.96 

South Africa 8.09 7.82 6.87 6.86 6.68 5.67 6.33 7.01 6.86 6.50 8.17 2.50 12.45 

Turkey 1.32 1.23 1.21 1.17 1.41 1.34 1.35 1.42 1.17 1.15 1.39 0.00 1.77 

Developed Markets

Australia2 0.91 0.96 0.89 0.88 0.75 0.78 0.73 0.79 0.88 0.96 0.79 0.93 0.48

Canada 1.03 1.02 0.99 1.00 1.30 1.20 1.16 1.17 1.00 0.92 1.08 0.92 1.61

Denmark 4.72 4.73 5.23 5.11 5.91 5.49 6.30 5.65 5.11 4.67 5.54 5.01 9.00

Euro area2 1.58 1.58 1.43 1.46 1.26 1.36 1.18 1.32 1.46 1.60 1.34 1.31 2.37

Hong Kong SAR 7.78 7.80 7.77 7.80 7.76 7.77 7.75 7.78 7.80 7.75 7.83 7.70 7.83

Japan 99.69 106.21 114.80 111.71 107.22 102.63 117.75 119.07 111.71 97.33 123.41 80.63 159.90

New Zealand2 0.79 0.76 0.76 0.77 0.66 0.72 0.68 0.70 0.77 0.82 0.69 0.81 0.39

Norway 5.10 5.09 5.39 5.44 6.67 6.08 6.74 6.24 5.44 4.96 5.97 5.27 9.58

Singapore 1.38 1.36 1.49 1.44 1.70 1.63 1.66 1.53 1.44 1.35 1.53 1.39 1.91

Sweden 5.94 6.01 6.44 6.47 7.19 6.66 7.94 6.85 6.47 5.84 6.98 5.09 11.03

Switzerland 0.99 1.02 1.16 1.13 1.24 1.14 1.31 1.22 1.13 0.98 1.22 1.10 1.82

United Kingdom2 1.98 1.99 2.05 1.98 1.79 1.92 1.72 1.96 1.98 2.11 1.94 2.11 1.37
199

EMERGING MARKETS

Table 11 (concluded)

Period on Period Percent Change

2008 End of period 2007 End of period End of period

Q1 Q2 Q3 Q4 2003 2004 2005 2006 2007

Emerging Markets

Latin America

Argentina –0.6 4.7 –1.9 0.0 14.7 –1.4 –1.9 –1.0 –2.8

Brazil 1.5 9.2 5.2 3.0 22.4 8.9 13.7 9.4 20.0

Chile 14.4 –17.6 3.3 2.5 21.5 6.7 8.5 –4.0 7.1

Colombia 10.2 –4.3 –2.4 0.3 3.1 18.1 3.0 2.1 11.0

Mexico 2.5 3.2 –1.2 0.2 –7.6 0.7 4.8 –1.7 –0.8

Peru 9.0 –7.1 2.6 2.9 1.5 5.6 –4.1 7.1 6.6

Venezuela 0.0 0.0 0.0 0.0 –13.1 –16.7 –10.7 0.0 0.0

Asia

China 4.1 2.3 1.4 2.9 0.0 0.0 2.6 3.4 7.0

India –1.8 –6.8 2.3 0.9 5.2 5.0 –3.5 1.8 12.3

Indonesia 1.9 0.0 –0.9 –3.1 6.3 –9.2 –5.7 9.3 –4.3

Korea –5.5 –5.3 0.9 –2.2 –0.5 15.2 2.5 8.6 –0.6

Malaysia 3.5 –2.2 1.3 3.0 0.0 0.0 0.5 7.1 6.7

Pakistan –1.7 –8.3 –0.4 –1.5 1.7 –3.7 –0.6 –1.8 –1.2

Philippines –1.2 –7.1 2.8 9.0 –3.5 –1.2 5.9 8.3 18.9

Taiwan Province of China 6.7 0.1 0.6 0.7 2.0 7.0 –3.3 0.7 0.5

Thailand –5.2 –6.0 –0.6 7.0 8.8 1.8 –5.1 15.7 19.0

Europe, Middle East, 

& Africa

Czech Republic 13.9 5.4 9.9 6.1 16.9 14.7 –8.7 17.9 14.4

Egypt 1.6 2.1 1.9 1.0 –25.1 1.3 6.1 0.5 3.2

Hungary 5.0 10.5 3.6 1.4 7.6 15.3 –15.0 11.9 9.7

Israel 8.3 6.2 5.8 4.2 8.0 1.6 –6.1 9.2 9.3

Jordan 0.0 0.1 0.1 –0.1 0.1 0.0 0.1 –0.1 0.0

Morocco 2.9 0.5 –0.1 1.8 –2.7 –9.2 –7.1 2.0 12.3

Poland 11.4 4.3 5.3 7.0 2.6 24.0 –7.2 11.8 17.5

Russia 4.9 0.2 3.6 0.9 9.3 5.5 –3.6 9.2 6.9

South Africa –15.2 3.5 2.5 0.1 28.2 18.0 –10.5 –9.7 2.1

Turkey –11.6 8.0 8.8 3.1 17.7 4.7 –0.6 –4.7 21.1

Developed Markets

Australia 4.3 5.0 4.5 –1.4 33.9 3.8 –6.1 7.6 11.0

Canada –2.6 0.4 7.4 –0.6 21.2 7.9 3.4 –0.3 16.8

Denmark 8.2 –0.2 5.2 2.2 19.8 7.8 –12.9 11.5 10.5

Euro area 8.2 –0.2 1.2 2.3 20.0 7.6 –12.6 11.4 10.5

Hong Kong SAR 0.2 –0.2 0.6 –0.3 0.4 –0.1 0.2 –0.3 –0.3

Japan 12.1 –6.1 7.3 2.8 10.8 4.5 –12.8 –1.1 6.6

New Zealand 2.6 –3.0 –1.9 1.1 25.0 9.5 –4.8 3.0 8.8

Norway 6.7 0.1 9.4 –0.9 4.1 9.6 –9.8 8.1 14.7

Singapore 4.7 1.2 3.0 3.1 2.1 4.2 –1.9 8.4 6.5

Sweden 8.9 –1.2 6.1 –0.5 20.9 8.0 –16.2 15.9 5.9

Switzerland 14.1 –2.7 4.9 2.7 11.7 8.7 –13.2 7.7 7.5

United Kingdom –0.1 0.4 1.9 –3.0 10.9 7.4 –10.2 13.7 1.3

Source: Bloomberg L.P.

1High value indicates value of greatest appreciation against the U.S. dollar; low value indicates value of greatest depreciation against the U.S. 

dollar. “All-Time” refers to the period since 1990 or initiation of the currency. 2U.S. dollars per unit.
STATISTICAL APPENDIX

200

Table 12. Emerging Market Bond Index: EMBI Global Total Returns Index

2008 End of Period 2007 End of Period End of Period 12-

Month 

High

12-

Month

Low

AllTime 

High1

AllTime

Q1 Q2 Q3 Q4 2003 2004 2005 2006 2007 Low1

EMBI Global 411 408 398 409 283 316 350 384 409 398 348 418 63

Latin America

Argentina 97 93 105 112 67 81 83 126 112 119 90 194 47

Brazil 636 651 622 633 390 446 505 580 633 672 581 672 68

Chile 204 199 190 197 162 172 177 185 197 206 185 206 98

Colombia 313 315 300 309 201 228 256 283 309 327 285 327 70

Dominican Republic 187 187 190 198 99 126 156 184 198 198 185 198 83

Ecuador 834 862 761 811 464 562 636 561 811 889 655 889 61

El Salvador 159 158 159 165 110 123 134 152 165 165 154 165 95

Mexico 390 382 369 377 284 308 333 353 377 395 354 395 58

Panama 691 694 666 691 452 511 567 637 691 712 635 712 56

Peru 641 639 620 633 431 485 514 591 633 667 579 667 52

Uruguay 181 186 182 188 97 129 151 177 188 192 175 192 38

Venezuela 546 565 574 563 393 484 562 634 563 598 527 638 59

Asia

China 299 295 281 289 241 253 260 271 289 302 273 302 98

Indonesia 160 150 157 159 . . . 121 133 154 159 161 148 161 98

Malaysia 248 244 232 240 194 207 215 224 240 249 225 249 64

Philippines 428 411 409 425 261 280 337 394 425 434 386 434 81

Vietnam 119 110 114 117 . . . . . . 101 112 117 120 107 120 98

Europe, Middle East, 

& Africa

Bulgaria 729 720 703 713 578 630 643 676 713 746 680 746 80

Egypt 175 176 168 171 140 150 155 161 171 178 164 178 87

Hungary 168 168 160 168 142 144 148 153 168 176 153 176 97

Iraq 124 130 101 115 . . . . . . . . . 102 115 134 91 134 91

Lebanon 240 250 225 236 177 195 212 215 236 250 222 250 99

Pakistan 120 110 118 111 160 107 112 123 111 124 108 160 91

Poland 385 375 355 373 290 312 327 340 373 387 340 387 71

Russia 619 614 585 607 426 475 538 568 607 627 568 627 26

Serbia1 121 122 122 121 . . . . . . 108 117 121 125 116 125 99

South Africa 371 373 366 373 297 323 337 349 373 379 354 379 99

Tunisia 164 162 157 160 127 138 143 149 160 166 152 166 98

Turkey 384 368 377 392 279 307 336 356 392 396 363 396 91

Ukraine 380 362 369 372 289 310 334 353 372 386 358 386 100

Latin America 373 375 366 372 252 285 316 354 372 383 345 383 62

Non-Latin America 482 471 460 476 342 374 413 443 476 486 445 486 72
201

EMERGING MARKETS

Table 12 (concluded)

Period on Period Percent Change

2008 End of period 2007 End of period End of period

Q1 Q2 Q3 Q4 2003 2004 2005 2006 2007

EMBI Global 0.6 –0.8 2.6 2.6 25.7 11.7 10.7 9.9 6.3

Latin America

Argentina –12.7 –4.7 –2.6 6.0 19.1 19.8 2.7 51.3 –11.1

Brazil 0.5 2.3 4.1 1.7 69.8 14.3 13.2 14.8 9.1

Chile 3.7 –2.2 1.7 3.5 8.3 6.0 3.2 4.1 6.4

Colombia 1.3 0.4 1.5 3.0 19.4 13.2 12.4 10.7 9.1

Dominican Republic –5.3 –0.2 –0.7 4.0 –15.3 27.2 24.1 18.0 7.3

Ecuador 2.9 3.3 14.0 6.5 101.5 21.1 13.2 –11.8 44.6

El Salvador –3.3 –0.9 1.1 3.6 11.9 11.5 8.8 14.1 8.0

Mexico 3.4 –1.9 3.1 2.1 11.6 8.6 8.1 6.0 6.9

Panama –0.1 0.6 2.5 3.7 14.4 13.0 11.1 12.3 8.5

Peru 1.2 –0.2 3.4 2.1 26.6 12.6 6.0 14.8 7.1

Uruguay –3.6 2.4 0.0 3.2 55.6 34.0 16.3 17.3 6.6

Venezuela –3.0 3.6 0.7 –1.9 39.9 23.2 16.1 12.8 –11.2

Asia

China 3.4 –1.3 2.4 2.9 4.5 5.1 3.0 4.1 6.7

Indonesia 1.0 –6.1 1.9 1.0 . . . . . . 9.7 15.9 3.0

Malaysia 3.2 –1.8 2.4 3.9 10.7 6.6 3.7 4.3 7.4

Philippines 0.8 –4.1 3.0 3.9 13.4 7.1 20.6 16.8 7.9

Vietnam 1.4 –7.3 3.0 2.4 . . . . . . . . . 10.6 4.5

Europe, Middle East, 

& Africa

Bulgaria 2.2 –1.2 3.0 1.6 10.2 8.9 2.1 5.1 5.6

Egypt 2.6 0.4 1.8 1.5 14.4 6.8 3.8 3.8 5.9

Hungary 0.1 –0.2 3.9 4.9 3.7 1.2 2.8 3.7 9.4

Iraq 7.4 4.8 0.4 13.5 . . . . . . . . . . . . 12.4

Lebanon 1.5 4.3 –0.3 5.0 19.5 9.9 8.7 1.6 9.9

Pakistan 7.9 –7.6 –5.1 –5.8 –0.2 –33.3 4.5 10.3 –10.0

Poland 3.0 –2.5 3.9 5.0 3.7 7.5 5.0 3.8 9.9

Russia 2.1 –0.9 2.9 3.7 22.4 11.5 13.3 5.5 6.9

Serbia1 –0.2 1.4 0.7 –0.9 . . . . . . . . . 8.3 3.7

South Africa –0.5 0.4 2.8 1.8 9.6 8.8 4.3 3.7 6.8

Tunisia 2.1 –1.2 2.8 2.4 13.3 8.7 3.7 3.8 7.8

Turkey –2.1 –4.2 2.6 4.1 30.8 10.0 9.5 6.1 10.2

Ukraine 2.4 –5.0 1.2 0.7 19.8 7.2 7.7 5.9 5.2

Latin America 0.1 0.7 2.7 1.9 33.0 13.4 10.9 11.9 5.2

Non-Latin America 1.2 –2.3 2.4 3.4 17.7 9.2 10.6 7.2 7.5

Source: JPMorgan Chase & Co.

1Data prior to 2006 refer to Serbia and Montenegro.
STATISTICAL APPENDIX

202

Table 13. Emerging Market Bond Index: EMBI Global Yield Spreads

(In basis points)

2008 End of Period 2007 End of Period End of Period 12-

Month

High

12-

Month

Low

AllTime

High1

AllTime

Q1 Q2 Q3 Q4 2003 2004 2005 2006 2007 Low1

EMBI Global 324 308 214 255 403 347 237 171 255 339 168 1631 151

Latin America

Argentina 400 614 398 410 5,485 4,527 504 216 410 614 303 7,222 185

Brazil 174 227 172 220 459 376 308 190 220 304 147 2,451 138

Chile 126 177 124 151 90 64 80 84 151 184 81 260 52

Colombia 168 221 166 195 427 332 244 161 195 286 109 1,076 95

Dominican Republic 254 463 252 281 1,141 824 378 196 281 489 150 1,750 122

Ecuador 618 596 616 614 799 690 661 920 614 779 538 4,764 436

El Salvador 296 285 175 199 284 245 239 159 199 307 113 434 99

Mexico 193 194 131 172 201 174 143 115 172 212 101 1,149 89

Panama 244 218 159 184 324 274 239 146 184 256 115 769 114

Peru 223 199 137 178 325 239 257 118 178 244 105 1,061 95

Uruguay 343 294 212 243 636 388 298 185 243 346 141 1,982 133

Venezuela 661 596 419 523 586 403 313 183 523 681 304 2,658 161

Asia

China 90 137 88 120 58 57 68 51 120 164 54 364 39

Indonesia 329 381 217 275 . . . 244 269 153 275 382 156 433 136

Malaysia 144 153 108 119 100 78 82 66 119 153 73 1,141 65

Philippines 273 303 184 207 415 457 302 155 207 303 143 993 132

Vietnam 283 368 156 203 . . . . . . 190 95 203 396 113 396 89

Europe, Middle East, 

& Africa

Bulgaria 92 204 90 153 177 77 90 66 153 240 57 1,679 42

Egypt 105 201 103 178 131 101 58 52 178 275 50 646 20

Hungary 163 134 80 84 28 32 74 58 84 187 62 196 –29

Iraq 545 474 639 569 . . . . . . . . . 526 569 730 430 730 376

Lebanon 594 469 491 493 421 334 246 395 493 622 358 1,082 111

Pakistan 562 687 386 535 . . . 233 198 154 535 702 207 2,225 122

Poland 112 115 69 67 76 69 62 47 67 132 56 410 17

Russia 208 197 133 157 257 213 118 99 157 225 97 7,063 87

Serbia1 389 332 206 304 . . . . . . 238 186 304 404 140 404 134

South Africa 271 232 115 164 152 102 87 84 164 307 83 757 50

Tunisia 214 197 105 140 146 91 81 83 140 244 67 394 48

Turkey 348 384 220 239 309 264 223 207 239 384 177 1,196 168

Ukraine 376 467 217 303 258 255 184 172 303 467 142 2,314 125

Latin America 347 313 227 275 518 415 272 180 275 362 180 1,532 157

Non-Latin America 297 303 196 227 248 239 179 159 227 310 150 1,812 142
203

EMERGING MARKETS

Table 13 (concluded)

Period on Period Spread Change

2008 End of period 2007 End of period End of period

Q1 Q2 Q3 Q4 2003 2004 2005 2006 2007

EMBI Global 69 –16 18 19 –322 –56 –110 –66 84

Latin America

Argentina –10 214 22 3 –857 –958 –4,023 –288 194

Brazil –46 53 8 28 –1,001 –83 –68 –118 30

Chile –25 51 49 22 –86 –26 16 4 67

Colombia –27 53 39 17 –206 –95 –88 –83 34

Dominican Republic –27 209 61 12 642 –317 –446 –182 85

Ecuador 4 –22 –13 0 –1,002 –109 –29 259 –306

El Salvador 97 –11 38 14 –127 –39 –6 –80 40

Mexico 21 1 18 31 –128 –27 –31 –28 57

Panama 60 –26 22 16 –122 –50 –35 –93 38

Peru 45 –24 17 30 –284 –86 18 –139 60

Uruguay 100 –49 35 15 –592 –248 –90 –113 58

Venezuela 138 –65 18 25 –545 –183 –90 –130 340

Asia

China –30 47 63 36 –26 –1 11 –17 69

Indonesia 54 52 32 27 . . . . . . 25 –116 122

Malaysia 25 9 44 10 –112 –22 4 –16 53

Philippines 66 30 19 13 –107 42 –155 –147 52

Vietnam 80 85 28 30 . . . . . . . . . –95 108

Europe, Middle East, 

& Africa

Bulgaria –61 112 32 70 –114 –100 13 –24 87

Egypt –73 96 102 73 –194 –30 –43 –6 126

Hungary 79 –29 13 5 –24 4 42 –16 26

Iraq –24 –71 12 –11 . . . . . . . . . . . . 43

Lebanon 101 –125 32 0 –355 –87 –88 149 98

Pakistan 27 125 80 39 –271 233 –35 –44 381

Poland 45 3 13 –3 –109 –7 –7 –15 20

Russia 51 –11 25 18 –221 –44 –95 –19 58

Serbia1 85 –57 36 48 . . . . . . . . . –52 118

South Africa 107 –39 32 43 –98 –50 –15 –3 80

Tunisia 74 –17 44 33 –127 –55 –10 2 57

Turkey 109 36 16 9 –387 –45 –41 –16 32

Ukraine 73 91 39 40 –413 –3 –71 –12 131

Latin America 72 –34 16 21 –463 –103 –143 –92 95

Non-Latin America 70 6 23 16 –196 –9 –60 –20 68

Source: JPMorgan Chase & Co.

1Data prior to 2006 refer to Serbia and Montenegro.
STATISTICAL APPENDIX

204

Table 14. Emerging Market External Financing: Total Bonds, Equities, and Loans

(In millions of U.S. dollars)

2007 2008

2002 2003 2004 2005 2006 2007 Q3 Q4 Q1 Q2

Total 157,999.4 214,973.7 325,729.4 462,221.8 572,561.7 741,346.2 172,745.8 191,622.5 101,011.5 167,124.2 

Africa 7,448.0 9,694.7 12,715.3 12,435.3 16,191.5 31,430.0 5,417.1 7,220.5 290.1 4,078.8 

Algeria 150.0 40.0 307.9 489.3 25.4 411.0 411.0 — — — 

Angola 350.0 1,542.0 2,900.0 3,122.7 91.9 74.6 — — — — 

Botswana — — 28.4 — — — — — — — 

Burkina Faso — — — 11.0 — 14.5 — — — — 

Cameroon — — 48.0 30.0 — — — — — — 

Cape Verde — — — — — 13.0 — — — — 

Central African Republic — — — — — 305.5 305.5 — — — 

Côte d’Ivoire — — — — — — — — — 45.0 

Djibouti — — 40.0 — — — — — — — 

Ethiopia — — 40.0 — — — — — — 100.2 

Gabon — — 22.0 — 34.4 1,000.0 — 1,000.0 — — 

Ghana 420.0 650.0 850.0 706.5 860.0 1,464.3 973.8 340.5 — — 

Kenya — 134.0 135.1 64.0 330.1 10.0 — — — 848.6 

Lesotho — — — — — 19.7 19.7 — — — 

Malawi — — 4.8 — — — — — — — 

Mali 150.4 287.6 288.9 — — 180.9 31.0 — 110.4 — 

Mauritius — — — 99.3 180.0 — — — 9.0 — 

Morocco — 467.1 803.5 1.9 341.4 1,966.4 431.3 287.2 — 196.9 

Mozambique — 35.5 422.4 — 38.8 — — — — 800.0 

Namibia — 35.0 — 50.0 100.0 — — — — 87.6 

Niger — 27.0 — — — — — — — — 

Nigeria 1,000.0 762.0 875.0 874.0 640.0 5,515.7 874.7 2,100.0 — — 

Senegal 40.0 — 10.0 — 31.6 — — — — — 

Seychelles — — — — 200.0 30.0 30.0 — — — 

South Africa 4,587.6 5,200.2 5,324.8 6,265.9 12,700.7 19,766.1 2,086.7 3,297.9 38.7 1,549.6 

Sudan — — 31.0 — — — — — — — 

Tanzania — — — 136.0 — — — — 112.0 — 

Tunisia 750.0 484.3 583.6 579.9 24.7 403.4 253.4 — — 402.0 

Uganda — — — — 12.6 — — — — — 

Zambia — 30.0 — — 505.0 255.0 — 195.0 20.0 — 

Zimbabwe — — — 4.8 75.1 — — — — 48.9 

Asia 76,536.3 100,246.0 152,357.7 195,322.2 243,069.2 315,300.1 71,497.8 90,172.4 62,971.8 47,483.3 

Bangladesh — 10.0 176.8 16.7 106.5 57.5 21.8 7.0 11.3 — 

Brunei Darussalam 129.0 — — — — — — — — — 

Cambodia — — — — 105.5 250.0 — — — — 

China 10,205.3 15,772.8 25,661.6 41,331.2 63,393.5 87,615.0 25,687.9 30,150.6 15,755.2 8,285.6 

Fiji — — — — 150.0 — — — — — 

Hong Kong SAR 14,623.0 15,647.8 19,291.2 20,943.4 26,146.9 23,641.3 3,917.1 7,310.2 2,450.3 4,154.9 

India 1,427.4 3,277.2 13,301.1 23,189.6 33,037.3 61,059.7 18,816.7 13,208.4 15,745.1 8,805.6 

Indonesia 1,122.5 5,207.2 4,115.3 5,195.5 8,364.3 8,340.7 1,848.6 2,734.1 4,044.5 4,309.5 

Korea 17,819.0 19,112.6 31,016.0 48,362.8 41,426.3 60,742.4 6,949.1 19,552.7 11,298.2 9,100.4 

Lao P.D.R. 101.4 — 210.0 1,000.0 — — — — — — 

Macao SAR — — 382.0 729.0 3,692.7 4,531.3 — 2,726.2 180.0 — 

Malaysia 5,976.3 5,743.7 7,977.8 6,193.2 7,707.4 7,111.7 749.8 2,301.3 236.0 599.7 

Marshall Islands 34.7 — — 24.0 170.0 1,069.3 118.0 557.1 — 204.0 

Mongolia — — — 30.0 6.0 85.0 — 10.0 500.0 4.0 

Nepal — — — — — — — — 5.0 — 

Pakistan 388.8 983.8 970.0 739.2 3,298.9 2,149.3 343.1 262.9 240.0 255.4 

Papua New Guinea 85.0 153.7 — — — 1,024.3 — 14.8 — — 

Philippines 6,345.5 6,405.4 6,358.3 6,194.8 7,172.5 6,648.4 3,644.2 161.2 600.4 694.7 

Singapore 4,949.0 7,448.1 11,949.3 14,624.9 20,096.7 19,974.3 2,941.8 3,542.9 5,116.3 5,755.0 

Sri Lanka 33.7 186.0 135.0 383.0 129.8 755.0 — 545.0 — 340.0 

Taiwan Province of China 10,230.7 16,040.6 26,558.0 19,085.5 22,374.8 24,986.0 5,317.0 5,611.7 6,238.9 3,181.1 

Thailand 2,672.5 3,860.0 4,141.3 6,310.9 5,232.8 2,617.4 840.0 686.4 136.7 1,341.7 

Vietnam 392.5 397.0 114.0 968.8 457.4 2,641.6 302.6 789.8 414.0 451.7 

Europe 29,639.2 45,102.0 70,204.0 104,340.5 133,624.8 164,830.4 29,742.1 33,309.2 19,357.9 58,386.0 

Albania — — — — — — — — 35.7 — 

Belarus — — 21.4 32.0 338.6 302.8 145.5 76.0 43.0 149.0 

Bulgaria 1,765.3 668.6 1,099.9 1,103.7 1,727.1 1,360.0 547.0 328.5 438.3 300.5 

Croatia 1,619.0 2,963.6 2,737.4 1,263.7 2,177.7 2,786.5 755.8 1,692.8 — 419.6 

Cyprus 550.1 653.9 1,178.4 1,189.9 3,839.0 3,099.9 485.3 158.8 69.5 1,320.7 

Czech Republic 520.2 2,072.0 4,066.2 4,001.1 2,181.4 4,262.7 1,103.1 936.1 1,266.9 5,697.7 

Estonia 485.0 455.9 1,181.4 692.8 473.7 299.2 89.2 38.0 178.8 117.7 

Faroe Islands — — — 85.3 273.8 431.2 — — — 217.4 

Gibraltar 8.2 — — 1,897.1 2,371.7 494.8 — — — — 
205

EMERGING MARKETS

Table 14 (concluded)

2007 2008

2002 2003 2004 2005 2006 2007 Q3 Q4 Q1 Q2

Europe (continued)

Hungary 1,311.3 4,551.6 9,260.3 9,341.7 7,328.7 5,330.8 267.9 976.8 1,745.3 5,956.5 

Latvia 51.9 70.7 881.6 516.1 1,457.4 1,614.7 550.1 55.8 1,115.8 — 

Lithuania 369.6 432.7 986.0 1,220.0 1,292.0 1,645.3 — 971.9 15.4 31.1 

Macedonia, FYR — 47.6 66.0 176.5 — 14.4 — 14.4 — — 

Malta — 114.8 242.7 — 256.0 — — — — — 

Moldova — — 7.0 13.1 — — — — — — 

Montenegro — 13.4 — — 0.8 21.4 — 21.4 — — 

Poland 6,023.9 7,812.4 5,259.4 17,007.6 8,319.2 7,401.4 1,499.4 728.6 577.3 5,127.0 

Romania 1,456.6 1,763.8 1,116.7 2,611.0 747.2 1,168.2 3.1 893.7 176.6 1,410.1 

Russia 8,102.0 11,198.6 22,121.2 37,003.6 64,706.8 87,964.7 11,770.1 20,480.3 7,410.9 31,530.4 

Serbia1 — — 213.4 1,252.6 60.2 568.6 176.5 202.8 7.8 — 

Slovak Republic 232.7 967.6 1,319.0 711.5 1,210.7 1,354.2 — — — — 

Slovenia 378.0 430.3 1,321.9 1,887.3 1,837.8 4,759.9 — 1,695.2 1,532.3 1,974.9 

Turkey 6,251.3 9,471.6 14,506.9 18,999.6 27,641.6 31,276.6 11,357.1 1,234.5 3,759.4 2,514.3 

Ukraine 514.0 1,413.0 2,617.1 3,334.4 5,383.3 8,672.9 991.9 2,803.5 984.9 1,619.2 

Middle East and 

Central Asia 12,497.2 12,995.8 33,909.8 63,940.3 103,395.1 96,825.9 20,569.6 30,944.5 6,056.2 30,030.0 

Armenia — — — 1.3 30.0 19.1 — — 11.0 — 

Azerbaijan — — 1,217.2 400.2 183.8 315.7 41.7 5.0 13.6 57.0 

Bahrain 924.5 2,361.3 1,888.6 2,913.8 3,825.7 6,170.1 760.0 40.0 370.0 55.0 

Egypt 670.0 155.0 1,465.0 3,855.8 4,379.6 5,602.1 2,788.1 1,122.4 1,220.0 3,366.4 

Georgia — 6.0 — 11.1 220.8 341.6 89.0 52.6 100.0 500.0 

Iran, I.R. of 2,842.4 952.3 2,419.4 1,928.8 142.5 — — — — — 

Iraq — — — 107.8 2,877.0 — — — — — 

Israel 390.2 2,050.0 3,977.9 5,113.0 3,518.4 2,662.2 587.8 794.2 717.9 1,364.0 

Jordan 80.9 — 199.4 — 60.0 180.0 — — — — 

Kazakhstan 1,064.5 1,801.3 6,376.2 8,199.1 16,655.8 18,050.7 2,852.4 5,157.7 222.9 4,268.0 

Kuwait 750.0 365.0 1,788.2 4,445.0 5,346.6 1,919.9 604.4 403.0 505.7 1,310.0 

Kyrgyz Republic 95.0 — — 2.0 — — — — — 0.8 

Lebanon 990.0 160.0 5,382.8 2,558.0 6,040.0 2,420.0 500.0 400.0 875.0 1,763.2 

Libya — — — — — 38.0 38.0 — — — 

Oman 2,417.0 907.8 1,328.6 3,320.7 3,430.2 3,580.7 1,428.3 — 450.0 96.0 

Qatar 1,571.7 880.8 2,042.7 10,768.5 11,467.0 14,700.5 5,849.7 7,983.0 673.5 3,465.0 

Saudi Arabia 300.0 969.5 2,749.6 5,791.0 9,115.5 7,110.6 70.0 780.6 52.0 6,564.0 

Tajikistan — — 5.2 1.2 — 2.0 — 2.0 — — 

United Arab Emirates 370.0 2,348.1 3,041.0 14,519.5 36,097.2 33,712.6 4,960.1 14,203.9 844.6 7,220.6 

Uzbekistan 31.0 38.7 28.0 3.6 4.9 — — — — — 

Latin America 31,878.8 46,935.1 56,542.5 86,183.4 76,281.1 132,959.9 45,519.3 29,975.9 12,335.6 27,146.1 

Argentina 824.2 100.0 1,790.0 20,663.0 3,343.6 9,946.1 5,252.5 860.0 1,026.0 265.0 

Belize 125.0 100.0 — — — — — — — — 

Bolivia 90.0 30.0 — 54.0 — — — — 100.0 — 

Brazil 10,229.0 14,134.1 16,669.8 27,957.3 33,931.1 73,322.6 26,878.2 19,443.7 6,041.7 14,883.7 

Chile 3,546.6 7,795.0 7,956.8 6,900.2 6,159.2 3,886.0 1,714.2 1,058.0 537.2 2,271.0 

Colombia 1,880.0 1,765.0 1,628.4 3,063.3 5,049.6 7,931.8 4,319.9 1,909.0 1,750.0 202.0 

Costa Rica 250.0 490.0 334.2 91.7 1.7 31.1 — 30.5 — 150.0 

Cuba — — 69.8 1.9 — — — — — — 

Dominican Republic 423.3 670.4 140.5 284.4 779.8 657.9 — — — — 

Ecuador 910.0 — — 759.0 19.1 104.0 89.0 15.0 — — 

El Salvador 1,810.0 481.0 340.2 454.5 1,326.6 — — — — — 

Grenada 100.0 — — — — — — — — — 

Guadeloupe 17.4 — — — — — — — — — 

Guatemala 44.0 300.0 439.3 365.0 — 15.0 — 150.0 — 350.0 

Haiti — — — — 134.0 — — — — — 

Honduras — — 119.0 4.6 — — — — — — 

Jamaica 300.0 49.6 905.3 1,466.6 1,076.1 1,275.0 2,157.2 1,200.0 1,500.0 626.0 

Mexico 9,583.3 15,783.6 19,930.0 14,261.4 17,186.3 18,500.6 2,626.4 3,287.3 752.0 2,383.4 

Nicaragua — — 22.0 — — — — — — — 

Paraguay — — — — — — — — 18.8 80.0 

Peru 1,133.0 1,445.0 1,388.2 2,583.9 1,489.9 5,805.1 2,471.9 772.3 610.0 1,285.0 

St. Lucia — 20.0 — — — — — — — — 

Trinidad and Tobago 213.0 46.0 415.0 100.0 2,708.0 955.4 — — — — 

Uruguay 400.0 53.1 — 1,061.2 2,700.0 1,148.3 10.0 — — — 

Venezuela — 3,672.5 4,394.0 6,111.3 376.1 9,381.0 — 1,250.0 — 4,650.0 

Source: Data provided by the Bond, Equity and Loan database of the International Monetary Fund sourced from Dealogic.

1Data prior to 2006 refer to Serbia and Montenegro.
STATISTICAL APPENDIX

206

Table 15. Emerging Market External Financing: Bond Issuance

(In millions of U.S. dollars)

2007 2008

2002 2003 2004 2005 2006 2007 Q3 Q4 Q1 Q2

Total 58,278.4 89,358.0 128,346.9 179,506.6 163,124.6 184,375.9 23,144.8 26,665.5 18,988.5 58,568.3

Africa 2,845.9 2,375.9 2,250.1 3,170.0 4,898.9 13,243.3 2,359.5 1,393.9 — 513.6

Gabon — ———— 1,000.0 — 1,000.0 — —

Ghana — ———— 950.0 750.0 200.0 — —

Morocco — 457.3 — — — 671.3 — — — —

Nigeria — — — — — 525.0 — — — —

Seychelles — — — — 200.0 30.0 30.0 — — —

South Africa 2,195.9 1,562.4 1,696.5 2,681.4 4,698.9 9,813.6 1,326.0 193.9 — 513.6

Tunisia 650.0 356.1 553.6 488.6 — 253.4 253.4 — — —

Asia 17,179.7 26,738.6 44,566.9 44,502.1 41,705.3 47,314.6 5,939.9 6,160.4 6,536.2 13,642.9

China 250.0 1,802.3 4,362.0 3,858.2 1,110.0 2,144.2 1,022.8 — — 300.0

Fiji — — — — 150.0 —————

Hong Kong SAR 1,230.9 1,868.8 3,316.8 4,626.9 3,595.8 5,122.6 — 420.0 288.5 766.2

India — 300.0 3,199.8 2,118.3 2,644.2 7,549.4 2,000.0 286.0 157.5 1,250.0

Indonesia 275.0 609.2 1,363.6 2,817.3 2,000.0 1,750.0 — — 2,000.0 2,200.0

Korea 8,777.3 12,303.3 17,717.7 17,953.7 18,345.6 22,250.3 2,462.7 4,954.4 3,270.7 6,816.0

Malaysia 1,168.9 897.7 1,975.0 1,184.1 2,076.2 918.6 203.8 — — —

Mongolia — ———— 75.0 — — 500.0 —

Pakistan — — 500.0 — 1,050.0 750.0 — — — —

Philippines 4,773.8 4,450.0 4,446.7 3,900.0 4,623.2 1,000.0 — — — —

Singapore 703.7 3,849.2 5,727.9 4,245.7 4,750.5 4,489.1 25.6 — 319.6 1,784.4

Sri Lanka — — 100.0 — — 500.0 — 500.0 — —

Taiwan Province of China — 358.0 457.4 806.0 304.7 — — — — 2.4

Thailand — 300.0 1,400.0 2,241.8 1,055.0 765.4 225.0 — — 523.8

Vietnam — — — 750.0 — — — — — —

Europe 14,866.1 22,787.0 33,016.7 52,290.5 50,649.5 59,776.4 5,259.0 9,063.9 6,862.3 26,669.6

Belarus — — — — 2.5 19.4 — — 3.0 —

Bulgaria 1,752.3 287.3 10.0 383.4 220.8 —————

Croatia 844.1 978.0 1,654.3 — 384.9 746.4 408.5 — — —

Cyprus 482.0 653.9 1,178.4 1,135.5 1,694.9 2,427.8——— 202.3

Czech Republic 429.4 337.6 2,546.7 1,345.2 907.4 2,168.9 515.9 855.9 144.9 3,182.4

Estonia 296.7 328.8 958.5 426.6 — 38.0 — 38.0 — —

Gibraltar 8.2 — — — — 400.8————

Hungary 71.3 2,441.7 5,002.1 7,351.4 6,900.9 4,088.2 — 726.8 1,466.1 3,441.6

Latvia — — 528.4 123.1 266.1 — — — 607.6 —

Lithuania 360.9 432.7 811.2 778.6 1,241.6 1,484.1 — 850.9 — —

Macedonia, FYR — — — 176.5 — —————

Poland 2,673.9 4,298.1 3,545.2 11,851.5 4,693.5 4,111.0 — 446.7 473.8 3,311.3

Romania 1,070.8 805.7 — 1,197.0 — ———— 1,162.5

Russia 3,080.0 4,587.8 7,150.8 15,365.7 20,804.6 29,990.6 2,284.6 5,004.5 539.6 14,509.3

Serbia — — — 1,018.5 — 165.2 — — — —

Slovak Republic 141.7 866.3 1,188.7 — 1,208.8 1,354.2 — — — —

Slovenia 30.2 — 67.3 156.5 — 1,614.8 — 141.1 1,477.3 —

Turkey 3,125.6 5,459.0 6,060.1 8,875.0 9,209.9 7,132.2 1,350.0 — 2,150.0 500.0

Ukraine 499.0 1,310.0 2,315.0 2,105.9 3,113.5 4,035.0 700.0 1,000.0 — 360.0
207

EMERGING MARKETS

2007 2008

2002 2003 2004 2005 2006 2007 Q3 Q4 Q1 Q2

Middle East & Central 

Asia 4,561.4 4,696.3 14,783.4 18,576.9 35,156.1 25,327.1 3,707.5 3,233.2 1,844.3 7,122.5

Azerbaijan — — — — 5.0 100.0 — — 13.6 26.0

Bahrain 584.5 1,311.3 665.6 1,296.7 1,120.0 1,767.7 200.0 — 350.0 —

Egypt — — — 1,250.0 — 1,803.5 1,053.5 — — —

Georgia — ———— 200.0 — — — 500.0

Iran, I.R. of 999.9 — — — — —————

Iraq — — — — 2,700.0 —————

Israel 376.1 1,800.0 2,250.0 1,177.9 1,500.0 — — — 250.0 1,000.0

Jordan 80.9 — 145.0 — — —————

Kazakhstan 550.0 825.0 3,225.0 2,850.0 7,055.8 8,808.6 429.7 807.4 — 3,540.0

Kuwait 750.0 200.0 500.0 500.0 1,137.0 575.0 475.0 — 305.7 —

Lebanon 990.0 160.0 5,382.8 1,780.0 5,741.6 2,300.0 500.0 400.0 875.0 1,763.2

Oman — — 250.0 — 25.0 — — — — —

Qatar — — 665.0 2,250.0 3,040.0 — — — — —

Saudi Arabia — 400.0 — 1,800.0 2,913.8 — — — — —

United Arab Emirates 230.0 — 1,700.0 5,672.4 9,917.9 9,772.4 1,049.3 2,025.8 50.0 293.3

Latin America 18,825.4 32,760.3 33,729.7 60,967.1 30,714.8 38,714.5 5,878.8 6,814.0 3,745.7 10,619.8

Argentina — 100.0 1,290.0 18,984.4 1,745.5 3,400.9 100.0 445.0 — 65.0

Belize 125.0 100.0 — — — —————

Brazil 7,209.7 11,803.7 9,716.4 17,769.0 12,303.9 10,091.9 402.0 2,930.0 1,245.7 4,929.0

Chile 1,632.3 3,200.0 2,350.0 900.0 1,100.0 250.0 — — — 99.8

Colombia 1,000.0 1,765.0 1,545.4 2,435.5 3,177.6 3,133.7 1,050.7 679.0 1,000.0 —

Costa Rica 250.0 490.0 310.0 — — —————

Dominican Republic — 600.0 — 196.6 550.0 430.0————

Ecuador — — — 650.0 — —————

El Salvador 1,745.0 348.5 286.5 375.0 625.0 —————

Grenada 100.0 — — — — —————

Guatemala — 300.0 380.0 200.0 — — — 150.0 — 350.0

Jamaica 300.0 — 809.0 1,050.0 880.0 625.0 2,157.2 1,200.0 1,500.0 526.0

Mexico 5,063.4 9,080.0 11,384.2 9,165.1 6,207.2 6,341.4————

Peru 1,000.0 1,250.0 1,298.2 2,155.0 445.0 4,449.0 2,169.0 160.0 — —

Trinidad and Tobago — — 100.0 100.0 980.7 900.0 — — — —

Uruguay 400.0 53.1 — 1,061.2 2,700.0 342.6 — — — —

Venezuela — 3,670.0 4,260.0 5,925.3 — 8,750.0 — 1,250.0 — 4,650.0

Source: Data provided by the Bond, Equity and Loan database of the International Monetary Fund sourced from Dealogic.

Table 15 (concluded)
STATISTICAL APPENDIX

208

Table 16. Emerging Market External Finance: Equity Issuance

(In millions of U.S. dollars)

2007 2008

2002 2003 2004 2005 2006 2007 Q3 Q4 Q1 Q2

Total 17,194.2 28,018.7 49,026.2 93,016.2 157,296.8 229,171.8 41,643.4 82,532.2 17,452.7 32,824.7

Africa 365.0 1,181.3 2,742.4 1,189.0 4,216.7 9,825.5 2,252.6 3,891.1 38.7 1,559.1

Algeria — — — — 25.4 — — — — —

Central African Republic — — — — — 305.5 305.5 — — —

Ghana — — — — — 9.8 9.8 — — —

Kenya — — — — — — — — — 848.6

Morocco — — 800.9 — 316.0 1,295.2 431.3 287.2 — 196.9

Namibia — — — — — — — — — 87.6

Nigeria — — — — — 1,324.2 824.2 500.0 — —

South Africa 365.0 1,181.3 1,910.5 1,184.2 3,800.2 6,890.9 681.9 3,103.9 38.7 377.1

Sudan — — 31.0 — — — — — — —

Zimbabwe — — — 4.8 75.1 — — — — 48.9

Asia 12,940.8 22,955.3 36,755.1 68,813.1 100,952.0 117,982.8 21,820.1 42,946.5 15,671.8 10,468.0

Bangladesh — — — 16.7 23.0 39.9 4.2 7.0 — —

Cambodia — — — — 105.5 250.0 — — — —

China 1,747.2 4,114.1 13,763.8 25,715.0 53,871.2 60,743.6 12,261.6 26,536.6 8,880.6 5,583.3

Hong Kong SAR 3,058.4 4,292.3 3,704.6 5,022.3 6,504.2 6,021.0 821.1 2,614.5 120.2 1,596.5

India 264.8 421.6 5,023.5 10,100.7 14,511.9 22,198.7 4,781.9 5,041.0 4,980.2 1,054.8

Indonesia 284.8 1,128.6 849.3 1,336.3 607.8 3,094.0 901.8 1,105.6 271.0 630.7

Korea 1,582.2 1,219.6 5,314.4 13,300.8 10,062.7 7,370.5 840.2 2,485.9 1,146.4 578.9

Macao SAR — — — — 1,316.8 581.3 — 581.3 — —

Malaysia 1,143.2 559.3 964.7 710.9 580.0 1,870.4 — 746.3 100.0 —

Pakistan — — — — 961.1 784.4 134.1 — — 109.3

Papua New Guinea 85.0 153.7 — — — 1,024.3 — 14.8 — —

Philippines — 103.9 47.0 740.2 1,646.4 2,556.2 1,289.2 111.2 105.4 169.4

Singapore 1,624.6 1,245.2 2,601.1 4,075.4 4,778.7 4,541.0 522.0 1,401.7 — 3.9

Sri Lanka 33.7 — — 55.5 — — — — — —

Taiwan Province of China 3,060.7 8,215.0 3,388.5 7,172.1 3,728.3 5,352.1 263.8 1,472.7 61.4 82.2

Thailand 56.3 1,501.9 1,098.4 567.2 2,254.5 943.1 — 686.4 6.7 569.0

Vietnam — — — — — 612.4 — 141.7 — 90.0

Europe 1,659.0 2,484.2 5,559.6 11,276.1 24,233.2 41,676.0 2,123.2 9,474.6 196.7 7,037.5

Bulgaria — — — 93.5 85.7 — — — — —

Croatia — — — — 500.9 1,377.6 — 1,377.6 — —

Cyprus — — — 54.4 1,178.4 20.8 — 20.8 — 28.4

Czech Republic — 1,091.5 174.4 295.1 287.3 278.0 197.8 80.2 — 2,515.2

Estonia 41.3 — — 266.2 24.2 216.1 44.1 — — —

Faroe Islands — — — — 67.7 225.1 — — — —

Gibraltar — — — 1,897.1 437.5 94.1 — — — —

Hungary — 13.2 884.7 48.8 — 191.8 — — — —

Lithuania — — — 51.2 — — — — 15.4 —

Poland 245.4 758.6 964.7 1,865.6 1,575.5 556.7 149.0 99.5 103.5 627.5

Romania — — — — 172.5 156.0 — 97.9 — —

Russia 1,301.0 570.0 2,554.9 6,458.2 18,706.9 33,540.2 1,023.7 6,806.5 — 2,846.1

Slovak Republic — — — 88.8 1.9 — — — — —

Slovenia — — — — — 453.6 — 453.6 — 305.0

Turkey 71.4 50.9 980.8 — 1,164.3 3,233.1 675.4 — — —

Ukraine — — — 157.1 30.5 1,332.9 33.1 538.7 77.8 715.3

Middle East & Central Asia 14.1 186.0 1,783.2 5,733.5 9,123.8 11,985.5 1,301.6 8,378.6 371.0 3,398.0

Bahrain — — — 87.2 420.5 266.4 — — — —

Egypt — — 141.0 1,116.5 483.7 722.4 — 722.4 — 527.7

Georgia — — — — 159.8 — — — 100.0 —

Israel 14.1 186.0 1,357.9 1,894.7 921.6 1,459.2 152.8 194.2 91.6 364.0

Kazakhstan — — — 1,548.2 4,303.6 5,031.4 977.5 3,201.3 152.9 67.0

Kuwait — — 260.7 — — — — — — 1,141.0

Lebanon — — — 778.0 248.4 — — — — —

Oman — — 23.6 148.4 — — — — — —

Qatar — — — — 1,173.8 171.4 171.4 — — 900.0

Saudi Arabia — — — — — 41.8 — 41.8 — —

United Arab Emirates — — — 160.5 1,412.3 4,293.0 — 4,218.9 26.6 398.4

Latin America 2,215.3 1,211.8 2,186.0 6,004.6 18,771.1 47,702.0 14,145.9 17,841.4 1,174.5 10,362.1

Argentina — — — — 987.1 1,319.2 622.5 59.1 — —

Brazil 1,148.5 556.2 1,830.5 4,254.0 13,888.9 38,848.0 9,718.0 15,952.7 1,174.5 7,978.7

Chile — 115.6 105.5 689.7 892.3 460.5 96.7 155.0 — —

Colombia — — — — 67.7 3,418.1 3,119.2 — — —

Mexico 1,066.8 540.0 250.1 1,060.9 2,358.2 2,932.8 534.4 1,447.3 — 2,383.4

Peru — — — — 576.9 723.3 55.1 227.3 — —

Source: Data provided by the Bond, Equity and Loan database of the International Monetary Fund sourced from Dealogic.
209

EMERGING MARKETS

Table 17. Emerging Market External Financing: Loan Syndication

(In millions of U.S. dollars)

2007 2008

2002 2003 2004 2005 2006 2007 Q3 Q4 Q1 Q2

Total 82,526.8 97,597.0 148,356.3 189,699.0 252,140.3 327,798.6 107,957.7 82,424.8 64,570.4 75,731.2

Africa 4,237.2 6,137.5 7,722.8 8,076.3 7,076.0 8,361.2 805.0 1,935.5 251.4 2,006.2

Algeria 150.0 40.0 307.9 489.3 — 411.0 411.0 — — —

Angola 350.0 1,542.0 2,900.0 3,122.7 91.9 74.6 — — — —

Botswana — — 28.4 — — — — — — —

Burkina Faso — — — 11.0 — 14.5 — — — —

Cameroon — — 48.0 30.0 — — — — — —

Cape Verde — — — — — 13.0 — — — —

Côte d’Ivoire — — — — — — — — — 45.0

Djibouti — — 40.0 — — — — — — —

Ethiopia — — 40.0 — — — — — — 100.2

Gabon — — 22.0 — 34.4 — — — — —

Ghana 420.0 650.0 850.0 706.5 860.0 504.5 214.0 140.5 — —

Kenya — 134.0 135.1 64.0 330.1 10.0 — — — —

Lesotho — — — — — 19.7 19.7 — — —

Malawi — — 4.8 — — — — — — —

Mali 150.4 287.6 288.9 — — 180.9 31.0 — 110.4 —

Mauritius — — — 99.3 180.0 — — — 9.0 —

Morocco — 9.8 2.6 1.9 25.4 — — — — —

Mozambique — 35.5 422.4 — 38.8 — — — — 800.0

Namibia — 35.0 — 50.0 100.0 — — — — —

Niger — 27.0 — — — — — — — —

Nigeria 1,000.0 762.0 875.0 874.0 640.0 3,666.5 50.5 1,600.0 — —

Senegal 40.0 — 10.0 — 31.6 — — — — —

South Africa 2,026.7 2,456.4 1,717.8 2,400.3 4,201.6 3,061.6 78.8 — — 659.0

Tanzania — — — 136.0 — — — — 112.0 —

Tunisia 100.0 128.2 30.0 91.2 24.7 150.0 — — — 402.0

Uganda — — — — 12.6 — — — — —

Zambia — 30.0 — — 505.0 255.0 — 195.0 20.0 —

Asia 46,415.8 50,552.1 71,035.7 82,007.0 100,411.9 150,002.6 43,737.7 41,065.5 40,763.8 23,372.4

Bangladesh — 10.0 176.8 — 83.6 17.6 17.6 — 11.3 —

Brunei Darussalam 129.0 — — — — — — — — —

China 8,208.1 9,856.4 7,535.7 11,757.9 8,412.3 24,727.2 12,403.4 3,614.0 6,874.6 2,402.4

Hong Kong SAR 10,333.7 9,486.7 12,269.8 11,294.2 16,046.8 12,497.7 3,096.0 4,275.8 2,041.6 1,792.1

India 1,162.7 2,555.5 5,077.8 10,970.7 15,881.2 31,311.6 12,034.8 7,881.4 10,607.4 6,500.9

Indonesia 562.7 3,469.4 1,902.4 1,041.8 5,756.5 3,496.7 946.8 1,628.5 1,773.5 1,478.7

Korea 7,459.6 5,589.7 7,983.9 17,108.2 13,017.9 31,121.7 3,646.1 12,112.4 6,881.1 1,705.5

Lao P.D.R. 101.4 — 210.0 1,000.0 — — — — — —

Macao SAR — — 382.0 729.0 2,375.9 3,950.1 — 2,145.0 180.0 —

Malaysia 3,664.2 4,286.8 5,038.1 4,298.2 5,051.2 4,322.8 546.0 1,555.0 136.0 599.7

Marshall Islands 34.7 — — 24.0 170.0 1,069.3 118.0 557.1 — 204.0

Mongolia — — — 30.0 6.0 10.0 — 10.0 — 4.0

Nepal — — — — — — — — 5.0 —

Pakistan 388.8 983.8 470.0 739.2 1,287.8 614.9 209.0 262.9 240.0 146.1

Philippines 1,571.7 1,851.4 1,864.7 1,554.6 902.9 3,092.2 2,355.0 50.0 495.0 525.3

Singapore 2,620.7 2,353.8 3,620.4 6,303.7 10,567.4 10,944.2 2,394.2 2,141.2 4,796.7 3,966.7

Sri Lanka — 186.0 35.0 327.5 129.8 255.0 — 45.0 — 340.0

Taiwan Province of China 7,170.0 7,467.6 22,712.1 11,107.4 18,341.9 19,633.9 5,053.2 4,139.0 6,177.6 3,096.5

Thailand 2,616.2 2,058.1 1,642.9 3,501.8 1,923.3 908.8 615.0 — 130.0 248.9

Vietnam 392.5 397.0 114.0 218.8 457.4 2,029.2 302.6 648.1 414.0 361.7

Europe 13,114.1 19,830.8 31,627.7 40,773.9 58,742.1 63,378.1 22,359.9 14,770.7 12,298.9 24,678.9

Albania — — — — — — — — 35.7 —

Belarus — — 21.4 32.0 336.1 283.5 145.5 76.0 40.0 149.0

Bulgaria 13.0 381.3 1,089.9 626.8 1,420.6 1,360.0 547.0 328.5 438.3 300.5

Croatia 774.9 1,985.5 1,083.1 1,263.7 1,291.9 662.6 347.4 315.3 — 419.6

Cyprus 68.1 — — — 965.7 651.3 485.3 138.0 69.5 1,090.0

Czech Republic 90.8 642.9 1,345.1 2,360.8 986.8 1,815.8 389.4 — 1,122.0 —

Estonia 147.1 127.1 222.9 — 449.4 45.1 45.1 — 178.8 117.7

Faroe Islands — — — 85.3 206.2 206.1 — — — 217.4

Gibraltar — — — — 1,934.2 — — — — —

Hungary 1,240.0 2,096.7 3,373.4 1,941.4 427.8 1,050.9 267.9 250.0 279.2 2,514.8

Latvia 51.9 70.7 353.2 393.0 1,191.3 1,614.7 550.1 55.8 508.2 —
STATISTICAL APPENDIX

210

Table 17 (concluded)

2007 2008

2002 2003 2004 2005 2006 2007 Q3 Q4 Q1 Q2

Europe (continued)

Lithuania 8.8 — 174.8 390.2 50.4 161.2 — 121.0 — 31.1

Macedonia, FYR — 47.6 66.0 — — 14.4 — 14.4 — —

Malta — 114.8 242.7 — 256.0 — — —— —

Moldova — — 7.0 13.1 — — — —— —

Montenegro — 13.4 — — 0.8 21.4 — 21.4 — —

Poland 3,104.6 2,755.7 749.4 3,290.4 2,050.2 2,733.7 1,350.4 182.5 — 1,188.2

Romania 385.9 958.1 1,116.7 1,414.0 574.7 1,012.2 3.1 795.8 176.6 247.5

Russia 3,721.0 6,040.8 12,415.5 15,179.7 25,195.4 24,433.9 8,461.8 8,669.3 6,871.3 14,175.0

Serbia — — 213.4 234.1 60.2 403.4 176.5 202.8 7.8 —

Slovak Republic 91.1 101.3 130.3 622.7 — — — —— —

Slovenia 347.7 430.3 1,254.6 1,730.8 1,837.8 2,691.6 — 1,100.5 55.0 1,669.9

Turkey 3,054.3 3,961.7 7,466.0 10,124.6 17,267.4 20,911.3 9,331.7 1,234.5 1,609.4 2,014.3

Ukraine 15.0 103.0 302.1 1,071.4 2,239.3 3,305.0 258.8 1,264.9 907.0 543.8

Middle East & Central Asia 7,921.7 8,113.5 17,343.2 39,630.0 59,115.2 59,513.2 15,560.6 19,332.7 3,840.8 19,509.5

Armenia — — — 1.3 30.0 19.1 — — 11.0 —

Azerbaijan — — 1,217.2 400.2 178.8 215.7 41.7 5.0 — 31.0

Bahrain 340.0 1,050.0 1,223.0 1,530.0 2,285.2 4,136.0 560.0 40.0 20.0 55.0

Egypt 670.0 155.0 1,324.0 1,489.3 3,895.9 3,076.1 1,734.6 400.0 1,220.0 2,838.8

Georgia — 6.0 — 11.1 61.0 141.6 89.0 52.6 — —

Iran, I.R. of 1,842.5 952.3 2,419.4 1,928.8 142.5 — — —— —

Iraq — — — 107.8 177.0 — — —— —

Israel — 64.0 370.0 2,040.4 1,096.8 1,203.0 435.0 600.0 376.3 —

Jordan — — 54.4 — 60.0 180.0 — — — —

Kazakhstan 514.5 976.3 3,151.2 3,800.9 5,296.4 4,210.7 1,445.3 1,149.0 70.0 661.0

Kuwait — 165.0 1,027.5 3,945.0 4,209.6 1,344.9 129.4 403.0 200.0 169.1

Kyrgyz Republic 95.0 — — 2.0 — — — — — 0.8

Lebanon — — — — 50.0 120.0 — — — —

Libya — — — — — 38.0 38.0 — — —

Oman 2,417.0 907.8 1,055.0 3,172.2 3,405.2 3,580.7 1,428.3 — 450.0 96.0

Qatar 1,571.7 880.8 1,377.7 8,518.5 7,253.1 14,529.2 5,678.4 7,983.0 673.5 2,565.0

Saudi Arabia 300.0 569.5 2,749.6 3,991.0 6,201.7 7,068.8 70.0 738.9 52.0 6,564.0

Tajikistan — — 5.2 1.2 — 2.0 — 2.0 — —

United Arab Emirates 140.0 2,348.1 1,341.0 8,686.6 24,767.1 19,647.3 3,910.9 7,959.2 768.0 6,528.9

Uzbekistan 31.0 38.7 28.0 3.6 4.9 — — —— —

Latin America 10,838.1 12,963.1 20,626.9 19,211.7 26,795.2 46,543.4 25,494.5 5,320.5 7,415.4 6,164.3

Argentina 824.2 — 500.0 1,678.6 611.0 5,226.0 4,530.0 356.0 1,026.0 200.0

Bolivia 90.0 30.0 — 54.0———— 100.0 —

Brazil 1,870.9 1,774.3 5,122.9 5,934.3 7,738.3 24,382.6 16,758.2 561.0 3,621.5 1,976.1

Chile 1,914.3 4,479.4 5,501.3 5,310.6 4,166.9 3,175.5 1,617.5 903.0 537.2 2,171.2

Colombia 880.0 — 83.0 627.8 1,804.4 1,380.0 150.0 1,230.0 750.0 202.0

Costa Rica — — 24.2 91.7 1.7 31.1 — 30.5 — 150.0

Cuba — — 69.8 1.9 — — — —— —

Dominican Republic 423.3 70.4 140.5 87.8 229.8 227.9 — — — —

Ecuador 910.0 — — 109.0 19.1 104.0 89.0 15.0 — —

El Salvador 65.0 132.5 53.8 79.5 701.6 — — —— —

Guadeloupe 17.4 — — — — — — —— —

Guatemala 44.0 — 59.3 165.0 — 15.0 — — — —

Haiti — — — — 134.0 — — —— —

Honduras — — 119.0 4.6 — — — —— —

Jamaica — 49.6 96.3 416.6 196.1 650.0 — — — 100.0

Mexico 3,453.0 6,163.6 8,295.7 4,035.4 8,620.9 9,226.4 2,092.0 1,840.0 752.0 —

Nicaragua — — 22.0 — — — — —— —

Paraguay ———————— 18.8 80.0

Peru 133.0 195.0 90.0 429.0 468.0 632.9 247.9 385.0 610.0 1,285.0

St. Lucia — 20.0 — — — — — —— —

Trinidad and Tobago 213.0 46.0 315.0 — 1,727.3 55.4 — — — —

Uruguay ————— 805.7 10.0 — — —

Venezuela — 2.5 134.0 186.0 376.1 631.0 — — — —

Source: Data provided by the Bond, Equity and Loan database of the International Monetary Fund sourced from Dealogic.
211

EMERGING MARKETS

Table 18. Equity Valuation Measures: Dividend-Yield Ratios

2007 2008

2003 2004 2005 2006 2007 Q3 Q4 Q1 Q2

Composite 2.28 2.29 2.28 2.14 1.56 1.81 1.56 1.78 2.13

Asia 1.97 2.20 2.42 1.88 1.32 1.60 1.32 1.51 2.08

Europe/Middle East/Africa 2.41 2.00 1.76 2.36 1.82 2.09 1.82 2.06 2.20

Latin America 3.26 3.24 3.07 2.56 1.99 2.11 1.99 2.22 2.18

Argentina 1.37 0.98 1.20 1.21 1.20 1.36 1.20 1.19 0.76

Bahrain 2.27 1.19 1.77 4.16 3.80 3.97 3.80 5.52 5.37

Brazil 4.23 4.24 3.98 3.38 2.00 2.62 2.00 2.34 2.29

Chile 2.95 4.62 2.99 2.07 2.40 2.33 2.40 2.81 2.79

China 2.31 1.82 2.56 1.29 0.70 0.90 0.70 0.91 1.75

Colombia 5.89 5.44 1.38 1.96 1.89 2.09 1.89 1.83 2.10

Czech Republic 5.04 4.19 1.42 3.71 2.67 2.88 2.67 2.99 3.69

Egypt 4.94 1.45 1.54 2.29 1.76 2.18 1.76 1.80 2.31

Hungary 0.91 1.73 2.05 1.83 3.04 2.49 3.04 2.95 1.04

India 1.74 1.70 1.25 1.07 0.71 0.94 0.71 0.81 1.07

Indonesia 3.42 3.35 2.74 2.18 1.87 2.00 1.87 2.07 2.29

Israel 1.20 1.83 1.58 2.55 2.64 2.64 2.64 2.72 2.66

Jordan 2.40 1.49 2.19 1.06 1.48 1.93 1.48 2.32 1.26

Korea 2.08 2.25 1.70 1.49 1.30 1.40 1.30 1.09 1.61

Kuwait . . . . . . . . . 2.97 3.01 3.07 3.01 3.60 4.36

Malaysia 3.02 3.50 4.33 3.72 3.38 4.10 3.38 4.63 4.92

Mexico 2.12 1.85 2.18 1.24 2.20 1.38 2.20 2.19 2.31

Morocco 4.65 2.71 3.61 2.22 1.85 2.05 1.85 1.60 2.64

Nigeria 4.11 3.70 3.14 2.29 1.47 2.00 1.47 1.21 1.77

Oman 5.38 3.32 2.15 4.64 3.25 3.75 3.25 4.93 4.75

Pakistan 7.47 6.98 2.50 3.96 3.25 3.10 3.25 3.39 4.71

Peru 2.83 3.10 3.45 3.83 3.65 2.80 3.65 3.59 3.62

Philippines 2.12 1.79 2.63 2.00 2.28 2.54 2.28 3.79 4.51

Poland 1.43 1.20 2.48 3.36 2.66 2.96 2.66 3.10 2.25

Qatar . . . . . . . . . 1.69 2.31 2.88 2.31 2.74 2.12

Russia 1.78 1.21 1.07 1.83 0.53 0.61 0.53 0.63 0.70

Saudi Arabia 2.58 2.05 1.25 2.65 2.18 3.01 2.18 2.82 2.51

South Africa 3.96 3.09 3.09 2.77 3.33 2.98 3.33 2.96 3.38

Sri Lanka 3.64 4.67 2.47 1.77 2.28 2.54 2.28 3.11 3.75

Taiwan Province of China 1.47 2.67 3.39 3.06 3.03 3.34 3.03 3.02 3.14

Thailand 1.64 2.24 3.05 4.51 3.81 3.34 3.81 4.00 4.04

Turkey 1.15 2.97 1.81 2.19 1.96 2.13 1.96 2.63 3.71

United Arab Emirates . . . . . . . . . 2.12 1.27 1.97 1.27 1.37 1.95

Venezuela 9.86 12.28 6.27 5.71 . . . . . . . . . . . . . . .

Source: Standard & Poor’s Emerging Market Database.
STATISTICAL APPENDIX

212

Table 19. Equity Valuation Measures: Price-to-Book Ratios

2007 2008

2003 2004 2005 2006 2007 Q3 Q4 Q1 Q2

Composite 1.96 1.86 2.65 2.73 3.67 3.47 3.67 3.21 3.11

Asia 2.06 1.78 2.11 2.43 3.69 3.51 3.69 3.09 2.76

Europe/Middle East/Africa 1.86 2.21 3.91 3.26 3.91 3.55 3.91 3.54 3.75

Latin America 1.83 1.58 2.30 2.91 3.27 3.20 3.27 3.12 3.21

Argentina 1.99 2.16 2.50 4.09 3.23 3.57 3.23 3.49 4.23

Bahrain 2.02 2.02 2.73 2.23 3.56 2.69 3.56 3.48 3.62

Brazil 1.79 1.93 2.16 2.68 3.30 3.08 3.30 3.09 3.29

Chile 1.87 0.55 1.93 2.43 2.54 2.62 2.54 2.41 2.46

China 2.55 2.03 1.81 3.12 6.26 6.24 6.26 4.59 3.91

Colombia 0.94 1.58 2.41 1.78 1.82 1.70 1.82 1.55 1.55

Czech Republic 0.99 1.58 2.35 2.39 3.12 2.91 3.12 2.80 2.95

Egypt 2.08 4.38 9.08 5.85 8.60 7.16 8.60 7.58 6.63

Hungary 2.00 2.78 3.08 3.08 3.24 3.48 3.24 2.64 2.46

India 3.50 3.31 5.15 4.89 7.90 6.23 7.90 5.79 4.95

Indonesia 1.62 2.75 2.50 3.35 5.57 4.47 5.57 5.10 5.23

Israel 2.61 2.58 3.00 3.48 4.37 4.25 4.37 3.79 3.98

Jordan 2.08 2.99 6.24 3.30 4.39 3.27 4.39 4.28 5.35

Korea 1.57 1.25 1.95 1.74 2.18 2.21 2.18 2.00 1.94

Kuwait . . . . . . 4.64 4.52 6.37 6.65 6.37 6.94 6.70

Malaysia 1.71 1.93 1.67 2.08 2.51 2.35 2.51 2.17 1.97

Mexico 2.02 2.51 2.88 3.84 3.58 3.87 3.58 3.64 3.25

Morocco 1.70 2.06 2.92 3.11 4.34 4.41 4.34 5.03 4.81

Nigeria 2.52 3.19 5.36 5.22 11.98 9.56 11.98 15.94 13.94

Oman 1.50 1.80 2.28 2.19 4.01 2.86 4.01 4.49 5.02

Pakistan 2.25 2.63 3.51 3.17 4.66 4.39 4.66 5.13 4.08

Peru 1.80 1.56 2.17 3.47 5.95 6.60 5.95 5.77 6.59

Philippines 1.06 1.35 1.73 1.92 2.76 2.65 2.76 2.27 1.64

Poland 1.76 2.04 2.53 2.52 2.84 2.85 2.84 2.43 2.15

Qatar . . . . . . 8.80 2.73 3.79 3.12 3.79 3.96 5.14

Russia 1.18 1.18 2.19 2.53 2.82 2.48 2.82 2.40 2.78

Saudi Arabia 3.56 6.50 14.54 7.57 9.95 7.25 9.95 8.49 8.33

South Africa 2.06 2.52 2.98 3.80 4.38 4.21 4.38 4.38 4.26

Sri Lanka 1.63 1.93 2.56 2.41 1.85 1.69 1.85 1.75 1.58

Taiwan Province of China 2.18 1.94 1.93 2.36 2.56 2.79 2.56 2.56 2.31

Thailand 2.84 2.03 2.06 1.85 2.46 2.37 2.46 2.41 2.14

Turkey 2.64 1.74 2.13 1.95 2.78 2.64 2.78 2.00 1.82

United Arab Emirates . . . . . . 9.98 3.07 4.69 3.40 4.69 4.30 4.58

Venezuela 1.10 1.18 0.72 2.59 . . . . . . . . . . . . . . .

Source: Standard & Poor’s Emerging Market Database.
213

EMERGING MARKETS

Table 20. Equity Valuation Measures: Price/Earnings Ratios

2007 2008

2003 2004 2005 2006 2007 Q3 Q4 Q1 Q2

Composite 21.7 16.5 18.9 17.7 23.4 22.0 23.4 20.6 19.7

Asia 30.3 16.8 17.9 18.0 26.9 25.3 26.9 22.6 20.4

Europe/Middle East/Africa 18.0 18.6 25.2 18.7 22.6 20.3 22.6 20.7 21.1

Latin America 13.3 12.8 12.2 15.2 17.2 17.0 17.2 16.5 16.8

Argentina 21.1 27.7 11.1 18.0 13.6 15.2 13.6 14.8 17.9

Bahrain 21.3 21.5 31.7 14.3 20.3 17.2 20.3 20.5 21.3

Brazil 10.0 10.6 10.7 12.7 16.6 15.5 16.6 15.5 16.5

Chile 24.8 17.2 15.7 24.2 22.3 23.1 22.3 21.1 21.6

China 28.6 19.1 13.9 24.6 50.5 49.2 50.5 37.1 31.7

Colombia 13.0 19.2 28.8 21.9 21.8 20.9 21.8 18.6 18.6

Czech Republic 10.8 25.0 21.1 20.0 26.5 24.4 26.5 23.8 25.1

Egypt 11.7 21.8 30.9 20.2 30.2 24.7 30.2 26.6 23.1

Hungary 12.3 16.6 13.5 13.4 14.0 15.1 14.0 11.4 10.6

India 20.9 18.1 19.4 20.1 31.6 25.1 31.6 23.2 19.8

Indonesia 39.5 13.3 12.6 20.1 31.7 26.1 31.7 29.0 29.7

Israel 75.6 39.7 20.0 25.3 31.5 31.0 31.5 27.3 28.6

Jordan 20.7 30.4 57.1 20.8 28.0 20.7 28.0 27.3 33.6

Korea 30.2 13.5 20.8 12.8 16.4 16.7 16.4 15.1 14.7

Kuwait . . . . . . 21.5 21.1 29.7 31.1 29.7 32.4 31.3

Malaysia 30.1 22.4 15.0 21.7 20.1 19.4 20.1 17.4 15.8

Mexico 17.6 15.9 14.2 18.6 17.2 19.5 17.2 17.4 15.5

Morocco 25.2 24.6 22.4 22.5 30.4 31.9 30.4 35.3 33.7

Nigeria 18.5 23.5 20.7 24.1 58.4 44.1 58.4 77.8 68.0

Oman 15.2 14.2 15.8 13.1 23.1 17.0 23.1 25.9 28.9

Pakistan 9.5 9.9 13.1 10.8 15.3 15.0 15.3 16.8 13.4

Peru 13.7 10.7 12.0 15.7 20.9 22.4 20.9 20.3 23.6

Philippines 21.1 14.6 15.7 14.4 17.7 17.6 17.7 14.5 11.1

Poland –353.0 39.9 11.7 13.9 15.6 15.8 15.6 13.3 11.7

Qatar . . . . . . 48.7 15.9 21.7 18.0 21.7 22.6 29.3

Russia 19.9 10.8 24.1 16.6 18.4 15.8 18.4 15.6 16.6

Saudi Arabia 27.2 50.6 104.8 52.0 70.1 49.8 70.1 59.8 58.7

South Africa 11.5 16.2 12.8 16.6 18.7 18.3 18.7 18.7 18.3

Sri Lanka 15.0 18.1 23.6 15.4 12.1 11.2 12.1 11.5 10.4

Taiwan Province of China 55.7 21.2 21.9 25.6 27.9 29.7 27.9 27.9 25.1

Thailand 16.6 12.8 10.0 8.7 11.7 11.1 11.7 11.4 10.2

Turkey 14.9 12.5 16.2 17.2 25.2 24.4 25.2 18.1 16.5

United Arab Emirates . . . . . . 54.7 13.4 19.7 14.8 19.7 18.0 19.2

Venezuela 14.4 6.0 5.1 13.1 . . . . . . . . . . . . . . .

Source: Standard & Poor’s Emerging Market Database.
STATISTICAL APPENDIX

214

Table 21. Emerging Markets: Mutual Fund Flows

(In millions of U.S. dollars)

2007 2008

2001 2002 2003 2004 2005 2006 2007 Q3 Q4 Q1 Q2

Bonds –444 606 3,153 1,947 5,729 6,233 4,295 –1,185 943 492 174

Equities –1,781 –1,512 8,500 2,784 21,706 22,441 40,827 16,637 22,049 –20,045 7,742

Global –67 –2,082 2,119 –5,348 3,148 4,209 15,223 2,623 11,904 –6,638 247

Asia –768 817 5,148 5,609 6,952 16,790 16,405 11,064 6,668 –12,065 2,238

Europe/Middle East/Africa –327 65 857 2,185 7,587 –1,877 –953 –324 1,533 157 2,756

Latin America –619 –312 376 338 4,020 3,319 10,153 3,274 1,944 –1,499 2,501

Source: Emerging Porfolio Fund Research, Inc.
215

FINANCIAL SOUNDNESS INDICATORS

Table 22. Bank Regulatory Capital to Risk-Weighted Assets

(In percent)

2003 2004 2005 2006 2007 2008 Latest

Latin America

Argentina 14.5 14.0 15.3 16.8 16.8 16.8 May

Bolivia 15.3 14.9 14.7 13.3 12.6 14.0 May

Brazil 18.8 18.6 17.9 18.9 18.7 18.1 March

Chile 14.1 13.6 13.0 12.5 12.2 12.4 March

Colombia 12.6 13.1 13.2 12.2 12.8 13.3 May

Costa Rica1 16.5 18.1 15.9 15.3 13.0 12.9 March

Dominican Republic 11.6 13.9 13.0 12.3 13.1 16.3 March

Ecuador 14.9 14.5 14.4 14.8 15.7 . . . November

El Salvador 12.8 13.4 13.5 13.8 13.8 14.3 May

Guatemala 15.6 14.5 13.7 13.6 13.8 12.4 February

Mexico2 14.4 14.1 14.5 16.3 16.0 16.0 March

Panama 17.6 17.8 16.3 15.8 13.6 13.6 March

Paraguay 20.9 20.5 20.4 20.1 16.8 . . . December

Peru 13.3 14.0 12.0 12.5 11.7 12.2 May

Uruguay3 18.1 21.7 22.7 16.9 17.8 17.7 March

Venezuela 25.1 19.2 15.5 14.3 12.1 11.9 May

Emerging Europe

Albania 28.5 21.6 18.6 18.1 17.1 17.2 March

Belarus 26.0 25.2 26.7 24.4 19.3 . . . December

Bosnia and Herzegovina 20.3 18.7 17.8 17.7 17.1 16.5 March

Bulgaria 22.0 16.1 15.2 14.5 13.9 14.5 March

Croatia 16.5 16.0 15.2 13.6 15.9 15.9 March

Czech Republic 14.5 12.6 11.9 11.4 11.5 12.3 March

Estonia 14.5 13.4 11.7 13.1 14.8 . . . December

Hungary 11.8 12.4 11.6 11.0 10.8 . . . December

Israel 10.3 10.8 10.7 10.8 11.1 . . . September

Latvia 11.7 11.7 10.1 10.2 11.1 12.6 March

Lithuania4 13.3 12.4 10.3 10.7 10.9 . . . December

Macedonia, FYR 25.8 23.0 21.3 18.3 17.2 . . . September

Moldova 31.6 31.4 27.2 27.9 29.6 28.7 May

Montenegro . . . 31.3 27.8 21.3 17.1 17.2 March

Poland 13.7 15.5 14.5 13.2 11.8 . . . September

Romania5 21.1 20.6 21.1 18.1 13.8 13.0 March

Russia 19.1 17.0 16.0 14.9 15.5 15.3 March

Serbia 31.1 27.9 26.0 24.7 27.9 27.4 March

Slovak Republic 22.4 18.7 14.8 13.0 12.4 . . . December

Slovenia 11.5 11.8 10.6 11.8 . . . . . . December

Turkey6 30.9 28.8 24.2 22.1 18.9 16.9 June

Ukraine 15.2 16.8 15.0 14.2 13.9 13.3 March

Western Europe

Austria7 14.5 12.4 11.8 11.8 12.7 . . . December

Belgium 12.9 12.9 11.5 11.9 11.2 12.3 March

Denmark 13.9 13.4 13.2 13.8 . . . . . . December

Finland5 18.7 19.1 17.2 15.1 15.1 . . . June

France 11.9 11.5 11.4 10.9 10.1 . . . December

Germany 13.4 13.2 12.2 12.5 . . . . . . December

Greece 12.0 12.8 13.2 12.2 11.2 10.4 March

Iceland 12.3 12.8 12.8 15.1 12.1 . . . December

Ireland8 13.9 12.6 12.0 10.9 . . . . . . December

Italy9 11.4 11.6 10.6 10.7 10.4 . . . December

Luxembourg 17.1 17.5 16.3 14.9 13.5 . . . December

Malta . . . 21.4 20.4 22.0 23.2 . . . December

Netherlands 12.3 12.3 12.6 11.9 13.2 13.5 March

Norway 12.4 12.2 11.9 11.2 11.7 . . . December

Portugal10 10.0 10.4 11.3 11.0 10.2 . . . December

Spain 12.6 12.3 12.2 11.9 11.4 . . . December

Sweden11 9.9 10.1 10.1 10.0 10.3 10.0 March

Switzerland 12.4 12.6 12.4 13.4 12.5 . . . June

United Kingdom 13.0 12.7 12.8 12.9 12.6 . . . December
STATISTICAL APPENDIX

216

Table 22 (concluded)

(In percent)

2003 2004 2005 2006 2007 2008 Latest

Asia

Bangladesh 8.4 8.8 7.3 8.3 10.0 . . . December

China –5.9 –4.7 2.5 4.9 7.7 . . . June

Hong Kong SAR 15.3 15.4 14.9 15.2 13.4 14.3 March

India 12.7 12.9 12.8 12.3 12.3 12.6 March

Indonesia 22.3 19.4 19.3 21.3 19.3 20.5 March

Korea 11.1 12.1 13.0 12.8 12.3 12.0 March

Malaysia 13.8 14.4 13.7 13.5 13.2 . . . December

Philippines12 17.4 18.7 17.8 18.5 15.9 . . . September

Singapore 17.9 16.2 15.8 15.4 14.0 . . . September

Thailand 13.4 12.4 13.2 13.6 14.8 . . . December

Middle East & Central Asia

Armenia 33.8 32.3 33.7 34.9 30.1 27.0 March

Egypt 11.1 11.4 13.8 15.1 . . . . . . December

Georgia 20.3 18.8 17.5 20.6 16.0 15.8 May

Jordan 15.9 17.8 17.6 21.4 20.8 . . . June

Kazakhstan 16.9 15.3 14.9 14.8 14.2 . . . December

Kuwait 18.4 17.3 21.3 21.8 20.4 . . . December

Lebanon 22.3 22.2 22.9 25.0 24.0 . . . June

Morocco 9.6 10.5 11.5 12.3 10.6 . . . December

Oman 17.6 17.6 18.1 17.2 13.4 . . . September

Pakistan 8.5 10.5 11.3 12.7 13.6 . . . September

Saudi Arabia 19.4 17.8 17.8 21.9 21.8 . . . December

Tunisia 9.3 11.6 12.4 11.3 11.0 . . . December

United Arab Emirates 18.6 18.9 17.0 16.7 14.4 . . . December

Sub-Saharan Africa

Gabon 19.9 22.3 19.8 17.8 14.3 . . . December

Ghana 9.3 13.9 16.2 15.8 14.8 15.4 December

Kenya 17.3 16.6 16.4 16.5 16.3 . . . December

Lesotho . . . 22.0 22.0 19.0 20.0 . . . March

Mozambique 17.0 18.7 16.0 12.5 14.2 . . . December

Namibia 14.8 15.4 14.6 14.2 15.8 . . . December

Nigeria 17.8 14.7 17.8 22.6 21.0 . . . December

Rwanda 14.6 18.3 14.7 . . . . . . . . . December

Senegal 11.7 11.5 10.8 12.9 13.5 . . . December

Sierra Leone13 27.3 25.1 26.4 36.0 38.7 . . . December

South Africa 12.4 14.0 12.7 12.3 12.8 . . . December

Swaziland 14.0 14.0 15.0 20.0 23.0 . . . June

Uganda 17.0 20.5 18.3 18.0 19.5 . . . December

Other

Australia 10.0 10.4 10.4 10.4 10.2 10.5 March

Canada 13.4 13.3 12.9 12.5 12.1 12.3 March

Japan14 11.1 11.6 12.2 13.1 12.9 12.3 March

United States15 13.0 13.2 12.9 13.0 12.8 12.8 March

Sources: National authorities; and IMF staff estimates.

Note: Due to differences in national accounting, taxation, and supervisory regimes, FSI data are not strictly comparable across countries. 1Banking sector excludes offshore banks.

2Commercial banks.

3In 2006, the Uruguay Central Bank changed the methodology for calculating the regulatory capital ratio, changing the weights and adding a 

factor to the denominator to account for market risk. Regulatory capital ratios are smaller in 2006 and 2007, compared to previous years, due to 

this change in calculation. The data exclude the state mortgage bank. 4Data exclude foreign bank branches.

5Break in the data series starting in 2003.

6Break in the data series in 2007. 

7Starting in 2004 data reported on a consolidated basis. 

8Domestic banks.

9Consolidated reports for banking groups and individual reports for banks not belonging to groups.

10For 2005 and 2006 the figures are for the sample of institutions that are already complying with IAS, accounting as of December 2004 for 

about 87 percent of the usual aggregate considered. 11Data for the four large banking groups.

12On a consolidated basis.

13From 2006 figures unadjusted; not directly comparable with previous years.

14For the end of the fiscal year, i.e., March of the following calendar year; for major banks.

15All FDIC-insured institutions.
217

FINANCIAL SOUNDNESS INDICATORS

Table 23. Bank Capital to Assets

(In percent)

2003 2004 2005 2006 2007 2008 Latest

Latin America

Argentina 11.9 11.8 12.9 13.4 13.1 12.6 May

Bolivia 12.1 11.5 11.3 10.0 9.6 9.0 May

Brazil 9.6 10.1 9.8 9.9 9.9 9.5 March

Chile 7.3 7.0 6.9 6.8 6.7 7.1 March

Colombia 11.6 12.1 12.3 12.0 11.4 11.2 May

Costa Rica1 11.3 9.9 10.5 11.2 10.7 10.4 March

Dominican Republic 8.4 9.4 9.7 10.1 9.5 8.9 March

Ecuador2 8.8 8.5 8.4 8.7 8.5 7.5 April

El Salvador 9.4 9.7 10.1 10.7 11.8 12.2 May

Guatemala 9.0 8.9 8.5 8.2 9.2 9.4 March

Mexico3 10.0 10.2 11.5 13.2 14.4 14.1 March

Panama4 12.2 13.2 12.8 12.0 13.7 13.8 May

Paraguay 9.5 10.5 11.0 12.5 11.6 10.2 April

Peru 9.3 9.8 7.7 9.5 8.8 8.0 May

Uruguay5 7.2 8.3 8.6 9.8 10.5 9.5 March

Venezuela 14.3 12.5 11.6 8.8 8.3 8.2 May

Emerging Europe

Albania 4.7 4.8 5.4 5.9 5.8 6.1 March

Belarus 20.4 20.1 19.8 17.8 15.9 . . . December

Bosnia and Herzegovina 17.0 15.7 14.4 13.8 13.1 . . . September

Bulgaria 13.1 10.2 7.4 7.3 7.7 8.2 March

Croatia 8.9 8.6 9.0 10.3 12.5 13.5 March

Czech Republic6 5.7 5.6 5.7 6.2 6.0 . . . September

Estonia 11.3 9.8 8.6 8.4 8.6 . . . December

Hungary 8.3 8.5 8.2 8.3 8.3 . . . December

Israel 5.3 5.5 5.6 5.9 6.2 . . . September

Latvia 8.4 8.0 7.6 7.6 7.9 8.4 March

Lithuania7 9.8 8.7 7.2 7.1 7.4 . . . December

Macedonia, FYR . . . . . . . . . . . . . . . . . .

Moldova 21.1 19.3 16.7 17.3 17.3 . . . December

Montenegro . . . 20.4 15.3 10.4 8.0 8.3 March

Poland 8.3 8.0 7.8 7.6 7.4 . . . September

Romania8 10.9 8.9 9.2 8.6 7.3 6.9 April

Russia 14.6 13.3 12.7 12.4 13.3 13.6 March

Serbia 22.5 18.8 16.0 15.6 17.1 17.8 March

Slovak Republic 8.9 7.7 9.7 8.0 10.6 . . . December

Slovenia 8.3 8.1 8.4 8.4 . . . . . . December

Turkey9 13.7 14.4 12.9 11.3 13.0 11.7 June

Ukraine 12.3 13.8 12.4 13.3 12.5 13.1 March

Western Europe

Austria 4.9 4.9 4.8 5.2 6.5 . . . December

Belgium 3.1 3.1 2.7 3.3 4.3 4.0 March

Denmark 5.6 5.2 5.0 5.8 6.1 . . . December

Finland 9.7 8.7 8.8 9.2 . . . . . . December

France 6.9 6.6 5.8 6.0 5.5 5.5 May

Germany 4.2 4.0 4.1 4.3 . . . . . . December

Greece10 6.9 5.3 5.9 6.7 6.6 6.2 March

Iceland11 7.1 7.1 7.4 7.8 6.9 . . . December

Ireland 5.2 4.9 4.7 4.3 4.5 4.1 May

Italy 7.0 6.9 6.8 7.1 7.7 . . . December

Luxembourg 5.8 5.5 5.3 5.0 4.6 . . . December

Malta . . . 7.9 6.8 8.6 . . . . . . December

Netherlands 4.3 3.9 4.2 3.0 3.3 3.5 March

Norway 5.9 5.9 5.2 5.0 . . . . . . September

Portugal12,13 5.8 6.2 5.8 6.2 6.2 . . . December

Spain 8.1 8.5 7.7 7.2 7.0 6.9 April

Sweden14 5.0 4.3 3.9 4.0 4.0 4.0 March

Switzerland 5.7 5.3 5.1 4.9 . . . . . . December

United Kingdom 9.8 9.6 9.1 8.9 . . . . . . December

Asia

Bangladesh 3.2 2.7 2.6 4.0 6.5 . . . December

China15 4.9 4.9 4.4 5.1 5.5 . . . September

Hong Kong SAR 10.6 10.8 11.8 11.2 12.0 . . . November
STATISTICAL APPENDIX

218

Table 23 (concluded)

2003 2004 2005 2006 2007 2008 Latest

Asia (continued)

India 5.7 5.9 6.4 6.6 6.4 . . . March

Indonesia 9.6 10.8 10.2 10.7 10.0 . . . November

Korea16 7.0 8.0 9.3 9.2 9.0 8.8 March

Malaysia 8.5 8.2 7.7 7.6 7.5 7.4 May

Philippines 13.1 12.5 11.8 11.7 11.7 . . . December

Singapore 10.7 9.6 9.6 9.6 9.3 . . . September

Thailand 7.4 8.0 8.9 8.9 9.5 . . . December

Middle East & Central Asia

Armenia 18.1 17.8 21.5 22.9 22.5 22.7 March

Egypt 4.9 5.4 5.4 5.5 5.1 5.1 March

Georgia 26.5 22.0 18.8 21.2 20.4 21.4 May

Jordan . . . 5.1 5.0 6.8 6.7 . . . June

Kazakhstan17 9.0 13.1 13.0 13.2 15.2 . . . December

Kuwait 10.7 12.1 12.7 11.7 12.0 . . . September

Lebanon 6.9 6.8 7.5 9.1 8.1 . . . December

Morocco 7.6 7.6 7.7 7.4 6.9 . . . December

Oman 12.6 12.9 13.7 13.2 . . . . . . June

Pakistan 5.4 6.5 7.6 9.4 10.2 . . . September

Saudi Arabia 8.8 8.0 8.8 9.3 9.9 . . . December

Tunisia 7.6 7.5 7.7 . . . . . . . . . December

United Arab Emirates 11.4 11.1 11.9 12.6 . . . . . . December

Sub-Saharan Africa

Gabon 13.1 13.2 11.1 10.2 7.0 . . . December

Ghana 12.0 12.5 13.0 11.9 11.8 . . . February

Kenya 11.8 11.9 12.1 12.4 12.4 . . . December

Lesotho 17.0 16.9 14.6 . . . . . . . . . December

Mozambique 9.0 9.5 8.0 6.1 6.4 . . . December

Namibia 8.3 8.8 7.8 7.5 7.9 . . . December

Nigeria 9.6 9.9 12.4 14.7 16.3 . . . December

Rwanda 8.9 10.1 9.4 9.2 . . . . . . April

Senegal 7.8 7.7 7.6 8.3 10.4 . . . December

Sierra Leone 21.1 22.5 20.0 16.9 17.7 . . . December

South Africa 8.0 8.2 7.9 7.9 7.9 . . . December

Swaziland 13.7 22.4 22.9 . . . . . . . . . December

Uganda 8.6 10.3 10.3 10.9 10.3 . . . December

Other

Australia16 5.2 5.1 5.2 4.9 4.6 4.1 March

Canada 4.7 4.4 4.4 5.7 5.5 5.3 March

Japan18 3.9 4.2 4.9 5.3 5.0 4.3 March

United States19 9.2 10.3 10.3 10.5 10.3 10.2 March

Sources: National authorities; and IMF staff estimates.

Note: Due to differences in national accounting, taxation, and supervisory regimes, FSI data are not strictly comparable across countries. 1Banking sector excludes offshore banks.

2Total assets include contingencies. 

3All deposit takers.

4General licensed banks.

5The data exclude the state mortgage bank.

6Total own funds.

7Capital is defined as bank shareholders’ equity and foreign bank branches’ funds received from the head office.

8Break in the data series starting in 2003.

9Break in the data series in 2007.

10Data on a nonconsolidated basis. From 2004 in accordance with IFRS.

11Commercial banks and six largest savings banks (five largest savings banks from 2006 due to a merger of two banks). 

12For 2005 and 2006 the figures are for the sample of institutions that are already complying with IAS, accounting as of December 2004 for 

about 87 percent of the usual aggregate considered. 13On accounting basis, consolidated.

14Data for the four large banking groups.

15Banking institutions (policy banks, state-owned commercial banks, joint stock commercial banks, city commercial banks, rural commercial 

banks, urban credit cooperatives, rural credit cooperatives, postal savings, foreign banks, and nonbank financial institutions). 16Tier 1 capital to total assets.

17Tier 1 capital to total assets for 2002–03.

18For the end of the fiscal year, i.e., March of the following calendar year; all banks.

19All FDIC-insured institutions.
219

FINANCIAL SOUNDNESS INDICATORS

Table 24. Bank Nonperforming Loans to Total Loans

(In percent)

2003 2004 2005 2006 2007 2008 Latest

Latin America

Argentina 17.7 10.7 5.2 3.4 2.7 2.8 May

Bolivia 16.7 14.0 11.3 8.7 5.6 5.4 May

Brazil 4.1 2.9 3.5 3.5 3.0 2.9 March

Chile 1.6 1.2 0.9 0.8 0.8 0.9 May

Colombia 6.8 3.3 2.7 2.6 3.2 3.9 May

Costa Rica1 1.7 2.0 1.5 1.5 1.2 1.3 March

Dominican Republic 6.5 5.8 5.8 4.6 4.0 4.2 March

Ecuador 7.9 6.4 4.9 3.3 2.9 3.1 April

El Salvador2 2.8 2.4 2.0 1.9 2.1 2.5 May

Guatemala 6.5 7.1 4.2 4.6 5.8 4.7 April

Mexico3 2.8 2.2 1.8 2.1 2.5 2.1 March

Panama4 2.5 1.8 1.8 1.4 1.3 1.4 March

Paraguay 20.6 10.8 6.6 3.3 1.3 1.3 April

Peru 5.8 3.7 2.1 1.6 1.3 1.3 May

Uruguay5 14.3 4.7 3.6 1.9 1.1 1.0 March

Venezuela 7.7 2.8 1.2 1.1 1.2 1.7 May

Emerging Europe

Albania 4.6 4.2 2.3 3.1 3.4 3.9 March

Belarus 3.7 2.8 1.9 1.2 0.7 . . . December

Bosnia and Herzegovina 8.4 6.1 5.3 4.0 3.0 3.1 March

Bulgaria 3.2 2.0 2.2 2.2 2.1 2.1 March

Croatia 8.9 7.5 6.2 5.2 4.8 4.8 March

Czech Republic 4.9 4.1 4.3 3.6 2.6 2.8 March

Estonia 0.4 0.3 0.2 0.2 0.5 . . . December

Hungary 2.6 2.7 2.5 2.5 2.4 . . . December

Israel 2.6 2.5 2.3 1.9 1.7 . . . September

Latvia 1.4 1.1 0.7 0.4 0.4 0.5 March

Lithuania6 2.4 2.2 0.6 1.0 1.0 . . . December

Macedonia, FYR7 22.1 17.0 15.0 11.2 9.1 . . . September

Moldova 6.4 6.9 5.3 4.4 3.7 . . . December

Montenegro . . . 5.2 5.3 2.9 3.2 3.0 March

Poland 10.4 9.2 7.7 3.6 3.1 9.7 April

Romania 8.3 8.1 8.3 7.9 9.7 9.7 April

Russia 5.0 3.8 3.2 2.6 2.5 2.4 March

Serbia8 24.1 22.2 23.8 4.1 3.8 4.8 March

Slovak Republic 3.7 2.6 5.0 3.7 2.5 . . . December

Slovenia 3.7 3.0 2.5 2.5 . . . . . . December

Turkey9 11.5 6.0 4.8 3.8 3.6 3.1 June

Ukraine10 28.3 30.0 19.6 17.8 13.2 13.1 March

Western Europe

Austria 3.0 2.7 2.6 2.1 . . . . . . December

Belgium11 2.6 2.3 2.0 1.4 1.2 1.2 March

Denmark12 1.5 1.1 0.8 0.6 . . . . . . December

Finland13 0.5 0.4 0.3 0.3 0.3 . . . June

France14 4.8 4.2 3.5 3.0 2.7 . . . December

Germany 5.2 4.9 4.0 3.4 . . . . . . December

Greece 7.0 7.0 6.3 5.4 4.5 4.7 March

Iceland15 2.1 0.9 1.1 0.8 . . . . . . December

Ireland 0.9 0.8 0.7 0.7 . . . . . . December

Italy16 6.7 6.6 6.2 5.1 4.8 . . . December

Luxembourg 0.5 0.3 0.2 0.2 0.2 . . . December

Malta . . . 6.5 3.9 2.8 1.8 . . . December

Netherlands 2.0 1.5 1.2 0.8 . . . . . . December

Norway 1.7 1.1 0.8 0.6 0.6 0.6 March

Portugal17,18 2.4 2.0 1.5 1.2 0.8 . . . December

Spain19 0.8 0.6 0.6 0.6 0.7 1.1 April

Sweden20 1.2 0.9 0.7 0.6 0.5 . . . December

Switzerland 1.3 0.9 0.5 0.3 . . . . . . December

United Kingdom 2.5 1.9 1.0 0.9 0.9 . . . December
STATISTICAL APPENDIX

220

2003 2004 2005 2006 2007 2008 Latest

Asia

Bangladesh 22.1 17.6 13.6 13.2 14.0 13.2 March

China21 20.4 12.8 9.8 7.5 6.7 . . . December

Hong Kong SAR22 3.9 2.3 1.4 1.3 0.9 0.8 March

India 8.8 7.2 5.2 3.3 2.5 2.8 March

Indonesia23 19.4 14.3 14.8 13.2 9.3 8.5 March

Korea24 2.6 1.9 1.2 0.8 0.7 0.8 March

Malaysia 13.9 11.7 9.6 8.5 6.6 . . . November

Philippines25 16.1 14.4 10.3 7.5 5.8 . . . December

Singapore 6.7 5.0 3.8 2.8 1.8 . . . September

Thailand 13.5 11.9 9.1 8.1 7.9 . . . December

Middle East and Central Asia

Armenia 5.4 2.1 1.9 2.5 2.4 4.0 March

Egypt 24.2 23.6 24.8 24.7 . . . . . . December

Georgia 7.4 6.2 3.8 2.5 2.6 3.8 May

Jordan 15.5 10.3 6.6 4.3 4.1 . . . June

Kazakhstan26 . . . 4.3 3.3 2.4 2.7 . . . December

Kuwait 6.1 5.3 5.0 3.9 3.2 . . . September

Lebanon 12.8 17.7 16.4 13.5 10.1 . . . December

Morocco 18.7 19.4 15.7 10.9 7.9 . . . December

Oman 12.5 9.9 6.5 4.6 3.2 . . . September

Pakistan 17.0 11.6 8.9 6.9 8.4 . . . March

Saudi Arabia27 5.4 2.8 1.9 2.0 2.1 . . . December

Tunisia 24.0 23.7 20.9 19.0 17.3 . . . December

United Arab Emirates 14.3 12.5 8.3 6.3 . . . . . . December

Sub-Saharan Africa

Gabon 13.9 16.0 14.1 10.7 7.6 . . . December

Ghana 18.3 16.3 13.0 7.9 6.4 8.7 December

Kenya 34.9 29.3 25.6 21.3 22.7 . . . December

Lesotho . . . 1.0 2.0 2.0 3.0 . . . March

Mozambique 14.4 6.4 3.8 3.3 2.6 . . . December

Namibia 3.9 2.4 2.3 2.6 2.8 . . . December

Nigeria 20.5 21.6 18.1 8.8 8.4 . . . December

Rwanda 52.0 27.0 27.2 . . . . . . . . . December

Senegal 13.3 12.6 11.9 16.8 18.6 . . . December

Sierra Leone28 7.4 12.1 20.9 26.8 31.7 . . . December

South Africa 2.4 1.8 1.5 1.1 1.4 . . . December

Swaziland 2.0 3.0 2.0 4.0 4.0 . . . June

Uganda 7.3 2.2 2.3 3.0 4.1 . . . December

Other

Australia29 0.3 0.2 0.2 0.2 0.2 0.3 March

Canada 1.2 0.7 0.5 0.4 0.7 0.9 March

Japan30 5.2 2.9 1.8 1.5 1.5 1.4 March

United States31 1.1 0.8 0.7 0.8 1.4 1.7 March

Sources: National authorities; and IMF staff estimates.

Note: Due to differences in national accounting, taxation, and supervisory regimes, FSI data are not strictly comparable across countries. 1Banking sector excludes offshore banks.

2Official definition based on past-due loans.

3Commercial banks.

4Banking system.

5The data exclude the state mortgage bank.

6From end-2005 nonperforming loans are loans with payments overdue past 60 days. Until 2004 they are defined as loans in “substandard,” 

“doubtful,” and “loss” loan categories. 7Includes only loans to the nonfinancial sector.

8Break in the time series starting in 2006. Prior to 2006, assets classified in risk categories C, D, and E. From 2006, loans overdue past 90 days. 9Break in the data series in 2007. 

10The increase in nonperforming loans in 2003 reflects a revision in the official definition.

11Unconsolidated data up to 2005; consolidated data from 2006.

12Accumulated impairment losses as a percentage of loans, guarantees, and impairment losses.

13Net of provisions. 

Table 24 (continued)
221

FINANCIAL SOUNDNESS INDICATORS

Table 24 (concluded)

14Gross doubtful debts. A break in the data series in 2006. 

15Commercial banks and six largest savings banks. 2005 and 2006 figures are for the largest banks.

16Banking groups. For the 2002–04 period, nonperforming loans include only substandard loans and bad debts. For the 2005–07 period, the 

aggregate includes also loans overdue past 180 days. 17For 2005 and 2006 the figures are for the sample of institutions that are already complying with IAS, accounting as of December 2004 for 

about 87 percent of the usual aggregate considered. 18On a consolidated basis. Nonperforming loans are defined as credit to customers overdue. 

19Doubtful exposures to other resident sectors over total lending to other resident sectors.

20Data for the four large banking groups.

21Major commercial banks (state-owned commercial banks and joint stock commercial banks).

22Loans classified as “substandard,” “doubtful,” and “loss.”

23Compromised assets ratio; includes reported nonperforming loans, restructured loans, and foreclosed assets for the 16 largest banks. Not 

directly comparable to the other indicators in the table. Starting from 2005 the ratio is based on financial information for the 15 largest banks as 

of December 2005. 24Loans classified “substandard” or below. 

25The data exclude IBL.

26Loans overdue past 60 days and other qualified loans.

27Gross nonperforming loans to net loans.

28Break in the data series in 2006. 

29Impaired assets to total assets. Figures exclude loans in arrears that are covered by collateral.

30For the end of the fiscal year, i.e., March of the following calendar year; for major banks.

31All FDIC-insured institutions.
STATISTICAL APPENDIX

222

Table 25. Bank Provisions to Nonperforming Loans

(In percent)

2003 2004 2005 2006 2007 2008 Latest

Latin America

Argentina 79.2 102.9 124.5 129.9 129.6 122.3 May

Bolivia 74.0 84.3 81.1 90.7 92.6 93.0 May

Brazil 171.8 214.5 179.8 179.9 181.8 181.7 March

Chile 130.9 165.5 177.6 198.5 210.4 187.5 May

Colombia 98.1 149.7 166.9 153.6 134.5 120.2 May

Costa Rica1 145.9 122.6 153.0 162.2 144.0 140.8 March

Dominican Republic 65.6 112.9 123.5 142.0 134.5 128.3 March

Ecuador 127.3 119.0 143.7 182.7 169.8 180.1 April

El Salvador 129.8 132.3 126.7 116.4 120.0 104.7 May

Guatemala . . . . . . 43.2 39.6 42.7 59.1 April

Mexico 167.1 201.8 232.1 207.4 169.2 184.0 March

Panama2 150.3 149.4 116.2 128.5 143.1 128.9 March

Paraguay 54.8 54.6 57.7 59.1 78.2 86.3 April

Peru 67.1 68.7 80.3 100.3 131.6 139.4 May

Uruguay3 91.4 106.8 118.8 218.6 93.3 . . . December

Venezuela 103.7 130.2 196.3 229.1 175.7 135.4 May

Emerging Europe

Albania . . . . . . . . . . . . . . . . . .

Belarus 29.9 32.4 48.4 51.3 58.9 . . . November

Bosnia and Herzegovina . . . . . . . . . . . . . . . . . .

Bulgaria4 50.0 48.5 45.3 47.6 . . . . . . September

Croatia 60.6 62.3 60.0 61.5 59.2 57.5 March

Czech Republic5 76.7 69.4 63.2 58.5 56.4 . . . September

Estonia 214.5 276.9 215.0 153.6 . . . . . . November

Hungary 47.3 51.3 54.4 53.9 58.1 . . . December

Israel . . . . . . . . . . . . . . . . . .

Latvia 89.4 99.1 98.8 116.6 129.8 120.4 March

Lithuania . . . . . . . . . . . . . . . . . .

Macedonia, FYR . . . . . . . . . . . . . . . . . .

Moldova 92.3 84.1 95.6 128.8 105.3 . . . December

Montenegro . . . 77.3 67.4 78.8 73.6 85.3 March

Poland 53.4 61.3 61.6 57.8 . . . . . . September

Romania6 12.6 16.1 14.4 18.2 25.3 29.1 April

Russia7 118.0 139.5 156.3 159.3 144.0 145.8 March

Serbia 54.0 58.9 47.8 . . . . . . . . . September

Slovak Republic 85.8 86.4 85.1 105.9 95.1 . . . December

Slovenia 81.0 80.1 80.6 84.3 . . . . . . December

Turkey8 88.6 88.1 89.8 90.8 88.4 84.4 June

Ukraine 22.3 21.1 25.0 23.1 26.3 26.7 March

Western Europe

Austria9 68.0 70.8 71.5 75.3 . . . . . . December

Belgium10 52.8 54.2 51.6 52.6 47.5 47.9 March

Denmark 63.0 66.0 75.7 . . . . . . . . . December

Finland 77.7 78.5 85.8 . . . . . . . . . December

France11 59.6 61.3 63.8 62.9 61.4 . . . December

Germany . . . . . . . . . . . . . . . . . .

Greece 49.9 51.4 61.9 60.9 . . . . . . June

Iceland12,21 77.5 80.9 112.9 99.6 84.1 . . . December

Ireland 96.8 85.4 73.5 56.3 . . . . . . December

Italy13 . . . . . . . . . 46.0 49.5 . . . December

Luxembourg . . . . . . . . . . . . . . . . . .

Malta . . . . . . . . . . . . . . . . . .

Netherlands12 73.8 69.2 65.5 56.0 . . . . . . December

Norway12 59.0 66.1 52.5 52.9 . . . . . . December

Portugal14,15 73.0 83.4 79.0 83.9 75.7 . . . December

Spain16 231.5 289.0 235.7 255.1 204.8 144.1 April

Sweden17 73.9 78.9 84.7 78.5 79.9 . . . December

Switzerland 89.9 90.9 116.0 122.6 . . . . . . December

United Kingdom12 69.8 61.5 54.0 54.6 . . . . . . December

Asia

Bangladesh 18.3 18.9 25.3 26.3 42.3 43.4 March

China18 . . . . . . . . . . . . 39.2 . . . December 

Hong Kong SAR . . . . . . . . . . . . . . . . . .

India 46.4 56.6 60.3 58.9 . . . . . . March

Indonesia 146.5 158.7 82.2 99.7 120.5 122.4 September
223

FINANCIAL SOUNDNESS INDICATORS

2003 2004 2005 2006 2007 2008 Latest

Asia (continued)

Korea 84.0 104.5 131.4 175.2 199.1 183.8 March

Malaysia 38.9 41.0 45.4 50.7 62.6 . . . November

Philippines 51.5 58.0 73.8 75.0 81.5 . . . December

Singapore 64.9 73.6 78.7 89.5 105.9 . . . September

Thailand 72.8 79.8 83.7 82.7 86.5 . . . December 

Middle East & Central Asia

Armenia 34.3 77.0 70.7 64.3 66.6 43.9 March

Egypt 57.0 60.2 61.5 68.2 . . . . . . December

Georgia 48.1 64.2 55.6 50.9 49.7 60.1 May

Jordan19 51.9 63.8 78.4 80.0 67.8 . . . June

Kazakhstan20 . . . 64.4 104.9 102.7 60.2 . . . December

Kuwait 77.7 82.5 107.2 100.6 92.0 . . . September

Lebanon . . . 57.3 64.1 72.4 78.1 . . . December

Morocco 54.9 59.3 67.1 71.2 75.2 . . . December

Oman 59.8 75.3 72.7 102.8 112.8 . . . September

Pakistan 63.9 71.6 76.8 79.0 71.7 . . . September

Saudi Arabia 128.2 175.4 202.8 182.3 142.9 . . . December

Tunisia 43.1 45.8 47.4 49.2 53.8 . . . December

United Arab Emirates 88.5 94.6 95.7 98.2 . . . . . . December

Sub-Saharan Africa

Gabon 53.9 53.6 55.5 57.4 59.8 . . . December

Ghana . . . . . . . . . . . . . . . . . .

Kenya 79.2 102.9 115.6 115.6 . . . . . . September

Lesotho . . . . . . . . . . . . . . . . . .

Mozambique . . . . . . . . . . . . . . . . . .

Namibia . . . 95.2 85.3 90.3 77.2 . . . December

Nigeria 76.4 96.2 81.0 59.5 . . . . . . December

Rwanda 58.4 60.2 56.7 . . . . . . . . . December

Senegal 75.3 75.7 75.4 52.0 53.8 . . . December

Sierra Leone21 65.0 56.6 44.2 73.6 65.9 . . . December

South Africa 54.2 61.3 64.3 . . . . . . . . . December

Swaziland . . . . . . . . . . . . . . . . . .

Uganda 76.5 97.8 103.8 74.4 69.3 . . . June

Other

Australia 131.8 182.9 203.0 202.5 183.7 128.6 March

Canada 43.5 47.7 49.3 55.3 42.1 36.7 March

Japan22 25.5 29.9 31.2 28.1 28.8 26.4 March

United States23 140.4 168.1 155.0 135.0 93.1 88.9 March

Sources: National authorities; and IMF staff estimates.

Note: Due to differences in national accounting, taxation, and supervisory regimes, FSI data are not strictly comparable across countries. 1Banking sector excludes offshore banks.

2General licensed banks.

3The data exclude the state mortgage bank.

4Provisions to nonstandard loans.

5Allowances for individually assessed financial assets divided by receivables on investment portfolio classified as “substandard,” “doubtful,” 

and “loss.” 6Nonperforming loans reflect unadjusted exposure to loans classified as “loss,” “doubtful,” and “substandard.” The steady level of 

nonperforming loans in the face of growing credit partly reflects Romania’s relatively conservative classification and provisioning requirements. 7Change in definition in 2004; not strictly comparable with previous years.

8Break in the data series in 2007. 

92006 data cover two of the large banks only; not strictly comparable with previous years.

10Unconsolidated data up to 2005; consolidated data from 2006.

11Coverage of doubtful loans to customers by provisions.

12Data for large banking groups.

13Banking groups. 

14For 2005 and 2006 the figures are for the sample of institutions that are already complying with IAS, accounting as of December 2004 for 

about 87 percent of the usual aggregate considered. 15On a consolidated basis. Nonperforming loans are defined as credit to customers overdue. 

16Allowances and provisions to doubtful exposures.

17Data for the four large banking groups.

18Major commercial banks.

19Provisions to classified loans net of interest in suspense.

20Provisions to classified loans.

21Break in the data series in 2006.

22For the end of the fiscal year, i.e., March of the following calendar year; coverage of nonperforming loans by provisions for all banks. 23All FDIC-insured institutions.

Table 25 (concluded)
STATISTICAL APPENDIX

224

Table 26. Bank Return on Assets

(In percent)

2003 2004 2005 2006 2007 2008 Latest

Latin America

Argentina –3.0 –0.5 0.9 1.9 1.5 1.7 May

Bolivia 0.3 –0.1 0.7 1.3 1.9 1.5 May

Brazil1 2.0 2.2 2.9 2.7 2.9 2.8 March

Chile 1.3 1.2 1.3 1.3 1.1 1.1 May

Colombia 1.9 2.7 2.7 2.5 2.3 2.7 May

Costa Rica1,2 1.9 1.7 2.1 2.0 1.5 1.2 March

Dominican Republic 2.1 2.3 2.1 2.2 2.6 2.6 March

Ecuador 1.1 1.2 1.5 2.0 1.9 2.3 April

El Salvador 1.1 1.0 1.2 1.5 1.2 1.5 May

Guatemala 1.1 1.3 1.6 1.2 1.6 1.5 April

Mexico1,3 1.6 1.8 2.7 3.1 2.8 2.9 March

Panama1,4 2.1 2.3 2.1 1.7 2.0 2.8 May

Paraguay1 0.4 1.7 2.1 3.0 2.8 3.5 April

Peru 1.1 1.2 2.2 2.2 2.5 2.6 May

Uruguay5 –1.1 –0.1 0.7 1.2 2.8 1.0 March

Venezuela 6.2 5.9 3.7 3.0 2.6 2.5 May

Emerging Europe

Albania 1.2 1.3 1.4 1.4 1.6 1.3 March

Belarus 1.5 1.5 1.3 1.7 2.3 . . . December

Bosnia and Herzegovina 0.4 0.7 0.7 0.9 0.9 0.2 March

Bulgaria 2.4 2.1 2.1 2.2 2.4 2.5 March

Croatia 1.6 1.7 1.6 1.5 1.6 1.6 March

Czech Republic 1.2 1.3 1.4 1.2 1.3 1.4 March 

Estonia1 1.7 2.1 2.0 1.7 2.6 . . . December

Hungary 1.5 2.0 2.0 1.8 1.4 . . . December

Israel 0.7 1.0 1.1 1.0 1.3 . . . September

Latvia 1.4 1.7 2.1 2.1 2.0 1.6 March

Lithuania6 1.2 1.3 1.1 1.5 2.0 . . . December

Macedonia, FYR7 0.5 0.6 1.2 1.8 2.0 . . . September

Moldova 4.4 3.7 3.2 3.4 3.9 4.2 May

Montenegro . . . –0.3 0.8 1.1 0.7 0.2 March

Poland 0.5 1.4 1.6 1.7 1.8 . . . September

Romania8 2.7 2.5 1.9 1.7 1.3 1.5 March

Russia9 2.6 2.9 3.2 3.2 3.0 0.7 March

Serbia –0.3 –1.2 1.1 1.7 1.7 2.7 March

Slovak Republic 1.2 1.2 1.2 1.3 1.1 . . . December

Slovenia10 1.0 1.1 1.0 1.3 . . . . . . December

Turkey11 2.3 2.3 1.7 2.5 2.6 2.3 June

Ukraine 1.0 1.1 1.3 1.6 1.5 1.4 March

Western Europe

Austria12 0.3 0.6 0.6 0.7 0.7 . . . September

Belgium13 0.5 0.6 0.7 0.7 0.4 . . . December

Denmark 0.9 0.9 1.0 1.0 . . . . . . December

Finland 0.7 0.8 0.9 1.0 . . . . . . December

France 0.4 0.5 0.6 0.7 0.4 . . . December

Germany –0.1 0.1 0.3 0.3 . . . . . . December

Greece 0.6 0.4 0.9 0.8 1.0 0.7 March

Iceland 1.3 1.8 2.3 2.6 1.5 . . . December

Ireland1 0.9 1.1 0.8 0.8 . . . . . . December

Italy 0.5 0.6 0.7 0.8 0.7 . . . December

Luxembourg14 0.6 0.7 0.7 0.9 0.9 . . . December

Malta . . . 1.3 1.4 1.1 1.0 . . . December

Netherlands 0.5 0.4 0.4 0.4 0.6 . . . December

Norway 0.6 0.9 0.9 0.8 . . . . . . December

Portugal15 0.8 0.8 0.9 1.0 1.0 . . . December

Spain 0.9 0.9 0.9 1.0 1.1 . . . December

Sweden16 0.5 0.6 0.7 0.7 0.7 0.6 March

Switzerland14 0.7 0.8 0.9 0.9 . . . . . . December

United Kingdom1 0.6 0.7 0.8 0.5 0.4 . . . December

Asia

Bangladesh 0.5 0.7 0.6 0.8 0.9 . . . December

China17 0.3 0.5 0.6 0.7 1.0 . . . June

Hong Kong SAR18 1.9 1.7 1.7 1.7 1.9 2.0 March

India 1.0 0.8 0.9 0.9 0.9 1.0 March

Indonesia1 2.6 3.5 2.5 2.6 2.8 2.7 March
225

FINANCIAL SOUNDNESS INDICATORS

2003 2004 2005 2006 2007 2008 Latest

Asia (continued)

Korea19 0.2 0.9 1.3 1.1 1.1 0.9 March

Malaysia1 1.3 1.4 1.4 1.3 1.5 . . . December

Philippines1 1.1 0.9 1.1 1.3 1.4 . . . December

Singapore 1.0 1.2 1.2 1.4 1.4 . . . September

Thailand 0.6 1.2 1.4 0.8 0.1 . . . December

Middle East & Central Asia

Armenia1 2.7 3.2 3.1 3.6 2.9 3.0 March

Egypt 0.5 0.6 0.5 0.9 0.8 . . . September

Georgia1 3.9 1.9 3.0 2.7 1.9 1.6 May

Jordan 0.7 1.1 2.0 1.7 1.6 . . . December

Kazakhstan1 2.0 1.2 1.6 1.4 2.2 . . . December

Kuwait 2.0 2.5 3.0 3.2 3.4 . . . September

Lebanon 0.7 0.7 0.7 0.9 1.0 . . . December

Morocco –0.2 0.8 0.5 1.3 1.5 . . . December

Oman 0.3 1.9 2.7 2.7 . . . . . . December

Pakistan1 1.9 1.8 2.8 3.1 3.0 . . . September

Saudi Arabia1 2.2 2.5 3.4 4.0 2.8 . . . December

Tunisia 0.6 0.4 0.5 0.7 0.9 . . . December

United Arab Emirates 2.3 2.1 2.7 2.2 2.0 2.2 March

Sub-Saharan Africa

Gabon 0.7 2.8 2.6 2.5 . . . . . . December

Ghana1 6.2 5.8 4.6 4.8 3.7 3.6 December

Kenya 2.3 2.1 1.0 2.8 3.2 . . . December

Lesotho . . . 3.0 2.0 2.0 1.0 . . . March

Mozambique 1.2 1.4 1.8 3.5 3.5 . . . December

Namibia 3.6 2.1 3.5 1.5 3.5 . . . December

Nigeria 1.7 3.1 0.9 1.6 2.2 . . . December

Rwanda 1.4 2.2 1.5 . . . . . . . . . December

Senegal 1.8 1.8 1.6 1.6 . . . . . . December

Sierra Leone 10.5 9.7 7.9 5.8 3.1 . . . December

South Africa 0.8 1.3 1.2 1.4 1.4 . . . December

Swaziland 4.0 3.0 3.0 6.0 3.0 . . . June

Uganda 4.5 4.3 3.6 3.4 3.9 . . . December

Other

Australia20 1.6 1.1 1.0 1.0 1.0 . . . December

Canada 0.7 0.8 0.7 1.0 0.9 0.3 March

Japan21 –0.1 0.2 0.5 0.4 0.2 0.3 March

United States22 1.4 1.3 1.3 1.3 0.8 0.6 March

Sources: National authorities; and IMF staff estimates.

Note: Due to differences in national accounting, taxation, and supervisory regimes, FSI data are not strictly comparable across countries. 1Before tax.

2Banking sector excludes offshore banks.

3Commercial banks.

4General licensed banks.

5The data exclude the state mortgage bank.

6Net income before extraordinary items and taxes to average total assets.

7Adjusted for unallocated provisions for potential loan losses.

8Break in the data series starting in 2003.

9Not annualized.

10Before extraordinary items and taxes.

11Break in the data series in 2007.

12Starting in 2004 data reported on a consolidated basis. 

13Gross profits. 

14Income before provisions and taxes to total assets.

15For 2005 and 2006 the figures are for the sample of institutions that are already complying with IAS, accounting as of December 2004 for 

about 87 percent of the usual aggregate considered. 16Data for the four large banking groups. The data refer to a four-quarter moving average for the assets. The profit is accumulated over four 

quarters and adjusted. 172007 figure is net income to end-of-period assets.

18Net interest margin, not comparable with the other indicators in the table.

19Excludes earnings from sale of equity stakes.

20Gross profits until 2003; return on assets (after taxes) from 2004.

21For the end of the fiscal year, i.e., March of the following calendar year; all banks. The denominator of the ratio uses end-period total assets. 22All FDIC-insured institutions.

Table 26 (concluded)
STATISTICAL APPENDIX

226

Table 27. Bank Return on Equity

(In percent)

2003 2004 2005 2006 2007 2008 Latest

Latin America

Argentina –22.7 –4.2 7.0 14.3 9.0 14.1 May

Bolivia 2.8 –1.2 6.4 13.3 21.2 18.2 May

Brazil1 21.1 22.1 29.5 27.3 28.9 29.4 March

Chile 16.7 16.7 17.9 18.6 16.2 15.7 May

Colombia 17.1 23.0 22.1 20.2 20.9 25.7 May

Costa Rica1,2 17.2 16.7 20.1 18.7 13.4 11.3 March

Dominican Republic 20.6 25.4 22.4 21.7 28.0 27.7 March

Ecuador 14.7 16.5 18.5 23.1 21.4 23.7 April

El Salvador 11.5 10.9 11.8 14.6 11.3 14.3 May

Guatemala 12.2 14.0 19.1 15.1 16.9 12.9 March

Mexico1,3 16.1 17.2 24.4 26.2 19.9 21.1 March

Panama4 16.9 16.7 15.7 13.3 15.7 20.9 March

Paraguay1 4.5 18.3 22.6 31.7 34.7 37.7 April

Peru 10.7 11.6 22.2 23.9 27.9 28.2 May

Uruguay5 –15.3 –0.9 7.6 12.7 27.7 33.9 March

Venezuela 44.0 45.2 32.2 31.6 32.4 30.8 May

Emerging Europe

Albania 19.5 21.1 22.2 20.2 20.7 16.3 March

Belarus 8.4 7.8 6.8 9.6 13.8 . . . December

Bosnia and Herzegovina 3.4 5.8 6.2 8.5 8.9 2.0 March

Bulgaria 22.7 19.6 21.4 25.0 24.8 28.6 March

Croatia 14.1 16.1 15.1 13.0 11.1 10.0 March

Czech Republic 23.8 23.3 25.2 22.5 24.5 26.0 March

Estonia 14.1 20.0 21.0 19.8 30.0 . . . December

Hungary 19.3 25.3 24.7 24.0 18.1 . . . December

Israel 14.1 17.9 19.4 17.6 22.0 . . . September

Latvia 16.7 21.4 27.1 25.6 24.2 19.5 March

Lithuania6 11.8 13.5 13.8 21.4 27.2 . . . December

Macedonia, FYR7 2.3 3.1 7.5 12.3 15.8 . . . September

Moldova 19.7 17.8 15.4 20.5 24.2 24.5 May

Montenegro . . . –1.4 5.3 6.8 6.2 2.6 March

Poland 5.8 17.1 20.7 21.9 23.7 . . . September

Romania 20.0 19.3 15.4 13.6 11.5 15.4 March

Russia8 17.8 20.3 24.2 26.3 22.7 4.8 March

Serbia –1.2 –5.3 6.7 10.0 10.2 15.3 March

Slovak Republic9 10.8 11.9 16.9 16.6 16.6 . . . December

Slovenia10 11.9 12.5 13.8 15.1 . . . . . . December

Turkey11 16.0 16.4 11.8 19.8 19.6 19.5 June

Ukraine 7.6 8.4 10.4 13.5 12.7 11.4 March

Western Europe

Austria12 7.0 14.8 14.8 16.9 16.8 . . . September

Belgium 13.6 15.8 18.5 22.4 13.2 . . . December

Denmark 15.4 13.7 16.3 17.1 . . . . . . December

Finland 11.3 12.4 10.1 11.1 15.6 . . . June

France 8.5 10.6 11.8 15.5 9.8 . . . December

Germany –1.5 1.9 9.2 7.5 . . . . . . December

Greece 8.9 6.4 15.9 12.8 14.8 11.2 March

Iceland13 22.1 30.9 41.7 39.1 22.4 . . . December

Ireland1 17.8 20.7 19.6 19.1 . . . . . . December

Italy 7.4 9.3 9.7 11.4 9.7 . . . December

Luxembourg14 34.9 39.8 37.8 55.6 47.0 . . . December

Malta . . . 13.2 14.3 11.7 10.7 . . . December

Netherlands 14.8 16.8 15.4 15.4 18.7 . . . December

Norway 9.6 14.6 18.0 15.7 15.9 8.3 March

Portugal15 13.9 12.8 16.8 16.9 15.2 . . . December

Spain 13.2 14.1 16.9 19.9 19.9 . . . December

Sweden16 12.3 14.6 17.4 18.0 17.0 15.9 March

Switzerland17 11.7 14.3 18.0 17.7 . . . . . . December

United Kingdom1 8.6 10.9 11.8 8.9 6.2 . . . December

Asia

Bangladesh 9.8 13.0 12.4 14.1 13.8 . . . December

China18 . . . 13.7 15.1 14.8 19.9 . . . June

Hong Kong SAR19 17.8 20.3 19.1 . . . . . . . . . December

India 18.8 20.8 13.3 12.7 . . . . . . March

Indonesia . . . 22.9 16.5 16.4 17.7 19.2 March
227

FINANCIAL SOUNDNESS INDICATORS

2003 2004 2005 2006 2007 2008 Latest

Asia (continued)

Korea 3.4 15.2 18.4 14.6 14.6 . . . December

Malaysia1 15.6 16.7 16.7 16.2 19.7 . . . December

Philippines 8.5 7.1 8.7 10.6 11.8 . . . December

Singapore 8.7 11.6 11.2 13.7 13.4 . . . September

Thailand 10.3 16.8 14.2 8.8 7.3 . . . December

Middle East & Central Asia

Armenia1 14.4 18.4 15.5 15.9 14.9 13.0 March

Egypt 9.8 10.6 9.6 17.4 14.3 . . . September

Georgia1 15.0 7.9 15.1 15.7 9.7 7.3 May

Jordan . . . 13.1 20.9 15.0 12.6 . . . June

Kazakhstan 14.2 11.5 16.6 14.6 18.2 . . . December

Kuwait 18.6 20.9 22.9 27.1 28.1 . . . September

Lebanon 10.9 9.3 11.0 10.2 12.1 . . . December

Morocco –2.0 10.9 6.3 17.4 20.6 . . . December

Oman 1.7 12.9 16.6 18.1 . . . . . . December

Pakistan1 35.4 30.5 38.2 35.2 30.1 . . . September

Saudi Arabia 22.7 24.3 28.5 30.5 . . . . . . December

Tunisia 7.6 5.1 6.5 7.7 9.0 . . . December

United Arab Emirates 16.4 19.9 22.5 18.2 22.0 21.2 March

Sub-Saharan Africa

Gabon 5.7 21.3 21.1 23.5 . . . . . . December

Ghana1 32.7 33.7 23.6 39.6 35.8 31.5 December

Kenya 23.2 22.0 25.0 28.6 32.3 . . . August

Lesotho . . . 27.0 15.0 27.0 8.0 . . . March

Mozambique 16.3 18.7 27.4 55.4 47.7 . . . December

Namibia 43.2 24.2 45.6 19.9 44.9 . . . December

Nigeria 19.8 27.4 7.1 10.4 13.1 . . . December

Rwanda 31.1 21.6 16.5 . . . . . . . . . December

Senegal 22.1 17.6 15.8 14.6 . . . . . . December

Sierra Leone20 67.1 73.2 52.5 21.1 10.3 . . . December

South Africa 11.6 16.2 15.2 18.3 18.1 . . . December

Swaziland 29.0 20.0 20.0 52.0 26.0 . . . June

Uganda 43.2 37.8 29.6 28.3 31.4 . . . December

Other

Australia21 24.2 16.0 14.7 16.8 18.1 . . . December

Canada 14.7 16.7 14.9 20.9 16.1 6.3 March

Japan22 –2.7 4.1 11.3 8.5 3.2 6.1 March

United States23 15.0 13.2 12.7 12.3 7.8 5.7 March

Sources: National authorities; and IMF staff estimates.

Note: Due to differences in national accounting, taxation, and supervisory regimes, FSI data are not strictly comparable across countries. 1Before tax.

2Banking sector excludes offshore banks.

3Commercial banks.

4General licensed banks.

5The data exclude the state mortgage bank.

6Capital is defined as bank shareholders’ equity and foreign bank branches’ funds received from the head office. Net income before 

extraordinary items and taxes. 7Adjusted for unallocated provisions for potential loan losses.

8Not annualized. 

92007 data do not include branches.

10Before extraordinary items and taxes.

11Break in the data series in 2007.

12Starting in 2004, data reported on a consolidated basis. 

13Commercial banks and six largest savings banks (five largest savings banks from 2006 due to a merger of two banks). 

14Net after tax income to paid-in capital.

15For 2005 and 2006 the figures are for the sample of institutions that are already complying with IAS, accounting as of December 2004 for 

about 87 percent of the usual aggregate considered. 16Data for the four large banking groups. 

17Gross profits. 

182007 figure is net income to end-of-period equity. 

192005 figure on a domestic consolidation basis; not strictly comparable with previous years.

20Break in the data series in 2006. 

21Gross profits until 2003; return on equity (after taxes) from 2004.

22For the end of the fiscal year, i.e., March of the following calendar year; all banks. The denominator of the ratio uses end-period data. 23All FDIC-insured institutions.

Table 27 (concluded)