© 2015 International Monetary Fund 

IMF Country Report No. 15/173 

UNITED STATES 

FINANCIAL SECTOR ASSESSMENT PROGRAM 

STRESS TESTING—TECHNICAL NOTE 

This Technical Note on Stress Testing on the United States was prepared by a staff team 

of the International Monetary Fund. It is based on the information available at the time it 

was completed in June 2015. 

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Washington, D.C. 

July 2015 
 

UNITED STATES 

FINANCIAL SECTOR ASSESSMENT PROGRAM 

 

TECHNICAL NOTE

STRESS TESTING 

 

Prepared By 

Monetary and Capital Markets 

Department 

This Technical Note was prepared in the context of 

an IMF Financial Sector Assessment Program (FSAP) 

mission to the United States of America, led by 

Aditya Narain and overseen by the Monetary and 

Capital Markets Department, IMF. Further 

information on the FSAP program can be found at 

http://www.imf.org/external/np/fsap/fssa.aspx

June 2015 
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CONTENTS 

GLOSSARY _________________________________________________________________________________________ 5 

EXECUTIVE SUMMARY ___________________________________________________________________________ 6

INTRODUCTION ________________________________________________________________________________ 11

IMF STAFF’S SOLVENCY STRESS TESTS FOR BANK HOLDING COMPANIES _________________ 12

A. Scope of the Test ______________________________________________________________________________ 12

B. Bank Holding Companies: An Overview _______________________________________________________ 14

C. Macroeconomic Scenarios ____________________________________________________________________ 17

D. Capital Standards _____________________________________________________________________________ 20

E. Models and Behavioral Assumptions __________________________________________________________ 24

F. Sensitivity Analyses ____________________________________________________________________________ 33

G. Results ________________________________________________________________________________________ 33

H. Network Analysis for Large BHCs _____________________________________________________________ 38

DISCUSSION OF SUPERVISORY AND COMPANY-RUN SOLVENCY STRESS TESTS __________ 43

A. Supervisory Stress Tests _______________________________________________________________________ 44

B. Company-run DFAST for 31 BHCs _____________________________________________________________ 48

IMF STAFF’S LIQUIDITY RISK ANALYSIS FOR BHCS __________________________________________ 50

A. Liquidity Metric ________________________________________________________________________________ 51

B. Results _________________________________________________________________________________________ 52

IMF STAFF’S SOLVENCY TESTS FOR INSURANCE _____________________________________________ 55

A. Scope of the Test ______________________________________________________________________________ 55

B. Scenario _______________________________________________________________________________________ 55

C. Valuation and Capital Standard _______________________________________________________________ 57

D. An Overview of Insurance Companies Soundness ____________________________________________ 58

E. Modeling Assumptions ________________________________________________________________________ 59

F. Results _________________________________________________________________________________________ 61

IMF STAFF’S LIQUIDITY ANALYSIS OF MUTUAL FUNDS _____________________________________ 68

IMF STAFF’S MARKET-PRICE BASED STRESS TESTS __________________________________________ 72

A. Systemic Risk Dashboard ______________________________________________________________________ 73
 

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B. Contingent Claims Analysis: Stress Testing for Systemic Risk _________________________________ 83

RECOMMENDATIONS FOR IMPROVEMENT ___________________________________________________ 93

REFERENCES _____________________________________________________________________________________ 94

BOXES 

1. NAIC Top Down Stress Test ___________________________________________________________________ 64

2. Additional IMF Stress Test Based on Statutory Accounting ___________________________________ 67

3. Systemic Risk Indicators Framework (SyRIN): A Primer ________________________________________ 82

FIGURES 

1. Structure of the U.S. Banking Sector ___________________________________________________________ 13

2. GDP Growth in the Baseline and Stress Scenario ______________________________________________ 20

3. IMF Stress Testing Framework: Bank Solvency ________________________________________________ 25

4. CET1 Ratio Under the Baseline and Stress Scenario ___________________________________________ 38

5. Credit and Funding Shock _____________________________________________________________________ 41

6. Contagion Path Triggered by BHC 1 Distress __________________________________________________ 43

7. DFA vs. IMF Stress Test Results ________________________________________________________________ 47

8. Liquidity Metric, Historical Run-Off Rates, and LCR Run-Off Rates ____________________________ 54

9. Medium-term Projections in a Low Interest Rate Environment, 2006–18 ______________________ 61

10. Top-Down Insurance Stress Tests ____________________________________________________________ 62

11. Mutual Fund Liquidity Analysis: The Waterfall Approach _____________________________________ 70

12. Results of the Liquidity Risk Analysis _________________________________________________________ 71

13. SRISK Market Implied Capital Shortfalls ______________________________________________________ 74

14. Early Warning Indicators _____________________________________________________________________ 74

15. Equity Price Misalignment Measures _________________________________________________________ 75

16. Fundamentals in Housing and Credit ________________________________________________________ 76

17. Credit Risk Networks _________________________________________________________________________ 77

18. Financial Account Net Exposures _____________________________________________________________ 79

19. Systemic Risk Indicators Framework, 2010Q4 and 2014Q4 __________________________________ 81

20. Financial System Connectivity ________________________________________________________________ 89

21. U.S. Financial System Default Probability Forecast ___________________________________________ 90

22. Spillover Map ________________________________________________________________________________ 92

TABLES 

1. Stress Testing Recommendations _____________________________________________________________ 10

2. Stress Testing: Overview of the Exercises Done by the IMF ____________________________________ 11

3. Variables Used in the IMF Stress Test __________________________________________________________ 18

4. Capital Standards ______________________________________________________________________________ 21

5. Capital Standards for Advanced Approach BHCs and Other BHCs ____________________________ 21

6. Balance Sheet Mapping _______________________________________________________________________ 27
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7. Loan Portfolio Mapping _______________________________________________________________________ 28

8. Projection Exercise _____________________________________________________________________________ 29

9. Models of Net Charge-Offs ____________________________________________________________________ 32

10. Dividend Distribution Schedule ______________________________________________________________ 33

11. Simulation Results of Credit and Funding Shock with Risk Transfers _________________________ 41

12. Main Differences Between IMF’s Top-Down and FRB’s Top-Down Approach ________________ 44

13. Liquid Assets _________________________________________________________________________________ 51

14. Outflow Items ________________________________________________________________________________ 52

15. Market Risk Parameters ______________________________________________________________________ 56

16. Impact of Natural Catastrophes and Pandemics _____________________________________________ 63

17. Changes in Duration and Credit Quality of Bond Portfolio ___________________________________ 65

18. CCA Stress Test Sample Data _________________________________________________________________ 85

19. Variables Used in CCA Stress Tests ___________________________________________________________ 86

APPENDICES 

I. Risk Assessment Matrix and Stress Test Matrix ________________________________________________ 97

II. Additional Data: Banking Sector ______________________________________________________________ 117

III. Additional Data: Insurance Sector ___________________________________________________________ 136

IV. Additional Results: Market-Based Tests ______________________________________________________ 137
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Glossary 

AFS Available for Sale 

AOCI Accumulated Other Comprehensive Income 

APT Asset Pricing Theory 

BCBS Basel Committee on Banking Supervision 

BHC Bank Holding Company 

BU Bottom-up 

CET1 Common Equity Tier 1 

CCA Contingent Claims Analysis 

CCB Conservation Capital Buffer 

CRSP Center for Research in Security Prices 

DFA Dodd-Frank Act 

DFAST Dodd-Frank Act Stress Test 

CDS Credit Default Swaps 

DFAST Dodd-Frank Act Stress Test 

FDIC Federal Deposit Insurance Corporation 

FSB Financial Stability Board 

FRB Federal Reserve Board 

GAMLSS General Adaptive Models of Location, Scale, and Shape 

GAIC Generalized Akaike Information Criteria 

GSIBs Globally Systemically Important Banks 

GSIIs Globally Systemically Important Insurers 

HTM Hold to Maturity 

LCR Liquidity Coverage Ratio 

MBSs Mortgage-Backed Securities 

MCSR Marginal Contribution to Systemic Risk 

NAIC National Association of Insurance Commissioners 

NBFI Nonbank Financial Institution 

NSFR Net Stable Funding Ratio 

OCC Office of Comptroller of the Currency 

OTC Over-the-Counter 

PELT Pruned Exact Linear Time 

P&C Property & Casualty Insurance 

PD Probability of Default 

RBC Risk-Based Capital 

RWAs Risk Weighted Assets 

SRISK Systemic Risk Measure 

SyRIN Systemic Risk Indicators 

TAC Total Adjusted Capital 

VaR Value at Risk 

WEO World Economic Outlook 
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EXECUTIVE SUMMARY1

1. A range of stress tests was used to quantify the potential impacts of risks and 

vulnerabilities in bank and nonbank sectors. The stress testing exercise reflected a broader 

evaluation of potential risks, embodied in the Risk Assessment Matrix (Appendix I). To provide a 

more comprehensive assessment than possible with any single approach, the stress testing exercise 

comprised several approaches. The FSAP team conducted top-down solvency tests for bank holding 

companies (BHCs) and insurance companies, liquidity risk analysis for BHCs and mutual funds, as 

well as market-price based stress tests. Moreover, the exercise was informed by the supervisory 

(top-down) stress tests performed by the U.S. authorities for the banking sector and the insurance 

sector, and by company-run (bottom-up) stress tests performed by BHCs. The exercise thus covered 

both banks and nonbanks (including insurance companies and mutual funds). It encompassed 

solvency and liquidity risks, as well as contagion risks. In the case of BHCs, the tests performed by 

IMF staff complement the Dodd-Frank Act stress test (DFAST) results. 

2. The stress tests run by the authorities and by companies under the DFA suggest that 

most large BHCs are resilient to shocks similar to the last crisis. The DFA requires the FRB to 

conduct an annual supervisory stress test of BHCs with total consolidated assets of $50 billion or 

more. It also requires all financial institutions with total consolidated assets of more than $10 billion 

to conduct company-run stress tests at least once a year. The results of the 2015 supervisory and 

company run stress tests, released in March, suggest that the system is resilient to severe shocks. 

Even in the severely adverse scenario (resembling the 2008–09 crisis), all the 31 BHCs have sufficient 

capital to absorb losses, which is the first time since the start of annual stress tests in 2009 that no 

firm fell below any of the main capital thresholds. The tests do not cover insurance and other 

nonbank financial institutions and do not capture network effects or analyze liquidity risks. 

3. The staff’s analysis benefitted from the relatively wide range of publicly available data, 

but was nonetheless subject to data constraints. Due to constraints on the authorities’ ability to 

share confidential supervisory information with the team, the analysis relied largely on publicly 

available data. The public data gathered was very extensive, but had notable gaps in some areas. For 

example, a lack of security-level granularity in publicly available data made full-fledged liquidity 

stress testing for BHCs and mutual funds a challenge. Data on interconnectedness among financial 

institutions have important gaps, although the authorities assisted the team in performing a 

contagion stress test for a sample of large BHCs. The insurance sector analysis was also constrained 

 

1

 The work on the note, prepared as part of the 2015 U.S. FSAP, was coordinated by Martin Čihák and carried out 

between October 2014 and March 2015.The main authors are Ivo Krznar (bank holding company stress tests, mutual 

funds stress test), Timo Broszeit (insurance stress tests), Dale Gray (market-based analysis), Ben Huston (marketbased analysis and support on bank holding company and mutual fund stress tests), and Juan Solé (network 

analysis). Important contributions were provided by Miguel Segoviano, Jay Surti, Nobuyasu Sugimoto, Sheheryar 

Malik, Naixi Wang, Deniz Igan, and Fabian Lipinsky. The note reflects comments and suggestions from other IMF 

colleagues. The FSAP team would like to express its gratitude to counterparts at the Federal Reserve Board (FRB), the 

U.S. Treasury, OCC, FDIC, SEC, NAIC, FIO, and other federal and state officials for all discussions and comments. The 

work has also benefitted from interactions with private sector participants and analysts. 
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by valuation practices in the United States, complexity of the insurance business and its regulation, 

and the absence of group-level risk-based capital. 

4. The solvency stress test considered two scenarios—baseline and stress—and a range of 

sensitivity checks. The baseline scenario was informed by the Blue Chip Economic Consensus and 

broadly reflected the IMF‘s World Economic Outlook (WEO) projections as of January 2015. The 

stress scenario was based on the severely adverse scenario from the DFAST, which was deemed 

appropriately stressful from the FSAP viewpoint. The scenario horizon was expanded to 5 years to 

bring it closer in line with recent FSAPs. The trajectories and co-movements of key variables were 

informed by post-war U.S. recessions, with scenario severity calibrated to be similar to the 2007–09 

recession. In the scenario, the unemployment rate rose by 4 percentage points over a two-year 

period. Real GDP was 4.5 percent lower than the baseline by the end of 2015 (GDP growth rates 

were negative for 5 quarters), equity prices fell by 60 percent in one year, house prices declined by 

25 percent over the first two years, corporate spreads rose by 330 basis points, and mortgage rates 

increased by 80 basis points. 

5. For BHCs, the staff’s solvency stress tests over the initial stressed period are largely in 

line with the DFAST results, and suggest that the system is generally robust, although some 

BHCs would fall below the hurdle rate in the stressed environment. 

 Banking stress tests covered the largest 31 BHCs—the same as under the DFAST—

constituting 85 percent of sectoral assets. The institutions were subjected to credit and 

liquidity risks in the context of a stress scenario. All tests were conducted based on publicly 

available, consolidated data as of September 2014. The solvency stress tests assessed the 

level of banks’ Basel III Common Equity Tier 1 ratios against a hurdle rate consisting of the 

regulatory minimum consistent with the Basel III transition schedule augmented by the 

capital conservation buffer and a capital surcharge for Globally Systemically Important Banks 

(GSIBs) which are both phased in over the forecast period. The hurdle rates in the IMF stress 

test were more stringent than in the DFAST where the hurdle rate was consistent with the 

Basel III transition schedule.

 The system-wide CET 1 ratio would fall by 21⁄2 percentage points in 2015 (or 21⁄4 percentage 

points relative to the baseline scenario) and no BHCs would fall below the hurdle rate in the 

first year, reflecting BHCs’ already high capital positions. 

 However, capital ratios do fall below the hurdle rate in subsequent years. Two BHCs fall 

below the hurdle rate in 2016 and an additional eleven BHCs do so during the recovery 

period. Recapitalization needs peak in 2019 at 180 percent of 2014 net income 

(corresponding to 1 percent of 2019 nominal GDP). While effects on capital ratios in the 

period of negative economic growth were largely driven by credit and trading losses, 

changes in capital ratios during the recovery period (2017–19) were mainly due to assumed 

increases in risk weighted assets, a higher hurdle rate due to phase-in of Basel III deductions, 

sluggish interest income growth, and higher dividend distributions. Although the results are 

conservative, since the BHCs would not likely increase their balance sheets if this were to 
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take them below the regulatory threshold, this does illustrate that some BHCs could face 

difficulties in supporting credit growth in the event of a large shock.

6. Staff’s liquidity risk analysis suggests that most BHCs have enough liquid assets to 

meet a liquidity shock similar to the 2008/2009 event. A few BHCs would face liquidity pressures 

due to deposit outflows in the short run and large unused commitments over a longer stress 

horizon. If faced with a much larger shock, as characterized by an approximation of the LCR run-off 

rates, liquid assets for many BHCs would not be sufficient to meet liquidity needs due to large 

withdrawal of wholesale funding. 

7. On the insurance side, stresses have a significant impact, especially in life insurance. 

The top-down group-level stress test for 43 insurance groups has limitations due to incomplete 

data, valuation practices in the United States, the complexity of the insurance business, and the 

absence of group-level risk-based capital. With these caveats, the team’s top-down stress tests 

suggest that market stresses would cause substantial reduction of shareholder equity of life insurers 

if a “fully market-consistent” valuation was applied; non-life insurers would be less affected. An 

equivalent of the “severely adverse” scenario would lead to at least 17 distressed companies (16 life 

insurers and 1 credit insurer). Shareholder equity of these groups would turn negative if the 

materialization of market risk shocks occurred instantaneously, thereby effectively reducing the full 

coverage of insurance liabilities with eligible assets. The current valuation regime, however, would 

not recognize the impact of these asset shocks except over time, as impairments are only required 

when losses are significant or prolonged. An additional exercise based on statutory accounting 

shows the sector in a more robust shape, as mark-to-market losses would only partially be reflected 

in the balance sheet. The results of this exercise are broadly in line with the top-down stress test 

performed by the NAIC. 

8. Calculations for managed funds underscore the liquidity risk in the asset management 

industry. The increased investment by open-ended mutual funds in relatively less liquid assets 

leaves them more susceptible to runs and suggests that their response to redemption pressures 

could exacerbate market stress. This risk was assessed by analysis geared to measuring whether 

markets would be able to absorb severe redemption pressures wherein these funds are forced to 

liquidate positions. Specifically, a top-down liquidity analysis was performed, covering some 9,000 

mutual funds representing around 80 percent of the industry. Assets sold by mutual funds hit by a 

stress shock were compared to data on dealers' inventory. The stress shock was defined as a one 

time, tail event (1 percent) redemption shock. The results of the stress test suggest that mutual 

funds that invest in municipal bonds and corporate bonds might face liquidity problems. While 

subject to important data limitations, the tests illustrate the danger that these funds might sell their 

assets at a fire-sale discount to meet redemptions. More work is needed on the quality of 

underlying data. The authorities are encouraged to perform liquidity risk analysis for the mutual 

fund industry on a regular basis and further strengthen guidance to the industry in this regard. 

9. A network stress-test methodology was used to assess potential spillovers among the 

largest domestic GSIBs. The methodology consists of simulating credit and funding shocks within a 
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network of institutions and tracking the contagion effects in terms of capital losses and path of bank 

failures. The methodology also allows for the assessment of the systemic impact arising from 

existing off-balance sheet financial linkages (e.g., credit default swaps). Due to data limitations, the 

exercise focused only on six largest U.S. GSIBs, accounting for 52 percent of BHCs’ total assets. The 

results indicate that the six largest BHCs hold enough capital to sustain a range of credit and 

funding shocks to individual counterparties within the network, and contagion risk appears 

contained. This is likely because direct exposures within the six-bank holding company network are 

not large enough relative to the initial capital of each institution to lead to second-round spillovers. 

Nonetheless, the calculations also suggest that risk transfer instruments, such as credit default 

swaps, alter dramatically the risk profile of financial institutions. The results thus illustrate the 

importance of monitoring and stress-testing off-balance sheet exposures, The results are also 

suggestive of the need to expand the data on exposures included in the network (e.g., exposure of 

the BHCs to money market funds), as well as consider richer market dynamics in the simulations 

(e.g., downward spirals in the value of certain financial assets). 

10. Nonbank financial institutions and markets account for a majority of systemic risk. The 

quantitative analysis of systemic risk suggests that marginal contributions to systemic risk outside of 

the banking system have increased. Banks are sizable, accounting for some 30 percent of assets, and 

their contribution to systemic risk remains large. Nonetheless, the staff’s analysis of 

interconnectedness of the different segments of the system illustrates that the systemic risks posed 

by bond funds, money market funds, separate funds and equity managers may be small in absolute 

terms, but they are large in relation to their size, reflecting their interconnectedness with banks and 

other financial sector components. 

11. Market-price based stress tests were used to illustrate the importance of cross-sectoral 

spillovers under stress. Market-price based analysis has important limitations that reflect the 

underlying modeling assumptions as well as the quality of the underlying market data. But given the 

relatively high level of development, high liquidity, transparency, and sophistication of U.S. financial 

markets, market-based models provide useful complementary information. The results suggested: 

 The system as a whole appears less vulnerable to large adverse macroeconomic shocks than 

during the recent crisis, but default probabilities would still rise to levels that would suggest 

significant systemic stress. 

 Cross-sector spillovers amplify the effects of shocks. U.S. banks, insurers, and other nonbank financial institutions tend to be adversely affected by credit risk shocks originating in 

other domestic sectors. And while spillovers from insurance companies to banks are small, 

when macroeconomic conditions are held constant, they are large otherwise. This suggests 

that the risks that insurers pose to the system stems from their own vulnerability to 

macroeconomic shocks. 

 Spillovers from the United States to the rest of the world can be large. This is particularly 

true for the foreign banking sector, while the foreign insurance sector is most negatively 
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affected by adverse changes in the credit profile of U.S. asset managers and insurers. 

Spillbacks from the rest of the world appear to be relatively modest. 

12. While the authorities’ stress testing is state-of-the art in many respects, the exercise 

has suggested some scope for enhancements (Table 1). These include addressing data gaps by 

collecting interbank exposures for the whole sample of BHCs that were stress tested and conducting 

a network analysis on a regular basis; establishing a liquidity stress testing framework; trying to link 

liquidity, solvency and network analysis in a systemic risk stress testing framework; reexamining 

some of the solvency stress test assumptions to make them consistent with historical evidence, for 

example, as part of sensitivity analyses; as well as performing regular liquidity stress tests for open 

ended mutual funds and further strengthening the guidance to the industry on liquidity risk analysis. 

Table 1. Stress Testing Recommendations 

General

 Conduct regular, comprehensive stress tests for all major financial sub-sectors that capture the 

impact of macro-financial factors, spillover and feedback effects between institutions and 

interactions between solvency and liquidity. 

 Conduct more intensive monitoring of systemic financial sector risks, including the use of marketbased solvency and shortfall measures. 

Banking 

 Conduct liquidity stress testing on a regular basis. 

 Collect consistent interbank exposure data, and run network analysis on a regular basis. 

 Link liquidity, solvency and network analyses in a systemic risk stress testing framework. 

 Reexamine DFAST solvency stress test assumptions (e.g., regarding loan and balance sheet growth 

and dividend distribution) to confirm that they are consistent with historical evidence. 

Insurance 

 Develop and perform insurance stress tests on a consolidated, group-level basis, especially for 

groups that are (i) designated as systemically important; (ii) engaged in material group-internal risk 

transfer, e.g. via captives; or (iii) exposed to non-linear market risks through the sale of products 

which include guarantees or optionalities, e.g. variable annuities. 

 Improve public disclosure by requiring insurance companies to disclose market risk sensitivities in a 

more harmonized manner. 

Mutual Funds

 Develop and perform liquidity risk analysis for the mutual fund industry on a regular basis; further 

strengthen guidance to the industry on liquidity risk analysis. 
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INTRODUCTION 

13. This note provides the methodology and results of stress tests of the financial sector 

carried out in the 2015 FSAP assessment for the United States. To obtain a more comprehensive 

assessment than possible with any single approach, the U.S. FSAP stress tests combined three groups 

of complementary approaches. The first group consisted of the IMF’s top-down stress tests for BHCs, 

insurance companies, and mutual funds. The second group included the Fed’s top-down (supervisory 

DFAST) tests for BHCs and bottom-up stress tests run by the companies (company run DFAST). The 

third group included a broad range of IMF’s top-down calculations using market-price data (Table 2 

and Appendix I). The findings of the stress tests were used to provide quantitative support for the 

FSAP’s stability risk assessment by estimating the impact from the realization of key tail risks and to 

facilitate policy discussions on risk mitigation strategies and crisis preparedness. 

Table 2. Stress Testing: Overview of the Exercises Done by the IMF

Exercise type Coverage Scenarios Cut-off date; data 

frequency; stress test 

horizon 

IMF top down 

(solvency) test 

31 Bank Holding Companies 

plus additional 2 BHCs and 2 

savings banks in the sensitivity 

analysis 

(6 systemic BHCs for network 

stress testing) 

Scenarios taken from 

DFAST, extended 

using WEO; 

sensitivity analysis 

and network analysis 

2014Q3; quarterly; 5 year 

stress test horizon 

Bank liquidity risk 

analysis 

31 Bank Holding Companies Range of adverse 

scenarios 

2014Q3; quarterly; 

instantaneous shock 

Insurance stress 

testing 

43 insurance groups (20 life, 16 

property & casualty, 5 health 

insurance, and 2 credit and 

mortgage insurance). 

Scenarios taken from 

DFAST 

End-2014 data 

Mutual fund 

liquidity risk 

analysis 

9,000 mutual funds (80 percent 

of the industry) 

Range of adverse 

scenarios 

2014Q3; quarterly, 

instantaneous shock 

Market-price 

based network 

analysis and stress 

testing 

210 institutions (U.S. banks, 

insurers, NBFIs, asset managers, 

nonfinancial firms; foreign 

banks and insurers) 

Scenarios taken from 

DFAST, extended 

using WEO 

2014Q3; 

daily 

Note: for details, see the Stress Test Matrix (Appendix Table 2). Table 2 focuses on IMF-run stress tests and does not include the 

supervisory and companies-run stress tests that informed this exercise. 
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IMF STAFF’S SOLVENCY STRESS TESTS FOR BANK 

HOLDING COMPANIES 

14. This section explains the top-down solvency stress tests of the IMF FSAP team. The 

section covers: (i) overview of scope, (ii) the state of the banking sector; (iii) the macroeconomic 

scenarios; (iv) the capital definitions and standards that were used for calculating and reporting 

results; (v) the stress test methodology and the use of models to map the macroeconomic scenarios 

into credit losses, income projections, balance sheet items and risk weighted assets; (vi) the 

behavioral assumptions governing capital actions in the stress test scenarios, (v) the network analysis 

performed and (vii) the results of the stress test. 

A. Scope of the Test 

15. The top-down test followed the balance sheet-based approach. This assesses solvency of 

individual BHCs under the baseline and stress scenarios through changes in net income and riskweighted assets. A range of sensitivity analysis was performed to supplement the two scenarios. This 

approach was comparable to the company run DFAST (bottom-up) and supervisory DFAST (topdown), despite some important differences (Table 12). It can be seen as complementary to the DFAST 

exercise. 

16. The stress test used publicly available, consolidated data. These cover individual BHCs 

from regulatory reports (FR Y-9C) provided by SNL Financial that take into account structural breaks 

resulting from bank mergers and acquisitions. 

17. The coverage of the IMF top-down test was the same as in the DFAST, which increased 

the comparability of results. Results of the test were calculated by individual institution. The stress 

test covered 31 largest BHCs (with total consolidated assets of $50 billion and more), which account 

for about 85 percent of the BHC assets and 70 percent of total banking sector assets, defined as total 

assets of BHCs, savings and loans holding companies (SLHC) and commercial and savings banks that 

are not part of any BHC or SLHC (Figure 1). The network analysis was based on six largest BHCs, 

accounting for 52 percent of total BHC assets. 

18. While the test’s coverage in terms of total banking sector assets is comparable with 

other FSAPs, some large depository institutions were not included. BHCs with assets of $10 

billion and more but less than $50 billion (which represent around 5 percent of the BHC assets) were 

not included. Most banking organizations (commercial banks and saving institutions) with assets of 

$50 billion and more that are regulated by OCC and the FDIC and are subject to bottom-up tests 

(company-run DFAST), were implicitly included in the top-down stress test, as they are subsidiaries of 

BHCs included in the top-down stress test. The exceptions are two banks (one regulated by the FDIC 

and the other by OCC) that are part of BHCs that will be subject to supervisory DFAST in the future 
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BANK HOLDING COMPANIES 

$16.8 trillion

SAVING AND 

LOANS HOLDING 

COMPANIES

$1.7 trillion

COMMERCIAL AND 

SAVINGS BANKS

$15.4 trillion

31 BANK HOLDING 

COMPANIES in the ST, $13.9

trillion

Two BHCs and two 

savings banks in the 

sensitivity analysis

$510 billion

CREDIT UNIONS

$1 trillion

and two banks that are part of large SLHCs (regulated by OCC)2. These institutions were stress tested 

as part of a robustness check (Figure 1). Moreover, one depository institution—the largest credit 

union—has assets exceeding $50 billion but was not included in the stress test, because it was not 

subject to risk-based capital requirements used by other federal banking regulatory agencies as of 

2014Q3.3

 Large SLHCs were not included in any stress test as they were not subject to capital 

requirements as of 2014Q3.4

 SLHCs with assets of $50 billion and more represent 10 percent of total 

holding companies’ assets with assets of $50 billion and more. They will be required to perform DFA 

company-run stress tests in the future. 

Figure 1. Structure of the U.S. Banking Sector 

Source: Federal Reserve Board, IMF Staff calculations. 

19. The cut-off date for the data was September 30, 2014. Minimum capital requirements 

used as hurdle rates were consistent with the revised capital regulatory standards that reflect Basel III 

capital standards including both the capital conservation buffer and a GSIB capital surcharge 

(calculated using the BCBS framework), on a phased-in basis, as minimums. The hurdle rates in the 

 

2

 There are also large U.S. branches of a foreign institution that could not be included in the solvency test as they do 

not hold any capital. 

3

 National Credit Union Administration, the regulator of all federal credit unions, issued (in January 2015) for 

comments a proposed rule that would amend the agency’s current risk-based net worth requirement by replacing the 

current risk-based net worth ratio with a new risk-based capital ratio for federally insured natural person credit 

unions. The new capital requirements would be more consistent with regulatory risk-based capital measures used by 

the FDIC, OCC and Board of Governors of the Federal Reserve.

4

 Their capital requirement schedule (Schedule HC-R) of the FR Y-9C was empty. Therefore, the calculation of their 

CET1 capital and their risk weighted assets was not possible. 
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IMF stress test were more stringent than in the DFAST’s, which were consistent with the Basel III 

transition schedule and did not include capital conservation buffer or a GSIB capital surcharge. 

B. Bank Holding Companies: An Overview 

20. This section provides an overview of BHCs included in the stress tests (Appendix Figure 

1). It analyzes the structure of balance sheets and income statements as well as off-balance sheet 

items. Moreover, it provides some detailed information on GSIBs. 

21. Assets of 31 largest BHCs rose by 11 percent since the last FSAP and 18 percent since 

the crisis. Total assets of the largest BHCs represent 80 percent of nominal GDP. The increase in 

assets was primarily driven by increases in cash, federal funds bought and reverse repos and 

available-for-sales securities portfolio. Much of this growth reflects impacts of Quantitative Easing 

policies on bank balance sheets—impacts that are likely to be at least partially reversed with the 

upcoming unwind. Cash now accounts for 12 percent of total asset compared to 3 percent before 

the crisis. The increase in federal funds and repo mostly reflect a large increase in 2009. Securities 

holding have expanded by 60 percent since 2008 due to increases in holdings of available for sale 

(AFS) securities—as holdings of mortgage-backed securities (MBSs), Treasury securities and foreign 

debt securities increased—which increases BHCs exposure to interest rate risk.5

 Half of the AFS 

portfolio pertains to MBSs, followed by Treasury securities (16 percent) and foreign debt securities 

(14 percent). As of 2014Q3 trading assets are lower by about 10 percent comparing to 2008 which is 

partly due to the implementation of the Volcker rule which severely restricts proprietary trading. 

22. Total net loans are the largest asset category accounting for 40 percent of total assets, 

slightly lower than in 2008 or before the crisis. While total loans have increased by 10 percent the 

structure of loans has changed since the crisis. Real estate loans, the largest loan type, account for 

28 percent of total loans, down from 36 percent in 2008. This was mainly due to lower revolving, 

open end real estate loans extended under lines of credit and close-end junior lien real estate loans 

as underwriting standards for those loans tightened considerably mostly by requiring more 

documentation and by imposing debt service ratio (OCC, 2014). On the other hand, loans to financial 

institutions increased their share to about 10 percent driven by increases in loans to non-depository 

financial institutions and loans for purchasing securities. Rapid loan growth since the beginning of 

2013 driven by business loans calls for continued vigilance given evidence of weakening 

underwriting, especially in the leveraged loans market.6

23. Deposit growth, which accounted for the bulk of funding growth, supported the 

growth of assets. Deposits are 40 percent larger than in 2008 and account for 53 percent of total 

liabilities and 132 percent of loans. The deposit-to-loan ratio is 21 percentage points higher than in 

 

5

 Moreover, at largest banks asset maturities have been stable but liability maturities have gotten shorter (Bednar and 

Elamin, 2014). Small banks are particularly exposed to interest rate risk as their asset maturities have gotten longer 

and liability maturities have gotten shorter. 

6

 Leveraged loan issuance in 2015 has started to decrease following intra-agency leveraged lending guidance. 
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INTERNATIONAL MONETARY FUND 15

2010 and 30 percentage points higher than before the crisis partly due to record corporate cash 

holdings. About 60 percent of deposits are money market deposits. Stable deposits7

 account for 

almost 90 percent of total deposits and less stable deposits have significantly decreased since the 

crisis due to lower large, short-term domestic and foreign time deposits. 

24. Deposit growth, along with deleveraging, has reduced BHCs’ reliance on wholesale 

funding. Non-deposit liabilities such as repos, trading liabilities and other wholesale funding are 

10 percent lower than in 2009. Wholesale funding (defined as repos, trading liabilities, subordinated 

notes and brokered deposits) account for 30 percent of total liabilities. The bulk of wholesale funding 

pertains to other borrowed money (50 percent) and repos (25 percent). The maturity of other 

borrowed money has been extended since 2008 and most of other borrowed money (70 percent) in 

2014 is related to unsecured liabilities and liabilities with maturity of 1 year and more. 

25. Total equity has increased by 70 percent driven by retained earnings and surpluses, 

which have doubled since the crisis, largely in response to the higher regulatory requirements. 

CET 1 capital ratio has doubled since the crisis to 12 percent at the end of 2014Q3. The leverage ratio 

(defined as CET1 over total assets) has more than doubled to 8 percent since the end of 2008. 

26. Off balance sheet activity has fallen since 2009 mostly due to lower holdings of 

derivatives, notwithstanding an increase in unused commitments. While the derivatives (credit 

equivalent) have fallen by 30 percent, unused commitments have increased by 5 percent since the 

crisis and still represent the largest off balance sheet item. The largest share of unused commitments 

pertains to consumer credit card lines (40 percent) and commercial and industrial loans (25 percent). 

The structure of unused commitments has changed since the crisis. Although unused credit card 

lines have fallen, unused commitments on commercial and industrial loans and loans to financial 

institutions have increased by 30 percent. Securities lending is the second largest off balance sheet 

item with a share of around 20 percent of total off balance sheet activity. Interest rate contracts are 

the largest component of derivatives portfolio (82 percent) followed by foreign exchange contracts 

(14 percent). Credit derivatives have been cut in half since 2009 and represent a small proportion of 

derivatives activities where most contracts are related to purchased or sold investment grade credit 

default swaps. Swaps and forward contracts dominate the derivative contracts. Almost the whole 

derivative portfolio is held for trading. In most of OTC derivatives transactions cash is the main 

collateral and major counterparties are banks and securities firms and non-financial corporate firms. 

27. While BHCs have made material improvements in nonperforming loans, underwriting 

standards have continued to loosen since 2011 (Appendix Figure 2). Economic recovery has been 

conducive to further strengthening of the BHCs’ balance sheets. Delinquent and non-performing 

loans have continued to fall since their peak in 2009. Delinquency rates and NPLs have been cut in 

half since end 2009 and now stand at 3.5 and 2.5 percent respectively. Most of bad loans consist of 

 

7

 Stable deposits are defined as total deposits minus brokered deposits minus large deposits and foreign deposits 

maturing within a year. 
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16 INTERNATIONAL MONETARY FUND

residential mortgage loans. Non-real estate mortgage delinquent loans are at the levels before the 

crisis. Net charge-off rates are considerably lower than in 2009 but still higher than before the crisis 

due to higher charge-offs for consumer loans. However, regulatory surveys from the OCC suggest 

looser underwriting in commercial real estate, commercial and industrial loans, and auto loans with 

some banks having significant exposure to subprime auto loans. Moreover, LTVs for CRE loans are 

approaching their pre-crisis levels suggesting continuing monitoring is needed. The largest BHCs 

seem resilient to the recent oil price drop since their direct loan exposure to energy-related 

companies is only in the range of 1.2 to 5 percent their total loans. 

28. While the BHCs have posted all time high profits in 2014Q3, there is a large dispersion 

of profitability indicators across BHCs. Net income has increased substantially since the last FSAP 

driven by lower provisions which have come down to pre-crisis levels and higher non-interest 

income. Net interest margins continue to compress as a result of protracted low interest rates, banks’ 

increased holding of liquid assets because of regulatory requirements as well as heightened loan 

competition. Non-interest expenses are 35 percent higher in 2014Q3 than in 2008, partly due to 

litigation-related charges and cyber security protection. Return on equity (ROE) is about 50 percent 

smaller than before the crisis mainly due to higher capitalization of BHCs. Return on assets are 

20 bps lower than before the crisis. While profitability of all BHCs is relatively high there are large 

differences across BHCs, which, for some large BHCs, is also due to litigation charges stemming from 

BHCs’ business practices leading up to the crisis.8

 Many BHCs are seeking to enhance their ROE by 

looking for new business, principally through new loan growth or reconfiguration of business models 

which increases the risk of relaxation of underwriting standards. 

29. There are large differences in business models across BHCs. BHCs can be differentiated 

based on the largest asset category. Most of the BHCs are focused on lending as the main business 

activity. The second type of BHCs is more involved in capital market activities. The largest asset item 

of the third type of BHCs pertains to AFS securities. In general, BHCs with high proportion of loans 

have lower leverage, lower off balance sheet activities and are less involved in wholesale activities 

such as reverse repo and trading. They use deposits as a major source of funding and are more 

profitable than other types of BHCs. On the other hand, BHCs that have large trading activities are 

less involved in lending but more involved in reverse repo transactions. They are funded more on the 

wholesale market and have higher leverage. They also have higher NPLs, which might imply that they 

can’t compete with BHCs whose lending represents their core business or that they are less 

constrained by regulatory capital ratios and are searching for yield by targeting riskier loans. The 

third type of BHCs, of which the largest three BHCs have large operations as custodian banks, is less 

involved in lending, trading and repo transactions. They have higher leverage and off balance sheet 

activities but at the same time large deposit base. 

30. Total assets of GSIBs have reached about $10 trillion and represent 75 percent of total 

assets of all BHCs included in the stress test (Appendix Figure 3). When derivatives positions and 

 

8

 The large BHCs continue to be plagued by very elevated litigation-related charges. 
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INTERNATIONAL MONETARY FUND 17

securities financing transactions are added their total exposure is about 35 percent higher than their 

total assets. Around 25 percent of their assets are related to foreign exposures and on average, they 

derive about 1⁄4 of their total net revenue from foreign business. The largest component of foreign 

loans pertain to commercial and industrial loans. Securitization, mostly of mortgage loans, represents 

11 percent of GSIBs assets but with large differences across BHCs. Securities (trading and AFS) 

account for 30 percent of their total assets. The structure of investment and AFS securities portfolios 

is very similar- MBSs (45 percent) and Treasury securities (around 20 percent) represent almost 2/3 of 

portfolios. 

31. GSIBs are interconnected with the rest of the financial system. Intra-financial system 

assets represent 22 percent of GSIBs’ assets while intra-financial system liabilities represent 16 

percent of their total liabilities with notable differences across GSIBs. The largest component of intafinancial system assets is the fair value and potential future exposure of OTC derivatives (52 percent), 

followed by deposits (20 percent). Most of the intra-financial liabilities pertain to deposits 

(47 percent; most of deposits were due to non-bank financial institutions) and OTC derivatives 

(35 percent). Almost half of OTC derivatives are cleared through a central counterparty. 

C. Macroeconomic Scenarios 

32. The solvency stress tests examined two macroeconomic scenarios: a baseline and a stress 

scenario over a five year horizon (Box 1, Appendix Figure 4). These scenarios were developed by the 

FRB in consultations with the OCC and the FDIC (over July and August 2014).9

 The scenarios 

consisted of the future paths of 28 economic and financial variables (six measures of economic 

activity and prices, four measures of developments in equity and property prices, six measures of 

interest rates and variables for the euro area, the United Kingdom, developing Asia, and Japan). 

33. The baseline scenario and the stress scenario over the initial three years reflected the 

supervisory baseline scenario and the severely adverse scenario under the Dodd-Frank Act 

Stress Tests (DFAST), respectively. The baseline scenario was very similar to the IMF’s latest WEO 

projections for the first three years of the horizon. The stress scenario reflected the severely adverse 

scenario under the DFAST10 for the first three years of the forecast horizon (up to 2017Q4). The 

 

9

 See Board of Governors of the Federal Reserve System: 2015 Supervisory Scenarios for Annual Stress Tests Required 

under the Dodd-Frank Act Stress Testing Rules and the Capital Plan Rule, October, 2014. 

10 Overall, the 2015 and 2014 DFAST severely adverse scenarios were very similar. The real GDP growth rate dynamics 

over the stress horizon (in terms of standard deviations) in 2015 and 2014 exercise were almost the same. The same 

applied to 28 other variables. There were two main differences between the 2015 and the 2014 DFAST exercise. First, 

the 2015 DFAST included wider corporate bond spreads, reflecting a general pullback from a variety of assets linked 

to risky corporate borrowers (high-yield bonds, leveraged loans, CLOs). The spreads widened to levels reached in the 

2008 recession. Second, a larger increase in the price of oil (to $110 per barrel) was assumed. This led the CPI headline 

inflation rate to reach 4.2 percent in the short run, before falling back to 1.6 percent by the end of the DFAST horizon. 

Market shock calibration was broadly similar to the 2014 exercise. In the adverse scenario a positive shock to shortterm interest rates was used in the 2015 DFAST, in order to explore the sensitivity of the financial system to uniquely 

different stresses than those reflected in the severely adverse scenario. 
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18 INTERNATIONAL MONETARY FUND

Variables used in the ST Projected from Exogenous variables 

Real GDP growth 

Unemployment rate

3-month Treasury rate

10-year Treasury yield

BBB corporate yield Differenced unemployment rate, real GDP growth, VIX, 10-

year Treasury yield

Mortgage rate Differenced unemployment rate, real GDP growth, VIX, 3-

month Treasury rate, 10-year Treasury yield

Dow Jones Total Stock Market Index 

((y/y))

Differenced unemployment rate, real GDP growth, real 

GDP growth squared, VIX, 3-month Treasury rate

House Price Index (y/y) Mortgage rate, y/y real GDP, y/y house price index (-1)

Commercial Real Estate Price Index (y/y) BBB corporate yield, y/y real GDP, y/y CRE index (-1)

Market Volatility Index (Level) Differenced unemployment rate, real GDP growth, real 

GDP growth squared

Variables added

Business interest rates 10-year Treasury yield

Consumer interest rates Differenced unemployment rate, 10-year Treasury yield

Federal funds rate 3-month Treasury rate

Extended to converge to the baseline by the end of the 

horizon

2018q1

2018q1

2014q4

scenarios were characterized by 10 variables from the DFAST and three additional variables not 

included in the DFAST (Table 3). 

34. For the first three years of the horizon (from 2014Q4 to 2017Q4), the IMF staff adhered 

to the supervisory scenarios. For the additional years (from 2018Q1 to 2019Q4), the paths for a 

selected subset of key indicators (GDP growth, unemployment, short- and long-term interest rates) 

were extended based on the latest WEO projections for the baseline. For the stress scenario, the 

paths for the key indicators were extended so as to converge to the baseline by the end of the 

horizon. The other variables from the DFAST (house prices, commercial real estate prices, VIX, Dow 

Jones stock price index, BBB corporate yield and mortgage rates) were extended using simple OLS 

regression models and projections for key indicators as exogenous variables (Table 3). Business and 

consumer interest rates11 and federal funds rate were added to the set of variables from the DFAST 

and were projected from 2014Q4 using regression models and projections of variables from the 

DFAST.

Table 3. Variables Used in the IMF Stress Test 

 Source: IMF Staff.

35. The baseline scenario was very similar to the average projections of economic 

forecasters.

12 It reflected a sustained, moderate expansion of U.S. economic activity converging to a 

 

11 Finance rate on personal loans at commercial banks (24 month loan) were used as consumer interest rates and 

bank prime loan rate was used as business interest rates. 

12 For example, the path for U.S. real activity and inflation was in line with the October 2014 consensus projections 

from Blue Chip Economic Indicators. 
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INTERNATIONAL MONETARY FUND 19

growth rate of about 2 percent and unemployment rate reaching 4.4 percent by the end of 2019. 

Gradual normalization in federal funds rate and Treasury yields starts in second quarter of 2015. 

Interest rates on mortgage loans, consumer loans and business loans follow broadly the dynamics of 

short-term rates. All assets prices (equity, house and commercial property) rise steadily 

accompanying the modest expansion of economic activity. 

36. The stress scenario was similar in severity to the 2007–09 recession. The stress scenario 

was based on the severely adverse scenario of the DFAST, which was deemed appropriately stressful 

from the FSAP viewpoint. Nonetheless, the scenario horizon was expanded to 5 years, bringing it 

closer to recent FSAPs. Following the approach adopted in the DFAST, the trajectories and comovements of key variables were informed by post-war U.S. recessions, with scenario severity 

calibrated to be similar to the 2007–09 recession. The unemployment rate was used as the primary 

basis for specifying the scenario13 and the other variables were set using a combination of economic 

models, typical paths of these variables in past recessions, and informed judgment.14 The severely 

adverse scenario in the 2015 DFAST (the shock was applied from 2014Q4) was characterized by a 

4 percentage point rise in the unemployment rate over a two-year period. It was assumed that: real 

GDP would be on average 6.6 percentage points lower than the baseline in 2015 (Figure 2);15 equity 

prices would fall by 60 percent in the first year; house prices would decline by 25 percent over the 

first two years; corporate spreads would rise significantly in 2015, reflecting a deterioration of U.S. 

corporate credit quality; mortgage rates would increase by 80 basis points; and market volatility 

would rise to levels the same as the peaks reached in the 2007–09 recession. Short-term interest 

rates would remain at zero by end of 2017, reinforcing the negative effects from protracted period of 

low interest rates, after which normalization would start. Long-term Treasury yields would first drop 

to 1 percent in 2014Q4 and then edged up slowly over the remainder of the stress testing horizon. 

The scenario also included a rise in oil prices to about $110 per barrel possibly reflecting a 

materialization of geopolitical risks. After 2017, most of the variables were assumed to converge to 

the levels in the baseline scenario. The stress scenario was complemented with sensitivity analyses to 

estimate the marginal impact of individual risks not captured by the scenario (the interest rate spike 

in particular). The supervisory DFAST calculations of net income losses also incorporated projected 

losses generated by operational risk events such as fraud, computer system, or other operating 

disruptions. 

 

13 After specifying the unemployment rate, the FRB specified paths of other macroeconomic variables based on the 

paths of unemployment using models or how these variables have typically evolved in the past U.S. recessions and 

informed judgment as some variables have taken divergent paths in previous recessions. FRB staff believes that this 

approach is more suited for developing the severely adverse scenario than a probabilistic approach, which is model 

dependent. 

14 See “Policy Statement on the Scenario Design Framework for Stress Testing,” 12 CFR 252, appendix A. 

15 GDP growth rates were negative for 5 quarters, implying a 4.6 percent cumulative loss of real GDP from 2014Q3 to 

2015q4. The projected dynamics follows that of GDP growth in the 2008 recession, which was the worst post-war U.S. 

recession in terms of output and employment losses and its duration. The cumulative loss of real GDP during the 

2008/2009 crisis was 3.6 percent from 2008Q3 to 2009Q2. 
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20 INTERNATIONAL MONETARY FUND

90

95

100

105

110

115

-1 0 1 2 3 4 5

U.S. FSAP stress test Macro-Scenarios 

Global financial crisis (2008q2=100) FSAP stress scenario (2014Q3=100)

FSAP baseline scenario (2014Q3=100)

Years

Figure 2. GDP Growth in the Baseline and Stress Scenario 

Source: FRB, IMF Staff calculations.

D. Capital Standards 

Capital definitions 

37. The capital definition applied in the stress test corresponded to Basel III capital 

standards. This was applied to all BHCs, recognizing that only advanced approaches BHCs were 

subject to Basel III capital rules in 2014, and non-advanced approached BHCs became subject to the 

rules from January 1, 2015. 

38. Hurdle rates included the CET1 minimum requirement, the capital conservation buffer, 

and the GSIB surcharge. The solvency stress test assessed the level of BHCs common equity Tier 1 

ratios of both advanced approaches and non-advanced approaches BHCs against the regulatory 

threshold consistent with the Basel III transition schedule but also accounting for capital conservation 

buffer and a G-SIBs capital surcharge (calculated using the BCBS framework), as minimums (Table 4). 

The phase-in for the Basel III framework that began during 2014 and the revised capital framework 

that introduced a new standardized approach to RWAs starting in 2015 were also considered 

(Table 5). A common equity surcharge associated with G-SIB status was also taken into account. It 

ranged from 1.0 to 2.5 percent, following Financial Stability Board (FSB) buckets corresponding to 

required level of additional loss absorbency, and it was phased-in between January 1, 2016 and end 

of 2018.16,17 

 

16 The FRB has invited comments on a proposal to establish a GSIB surcharge for U.S. BHCs that is based on the BCBS 

methodology, augmented to address specific risks to U.S. financial stability. Under the proposal, estimated surcharges 

for BHCs that would be identified as GSIBs currently would range from 1.0 to 4.5 percent of a firm’s total riskweighted assets, as compared from buffers that range from 1.0 to 2.5 percent, under the BCBS methodology. 17 The impact on a leverage ratio defined as Tier 1 capital ratio over total assets was also considered but were not 

reported. The results related to the leverage ratio confirmed the results based on risk sensitive capital measures since 

BHCs have to calculate risk-weighted assets using the standardized approach (or generalized approach in the past). 

Analyzing the Basel III leverage ratio would require forecasting off-balance sheet items (derivatives exposures, 

securities financing transaction exposures and other off-balance sheet items) which was out the scope of this stress 

testing exercise due to lack of granular data for off-balance sheet exposures. 
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INTERNATIONAL MONETARY FUND 21

Capital Ratio Aspect of the Ratio Q4 2014 2015 2016 onwards

Advanced approaches BHCs

Capital in numerator

Revised (Basel III) 

capital framework

Revised (Basel III) capital 

framework

Revised (Basel III) capital 

framework

Denominator

General approach 

RWAs

Projected RWAs, stand. 

RWAs switch, include op. 

risk RWAs

Projected RWAs, include op. 

risk RWAs

Capital in numerator

Revised (Basel III) 

capital framework

Revised (Basel III) capital 

framework

Revised (Basel III) capital 

framework

Denominator Average assets Average assets Average assets

Other BHCs

Capital in numerator

n/a but Basel III ratio 

estimated 

Revised (Basel III) capital 

framework

Revised (Basel III) capital 

framework

Denominator

General approach 

RWAs

Projected RWAs, stand. 

RWAs switch Projected RWAs

Capital in numerator

Basel I-based but 

Basel III used

Revised (Basel III) capital 

framework

Revised (Basel III) capital 

framework

Denominator Average assets Average assets Average assets

Tier 1 leverage ratio

Common equity tier 1 ratio

Tier 1 leverage ratio

Common equity tier 1 ratio

2014 2015 2016 2017 2018 2019

I. Basel III phase-in minimum CET1 4.0 4.5 4.5 4.5 4.5 4.5

II. Capital conservation buffer 0.625 1.250 1.875 2.5

III. Total CET1 ratio (I.+II.) 4.0 4.5 5.125 5.750 6.375 7.0

IV. GSIB surcharge \1 0.25 0.50 0.75 1.00

V. Total CET for GSIBs (III. + IV.) 4.00 4.50 5.375-5.75 6.25-7.0 7.125-8.25 8.0-9.5

Phase in of deductions from CET1 \2 20 40 60 80 100 100

Phase out of existing AOCI capital adjustments \3 80 60 40 20 0 0

Table 4. Capital Standards 

(percent) 

\1 GSIB surcharge factor (the factor was multiplied by GSIB surcharge). 

\2 Applied to intangible assets and DTAs. 

\3 For advanced approached BHCs only. 

Source: FRB, IMF Staff. 

Table 5. Capital Standards for Advanced Approach BHCs and Other BHCs 

Source: FRB, IMF Staff.

39. CET1 capital for the base period was estimated for non-advanced approaches BHCs. 

Non-advanced approaches BHCs became subject to Basel III capital rules from 2015Q1 and did not 

report CET1 capital (on Schedule HC-R, Part I.B. of FR Y-9C) as of September 2014. Therefore, SNL’s 

estimate of CET1 (after deductions and adjustments) were used. SNL calculates the CET1 as: Tier 1 

Capital - Non-qualifying Perpetual Preferred Stock - Preferred Stock & Surplus - Qualifying: Non 
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22 INTERNATIONAL MONETARY FUND

Controlling Interests—Qualifying: Restricted Core Capital Elements—Qualifying: Mandatory 

Convertible Securities.18

40. Deductions from CET1 were needed to calculate phase in of deductions from CET1. 

Most deductions pertained to goodwill, intangible assets, and deferred tax assets (DTAs).19 To 

calculate phase in of deductions from CET1 the following strategy was implemented: 

 The deductions were reported by the advanced approaches BHCs only (in Schedule HC-R). 

For non-advanced approaches BHCs intangible assets deducted from CET1 were 

approximated by intangible assets other than goodwill and mortgage servicing assets (MSA) 

(from Schedule HC-M) adjusted for deferred tax liabilities (DTLs) associated with intangible 

assets.20 A deduction related to goodwill, net of deferred tax liabilities, were reported by all 

BHCs. A deduction related to DTAs for non-advanced approaches BHCs was approximated by 

DTAs deducted from Tier 1 capital. 

 The nominal value of all deductions was assumed to stay constant over the stress testing 

horizon (as in the supervisory DFAST). Each deduction had to be considered separately since 

there was no transition provision for goodwill while intangible assets and DTAs followed the 

Basel III transition provisions.21 Deductions were calculated by multiplying intangible assets 

and DTAs by the transition provision factor. Only the difference between the deduction in 

period t+1 and t was subtracted from CET1 capital in period t since CET1 in period t was 

already defined as CET1 after adjustments and deductions.22 No assumptions were made 

about banks' behavioral responses to phase-ins. 

41. The treatment of accumulated other comprehensive income (AOCI) reflected the Basel 

III transition arrangements. Consistent with Basel III transition arrangements, only 20 percent of 

AOCI was incorporated into CET1 capital in 2014 and additional 20 percent in every year after 201423

for advanced approaches BHCs. Consistent with the supervisory DFAST, it was assumed that nonadvanced approaches BHCs would opt out of including AOCI. In comparison to Fed’s stress test that 

 

18 SNL’s estimates of CET1 capital were very similar to pro-forma estimates of CET1 reported by non-advanced 

approaches BHCs in their financial reports. 

19 Other adjustments and deductions account, on average, for 3.2 of total deductions or 0.3 percent of CET1 after 

adjustments and deductions. 

20 It was assumed that, for non-advanced approaches BHCs, the same proportion of intangible assets is related to 

deferred tax liabilities associated with intangible assets which was equal to average share of DTLs associated with 

intangible assets for advanced approaches BHCs (defined as the difference between intangible assets other than 

goodwill and MSA (from Schedule HC-M) and non-phase in value of intangible assets deducted from CET1 (from 

Schedule HC-R). 

21 The amount for intangible assets, and DTAs reported in Schedule HC-R (BHCA P842, P843) in 2014Q3 were already 

a result of applying transition provisions in 2014 (20 percent). 

22 In other words, what matters for CET1 is the marginal effect of higher transition provision factor. 

23 No assumptions were made on possible behavioral assumption of BHCs with respect to transition provisions such 

as the fact that BHCs might reallocate their securities portfolio from AFS to HTM as higher proportion of AOCI flows 

through to CET1 capital. 
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INTERNATIONAL MONETARY FUND 23

held the components of AOCI other than unrealized gains (losses) on AFS securities constant over 

the planning period, in the IMF top-down test the aggregate AOCI was modeled as the structure of 

AOCI is not publicly available information. As in the case of deductions, only the difference between 

AOCI in period t+1 and t was added to CET1 capital in period t since CET1 in period t was already 

defined as CET1 after AOCI. 

Risk-weighted assets 

42. Total risk weighted assets (RWAs) were projected for each BHC that participated in the 

stress test. The two components of RWAs (credit RWAs for total assets and off-balance sheet items 

and market RWAs24) were challenging to model separately with publicly available data,25 and making 

simplified assumptions about each component of total RWAs could yield misleading results.26

Nonetheless, the dynamics of total RWAs followed closely the dynamics of total assets which was 

projected in the exercise. Therefore, the year-on-year growth rate of total RWAs was modeled in a 

panel regression model with fixed effects as a function of year-on-year growth rate of total assets. 

Interest rates were added as an exogenous variable to reflect the assumption that the credit 

portfolio’s underlying risk features does not remain constant27 throughout the horizon thereby 

making the projection of RWAs risk sensitive. While BHCs can qualify for using the advanced 

approach credit risk RWAs from January 1, 2016,28 it was assumed that the relationship between 

RWAs and total assets found before 2016 would hold also after 2016. 

43. Operational risk RWAs were included in calculation of total RWAs for advanced 

approaches BHCs that exited the parallel run, given the requirements of the Collins 

amendment.

29 Operational risk capital charge for non-advanced approaches banking organizations 

and advanced-approaches BHCs that have not exited the parallel run was not applied. Since there 

 

24 Capital standards require all BHCs with significant market risk to measure their market risk exposure and hold 

sufficient capital to protect against the risk of loss attributable to this exposure. In general, a bank holding company is 

subject to the market risk capital rules if its consolidated trading activity, defined as the sum of trading assets and 

liabilities as reported in its FR Y-9C report for the previous quarter, equals: (1) 10 percent or more of the bank holding 

company’s total assets as reported in its FR Y-9C report for the previous quarter, or (2) $1 billion or more. A bank 

holding company’s measure for market risk for its covered positions is the sum of its value-at-risk (VAR)-based, 

stressed VaR-based, incremental risk, and comprehensive risk capital requirements plus its specific risk add-ons and 

any capital requirement for de minimis exposures. A BHC’s market risk equivalent assets equal its measure for market 

risk multiplied by 12.5 (the reciprocal of the minimum 8.0 percent capital ratio). 

25 The initial objective was to calculate credit RWAs based on the projection of BHCs’ total assets and the average risk 

weight computed using total asset and total credit RWAs and holding these weights over the stress horizon constant. 

This would have, however, required modeling credit RWAs for off-balance sheet items (that depend on off-balance 

sheet items) and market RWAs, which was challenging due to RWAs’ volatility. 

26 Shares of market RWAs or credit RWAs for off-balance sheet items in total RWAs, credit RWAs for balance sheets, 

total assets or nominal GDP are not stable over time. 

27 For example, borrowers might become riskier as the debt burden becomes larger due to higher interest rates. 

28 The standardized approach to calculate RWAs is used to establish the minimum "generally applicable" capital floor 

requirements—a BHCs has to calculate RWAs using both standardized and advanced approach but is required to use 

higher RWAs (under the Collins amendment) for calculation of capital requirements. 

29 A capital charge for operational risk was not reported in FR Y-9C report. Advanced approaches BHCs that exited 

parallel run report this charge on FFIEC 101 report, Schedule B, item 35. 
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24 INTERNATIONAL MONETARY FUND

was no meaningful way to project RWAs for operational risk, it was assumed that the share of 

operational RWAs in total assets stays the same over the stress testing horizon. RWAs for advanced 

approaches BHCs that exited the parallel run were projected without operational RWAs. Projection of 

operational RWAs was then added to projected RWAs to calculate projected total RWAs. 

44. The increase in risk weighted assets due to the implementation of standardized 

approach was applied to projected total RWAs.

30 Credit RWAs under the standardized approach 

were not possible to calculate as publicly available data were not granular enough to apply the new 

weights to calculate credit RWAs.31 The increase in credit risk RWAs, due to introduction of 

standardized approach, was applied in 2015 onwards based on the calculated average increase in 

RWAs reported for the 2014 DFA stress testing exercise.32 The average increase of RWAs due to 

implementation of standardized approach was 9 percent which is in line with BCBS Basel III 

monitoring exercise estimates of RWA changes due to Basel III rules, as per Table A.13 in BCBS 

(2014).33

E. Models and Behavioral Assumptions 

45. Quarterly data from 1991 to 2014Q3 from FR Y-9C report and a set of panel regression 

models were used to forecast each BHCs’ main components of balance sheets and income 

statements (Figure 3).34 Projections of balance sheets (Step 1, Table 8) over the stress testing 

 

30 As of 2014Q3, in the FR Y-9C report, Schedule HC-R, Part II., BHCs were required to report risk-weighted assets 

using general approach only. Credit RWAs for BHCs were calculated under the general approach in 2014, and under 

the standardized approach from January 1, 2015. 

31 The main differences in weights between the Basel I general approach to RWAs and the Basel III standardized 

approach to RWAs are: risk weights for exposures to foreign governments depend on the sovereign’s OECD country 

risk classification (0–150 percent instead of 0, 20 and 100 percent), to certain supranational entities and multilateral 

development banks (0 percent instead of 20 percent), exposures to public sector entities depend on the sovereign’s 

OECD country risk classification (20-150 percent instead of 20, 50 and 100 percent), exposures to foreign banks 

depend on the sovereign’s OECD country risk classification (20-150 percent instead of 20 and 100 percent), exposures 

to qualifying securities firms (100 percent instead of 20 percent), high volatility CRE loans (150 percent instead of 

100 percent); exposures to OTC derivatives (50 percent risk weight ceiling is removed); approach to calculating RWA 

for securitization exposures, collateralized transactions, cleared derivatives, equity exposures to investment funds, 

unsettled transactions amended, equity exposures (0–600 percent instead of 100 percent). 

32 The range of RWA increases was wide which could have introduced a bias in the forecast of RWAs. 

33 The latest BCBS’s Basel III monitoring exercise estimates the increase in total RWAs to be 8.3 percent for Group 1 

banks and 6.5 percent for Group 2 banks. 

34 When projecting each item, several approaches were considered for robustness purposes. Lags of dependent 

variable, of independent variables, fixed effects versus pooled estimates and different definitions of dependent 

variables (year-on-year, q-o-q and shares) and the impact of outliers were explored. The initial set of potential 

regressors for each model was chosen on the basis of economic intuition or evidence from the literature. The “best” 

model was chosen based on R^2 as well as the sign of estimated coefficients and its statistical significance. Some 

variables were not included in the model (despite economic logic) due to multicollinearity and/or their marginal 

contribution to higher R^2 when included in the model. Many models perform best with variables specified as yearon-year growth rates. While R^2 was always higher in a specification with lagged dependent variable only one 

equation (the loan equation) has a lagged dependent variable. The disadvantage of using lags of dependent variable 

might be that the autoregressive term creates too much persistence at the time when the economy turns and might 

not produce effects large enough to reflect that turn. Moreover, the lagged term might take over most of the 

explanatory power over other explanatory variables rendering them useless for the stress testing purposes. Lagged 

dependent variables were only considered in cases where it was reasonable to expect that dependent variable might 

(continued) 
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DATA

Historical data on macroeconomic, 

financial variables, individual BHC data on 

Balance sheet and Income stamement 

Items

Project total RWAs

MODELS/ASSUMPTIONS ESTIMATION

Panel regression models 

Regression models on aggregate data

Constant shares 

Project Income 

Statements

MACROECONOMIC 

SCENARIOS

PROJECTION

Estimate relationships between 

Income Statement and Balance sheet 

items (dependent variables) and 

macroeconomic, financial variables 

and BHCs specific variables

Project Balance 

Sheet (or use 

assumptions of 

balance sheet 

dynamics)

Common 

equity Tier 

1 Capital 

ratio

horizon were used for the purposes of projecting total RWAs and income statement items (Step 2). 

Projections of RWAs and net income, with assumptions on dividend distribution, Basel III deductions 

and AOCI determined capital requirements over the stress testing horizon (Step 3). In comparison to 

the DFAST, asset disposals and acquisitions over time were not considered. 

Figure 3. IMF Stress Testing Framework: Bank Solvency 

Source: IMF Staff.

46. The models used were intended to capture how the balance sheet, RWAs, and net 

income of each BHC are affected by the macroeconomic and financial conditions (that served 

as independent variables) described in the scenarios. In those cases where the panel modeling 

approach was not appropriate, due to highly volatile individual bank data or insignificant 

relationships with macroeconomic and financial variables, modeling the particular variable at the 

aggregate level was tried. Projections of aggregate variables were then distributed to each BHC 

based on their market share or 2014 DFAST results. 

Balance sheet growth projections 

47. The growth rate of total assets was assumed to be equal to the growth rate of the 

largest asset category, accounting for smaller volatility of the growth rate of total assets.35 In most 

 

exert some persistence even in the event of a shock (e.g. loans). Outliers from the loan equation, net interest expense, 

net interest income, net charge offs equations, which for the loan equations and net charge-offs equations had an 

important impact on estimated results, were taken out from the sample. Outliers were not taken out from equations 

for trading, provisions, AOCI and non-interest incomes since stress testing is focused on tail events and in these cases 

statistical outliers should not be removed from the sample. For the same reason, using techniques such as winsorizing 

the top and bottom 5 percent seemed as too blunt an instrument to address issues related to outliers. Therefore, as a 

general rule, only a small number of extreme outliers where the specific data points could not be clearly explained, 

were taken out from equations (the outliers represented less than 1 percent of the sample size, except for the loan 

equation, where a slightly higher proportion of outliers was taken out). 

35 Year-on-year growth rates of total assets were “filtered” and projected using pooled panel regression model with 

year-on-year growth rate of loans as the only independent variable. 
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cases, this meant that assets grew in line with total loans. In several cases, that meant that assets 

grew in line with trading assets. The projection of BHCs’ total assets and loans was used for 

projecting income statement items and RWAs.36 

48. Modeling individual BHC’s total loans proved to be more straightforward than 

modeling each loan portfolio item (residential real estate, CRE, business loans, consumer loans, 

loans to foreign governments, loans to financial institutions and other loans, Table 6 and 7).37 A 

panel, fixed-effects model of a year-on-year growth rate of net total loans was estimated and 

projected at the bank by bank level. It was assumed that lower economic activity, increases in interest 

rates and higher market turbulence (as a proxy for risk aversion) would lower the demand for loans. 

Therefore, independent variables included: year-on-year growth rate of real GDP, year-on-year 

changes in interest rates, and the VIX. The model also included lagged dependent variable to 

account for persistence in the growth rate of loans.38 The growth rate of loans was used in projection 

of net interest income and total assets and deposits. Based on a strong historical relationship 

between year-on-year growth rates of loans and deposits (correlation equal to 0.85) it was assumed 

that the growth rate of deposits is equal to the growth rate of loans over the stress testing horizon. 

In the sensitivity analysis different growth rates of loans and total assets (in line with Fed’s top down 

approach) in the stress scenario were analyzed. 

49. Total assets of Goldman Sachs and Morgan Stanley were primarily determined by the 

dynamics of their trading assets, which were projected separately.39 Trading assets were 

projected in a panel regression with fixed effects where year-on-year growth rate of trading assets of 

both companies were determined by VIX, real GDP growth rate, and the federal funds rate. The first 

two regressors were expected to control for factors that affect trading activity whereas the interest 

rates was included as a regressor to control for the effects on asset prices. The growth rates of total 

assets for the two companies over the stress horizon were assumed to be equal to the growth rate of 

projected trading assets. 

 

36 Other balance sheet items (cash, HTM securities, ATM securities, repos, trading assets and other assets) could not 

be consistently modeled with the available data, either on individual bank’s (as year-on-year growth rates or as the 

share in total assets) or on aggregate data basis. That directly implied that projecting specific categories of credit risk 

RWAs was not possible and differences across BHCs’ stress test results could not be explained by differences in the 

composition of their loan portfolio. 

37 Federal Reserve staff suggested that loans secured by owner- occupied nonfarm nonresidential properties should 

be classified as business loans and rather than CRE loans. However, many BHCs reported the sum of loans secured by 

owner- occupied nonfarm nonresidential properties and loans secured by other nonfarm nonresidential properties 

but not the items separately. This is the reason why it was assumed that both loans type were CRE loans. 

38 To take into account that OLS estimator is inconsistent when lagged dependent variable is used as an explanatory 

variables the Arellano-Bond estimators and additional lags of dependent variables as instruments were used. Since 

the estimated coefficients were very similar to OLS estimates, the model estimated with OLS was used in the 

projection exercise. 

39 Data in FR Y-9C for both companies start from 2009. Data for total assets and trading assets were extended to 2004 

using companies’ quarterly financial reports. 
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Table 6. Balance Sheet Mapping 

Source: FRB, IMF Staff.
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Table 7. Loan Portfolio Mapping 

Source: FRB, IMF Staff. 

Loan losses and net income projections: methodology 

50. The projections of revenues, expenses, and loan losses were based on the IMF’s 

projections of the balance sheet for each BHC over the planning horizon. Most components of 

pre-provision net revenue (including components of net interest income, noninterest income, and 

noninterest expenses, Table 8) were modeled using data on historical revenues and operating and 

other non-credit-related expenses reported on the FR Y-9C report in a simple panel regression 
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model framework (Tables 23 and 24).40 Projections of all independent variables were taken from the 

scenarios. 

Table 8. Projection Exercise 

Source: IMF Staff. 

51. Provisions for loan losses. 

 In the first approach, which was used to calculate the results in the benchmark case, 

aggregate provisions were modeled as the ratio of total provisions over aggregate net loans 

as a function of real GDP growth, differenced unemployment rate, credit spreads, growth 

rates of house prices, growth rate of VIX41 taking into account that effect on provision in 

 

40 The starting point for the choice of regressors in all the models was the New York Fed’s CLASS model (Hirtle and 

others, 2014) and models of net charge-off rates in Francisco B. Covas, Ben Rump, and Egon Zakrajsek (2014). It was 

expected that higher GDP growth, falling unemployment, lower spreads, increases in house prices, and lower market 

volatility would lower provisions. 

41 It was expected that higher GDP growth, falling unemployment, lower spreads, increases in house prices and lower 

market volatility would lower provisions. 
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periods of stress are more pronounced.42 Projected aggregate provisions were distributed 

among BHCs using individual bank’s share of provisions in total provision in the 2014 DFAST 

exercise. This approach was used to benchmark projections of provision using other 

approaches as the model of aggregate provisions managed to capture the spike in provisions 

during 2008/2009 crisis. 

 In the second approach, used in the sensitivity analysis, it was assumed—as in the last FSAP’s 

stress testing exercise—that provisions are equal to net charge offs. Models of net charge off 

rates by loan types43 that capture the historical behavior of net charge-offs over 

corresponding type of loans relative to changes in macroeconomic and financial market 

variables were considered (Table 9). The predicted net charge-off rates were multiplied by 

loan balances. The growth rate of each loan type was assumed to be equal to the projected 

growth rate of total loans. While there was a close relationship between provisions and net 

charge off rates during normal times, the disadvantage of this approach was that provisions 

increase more quickly than realized net charge-offs during stress times.44 The IMF team’s 

analysis showed that while the net charge offs dynamics compares well to the dynamics of 

provisions, they lag 2 to 3 quarters.45 In the sensitivity analysis, the effect of projecting total 

net charge-offs and total provisions (instead of by loan types) was also explored. 

52. Net interest income. Net interest income was projected using fixed effects panel regression 

and the annual difference of net interest income as the dependent variables and the annual 

difference of a product of total net loans and loan interest rates and the annual difference of a 

product of total interest bearing deposits and deposit rate46 as explanatory variables. By including 

loans and deposits as an independent variable the macroeconomic environment as well as bank 

specific characteristics were taken into account. A projection of loans was taken from projection 

exercise of BHC’s balance sheets and interest bearing deposits were assumed to grow at year-onyear growth rate of loans. Deposit interest rates were assumed to be equal to the Federal funds rate. 

For the purposes of estimation, the loan interest rate was defined as a weighted average of 

mortgage, business, consumption lending rate and federal funds rate that approximated the interbanking interest rate adjusted for each BHC’s loan portfolio structure.47 

 

42 This was modeled by including an interaction term between the growth rate of VIX and dummy variable which took 

value of 1 every time the growth rate of VIX was positive. 

43 Net charge offs for loans to government were not considered as they were very small in the last 15 years. 

44 Conversely, as the economy recovers provisions fall more quickly in advance of net charge-offs. 

45 This was taken into account when modeling and projecting net charge offs. 

46 These variables are the main determinants of net interest income. 

47 For each BHC, the weights were defined to reflect the structure of BHCs’ loan portfolio (divided into real estate 

loans, CRE loans, business loans, consumer loans and loans to financial institutions, Table 7). 
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53. Non-interest income excluding trading income. This item was projected using a panel 

regression model with non-interest income (excluding trading income) over total assets as the 

dependent variable and the growth rate of VIX, unemployment rate and lending rates as 

independent variables. It was expected that during period of market turbulence (higher growth rates 

of VIX) trading from fees and commissions goes down as brokerage, underwriting, securitization fall. 

The same would be true if unemployment is high implying economic activity is low. Lending rates 

were included in the regression to control for substitution effect—it was expected that higher 

lending rates would make BHCs shift from non-interest income activities to interest income activities. 

54. Trading income. Trading income was modeled as aggregate trading income that includes 

gains on AFS and hold to maturity (HTM) securities over total aggregate assets in a regression with 

the following independent variables: year-on-year growth rate of VIX, the interaction term between 

year-on-year growth rate of VIX and a dummy variable that took the value of 1 when the growth rate 

of VIX was positive to account for any non-linearity between market volatility and trading losses in 

times of stress, the change in credit spread48 and the change in term spread. Projected trading 

income was distributed among BHCs based on their 2014 share of trading income and gains on AFS 

and HTM securities in total trading income that includes total gains on AFS and HTM securities. 

55. Non-interest expenses. This item was modeled as a year-on-year growth rate in a panel 

regression with year-on-year growth rate of total assets as the only independent variable and fixed 

effects. The assumption was that non-interest expenses depend on the size of the business which is 

ultimately related to the size of the balance sheet. 

56. Taxes. Taxes were set at 28 percent—the pooled average level of the tax rate over the last 

25 years. 

57. Extraordinary items and minority interest. It was assumed that these items are equal to 

zero as, in general, this item did not contribute much to the net income. 

58. AOCI. Aggregate total AOCI49 was modeled as the ratio of AOCI to aggregate assets in a 

regression with yearly change in BBB yields, 10 year Treasury bond, real GDP growth and the 

interaction term between real GDP growth and a dummy that takes value of 1 when GDP growth rate 

was negative to account for potential “non-linear” effects on unrealized losses in downturns. 

Projected total AOCI was distributed among BHCs using proportions of individual BHCs’ AOCI losses 

in total AOCI losses in the 2014 DFAST exercise. 

 

48 Higher spreads were expected to signal lower profitability going forward. 

49 Granular data on AOCI and OCI components are not reported in FR Y-9C report. 
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1. Real estate loans (1. from Column B)

a. Residential Real Estate Real GDP, unemployment rate, house prices, mortgage rates, 

VIX

b. Commercial Real Estate Real GDP, unemployment, CRE prices, corporate interest rates, 

VIX

2. Business loans Unemployment, VIX, corporate spreads

3. Consumer loans (5. + 8. from Column A-Column B) Unemployment, house prices, consumer interest rates

4. Loans to financial institutions Real GDP, federal funds rate, VIX

5. TOTAL Real GDP, unemployment rate, house prices, lending rates, 

corporate spreads

Column A (IMF mapping of Schedule HI-B) Column B Independent variable

Table 9. Models of Net Charge-Offs 

Source: IMF Staff.

59. The following income statement items were not considered: (i) losses related to 

operational risk events, mortgage repurchases, or OREO (ii) HFS/FVO loan losses as the data on these 

items were not publicly available and (iii) deferred tax assets (DTAs). 

Capital action assumptions 

60. A dividend distribution rule was defined where dividend distribution depends on the 

CET1 ratio. It was also assumed that BHCs do not issue new shares or make repurchases during the 

stress test horizon similar to the supervisory DFAST.50 In comparison to DFAST assumptions on 

dividend payments where common stock dividend payments continue at the same level as in 2014, 

the following rule for determining dividend payments was assumed: 

 Dividend payouts were payable out of the current year’s profit using the Basel III capital 

conservation rule taking into account transition provisions and GSIB surcharge (Table 10). 

Dividends were assumed to be paid out of current period net income after taxes by BHCs 

that were in compliance with the capital requirement equal to the hurdle rate. A maximum 

allowed dividend payout was assumed to be equal to the dividend payout ratio (dividends 

over net income after taxes) in 2014. If a bank fell below the hurdle rate before dividend 

distribution, it was considered capital constrained and followed a schedule of dividend 

payouts per Table 10. If a bank fell below the hurdle rate because of dividend distribution, it 

was assumed that the bank’s dividend payout was limited to a level that ensures the hurdle 

rate is not breached. This rule applied only if a bank earned a positive net income. If net 

 

50 In the supervisory DFAST it was assumed that BHCs can make new issuances related to employee stock ownership 

plans. 
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Capital ratio 

(CET 1): 2014

Capital ratio 

(CET 1): 2015

Capital ratio 

(CET 1): 2016

Capital ratio 

(CET 1): 2017

Capital ratio 

(CET 1): 2018

Capital ratio 

(CET 1): 2019 Assumed dividend payout

4.5-4.656 4.5-4.813 4.5-4.969 4.5-5.125 0% x Net income (t)

4.656-4.813 4.813-5.125 4.969-5.438 5.125-5.75 20% x EDPR x Net income (t)

4.813-4.969 5.125-5.438 5.438-5.906 5.75-6.375 40% x EDPR x Net income (t)

4.969-5.125 5.438-5.75 5.906-6.375 6.375-7.0 60% x EDPR x Net income (t)

>4.0 >4.5 >5.125 >5.75 >6.375 >7.0 Effective div. payout rate in 2014 (EDPR)

income was negative it was assumed that there was no dividend payout. If a bank was above 

the threshold it paid a maximum allowed proportion of dividend. 

 In the sensitivity tests, an assumption that dividends remained fixed (in nominal terms) at 

their 2014 value was explored. 

Table 10. Dividend Distribution Schedule\1

\1 GSIB surcharge was also taken into account (not shown in the table). 

Source: IMF Staff.

F. Sensitivity Analyses 

61. To account for some of the differences between the IMF top-down stress test and the 

supervisory DFAST a range of sensitivity analyses were performed. These included: (i) assuming 

that loans and balance sheet grow at the similar rate as in the supervisory DFAST; (ii) assuming 

constant dividend distribution in the stress test like in the supervisory DFAST; (iii) assuming hurdle 

rates from DFAST and assuming that total RWAs are a sum of credit risk RWAs and market risk RWAs 

(and not including operational RWAs) and (iv) assuming all the elements of DFAST in the same 

scenario (constant dividends, fixed loan supply and total RWAs defined as a sum of credit and 

market RWAs only). Moreover, additional sensitivity analyses were performed: (i) including an oil 

price shock in the stress scenario; (ii) calculating the impact of a large interest rate shock in first year 

on the stress scenario; (iii) using different measures to calculate provisions in the stress scenario and 

(iv) extending the scope of the stress testing exercise to include all banking organizations with 

consolidated assets of $50 billion or more that are subject to Basel III capital requirements. 

G. Results 

62. The solvency stress test suggests that no BHCs would fall below the hurdle rate during 

the first year of severe economic distress (2015). This was due to high BHCs’ capital position in 

the base year (11.7 percent) relative to the CET1 regulatory threshold (4.5 percent) and large profits 

in the base period. The system-wide CET 1 ratio fell by 21⁄2 percentage points in 2015 (Figure 4) 

relative to the base year or 2.2 percentage points relative to the baseline scenario in 2015. 

63. The results in the year of downturn were mainly driven by the increases in provisions 

and trading losses (Appendix Figure 5). 
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 Compared to the base period (2014Q3), the system wide CET1 remained intact in the first 

quarter of the stress testing horizon (2014Q4).51 Net income fell sharply from its annualized, 

cumulative all-time high level in 2014Q3 of about $120 billion to a $30 billion net loss due to 

the impact of the initial negative shock in 2014Q4 on credit losses that doubled in 

comparison to 2014Q3 levels. On the other hand, lower RWAs due to slowdown in lending 

and total assets affected by the initial negative shock cushioned the negative effect of net 

losses on the CET1 ratio in 2014Q4. The modest negative impact of dividend distribution was 

not felt due to the capital conservation rule kicking in from 2014Q4 onwards. 

 Provisions for credit losses and trading losses play the major role in dynamics of CET1 ratios 

in 2015. The lower CET1 ratio in 2015 was mainly a result of higher provisions, which were 

four times higher than at the end of 2014 (subtracting -2.3 percentage points from CET1 ratio 

which is 8 times higher than in the base period), trading losses (-0.6 percentage points), 

negative AOCI and higher deductions from CET1 (each contributing with -0.3 percentage 

points). Net interest income fell by 5 percent compared to the end of 2014 due to lower loan 

demand and despite higher spreads. Noninterest income also fell by more than 10 percent. 

The impact of the implementation of the standardized approach to calculate RWAs for credit 

risk (which raised total RWAs by about 9 percent in 2015) was cushioned by lower loans and 

total assets and RWAs stayed stable in comparison to 2014. 

64. Two BHCs would fall below the hurdle rate in the first year of the recovery (2016). The 

system-wide CET1 would fall by additional 0.3 percentage points in 2016. One BHC fell below the 

hurdle rate mainly due to relatively low profitability in the baseline. 

65. Eleven additional BHCs would fall below the hurdle rate by the end of the stress testing 

horizon due to “too rapid” expansion of their balance sheet. Notwithstanding the favorable 

economic environment, system-wide CET1 would fall by additional 0.6 percentage point in 2017–

2018 after recovering in the last year of the stress testing horizon (by 0.1 percentage points). Despite 

a recovery in BHCs’ capital position in 2019 a number of BHCs would fall below the hurdle rates. The 

BHCs that fell below the hurdle rate are the ones that on average had higher projected and historical 

(as reflected in the fixed effects in the loan equation) credit and total assets growth. Although this 

reflects the unrealistic assumption that BHCs would increase balance sheets even if this would take 

them below the regulatory threshold, this still suggests that these BHCs may need to raise additional 

capital if they are to be in a strong position to support a recovery in the face of an adverse scenario. 

66. Changes in CET1 ratios in the recovery period were mainly driven by higher RWAs due 

to the expansion of BHCs’ balance sheets.

 

51 The CET1 ratio increased by 1⁄2 percentage points in the first three quarters of 2014 despite higher RWAs due to the 

implementation of Collins amendment for advanced approaches BHSs that exited the parallel run that lowered the 

CET1 by about 0.7 percentage points. 
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 The first year of the recovery (2016) was still characterized by net losses (after taxes) which 

together with higher RWAs and deductions from CET1 resulted in lower CET1 ratios. 

Provisions fell comparing to 2015 but stayed at relatively high level. Provisions and higher 

non-interest expense due to expansion of balance sheets primarily contributed to net losses. 

The growth rate of loans, total assets and RWAs picked up due to higher economic activity 

and lower market volatility that also contributed to higher non-interest income, including 

trading income. 

 Higher RWAs weigh on CET1 ratios in the last three years of the stress testing horizon. While 

the cushioning impact of net interest income and non-interest income got stronger—

reflecting the favorable economic environment—capital ratios deteriorated by increasing 

RWAs subtracting 80bps from CET1 in each of the last three years and increases in noninterest expenses due to the expansion of BHCs’ balance sheets (by 10 percent annually). 

While provisions and deductions from CET1 still played a role, their impact became very small 

by the end of the stress testing horizon. 

67. Recapitalization needs are manageable. Recapitalization needed to bring all BHCs to the 

hurdle thresholds peaks in 2019 at 180 percent of their annualized 2014Q1–Q3 net income—which 

corresponds to 1 percent of 2019 nominal GDP. 

68. CET1 ratios were projected to fall in the baseline; this is driven by increases in the 

RWAs despite positive net incomes. The system-wide CET1 ratio fell by 90bps in 2019 comparing 

to the base period. The negative impact of higher RWAs and expansion in total assets due to 

favorable economic environment was larger than the positive impact of all time high profits reported 

by the BHCs in the period 2015–2017. The impact of higher RWAs throughout the stress testing 

horizon was reinforced by the implementation of the standardized approach in 2015. Moreover, the 

impact of net income got weaker from 2015 due to lower contribution of net interest income which 

came down as a result of tighter spreads and lagged effects of rising policy rates on lending rates. 

Dividend distribution played a role too, subtracting on average 40bps from CET1. Even under the 

baseline scenario 3 BHCs fell below the hurdle rate in 2018 and seven more in 2019. These are the 

same BHCs that failed the test under the stress scenario mainly due to rapid expansion of their 

balance sheets in the second part of the stress testing horizon. 

69. Sensitivity analyses were performed with respect to loan and total assets dynamics, 

dividend distribution rule, oil price shock, interest rate shock and different models of 

provisions (Figure 4). The results show that: 

 Loan dynamics: By using an assumption that loans and total assets do not fall during the 

period of downturn52 CET1 ratios fall more in 2015 and less in subsequent periods. System-

 

52 It was assumed that loans remain at their 2014Q3 level whereas total asset grow at the yearly rate of 2 percent. 

These growth rates are similar to DFAST assumptions. 
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wide CET1 ratio fell by additional 80bps due to higher RWAs when compared to the stress 

scenario but increased on average by 40bps a year in the last three years due to lower 

increases in RWAs resulting from lower growth rate in total assets then in the stress scenario. 

 Dividend distribution: Holding dividends constant at their 2014Q3 level (as in the DFAST) 

would subtract additional 30bps from the system wide CET1 ratio each year. 

 DFAST hurdle rate, no operational RWAs: Despite lower hurdle rates and higher initial capital 

ratios, the same two BHCs would fall below the thresholds. Additional 3 BHCs would fall 

below the threshold in the recovery period. 

 Constant loans and dividends, no operational RWAs: The system wide CET1 fell by 380 bps in 

2015. Under this scenario two additional BHCs would fall below the regulatory minimum of 

4.5 percent mainly due to dividend distribution which, in the benchmark, are cut to zero due 

to negative net income in 2016.

 Oil price shock: Including oil prices as one of the determinants of provisions and applying the 

oil price shock where oil prices fell by 25 percent 2014Q4 and additional 60 percent by the 

end of 2015 would increase total provisions by around 3 percent.

 Interest rate shock: Applying the interest rate shock to the 3-month Treasury yields of 450bps 

in 2015 would not change the results significantly since higher short term rates would have 

both positive and negative effects on income statements and balance sheets. On one hand, 

higher short-term rates would reduce loans growth rate and subsequently total assets and 

RWAs. On the other hand, higher short-term interest rates would result in higher credit losses 

(by 11 percent in 2015) and losses on AOCI (almost twice as higher in 2014Q4–2015Q4 than 

in the stress scenario).53 While these losses would be higher than in the stress scenario they 

would not be large enough to make a material impact on the results since the other variables 

have more pronounced effects on credit and trading losses than interest rates. However, the 

effects of higher interest rates on GDP growth were not analyzed. 

 Models of provisions: The only model that can capture the spike in provisions in 2008/2009 is 

the model of aggregate provisions which projected provision in the period 2014Q4–2016Q4 

at the level of $390bn. A panel model of total provisions and a panel model of net charge 

offs resulted in the same projection of total provision—$280bn over the same period. 

Comparing to the benchmark stress scenario, this would correspond to system-wide CET1 

ratio increase of about 80bps in 2015 and 15bps in 2016. While panel models project lower 

 

53 A rise in short-term rate might also induce deposit outflows, something which is not captured by the stress testing 

framework. Moreover, the potential losses resulting from an interest rate shock due to cash-flow effects were not 

estimated due to data limitations. 
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provision in the first two years the projections are more persistent and results in higher total 

provision over the whole stress testing horizon than the model of aggregate provisions. 

 Structure of loan losses: Modeling net charge-offs by loan type allows comparison of losses 

by loan type. An increase in credit losses is mainly driven by an increase in default rates and 

the size of exposures on the household sector. Around 35 percent of losses in 2015–2016 

come from consumer loans and 25 percent comes from residential real estate exposures. 

While the share of commercial real estate and business loans is similar to 2008–2009, loans 

to financial institutions (and other loans) would account for much higher proportion of losses 

given their size in 2014 which is much higher than before the crisis. 

 Expanded coverage of the stress test: Expanding the coverage of the stress test to include all 

the deposit taking financial institutions with asset size of $50 billion and more that reported 

Basel III capital ratios as of 2014Q3 requires including additional two BHCs54 and two large 

savings banks.55 The same methodology was used to assess the capital adequacy of the four 

financial institutions as in the main stress test.56 The results show that one financial institution 

would fall below the threshold in 2016 mainly due to higher provisions and relatively low 

capitalization in the base period. Total recapitalization needs would increase by 5 percent 

due to the recapitalization of this financial institution. 

 

54 These two banking organizations are subsidiaries of foreign banking organizations that are currently relying on 

Supervision and Regulation Letter SR 01-01 issued by the FRB. Supervision and Regulation Letter SR 01-01 generally 

provides that a U.S. BHC that is owned and controlled by a foreign bank that is a financial holding company that the 

Federal Reserve has determined to be “well-capitalized” and “well-managed” is not required to comply with the 

Federal Reserve’s capital adequacy guidelines. Until July 21, 2015, the stress testing rule will not apply to any BHC that 

is a subsidiary of a foreign banking organization that is currently relying on Supervision and Regulation Letter SR 01- 

01. However, Section 171 of the Dodd-Frank Wall Street Reform and Consumer Protection Act requires that, 

commencing July 21, 2015, U.S. domiciled BHC subsidiaries of foreign banks may no longer rely upon SR-Letter 01-01. 

55 These two banks are part of large SLHCs.

56 However, the provisions for loan losses were projected using the panel regression since the four financial 

institutions were not part of supervisory DFAST and projected total provisions could not be distributed based on their 

provisions’ share in total provisions reported for the supervisory DFAST. 
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0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

2014q3 2014 2015 2016 2017 2018 2019

Regulatory threshold

System wide CET1 

under stress

Max GSIB CET1 

charge

Min GSIB CET1 

charge

System wide CET1 

under baseline

6.0%

7.0%

8.0%

9.0%

10.0%

11.0%

12.0%

13.0%

2014q3 2014 2015 2016 2017 2018 2019

CET1 ratio- sensitivity analysis

Benchmark (stress scenario) Constant loans

Provisions=Net charge offs Constant dividend

Interest rate shock No operational RWAs

No op. RWAs, constant div., constant loans

Figure 4. CET1 Ratio Under the Baseline and Stress Scenario 

Note: The whisker boxes for each year display the distribution of CET1 ratios, projected using the IMF methodology, for 31 BHCs

in terms of distribution’s moments: the bottom and top of the whisker box are the first and third quartiles, the band inside the 

box is the second quartile (median), the diamonds represent an un-weighted average CET1 ratio for 31 BHCs, the lower and the 

upper whisker represent the minimum and the maximum CET1 ratio respectively. The system-wide CET1 ratios are defined as 

projected total CET1 capital over total RWAs (sum over all BHCs). 

Source: IMF Staff calculations.

H. Network Analysis for Large BHCs 

70. To assess potential spillovers among the six largest U.S. G-SIBs, FRB staff implemented 

an updated version of the network stress-test methodology developed in Espinosa and Solé 
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(2011). The network stress-tests were conducted by the staffs of the Federal Reserve Board (FRB) 

and the IMF in order to assess contagion risks among six U.S. BHCs designated as globally systemic.57

This methodology consists of simulating credit and funding shocks within a network of institutions 

and then tracking the contagion effects in terms of capital losses and path of bank failures. In 

addition, the methodology also allows for the assessment of the systemic impact arising from 

existing off-balance sheet financial linkages (e.g., credit default swaps). 

71. To preserve data confidentiality, Fund staff provided the FRB the software necessary to 

implement the network stress-tests but had no access to the actual data. In turn, the staff of the 

FRB ran several simulations and robustness checks for a range of model parameters. The output of 

these simulations was reported back to the Fund. 

The data 

72. The FRB maintains a dataset that was used to execute the Espinosa and Solé algorithm 

for six systemic BHCs in the United States. The confidential dataset contained information on 

these BHCs’ capital levels, credit and funding exposures, as well as credit default swaps (CDS) 

contracts. The six institutions under consideration hold capital of around 9 to 12 percent of their 

total assets. Further details of the data are as follows: 

 Credit exposure data: For each of the six BHCs the FRB has an estimate of the credit loss that 

would be borne if one of the other five institutions went into default. This credit exposure 

estimate incorporates both direct credit losses—i.e., the losses that result from the default on 

a loan—and indirect credit losses—i.e., which would include losses that result from replacing 

defaulted derivative positions and losses on owned securities that have been issued by the 

defaulting BHC. 

 Funding exposure data: For each of the BHC, the FRB collects data on the amount of funding 

received from the other five counterparties. This amount includes secured and unsecured 

funding, as well as repo transactions of all maturities. Thus, the entire amount of a BHC’s 

funding exposure represents an estimate of the total amount of borrowings that would have 

to be replaced by that BHC if the counterpart entered default. 

 Risk transfer data: The FRB also has data on the amount of credit risk that has been 

transferred between the six BHCs. In particular, the data measure the amount of single-name 

notional CDS exposure that each bank has with respect to the five other BHCs in the sample 

(i.e., the notional amount of CDS protection that bank i has sold to bank j on reference entity 

bank h). Hence, note that the data do not include any CDS index trades nor does it contain 

data on more complex exposures such as CDS options. 

 

57 Eight companies are designated as GSIBs. Data were not sufficient to include the other two GSIBs in the exercise. 
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Simulation and results 

73. The network stress-test exercise comprised four different sets of simulations designed 

to capture key dynamics at play during the 2007–08 financial crisis. The first set of simulations 

examines the domino effects triggered if each of the six BHCs defaulted (one at a time) on their 

respective credit commitments. The second set of simulations assesses the effects of a credit-plusfunding event, where the default of an institution also leads to a liquidity squeeze for those 

institutions funded by the defaulting institution. In this case, the credit shock is compounded by a 

funding shock and the associated fire sale losses.58 The third and fourth sets of simulations build on 

the previous two by incorporating the credit default exposures of each BHC.59 The simulations were 

conducted with quarterly data for each quarter between 2013Q1 and 2014Q3. 

74. The results indicate that the six BHCs hold enough capital to sustain a range of credit 

and funding shocks to individual counterparties within the network. As reported by the FRB, 

most simulations did not trigger contagion chains among the six institutions under consideration. 

This result likely emerges from the fact that direct exposures within the six-BHC network are not 

large enough (relative to the initial capital of each institution) to lead to second-round spillovers. 

Nonetheless, the positive results are also suggestive of the need to expand the data on exposures 

included in the network (e.g., exposure of the six BHCs to money market funds), as well as consider 

richer market dynamics in the simulations (e.g., downward spirals in the value of certain financial 

assets). 

75. The simulation results show that the six BHCs hold enough capital to sustain shocks to 

a single counterparty within the network. In the four simulations considered, all BHCs appear to 

have enough capital to sustain the credit and funding losses individually impinged by the other BHCs 

in the network. For example, Table 11 shows results for the credit-plus-funding shock with risk 

transfers: in all instances the capital losses born by each BHC are not large enough to trigger a 

second round of contagion. Nonetheless, the losses could be substantial for some BHCs (e.g., BHC 4 

could suffer losses of up to 2.5 percent of its initial capital), and could in turn lead to further funding 

difficulties for that institution if market concerns arise given the relatively large loss of capital. 

 

58 When liquidity is tight and in the absence of alternative sources of funding, a bank may be forced to sell part of its 

assets in order to restore its balance sheet identity. The network stress-test replicates the situation where, as in the 

2007–08 crisis, a bank is able to replace only a fraction of the lost funding and its assets trade at a discount (i.e., their 

market value is less than their book value). To err on the side of caution, the initial set of parameter values is arguably 

quite adverse. The simulations assumed that 35 percent of the funding provided by a defaulting institution cannot be 

replaced and that asset fire sales are conducted at 50 percent loss relative to book value. Finally, a bank is considered 

under distress if its capital to assets ratio falls below the prompt-corrective action (PCA) threshold of four percent. 

59 As illustrated below, contingent exposures such as credit default swaps deserve special consideration in times of 

stress because they activate dormant linkages across financial institutions and bring new exposures onto the balance 

sheet of an institution. 
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Table 11. Simulation Results of Credit and Funding Shock with Risk Transfers 

Source: FRB, IMF Staff calculations.

76. The results also illustrate the importance of monitoring (and stress-testing) off-balance 

sheet exposures. Risk transfers such as credit default swaps can alter dramatically the risk profile of 

financial institutions. The simulations conducted allow an assessment of the potential impact that 

CDS exposures could have on each BHC’s capital. Figure 5 shows, for example, that the losses to BHC 

5 from the failure of each of its counterparties (one at a time) would be dramatically different 

depending on whether CDS are taken into account. The chart suggests that BHC 5 is actually 

hedging its exposures to BHCs 1, 2, and 3 via CDS, but that it is actually increasing its exposure to 

BHCs 4 and 6 via this market. This type of analysis could be useful to regulators to monitor the 

interaction between on- and off-balance sheet exposures. 

Figure 5. Credit and Funding Shock 

Note: Credit and funding shock – impact of CDS exposures on capital losses of BHC 5 (in percent of initial capital) 

Source: FRB, IMF Staff calculations.
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42 INTERNATIONAL MONETARY FUND

77. The results appear robust to further simulations with stressed values of the model’s 

parameters. The FRB conducted robustness tests by assigning extreme values to the model’s 

parameters that measure the severity of an institution’s funding squeeze vis-à-vis the defaulting 

BHCs, the loss of asset value due to the fire sales associated with the funding squeeze, and by 

increasing the capital level below which an institution is considered under distress.60

78. Additional calculations by the FSAP team identified a combination of severe factors 

under which contagion would take place. A situation where funding markets are severely impaired 

and assets trade at heavy discounts could lead to a chain reaction of BHCs going into distress. For 

this severe scenario, the FSAP team assumed that only 65 percent of the short-term funding 

provided by a defaulting institution is rolled over by other market participants and that asset fire 

sales take place at 25 percent of book value. Given the team’s lack of direct access to confidential 

supervisory information, it was assumed that all BHCs have initial capital levels equivalent to 8 

percent of risk-weighted assets and that if a bank loses more than 5 percent of its initial capital in 

one round, it suffers “distress”, which triggers the next round of contagion. Under these simplifying, 

though admittedly stark, assumptions, it is possible to trace which institutions would be more 

vulnerable through the contagion chain. For example, as shown in Figure 6, one such path would be 

triggered by distress in BHC 1 and would lead to three successive rounds of contagion affecting BHC 

4 first, then BHCs 5 and 6 in a second round, and finally BHC 3. Note that BHC 2 would not go into 

distress in this scenario. These calculations are for illustration only, and more data on, for example, 

initial capital levels and exposure of the BHCs to specific financial instruments (e.g., reliance on shortterm funding) to construct more accurate scenarios. 

 

60 The robustness tests include (i) assuming that none of the funding provided by the defaulting institution can be 

replaced (and hence larger sales of assets are needed), (ii) assuming that fire sales of assets take place at 1⁄4 of the 

book value of the assets, and (iii) raising to 6 percent the capital threshold below which distress in one bank is 

transmitted to others. 
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Figure 6. Contagion Path Triggered by BHC 1 Distress 

Source: IMF staff. 

79. While somewhat reassuring, the results also point to the need to expand the FRB’s 

dataset and consider richer market dynamics in the simulations. As explained, the FRB’s dataset 

only includes data on direct exposures among the six systemic BHCs. Thus, the stress-tests are 

unable to assess the potential impact of contagion feedbacks arising from other segments of the 

financial sector (e.g., if the six BHCs are exposed to a common funding source prompt to runs, such 

as money market funds; or a common credit exposure, such as CDS contracts referenced to a 

beleaguered sovereign). Similarly, new simulations that comprise downward spirals in the value of 

certain financial assets (e.g., asset-backed securities) could be designed and added to the network 

stress-test. Fund and FRB staffs have held conversations in this regard. 

DISCUSSION OF SUPERVISORY AND COMPANY-RUN 

SOLVENCY STRESS TESTS 

80. This section summarizes the solvency stress tests conducted for the 2015 DFA stress 

testing exercise and discusses the differences vis-à-vis the FSAP team’s analysis. The stress 

testing frameworks of both company-run DFAST and supervisory-run DFAST for the 31 BHCs are 

publicly available information. Details of the company-run solvency stress test for BHCs can be found 

in Board of the Governors of the Federal Reserve System, 2013, “Comprehensive Capital Analysis and 

BHC 1 BHC 4

BHC 5

BHC 6

BHC 3
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IMF top down approach Supervisory DFAST

Capital hurdle rates

Basel III minimum 

requirement+capital conservation 

buffer+GSIB surcharge

Basel III minimum 

requirement

Risk weighted assets Includes operational RWAs for 

advanced approaches BHCs

Does not include operational 

RWAs for advanced 

approaches BHCs

Projected loans and total 

assets Projected using a model

Forecasted conditional on 

the assumption that credit 

supply is maintained at longrun historical levels

Data used Publicly available data Granular supervisory data

Length of scenarios 5 years 9 quarters

One-time add-on shock - Global market shock and 

Counterparty shock

Important Income statement 

items not included

Losses related to operational risk 

events, mortgage repurchases, or 

OREO and HFS/FVO loan losses 

Dividend distribution Dividend distribution rule as a function 

of capital ratio

Constant dividend at the 

2014 level

Review 2014 Summary Instructions and Guidance”, November 2013 and Board of the Governors of 

the Federal Reserve System, 2013, “Capital Planning at Large Bank Holding Companies: Supervisory 

Expectations and Range of Current Practices”, August 2013, whereas the details of the supervisory 

DFAST can be found in Board of the Governors of the Federal Reserve System, 2015, “Dodd-Frank 

Act Stress Test 2015: Supervisory Stress Test Methodology and Results”, March 2015. 

A. Supervisory Stress Tests 

81. There are important differences between the IMF approach and the authorities’ 

approach, with potentially significant impacts on results (Table 12). The hurdle rates in the IMF 

stress test were more stringent: there are differences in the calculation of risk-weighted assets, and 

capital ratios, such as the inclusion of operational RWAs in the IMF stress tests, that on average 

resulted in lower ratios for advanced approaches BHCs. The level of the granularity of the data and 

subsequently the methodology used by different approaches was different—the authorities used 

much more granular data. The dividend distribution rule used by the IMF was a function of the level 

of capital which was a less conservative assumption than the one used by the authorities. Loans and 

total assets were projected in the IMF stress test whereas they were forecasted conditional on the 

assumption that credit supply is maintained at long-run historical levels in the DFAST. Some losses 

were not covered by the IMF stress test due to lack of historical data. Finally, while the authorities 

used a 9-quarter stress horizon and 3-year forecast scenarios, the IMF test focused on stress testing 

horizon that spanned over 5 years. However, IMF forecasts over the first three years were consistent 

with the forecasts of the FRB under the baseline and stress scenarios. 

Table 12. Main Differences Between IMF’s Top-Down and FRB’s Top-Down Approach 

Source: IMF Staff 
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82. An in-depth analysis of the results of the DFAST scenario is limited by constraints on 

publicly available information. Detailed information on capital, RWAs and income statement items 

were not publicly available. Only the minimum and the ending capital ratios over the stress testing 

horizon of nine quarter were published. RWAs were published as of the end of the stress testing 

horizon. Income statement items were published as a cumulative value of pre-provision revenue, 

other revenue, provisions, realized losses/gains on securities (AFS/HTM), trading and counterparty 

losses, other losses/gains, net income before taxes and other comprehensive income. AOCI included 

in capital was published as of the end of the stress testing horizon. Loan losses (cumulative over the 

nine quarter period) and loan losses structure was published by type of loan. All the results were 

presented both in the aggregate for the 31 BHCs and for individual BHCs for severely adverse and 

adverse scenario. 

83. With this caveat, the results of the supervisory DFAST suggest that, in the aggregate, 

BHCs are resilient to shocks for the severely adverse scenario. All BHCs stay above the regulatory 

minima. Even if the IMF hurdle rates were used, no BHC failed the stress test. Over the nine quarter 

of the stress testing horizon the system-wide CET1 would fall to 7.6 percent (its minimum) and to 

7.8 by the end of 2016. If compared to the IMF estimate of the system wide CET1 in 2014Q3 (of 

12.4 percent) the CET1 ratio at its minimum would fall by 480 basis points, compared to 2014Q3. This 

is both due to increase in RWAs, which mainly reflects the implementation of standardized approach 

in 2015 and the assumption on the loan supply, as well as net income losses that were projected to 

be -$222 billion. 

84. The losses were mainly driven by loan losses ($340 billion) and trading and 

counterparty credit losses from a global market shock ($103 billion). Projected losses on 

mortgage 61and consumer loans62 represent 56 percent of projected loan losses driven by higher 

unemployment rate and lower house prices. The largest losses pertained to credit card losses 

($83 billion). The nine-quarter cumulative loss rate of 6.1 percent, with significant differences across 

BHCs, is high by historical standards and more severe than any recession since the 1930ties. Trading 

losses at the six BHCs and counterparty losses at the eight BHCs ranged between $1bn and 

$24 billion across the eight BHCs. 

85. Low level of pre-provision revenue mainly reflected low projected net interest income 

and non-interest income. This is consistent with low interest rates and flattening of the yield curve 

in the first part of the stress testing horizon and falling asset prices, rising equity market volatility and 

falling economic activity. 

86. Under the adverse scenario BHCs would report moderate declines in capital ratios. The 

adverse scenario simulates a mild recession but with a sharp increase in short term rates that affect 

BHCs’ funding costs. The projected capital ratios are smaller than those under the severely adverse 

 

61 Mortgage loans were defined as first-lien mortgages, domestic and junior liens and HELOCs, domestic mortgage 

loans. 

62 Consumer loans were defined as credit cards and other consumer loans. 
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scenario. The main difference is higher pre-provision revenue driven by higher net interest income 

due to higher interest rates. In the publicly available results, the authorities did not indicate any 

impact of higher interest rates on loan delinquency. However, AOCI is three times larger than in the 

severely adverse scenario due to higher interest rates. 

87. In aggregate, the CET1 projections in the DFAST and IMF top-down approach are 

similar (Figure 7). The minimum CET1 ratio in the benchmark IMF stress test is higher than in the 

DFAST, mainly reflecting less conservative loan dynamics and lower dividend distributions for firms 

constrained by the hurdle rate. However, when DFAST assumptions on loan supply, dividend 

distribution and operational RWAs were introduced in the IMF stress test the CET1 ratio in the 

benchmark case came down close to the DFAST CET1 ratio. 

88. However, while the aggregate capital ratio may be similar, RWAs in DFAST were higher 

than in IMF stress tests. Total RWAs in DFAST were 6 percent higher than in the IMF benchmarks 

case (again reflecting the difference in credit dynamics that outweigh the addition of the operational 

RWAs to the IMF model) but also 4 percent higher than in the IMF stress test with the DFAST 

assumptions. If the DFAST estimates of the 2015 standardized RWAs increase for individual BHCs 

were applied in the IMF stress test the total RWAs would be very similar. 

89. Correspondingly, the aggregated capital level in the DFAST was higher than in IMF’s 

stress test. Although it was not possible to decompose the underlying factors driving this result 

based on publicly available information, this difference could be due to a number of reasons. First 

net income losses could have been smaller in DFAST. However, this was not the case, and in fact net 

income losses in the two stress tests were very similar due to the fact that projected provisions were 

almost the same and that the sum of pre-provision revenue and trading, counterparty and other 

losses was very similar to pre-provision revenue in the IMF stress test. Therefore, one or a 

combination of the following factors could have led to a higher estimate of capital in the DFAST: 

(i) taxes; (ii) deductions from CET1; (iii) dividends; (iv) extra-ordinary items; (v) change in valuation of 

allowances. Deductions and dividends were presumably the same in both stress tests in the case 

where IMF stress test took DFAST assumptions. However, since the details of these other factors were 

not part of the published results, an accounting of which of these led to the higher aggregate capital 

estimate could not be ascertained. 

90. Moreover, bank-specific capital ratios differed significantly between the two exercises. 

About one third of BHCs in the DFAST have either higher or lower CET1 ratio by 2.5 percentage 

points. For a few large BHCs IMF estimates of CET1 ratios were higher due to high trading and 

counterparty default losses in the DFAST for these BHCs. For the rest of the BHCs, IMF estimates of 

net income losses were mainly higher, including for the two BHCs that would fall below the capital 

hurdle rates. This may be due to the modeling approach of the IMF stress testing framework, which 

was different from the DFAST due to granularity of the data. 
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0

2

4

6

8

10

12

14

16

Actual 2014q3 Ending Minimum

CET1 ratios, DFAST versus IMFST

DFAST, supervisory IMFST

8,200

8,400

8,600

8,800

9,000

9,200

9,400

9,600

9,800

10,000

10,200

Actual 2014q3 Basel III standardized approach, 2016q4

RWAs, DFAST versus IMFST

DFAST, supervisory IMFST IMF, DFAST assumptions

-300

-200

-100

0

100

200

300

400

500

DFAST, supervisory DFAST, company 

run

IMFST IMFST, DFAST 

assumptions

Income statement items, cumulative 2014q4-

2016q4, DFAST versus IMFST

Pre-provision revenue Provisions

Net income before taxes Trading and counterparty losses

Other losses

Figure 7. DFA vs. IMF Stress Test Results 

CET1 ratios (in percent) RWAs (in billion USD) 

 Note: CET1 ratios are defined as total CET1 over total RWAs. 

(sum over all 31 BHCs) 

 Income statement items 
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48 INTERNATIONAL MONETARY FUND

Mortgages

25%

Business 

loans

16%

CRE loans

12%

Consumer 

loans

36%

Other

11%

Structure of loan losses, IMFST

Mortgages

21%

Business 

loans

19%

CRE loans

10%

Consumer 

loans

45%

Other

5%

Structure of loan losses, companyrun DFAST 

Mortgages

22%

Business 

loans

20%

CRE loans

15%

Consumer 

loans

35%

Other

8%

Structure of loan losses, 

supervisory DFAST

Structure of loan losses 

Source: IMF Staff calculations. 

 

B. Company-run DFAST for 31 BHCs 

91. Three bottom-up stress tests were reviewed by the agencies: one by the FRB63 one by 

the OCC and one by the FDIC. All the tests relied on banking companies’ internal consolidated data 

to assess solvency of individual companies under different macroeconomic scenarios through 

 

63 The Fed’s bottom up stress test described here is the DFA company-run stress test which is different from the 

company-run stress test in the FRB’s Comprehensive Capital Assessment and Review (CCAR) in terms of capital 

actions and the scope. The DFA company-run stress test includes BHCs with total assets larger than $10 billion. The 

Federal Reserve’s annual CCAR is an assessment of the capital adequacy of U.S. BHCs with total consolidated assets of 

$50 billion or more and of the practices these companies use to manage their capital. CCAR is a broad supervisory 

program that includes bottom-up stress testing but also assesses BHC’s own practices for determining capital needs, 

including their practices around risk measurement and management, capital planning as well as internal controls and 

governance around these practices. 
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changes in net income and risk-weighted assets. The cut-off date of both tests of the data was 

September 2014.64 

92. The DFA company run stress test covered BHCs with consolidated assets of $10 billion 

or more, which account for about 90 percent of total BHC assets. OCC’s stress tests covered 

national banks and federal savings association with total consolidated assets over $10 billion. FDIC’s 

stress tests covered FDIC-insured state banks that are not members of the Federal Reserve System 

and FDIC-insured state-charted savings associations with total consolidated assets of more than 

$10 billion. Instructions to companies, together with scenarios, were issued on October 23, 2014. 

93. This note focuses on the results of the company run DFAST for 31 BHCs. Company-run 

stress testing results were reported to the primary supervisor on January 6, 2015 for companies with 

assets size of more than $50 billion and on March 31, 2015 for companies with assets size of more 

than $10 billion but less than $50 billion. While the results were not published by companies’ 

supervisors, banking organization with assets of $50 billion or more, including 31 BHCs included in 

the supervisory DFAST, disclosed a summary of the results of the bottom-up stress test, under the 

severely adverse scenario in March 2015. Other companies will be required to publish the bottom-up 

stress testing results in the period from June 15 to June 30. To ensure comparability with the IMF 

top-down stress test and the supervisory DFAST, and given the appropriate coverage of the topdown tests and the timing of the FSAP (the second mission took place during the last week of 

February and the week of March), only the results of the DFA bottom up stress test for largest BHCs 

(with total assets of $50 billion and more) are presented in this note. 

94. The capital definition applied in the stress tests corresponded to that required by local 

regulation, i.e., Basel III65 (subject to phase-in) for advanced approaches BHCs and non-advanced 

approaches BHCs (from January 1, 2015) and Basel I for non-advanced approached BHC for the first 

quarter of the stress test horizon (last quarter of 2014).66 In order to assess the potential impact of 

negative shocks on the capital requirement metrics over the stress horizon, companies were required 

to assume consistency with the Basel III transition schedule. The stress tests incorporated the 

transition arrangements and minimum capital requirements from the revised regulatory capital 

framework implementing the Basel III capital reforms from January 2014 and Basel I capital standards 

for non-advanced banking organization applied for 2014 only. Starting in 2015, the revised capital 

framework introduced a new standardized approach for risk weighting assets, which replaced the 

calculation of risk weights using the general risk-based capital approach. 

 

64 The annual stress testing cycle will be shifted by 90 days beginning with 2016 stress testing cycle. For example, the 

2016 cycle will begin on January 1, 2016 instead of October 1, 2015. 

65 Basel III capital standards have been implemented by the OCC, FDIC and the FRB in January 1, 2014 for advanced 

approaches banking organizations. The standards were implemented on January 1, 2015 for all other banking 

organizations. The revised capital rules apply to national banks, state member banks, Federal savings associations, and 

top-tier savings and loan holding companies and top-tier bank holding companies domiciled in the U.S. not subject 

to the Boards’ Small Bank Holding Company Policy Statement (with assets less than $500 million). 

66 RWAs under Basel I were calculated using the general risk-based capital approach (12 CFR 225, Appendix A and E). 
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95. The results of the company run stress test were disclosed by BHCs in the supervisory 

DFAST format. As in the supervisory DFAST, detailed information on capital, RWAs and income 

statement items were not publicly available. While capital ratios and income statement items were 

published in the same format as in the supervisory DFAST for all BHCs, six BHCs did not publish their 

RWAs which precluded the analysis of the results in the aggregate for the 31 BHCs. 

96. On average, the individual results of the company run test were more optimistic than 

the results of the supervisory DFAST or the IMF stress test. The tests suggest that BHCs are 

resilient to shocks for the severely adverse scenario. All BHCs stay above the regulatory minima, even 

if the IMF hurdle rates were used. Over the nine quarter of the stress testing horizon the unweighted system-wide average CET1 would fall from 12.9 percent to 9.4 percent (its minimum). The 

average CET1 ratio in the company run stress test is 90bps higher than in the supervisory DFAST and 

175bps higher than in the IMF stress test. Higher CET1 ratios are both due to lower increase in RWAs, 

for the BHCs that reported their projection of RWAs, as well as lower net income losses that were 

projected to be -$190 billion (compared to -$222 billion in the supervisory DFAST and -$224 billion 

in the IMF stress test). 

97. The differences in the net income losses were mainly driven by loan losses 

($250 billion). While trading and counterparty losses ($108 billion) were almost the same as in the 

supervisory DFAST, projected loan losses were much lower by the BHCs than by the FRB or the IMF. 

Moreover, the structure of loan losses was different in the company run stress test than in the 

supervisory DFAST or the IMF stress test. For example, losses on consumer loans represent 

45 percent of projected loan losses in the company run stress test, compared to 35 percent in the 

supervisory DFAST or the IMF stress test (Figure 7). 

98. While the correlation between BHC-specific capital ratios for the company run and the 

supervisory stress test is high,67 some BHCs reported significantly higher CET1 ratios. 

Comparing to the supervisory DFAST results, in the company run DFAST five BHCs have higher CET1 

ratio by 2.5 percentage points. This is due to lower losses and RWAs projected by the BHCs. Many 

BHCs argue that the differences in projected net income statement items and RWAs may be due 

different modeling approaches employed by the BHCs. 

IMF STAFF’S LIQUIDITY RISK ANALYSIS FOR BHCS 

99. A liquidity risk analysis was done by the IMF team in order to assess the resilience of 

the banking sector with respect to sudden, sizable withdrawals of funding. The analysis was 

done as of 2014Q3 on a bank-by-bank basis and included the same BHCs as in the solvency stress 

test. 

 

67 The correlation between CET1 ratios in the company-run stress test and in the supervisory stress test is 0.80. The 

correlation between CET1 ratios in the company-run stress test and in the IMF stress test is 0.47 and the correlation 

between CET1 ratios in the supervisory stress test and in the IMF stress test is 0.49. 
UNITED STATES

INTERNATIONAL MONETARY FUND 51

HAIRCUT

Level 1 assets

Cash and balances due from depository institutions 0

U.S. Treasury securities 0

Level 2 assets

Level 2A assets

U.S. government agency obligations 15

MBSs (guaranteed by GNMA, issued by FNMA, FHLMC, 

issued or guaranteed by U.S. government agencies or 

sponsored agencies)

15

Level 2B assets

Other domestic debt securities

Other foreign debt securities

Investments in mutual funds and other equity securities

50

100. Due to data constraints, the LCR or the NSFR was not possible to calculate.

68 The 

liquidity metric calculated by the IMF team was defined and calculated based on publicly available 

data reported in FR Y-9C report (Schedules HC, HC-B, HC-D, HC-E, HC-L and HC-M). 

A. Liquidity Metric 

101. The liquidity metric measured whether BHCs have adequate levels of liquid assets that 

can be converted into cash to meet their liquidity needs. The liquidity metric was defined as the 

ratio between the stocks of liquid assets to the total cash outflow. While the items that were included 

in the numerator and denominator of the metric were informed by the LCR definitions used in BCBS 

(2013) (Table 13, 14) there was no attempt to replicate the LCR calculation based on publicly 

available data.69 Haircuts were taken from the LCR and run-off rates were calibrated based on the 

2008/2009 episode.70 Two stress horizons were assumed over which the withdrawal of funding took 

place: 1 quarter and 3 quarters. In the sensitivity analysis the run-off rates were calibrated based on 

the LCR. 

Table 13. Liquid Assets 

 Source: IMF Staff 

 

68 Banks will start reporting their LCRs by the end of 2015. 

69 High quality liquid assets in the U.S. final rule on the LCR does not include securities issued or guaranteed by PSEs 

(e.g., state, local authority or other governmental subdivision below the sovereign level) such as municipal securities 

or RMBSs. Moreover, claims issued or guaranteed by a U.S. GSE are not included in level 1 liquid assets and corporate 

debt securities are not included in Level 2A assets. 

70 The peak was identified for each outflow item in the period 2008-2009. Each outflow item is a sum of the 

corresponding outflow item across 31 BHCs. The run-off rate is then defined as the percentage difference between 

the value of a given outflow items at its peak and its value next quarter (1q stress horizon) or three quarters from the 

peak (3q stress horizon). 
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52 INTERNATIONAL MONETARY FUND

HIST. RUNOFF RATE, 1qHIST. RUNOFF RATE, 3qLCR RUNOFF RATE

Deposits

Time deposits of less than $100,000 0 15 3

Interest bearing demand deposits, NOW, ATS and other transaction accounts 3 0 3

Time deposits of $100,000 or more 8 21 5

Money market deposits accounts and other savings accounts 0 0 10

Noninterest bearing balances 12 8 10

Deposits in foreign offices 4 15 5

Unsecured wholesale funding

Unsecured borrowing 4 11 100

Subordinated notes and debentures 4 4 100

Secured wholesale funding

Securities sold under agreement to repurchase 0 0 15

Secured borrowing 13 22 50

Liabilities for short position 1 35 100

Currently undrawn committed credit and liquidity facilities 

Revolving, open-end loans secured by 1-4 family residential properties 5 13 5

Unused consumer credit card lines 1 12 5

Other unused credit card lines 1 12 5

Commitments to fund CRE, construction and lan development loans secured by RE 6 14 10

Commitments to fund CRE, construction and lan development loans not secured by RE 8 27 10

Securities underwriting 44 70 10

Commercial and industrial loans 2 5 10

Loans to financial institutions 2 5 40

All other unused commitments 2 5 100

Other contingent funding liabilities (such as guarantees, letters of credit,

revocable credit and liquidity facilities, etc) 

Financial standby letters of credit 2 4 10

Performace standby letter of credit 2 18 10

Commercial and similar letters of credit 5 26 10

Table 14. Outflow Items 

 Source: IMF Staff 

B. Results 

102. The results of the analysis give some comfort that the system is able to meet liquidity

requirements, but there are pockets of vulnerability. The analysis suggests that most, but not all, 

BHCs have enough liquid assets to meet a liquidity shock similar to 2008/2009 event (Figure 8).71

Several BHCs would face liquidity pressures due to deposit outflows in the short run and large 

unused commitments over a longer stress horizon. If faced with a much larger shock, as 

characterized by the LCR run-off rates, liquid assets for many BHCs would not be sufficient to meet 

liquidity needs due to large withdrawal of wholesale funding. Wholesale funding plays an important 

role in this case since the run-off rate on wholesale funding is much larger than what happened in 

2008/2009. 

 

71 The Fed’s estimates indicate that the majority of the largest U.S. banks would meet the LCR. 
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INTERNATIONAL MONETARY FUND 53

103. While the liquidity metric offers some insights into the liquidity risks, the results come 

with many caveats. While ideally liquid assets should include unencumbered liquid assets only, the 

liquidity metric here includes both types of assets which will inflate liquid assets.72 Also, the liquidity 

metric does not include pledged assets to BHCs which deflates the liquid assets measure. Moreover, 

inflows were not considered as part of liquidity analysis which would make the liquidity metric 

smaller than what it would otherwise be. Outflows on derivatives contract were not considered. Due 

to unavailability of granular data, the actual run-off rates included in the sensitivity analysis had far 

smaller variation than those in the LCR. Finally, current regulatory report lack important elements to 

run an accurate liquidity stress test such as liability tenor information, inflows resulting from 

maturing transactions and relevant contractual terms embedded in derivatives contracts. 

 

72 Publicly available data do not allow differentiating between encumbered and unencumbered assets. 
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54 INTERNATIONAL MONETARY FUND

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

Liquid assets and outflows

Cash U.S. Treasury securities GSE MBSs

Other Unsec. wholesale funding Sec. wholesale funding

Undrawn commitments Other Deposits

Approximation 

of the LCR

run-offs

1q run-offs 3q run-offs

trillion USD

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

1q 3q "LCR"

Liquidity metric (Liquid assets/Outlfow)

Approximation

of the LCR runoff rates

Historical run-off 

rates (1 quarter)

Historical run-off 

rates (3 quarters)

Figure 8. Liquidity Metric, Historical Run-Off Rates, and Approximation of LCR Run-Off 

Rates 

 

Note: The whisker boxes display the distribution of the liquidity metric, calculated using the IMF methodology, for 31 BHCs in 

terms of distribution’s moments: the bottom and top of the whisker box are the first and third quartiles, the band inside the box 

is the second quartile (median), the diamonds represent un-weighted average of liquidity metrics for 31 BHCs, the lower and the

upper whisker represent the minimum and the maximum liquidity metric, respectively. 

Source: IMF Staff calculations.
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INTERNATIONAL MONETARY FUND 55

IMF STAFF’S SOLVENCY TESTS FOR INSURANCE 

104. This section reports on the top-down solvency stress tests for insurance companies 

performed by the IMF FSAP team. In addition, it also includes a summary of a top-down stress test 

performed by the National Association of Insurance Commissioners (NAIC) for this FSAP. 

A. Scope of the Test 

105. The scope of the exercise was broad in terms of risk categories included and 

methodological approaches. The stress test included a sample of 44 insurance groups, of which 

22 are predominantly active in the life insurance business, 15 in property & casualty (P&C) business, 

five in health insurance, and two in credit and mortgage insurance. All of the groups in the sample 

are publicly listed. The stress test included the three groups designated both as global systemically 

important insurers by the Financial Stability Board (FSB) and as systemically important by the 

Financial Stability Oversight Council (FSOC). Overall, the sample represents 40 percent of the 

domestic insurance sector in terms of gross written premiums. The separate top-down stress test 

performed by the NAIC, which also informed the IMF’s sensitivity analyses, covered all U.S. life and 

P&C insurers filing with the NAIC. 

106. The stress test used publicly available, consolidated data of insurance groups from 

regulatory returns provided by SNL Financial and Bloomberg. Further sector-wide data was 

provided by the NAIC to enhance the granularity of the exercise. The separate stress test performed 

by the NAIC used legal entity data filed with the NAIC, including both publically available information 

as well NAIC risk-based capital data filed exclusively with the NAIC and state insurance regulators. 

The risk-based capital data was only made available to the IMF in aggregate format and not on an 

individual company basis. 

B. Scenario 

107. The insurance top down stress test was built on the DFA stress test specifications. Given 

the nature of insurance business and its balance sheet structure, the main focus of the stress test was 

on investment assets and, therefore, the market risk parameters of the DFAST. The market risk 

stresses (Table 15) included shocks to bond holdings (sovereigns, municipals, and corporates), 

securitizations, equity, property and other investments (such as hedge funds and private equity). In 

addition, like in the severely adverse scenario of the DFAST, lower swap rates were assumed, in a 

range of minus 21 to minus 143 basi–s points for maturities of one year and 30 years, respectively. 

Broadly speaking, the market risk parameters reflect a severe market distress similar to the situation 

observed at the height of the financial crisis in 2008–09. All stresses were assumed to occur 

instantaneously. 
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56 INTERNATIONAL MONETARY FUND

Table 15. Market Risk Parameters 

Source: IMF Staff assumptions based on DFAST. 

108. The lack of detailed data on the investments of insurance undertakings made it 

necessary for the FSAP team to simplify some of the stresses provided in the DFAST, or to rearrange some risk factors. As an example, the equity shock was not calculated on a per-country 

basis, but an overall weighted shock was generated based on the geographical breakdown of the 

equity exposures of the U.S. insurance sector. In a similar vein, the shock for non-U.S. sovereign 

bonds was derived, split into the rating categories defined by the NAIC.73 For the property shock, it 

was assumed that the price developments in the commercial real estate sector as defined in the 

DFAST for the twelve quarters from 2014-Q4 until 2017-Q4 would occur within just one period. 

 

73 The NAIC defines six categories of credit quality. They correspond to the rating categories used by Standard&Poor’s 

as follows: NAIC 1: AAA – A; NAIC 2: BBB; NAIC 3: BB; NAIC 4: B; NAIC 5: CCC; NAIC 6: CC or lower, and defaulted. 

Market shocks Change (bp) Market value 

change (%)

Changes in USD 

swap/discount curve Change (bp)

Bond spreads 1M -12,6

 U.S. Government 58,4 3M -11,3

 Other Gov - NAIC 1 56,8 6M -14,9

 Other Gov - NAIC 2 190,9 9M -17,1

 Other Gov - NAIC 3 352,1 1Y -20,9

 Other Gov - NAIC 4 377,0 2Y -47,0

 Other Gov - NAIC 5 1.661,0 3Y -69,8

 Other Gov - NAIC 6 1.661,0 5Y -94,0

 U.S. Municipals - NAIC 1 66,0 7Y -108,5

 U.S. Municipals - NAIC 2 372,0 10Y -116,4

 U.S. Municipals - NAIC 3 429,0 15Y -125,8

 Corp - NAIC 1 248,1 20Y -134,2

 Corp - NAIC 2 640,3 30Y -142,6

 Corp - NAIC 3 985,3

 Corp - NAIC 4 922,3

 Corp - NAIC 5 1.878,0

 Corp - NAIC 6 1.878,0

RMBS -51,3%

CMBS -50,3%

Other Securitizations -28,7%

Mortgage loans -12,2%

Equity interest -28,9%

Property (CRE) -28,3%

Cash 0,0%

Other investments -28,9%
UNITED STATES

INTERNATIONAL MONETARY FUND 57

C. Valuation and Capital Standard 

109. An important point of context for the stress tests is that statutory accounting of U.S. 

insurers is based on U.S. GAAP, which in some instances differs from a “fully marketconsistent” approach to the valuation of assets and liabilities. Under statutory accounting, the 

liabilities of P&C insurers are generally not discounted which adds a layer of conservatism. Also 

under statutory accounting, life insurance liabilities are discounted with a rate that is set at the time 

when the policy is sold to the policyholder or with a discount rate based on the expected return of 

assets associated with the insurance liabilities. The NAIC calculated that the discount rate on all life 

insurance policies averaged approximately 4 percent in 2013, which is above current market rates.74

Under statutory accounting, amortized cost is the predominant accounting regime for fixed income 

assets,75 which means that neither unrealized gains nor losses are recognized. For the stress test, this 

results in a significant difference in the impact of a shock to the risk-free interest rate. In a truly 

economic balance sheet with a fully market-consistent valuation of both assets and liabilities lower 

interest, as specified in the scenario, mean that the liabilities of a life insurer increase more than its 

assets, given a structural mismatch of assets and liabilities that is very common in that type of 

business. While the duration mismatch is usually smaller for non-life insurers, the same mechanics 

apply. State insurance regulation requires that companies perform an asset adequacy analysis at 

least annually to measure the structural mismatch of assets and liabilities under a range of different 

interest rate scenarios. 

110. Under statutory accounting, also the impairment rules for life insurers differ from a 

fully market-consistent regime. Investment assets are impaired only when the fair value loss is 

deemed to be other than temporary. Once impaired, a bond cannot be written back up to its original 

fair value after recovery. 

111. In the absence of a group capital requirement for insurers, the hurdle rate for the IMF’s 

top-down stress test was set, generously, as the complete extinction of shareholder equity. 

This means that the capital deemed to cover unexpected losses and serving as “the first line of 

defense” would no longer be in place. Groups with negative shareholder equity after stress clearly 

failed the stress test. However, this perspective holds true only in a full fair-value regime. With 

negative shareholder equity, assets are smaller than liabilities. While such a non-coverage of 

liabilities might potentially cause policyholders to surrender their life insurance policies (a “run” 

situation), such a behavior is partly disincentivized by surrender penalties, loss of insurability, and the 

federal income tax treatment of life insurance. Further, a company’s insurance book can be run off in 

 

74 The average net portfolio yield for life insurance companies was 5.11 percent in 2013. 

75 The actual treatment depends on the credit quality: For life insurers, fixed income assets in NAIC credit quality 

buckets 1 to 5 are valued at amortized costs, and so are, for P&C insurers, assets in buckets 1 and 2. For the remaining 

credit quality buckets (6 in life and 3 to 6 in P&C) the lower value of amortized cost and market value is used. 
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58 INTERNATIONAL MONETARY FUND

a relatively ordered way, because of the powers the state insurance regulator has in a troubled 

company context, thereby widely limiting contagion effects. 

D. An Overview of Insurance Companies Soundness76

112. Balance sheet and asset quality. The aggregated balance sheet of the 44 insurance groups 

in the stress testing sample increased from 2009 to 2013 by 25 percent to $4.5 trillion. The largest 

group within this sample consists of life insurers, with aggregated assets of $3.1 trillion. Non-life 

insurers and health insurers complete the sample with aggregated assets of $1.1 trillion and 

$0.3 trillion, respectively. The share of the non-life sector fell, owing to the continuing restructuring 

of AIG, which divested parts of its business in 2010 and 2011. In the aggregate, the ratio of 

shareholder equity to balance sheet assets was 8.3 percent for life insurers in 2014, and was 

22.9 percent and 33.1 for non-life insurers and health insurers, respectively. Non-life insurers hold 

higher amounts of shares and also shorter durations in their bond portfolio than life insurers. Health 

insurers have in comparison the most conservative asset allocation, with relatively high allocations in 

sovereign bonds. 

113. In recent years, there was a tendency among life insurers to invest in longer maturities. 

In 2014, the median duration reached 7.6 years for the life insurers in the sample (an increase from 

7.2 years in 2009). In comparison, median asset durations for non-life companies and health insurers 

were 5.4 years and 5.2 years, respectively. Also, the median share of non-investment grade bonds in 

the bond portfolio of life insurers reached 5.4 percent, compared to 4.1 percent for non-life insurers 

and 4.4 percent for health insurers. The share of non-investment grade bonds among life insurers 

has been decreasing since 2009, while it has been increasing for non-life, albeit from a lower base. 

114. Income statement. In terms of revenues (premiums, capital gains and investment income 

being the main components), health insurers form the largest group in the stress test sample. Their 

share reaches 42 percent, mainly coming from recurring premiums. Life and non-life insurers account 

for 32 percent and 26 percent, respectively. Between 2009 and 2014, revenues of life insurers 

increased by 36 percent. Non-life insurers increased their revenues by 11 percent and health insurers 

by 46 percent. 

115. Nearly all insurers in the stress test sample reported a positive net income in 2014. 

Returns on equity were highest in the health sector with a median of 14.0 percent. Non-life insurers 

reported 11.7 percent and life insurers 8.2 percent. Compared to 2013, the return on equity improved 

substantially for non-life and health insurers, while declining for life insurers. 

116. Life insurers have benefitted from positive capital market developments since 2009 

with both stock markets and bond markets improving, but have struggled in the face of lower 

 

76 This section describes the structural developments of the 43 insurance groups included in the top-down stress test 

for the period from 2009 to 2014. 
UNITED STATES

INTERNATIONAL MONETARY FUND 59

interest rates. Investment yields have been declining over the last years as higher-coupon bonds 

expire and are replaced by lower-yielding new issues. For P&C insurers, super storm “Sandy” in 2011 

was the latest major catastrophe event, resulting in a median combined ratio77 above 100 percent. 

Since then, the combined ratio has stabilized below 100 percent, with the median in 2014 being 

94 percent. Over the last five years, changes of the combined ratio have predominantly been driven 

by the loss ratio, while operating expenses have been stable on average, around 32 percent. 

117. While the implementation of the Affordable Care Act resulted in rising premium 

income of health insurers, uncertainties remain about the future profitability of policies sold 

under this program. The IMF staff observed that higher premiums and positive profit margins have 

benefitted health insurers after the first full year of business under the Affordable Care Act. Already 

since the enactment of the new system in March 2010, stock prices have significantly outperformed 

the S&P 500. Nevertheless, risks continue to exist for the health insurance industry, notably 

administrative risks with regard to regulated prices and legal challenges to subsidies provided to 

policyholders. Also the medium-term behavior of policyholders is unknown, especially lapse rates 

could be higher than in other health insurance lines. Finally some uncertainties about future claims 

exist as the Affordable Care Act has led to a shift in the average risk profile of the policyholder by 

attracting higher-risk cohorts of customers. 

E. Modeling Assumptions 

118. Assuming a fully market-consistent valuation impact of the shock, the value of 

investment assets and ultimately shareholder equity declines substantially.

 78 Credit spread 

increases are multiplied with the duration of the respective asset class, derived from the maturity 

buckets provided in statutory reporting, also resulting in a lower value of investment assets. 

119. Separate accounts have not been included in the stress test as investment losses are 

generally passed on to policyholders. Such accounts are offered by 17 companies in the sample 

(with a median share of total reserves of 53 percent amongst those companies offering separate 

accounts). No breakdown of investments or detail on the guarantees provided in these accounts is 

available in the consolidated public filings, so it was assumed that the asset allocation matches the 

group-wide asset allocation and no economic loss remains with the insurance company. The market 

risk shocks were accordingly applied only to the investment assets held in the general account. This 

simplifying approach might underestimate the effect of the stress scenario in some cases, especially 

when an insurance company has issued a guarantee for the separate accounts (or parts of them). 

 

77 The combined ratio shows the sum of losses and operating expenses divided by premiums (or loss ratio plus 

expense ratio). Values above 100 indicate losses in underwriting business. 

78 Only the stress impact was assessed based on market value changes. A pre-stress transformation of the insurers’ 

balance sheet from statutory accounting to market valuation was beyond the scope of this top-down exercise. 
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60 INTERNATIONAL MONETARY FUND

120. In an economic balance sheet approach, the shock to the risk-free rate needs to be 

applied to both assets and liabilities. While the duration on the asset side can be approximated 

based on maturity buckets, no detailed information on the duration of liabilities is available. It was 

therefore assumed, based on suggestions by market participants, that the duration of liabilities 

exceeds the duration assets by two years in the case of life insurers, and by one year in the case of 

non-life and health insurers. 

121. The stress test does not take into account any mitigating effect from hedging. Insurance 

companies usually apply a sophisticated hedging strategy with regard to their interest rates (mainly 

via swaps and swaptions), and also hedge against declines in the stock market via options and 

futures. As these hedging activities can vary substantially among companies, it is difficult to estimate 

the mitigating effect in times of stress. In any case, it is very likely that the stress test gives a 

maximum impact. 

122. For the sensitivity tests, the IMF team built on various approaches developed by the 

NAIC. This was with regard to modeling the effect of (a) major natural catastrophes, (b) a pandemic, 

and (c) a prolonged period of low interest rates. The results of these analyses were not added to the 

outcome of the main stress scenario, although it is possible to assume that the stress scenario occurs 

at the same time as a major natural catastrophe or a pandemic. 

123. The IMF team has specified three types of natural catastrophes, for which the NAIC has 

provided approximate results. These were based on re-assessing historic events of a similar type 

and by cross-checking this against newly introduced amendments to RBC filings of companies. The 

catastrophe events included a hurricane in Florida, similar to but worse than hurricane Andrew in 

1992, causing an industry-wide loss of at least $40 billion. As a second event, an earthquake in 

California, similar to but worse than the Northridge earthquake in 1994 was specified; the earthquake 

should have a magnitude of at least 7.2 and the insured loss should be greater than $35 bn. Finally, a 

series of three major tornados should be assumed to occur in the Midwest of the United States, each 

causing an insured loss of at least $4 billion. Each of these events, which have been modeled to occur 

independently from each other, would reduce the capital position of exposed P&C insurers. 

124. Similarly to catastrophic events which mainly affect the P&C sector, a shock to 

mortality rates in the form of a pandemic was modeled in the life insurance sector. This 

mortality shock is calibrated as a pandemic with 1.5 additional deaths per 1,000 which is considered 

a 1-in-200 year event (Swiss Re 2007). While the increased mortality rate is well below numbers 

reported for the influenza pandemic in 1918–19, when more than 5 per 1,000 people ceased in the 

United States, the situation is not directly comparable to today’s standards of healthcare and 

governments’ responsiveness. The results for both the catastrophe shock and the pandemic shock do 

not include potential macroeconomic implications of such an event which in turn could have a 

further negative effect on capital markets, as recent events like e.g. the SARS outbreak in Hong Kong 

in 2002–03 have shown. 
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INTERNATIONAL MONETARY FUND 61

125. With regard to the prolonged period of low interest rates (“low for long”), the NAIC 

has provided an analysis that compares the net investment yield of life insurers against the 

average credited rate. The horizon for this analysis (Figure 9) was from 2014 until 2018. The 

investment yield was assumed to decline linearly based on the trend observed since 2006. 79 The 

average credited rate would also decline, as new business would be issued with lower contractual 

guaranteed interest rates. For example, currently issued annuities contain an interest rate guarantee 

of 1 percent. The spread between investment yield and credited rate, multiplied with the projected 

reserves gives the impact on the profitability of the insurer.

Figure 9. Medium-term Projections in a Low Interest Rate Environment, 2006–18 

Source: NAIC 

F. Results 

126. The stress test shows a significant impact on the U.S. insurance sector, especially in the 

life business (Figure 10). The results for the life insurance companies show a wide dispersion, but 

11 out of 22 groups would report negative shareholder equity after stress if a fully market-consistent 

accounting regime was in place. The effect is larger than what has been observed historically, e.g., in 

the recent global financial crisis life insurers had the ability to hold their investment assets until 

maturity without the need to sell them at depressed price—Box 2 provides further analysis on how 

 

79 This assumption is a simplification as the downward trend in portfolio yields might flatten out in the future when 

fixed income instruments with higher coupons have already expired. 

0,0%

1,0%

2,0%

3,0%

4,0%

5,0%

6,0%

7,0%

Declining spread between portfolio yield

and guaranteed rate

Net portfolio yield Guaranteed credited rate
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62 INTERNATIONAL MONETARY FUND

stresses unfold under statutory accounting. The other insurance segments are much less affected, 

given their lower exposure to investment risks. No nonlife or health group would be in distress, 

suggesting that the respective sectors are in a more robust shape. Overall, the losses in shareholder 

equity amount to $267 billion, or 45 percent of pre-stress equity. Life insurers contribute to this 

amount by $187 billion, while $72 billion come from P&C insurers, $6 billion from health insurers, 

and $2 billion from credit insurers. Out of the total loss, $104 billion is attributed to the distressed 

companies. 

127. The main contribution to the overall loss in shareholder equity, calculated in a fully 

market-consistent way, comes from the credit spread increase in the corporate bond portfolio. 

For the full sample, this corporate bond shock (also applied to hybrids) accounts for 62 percent of 

the overall loss. Further notable contributions come from the shock to sovereign bonds and GSEs, 

the shock to the securitization portfolio, and the shock to share prices (each with 7 percent). Given 

their larger holdings in shares, P&C insurers are relatively more affected by the equity market shock, 

while health insurers would record larger losses stemming from their sovereign bond portfolio. For 

credit insurers, the full impact of higher credit spreads and higher default rates would likely be 

higher than the numbers suggest as also their liabilities would be affected. 

Figure 10. Top-Down Insurance Stress Test 

Life insurance companies most affected in the stress 

test 

Highest contribution from market value losses of 

corporate bonds 

Source: SNL Financial and IMF Staff calculations 

-200%

-180%

-160%

-140%

-120%

-100%

-80%

-60%

-40%

-20%

0%

Life P&C Health Credit

Reduction in shareholder equity

Inter-quartile range Median 0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Life P&C Health Credit

Contribution to total loss in shareholder equity

other market risk

stresses

stress on property

stress on equity

shock on securitizations

stress on corp. bonds

and hybrids

stress on sov. bonds,

GSEs, munis etc.

stress on risk-free rate

(swap/discount rate)
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128. Among the distressed companies, smaller and medium-sized institutions are in a 

majority. The aggregated balance sheet assets of the 11 companies amount to around $490 billion 

(11 percent of the full sample). Their pre-stress shareholder equity declines from $55 billion to a 

negative $48 billion under stress. 

129. A large catastrophic event or a pandemic, seen in isolation, are likely manageable for 

both the life and the non-life sector (Table 16). The most expensive event would be the Florida 

hurricane which, modeled as a 1-in-250 year event could cause insured losses of around $80 billion. 

An earthquake in California with the same expected occurrence frequency could result in a loss of 

$34 billion, since the vast majority of California earthquake exposure for residential properties is 

through the California Earthquake Authority. A series of three severe tornados in the Midwest of the 

United States shows only rather contained effects, substantially below the claims expected in the 

other two scenarios. A pandemic with 1.5 additional deaths per 1,000 which is considered a 1-in200 year event (Swiss Re, 2007), could cost the U.S. life insurance industry between $20 and 

$25 billion. The hurricane and earthquake are estimated net of reinsurance. The pandemic is 

estimated on a gross basis, not taking into account the mitigating effect of reinsurance and 

alternative risk transfer, so that only parts of these amounts would ultimately be borne by U.S. 

insurance sector. However, the results for both the catastrophe shock and the pandemic shock do 

not include potential macroeconomic implications of such an event which in turn could have a 

further negative effect on capital markets. 

Table 16. Impact of Natural Catastrophes and Pandemics 

Source: NAIC 

130. A scenario of prolonged low interest rates poses a slow burning risk which could 

become a solvency risk for life insurers in a few years. For the period from 2006 to 2013, the 

industry-wide spread between the net portfolio yield and the guaranteed credited rate declined by 

57 basis points. Assuming that interest rates remain at their current levels, the spread could, linearly 

projected, continue to decline further as the lower rates influence the average guaranteed credited 

rate at a significantly slower speed than the portfolio yield. The modeling is based on several 

restrictive behavioral assumptions, especially with regard to the asset allocation, but clearly indicates 

that the risk of low interest rates requires intense monitoring as it influences the business model of 

life insurance substantially. While at the moment, the risk only reduces profitability of the companies, 

potential negative spreads between investment yields and guaranteed rates could result in widespread losses ultimately resulting in a weaker capital position of the sector. 

net claims in USD billion 1-in-100 years 1-in-200 years 1-in-250 years

Florida hurricane 45 - 50 75 - 83

California earthquake 22 34

Pandemic 20 - 25
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64 INTERNATIONAL MONETARY FUND

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Life P&C

Contribution to loss in total adjusted capital

Catastrophe losses

Variable annuity losses

Other investments

Property

Equity

Mortgage loans

CMBS

Non-agency RMBS

Corporate bonds

(below inv. grade)

Municipal bonds

Sovereign bonds

Detail of Stresses Change in value 

(in percent)

Bonds

 Sovereign bonds 2,0

 Municipal bonds -5,0

 Corporate bonds (below inv. grade) -5,0

 Non-agency RMBS -28,0

 CMBS (Senior) -5,0

 CMBS (Mezzanine) -25,0

 CMBS (Subordinate) -25,0

Mortgage Loans -5,0

Equity

 Unaffiliated Common Stock -40,0

 Affiliated Common Stock -30,0

 Preferred Stock -10,0

Property -30,0

Other Investments ("Schedule BA") -30,0

Variable Annuity Losses (10% of maximum guaranteed amount)

Catastrophe Losses (1-in-250 year event)

Box 1. NAIC Top-Down Stress Test 

In parallel to the top down exercise performed by the IMF team, the NAIC has also run a top down stress test 

based on end-2013 statutory data for the whole U.S. insurance sector. Also the NAIC stress test shows a 

substantial impact, though less pronounced as no shock is applied to investment grade corporate bonds and 

sovereign bonds are expected to increase in value (combining the effect of potentially higher credit spreads 

and offsetting lower risk-free interest rates). 

In addition to immediate losses in the 

general accounts, also guaranteed 

components of separate accounts 

were included. In those cases where 

an insurance company guarantees 

certain benefits of separate account 

products, a shock is modeled by 

applying a factor of 10 percent to the 

company’s maximum possible 

guaranteed amount that would be 

diverted from the general account to 

the separate account. 

The exercise further included two 

natural catastrophes, a Florida 

hurricane and a California 

earthquake, each with a 1-in-250 year 

probability and both occurring at the 

same time. 

Based on statutory accounting, the U.S. insurance industry as a whole shows some robustness although the 

capital impact is significant. While there 

are some companies which would fall 

below the regulatory thresholds, the 

average decline in total adjusted capital 

amounts to $166 billion in the life sector (-

35 percent) and $267 billion in the P&C 

sector (-33 percent). Even with stressed 

capital, both sectors would, on average 

reach RBC coverage ratios of 659 percent 

and 447 percent, respectively. 

The main contribution to the overall 

changes comes from the securitization 

portfolio and other investment assets in 

the life sector, while for P&C insurers, the 

equity shock and the catastrophic events 

have the largest impact. 

Source: NAIC 
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131. The results show the life insurance sector in a very challenging position. Not only could 

market turbulences cause huge losses in terms of fair value accounting (less so in the current 

statutory accounting as long as the market stress is not prolonged or substantial), but vulnerabilities 

persist. The low-yield environment could, if continued, erode the profitability of the sector and 

ultimately also deteriorate the capital position. Net investment yields would need to remain above 

4 percent to ensure profitability. The sector has already reacted and is actively changing its product 

mix, offering more policies where investment risks are partially or fully passed on to the policyholder. 

132. The low interest rate environment is challenging especially for life insurers and caused 

many companies to change their investment behavior. As insurers search for yield, durations in 

the bond portfolio have increased since 2009 (Table 17). The share of bonds below investment grade 

has declined for the life companies in the stress test sample (from 7.5 to 5.4 percent). Over the same 

period, the share went up significantly for non-life and health insurers, albeit from a low base. Also 

within the investment grade bond portfolio there was a clear tendency to take on more credit risk by 

expanding the relative share of BBB-rated assets. Some movement into alternative investments 

(hedge funds, private equity) has been observed, but this is still of a smaller dimension and rather 

restricted to larger insurance companies who are expected to have an adequate risk management in 

place. 

Table 17. Changes in Duration and Credit Quality of Bond Portfolio 

Duration 

2009 

Duration 

2014 

Share of 

non-IG in 

bond 

portfolio 

2009 (in 

percent) 

Share of 

non-IG in 

bond 

portfolio 

2013 (in 

percent) 

Share of 

BBB in IG 

bond 

portfolio 

2009 (in 

percent) 

Share of 

BBB in IG 

bond 

portfolio 

2009 (in 

percent) 

Life 7.2 7.6 7.5 5.4 33.9 37.6 

Non-life 5.1 5.4 1.9 4.1 8.7 15.0 

Health 4.2 5.2 1.0 4.4 10.1 14.9 

Source: SNL Financial and IMF staff calculations 

Notes: based on insurance stress test sample

133. While life insurance companies are exposed to the risk of prolonged low interest rates, 

also a sharp upward shock to interest rates poses a material risk. The market value of the bond 

portfolio would decline, even more so with the longer durations insurers are holding now. In 

statutory accounting, however, life insurers would be able to carry many of these assets at amortized 

cost, unless their decrease in value was determined to be other than temporary. Rising interest rates 

would likely lead to an increase in policy surrenders when policyholders switch into higher-yielding 

assets (within and outside the insurance sector), leaving companies with a liquidity drain and 

potentially some losses for those products where no surrender penalty applies. While the share of 

companies that fund themselves on the capital market is rather small in the insurance sector, higher 

interest rates drive up funding costs for those who issue bonds on a regular basis. 
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134. P&C insurers showed greater robustness in the stress tests. After some years with only 

few large catastrophes, the capital positions have improved to weather times of stress. However, 

competition and price pressure have increased with some products, and investment yields have 

decreased, requiring companies to improve their cost structure and also to remain prudent in their 

underwriting. 

135. Insurance companies are able to mitigate some of the effects of the stress. A range of 

life insurance products includes profit sharing features between the insurance company and the 

policyholder which allow the insurer to (partially) pass on investment losses by reducing discretionary 

benefits. In a risk-based solvency regime, it is also possible for the companies to de-risk their 

investments in order to reduce their capital requirements, resulting in higher solvency ratios; similarly 

ceding risks to a reinsurance company could be considered. Finally, dividend policies, both upstream from subsidiaries to the top (holding company) level and from the top level to shareholders 

can be actively managed, especially with larger and diversified groups. As the range of management 

actions is very broad, no general modeling result can be provided based on publicly available data. 
UNITED STATES

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-40%

-35%

-30%

-25%

-20%

-15%

-10%

-5%

0%

Life P&C

Reduction in shareholder equity

Inter-quartile range Median

Box 2. Additional IMF Stress Test Based on Statutory Accounting 

To evaluate the effect of differences in accounting methods, the IMF team complemented its mark-to-market 

stress test with an additional statutory accounting exercise, based on the same sub-samples of life and P&C 

insurers1

 and end-2014 data. The difference in the results of both approaches reveals the impact the valuation 

regime can have on an insurance company’s capital position. 

The following modeling steps have been taken: 

 Mark-to-market impairment, based on the DFAST severely adverse scenario, for holdings in equity, 

corporate bonds below investment grade, and other investment assets; 

 Default losses in the corporate bond and securitizations portfolio (default rates are based on 

observations from 2008-092

, with an assumed 50 percent loss given default rate); 

 A catastrophic event (for P&C) and a pandemic causing higher mortality rates (for life) which result in 

net cash outflows of $25 billion in each of the two sectors (outflows are assumed to be distributed 

among companies according to their respective market share); 

 The need to liquidate investment assets to match the cash outflows, realizing losses at distressed 

market levels; it is assumed that companies would sell U.S. treasury bonds, municipals and GSE issues 

first. 

The aggregated reduction in statutory capital for life and P&C companies under this modeling approach 

amount to $111 billion, which represents 22 percent of the pre-stress statutory capital. The median loss in the 

life sample is 26 percent, while for P&C insurer it is 23 percent. One life insurer would be in distress as its 

statutory capital turns negative, for one other life insurer the reduction amounts to more than 75 percent. 

Most of the overall impact can be attributed to the impairment of investments which accounts for 69 percent of 

the change in statutory capital. The catastrophic (for P&C) or pandemic (for life) event contributes 18 percent, 

the defaults in the investment grade corporate bond and securitizations portfolio 13 percent and the realized 

losses in the forced sell-off only contribute marginally (1 percent). The differences between the life and the P&C 

sector are not very pronounced. 

The results of this exercise are broadly in line with those of the NAIC stress test, which was also based on 

statutory accounting. Some divergence however exists due to a different scenario design; as an example, the 

NAIC stress test does not include the impact of a pandemic event in the overall results of life insurers.

Notes: 

/1 Health and credit insurers have not been included in this exercise. 

/2 Default rates are calculated as averages of default rates provided by Fitch, Moody’s and Standard&Poor’s for the period from

mid-2008 to mid-2009 via the Central Repository (CEREP) set up by the European Securities and Markets Authority (ESMA).
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68 INTERNATIONAL MONETARY FUND

IMF STAFF’S LIQUIDITY ANALYSIS OF MUTUAL FUNDS 

136. Open-ended mutual funds’ investments are exposed to redemption risk. 

 Such funds may be more susceptible to runs when their investments are directed into 

markets that are less liquid. Their liabilities are liquid due to a regulatory obligation to meet 

investor redemption demand in-cash within 7 days, yet they may possess neither the 

balance-sheet liquidity capacity nor access to robust-to-severe stress back-up lines of 

liquidity. This could leave funds with no recourse but to sell assets in the open market, even 

at a steep discount. 

 The rapid expansion of such funds into fixed-income markets has raised concerns about the 

potential for investor runs on funds to exacerbate asset market stress. The markets for 

corporate bonds, emerging market debt, bank loans, and municipal bonds are less liquid 

than equities, U.S. Treasury, and GSE securities. Moreover, there has been a notable decrease 

in trading liquidity in corporate bonds since the crisis. 

137. IMF staff has performed an analysis of liquidity in U.S. mutual funds, and important 

caveats apply. This was the first time this type of analysis was performed in an FSAP context, and 

while publicly available data are voluminous they are incomplete in important respects. Moreover, 

the analytical basis for analyzing the liquidity position of mutual funds is still nascent. The analysis 

could therefore be only exploratory in nature, but still provides interesting results that help point to 

possible vulnerabilities and areas where further analysis may be warranted. 

138. The analysis was geared to measuring whether markets would be able to absorb severe 

redemption pressures wherein these funds are forced to liquidate positions. Ideally, an asset 

pricing model would be deployed to examine the (marginal) impact of redemption spikes on asset 

prices or (bid-ask) spread measures of market liquidity. Absent such a model, the approach taken in 

this exercise was to assess whether a standard metric of available trading liquidity, dealer inventory 

in specific assets markets80, is sufficient to absorb redemption demand in a tail risk scenario. Dealer 

inventory was selected as a proxy for trading liquidity because it was believed to provide a useful 

indication of market appetite for a variety of asset classes. 

139. Specifically, a top-down liquidity risk analysis was performed. Close to 9,000 mutual 

funds representing around 80 percent of the industry were analyzed. The funds were divided into 

 

80 This metric has been used in many analysis as an indicator of market liquidity, including by OFR (2014), IMF (various 

2014 GFSR chapters), BIS (Fender and Lewrick, 2015), and New York Fed (Adrian and others, 2013). Also, the FSOC’s 

2015 Annual Report (FSOC, 2015) notes that broker-dealers have significantly reduced their inventories of certain 

fixed income securities, pointing out that “market liquidity may be impaired if broker-dealers are less willing or unable 

to intermediate supply and demand imbalances.” A recent analysis by Goldman Sachs (2015) suggests that changes in 

the inventory level of corporate bonds at primary dealers are not correlated with mutual fund outflows. However, they 

also find that, based on the “taper tantrum” experience, dealer inventory and mutual fund outflows are positively 

correlated which suggests that dealers inventory might be a good metric of market liquidity in the stress period. 
UNITED STATES

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69 styles81 capturing their investment objectives. For each market under consideration (mortgage, 

corporate, municipal, government bonds), the universe of funds captured by this approach includes 

both index funds and hybrid funds. Opting for this style-based approach means greater coverage of 

funds investing in each market of interest, but it also entails a greater aggregation in the markets 

analyzed. For example, it is possible to stress market liquidity in corporate bonds markets but not 

separately for investment grade and high yield. The choice of such an approach is justified by the 

fact that it is important to capture a full universe of funds. The calculations were based on granular 

data on mutual funds from the Center for Research in Security Prices (CRSP). The cut-off date for the 

analysis was the third quarter of 2014. 

140. The IMF staff compared assets sold by mutual funds hit by a shock to data on dealers' 

inventory. If dealers’ inventory would be smaller than assets sold by mutual funds this would 

indicate potential liquidity pressure on mutual funds that invest in the assets sold in that particular 

market. This might also give rise to fire-sale risks on that particular market and might imply that 

investors in the funds exposed to those markets have to take a haircut on their investment. 

141. The shock was defined as a one time, tail event redemption shock. The distribution of 

net flow rates, including both net inflows and net outflows, by fund style was analyzed. Net flow rates 

were defined on the monthly basis as a simple average of net flow rates of all mutual funds of the 

same style over the period 1998-2014Q3. The first percentile of net flow rate distribution was taken 

as the stress redemption shock. Averaging redemptions across component market segments (such as 

investment grade and high yield) was a simplifying assumption that may affect the results of the 

analysis. 

142. Once a mutual fund is hit by a redemption shock it would have to sell its assets to meet 

redemptions. The following two sets of assumptions on redemption induced assets sold were made: 

 Approach 1 (“pro rata”): Pro-rata selling of assets was assumed i.e. assets were sold to meet 

the redemptions by making sure that the structure of assets is intact. This assumption is a 

natural one to adopt for the case of index funds which would be expected to sell assets to 

meet redemption demand in a way that seeks to keep portfolio weights unchanged to 

continue minimizing tracking error relative to their benchmark. 

 Approach 2 (“waterfall”): Mutual funds were assumed to rank order assets held by their 

liquidity characteristics, as captured by the LCR haircut hierarchy, selling assets to meet 

redemptions in descending order of liquidity. Specifically, the assets were assumed to sold in 

 

81 In the context of this technical note, mutual fund styles, such as equity-based or fixed income-based investment 

styles, follow a modified version of the CRSP style codes. These modified CRSP style codes consist of up to four 

characters, with each character position representing a distinct style attribute and an increased level of granularity. 

Modified CRSP style codes were created by IMF staff in consultation with CRSP, and differ from traditional style codes 

in that they establish a subset/superset relationship between related styles. 
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70 INTERNATIONAL MONETARY FUND

Proportion of asset sold

1. Cash 100%

2. Government securities 100%

3. MBS 100%

4. Equity 20%

5. Corporate bonds 20%

6. Munis 100%

7. Equity 40%

8. Corporate bonds 40%

Redemption shock: 

1st percentile

Total corporate 

bonds sold

Dealers' inventory of 

corporate bonds

the following order (Figure 11):82 cash is the first asset to be used to meet redemptions, 

government securities are second, MBSs are third (assuming most are GSE-backed MBSs), 

then 20 percent of equity and corporate bonds, then municipal bonds (whole portfolio) and 

then an additional 40 percent of equity and corporate bonds. 

 Under both approaches, realized assets sales due to the tail event shocks were added up 

across all funds included in the exercise and for each asset market and then compared to 

dealer inventory, which is was used as a proxy for assessing market makers’ (dealers) 

willingness and/or ability to make markets in, and as an indicator of general market demand 

for, a given asset class. 

Figure 11. Mutual Fund Liquidity Analysis: The Waterfall Approach 

Source: IMF Staff 

143. The results of the analysis, under both approaches, suggest that municipal bonds and 

corporate bonds markets may face significant stress when faced with tail event redemption 

shocks. The analysis illustrates the danger that funds that invest in corporate and municipal bonds 

might sell these assets at a fire-sale discount to meet redemptions. Under the same tail event shock, 

municipal bonds that might be sold to meet the redemptions could be three (in the case of the 

assumed ordering of assets sold) to four times (in the case of pro-rata asset selling) larger than the 

dealers’ inventory of municipal bonds (Figure 12). Similarly, the analysis shows that the volume of 

corporate bonds sold under severe stress by mutual funds could be up to seven times larger than 

what dealers currently hold in inventory. 

 

82 The assumed ranking of assets sold was based on economic reasoning and expert insights. Nonetheless, it might 

not apply for funds of different styles. Ideally, the ranking should take into account their mandate and their strategy, 

which was resolved by the first approach. 
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0

50,000

100,000

150,000

200,000

250,000

300,000

MBSs Munis Corporate bonds

Sold assets under stress and dealers' inventories, million USD

Sold assets- assumed ordering Dealers' inventory Sold assets- pro-rata

Figure 12. Results of the Liquidity Risk Analysis 

Note: “Sold assets- pro-rata” represent asset sold by mutual funds hit by a tail event redemption shock that have to sell their

assets pro-rata i.e., by making sure that the structure of assets is intact (approach 1). 

“Sold assets- assumed ordering” represent asset sold by mutual funds hit by a tail event redemption shock that have to sell their 

assets in descending order of liquidity (approach 2)

Source: IMF Staff calculations.

144. A number of caveats precluded a more detailed analysis of the liquidity risks in the 

mutual fund industry. First, not all mutual funds were included in the analysis, and a larger sample 

would make the assets sold under stress larger, underscoring the vulnerabilities identified. Second, 

the data used in the analysis may not have been granular enough (both on the mutual funds’ asset 

structure and on the structure of dealers’ inventories) to fully capture the liquidity dynamics of a 

potential asset fire-sale. For example, both domestic and foreign corporate bonds were bundled in 

the same category of corporate bonds and compared to dealers’ inventories of corporate bonds. This 

prevented the analysis from discriminating among potential differences in the liquidity profiles of the 

two bonds types.83

145. To address the caveats and more closely examine the potential illiquidity of mutual 

fund assets, IMF staff performed a separate security-level analysis of mutual fund portfolio 

holdings. This was done by combining portfolio security information from CRSP with security issue(r) 

information from the CUSIP Global Service’s CUSIP Master File and the Mergent Fixed Income 

Securities Database, and then aggregating security holdings into homogenous asset classes, based 

upon their perceived liquidity characteristics. Unfortunately, the issuer information from the latter 

 

83 A separate liquidity risk analysis was performed for MMMFs. The test was geared toward measuring the extent to 

which MMMFs have enough liquid assets to meet in an orderly manner redemptions given a market shock. A liquidity 

metric was adopted to assess whether the MMMFs analyzed had adequate levels of liquid assets that could be 

converted into cash to meet the liquidity demands of a 1 percent probability net outflow (i.e., the 1 percentile of the 

historical distribution of net flows). In this case, haircuts were applied to liquid assets, in line with the Basel III Liquidity 

Coverage Ratio, to ensure that an unexpected market event could potentially cause certain previously liquid securities 

to become illiquid. The results suggest that the MMMFs analyzed would not face significant liquidity risks as their 

liquid assets were three to five times larger than the liquidity demanded by the tail event redemptions. However, only 

a small subset of MMFs with detailed asset data was available in the CRSP dataset used in the analysis. 
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72 INTERNATIONAL MONETARY FUND

two sources was found to contain a significant domestic equity reporting bias or to only cover a 

small fraction of the securities present in the CRSP holdings database.84 Accordingly, the aggregated 

asset classes were deemed unrepresentative of the broader mutual fund holdings universe, and 

liquidity assessments using these asset classes as inputs were not pursued further. 

146. In the absence of data constraints, the “ideal” liquidity analysis would utilize detailed 

supervisory data on fund holdings and explicitly take into account individual fund 

characteristics and market-level liquidity information. This includes information such as 

investment mandates and trading volumes, when estimating redemption risk and the potential for 

asset fire sales. For each individual fund, such an analysis would: (i) determine tailored liquidity 

waterfalls based upon investment mandates, (ii) apply extreme redemption shocks to assess the 

immediate demand for cash, (iii) use the liquidity waterfall to estimate which, and how much of, each 

security should be sold to meet redemptions, and (iv) use cumulative information on security bid-ask 

spreads and trading volumes to estimate what the sales price of each security sold would be. The 

liquidity analysis would then aggregate the value of the securities sold across funds into granular 

asset classes, and compare these values to what would have been obtainable under ordinary market 

conditions in order to determine which particular asset classes may be most prone to fire-sale 

dynamics. 

147. The authorities are encouraged to step up work to assess susceptibility of markets to 

extreme mutual fund redemptions. The authorities should further clarify the guidance on liquidity 

risk analysis performed by the industry. It is important that the authorities mobilize the resources 

necessary to regularly conduct liquidity risk analyses as part of their overall approach to mutual fund 

industry oversight. 

IMF STAFF’S MARKET-PRICE BASED STRESS TESTS 

148. This section presents the results of IMF staff’s market-price based analysis and stress 

tests. This exercise is intended to complement the analysis above by taking into account the 

information about risk that is embodied in market prices, which allows consideration of correlations 

between institutions and higher frequency and more timely assessments. There are important 

caveats that must be acknowledged, however. The findings are necessarily sensitive to: 

methodological issues or choices – such as simplifying assumptions; the use of imperfect proxies; the 

selection of sample periods; heterogeneity across model, data, and variable definitions; and potential 

endogeneity.85 Accordingly, in many cases these findings should be interpreted as informative 

 

84 The CUSIP master file was found to contain information for 56 percent of the securities with CUSIPs in the CRSP 

holdings database. Of these observations with available records, about 90 percent were for equities. The Mergent 

database contains information for only 8 percent of the securities with CUSIPs in the CRSP holdings database. 

Crosschecks on the CRSP holdings database using two–digit issue CUSIP codes found that the broad breakdown of 

equity and fixed income securities differed materially from what was observed in the sample of aggregated results. 

85 Where possible, IMF staff made attempts to mitigate methodical issues by adding control variables and by 

performing robustness checks, however, it was not always possible to completely eliminate their influence. 

Simplifying assumptions were often used when more complex and realistic processes could not be utilized, and these 

(continued) 
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approximations, as opposed to precise estimates, which primarily highlight the co-behavior of risk 

factors rather than causal relationships between them. Despite these caveats, the market-price based 

analysis and stress tests are useful tools for examining the financial stability landscape. For this 

reason, they have been used extensively in previous work by IMF staff as well as many central banks 

and others analyzing systemic risk. 

149. The section is divided into two complementary parts. First, a broad survey of available 

systemic risk measures is presented and their historical evolution is examined. Second, a stress 

testing analysis of the market-implied interaction between default risk and the macroeconomic 

environment is conducted using the Contingent Claims Analysis (CCA) framework. Both parts use 

high-frequency, forward-looking market consensus information to “cross-check” the findings of IMF 

staff’s other stress test methodologies. They also extend stress-test coverage to sectors which are not 

traditionally subject to microprudential oversight, such as non-bank non-insurance financial 

institutions, and help to compensate for a lack of access to supervisory data. 

A. Systemic Risk Dashboard 

150. To limit model risk, this technical note adopts a multi-model approach to identifying 

systemic risk in the United States. There exist numerous definitions of systemic risk, and the 

methodologies employed to measure such risk are diverse as well. Accordingly, this technical note 

adopts a multi-faceted approach, called a “systemic risk dashboard”, which employs a range of 

measures in an effort to identify the numerous dimensions in which a threat to financial stability may 

arise. The dashboard helps to inform the risk-based assessment process and to guard against 

another important type of risk known as “model risk”, which is the excessive reliance on a single 

modeling framework.86 

Key Dashboard Findings for the United States 

 Market-based measures point to a reduction in the systemic risks of banks. Systemic 

RISK87 (“SRISK”)—a well-known measure of market-implied capital shortfall for a given bank 

 

assumptions may not always accurately reflect actual dynamics. Specific factors can often be technically difficult to 

identify and better proxies may exist than the ones that were used to approximate them. Similarly, the categories 

used in analyses may have been defined or aggregated in either too broad and or too narrow a manner to 

unambiguously identify the impact of important effects. Sample periods may not have fully captured the evolution of 

relevant tail risks, which could potentially influence estimated relationships and limit the comparability of findings 

across different risk measures. Finally, endogeneity may not have always been fully controlled for, complicating 

identification in models with jointly estimated variables. Endogeneity can arise either because of reverse causation or 

because of omitted explanatory variables, and its presence makes it difficult to determine whether findings are truly 

causal or simply driven by a set of common unidentified factors. 

86 Not all measures presented in the systemic risk dashboard are appropriate for assessing all dimensions of systemic 

risk. For a fuller discussion of these metrics, including their strengths and weakness, see Blancher and others (2013) 

and Office of Financial Research Working Paper #1 as well as their references. 

87 Specifically, SRISK is an estimate of the capital that a financial firm would need to raise if a severe financial crisis 

were to occur. See Acharya and others (2012) for additional details. 
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74 INTERNATIONAL MONETARY FUND

or given banking system—suggests that systemic risk posed by banks has declined towards 

to its pre-crisis average. SRISK capital shortfalls peaked at approximately one trillion dollars 

in early 2009 but have now fallen to 300 billion dollars. This level is roughly commensurate 

with average pre-crisis shortfall estimates and constitutes approximately 2 percent of GDP 

(Figure 13). One caveat is that SRISK exclusively measures systemic risk in the banking sector. 

Risks posed by nonbanks are covered in subsequent analysis. 

Figure 13. SRISK Market Implied Capital Shortfalls 

Source: NYU Stern Volatility Lab, as of end 2014Q 

Figure 14. Early Warning Indicators

Source: IMF staff calculations. 

 Standard early warning indicators of banking distress are also reassuring. Both financial 

cycles88 and credit-to-GDP gap89 measures (Figure 13)—widely-used early warning indicators 

 

88 Financial cycles are computed using the BIS bandpass filter methodology (BIS, 2011; Otker-Robe et al, 2015) and 

capture the co-movement between credit growth and residential property prices. Empirical evidence finds that 

(continued) 

0

0.2

0.4

0.6

0.8

1

1996 1999 2002 2005 2008 2011 2014

Cycle Amplitde

Financial Cycle Position

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

1996 1999 2002 2005 2008 2011 2014

Deviation from Trend

Credit-to-GDP Gap

U.S. cycle inflection point 
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INTERNATIONAL MONETARY FUND 75

of impending domestic financial crises—signal that the United States banking system is 

relatively healthy from a cyclical perspective. Downward inflections in financial cycles and 

positive credit-to-GDP gaps often coincide with periods of financial distress. 

Figure 15. Equity Price Misalignment Measures

Source: IMF staff calculations. 

 Equity price indicators suggest that valuations may be stretched relative to 

fundamentals. Percentage deviations of observed equity prices from theoretical prices 

based on Asset Pricing Theory (left) and the Equity Composite Z-score90 (right) (Figure 14) 

indicate the degree to which equity prices are misaligned with economic fundamentals. 

Figure 14 shows that these indicators are either positive or have been trending into positive 

territory, which suggests that equity price levels may be approaching unsustainably high 

levels.

 Housing price indicators are at normal levels, but need to be closely monitored. Turning 

to measures of credit and housing price sustainability (Figure 16), credit-based measures 

(top) are still well below their pre-crisis levels, indicating that excessive credit growth does 

not pose an immediate financial stability risk to the United States. However, growth in 

 

downward inflections in a country’s financial cycle can be a predictive sign of an impending domestic financial crisis. 

For example, the dashed black line shows a downward inflection for the United States in 2007, which suggests the 

possible onset of a financial crisis in the following years (i.e., 2008-09). 

89 The credit-to-GDP gap is defined according to current Basel Committee on Banking Supervision guidance (BCBS, 

2010) as the difference between the credit-to-GDP ratio to its long term trend, calculated using a one-sided HP filter 

with a smoothing parameter of 400,000. When a country’s credit-to-GDP gap shows a positive deviation from trend, 

this is taken as a signal that domestic credit growth may be excessive and contributing to a credit bubble. 

90 The Equity Composite Z-Score is defined as the arithmetic mean of the deviation of observed equity prices from 

APT-implied equity prices (Figure 13 left) and other traditional valuation measures such as dividend yields, price-tobook, price-to-earnings, 12-month forward price-earnings, and price-to-cashflow ratios. All component values are 

normalized into z-scores and aggregated to the country-level prior to averaging. Positive values indicate equity 

overvaluations. 

-60

-40

-20

0

20

40

2004 2006 2008 2010 2012

Percent

Deviation from APT-Implied Equity Prices*

-1.70

-1.20

-0.70

-0.20

2004 2006 2008 2010 2012 2014

Z-score

Equity Composite Z-score
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76 INTERNATIONAL MONETARY FUND

housing prices (bottom) has fully regained its pre-crisis momentum and needs to be carefully 

monitored. 

Figure 16. Fundamentals in Housing and Credit

Source: IMF staff calculations. 

 The potential threat posed to the financial system from credit risk migration has declined 

significantly since 2007-09, but remains a concern. An analysis of U.S.- and foreign entitybased credit risk networks (Figure 17) suggests that there has been material decline in the 

susceptibility of institutions to credit risk migration, as denoted by the decrease of dense, red 

connections in the current period relative to 2007–09. Nonetheless, the role played by GSIBS in 

the transfer of credit risk appears to remain important. 

-6

-1

4

9

2004 2006 2008 2010 2012 2014

Percent Growth (YoY)

Total Credit to the Non-Financial Private Sector

-15

-10

-5

0

5

10

2004 2006 2008 2010 2012 2014

Percent Growth (YoY)

Bank Credit to the Non-Financial Private Sector

50

60

70

80

90

100

110

120

130

140

2000Q1 2004Q1 2008Q1 2012Q1

Ratio Level ( Base Year = 2009)

Price-to-Income Ratio

-20

-10

0

10

20

30

2004 2006 2008 2010 2012 2014

Percent (YoY)

Real Housing Price and Mortgage Rate Growth

Mortage Rate Growth Real Housing Price Growth
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Figure 17. Credit Risk Networks

2007–09 2012–14 

Source: IMF staff calculations. 

Note: The credit risk networks shown were created by applying bivariate Granger-Causality tests to time-series of 198 institutions’ firstdifferenced default probabilities. These tests were applied 21 times at differing specifications and the color the connecting lines reflects 

the robustness of the identified Granger-casual connections. Red connections were found in 95 percent or more of trials, dark gray 

connections were found in 70 percent or more of trials, and light gray connections were found in at least 50 % of trials. Thick black 

connections represent membership in the network’s minimum spanning tree. Node size represents the GSIB vs non-GSIB (large vs. small) 

network dimension and node color (blue vs. gold) reflects the domestic vs. foreign dimension. The 21 testing specifications are

combinations of three different test significance levels (1%, 5%, and 10%) and seven different lag orders (orders 1 through 7).

 Individual financial system sectors are exposed to each other, and to the rest of the world, 

through a common set of financial instruments (Figure 18).

 Asset managers, insurers, pensions, and households are sizable net claimants on the 

corporate equities and corporate bonds issued by other sectors, such non-financial 

corporations (“corporate”) and nonbank financial institutions (NBFIs).

 Asset managers, insurers, pensions, and households, in addition to banks, also serve as 

notable net claimants on the government through their holdings of treasury, agency, and 

municipal debt securities.

 The largest net claims on fund shares, the primary instrument used by asset managers to 

raise capital, are attributable to the households and pensions, although banks, insurers, and 

non-financial corporate also possess net claims on this sector as well. 

 Money market instruments act an important conduit between households, asset managers 

and banks.

 Banks, corporates, households, and GSEs have net exposures to all other sectors, and to 

abroad, via an assortment of deposits, loans, and mortgages.

 Government and pensions are significantly exposed to households and corporates via 

pension entitlements.
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 On a net basis, the rest of the world is exposed to the U.S. financial system primarily through 

net claims on money market instruments, corporate equities, and other direct investment. 

Foreign net claims on the United States take the form of shares in investment funds, 

government securities, corporate bonds, loans, mortgages, and miscellaneous deposits.

 Publicly available locational data on OTC credit-default-swaps (CDS) shows that both the rest 

of the world and the aggregate U.S. financial system have sizable outstanding exposures to 

these instruments. Unfortunately, additional detail on the nature of these credit risk 

exposures, or other OTC derivatives exposures, cannot be obtained without access to 

confidential trade information.
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Figure 18. Financial Account Net Exposures 

Source: FRB; BIS; IMF staff calculations 

Note: Yellow nodes denote sectors and blue nodes denote instruments. Node size represents the total value of balance sheet 

assets held by each sector or cumulatively outstanding for each instrument as of 2014Q3. The directed lines between each 

node, and the color of these lines, represent the net exposure of a given sector to a given instrument. Black lines denote the net 

claims of a sector, and red lines denote net liabilities of a given sector, in the form of a given instrument. Line thickness signifies 

the magnitude of net claims (red lines) and net liabilities (black lines). The thicker (thinner) a line, the larger (smaller) the size of 

a given net exposure. Net exposures were calculated for each sector-instrument combination by subtracting (netting) liabilities

from asset claims. 

All lines are directed, but arrowheads may not always appear due to formatting limitations. Black lines without visual 

arrowheads are always directed from a sector to an instrument. Similarly, arrow-less red lines are always directed from an 

instrument to a sector. The dashed line effects do not have a semantic significance and are used only as a visual aid. 

The instrument “CDS” represents the aggregate, over-the-counter (OTC) credit default swap (CDS) exposure attributable to 

different domiciles as calculated by official BIS statistics. The box titled “U.S. CDS Exposure” represents the aggregate OTC CDS 

exposure of U.S. counterparties that cannot to be attributed to specific domestic sectors. Detailed sector and instrument 

definitions are presented in Appendix Table 7. 
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 Comprehensive measures of systemic risk suggest that nonbanks contribute more to 

systemic risk than banks. An important analytical tool in this regard is the Systemic Risk 

Indicators (SyRIN) framework, which uses distress-based metrics91 to assess the level of 

systemic risk present in a given set of financial sectors. SyRIN has been used in earlier studies, 

for example in the October 2014 Global Financial Stability Report (IMF, 2014b).92 An 

examination of the U.S. financial system using this framework finds that although systemic 

risk in the United States appears to be declining towards pre-crisis level, confirming the 

findings from narrower measures such as SRISK, areas of concern remain. The framework 

highlights that in terms of asset size, banks account for less than 30 percent of the U.S. 

financial system, with more than 70 percent accounted for by nonbanks, and these asset 

shares were broadly unchanged between 2010Q4 and 2014Q4 (Figure 19, top chart). But the 

SyRIN framework also shows that there are parts of the nonbank sector that contribute to 

systemic risk more than one could expect based on their size. The marginal contribution to 

systemic risk (MCSR) of the U.S. high yield and insurance sectors is disproportionately large 

relative to their share of financial system assets. At the same time, for example, the pension 

sector’s ratio of MCSR to size is approximately 0.6. This means that the pension sector 

contribution to systemic risk is lower relative to its size, hence this sector appears to diversify 

risk from the financial system (Figure 19, bottom chart). A longer-term analysis of the ratio of 

MCSR relative to size suggests that these ratios for High Yield Bond Funds, IG Bond Funds 

and Hedge Funds have shown a significant increasing trend.

 

91 In the SyRIN framework, “distress” is defined as an event whereby the index used to represent returns in a given 

sector falls to a level which history would suggest should only be observed 1 percent of the time. SyRIN estimates of 

joint distress incorporate co-movements in market prices. Market prices can reflect consensus views on distressed 

portfolio reallocations and other indirect effects. 

92 See Box 3 and Chapter 2 of IMF (2014b) and Segoviano and others (2015) for additional details. SyRIN and CCA 

(presented later) are independent frameworks and their findings, definitions, and approaches to measuring systemic 

risk should not be conflated. 
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Figure 19. Systemic Risk Indicators Framework, 2010Q4 and 2014Q4 

Sector Sizes (percent of total financial system assets)

Marginal Contribution to Systemic Risk, As a Multiple of Size 

Source: IMF staff calculations based on methodology described in Segoviano and Goodhart (2009) and expanded in the October 

2014 Global Financial Stability Report (IMF 2014b); and Segoviano and others (2015). 

Note: The top chart shows sectors’ shares of financial system total assets. The bottom chart shows each sector’s contribution to 

systemic risk relative to its asset size. Ratios exceeding 1.0 indicate that the systemic importance of a given sector is greater than 

what would be suggested by asset size alone. In the SyRIN framework, “distress” is defined as an event whereby the index used to 

represent returns of a given sector falls to a level that should only be observed 1 percent of the time based on history. The SyRIN 

definition of “distress” is independent of the Contingent Claims Analysis (CCA) definition of “default.” HY = high yield, IG= 

investment grade, MMMFs = money market mutual funds. 

151. In sum, the findings of the Systemic Risk Dashboard suggest that although threats to 

financial stability have diminished since the peak of the crisis, they remain worthy of 
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continued close monitoring, especially with regard to nonbanks. Financial stability indicators in 

the United States have broadly improved, but areas of concern do exist, especially outside the 

banking system. Equity prices levels, housing price growth, and the vulnerability of banks to distress 

originating in the high yield and insurance sectors should be assessed more closely. The metrics 

presented thus far focus on historical developments in the financial system and serve as a baseline 

assessment. Against this background, the next section uses a hybrid market/balance-sheet 

framework known as Contingent Claims Analysis (CCA) to explore how financial risks may evolve 

under the IMF/DFAST stress scenario. 

Box 3. Systemic Risk Indicators (SyRIN) Framework: A Primer

Contagion through interconnectedness across financial institutions and sectors plays an important role 

in the realization of systemic risk. The recent crisis underlined that proper estimation of contagion risks 

among financial institutions and sectors in a financial system is essential for effective financial stability 

assessment. The realization of simultaneous large losses in various entities would affect a financial stability, and 

thus represents a major concern for regulators. Thus, the analysis of systemic risk should aim at understanding 

these contagion risks (due to direct and indirect linkages across financial institutions) and their changes across 

the economic cycle. 

The SyRIN framework used in the market-price based systemic risk assessment draws upon recent 

financial stability literature and a wealth of previous analytical work to estimate systemic risk. Earlier 

versions of this approach were used in the 2010 FSAP as well as the October 2014 Global Financial Stability 

Report (IMF, 2014b). The SyRIN employs distress-based metrics to assess the level of systemic risk present in a 

given set of financial sectors. The SyRIN framework is independent of the CCA analysis, and their findings, 

definitions, and approaches to measuring systemic risk should not be conflated. 

The SyRIN framework conceptualizes the financial system as a portfolio of entities, which spans banks 

and non-banks alike, and incorporates the effect of interconnectedness into its risk estimates. Most 

empirical literature on systemic risk measurement has tended to focus on a single sector, typically the banking 

sector, without taking into account the rest of the financial system. Research concerned with analyzing risks 

associated with nonbank financial institutions has only recently started to emerge. By providing a 

comprehensive treatment of both bank and non-bank financial sectors, SyRIN is able to analyze system-wide 

risks in a manner consistent with empirical facts (Espinoza and Segoviano 2014). For the purposes of this 

Technical Note, the SyRIN portfolio includes the largest domestic banks, insurance companies, pensions, mutual 

fund and hedge fund sectors. 

Contagion through interconnectedness in the financial system can happen due to direct and indirect 

linkages across sectors. Direct linkages occur through the direct exposure channel via inter-sector exposures 

(interbank deposits, lending, syndicated loans), derivative transactions and exposures counterparty bankruptcy. 

Indirect linkages can be due to exposures to common risk factors which surface in periods of economic and 

financial distress, either through the asset liquidation channel or through liquidity-induced fire-sales. Other 

associated factors include general increases in investor risk aversion during stress periods, the effect of 

“herding” on investor portfolio reallocation decisions, and actions taken by short-sellers to drive down stock 

prices for financial firms which follow complex or opaque business models or rely on embedded leverage. 

SyRIN estimates are drawn from a multivariate joint probability density which accounts for financial 

system interconnectedness. This density models the distribution of asset price returns for a financial system 

portfolio that explicitly accounts for the dependence structure (defined in terms of direct and indirect linkages) 

of its portfolio components. This approach allows SyRIN estimates to capture the joint effect of changes in asset 

values for sectors throughout a financial system. To infer this density, SyRIN uses a robust, non-parametric 

approach that incorporates endogenous changes in sector co-dependence. When economic conditions 
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deteriorate, the density’s “distress” dependence structure increases, thereby increasing its probabilistic 

estimates that sectors will fall into distress.

SyRIN uses a nonparametric multivariate density to measure financial stability from several 

complementary perspectives. Measurement takes the form of estimation (via simulation) of distressed losses 

to the financial system, and is the basis for the calculation of a given sector’s MCSR. The MCSR captures the 

effect of portfolio sectors’ interconnectedness (co-dependence) and relative size, and can be used in 

conjunction with complimentary joint and conditional probability estimates, which are also drawn from SyRIN’s 

multivariate density, to round out its assessment of systemic risk. 

B. Contingent Claims Analysis: Stress Testing for Systemic Risk 

152. Contingent Claims Analysis (CCA)93 uses equity prices and accounting information to 

measure the credit risk of institutions with publicly traded equity. The CCA framework is useful 

because it provides forward-looking default probabilities which take into account both leverage 

levels and market participants’ views on credit-quality. Another benefit is that it provides a 

standardized benchmark of credit risk, known as default probabilities, that facilitate cross-sector and 

cross-country comparisons. However, CCA does suffer from some theoretical shortcomings. Namely, 

it can only be applied to entities with either publicly-traded equity or very liquid CDS spreads, and it 

cannot capture liquidity or (financing) roll-over risk. CCA risk measures are calculated at the firmlevel and complement the high-level survey of risk measures presented in the systemic risk 

dashboard with micro-based information on financial risks. 

153. The central idea behind CCA is that an institution’s risk of default is driven by the level 

and uncertainty in its asset values relative to the promised payments on its debt obligations. 

Assets of a financial institution or corporation are uncertain and change due to factors such as profit 

flows and risk exposures. Default risk over a given horizon period is driven by uncertain changes in 

future asset values relative to promised payments on debt– where these payments are often referred 

to as the “default barrier”. As first introduced by Merton (1973), the key financial insight used to 

quantify this asset/debt inter-relationship is that equity values can be modeled as an implicit call 

option on assets, with an exercise price equal to a default barrier, and that risky debt can be modeled 

as the default-free value of debt minus an implicit put option (i.e. an expected loss due to default). 

Accounting information on an institution’s debts and market information on the price of its 

outstanding equity are used to calibrate CCA balance sheet risk indicators and to estimate forwardlooking probabilities of default. 

154. The CCA and SyRIN are two different frameworks that are independent of and 

complementary to each other; their findings, definitions, and approaches to measuring 

systemic risk should not be conflated. Importantly, CCA uses “default probabilities” as its measure 

of credit/default risk, whereas SyRIN uses “distress probabilities” in its financial risk estimates. CCA 

default probabilities are derived from a structural model (Merton, 1973) and signify the chance that 

 

93 For a fuller description of CCA, see the 2010 US FSAP Technical Note on Stress Testing July 2010 pages 59-62 and 

Appendix VIII pages 98 -99 and IMF WP 13/218. 
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the value of a given financial institution’s (market-adjusted) assets will fall below a specific liability 

threshold (its default barrier). SyRIN distress probabilities are defined as the chance that the value of 

a given financial sector’s market equity will fall below the 1st percentile value of its historical equity 

price distribution. Also, the sectors analyzed in the SyRIN and CCA approaches are not necessarily 

identical. For example, only SyRIN’s analysis includes sectors such as pensions and high yield bonds 

funds and only CCA’s analysis includes foreign banking and insurance sectors. (See relevant sections 

of the market-price based stress tests for additional details.) 

CCA Stress Test Data 

155. The historical default probability estimates used in the CCA stress tests were acquired 

from CreditEdge.94 CreditEdge follows several broad steps in its production of this key stress testing 

input. 

i. For each institution, daily equity values, equity volatilities, and default barriers are 

calculated. 

ii. The inputs from step (i) are then used to simultaneously solve two structural CCA 

equations and estimate market-implied asset values and market-implied asset volatility. 

iii. “Distance-to-default” indicators are computed using the inputs from the first two steps 

and are then mapped to empirically observed one-year default probabilities using 

Moody’s extensive historical default database. 

156. Default probabilities can also be mapped to credit-risk boundaries, such as credit 

spreads or ratings. Empirical research suggests that an approximate investment-grade “safe zone” 

for financial institutions corresponds to an expected one-year default probability of 0.5 percent or 

less.95

157. The CCA stress tests covered 210 institutions from a mixture of domestic and foreign 

domiciles and eight different sectors (Table 18). The eight sectors consisted of: domestic banks 

("banks"); life, health, and property/casualty insurers ("insurers"); investment management 

companies, REITS, and private equity firms ("asset managers"); large, publicly-traded nonfinancial 

firms (corporates); other nonbank financial institutions (NBFIs), government sponsored housing 

enterprises ("GSEs");96 all non-U.S. domiciled G-SIBs ("foreign banks"), and all G-SIIs as well as other 

large foreign insurers ("foreign insurers"). In addition, an overall U.S. financial system sector was 

created using data pooled from among all the domestic U.S. sectors, excluding GSEs. 

 

94 Data were generated using CreditEdge’s 9.0 Public Firm Model. See Moody’s Analytics EDF 9: Introduction and 

Overview (February 2015) for additional details.

95 The safe zone boundary value is based upon extensive research by Moody’s Analytics, shared with IMF staff. 

96 Due to data quality constraints, default probabilities for GSEs which correspond to the period following their 

conservatorship were omitted from the final results of the CCA stress tests. 
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Table 18. CCA Stress Test Sample Data

Number Selection Criteria

Asset Managers 41 10 billion USD plus market cap 

NBFIs 13 10 billion USD plus market cap 

Insurers 44 20 billion USD plus market cap 

Corporates 32 

Includes the largest non-financial DJIA public companies, 

auto makers that received government support, “new 

economy” technology companies with large and rapidly 

growing market capitalization (e.g., Facebook) 

Banks 46 20 billion USD plus market cap 

GSEs 2 Must have entered government conservatorship 

Foreign Banks 20 All foreign banks designated by the FSB as GSIBs 

Foreign Insurers 12 All foreign insurers designated by the FSB as GSIIs plus the 

largest non-U.S. domiciled global insurers 

Total 210

Source: IMF staff calculations 

158. The CCA stress tests utilized a connectivity variable and a number of credit risk and 

macroeconomic variables (Table 19). For comparability purposes, the macro variables used in the 

stress testing scenarios were identical to those used in the top-down balance-sheet based stress 

tests run for banks. Daily historical data was used in estimations and covered a ten-year period 

spanning end 2004Q3 to end 2014Q3. Additional information is listed below: 

 Daily default probability data was acquired from CreditEdge and daily, monthly, and quarterly 

macroeconomic data was from obtained from Haver Analytics and Bloomberg. 

 Inter-connectivity data was calculated by IMF staff, using the process outlined in the 

subsequent section on the stress testing methodology, on a rolling monthly basis over the 

period of 2004–2014. 

 All quarterly and monthly variables, including connectivity, were temporally disaggregated to 

a daily frequency using the Chow-Lin Max-Log methodology. This includes all data contained 

in the baseline and stress scenarios as well. 

 The connectivity measure used for projections was extended forward in time using the 

following two assumptions: (i) the connectivity trend which was observed in 2008–2010 
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would repeat itself, then (ii) remain at its post-crisis historical average once this value has 

reached. 

 All default probabilities used in the CCA stress test models are median one-year, expected 

default probabilities. For each sector, daily median default probability time series were 

computed from observations pooled at the sector level. Daily U.S financial system median 

default probabilities were generated from observations pooled from all domestic sectors, 

excluding GSEs. 

Table 19. Variables Used in CCA Stress Tests 

Source: IMF staff. 

CCA Stress Test Methodology 

159. To estimate the relationship between connectivity, macroeconomic factors, and 

median default probabilities, the CCA stress tests utilized a class of models known as General 

Adaptive Models of Location, Shape, and Scale (GAMLSS). GAMLSS, described in detail in 

Stasinopoulos and Rigby (2007), is an extremely flexible model class which allows one to: (i) utilize a 

wide variety of distributions to characterize the response variable and (ii) explicitly model the first 

four moments of these distributions as functions of exogenous conditions. As a result, the framework 

is well suited to address the presence of tail-risks, nonlinearities, and deviations from the normality 

assumption. The default probability data used in the CCA exercise exhibited all of these latter 

characteristics, thereby motivating the choice of the GAMLSS model class for stress testing purposes. 

160. The GAMLSS modeling process follows an iterative approach consisting of eight steps. 

i. Fit approximately 100 different distributions to the response variable (i.e., median 1-year 

default probabilities). 
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ii. Compare the quality of each fit using the Generalized Akaike Information Criteria (GAIC) and 

select the distribution with the best score. 

iii. Using the distribution chosen in step (i), run information criteria based selection 

procedures97 to identify the independent variables with the most linear explanatory power. 

iv. For variables not selected in step (iii), transform them using additive terms (e.g., orthogonal 

polynomials, penalized basis splines, etc.) and repeat the prior step’s selection procedure. 

v. Once a final set of independent variables has been determined, experiment with different 

additive terms to enhance model fit. 

vi. Compare all experimental models generated in step (v) using the GAIC and select the model 

with the best score. 

vii. Repeat steps (i) through (vi) for each distributional parameter (i.e., the mean, variance, 

skewness, and kurtosis).

viii. Use diagnostics to assess whether the model residuals are supportive of the assumed 

response distribution. If not, return to step (i) and repeat the process using the next best 

GAIC-identified distribution.

161. To avoid over-fitting and to test the out-of-sample predictive power of stress test 

projections, stiff penalties98 were imposed on model complexity when computing GAIC-based 

model selection scores and a back-testing regime was used throughout the modeling process. 

The back-testing regime consisted of (i) using a quantile-based sampling algorithm99 to partition the 

overall dataset into separate validation, testing, and training datasets, and (ii) using the validation 

dataset for calculating initial distributional fits; using the training dataset to estimate the regression

coefficients and for variable selection; and using the testing dataset to gauge out-of-sample model 

performance.100

 

97 The model selection procedure is a generalized version of the AIC-based algorithm of Venables and Ripley (2002). 

The procedure performs forward and backward model selection over a model space that is bounded by user-specified 

lower and upper limits on model complexity. The selected model will have the highest GAIC among all possible 

models considered and will exhibit a level of complexity which is somewhere between that of the lower and upper 

complexity limits. 

98 Following suggested best practice (Kim and Gu, 2004), a penalty value of k=2.8 was selected when computing GAIC 

scores. 

99 Quantile-based sampling was used to create data partitions by first splitting a dataset into quasi-homogenous 

subgroups, where group membership was based on percentile values, and then performing random sampling within 

each subgroup. This method of random sampling is used to preserve the distributional structure of samples drawn 

from each split of the response variable and to help balance the data partitions. 

100 Out-of-sample model performance was assessed by comparing the global deviance and mean prediction error 

scores of training and testing dataset model fits. Large discrepancies in scores suggest poor out-of-sample 

performance. 
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162. The GAMLSS model for the overall U.S. financial system suggests that macroeconomic, 

sector credit risk, and interconnectivity factors influence credit risk levels in the United States 

in significant, and often non-linear, ways. The model finds that median 1-year default probabilities 

for the U.S. financial system are expected to fall linearly with rises in long-term Treasury yields, 

housing and commercial real-estate prices and with appreciation of the U.S. dollar against the Euro. 

Default probabilities are expected to increase linearly with BBB-rated corporate bond yields and 

bank, insurer, asset manager, other non-bank financial institution, foreign bank, and foreign insurer 

median default probabilities. The magnitude of these co-movements differs by for each variable, but 

changes in BBB corporate bond yields, long-term Treasury yields, and asset manager default 

probabilities have some of the greatest effects on financial system credit risk. The model also finds 

many non-linear relationships. U.S. credit risk slightly rises with euro-area consumer inflation, and 

falls with declines in real GDP growth and the VIX and Dow Jones Industrial indices. Overall credit risk 

in the U.S. also rises with increases in corporate credit risk, but only in non-linear statistically 

significant manner. A simplified summary of the final specification for the overall U.S. financial system 

model is presented in Appendix Table 5. Appendix Figure 8 shows the estimated relationships for the 

model’s four non-parametric additive terms. 

163. A single connectivity measure was calculated and used as an explanatory variable in 

each of the estimated GAMLSS models. This connectivity measure was calculated using 

institutional-level default probability time-series data from the following five domestic sectors: 

banks, insurers, asset managers, other non-bank financial institutions, and nonfinancial corporates. 

This measure, formally known as a “global clustering coefficient”, was derived using the following 

three-step process: 

 Perform Spearman Rank Correlation Tests to identify institutional default probabilities that 

are correlated at the .0001 percent significance level. 

 Construct an adjacency matrix from the test results, and use this matrix to derive a 

“correlation network.” 

 Calculate the network’s global clustering coefficient score. 

The above three-step process was repeatedly applied to one-month rolling windows spanning the 

period of 2004Q3 to 2014Q3. Monthly values were then temporally disaggregated using the ChowLin Max-Log methodology to generate a daily connectivity time series. Figure 20 shows the 

corresponding quarterly series, which was calculated using both domestic and foreign sectors. The 

figure underscores the increase in connectivity over time. Statistical tests suggest that a structural 

break occurred in 2008. After the break, connectivity has a higher mean and lower variance, 

suggesting that the financial system is more “consistently connected” post-2008.
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Figure 20. Financial System Connectivity 

Source: IMF staff calculations based on data from Moody’s CreditEdge. 

Note: The clustering coefficient measures network connectivity on a scale from 0 to 1 and its values reflect the probability that a set 

of points within a given network will share a direct relationship. The clustering coefficient scores for the U.S. financial system were 

calculated by first applying correlation tests to one-year, rolling windows of firm-level measures of default risk and assessing the 

clustering present in these test results. The correlation tests are based on two-sided, non-parametric Spearman rank correlation 

tests, performed at the 0.001 percent significance level. The score at each point in time represents the connectivity present between 

firms with highly correlated default risk over the prior year-to-date. The firm-level measures of default risk used in the underlying 

calculations were monthly default probabilities for approximately 210 domestic and foreign entities. The red line is a structural 

break, as identified by a pruned exact linear time (PELT) mean-variance test performed at a 1 percent significance level. 

Connectivity has a higher mean and lower variance after the break which suggests that the financial system is more “consistently 

connected” post-2008. 

CCA Stress Test Results 

164. The CCA stress tests estimated the relationship between connectivity, the 

macroeconomic environment, and median default probabilities using separate GAMLSS 

models for each sector and for the overall U.S. financial system. These models were used to 

assess the impact of macroeconomic changes and fluctuations in connectivity under the “baseline” 

and “stress” scenarios and to individually project default probabilities for the overall U.S. financial 

system and for five domestic and two foreign sectors. The CCA stress test scenarios are identical to 

those used in the balance sheet stress tests for banks (i.e., identical to the IMF/DFAST scenarios). In 

order to isolate the impact of potential spillovers, the CCA stress tests controlled for a number of 

different factors. The use of quantile-based default probabilities served as a control for idiosyncratic 

risk at the firm level. The presence of macro variables with high explanatory power in the estimated 

regressions was used to control for macroeconomic risks and the inclusion of a statistically significant 

connectivity measure was used as a control for other relational changes between sectors. 101 Given 

 

101 Specifically, the connectivity measure was used to assess whether changes in default probabilities of one sector 

could be directly/indirectly attributed to changes in the credit risk of another sector. 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Clustering Coefficient Score
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these controls, the relationships estimated between sector default probabilities are believed to 

effectively capture the effect of intersectoral spillovers. 

165. Under the stress scenario, expected one-year median default probabilities for the U.S. 

financial system are projected to increase from 0.20 percent to 0.65 percent, about two-thirds 

the level seen at the height of the 2008–09 financial crisis. Figure 21 shows the historical 

evolution of overall system median default probabilities along with forecasts under the baseline and 

stress scenarios. On average, default probabilities for all sectors (Appendix Figure 9), are expected to 

increase to about two thirds of their 2008–09 levels, with the exception of corporates and asset 

managers which are expected to experience smaller increases. Using a threshold of 0.5 percent or 

lower as a low credit risk boundary,102 under the stress scenario banks, insurers, NBFIs, and foreign 

banks exit this “safe zone” during the period of peak stress which occurs in 2015–16. Corporates, 

asset managers, and foreign insurers never breach this boundary though, even under severely 

adverse macroeconomic conditions. Only projections for the overall U.S. financial system model 

explicitly take into account changes in the estimated default probabilities of other sectors. Individual 

sector projections (Appendix Figure 9) were generated exclusively using macroeconomic and 

connectivity factors. 

Figure 21. U.S. Financial System Median Default Probability Forecast

Source: IMF staff calculations. 

Note: Blue lines indicate 25th and 75th percentile values of the distribution of historical estimates of U.S. institution 1-year ahead 

default probabilities. The dashed black line denotes the median value of the distribution of historical estimates of U.S. institution 1-

year ahead default probabilities. The solid red and black lines denote median 1-year ahead default probabilities projected by the 

CCA stress tests under the stress and baseline scenarios, respectively. To better show projection details, the y-axis has been 

truncated at 1 percent. The line denoting the 75th percentile reached a maximum value of 2.5 percent in 2008-09. Only projections 

for the overall U.S. financial system model explicitly take into account changes in the estimated default probabilities of other 

sectors. Individual sector projections (Appendix Figure 9) were generated exclusively using macroeconomic and connectivity 

factors. 

 

102 The threshold value of 0.5 percent is based upon extensive research done by Moody’s Analytics and was conveyed 

to IMF staff via personal communication. 
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166. The analysis shows the importance of spillovers across sectors. Spillovers from the United 

States to the rest of the world were found to be large, but spillbacks from rest of the world to the 

United States appear relatively limited. The estimated effect of credit risk shocks (Figure 22) varies 

from sector to sector, but several observations are worth noting. 

 Shocks to the credit profiles of U.S. asset managers, NBFIs, insurers, and foreign banks 

increased U.S. bank credit risk the most.103

 Increases in overall U.S. credit risk and credit risk in foreign insurers most negatively 

impacted the foreign banking sector, whereas the foreign insurance sector is most negatively 

affected by adverse changes in the credit profile of U.S. asset managers and insurers. 

 The effect of a rise in default risk in foreign banks and foreign insurers on U.S. default risk 

appears small. Excluding the impact of macro factors, non-U.S. domiciled banks and insurers 

were found to collectively account for only 3 percent of median U.S. default probabilities. 

Under the 2015–16 peak period of the stress scenario, this contribution is projected to fall by 

an additional one-third, to 2 percent of the total attributable to non-macro factor 

components. 

 

103 The effect of insurers on banks may appear quantitatively smaller than suggested by the SyRIN approach used in 

the previous section. This reflects methodological differences between the two approaches, namely the fact that 

SyRIN focuses on the joint probability of distress, while the present approach explicitly takes into account the effect of 

macroeconomic variables on probabilities of distress. In other words, there are risks of spillovers from insurers to 

banks, but they partly reflect common (macroeconomic) factors.
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Figure 22. Spillover Map 

Effect on Sector X of a 0.5 percent Default Probability Increase in Sector Y

Source: IMF staff calculations. 

Note: The figure shows the marginal contribution (in percent) of a one-time shock on a recipient sector’s default probabilities given 

a severe 50 basis point (0.5 percent) shock to an originating sector’s default probabilities. The results were calculated using the 

GAMLSS inter-sector relationships estimated as of end 2014Q3. Sector end of period default probabilities were separately 

increased by 50 basis points and shocks were measured as the deviation in a recipient sector’s probability of default from its 

unshocked historical baseline. Dashed red lines denote cross-border effects of credit risk shocks. For this exercise, spillover effects 

were captured by incorporating historical estimates of median sector default probabilities as independent variables into the final 

specification of each of the sector projection models used to generate the results shown in Appendix Figure 8. 
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INTERNATIONAL MONETARY FUND 93

RECOMMENDATIONS FOR IMPROVEMENT 

167. While the authorities’ solvency stress test for BHCs are well advanced, and state-ofthe-art in many respects, there is scope for enhancement. It would be useful to try to link 

liquidity, solvency and network analysis in a systemic risk stress testing framework. For example, Bank 

of Canada’s Macro Financial Risk Assessment Framework captures the various sources of risk 

(solvency, liquidity and spillover effects) within a single stress testing framework (Bank of Canada, 

2014). Moreover, reexamining some of the solvency stress test assumptions to make them consistent 

with historical evidence would be useful. For example, there may be merit in reexamining the stress 

test assumptions on loan and asset growth as well as dividend distribution. 

168. Increasing the coverage of the tests would be helpful. In particular, the FRB should 

include the largest SLHCs in the supervisory stress tests once they start performing company-run 

stress tests (from 2017). 

169. Establishing a regular liquidity stress testing framework for banks will be an important 

further step. The announced Comprehensive Liquidity Analysis and Review (CLAR), that is expected 

to be launched by the end of 2015, is a step is the right direction. This will complement the solvency 

testing under the DFA. 

170. Another area for improvements relates to modeling network contagion. The network 

contagion exercise here illustrates the need to expand the FRB’s data on interbank exposures to 

include a richer set of dynamics and a broader range of counterparties. 

171. In insurance, the focus should be on developing and performing insurance stress tests 

on a consolidated, group-level basis. This is especially important for groups that are (i) designated 

as systemically important; (ii) engaged in material group-internal risk transfer, e.g., via captives; or 

(iii) exposed to non-linear market risks through the sale of products which include guarantees or 

optionalities, e.g., variable annuities. It would also be useful to improve public disclosure by requiring 

insurance companies to disclose market risk sensitivities in a more harmonized manner. 

172. Regular system-focused liquidity risk analysis for the mutual fund industry should be 

done on a regular basis. At present, a considerable range of bottom-up analyses is performed by 

the industry. According to the authorities, rule-making is forthcoming to standardize stress tests by 

mutual funds with consolidated assets of $10 billion or more. The authorities are encouraged to 

further clarify the guidance to the industry on liquidity risk analysis, and to start conducting regular 

top-down analysis to provide a more holistic picture of the industry’s contribution to systemic risk. 

173. The authorities are encouraged to conduct more intensive monitoring of systemic 

financial sector risks, including the use of market-based solvency and shortfall measures. 

Market-price based stress tests employ forward-looking, higher-frequency, market consensus 

information that, when used appropriately, can add value to traditional stress tests in a variety of 

ways. While the market-price based analysis has its limitations, it can be a useful “cross-check” to 

corroborate the findings of other stress test methodologies. They can also be readily extended to 

assess the safety and soundness of sectors which are not traditionally subject to bank-like 

supervisory oversight. 
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Acharya, Viral, Robert Engle, and Matthew Richardson, Capital Shortfall: A New Approach to Ranking 

and Regulating Systemic Risks, AEA, January 7, 2012—SRISK Model, NYU Lab (New York: 

New York University). 

Adrian, Tobias, Michael Fleming, Jonathan Goldberg, Morgan Lewis, Fabio Natalucci, and Jason Wu , 

2013: “Dealer balance sheet capacity and market liquidity during the 2013 selloff in fixedincome markets”, Liberty Street Economics, 16 October. 

Bank of Canada, 2014, “Stress Testing the Canadian Banking System: A System-Wide Approach”, 

Financial System Review, June (Bank of Canada) 

Basel Committee on Banking Supervision (BCBS), 2010a, “Guidance for national authorities operating 

the countercyclical capital buffer” http://www.bis.org/publ/bcbs187.pdf (BCBS: Basel). 

———, 2010b, “Basel III: International Framework for Liquidity Risk Measurement, Standards and 

Monitoring,” December (BCBS: Basel). 

———, 2013, “Basel III: The Liquidity Coverage Ratio and liquidity risk monitoring tools,” January 

(BCBS: Basel). 

———, 2014, “Basel III Monitoring Report,” September (BCBS: Basel). 

Blancher, Nicolas, Srobona Mitra, Hanan Morsy, Akira Otani, Tiago Severo, and Laura Valderrama, 

2013, “Systemic Risk Monitoring “Sysmo” Tool Kit - A User Guide”, IMF Working Paper 

13/168. http://www.imf.org/external/pubs/cat/longres.aspx?sk=40791

Bednar, William, and Mahmoud Elamin, 2014. “Rising Interest Rate Risk at US Banks,” Federal 

Reserve Bank of Cleveland, Economic Commentary, 2014–12. 

Bisias, Dimitrios, Mark Flood, Andrew Lo, and Stavros Valavanis, 2012, “A Survey of Systemic Risk 

Analytics,” Office of Financial Research, US Department of Treasury.

Čihák, Martin, 2007, “Introduction to Applied Stress Testing,” IMF Working Paper 07/59 

(Washington: International Monetary Fund), available at 

http://www.imf.org/external/pubs/ft/wp/2007/wp0759.pdf. 

Espinoza, Raphael, and Miguel Segoviano, 2014, “Probabilities of Default and the Market Price of 

Risk in a Distressed Economy,” IMF Working Paper 11/75 (Washington: International 

Monetary Fund). 
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Espinosa-Vega, Marco, and Juan Solé, 2011, “Cross-Border Financial Surveillance: A Network 

Perspective,” Journal of Financial Economic Policy, Vol 3 (3). 

Fender, Ingo and Ulf Lewrick, 2015: “Shifting tides – market liquidity and market-making in fixed 

income instruments”, BIS Quarterly Review, March 2015 (Basel: BIS). 

Financial Stability Oversight Council, 2015, 2015 Annual Report (Washington: Financial Stability 

Oversight Council). 

Glasserman, Paul and Gowtham Tangirala, 2015, “Are the Federal Reserve’s Stress Test Results 

Predictable?” OFR Working Paper No. 15–02, March 3. 

Goldman Sachs, 2015, “Inventories and liquidity likely to dip on summer seasonal”, The Credit Line, 

June 25, 2014. 

Gray, Dale, Robert Merton, and Zvi Bodie, 2008, “A New Framework for Measuring and Managing 

Macrofinancial Risk and Financial Stability,” Harvard Business School Working Paper No. 

09/15 (Cambridge, MA: Harvard Business School). 

Gray, Dale, and Samuel Malone, 2008, Macrofinancial Risk Analysis (London: Wiley Finance). 

Gray, Dale, Marco Gross, Joan Paredes, and Matthias Sydow, 2013, “Modeling Banking, Sovereign, 

and Macro Risk in CCA Global VAR” , IMF Working Paper 13/218 (Washington: International 

Monetary Fund). 

Hirtle, Beverly, Kovner, Anna Vickery, James and Meru Bhanot, 2014, “Assessing Financial Stability: 

The Capital and Loss Assessment under Stress Scenarios (CLASS) Model”, Federal Reserve 

Bank of New York Staff Report 663, February. 

International Monetary Fund (IMF), 2010, U.S. Financial Sector Assessment Program—Technical Note 

on Stress Testing, IMF Country Report 10/244 (Washington: International Monetary Fund). 

———, 2011, “United Kingdom FSAP Update: Stress Testing the Banking Sector Technical Note,” 

IMF Country Report 11/227 (Washington: International Monetary Fund). 

———, 2014a, “People’s Republic of China–Hong Kong Special Administrative Region: Technical 

Note on Stress Testing the Banking Sector,” IMF Country Report 14/210, July 16 (Washington: 

International Monetary Fund), available at 

http://www.imf.org/external/pubs/ft/scr/2014/cr14210.pdf. 

———, 2014b, Global Financial Stability Report October 2014: Risk Taking, Liquidity, and Shadow 

Banking: Curbing Excess While Promoting Growth (Washington: International Monetary 

Fund), available at http://www.imf.org/external/pubs/ft/gfsr/2014/02 . 
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Jobst, Andreas, Heiko Hesse, Li Lian Ong, and Christian Schmieder, 2013, “A Framework for 

Macroprudential Liquidity Stress Testing: Concepts and Application to S-25 and Other G-20 

Country FSAPs,” IMF Working Paper 13/68 (Washington: International Monetary Fund). 

Kim, Y.J. and C. Gu, 2004, Smoothing spline Gaussian regression: More scalable computation via 

efficient approximation. J. Roy. Statist. Soc. Ser. B 66, pp. 337–356. 

Merton, Robert, 1973, Theory of rational option pricing, Bell Journal of Economics and Management 

Science, Vol. 4 (1), pp. 141–183. 

Office of Financial Research, 2014, 2014 Annual Report,(Washington: Office of Financial Research). 

Segoviano, Miguel, and Charles Goodhart, 2009, “Banking Stability Measures”, IMF Working Paper 

09/4 (Washington: International Monetary Fund). 

Segoviano, Miguel, and others, 2015, “Systemic Risk and Interconnectedness (SyRin): A 

Comprehensive Multi-sector Framework, forthcoming IMF Working Paper (Washington: 

International Monetary Fund). 

Schmieder, Christian, Heiko Hesse, Benjamin Neudorfer, Claus Puhr, and Stefan Schmitz, 2012, “Next 

Generation Balance Sheet Stress Testing—Liquidity Module,” IMF Working Paper 12/3 

(Washington: International Monetary Fund). 

Schmieder, Christian, Claus Puhr, and Maher Hasan, 2011, “Next Generation Balance Sheet Stress 

Testing,” IMF Working Paper 11/83 (Washington: International Monetary Fund). 

Stasinopoulos, Dimitrios, and Robert Rigby, 2007, “Generalized Additive Models for Location, Scale 

and Shape (GAMLSS) in R”, Journal of Statistical Software, Vol. 23 (7). 

Swiss Re, 2007, Pandemic influenza: A 21st century model for mortality shocks (Zürich: Swiss Re). 

Venables, William, and Brian Ripley, 2002, Modern Applied Statistics with S. Fourth Edition. Springer. 

ISBN 0-387-98825-4. 
Appendix I. Risk Assessment Matrix and Stress Test Matrix

Appendix Table 1. Risk Assessment Matrix

Nature/Source of Risk Likelihood of Severe Realization of 

Threat in the Next 1–3 Years 

 Expected Impact on F

Threat is R

1. Abrupt surge in market volatility 

High Recent compression in volatility and risk premia could unwind. Stress in credit markets (especially cov-lite loans) could be exacerbated by increased exposure to more risky borrowers, rising leverage, and weaker underwriting standards. Impaired trading liquidity for high yield issues could aggravate the risks. Bond repricing could lead to a run on mutual funds. Run risk may be intensified by the increased holdings of retail investors (over the past five years, the share of credit instruments held by retail funds has increased 

substantially, to 37 percent of total credit holdings). Duration and interest rate risk could materialize, as they are both at recent historical highs and financial institutions’ portfolio allocations to fixed income instruments remain above the recent historical trend. 

High A 50 bps permanent increase icould subtract about 1⁄2 percenSpikes in term premia could imRuns from mutual funds can leloop between outflows and ass

2. Imbalances from protracted period of low interest rates 

Medium Search for yield leads to excess leverage, weaker underwriting standards 

and potential mispricing of risk. Continued low interest rates can give rise 

to new configuration of risk in the insurance and pension fund industry. In combination with the relatively weaker supervision in the nonbank sector, this can further increase intermediation outside the banking system and 

purchases of riskier assets by traditional and shadow banking system (e.g. asset managers). 

High If unaddressed, distortions couinstability with significant econspillovers to the rest of the wo

3. Operational risk High 

Operational risk stemming from, for example, software or hardware failure, a cyber event, or a major natural disaster. 

Medium Disrupting or destroying a critito sizeable impacts on the finaif a large solar storm similar in the world now (an event with alikelihood in the next 10 years)trillion dollars with power and for months. 

INTERNATIONAL MONETARY FUND 97
Note: The risks are ordered first by impact (high to low), and second by likelihood (high to low). 

Appendix Table 1. Risk Assessment Matrix (concluded)

Likelihood of Severe Realization of 

Threat in the Next 1–3 Years 

 Expected Impact on 

Threat is R

4. Protracted period of slower growth and lower inflation in advanced and emerging economies 

High Lower-than-anticipated potential growth and persistently low inflation leads to secular stagnation in advanced economies. Maturing of the cycle, misallocation of investment, and incomplete structural reforms leads to prolonged slower growth in emerging markets. 

Medium Slower growth in advanced ancould subtract about 1⁄2 perce

5. Geopolitical risks Medium 

Spillover effects from increased geopolitical tensions surrounding Russia/ Ukraine; heightened geopolitical risks in the Middle East, leading to a sharp rise in oil prices. 

Low Geopolitical tensions would crin global financial, trade and cin oil prices would have a negeconomy and financial sector safety resulting in dollar apprepercent increase in oil prices asubtract about 0.2 percent of 

6. Bond market stress from a reassessment in sovereign risk 

Low Interest rates could spike if the budget is not passed in 2015 or the federal borrowing limit is not raised (owing to political gridlock). Protracted failure 

to agree on a credible plan for fiscal sustainability could lead to a rise in the risk premium.

High The economic cost of a sharp premium could be sizeable. If could have severe global spilloA 200bps increase in the bencwould subtract 2.5 and 1.5 pegrowth in 2015 and 2016, resp

98 INTERNATIONAL MONETARY FUND
Appendix Table 2. Stress Test Matrix: Solvency, Liquidity, and Network Effects

Domain Assumptions 

Bottom-Up by Financial 

Institutions 

Top-Down by Authorities Top-do

BANKING SECTOR: SOLVENCY RISK

1.Institutional Perimeter

Institutions included

 31 largest bank holding companies (with total consolidated assets of $50 billi

Market share  85 percent of total banking holding companies’ assets Data and baseline date

 BHCs’ own data 

 Consolidated banking group 

 Baseline date: 2014 Q3 

 Supervisory data (FR Y-14) 

 Consolidated banking group 

 Baseline date: 2014 Q3 

 Publicly av

 Consolida

 Baseline d

2. Channels of Risk Propagation

Methodology  BHCs’ internal models consistent 

with supervisory expectations 

 Fed’s top-down approach similar 

to balance sheet-based approach based on granular data 

 Balance sh

Satellite Models for MacroFinancial linkages

 Macro-financial linkages: 

Companies were required to calculate, under the scenarios mentioned over the stress horizon, potential losses, pre-provision net revenue, provision for loan losses and capital levels. The FRB generally expects BHCs to use model or other quantitative methods for estimating profits and balance sheet items or process that are well supported, transparent and repeatable in instances where qualitative approaches are more appropriate. 

 A general expectation was that: (i) 

companies develop internal models that best capture their 

 Macro-financial linkages: Preprovision net revenue was 

forecasted using a series of autoregressive models that relate the components of a BHC’s revenues and non-credit-related expenses, expressed as a share of relevant asset or liability balances, to BHC characteristics, and to macroeconomic variables; the models were developed using pooled historical data from many financial institutions, either supervisory data collected by the FRB or proprietary industry data. 

 The projections of revenues, 

expenses, and losses were based on the Fed’s projections of the 

 Macro-fin

statement sheet itemassets) weforecasted regression (ii) univariusing aggincome, Aeffects). Bincluded mexplanato

INTERNATIONAL MONETARY FUND 99 
Domain Assumptions 

Bottom-Up by Financial 

Institutions 

Top-Down by Authorities Top-do

risks, rather than relying on models that closely mirror supervisory models, (ii) Data used should have contained sufficiently adverse outcomes, (iii) balance sheet and net income items to be projected at sufficient granularity. 

 Companies used a range of 

quantitative approaches to estimate losses. These include loan losses, losses of available-for-sale and held-to-maturity securities, operational risk losses, trading losses. 

 Methods used to estimate loan 

losses ranged from accounting based loss approach (charge-off and recovery) or expected losses (calculated a function of probability of default, loss given default and exposure at default). Projections of income statement items should have been consistent with scenario conditions and the current and projected paths of on and off balance sheet exposures. 

balance sheet for each BHC over

the planning horizon. Most components of pre-provision net revenue which contained consolidated income statement and balance sheet information for each BHC (including components of interest income, noninterest income, and noninterest expenses) were projected using data on historical revenues and operating and other non-credit-related expenses reported on the FR Y-9C report. 

 Loan losses were projected 

separately for different categories of loans based on the type of obligor (e.g. consumer, commercial loans), collateral (e.g. residential real estate, CRE), loan structure (e.g. revolving credit lines) and accounting treatment (accrual or fair value). Loan data and information about loan portfolios, including borrower characteristics, collateral characteristics, characteristics of the loans or credit facilities, amounts outstanding and yet to 

100 INTERNATIONAL MONETARY FUND
Domain Assumptions 

Bottom-Up by Financial 

Institutions 

Top-Down by Authorities Top-do

 be drawn down (for credit lines), 

payment history, and current payment status came from FR Y-14 report. Data were collected on individual loans or credit facilities 

Stress test horizon

 2014Q4-2016Q4  2014Q4-2016Q4  2014Q4-2

3. Tail shocks Scenario 

analysis 

 Baseline: reflected the average 

projections from the most recently available consensus view of the macroeconomic outlook expressed by professional forecasters, government agencies and other public sector organizations 

 Adverse: represented conditions 

that are more adverse than those in the baseline scenario but less severe than those in the severely adverse scenario. It was formulated as one-half or twothirds of the deviation of the paths of the variables relative to the baseline or severely adverse scenario. It also includes specific risk assumptions not found in the other scenarios, such as the rise in short-term interest rates. 

 Severely adverse (stress): 

reflected conditions of a typical or 

 Baseline: reflected the average 

projections from the most recently available consensus view of the macroeconomic outlook expressed by professional forecasters, government agencies and other public sector organizations 

 Adverse: represented conditions 

that are more adverse than those in the baseline scenario but less severe than those in the severely adverse scenario. It was formulated as one-half or twothirds of the deviation of the paths of the variables relative to the baseline or severely adverse scenario. It also includes specific risk assumptions not found in the other scenarios, such as the rise in short-term interest rates. 

 Severely adverse (stress): 

reflected conditions of a typical or 

 Baseline: 

broadly sithat follow WEO; 

 Stress: DF

scenario tshaped re2016 follorecovery. 

INTERNATIONAL MONETARY FUND 101
Domain Assumptions 

Bottom-Up by Financial 

Institutions 

Top-Down by Authorities Top-do

specific recreation of a post-war U.S. recession. In the scenario unemployment rate rose by a 4 percentage point rise over a twoyear period. Real GDP was 4.5 percent lower than the baseline by the end of 2015 (GDP growth rates were negative for 5 quarters), equity prices fell by 60 percent in one year, house prices declined by 25 percent over the first two years, corporate spreads rose by 330 basis points, and mortgage rates increased by 80 basis points. 

 BHCs’ developed scenarios: 

baseline and severely adverse which reflected a company’s idiosyncratic risks and unique vulnerabilities to different factors including firm-specific events 

specific recreation of a post-war U.S. recession. In the scenario unemployment rate rose by a 4 percentage point rise over a twoyear period. Real GDP was 4.5 percent lower than the baseline by the end of 2015 (GDP growth rates were negative for 5 quarters), equity prices fell by 60 percent in one year, house prices declined by 25 percent over the first two years, corporate spreads rose by 330 basis points, and mortgage rates increased by 80 basis points. 



Sensitivity analysis/one time add-on shock 

 Global market shock: an add-on 

shock applied to six companies with significant trading activity to their trading book and private equity positions (including their CVA) as of October 16, 2014; entailed estimating trading and counterparty mark-to-market losses and incremental default risk 

 Global market shock: an add-on 

shock applied to six companies with significant trading activity to their trading book and private equity positions (including their CVA) as of October 16, 2014; entailed estimating trading and counterparty mark-to-market losses and incremental default risk 

 Interest ra

 Oil price s

2014-201

 Provisions 

offs in a pframewor

 Constant 

 Constant 

assets groyear 

102 INTERNATIONAL MONETARY FUND
Domain Assumptions 

Bottom-Up by Financial 

Institutions 

Top-Down by Authorities Top-do

on their trading exposures. This shock consisted of moves in market prices and rates, much larger than in any scenario provided by the supervisors and was assumed to be an instantaneous event which immediately affects the market value of the trading assets and liabilities. 

 Counterparty default shock: an 

add-on shock involved the instantaneous and unexpected default of the companies’ counterparty with the largest net stressed losses as of October 16, 2014. This shock was applied to eight large and highly interconnected companies. The shock was applied to companies’ securities lending and repurchase/reverse repos and derivative exposures.

on their trading exposures. This shock consisted of moves in market prices and rates, much larger than in any scenario provided by the supervisors and was assumed to be an instantaneous event which immediately affects the market value of the trading assets and liabilities. 

 Counterparty default shock: an 

add-on shock involved the instantaneous and unexpected default of the companies’ counterparty with the largest net stressed losses as of October 16, 2014. This shock was applied to eight large and highly interconnected companies. The shock was applied to companies’ securities lending and repurchase/reverse repos and derivative exposures. 

 No operat

calculatio

 DFAST hu

4.Risks and Buffers

Risks/factors assessed (How each element is derived, assumptions)

 Credit risk (granular sectoral 

exposures). 

 Credit risk (granular sectoral 

exposures). 

 Tax rate: After-tax net income (or 

loss) was calculated by applying a consistent tax rate to pre-tax net income (or loss) for all BHCs. 

 Credit risk 

corporateinstitution

 Taxes: set 

tax rate ov

INTERNATIONAL MONETARY FUND 103
Domain Assumptions 

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Institutions 

Top-Down by Authorities Top-do

Behavioral adjustments 

 It was expected that companies 

project balances for each major segment of the balance sheet consistent with scenario conditions and competitive and strategic factors. 

 For supervisory scenarios two sets 

of capital actions were used. One using BHC’s planned capital action and one using the DFA stress test capital action assumptions. For BHC-defined scenarios the companies used planned capital actions- a BHC’s planned capital actions under the BHC-baseline scenario and alternative capital actions- a BHC’s assumed capital action under the BHC stress scenario 

 Under the DFA stress test capital 

action assumption, for the first quarter of the stress horizon, companies took into account its actual capital actions as of the end of the calendar quarter. For each of the second through ninth quarter, companies were required to use a standardized set of capital action assumptions that are specified in the Dodd-Frank Act 

 The FRB projection of BHC 

balances sheets began with a set of models that relate total assets in the banking industry and important subcomponents, such as total loans and non-loan assets, to nominal GDP and other macroeconomic factors, including a measure of loan supply. These relationships were estimated using aggregate data and were used to project the growth in industry assets and its subcomponents over the planning horizon under each of the supervisory scenarios. 

 In the adverse and severely 

adverse scenarios, the measure of loan supply was assumed to remain at its long-run historical average over the planning horizon 

 Industry assets, loans, and trading 

assets were allocated to each BHC based on its shares of these positions at the beginning of the planning horizon. 

 All types of loans at all BHCs were 

assumed to grow at the same rate, which meant that the mix of loans at each BHC was constant over the planning horizon. 

 Balance sh

deposits gbe equal tmodeled apanel regand macro exogenou

 Dividend 

followed calso takinsurchargemaximum to dividen(dividends the base-ycapital cowere paid records pr

 Asset disp

over time the portforemained with matuwith simila

104 INTERNATIONAL MONETARY FUND
Domain Assumptions 

Bottom-Up by Financial 

Institutions 

Top-Down by Authorities Top-do

stress test rules. The assumptions were the following: (i) Common stock dividend payments were assumed to continue at the same level as the previous year; (ii) Scheduled dividend, interest or principal payments on any other capital instrument eligible for inclusion in the numerator of a regulatory capital ratio were assumed to be paid; (iii) No redemption or repurchase of any capital instrument eligible for inclusion in the numerator; (iv) New common stock, preferred stock or other instruments that would be included in regulatory capital, except for common stock issuance associated with expensed employee compensation, could not be issued. 

 Trading assets were assumed to 

grow at the growth rate of total assets, adjusted for changes in mark-to-market values reflecting industry-wide trading asset composition, while non-loan assets other than trading assets and securities grew at the same rate for all BHCs. 

 Securities growth at each BHC was 

set so that total asset growth equals the projected rate given the growth of other types of assets at that firm. 

 Balance sheet projections 

incorporated expected changes to a BHC’s business plan, such as mergers, acquisition, and divestitures, that are likely to have a material impact on the its capital adequacy and funding profile 

 Any capital actions that are 

designed to offset the impact of the stress scenario on the bank are not allowed 

 Under the DFA stress test capital 

action assumption, for the first quarter of the stress horizon, companies took into account its 

INTERNATIONAL MONETARY FUND 105
Domain Assumptions 

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Institutions 

Top-Down by Authorities Top-do

 actual capital actions as of the end 

of the calendar quarter. 

 For each of the second through 

ninth quarter common stock dividend payments were assumed to continue at the same level as the previous year 

5. Regulatory and Market-Based Standards and Parameters

Calibration of risk parameters 

 Company own calibration of 

parameters 

 PDs were generally modeled as 

part of a transition process in which loans move from one payment status to another (e.g., from current to delinquent) in response to economic conditions. LGD were typically defined as a percentage of EAD and was based on historical data. 

 Total RWA

panel regtotal assetmake RWA

 2015 switc

approach calibrated RWAs usinapproachegeneralize

 Operation

approacheparallel rugrow with 

Regulatory/Accounting and Market-Based Standards

 Capital standards: The capital 

definition corresponded to that required by local regulation, i.e., Basel III for advanced approaches BHCs and non-advanced approaches BHCs (from January 1, 2015) and Basel I for nonadvanced approached BHC for the first quarter of the stress test 

 Capital standards: The capital 

definition corresponded to that required by local regulation, i.e., Basel III for advanced approaches BHCs and non-advanced approaches BHCs (from January 1, 2015) and Basel I for nonadvanced approached BHC for the first quarter of the stress test 

 Capital sta

standards estimated approacheapproachefrom Janu

 Hurdle rat

(regulatorcapital co

106 INTERNATIONAL MONETARY FUN 106 INTERNATIONAL MONETARY FUND
Domain Assumptions 

Bottom-Up by Financial 

Institutions 

Top-Down by Authorities Top-do

horizon (last quarter of 2014). Companies were required to assume consistency with the Basel III transition schedule 

 Starting in 2015, the revised capital 

framework introduced a new standardized approach for risk weighting assets, which replaced the calculation of risk weights using the general risk-based capital approach. 

 Capital metrics: Tier 1 common 

capital ratio, common equity tier 1 ratio, Tier 1 capital ratio, total capital ratio and tier 1 leverage ratio 

horizon (last quarter of 2014). Companies were required to assume consistency with the Basel III transition schedule 

 Starting in 2015, the revised capital 

framework introduced a new standardized approach for risk weighting assets, which replaced the calculation of risk weights using the general risk-based capital approach. 

 Capital metrics: Tier 1 common 

capital ratio, common equity tier 1 ratio, Tier 1 capital ratio, total capital ratio and tier 1 leverage ratio 

 Hurdle rates: Basel III minimum 

capital requirements; capital conservation and GSIB surcharge were not considered as part of hurdle rates 

capital subuckets); from CET1 phase outtaken into advanced 

 Risk-weighted assets: generalized 

approach (2014Q3-2014Q4); standardized approached to calculating RWAs from January 2015 

 RWAs were projected based on 

corresponding projections of onand off-balance sheet exposures and their risk attributes were 

 Risk-weighted assets: generalized 

approach (2014Q3-2014Q4); standardized approached to calculating RWAs from January 2015 

 Two components of RWAs were 

projected: market RWAs and two types of credit RWAs, generalized RWAs under the capital framework 

 Risk-weig

approach standardizcalculating 2015 

 Total RWA

panel regtotal assetmake RWA

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Domain Assumptions 

Bottom-Up by Financial 

Institutions 

Top-Down by Authorities Top-do

consistent with the scenario conditions 

and standardized RWAs under the revised capital framework. 

 Generalized risk weights were 

imputed from FR Y-9C data. These weights were held fixed throughout the forecast horizon. Credit RWAs were calculated under the revised capital framework implementing the Basel III regulatory capital regime in the United States (standardized approach RWAs) from 2015q1 onwards. The weights used to calculate credit RWAs were held fixed throughout the planning horizon. Market RWAs were broken down into components that are cyclical and non-cyclical. The first group, which includes value at risk and the incremental risk charge, was projected based on the volatility of the trading portfolio of the BHCs. The second group, which includes value at risk, stressed value at risk, the specific risk charge, and the comprehensive risk charge, was assumed to evolve according to projections of the BHCs’ trading 

 2015 switc

approach calibrated RWAs usinapproachegeneralize

 Operation

approacheparallel rugrow with 

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Domain Assumptions 

Bottom-Up by Financial 

Institutions 

Top-Down by Authorities Top-do

 assets 

 Operational RWAs were not 

considered 

BANKING SECTOR: LIQUIDITY RISK

1.Institutional Perimeter

Institutions included

 N.A.  N.A.  3

Market share  N.A.  N.A. Data and baseline date

 N.A.  N.A.  Publicly av

 Consolida

2014Q3. 

2. Channels of Risk Propagation

Methodology  N.A.  N.A.  Liquidity m

the ratio ooutflows uoutstandi

3. Tail shocks Size of the 

shock

 N.A.  N.A.  Shocks re

factors (harates) appliquid assewere infordynamics outflow ca

4.Risks and Buffers

Risks  N.A.  N.A.  Funding li

 Market liq

Buffers  N.A.  N.A.  Liquid ass

INTERNATIONAL MONETARY FUND 109
Domain Assumptions 

Bottom-Up by Financial 

Institutions 

Top-Down by Authorities Top-do

5. Regulatory and Market-Based Standards and Parameters

Calibration of risk parameters 

 N.A.  N.A.  Benchmar

based on calibrated experienc

 Sensitivity 

calibrated 

Regulatory standards 

 N.A.  N.A.  Threshold 

BANKING SECTOR: SPILLOVER RISKS

1.Institutional Perimeter

Institutions included

 N.A.  N.A.  6 largest B

Market share  N.A.  N.A.  52 percenData and baseline date

 N.A.  N.A.  2014Q3 

2. Channels of Risk Propagation

Methodology  N.A.  N.A.  Espinosa a

analysis 

3. Tail shocks Size of the 

shock

 N.A.  N.A. 

4. Risks Risks  N.A.  N.A. 

INSURANCE SECTOR: SOLVENCY RISKS

1.Institutional Perimeter

Institutions included

 N.A.  Full coverage of solo entities 

- 751 life insurers - 2,569 P&C insurers 

 43 Insuran

- 20 life in- 16 P&C - 5 health 

110 INTERNATIONAL MONETARY FUND
Domain Assumptions 

Bottom-Up by Financial 

Institutions 

Top-Down by Authorities Top-do

 - 2 credit 

Market share  N.A.  100 percent  40 percen

premiums

Data  N.A.  Statutory accounting  U.S. GAAP 

 Additiona

statutory 

Baseline date  N.A.  End-2013  End-2013 

2. Channels of Risk Propagation 

Methodology  N.A.  Investment assets: market value 

changes after price shocks, affecting the total adjusted capital (TAC) 

 In sensitivity analysis: Profitability 

negatively affected through prolonged period of interest rates: spread between net portfolio yields and guaranteed credited rates declining, ultimately resulting in weaker capital position 

 Investmen

changes aaffecting s

Stress test horizon

 N.A.  Instantaneous shock 

(except for “low-for-long” analysis: 5 years, i.e. 2014-2018) 

 Instantane

(except foanalysis: 5 

3. Tail shocks Sensitivity 

analysis 

 N.A.  Natural catastrophes: 

- Florida hurricane (similar to but 

worse than “Andrew”, 1992) 

- California earthquake (similar 

to but worse than “Northridge” event, 1994) 

- Series of tornados (three 

tornados, classified as EF5, in 

 Natural c

- Hurrica

worse 

- Earthq

worse event, 

- Series 

tornad

INTERNATIONAL MONETARY FUND 111
Domain Assumptions 

Bottom-Up by Financial 

Institutions 

Top-Down by Authorities Top-do

 the Midwest of the United 

States) 

 Prolonged period of low interest 

rates (“low-for-long”): assuming unchanged level of interest rates until 2018

- the Mi

States) 

 Pandemic 

 Prolonged 

rates (“lowunchangeuntil 2018 

4.Risks and Buffers

Risks/factors assessed 

 N.A.  Market value shocks: 

- Sovereign bonds: +2% - Municipal bonds: -5% - CMBS and RMBS: between 

-5% and -28% 

- Mortgage loans: -5% - Equity: -40% for unaffiliated, 

-30% for affiliated, -10% for preferred 

- Property: -30% - Other investment assets: -30% 

 Potential loss from guarantees in 

variable annuities: 10% of the maximum guaranteed amount 

 Catastrophic risk: 1-in-250 year 

event for the Florida hurricane and the California earthquake 

 Market va

- CMBS

betwe

- Mortg- Equity- Prope- Other 

-28.9% 

 Credit spr

- U.S. s- Munic

+66.0+429

- Other 

+56.7+1,66

- Corpo

+248+1,87

 Shock to t

interest ra1 year and 

112 INTERNATIONAL MONETARY FUND
Domain Assumptions 

Bottom-Up by Financial 

Institutions 

Top-Down by Authorities Top-do

 Catastrop

event for and the C

 Pandemic

+1.5 addit

Risk aggregation 

 N.A.  Sum of individual shocks  Sum of in

Buffers  N.A.  Coverage of risk-based capital 

(RBC) 

 Sharehold

Behavioral adjustments 

 N.A.  None  None 

ASSET MANAGERS: LIQUIDITY RISKS

1.Institutional Perimeter

Institutions included

 N.A.  N.A.  Largest m

mutual fudifferent s

Data and baseline date

 N.A.  N.A.  2014Q3 

2. Channels of Risk Propagation 

Methodology  N.A.  N.A.  Comparin

capacity oasset class orderly mmutual fuportfolio cdata on d

 Assuming 

be sold to 

 Redempti

individual 

INTERNATIONAL MONETARY FUND 113
Domain Assumptions 

Bottom-Up by Financial 

Institutions 

Top-Down by Authorities Top-do

 same style

calculated as an averacross all same style

redemptio

 For MMFs

the LCR ca

Stress test horizon

 N.A.  N.A.  One quart

by a run oby an assu

3. Tail shocks Sensitivity 

analysis 

 N.A.  N.A.  N.A. 

4.Risks and Buffers

Risks  N.A.  N.A.  Liquidity r

fund) 

Buffers  N.A.  N.A.  Liquid ass

 Capacity o

absorb seasset to mcomparing portfolio owith dealesame secu

MARKET-PRICE BASED: CREDIT/DEFAULT RISKS AND CROSS-BORDER SPILLOVER RISKS 

1.Institutional Perimeter 

Institutions included 

 N.A.  N.A.  210 institu

traded eqdifferent sfinancial a

114 INTERNATIONAL MONETARY FUND
Domain Assumptions 

Bottom-Up by Financial 

Institutions 

Top-Down by Authorities Top-do

 sectors an

sectors) 

 178 institu

the U.S. annon-U.S. j

 Domestic 

banks, inscorporateother noninstitution

 Foreign se

banks (noinsurers (nother larg

 Market Share  U.S. Banks 

percent oassets) 

 U.S. corpo

and otherinstitutiontotal secto

 Foreign b

of GSIB an

 Data and 

baseline date 

 All data fr

2014Q3 

 Credit risk 

 Macro dat

Datastrea

 Connectiv

INTERNATIONAL MONETARY FUND 115
Domain Assumptions 

Bottom-Up by Financial 

Institutions 

Top-Down by Authorities Top-do

 IMF using 

 Non-daily 

disaggregusing Chomethodol

2. Channels of Risk Propagation 

Methodology  Contingen

 General A

Location, (GAMLSS) 

3. Tail shocks Size of the 

shock 

 DFA/IMF s

macroeco

 One time 

counter-pprobabilit

4. Risks Risks  Macro risk

 Interconn

 Cross-bor

spillover r

 Credit risk 

 Default ris

116 INTERNATIONAL MONETARY FUND
UNITED STATES

INTERNATIONAL MONETARY FUND 117

-2,000,000,000

0

2,000,000,000

4,000,000,000

6,000,000,000

8,000,000,000

10,000,000,000

12,000,000,000

14,000,000,000

16,000,000,000

2000 2002 2004 2006 2008 2010 2012 2014q3

Structure of assets

Cash Allowance Gross loans Fed. funds/repos Securities Trading Other assets

0

2,000,000,000

4,000,000,000

6,000,000,000

8,000,000,000

10,000,000,000

12,000,000,000

14,000,000,000

16,000,000,000

2000 2002 2004 2006 2008 2010 2012 2014q3

Structure of liabilities

Deposits Fed. funds/repos Trading Other liabilities Total equity Other borrowed money Subordinated notes 

0

500,000,000

1,000,000,000

1,500,000,000

2,000,000,000

2,500,000,000

2000 2002 2004 2006 2008 2010 2012 2014q3

Structure of securities

AFS securities HTM securities

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2000 2002 2004 2006 2008 2010 2012 2014q3

Structure of liabilities

Wholesale funding Deposits Equity Other

Treasury 

securities

16%

U.S. government 

agency 

obligations

3%

Municipal 

securities

7%

MBSs

49%

ABSs

4%

Structured 

financial 

products

4%

Domestic debt 

securities

2% Foreign debt 

securities

14%

Mutual 

funds and 

equity

1%

Structure of AfS securities portfolio Pass-through RMBS 

guaranteed by 

GNMA

10%

Pass-through RMBS 

issued by FNMA and 

FHLMC

47%

Other pass-through 

RMBS

0%

RMBS 

issued/guaranteed 

by GSEs

22%

RMBS collateralized 

by MBS 

issued/guaranteed 

by GSEs

0%

Other 

RMBS

11%

Pass-through CMBS 

issued/guaranteed 

by FNMA, FHLMC, 

GNMA

2%

Other pass-through 

CMBS 

0%

CMBS 

issued/guaranteed 

by GSEs

2%

Other CMBS

6%

Structure of MBS portfolio

Appendix II. Additional Data: Banking Sector

Appendix Figure 1. Developments in the Banking Sector 

 
UNITED STATES

118 INTERNATIONAL MONETARY FUND

Treasury 

securities

9%

U.S. government 

agency 

obligations

2%

Municipal 

securities

2%

MBSs

10% ABSs

1%

Debt securities

24%

Real estate loans

2%

Commercial 

and 

industrial 

loans

2%

Consumer loans

0%

Other 

loans

1%

Other trading 

assets

29%

Derivatives

18%

Structure of trading portfolio Other 

trading 

liabilities

1%

Derivatives

40%

Short sale-Equity

18%

Short sale-Debt 

securities

41%

Short saleOther assets

0%

Structure of trading liabilities

CP

7%

Other 

borrowed with 

maturity <1

24% Other 

borrowed with 

maturity >1

69%

Structure of other borrowed money

Secured 

29%

Unsecured

71%

Structure of other borrowed money

Other 

borrowed 

money

47%

Fed 

funds/repos

24%

Trading 

liabilities

18%

Subordinated 

notes

5%

Brokered 

deposits

6%

Structure of wholesale funding

0%

2%

4%

6%

8%

10%

12%

14%

2000 2002 2004 2006 2008 2010 2012 2014q3

CET1 and leverage (CET1/total assets) ratio

Appendix Figure 1. Developments in the Banking Sector (continued) 

 

 
UNITED STATES

INTERNATIONAL MONETARY FUND 119

Deposits in 

domestic 

offices

82%

Deposits in 

foreign offices

18%

Structure of deposits

Noninterest 

bearing

25%

Interest bearing 

demand 

deposits

6% Money market 

deposits

59%

Time deposits of 

less than 

$100.000

5%

Time 

deposits of 

more than 

$100.000

5%

Structure of domestic deposits

Broked deposits 

less than 

$100.000

3%

Broked deposits 

more than 

$100.000

1%

Time deposits of 

more than 

$100.000 

maturing within 

a year

3%

Foreign deposits 

maturing within 

one year

4%

Stable deposits

89%

Structure of domestic deposits

0

1E+09

2E+09

3E+09

4E+09

5E+09

6E+09

2000 2002 2004 2006 2008 2010 2012 2014q3

Structure of loans

Residential real estate loans Commercial real estate loans 

Business loans Consumer loans

Loans to financial institutions and other loans Loans to foreign government and official institutions

0%

20%

40%

60%

80%

100%

2000 2002 2004 2006 2008 2010 2012 2014q3

Structure of Real estate mortgages

Revolving, open-end loans sec. by 1-4 family property and extended under lines of credit

Closed-end, sec. by 1-4 family properties, first lien

Closed-end, sec. by 1-4 family properties, junior lien

0%

20%

40%

60%

80%

100%

2010 2012 2014q3

Structure of loans to FIs and other loans

Loans to nondepository fin. institutions Loans to foreign banks

Loans to US depository institutions Loans for purchasing securities

Other loans

Appendix Figure 1. Developments in the Banking Sector (continued) 

 

 
UNITED STATES

120 INTERNATIONAL MONETARY FUND

0

100,000,000

200,000,000

300,000,000

400,000,000

500,000,000

2000 2002 2004 2006 2008 2010 2012 2014

HTM securities

Government agency securities GSE securities

Treasury securities State securities

Total MBS Total ABS

US debt securities Foregin debt securities

Structured products

0

500,000,000

1,000,000,000

1,500,000,000

2,000,000,000

2,500,000,000

2000 2002 2004 2006 2008 2010 2012 2014

AfS securities

Government agency securities GSE securities

Treasury securities State securities

Total MBS Total ABS

US debt securities Foregin debt securities

Structured products Mutual funds and equity

0

500,000,000

1,000,000,000

1,500,000,000

2,000,000,000

2009 2010 2011 2012 2013 2014

Trading asset securities

Government agency securities Treasury securities

State securities Total MBS

Other trading assets Loans

Structured products Derivatives with positive FV

Other debt securities

0

100,000,000

200,000,000

300,000,000

400,000,000

500,000,000

600,000,000

700,000,000

800,000,000

900,000,000

2009 2010 2011 2012 2013 2014

Trading liabilities

Short position-other assets Derivatives with negative FV Short position-equity

Structured products Short position-debt Other trad. Liabilities

0

1,000,000,000

2,000,000,000

3,000,000,000

4,000,000,000

5,000,000,000

6,000,000,000

7,000,000,000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Domestic deposits

Dom. Trans. Accounts (CBS) Dom. Nontrans. Accounts (CBS)

Dom. Trans. Accounts (ODI) Dom. Nontrans. Accounts (ODI)

0

200,000,000

400,000,000

600,000,000

800,000,000

1,000,000,000

1,200,000,000

1,400,000,000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Less stable deposits

Brokered Deposits < $100K,<= 1 Year Brokered Deposits< $100K,> 1 Year 

TimeDep>=$100K<=1Yr Non-U.S Time Dep<=1yr 

Appendix Figure 1. Developments in the Banking Sector (continued) 

 

 
UNITED STATES

INTERNATIONAL MONETARY FUND 121

Futures

3%

Forwards

17% Exchange 

tradedWritten 

options

2% Exchange 

tradedPurchased 

options

3%

OTC- written 

options

7% OTCpurchased 

options

7%

Swaps

61%

Derivatives position, gross amounts

Interest rate 

contract

82%

FX contracts

14%

Equity 

derivatives

3%

Commodity 

and other 

contracts

1%

Derivatives position, gross amounts

Gross notional 

held for 

trading

99%

Gross notional 

held for other 

purposes

1%

Derivatives position, gross amounts

0

1E+09

2E+09

3E+09

4E+09

5E+09

6E+09

7E+09

8E+09

9E+09

1E+10

2001 2003 2005 2007 2009 2011 2013

Structure of off-balance sheet items

Financial standby letters of credit Securities lent Unused commitments Derivatives-credit equivalent Other

Revolving, 

open-end loans 

secured by 1-4 

family 

residential 

properties

7%

Unused 

consumer 

credit card lines

49%

Other unused 

credit card lines

3%

Commitments 

to fund CRE, 

construction 

and lan 

development 

loans secured 

by RE

2%

Commitments 

to fund CRE, 

construction 

and lan 

development 

loans not 

secured by RE

1%

Securities 

underwriting 

0%

Commercial 

and industrial 

loans

31%

Loans to 

financial 

institutions

7%

Unused commitments

0

1,000,000,000

2,000,000,000

3,000,000,000

4,000,000,000

5,000,000,000

6,000,000,000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Unused commitments

Unused Cmt:1-4 Fam Res Lines Unused Commitments: Credit Card Lines 

Unused Cmt:Tot Comm RE Lnes Unused Cmt:Secs Underwriting 

Unused Commitments: Other Commitments 

Appendix Figure 1. Developments in the Banking Sector (continued) 

 
UNITED STATES

122 INTERNATIONAL MONETARY FUND

Fair value of 

collateral

22%

Cash US dollar

17%

Cash other 

securities

2%

US Tresury 

bonds

1%

US 

governement 

agency 

securities

0%

Corporate 

bonds

2%

Equity 

securities

6%

All other 

collateral

50%

OTC derivatives, collateral

Banks and 

securities 

firms

54%

Monoline 

Financial 

guarantors

0%

Hedge funds

2%

Sovereign 

governments

6%

Corporations 

and all other 

counterpartie

s

38%

OTC derivatives, counterparties

CDS

96%

TRS

1% Credit options

3%

Other credit 

derivatives

0%

Credit derivatives, sold protection, notional 

amounts

CDS

96%

TRS

1% Credit options

3%

Other credit 

derivatives

0%

Credit derivatives, purchased protection, 

notional amounts

Investment 

grade

72%

Subinvestmen

t grade

28%

Credit derivatives, sold protection

Investment 

grade

73%

Subinvestmen

t grade

27%

Credit derivatives, purchased protection

Appendix Figure 1. Developments in the Banking Sector (continued) 

 
UNITED STATES

INTERNATIONAL MONETARY FUND 123

0

2,000,000,000

4,000,000,000

6,000,000,000

8,000,000,000

10,000,000,000

12,000,000,000

14,000,000,000

2000 2002 2004 2006 2008 2010 2012 2014

Credit derivatives- protection bought and sold

Sold Protection Purch Protection

0

1,000,000,000

2,000,000,000

3,000,000,000

4,000,000,000

5,000,000,000

6,000,000,000

7,000,000,000

8,000,000,000

9,000,000,000

2009 2010 2011 2012 2013 2014

Credit derivatives- protection bought, investment 

grade vs. subinvetment

Purchased protection, investment Purchased protection, subinvestment

0

1,000,000,000

2,000,000,000

3,000,000,000

4,000,000,000

5,000,000,000

6,000,000,000

7,000,000,000

8,000,000,000

2009 2010 2011 2012 2013 2014

Credit derivatives- protection sold, investment grade 

vs. subinvetment

Sold protection, investment Sold protection, subinvestment

Appendix Figure 1. Developments in the Banking Sector (continued) 

 

 
UNITED STATES

124 INTERNATIONAL MONETARY FUND

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.10

2000 2002 2004 2006 2008 2010 2012 2014q3

Deliquencies (as % of total net loans)

Delinquency- business Delinquency- consumer Delinquency- CRE

Delinquency- Fis and other loans Delinquency- RRE

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

2000 2002 2004 2006 2008 2010 2012 2014q3

NPLs (as % of total net loans)

NPLs- business NPLs- consumer NPLs- CRE NPLs- FI NPLs- government NPLs- RRE

-20,000,000

0

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

140,000,000

160,000,000

180,000,000

2000 2002 2004 2006 2008 2010 2012 2014q3

Net charge offs

Net charge offs- business Net charge offs- consumer Net charge offs- CRE

Net charge offs- FI Net charge offs- government Net charge offs- RRE

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

2000 2002 2004 2006 2008 2010 2012 2014q3

Deliquencies (as % of corresponding loan type)

Delinquency- business Delinquency- consumer Delinquency- CRE

Delinquency- Fis and other loans Delinquency- RRE

0%

20%

40%

60%

80%

100%

2000 2002 2004 2006 2008 2010 2012 2014q3

Structure of deliquencies

Delinquency- business Delinquency- consumer Delinquency- CRE

Delinquency- Fis and other loans Delinquency- government Delinquency- RRE

Delinquency- securities

Appendix Figure 1. Developments in the Banking Sector (continued) 

 
UNITED STATES

INTERNATIONAL MONETARY FUND 125

-40,000,000

-20,000,000

0

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

140,000,000

-1,000,000,000

-800,000,000

-600,000,000

-400,000,000

-200,000,000

0

200,000,000

400,000,000

600,000,000

800,000,000

1,000,000,000

2000 2002 2004 2006 2008 2010 2012 2014q3

Net income

Interest income Interest expense

Provisions for loan and lease losses Noninterest income excluding trading

Trading revenue including AFS and HTM Noninterest expense

Applicable income taxes Extraordinary items 

Minority interests Net income- right

-0.4%

-0.2%

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

1.4%

-5.0%

0.0%

5.0%

10.0%

15.0%

20.0%

2000 2002 2004 2006 2008 2010 2012 2014q3

ROE and ROA

ROE ROA-right

Appendix Figure 1. Developments in the Banking Sector (continued) 

Source: FRB, SNL, IMF Staff calculations
UNITED STATES

126 INTERNATIONAL MONETARY FUND

0

10

20

30

40

50

60

70

80

90

100

03 04 05 06 07 08 09 10 11 12 13 14 03 04 05 06 07 08 09 10 11 12 13 14 03 04 05 06 07 08 09 10 11 12 13 14

CRE - Commercial Construction CRE - Residential Construction CRE - Other

Tightened Unchanged Eased

Commercial Underwriting Trends, by Product Type

0

10

20

30

40

50

60

70

80

90

100

03 04 05 06 07 08 09 10 11 12 13 14 03 04 05 06 07 08 09 10 11 12 13 14 06 07 08 09 10 11 12 13 14

Leveraged Loans Large Corporate Loans Commercial Leasing

Tightened Unchanged Eased

Commercial Underwriting Trends, by Product Type

0

10

20

30

40

50

60

70

80

90

100

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Tightened Unchanged Eased

Overall Commercial Credit Underwriting Trends

(Percentage of Responses)

Appendix Figure 2. Underwriting Standards 
UNITED STATES

INTERNATIONAL MONETARY FUND 127

0

10

20

30

40

50

60

70

80

90

100

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Tightened Unchanged Eased

Overall Credit Underwriting Trends - Retail

0

10

20

30

40

50

60

70

80

90

100

03 05 07 09 11 13 04 06 08 10 12 14 03 05 07 09 11 13 04 06 08 10 12 14

Affordable Housing Credit Cards Home Equity -

Conventional

Home Equity - high LTV

Tightened Unchanged Eased

Retail Underwriting Trends, by Product Type

0

10

20

30

40

50

60

70

80

90

100

03 04 05 06 07 08 09 10 11 12 13 14 03 04 05 06 07 08 09 10 11 12 13 14 03 04 05 06 07 08 09 10 11 12 13 14

Indirect Consumer Direct Consumer Residential Real Estate

Tightened Unchanged Eased

Retail Underwriting Trends, by Product Type

Appendix Figure 2. Underwriting Standards (continued) 

Source: OCC
UNITED STATES

128 INTERNATIONAL MONETARY FUND

-

500,000,000 

1,000,000,000 

1,500,000,000 

2,000,000,000 

2,500,000,000 

3,000,000,000 

3,500,000,000 

4,000,000,000 

Bank of 

America

Goldman 

Sachs

J.P. 

Morgan

Morgan 

Stanley

Wells 

Fargo

Citigroup State 

Street

New York 

Mellon

Total exposure and total assets

Total assets Total exposure

0%

10%

20%

30%

40%

50%

60%

Bank of 

America

Goldman 

Sachs

J.P. 

Morgan

Morgan 

Stanley

Wells 

Fargo

Citigroup State 

Street

New York 

Mellon

Foreign claims and liabilites (% of total assets)

Claims Liabilities

0%

10%

20%

30%

40%

50%

60%

Bank of 

America

Goldman 

Sachs

J.P. 

Morgan

Morgan 

Stanley

Wells 

Fargo

Citigroup State 

Street

New York 

Mellon

Foreign net revenue (in % of total revenue)

Funds deposited or 

lent to unaffiliated 

financial institutions

20% Undrawn 

committements 

10%

Secured debt 

securities

2% Senior unsecured 

debt securities

3% Subordinated 

debt securities

0%

Commercial paper

0%

Stocks

5% Net positive current 

exposure of SFT8%

OTC derivatives- net 

positive value 8%

OTC derivativesPotential exposure 

44%

Intra-financial system assets

Deposits due to 

depository 

institutions

11%

Deposits due to 

non-depository 

financial 

institutions

36%

Undrawn 

committed lines 

0%

Net negative 

current exposure 

of SFTs 

18%

OTC derivativiesNet negative fair 

value

8%

OTC derivativesPotential future 

exposure

27%

Intra-financial system liabilities

Real 

estate 

loans

12%

Commercial 

and industrial 

loans

37%

Loans to dep 

inst & other 

banks accept

13%

Loans to 

foreign govts 

and inst

1%

Loans to 

individuals

12%

Agricultural 

loans

0%

Other foreign 

loans

24%

Lease 

financing 

receivables

1%

Foreing loans

Appendix Figure 3. GSIBs 

 

 
UNITED STATES

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U.S. 

Treasury 

securities

18%

U.S. agency 

securities (excl 

MBS)

2%

Municipal 

securities

8%

Mortgagebacked 

securities

45%

Asset-backed 

securities

9%

Other debt 

securities

17%

Mutual funds 

and equity 

securities

1%

Investment securities

U.S. 

Treasury 

securities

18%

U.S. agency 

securities (excl 

MBS)

2%

Municipal 

securities

8%

Mortgagebacked 

securities

42%

Asset-backed 

securities

10%

Other debt 

securities

19%

Mutual funds 

and equity 

securities

1%

AfS securities

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Bank of 

America

Goldman 

Sachs

J.P. 

Morgan

Morgan 

Stanley

Wells 

Fargo

Citigroup State 

Street

New York 

Mellon

Investment and AfS securites (% of total assets)

Investment securities AfS securities

0%

5%

10%

15%

20%

25%

30%

35%

Bank of 

America

Goldman 

Sachs

J.P. 

Morgan

Morgan 

Stanley

Wells 

Fargo

Citigroup State 

Street

New York 

Mellon

Securitization activity (% of total assets)

1–4 family 

residential 

loans

84%

Home equity 

lines

1%

Credit card 

receivables

0%

Auto loans

0%

Commecial 

and industrial 

loans

0%

All other 

loans and 

leases

15%

Securitization activity

0%

10%

20%

30%

40%

50%

60%

70%

80%

Bank of 

America

Goldman 

Sachs

J.P. 

Morgan

Morgan 

Stanley

Wells 

Fargo

Citigroup State 

Street

New York 

Mellon

Intra-financial System Assets and Liabilities (% of total 

assets)

Assets Liabilities

Appendix Figure 3. GSIBs (continued) 

 

 
UNITED STATES

130 INTERNATIONAL MONETARY FUND

0%

1000%

2000%

3000%

4000%

5000%

6000%

Bank of 

America

Goldman 

Sachs

J.P. 

Morgan

Morgan 

Stanley

Wells 

Fargo

Citigroup State 

Street

New York 

Mellon

OTC derivatives (in % of total assets)

OTC derivatives 

cleared through 

a central 

counterparty

53%

OTC derivatives 

settled 

bilaterally

47%

Structure of OTC derivatives

Held for 

trading

99%

Non-traded

1%

Derivatives

Interest rate 

contracts

82%

Foreign 

exchange 

contracts

14%

Equity, 

comm, & other 

contracts

4%

Derivatives

Futures and 

forwards

52% Written 

options

24%

Purchased 

options 

24%

Derivatives

One year or 

less

61%

Over 1 year 

to 5 years

24%

Over 5 

years

15%

Derivatives

Appendix Figure 3. GSIBs (continued) 

 

Source: FRB, SNL, IMF Staff calculations 

 
UNITED STATES

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-10.0

-8.0

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2019

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2019

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2007

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2014

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2015

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2016

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2017

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2018

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2019

0

5000

10000

15000

20000

25000

30000

Q1 

2007

Q1 

2008

Q1 

2009

Q1 

2010

Q1 

2011

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2013

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2014

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2015

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2016

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2017

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2018

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2019

0

50

100

150

200

250

Q1 

2007

Q1 

2008

Q1 

2009

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2010

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2011

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2012

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2013

Q1 

2014

Q1 

2015

Q1 

2016

Q1 

2017

Q1 

2018

Q1 

2019

Appendix Figure 4. Macroeconomic Scenarios: Main Variables 

 GDP growth, q-o-q, annualized, in % Unemployment rate, in % 

 3-month Treasury yield, in % 10-year Treasury bond yield, in % 

 Dow Jones Industrial Stock Market Index House price index 
UNITED STATES

132 INTERNATIONAL MONETARY FUND

0

50

100

150

200

250

300

350

Q1 

2007

Q1 

2008

Q1 

2009

Q1 

2010

Q1 

2011

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2012

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2013

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2014

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2015

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2016

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2019

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10

20

30

40

50

60

70

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90

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2007

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2013

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2014

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2015

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2016

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2017

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2018

Q1 

2019

-5.0

0.0

5.0

10.0

15.0

20.0

Q1 

2007

Q1 

2008

Q1 

2009

Q1 

2010

Q1 

2011

Q1 

2012

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2013

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2014

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2015

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2016

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2017

-12.0

-10.0

-8.0

-6.0

-4.0

-2.0

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2.0

4.0

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Q1 

2007

Q1 

2008

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2009

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2010

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2011

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2012

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2013

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2014

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2015

Q1 

2016

Q1 

2017

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

Q1 

2007

Q1 

2008

Q1 

2009

Q1 

2010

Q1 

2011

Q1 

2012

Q1 

2013

Q1 

2014

Q1 

2015

Q1 

2016

Q1 

2017

Appendix Figure 4. Macroeconomic Scenarios: Main Variables (continued) 

 Commercial Real Estate Price Index Market volatility index 

 Euro area GDP growth Developing Asia real GDP, q-o-q rate, % 

 UK GDP growth 

Source: FRB, IMF Staff calculations 

 
UNITED STATES

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Net interest income Non-interest 

income 

excluding 

Trading income Noninterest 

expense

AOCI Provisions Provisions

Dependent variable specification d4 Share in total assets Share in total assets y/y Share in total assets Share in total net loans Share in total net loans

Panel/Aggregate Panel Panel Aggregate Panel Aggregate Aggregate Panel

Lag of dependent variable No No No No No No No

d4(Loans x Lending rate) 0.001775***

d4

(Deposits x Depoit rate) -0.001586***

Total assets, y/y 0.856675***

Lending rate -0.000118*** 0.000133***

Real GDP, y/y 0.000260*** -0.000323*** -0.000194***

Real GDP, y/y x Dum (=1 if y/y<0) 0.000682***

Unemployment rate -0.000408***

d(Unemployment rate) 0.001762***

d4

(Unemployment rate) 0.001174***

d4

(10-year Treasury yield) -0.000512***

d(10-year Treasury yield - 3 month 0.000773***

d4

(BBB corporate yield) -0.000112

BBB corporate yield - 3-month rate -0.000819*** 0.000130 0.000336***

Mortgage rate

House Price Index (y/y) -6.85E-05*** -8.70E-05***

Market Volatility Index, y/y -0.00000313*** 0.00000884*** -6.58E-06

Market Volatility Index, y/y x Dum(=1 if 

y/y>0)

-1.76E-05*** 1.90E-05**

Market Volatility Index, level 1.77E-05***

Constant 26819.86*** 0.009149*** 0.001206*** 2.369656*** -0.001447*** 0.000369

R^2 0.53 0.81 0.41 0.39 0.54 0.84 0.56

Number of observations 2231 2287 95 2232 97 95 2237

Net loans Total assets RWAs NCOs bussiness 

loans

NCOs consumer 

loans

NCOs RRE loans NCOs CRE loans NCOs financial 

institutions loans

NCOs total

Dependent variable specification y/y y/y y/y Share in loans Share in loans Share in loans Share in loans Share in loans Share in loans

Panel/Aggregate Panel Panel Panel Panel Panel Panel Panel Panel Panel

Lag of dependent variable Yes No No No No No No No No

Net loans, y/y 0.346118*** 0.793545***

Total assets, y/y 0.899295***

Lending rate 0.907326*** 0.00002

d4

(Lending rate) -2.299608***

Real GDP, y/y 1.45978*** -0.00007** -0.000123** -0.000467*** -0.000242***

Real GDP, y/y x Dum (=1 if y/y<0)

Unemployment rate 0.000688*** 0.001498***

d(Unemployment rate) 0.00006

d4

(Unemployment rate) 0.001008*** 0.000851***

BBB corporate yield - 3-month rate 0.000178*** 0.000271***

d4

(Mortgage rate) 0.000247***

House Price Index (y/y) -0.000114*** -0.00004*** -0.00008***

Commercial Real Estate Price Index 

(y/y)

-0.00004***

Market Volatility Index, level -0.070382*** 0.00001 0.00003*** 0.00004*** 0.00007*** 0.00001**

Business interest rates 0.000508***

d4

(Consumer interest rates) 0.000598***

d4

(Federal funds rate) 0.000187

Constant 2.808341*** 2.584347*** -4.794070*** 0.000981*** 0.004841*** -0.003267*** -0.011038 0.000731 0.001724***

R^2 0.39 0.76 0.84 0.37 0.56 0.46 0.28 0.08 0.56

Number of observations 2088 2253 1769 2179 2199 2166 2199 2287 2172

Appendix Table 3. Estimation Results: Income Statement Items 

Source: IMF Staff calculations

Appendix Table 4. Estimation Results: Balance Sheet Items, Net Charge offs, RWAs 

Source: IMF Staff calculations
UNITED STATES

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0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

2014 2015 2016 2017 2018 2019

5.0%

7.0%

9.0%

11.0%

13.0%

15.0%

-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

2014q3 2014 2015 2016 2017 2018 2019

NET INTEREST INCOME NONINTEREST EXPENSE

NONINTEREST INCOME TRADING INCOME

PROVISIONS FOR CREDIT LOSSES OTHER (dividends, taxes, AOCI, other ded.)

RWAS CHANGE IN CET1

CET1 (%)-right

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

2014q3 2014 2015 2016 2017 2018 2019

Regulatory threshold

Max GSIB CET1 

charge

Min GSIB CET1 

charge

(1,000,000,000)

(800,000,000)

(600,000,000)

(400,000,000)

(200,000,000)

-

200,000,000 

400,000,000 

600,000,000 

800,000,000 

1,000,000,000 

2014q3 2014 2015 2016 2017 2018 2019

NET INTEREST INCOME NONINTEREST EXPENSE NONINTEREST INCOME

TRADING INCOME PROVISIONS FOR CREDIT LOSSES TAXES

DIVIDENDS AOCI OTHER DEDUCTIONS

NET INCOME

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

2014 2015 2016 2017 2018 2019

Appendix Figure 5. Banking Stress Test Results: IMF Top-Down 

CET1 ratio 

 

Contributions to CET1 ratio 

 Net income over the stress horizon Recapitalization needs (share of 2014 net income) 

 

Recapitalization needs (share of nominal GDP)

Source: IMF Staff calculations 
UNITED STATES

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Appendix Figure 6. U.S. Cross-Border Assets, Liabilities, and Flows 

(Billions US$)

Source: IMF, BIS, IMF staff calculations

 
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136 INTERNATIONAL MONETARY FUND

Appendix III. Additional Data: Insurance Sector

Appendix Figure 7. Insurance Assets 

Source: SNL Financial and IMF staff calculations 

44%

3% 0% 5% 2% 0% 4%

37%

5%

Breakdown of assets

Bonds

Stocks

Mortgage loans

Real estate

Cash, cash equivalents, other

short-term investments

Derivatives

Other investment assets

Separate account assets

Other assets

7%

5%

19%

56%

13%

Breakdown of bond portfolio

U.S. sovereign

Non-U.S. sovereign

U.S. municipals and

GSEs

Corporate bonds and

hybrids

Non-GSE

securitizations

0% 20% 40% 60% 80% 100%

Life

P&C

Health

Credit

Breakdown of bond portfolio by maturity 

buckets

1 Year or Less Over 1 Year Through 5 Years

Over 5 Years Through 10 Years Over 10 Years Through 20 Years

Over 20 Years

0% 20% 40% 60% 80% 100%

Life

P&C

Health

Credit

Breakdown of bond portfolio by rating

NAIC 1 (AAA-A) NAIC 2 (BBB)

NAIC 3 (BB) NAIC 4 (B)

NAIC 5 (CCC) NAIC 6 (CC or lower, defaulted)

0,0

1,0

2,0

3,0

4,0

5,0

6,0

7,0

8,0

9,0

Life P&C Health Credit

Assumed average duration of bond portfolio
UNITED STATES

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Appendix IV. Additional Results: Market-Based Tests 

Appendix Table 5. GAMLSS Overall U.S. Financial System Model

Variable name Additive term structure Coefficient estimate P-value 

Intercept term -5.55E+00 < 2e-16 *** 

5-year treasury yield -4.21E-02 < 2e-16 *** 

Dow Jones stock market index free knot N/A; fully nonparametric; see term 

plot (Figure I) 

 

VIX localized regression (LOESS) N/A; fully nonparametric; see term 

plot (Figure I) 

 

House price index -1.56E-03 < 2e-16 *** 

CRE price index -2.00E-03 < 2e-16 *** 

USD/euro exchange rate -2.59E-03 < 2e-16 *** 

BBB corporate yield 5.41E-02 < 2e-16 *** 

Real GDP growth rate free knot N/A; fully nonparametric; see term 

plot (Figure I) 

 

Euro area inflation varying coefficient 1.00E-02 1.57e-07 *** 

Median 1-year default probability for 

Asset Manager sector 

 6.14E+01 < 2e-16 *** 

Median 1-year default probability for 

Bank sector 

 3.71E+01 < 2e-16 *** 

Median 1-year default probability for 

Insurer sector 

 1.29E+01 4.58e-07 *** 

Median 1-year default probability for 

NBFI sector 

 9.79E+00 3.83e-13 *** 

Median 1-year default probability for 

Corporate sector 

localized regression (LOESS) N/A; fully nonparametric; see term 

plot (Figure I) 

 

Median 1-year default probability for 

Foreign Bank sector 

 3.37E+00 2.74e-11 *** 

Median 1-year default probability for 

Foreign Insurer sector 

 3.196E-00 0.000129 *** 

Global clustering coefficient score first order orthogonal polynomial 1.89E+00 < 2e-16 *** 

Global clustering coefficient score second order orthogonal 

polynomial 

-3.77E-01 < 2e-16 *** 

Global clustering coefficient score third order orthogonal polynomial 2.17E-01 < 2e-16 *** 

Significance codes: 0 ***, 0.001**, 0.01*, 0.05'', 0.1''' 

Number of observations in the fit: 1244 

Degrees of Freedom for the fit: 44.94656 

 Residual Degrees of Freedom: 1199.053 

Global Deviance: -20500.05 

 AIC: -20410.15 
UNITED STATES

138 INTERNATIONAL MONETARY FUND

 SBC: -20279.75 

Distribution type: Inverse Gaussian 

Link function for the mean: Log 

Generalized R-squared: 0.9965047 

Dependent variable: Median 1-year default probability 

for Overall U.S. Financial System 

 

Source: IMF staff calculations 

Appendix Figure 8. Overall U.S. Financial System Model: Non-Parametric Term Plots

 

Soure: IMF staff calculations 

Note: red lines denote the fit of a GAMLSS non-parametric additive term for the response variable (median 1-year ahead U.S. 

financial system default probabilites) against a given explainatory variable. 
UNITED STATES

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Appendix Figure 9. Sector Default Probability Forecasts
UNITED STATES

140 INTERNATIONAL MONETARY FUND

Source: IMF staff calculations. 

Note: Blue lines indicate 25th and 75th percentile values of the distribution of historical estimates of U.S. institution 1-year ahead 

default probabilities. The dashed black line denotes the median value of the distribution of historical estimates of U.S. institution 1-

year ahead default probabilities. The solid red and black lines denote median 1-year ahead default probabilities projected by the 

CCA stress tests under the stress and baseline scenarios, respectively. To better show projection details, the y-axis has been

truncated for domestic banks, insurers, asset managers, and NBFIs. The blue lines denoting the 75th percentile reached maximum 

values of 4 %, 6.5 %, 2 %, and 16 %, respectively, for these four sectors in 2008-09. Only projections for the overall U.S. financial 

system model (Figure 18) explicitly take into account changes in the estimated default probabilities of other sectors. Individual 

sector projections were generated exclusively using macroeconomic and connectivity factors. 

Appendix Figure 10. Additional Diagnostics for the Overall U.S. Financial System Model

Worms plot (above) of the model’s normalized quantile 

residuals suggests that the first two moments of the response 

distribution have been satisfactorily modeled. Curved dotted 

lines denote 95% CIs. The fitted central red line is fairly straight 

which is supportive of good model fit. 

A correlation matrix of model predictors (above) shows that 

there is low correlation among the model’s predicator variables 

and that the modeling assumption of uncorrelated predictors 

has not been grossly violated. (This low correlation is a benefit 

of including orthogonalized additive terms in the model.) 
UNITED STATES

INTERNATIONAL MONETARY FUND 141

Model normalized quantile residuals closely follow a standard 

normal distribution which is a sign that the choice of 

distribution used to model the response variable was 

appropriate 

Quantile-Quantile plot (above) shows that the model’s 

normalized quantile residuals closely follow a standard normal 

distribution. This is another sign that the choice of distribution 

used to model the response variable was appropriate. 

Source: IMF staff calculations 

Appendix Table 6. Significance of GAMLSS Sector Model Spillover Effects

Distribution 

Type Beta Gamma Inverse 

Gamma 

Inverse 

Gamma 

Generalized 

Inverse 

Gaussian 

Generalised 

Gamma 

Lopatatsidis 

Green 

Beta 

Recipient 

Sector 

(right) 

Banks Insurers Corporates Asset 

Managers NBFIs Foreign 

Banks 

Foreign 

Insurers 

Overall U.S. 

Financial 

System 

*** 

Banks *** *** ** 

Insurers *** ** *** *** 

Corporates 

Asset 

Managers ***   **  

NBFIs ** *** 

Foreign 

Banks * *** 

Foreign 

Insurers ** *** 

Generalized Rsquared 0.9731212 0.9886623 0.9840041 0.9640468 0.9128003 0.9841397 0.9904947 

Significance codes: 0 ***, 0.001**, 0.01*, 0.05'', 0.1''' 

Source: IMF staff calculations. 

Note: Cells in the array above signify the statistical significance of the positive credit risk shocks illustrated in Figure 22. 

“Distribution type” refers to the assumed distribution of the response variable for each model (i.e., median 1-year ahead sector 

default probabilities). In the case of the overall U.S. financial system model only the effect of foreign sectors was considered. Unlike 

sector-specific models, response variable values for the overall model were drawn from a pool of domestic sector observations. 

This may prevent a proper comparison of shocks between the overall U.S. system and other domestic sectors. 

 
UNITED STATES

142 INTERNATIONAL MONETARY FUND

Appendix Table 7. Financial Account Net Exposures: Sector and Instrument Definitions

Sector/Instrument Name Component Financial Account Categories 

Households Households and Nonprofit Organizations 

Corporates Non-Financial Business 

Government General Government; Monetary Authority Domestic 

Financial 

GSEs Government Sponsored Enterprises; Agency- and GSEBacked Mortgage Pools 

Banks Private Depository Institutions 

Insurers Property-Causality Insurance Companies; Life Insurance 

Companies; 

Pensions Private and Public Pension Funds 

Asset Managers Money Market Mutual Funds; Mutual Funds; Closed End 

and Exchange Traded Funds; Real Estate Investment 

Trusts 

NBFIs Issuers of Asset-Backed Securities; Finance Companies; 

Security Brokers and Dealers; Holding Companies; 

Funding Corporations 

Rest of World Rest of World 

Money Market Net Interbank Transactions; Checkable Deposits and 

Currency; Time and Savings Deposits; Federal Funds and 

Security Repurchase Agreements; Open Market Paper 

Fund Shares Money Market Mutual Fund Shares; Mutual Fund Shares 

Treasury Securities Treasury Securities 

Other Government Securities Treasury Securities; Agency- and GSE-Backed Securities; 

Municipal Securities and Loans 

Corporate Bonds Corporate and Foreign Bonds 

Corporate Equities Corporate Equities 

Deposits, Loans, Mortgages and Interbank Transactions U.S. Deposits in Foreign Countries; Other Loans and 

Advances; Total Mortgages; Consumer Credit; Security 

Credit; Depository Institution Loans Not Elsewhere 

Classified; Net Interbank Transactions 

Other Instruments Special Drawing Rights (SDRs) Certificates and Treasury 

Currency; Trade Credit; Life Insurance Reserves; Pension 

Entitlements; Taxes Payable by Businesses; Proprietors' 

Equity in Noncorporate Business; Direct Investment; 

Total Miscellaneous Financial Claims 

CDS *BIS official statistics on credit-default swap notional 

amounts outstanding as of 2014Q2, adjusted for doublecounting 

Source: FRB’s Financial Accounts of the United States; Haver; BIS; IMF staff estimates.