2014 Annual Report

OFFICE OF FINANCIAL RESEARCH

i

LETTER FROM THE DIRECTOR

I am pleased to present the Office of Financial Research 2014 Annual Report to Congress. 

This report — our third — analyzes potential threats to U.S. financial stability, documents our 

significant progress in meeting the mission of the Office, and reports on key research findings. 

Over the past year, the U.S. financial system has continued to recover and strengthen. 

Compared with the period just before the financial crisis, threats to financial stability are moderate. But that relatively benign backdrop is no cause for complacency.

Rather, there is good reason to watch financial developments closely. Since our 2013 report, 

several financial stability risks have increased. The three most important are excessive risktaking in some markets, vulnerabilities associated with declining market liquidity, and the 

migration of financial activities toward opaque and less resilient corners of the financial 

system. 

The Financial Stability Oversight Council identified similar issues in its annual report six months 

ago and recommended steps to address them. Our report, as in 2013, builds on and complements the Council’s comprehensive perspective and policy recommendations with a more 

in-depth look at specific issues and an evaluation of policy options. 

Last year, we introduced our benchmark tool for assessing and monitoring threats — the OFR 

Financial Stability Monitor, which enables us uniquely to look across the financial system and 

spot threats wherever they arise. The further development of this monitor presented here is 

just one way we fulfill our mission to develop and maintain tools and metrics to assess and 

monitor vulnerabilities in the financial system. 

Our ability to identify and assess vulnerabilities and the quality of our judgments do not hinge 

on tools alone. They also depend on the quality of our raw materials — that is, the data and 

information we employ. What makes the OFR unique is our mission to improve the quality 

and scope of financial data. Global data standards are essential for data quality, so efforts 

to improve data quality require the engagement and cooperation of financial regulators and 

market participants worldwide.

For example, the Office has led the global Legal Entity Identifier (LEI) initiative from the start. 

This signature project is now self-sustaining, yet realizing the full benefits of the LEI system 

requires ubiquitous adoption. Consequently, I continue to call for regulators around the world 

to require the use of the LEI — and other available standards — in regulatory reporting. 

In a second data-quality project, we are helping the Commodity Futures Trading Commission 

to improve the quality of derivatives data reported to swap data repositories. Those data are 

essential for assessing exposures and interconnections across the financial system. 

Collaboration on efforts to fill data gaps is also necessary. We have just launched a landmark 

pilot project with the Federal Reserve to collect previously unavailable data for bilateral 

repurchase agreements, or repos. This project marks the first time the Office will collect data 

Photo credit: © iStockphoto/polygraphus
2014 OFR Annual Report ii

directly from financial companies. This collection and subsequent analysis of this key segment 

of short-term, wholesale funding markets will inform our future data collection initiatives. 

Four years have passed since the signing of Dodd-Frank, and a great deal has been accomplished. But the challenges of providing data and analysis for use by financial stability policymakers, and of evaluating financial stability policies and tools, remain consequential. 

Moreover, financial innovation and migration of financial activity to different markets, institutions, and jurisdictions will always tax our capacity to measure and analyze financial activity. 

World-class thinking is required to meet these challenges. For our part, the OFR needs the 

independence, the flexibility, and the resources to attract and retain the core, superior talent 

required to achieve our mission. At the same time, our approach to our work is collaborative 

by necessity. The breadth and scope of our mission exceed our stand-alone capacity, regardless of our talent and resources. That is why we view the OFR as part of a virtual research-anddata community that extends the analytical capability of the Office, expands our capacity to 

meet urgent needs, and complements the work of others in this community. 

The vision that we all share is of a transparent, efficient, and stable financial system. Our work 

so far has given us a strong sense of what success looks like. That perspective has informed 

a soon-to-be-published strategic plan that articulates our mission and goals for the next five 

years, and a roadmap to achieve them. First, we plan to be an essential source of data and 

analysis for monitoring threats to financial stability. Second, we will promote the identification 

and adoption of standards that improve the quality and utility of financial data. Third, our 

leading edge research will improve financial stability monitoring and the scope and quality of 

financial data, and inform policy and risk management. 

I am deeply grateful for the opportunity to lead this extraordinary organization and our talented team of dedicated professionals for a fourth year. Our goals are ambitious but fulfilling 

our mission requires aiming high. I am more confident than ever that we are building a valuable institution that will help assure a stronger and safer financial system in the future. 

Richard Berner

Director, Office of Financial Research

LETTER FROM THE DIRECTOR continued
iii

TABLE OF CONTENTS

1 Executive Summary ......................................................................1

2 Analyzing Threats to Financial Stability .......................................5

2.1 Analytic and Monitoring Framework ..........................................................5

2.2 Market Developments and Financial Stability Monitoring .........................6

 Back-testing the Financial Stability Monitor ..................................................14

 Building Tools for Financial Stability Monitoring ..........................................15

2.3 Potential Threats to Financial Stability ..................................................... 16

 Carry Trades Rise When Volatility Is Low ..................................................... 23

 The OFR Financial Stress Index . 25

Russian and Eastern European Developments ............................................ 27

Bank Loan Funds and Liquidity Mismatches .................................................31

Assessing the Vulnerability of Agency mREITs ............................................. 37

3 Evaluating Macroprudential Policy Tools ..................................45

3.1 Framework for Policy Tool Evaluation.......................................................45

3.2 Structural Policies to Promote Resilience in Financial Institutions ...........48

3.3 Structural Policies to Promote Market Resilience.....................................57

Money Market Fund Reform ...........................................................................59

3.4 Structural Policies to Promote Resilience in Clearing Infrastructure ........63

3.5 Policies to Address Cyclical Financial Excesses ........................................ 67

International Experience with Countercyclical Capital Buffers .................. 68

4 Research on Financial Stability ...................................................73

4.1 Fundamental Research Agenda................................................................73

4.2 Agent-based Models ................................................................................ 74

4.3 Visual Tools for Understanding Financial Stability ....................................78

4.4 Credit Markets ..........................................................................................84

4.5 Recent and Forthcoming OFR Research...................................................88

5 Advancing Data Standards ........................................................91

5.1 Data Standards Agenda ............................................................................91

 What Are Financial Data Standards? ........................................................... 92
2014 OFR Annual Report iv

5.2 Trends in Data Standards 93

5.3 Legal Entity Identifier................................................................................94

Evaluating the Benefits and Costs of the LEI ............................................... 95

5.4 Standards for Derivatives Markets............................................................98

What Are Taxonomies?..................................................................................101

5.5 Universal Loan Identifiers........................................................................102

5.6 Reference Databases ..............................................................................102

What Is an Ontology? ....................................................................................103

6 Addressing Data Gaps .............................................................105

6.1 Data Gaps Agenda .................................................................................105

6.2 Data Gaps Initiatives ............................................................................... 107

6.3 Regulatory Collections to Address Data Gaps....................................... 112

Hedge Fund Leverage and Strategy............................................................114

6.4 Collaboration and Data Sharing Initiatives ............................................. 119

7 Outreach, Collaboration, and Infrastructure ..........................125

7.1 Collaboration and Outreach Drive the OFR’s Work............................... 125

OFR Responsibilities Under the Dodd-Frank Act.......................................126

7.2 OFR Vision, Mission, and Operations ..................................................... 128

OFR’s Vision, Mission, and Goals for FY 2015-19.........................................128

7.3 OFR Organization and Workforce .......................................................... 130

8 Agenda Ahead.......................................................................... 131

Glossary.......................................................................................... A.1

Bibliography ....................................................................................B.1
1

31

Executive Summary

EXECUTIVE SUMMARY

This third annual report of the Office of Financial Research (OFR) 

assesses threats to U.S. financial stability, outlines OFR research that 

supports the assessment, and describes the OFR’s progress in meeting 

our mission. The report also evaluates policy initiatives for promoting 

financial stability and describes our work to improve the quality and 

scope of financial data and analysis.

The OFR, financial regulators, and other policy institutions have made 

significant progress since our last annual report in assessing the buildup 

of vulnerabilities in the financial system, improving the quality and 

scope of financial data, and developing and implementing new policy 

tools that — although largely untested — are designed to make the 

financial system stronger and more transparent.

However, several threats to financial stability have risen over the past 

year. This report highlights three specific risks. First, we see material 

evidence of excessive risk-taking during the extended period of low 

interest rates and low volatility. Second, markets have become more 

brittle because liquidity may be less available in a downturn and the risk 

of asset fire sales and runs in short-term wholesale funding markets 

remains unresolved. Third, we are concerned that financial activity is 

migrating toward areas of the financial system where threats are more 

difficult to assess because information is not available, and that activity 

may be consequential. Gaps in analysis, data, and policy also persist, 

despite progress in narrowing them. If left unaddressed, these threats 

could adversely affect financial stability. 

This annual report describes our:

• Financial Stability Monitor and other tools to help policymakers

and market participants understand and assess vulnerabilities 

and potential threats to financial stability; 

• analysis of the macroprudential policy toolkit regulators are developing, including key areas of 

progress and remaining issues, such as market liquidity risks, risks of runs and asset fire sales, and 

the need to address cyclical market excesses;

• work to make data standards in general and the Legal Entity Identifier in particular widespread 

in regulatory reporting and market practice, and OFR collaboration with the Commodity Futures 

Trading Commission (CFTC) to promote data standards across derivatives markets; and

• efforts to address data gaps, such as the OFR’s new collaboration with the Federal Reserve to 

gather data about repurchase agreement (repo) markets.

This report fulfills the 

requirement in the DoddFrank Wall Street Reform 

and Consumer Protection 

Act of 2010 for the OFR 

to report annually to 

Congress. As required, 

the report includes: (1) 

an analysis of any threats 

to the financial stability 

of the United States, (2) 

the status of our efforts 

in meeting the mission 

of the Office, and (3) key 

findings from our research 

and analysis of the 

financial system.
2014 OFR Annual Report 2

Analyzing Threats to Financial Stability

Chapter 2 details where risks have increased over the 

past year. The focal point of this analysis is our Financial 

Stability Monitor, introduced last year, but refined, broadened, and deepened for this report. The monitor displays 

the buildup of vulnerabilities across five broad categories 

of risk — macroeconomic, market, credit, funding and 

liquidity, and contagion — based on a set of models, surveys, financial data, and other indicators. 

The monitor shows that although overall risks to financial stability are not particularly elevated compared to the 

pre-crisis period, some have clearly intensified over the past 

year. One particular concern is market risk, which is the 

vulnerability of investor portfolios to large losses because of 

unanticipated adverse movements in interest rates, exchange 

rates, and other asset prices. The monitor also shows elevated risks among nonfinancial corporations in the United 

States because of relaxed lending standards, lower credit 

quality, higher debt levels in relation to total assets, and 

thinner cushions to counteract shocks. Market liquidity risks 

have also increased, in part reflecting structural changes in 

the way liquidity is provided.

Recent volatility in financial markets focused attention on 

some of the vulnerabilities that have been growing over 

the past several years. Although accommodative monetary 

policy has helped to foster economic recovery and promote 

bank balance-sheet repair, the prolonged period of low 

interest rates has also suppressed volatility and encouraged 

greater risk-taking by market participants. 

We also remain concerned about structural vulnerabilities 

related to short-term wholesale funding markets because 

incentives still exist for fire sales of assets during periods of 

stress. Short-term funding markets are instrumental in providing liquidity to keep the global financial system operating. 

Potential spillovers from an inevitable reversal in the stance 

of monetary policy are an additional cause of concern. 

The impending change in policy poses risks for market 

participants who have bet on sustained low volatility or low 

interest rates. The buildup of excesses is not unique to the 

United States. Emerging markets, for instance, show some 

parallels. Tighter global links mean that future shocks will 

be more quickly transmitted, likely resulting in broader 

disruptions. 

Evaluating Macroprudential Policy Tools 

Although the OFR is not a policymaking entity, the DoddFrank Act directed us to provide analysis and advice about 

policies designed to curb risks to the financial system.

In this country and overseas, regulators have made notable 

progress developing policies designed to make the financial 

system less vulnerable to shocks and less likely to be the 

source of shocks. For example, U.S. banking regulators have 

overhauled the requirements regarding the capital that banks 

must hold as a buffer to shocks, and they are beginning to 

introduce requirements on liquidity. Under the Dodd-Frank 

Wall Street Reform and Consumer Protection Act of 2010 

(Dodd-Frank Act), they also now require the largest banks 

to undergo supervisory stress tests and to submit resolution 

plans, or “living wills,” to help restore market discipline. The 

Financial Stability Oversight Council (Council) has begun 

to designate nonbank financial companies for enhanced 

prudential standards and supervision by the Federal Reserve 

under another Dodd-Frank Act mandate. 

In Chapter 3 of this report, we explore the benefits and 

challenges of the new set of policies, most of which focus 

on the largest banks or nonbank financial companies. We 

also discuss risks in mortgage securitization markets and the 

steps that regulators have taken, or could take, to address 

them, focusing on the final risk retention or “skin in the 

game” rule released by regulators in October. 

Most reforms so far have been aimed at structural vulnerabilities. We observe in Chapter 3 that tools are also needed 

to address cyclical excesses. The one new cyclical tool 

introduced in the United States is the countercyclical capital 

buffer. As agreed under the international Basel III accord, 

regulators can require banks to hold an additional buffer 

of capital during boom times as a cushion against potential 

future losses. We note that U.S. regulators have not said 

how the buffer will be used or what will trigger its use. In 

addition, the buffer is a blunt tool that may not be suitable 

for addressing excesses in specific credit markets.

The leveraged lending market provides a test case of the 

current approach to cyclical excesses. The response to these 

issues has been led by bank regulators, who regulate the 

largest institutions that originate leveraged loans, often for 

sale to asset managers through various instruments. Despite 

stronger supervisory guidance and other actions, excesses in 

this market show little evidence of easing.
3 Executive Summary

Research on Financial Stability

In Chapter 4, we highlight three multiyear research projects 

at the OFR. The first uses agent-based models to simulate the spread of contagion across multiple channels for 

risk transmission in financial markets during crises. These 

models hold great promise for understanding the dynamics 

of fire sales and other chains of complex events. The 

models attempt to capture the behaviors and interactions 

of diverse market participants by considering the roles 

each agent plays in stress events as they unfold. The second 

project describes the OFR’s exploration of visualization 

techniques that go beyond familiar line charts and bar charts 

to help analysts monitor financial stability. Effective and 

innovative visualizations reveal key patterns and connections 

in complex financial data. The OFR is also developing interactive visualizations for displaying these insights online.

The third section of Chapter 4 describes ongoing OFR 

research on credit default swap markets. Research is focused 

on the role of information flows on credit default swap 

activity and pricing and how transaction sizes affect prices 

and liquidity under different market conditions. In addition, 

the chapter discusses implications for financial stability posed 

by central counterparty clearing of credit default swaps.

Advancing Data Standards 

In Chapter 5 of the report, we describe the OFR’s work to 

promote and develop financial data standards, which are 

essential to make data suitable for financial stability analysis 

and for financial companies’ internal risk management.

The global Legal Entity Identifier (LEI) project is the most 

advanced of these standards, and it took major strides 

forward in 2014 with the completion of the governing 

structure for the LEI system. The LEI is like a bar code for 

identifying entities that engage in financial market transactions. It is a linchpin for making connections in the massive 

volumes of financial data that course through the international economy every day. To date, about 300,000 LEIs have 

been issued to entities around the world and 19 utilities have 

been approved to issue LEIs for use in regulatory reporting. 

Regulations requiring the LEI are being issued at an accelerating pace across the world. The OFR’s Chief Counsel 

chairs the international committee overseeing the system.

As momentum builds and the LEI system grows, the benefits are growing as well, including efficiencies for financial 

companies in internal reporting and in collecting, cleaning, 

and aggregating data. We also expect to reduce companies’ 

reporting burdens by generating efficiencies in reporting 

data to regulators. 

In 2014, the OFR began work to help the CFTC and other 

regulators improve data quality in swap data repositories. 

This is a critical initiative for market participants and regulators to make sense of the vast amounts of new market data 

that these repositories are beginning to collect. 

The OFR also created plans in 2014 to prepare and publish 

reference databases for financial entities and companies and 

financial instruments, as required in Dodd-Frank. The LEI 

system will provide all needed inputs to create and maintain 

a financial entity database. As an outgrowth of projects 

such as the work with the CFTC, the OFR has also begun 

to develop formats and standards for reporting financial 

transaction and position data and for identifying financial 

instrument types. In addition, the OFR will develop a prototype of the financial instrument reference database. 

Data sharing is critical to our mission. The Office continues 

to work with Council member organizations to develop 

protocols and procedures for securely sharing data for monitoring and analysis. 

Addressing Data Gaps

In Chapter 6, we discuss our progress in filling gaps in 

the data available for monitoring and analyzing financial 

stability. In October, we announced a pilot project with the 

Federal Reserve to gather data about the market for bilateral repos. This project focuses on a critical gap in the data 

needed for financial stability analysis. A repo is essentially a 

collateralized loan, when one party sells a security to another 

party with an agreement to repurchase it later at an agreed 

price. Repos are an important source of short-term funding 

for the financial industry. The U.S. repo market provides 

more than $3 trillion in funding every day. The bilateral repo 

market, which constitutes half of the total market, is not 

only opaque, but also vulnerable to runs and fire sales.

Our ability to evaluate financial developments has benefited 

significantly as regulators introduced new data collections 

and expanded old ones in recent years. For example, the 

Securities and Exchange Commission’s (SEC’s) new Form 

PF has provided unprecedented insights about hedge funds 

and other private funds. Chapter 6 explains our analysis of 

hedge fund leverage using information from that form.
4 2014 OFR Annual Report

Outreach, Collaboration, and Infrastructure 

Chapter 7 describes the OFR’s progress in building its organization as a valued source of high-quality data and expert 

research and in collaborating with member agencies of the 

Council, outside researchers, and regulators overseas. This 

collaboration is central to the mission of the OFR.

In the 2014 fiscal year, we organized workshops and conferences in collaboration with the Council and its member 

agencies. This year, our Financial Research Advisory 

Committee met twice, and presented more than a dozen 

recommendations that span our work in research, risk management, and data standards. We awarded three grants to 

outside experts to promote financial stability research under 

a joint program with the National Science Foundation. OFR 

senior managers and research staff members participated 

in dozens of public events, both for general and technical 

audiences, increasing awareness of our activities and promoting further collaborations with outside organizations.

Our forthcoming strategic plan for FY 2015-19 describes 

our vision, mission, goals, and objectives, and discusses specific ways we will carry out that mission. The plan is a blueprint to guide our activities, set our priorities, and inform 

the public about our mission and how we work to achieve 

it. The plan also contains metrics required by law to evaluate 

our performance and hold us accountable to oversight and 

the public. 

Information technology is critical to carry out our mission. 

In 2014, we completed installation of a world-class, analytic 

environment for collecting, storing, aggregating, and maintaining large volumes of data. We also installed computing 

tools to support complex financial models, cutting edge 

visualization, and analysis. The Office uses a wide range of 

security tools to assure protection of confidential, nonpublic information and has created a proprietary recovery 

site to assure business continuity.

We continue to hire highly qualified employees. In the past 

three years the staff has increased from 30 to nearly 225 

employees. As detailed in our forthcoming 2014 Human 

Capital Report to Congress, we plan a steady-state total 

workforce of approximately 300.

Agenda Ahead

In Chapter 8, we describe our plans for 2015 and beyond. 

Our research priorities include:

• broadening our market monitoring framework, 

including the publication of a Financial Markets 

Monitor, a version of which is currently presented 

only to the Council; 

• developing a suite of additional monitors and dashboards, focused on money market funds, hedge 

funds, and credit default swap markets;

• publishing working papers that describe in greater 

detail the methodology behind our Financial Stability 

Monitor and Financial Stress Index, as well as a series 

of short papers on significant threats to financial 

stability;

• expanding our analysis of stress tests and other macroprudential policy tools; and

• publishing research on financial stability, risk 

management, and related topics, including working 

papers on agent-based models, visualization techniques, and credit default swap markets, as described 

in Chapter 4.

Our data priorities include:

• advocating for the global implementation of the LEI 

in regulation and market practice;

• promoting data standards in derivatives markets, in 

collaboration with the CFTC;

• leading or contributing to the development and 

implementation of new standards, such as universal 

loan identifiers in the mortgage market;

• collecting data on the repo market, in collaboration 

with the Federal Reserve; 

• filling the data gaps discussed in Chapter 6, particularly to help us understand risks in asset management 

activities and short-term wholesale funding markets; 

and

• creating a prototype financial instrument reference 

database to promote market transparency.

Our institutional priorities continue to focus on building 

our expert workforce and our technological capabilities, 

including a leading-edge intranet for the OFR staff and 

deployment of the new OFR public website. 

In 2015, we will continue to collaborate with the Council 

and its member agencies and with our network of outside researchers, industry experts, and others. We will also 

continue to engage with our stakeholders in Congress 

and expand our grants program with the National Science 

Foundation.
5

2 ANALYZING THREATS 

TO FINANCIAL STABILITY

Analyzing Threats to Financial Stability

Financial markets came under pressure in September and October, 

exposing some of the vulnerabilities and risk-taking that have 

been promoted by several years of low interest rates and low volatility. 

In this chapter, we highlight concerns about: (1) excessive risk-taking 

and positioning, with a focus on interest rate risks, credit risks, and 

volatility risks; (2) market structure and liquidity issues, with a focus on 

the fragmentation of market liquidity and the persistent risks of asset 

fire sales and runs; and (3) the migration of activities due to financial 

innovation and regulatory arbitrage. 

2.1 Analytic and Monitoring Framework

Seeking higher returns, market participants have taken significant duration, credit, and liquidity risk during 

six years of low interest rates, low volatility, and ample funding liquidity. Our analysis focuses on the risk 

that an unanticipated interest rate or volatility shock could reveal those vulnerabilities. 

Since our last annual report, market excesses and consequent threats 

to financial stability have been increasingly evident across a number of

dimensions. For example, throughout most of 2014, low volatility and

compressed risk premia persisted across asset classes, while nonfinancial corporate credit fundamentals deteriorated. While an accommodative monetary policy helped support the economic recovery and 

promoted balance-sheet repair by lowering borrowing costs, it also 

created incentives for risk-taking, with potential consequences for 

financial stability. 

The decline in risky asset prices and increase in volatility that occurred 

in September and October 2014 exposed some of those excesses. That 

some markets proved to be surprisingly brittle under this modest stress strongly suggests that both cyclical 

and structural vulnerabilities have increased over the past year. Whether they turn out to be serious vulnerabilities will only be revealed in time by larger market shocks. In this chapter, we describe the framework 

and indicators we use to track and analyze vulnerabilities.

Market Developments and Financial Stability Monitoring. Section 2.2 describes our monitoring 

activities, focusing on our Financial Stability Monitor, which tracks and quantifies five categories of risk 

based on a host of underlying indicators, and our Financial Stress Index, which tracks risks on a real-time 

basis. 

Potential Threats to Financial Stability. In Section 2.3, we highlight the key cyclical and structural 

vulnerabilities that concern us, based on the results of our Financial Stability Monitor, market intelligence 

Since our last annual 

report, market excesses 

and consequent threats 

to financial stability have 

been increasingly evident 

across a number of 

dimensions.
6 2014 OFR Annual Report

The episode revealed a number of underlying vulnerabilities. First, during a protracted period of low interest rates 

and the Federal Reserve’s quantitative easing, investors may 

have taken low volatility for granted and underestimated the 

potential for a reversal. While quantitative easing policies are 

intended to encourage investors to buy risky assets, there 

is also a risk that the perceived reversal of such policies 

will lead investors to turn the other way, triggering market 

instability. 

Similarly, investors may have become too sanguine about the 

availability of market liquidity — the ability to transact in 

size without having a significant impact on price — during 

both good times and bad. While structural changes in the 

provision of market liquidity are not fully understood, 

financial stability analyses in recent years, including the 

OFR’s previous annual reports, have noted the potential fragility of market liquidity during a market shock, due in part 

to the reduced willingness or capacity of broker-dealers to 

provide liquidity (see OFR, 2013a; IMF, 2014c; and Market

Liquidity Risks in Section 2.3). The recent market dislocation showed those concerns to be valid, as market liquidity 

quickly vanished in traditionally liquid markets such as U.S. 

Treasuries, cash, and futures markets, leading to less market 

depth and further sharp price declines. (Reduced market 

depth increases the transaction cost of executing a trade in 

reasonable size.) 

The liquidity strains in the U.S. Treasury market spread 

quickly to other markets, affecting related asset classes 

such as interest rate futures, swaps, and options to differing degrees. A liquidation of speculative short positions 

in interest rate markets also contributed to the instability. 

Anecdotally, some of the price movements appeared continuous, suggesting that a high volume of transactions was 

executed by algorithmic trading systems. 

Although the dislocation that peaked in mid-October was 

fleeting, we believe there is a risk of a repeat occurrence, 

given the increased prevalence of algorithmic trading, a 

shift in risk preferences by broker-dealers, and the persistent incentives for risk-taking. The potential for a rapid 

and severe adjustment in prices followed by a reversal in 

derivatives markets or fixed-income markets — which are 

large, interconnected, and widely used for hedging and 

risk management — raises a host of financial stability 

concerns.

Despite the market gyrations, overall demand for risky 

assets has not abated (see Figure 2-1). Investors continue 

gathered from a range of sources, and other surveillance 

tools. Financial stability risks have risen since our last report, 

centered in three areas:

• Excessive risk-taking, including: (1) interest rate 

risks and operational challenges posed by a normalization in monetary policy; (2) credit risks related to 

excessive risk-taking and a weakening in nonfinancial 

corporate credit fundamentals; (3) volatility risks, 

characterized by reaching-for-yield and herding 

behavior following a long period of low volatility; and 

(4) emerging market risks, as capital outflows may 

reveal underlying fundamental weaknesses.

• Market structure and liquidity issues, including: 

(1) market liquidity risks, caused by fragmentation and 

structural changes in various market segments; (2) 

run risks and asset fire sale risks in wholesale funding 

markets; and (3) market infrastructure vulnerabilities 

in equity markets, stemming from complexity and 

lack of transparency.

• Financial innovation and migration of activities

from more tightly to less tightly regulated parts of the 

financial system, such as: (1) captive reinsurance companies, (2) mortgage servicing rights, and (3) single 

family rental securitization. 

2.2 Market Developments and 

Financial Stability Monitoring

This section describes market developments over the past 

year and our monitoring activities, including an update of 

the Financial Stability Monitor, which we introduced in our 

2013 annual report. 

Review of 2014 Market Developments

Following a prolonged period of calm, investors’ concerns 

about extended valuations and global economic growth triggered a broad-based reassessment of risk in September and 

October 2014. Global risky assets sold off, volatility spiked, 

and global sovereign bond yields fell amid a flight to safety. 

Measures of tail risk — the risk of extremely rare events — 

also increased, as demonstrated by demand for protection 

against adverse future moves in market prices. The dislocation was large and unexpected, but short-lived. Expectations 

for continued monetary policy accommodation helped asset 

prices stabilize and partially recover. But investor sentiment 

remains fragile. 
7 Analyzing Threats to Financial Stability

Figure 2-1. Risk Tolerance Financial Market Heat Map

Overall risk appetite has firmed since our last annual report

Note: Each indicator is scaled to a percentile range rank and compared to the levels prevailing over the past three years. The dotted vertical lines 

represent the average across all five asset classes for 2013 and 2014. The direction of the arrow displays the change in the risk appetite between 

the two years. P/B = Price-to-Book ratio (a ratio used to compare a stock’s market value to its book value); EM = emerging market; LIBOR = London 

Interbank Offered Rate; OIS = Overnight Index Swap; EONIA = Euro Overnight Index Average (1-day interbank interest rate for the euro zone); V2X = 

European equivalent of VIX; ATM =at-the-money (for options, where the strike price is the same as the current spot price of the underlying security); 

vol = volatility; VXY = measure of volatility in a basket of currencies; MOVE = Merrill Option Volatility Estimate Index; IG = investment grade; HY = 

high-yield; EMBI = J.P. Morgan’s Emerging Markets Bond Index; CEMBI = J.P. Morgan’s Corporate Emerging Market Bond Index; GBI-EM = J.P. Morgan’s Government Bond Index-Emerging Markets (an emerging market benchmark index that tracks local currency bonds); CDX EM = Markit’s index of 

credit default swaps covering emerging market companies.

Sources: Bloomberg L.P., OFR analysis

Lower Risk Appetite Higher

2013 

average

2014 

average

Change from 2013 to 2014

Increasing risk appetite

Decreasing risk appetite

EQUITY VALUATION

S&P 500 (P/B Ratio)

MSCI EM Equities (P/B Ratio)

Nikkei (P/B Ratio)

MSCI Europe Equity (P/B Ratio)

VOLATILITY

U.S. (VIX)

Europe (V2X)

USD/JPY ATM vol (6m)

EUR/USD ATM vol (6m)

EM-VXY (3m)

US Swaption MOVE Index (3m)

Euro Swaption MOVE Index (3m)

Brent oil (Realized Vol)

CBOE SKEW Index

FUNDING/LIQUIDITY

Libor/OIS (3m)

Euribor-EONIA (3m)

EUR/USD basis (2y)

USD/JPY basis (2y)

US Swap Spread (2y)

CREDIT

U.S. IG Corp (OAS)

U.S. HY Corp (OAS)

Euro IG Corp (OAS)

Euro HY Corp (OAS)

EMBI global (OAS)

CEMBI (OAS)

GBI-EM (Yield)

iTraxx SubDebt CDS (5y)

CDX EM (5y)

FLIGHT TO SAFETY

U.S. Treasury Term Premium (10y-2y)

U.S. T-Bill (4w yield)

US Treasuries (10y yield)

Germany (10y yield)

USD/Yen

USD/Swiss Franc

Precious Metals (Realized Vol 1m)
8 2014 OFR Annual Report

to be rewarded for taking credit, duration, and liquidity 

risk. Even after taking into account the broad-based market 

dislocation that occurred in September and October, higher 

risk assets such as eurozone peripheral bonds, emerging 

market sovereign and corporate debt, and U.S. corporate 

bonds remain among the better performing assets this year 

(see Figure 2-2). 

Overall global financial and monetary conditions remain 

broadly accommodative, reflecting the outlook for slow 

economic growth and low inflation (see Figure 2-3). While 

the Federal Reserve and Bank of England have spelled out 

the circumstances that would entail future changes in their 

policy stances, currently they remain committed to accommodative policies. The Bank of Japan and the European 

Central Bank have taken additional measures to ease policy 

further. 

Accommodative global monetary policy, coupled with the 

Federal Reserve’s purchases of large amounts of low-risk 

assets and changes in risk sentiment, helped to compress 

volatility and risk premiums (the returns in excess of the 

return earned on a risk-free investment). 

These conditions encouraged investors to increase their 

holdings of long-dated securities and products with riskier 

credit attributes in a search for higher returns. Over the 

past five years, investors moved out of money market 

instruments and into riskier assets such as leveraged loans, 

high-yield corporate credit, eurozone peripheral bonds, and 

emerging market equities. Investors moved into global equities more slowly (see Figure 2-4). For instance, cumulative 

flows from U.S.-domiciled mutual funds into bonds have 

increased by nearly $1 trillion since the end of 2008, while 

the flow into equities has been a mere trickle. As a result, 

some investors (particularly those with unhedged positions 

and duration mismatches) grew more heavily exposed to an 

abrupt correction in the fixed-income markets compared to 

the pre-crisis period. 

During the recent bout of volatility, investors partly 

unwound their positions in eurozone peripheral credits, U.S. 

equities, and high-yield and leveraged loans. But the liquidation was not enough to offset the extended long positions 

that investors had built up over the past few years. On the 

contrary, the fleeting nature of the episode ultimately had 

the effect of reinforcing demand for duration, credit, and 

liquidity risk, and led many investors to reestablish such 

positions. 

Figure 2-2. Total Year-to-Date Returns (percent)

Most risky assets show positive returns this year, despite 

market stress in late September and October

Although the dislocation that peaked in 

mid-October was fleeting, we believe 

there is a risk of a repeat occurrence, 

given the increased prevalence of 

algorithmic trading, a shift in risk 

preferences by broker-dealers, and the 

persistent incentives for risk-taking.

Note: Data are year-to-date through September 15, 2014, just 

before a period of market stress began, and through October 17, 

2014, when the stress event had largely subsided.

Sources: Bank of America Merrill Lynch, Bloomberg L.P., JPMorgan Chase 

& Co., OFR analysis

-15 -10 -5 0 5 10 15

S&P GSCI Commodities

EuroStoxx 50

Emerging market equities

S&P Leveraged Loan Index

GBI-EM Global (USD terms)

U.S. high-yield corp. bonds

S&P 500 Index

Euro high-yield corp. bonds

Treasuries/agencies

Trade-weighted USD index

Emerging market corporate

bonds

EMBI global external bonds

U.S. investment grade

corp. bonds

Oct 2014

Sep 2014
9 Analyzing Threats to Financial Stability

Unlike prior to the financial crisis, investors have not significantly increased balance-sheet leverage to boost returns. 

That’s partly because of constraints on banks from increases 

in their capital requirements. Overall, the aggregate amount 

of nonderivatives-based leverage in the financial system has 

remained stable and is below peak levels. Total outstanding 

repurchase agreements (repos) and the issuance of financial 

products with embedded leverage are below pre-crisis levels. 

However, we lack detailed information on derivatives-based 

leverage; it is an area that suffers from data opacity and 

warrants further analysis. 

There are a few worrying exceptions to this low-leverage 

trend. While financial institutions have reduced the amount 

of debt on their balance sheets compared to their assets, 

debt accumulation has increased among nonfinancial companies and the government. Managers of structured investment products such as collateralized loan obligations are 

also increasing their use of leverage, and total margin debt 

(debt used to purchase securities) has risen. These trends 

require close monitoring. 

Financial Stability Monitoring Activities

The OFR continues to build and refine our financial stability 

monitoring and analytical toolkit to include a spectrum of 

monitors, models, metrics, and visualization tools. We will 

continue to gather market intelligence and acquire data to 

quantify and analyze vulnerabilities and risks. 

The OFR’s Financial Stability Monitor provides a high-level 

summary of the buildup and overall level of vulnerabilities 

in the financial system as of September 30, 2014 compared 

to October 31, 2013 (see Figure 2-5). Several financial 

stability risks have risen since our last report, notably those 

that measure market and liquidity risks. While overall threats 

are not particularly elevated compared to the peak of the 

2007-09 financial crisis, a number of risks are close to those 

prevailing in late 2005 or early 2006. Highlights include:

• Macroeconomic risks remain little changed. The 

U.S. macroeconomic outlook has improved amid 

stronger sentiment and stable quarterly growth. 

Market perceptions of tail risks have diminished, as 

reflected by a decline in global market-implied sovereign credit risk. Geopolitical tensions in emerging 

markets and uncertainty about growth in the eurozone 

have had limited spillover effects here (see Emerging 

Markets in Section 2.3). However, the risk of slow 

growth and low inflation — which some have called 

Figure 2-4. Cumulative Mutual Fund Flows to Bonds, 

Equities, and Money Market Instruments ($ billions)

Quantitative easing encouraged investors to shift out of cash 

into riskier assets

Figure 2-3. Global Monetary Policy Rates (percent)

Amid a weak economic outlook, global monetary policy 

remains broadly accommodative

6

Sources: Haver Analytics, OFR analysis

0

600

1,200

1,800

2,400

3,000

Mar

1990 1994 1998 2002 2006 2010 2014

Equities

Bonds

Money market instruments

Note: Rates are weighted by gross domestic product and include 

Australia, Belgium, Brazil, Canada, China, France, Germany, India, 

Italy, Japan, Mexico, the Netherlands, Norway, Poland, Russia, 

South Korea, Sweden, Switzerland, Turkey, the United Kingdom, 

and the United States. 

Sources: Bloomberg L.P., OFR analysis

-2

0

2

4

1993 1996 1999 2002 2005 2008 2011 2014

Nominal

Real
10 2014 OFR Annual Report

Figure 2-5. OFR Financial Stability Monitor

Notes: Green signifies low financial stability risks, while red signifies elevated risks. The figure represents a series of underlying indicators 

based on maximum levels prevailing from January 1, 1990 (if available) to the present. Each risk category is constructed as a weighted average across the prevailing risk levels, with weights assigned based on the back-test performance of each of the indicators in the underlying 

categories. Positioning is determined by the weighted average z-scores of the underlying indicators since our last annual report. Data are as 

of September 30, 2014 and October 31, 2013. Some risk subcategories were revised to include indicators recently added to the Financial 

Stability Monitor. The OFR will issue a working paper in 2015 detailing the components that make up the monitor. 

Sources: Bloomberg L.P., Haver Analytics, OFR analysis

Less Risk More Risk

2013 2014 2013 2014

Funding Stress

Market Liquidity

Balance-Sheet Liquidity

Intermediation

Corporate Sector

Households

Banking Sector

Joint Distress

Asset Market Interdependence

Interconnectedness

Volatility

Interest Rate Risk

Positioning

Economic Activity

Sovereign Risk

Ination Uncertainty

External Sector

SUBCATEGORIES

(underlying indicators)

RISK CATEGORY

MACRO

CONTAGION

MARKET

FUNDING/

LIQUIDITY

CREDIT

Several financial stability risks have risen since our last report, 

notably those that measure market and liquidity risks.
11 Analyzing Threats to Financial Stability

“secular stagnation” — leaves some economies and 

financial systems vulnerable to shocks. 

• Market risk has increased across a number of 

measures. Duration risk (the sensitivity of bond 

investments to a change in interest rates) remains 

elevated, and could potentially expose investment 

portfolios to sizeable losses in the event of an 

unanticipated rise in interest rates (see Credit Risks 

and Interest Rate Risks in Section 2.3). There is 

evidence of valuation misalignments and crowded 

positioning in some market segments, posing the 

risk of a disorderly withdrawal if market participants 

exit similar positions simultaneously. These buildups 

have occurred amid historically low volatility, which 

increases the potential for excessive risk-taking and 

high leverage and could worsen the consequences 

if volatility suddenly spikes (see Volatility Risks in 

Section 2.3). 

• Credit risk measures are mixed — improving 

in the household and banking sectors, but 

continuing to deteriorate at the margin in the 

corporate sector. Overall bank fundamentals 

in the United States are strong, with much of the 

improvement attributed to enhanced capital and 

liquidity buffers to comply with new regulations. 

Delinquencies, default rates, and debt overhangs have 

continued to abate since the financial crisis; indeed, 

delinquencies and charge-offs at commercial banks 

for nonmortgage consumer and commercial loans are 

near record lows. However, overseas banks, particularly in the eurozone, show unresolved structural 

weaknesses and balance-sheet repair is incomplete 

(see IMF, 2014c). Our concerns focus on the buildup 

of risks in U.S. nonfinancial corporates. Although 

delinquencies in this sector remain low, we expect 

that the credit cycle, when it turns, will be exacerbated by the combination of increased balance-sheet 

leverage, reduced compensation for risk, lower credit 

quality, weaker covenants, and easing lending standards (see Credit Risks in Section 2.3). 

• Measures of funding appear stable, but market 

liquidity is a concern. Monetary policy is still 

accommodative and wholesale funding markets show 

reduced concentration risk, maturity risk, and intraday 

credit risk, while collateral quality has improved. 

However, these are precisely the circumstances that 

have in the past fostered excessive leverage, maturity 

transformation, and liquidity transformation. And 

funding markets can shift rapidly in the event of an 

Figure 2-6. Financial Stability Monitor Risk Classification and Examples of Indicators

Source: OFR analysis

Risk Definition Indicators

Macroeconomic

Evaluates risks that have the potential to 

affect financial stability through various macro 

channels such as growth, external balances, fiscal 

vulnerabilities, and confidence channels.

Financial conditions, output gap, sovereign debt 

levels and financing costs, foreign exchange 

reserves, current account balances, consumer and 

business confidence, inflation volatility, and inflation 

expectations

Market Assesses the risk of destabilizing losses across key 

asset classes and investment strategies as a result 

of adverse movements in asset prices. 

Duration, positioning, risk premiums valuations, and 

volatility

Credit Measures the propensity of a counterparty to 

meet its financial obligations, and includes marketimplied and balance-sheet measures of risk.

Corporate credit spreads, balance-sheet leverage, 

lending conditions, delinquencies, asset quality of 

households, corporates, banks, and nonbank financial 

institutions

Funding/

liquidity

Captures market liquidity, balance-sheet liquidity 

ratios, stress in funding markets, and the potential 

for vulnerabilities that arise from excessive 

leverage. 

Broker-dealer inventories, turnover, volume, cash 

balances, dependence on wholesale funding, 

changes in short-term investor assets under 

management and tenors, foreign exchange basis 

swaps, short-term funding rates/spreads 

Contagion Measures the vulnerability of the financial system 

to sudden shocks that may spread as a result of 

interconnectedness.

Contingent claims analysis, conditional value at risk, 

systemic expected shortfall, distressed insurance 

premium, network analysis, cross-border exposures, 

sovereign-bank exposures, correlation risk
12 2014 OFR Annual Report

The Financial Stability Monitor cuts across different geographies, sectors, and measurement methods (see Figure 

2-7A). We concentrate primarily on risks directly related to 

U.S. financial stability, but we also take into account spillovers or shocks to and from other major economies and 

large, complex financial institutions. The monitor includes 

indicators for sectors such as government, households, corporations, banks, and nonbank financial services firms. The 

monitor uses model-based indicators, as well as measures 

based on balance sheets, surveys, and markets.

The Financial Stability Monitor is constructed with five risk 

categories (see Figure 2-7B). Each category is evaluated 

using a series of underlying indicators based on maximum 

and minimum daily levels prevailing from January 1, 1990 

(if available) to the present. Each indicator is weighted 

according to its performance in a series of tests. Indicators 

are then combined into subcategories, such as interconnectedness or joint distress. Several subcategories make up each 

risk category, which is constructed as a weighted average of 

the prevailing risk levels for each indicator. Results are summarized on a heat map, in which green indicates that risks to 

stability are low and red indicates they are elevated.

The Financial Stability Monitor aims to identify underlying 

vulnerabilities that may predispose a system to a crisis. We 

have a good sense of preconditions that lead to crises: for 

example, excessive leverage, excessive risk-taking, a rapid 

rise in capital flows, and reduced policy buffers. Indicators 

were partly selected based on the experience with historical 

episodes of stress. However, every crisis has been unique, 

both in terms of the triggers and the propagation. Had we 

designed the monitor prior to the 2007-09 global financial crisis, we would likely have underestimated the role of 

funding markets in transmitting stress or underestimated the 

importance of macrofinancial linkages. (Given the benefit 

of hindsight, the monitor shows a pronounced buildup of 

risk during the runup to the crisis.) The lesson is that we will 

need to adjust our framework as new, more forward-looking 

metrics are developed, as our understanding about the transmission of risk evolves, and as the system is tested during 

future periods of stress. The Financial Stability Monitor is a 

dynamic framework that we will adjust as conditions evolve. 

The monitor’s main limitations include:

• It may miss some vulnerabilities, especially those that 

are hard to quantify. For instance, containing operational risk, and cyberattacks in particular, is important 

to assure infrastructure resilience. But it would be a 

unanticipated market disturbance, especially when 

it is accompanied by leverage or a liquidity mismatch (see Run Risks and Asset Fire Sale Risks

in Section 2.3). Corporate balance-sheet liquidity 

remains strong, measured by cash balances, although 

somewhat diminished since our last annual report. 

We remain concerned that traditional liquidity providers are less able than they were before the crisis 

to intermediate and provide liquidity in the event of 

a market disruption (see Market Liquidity Risks

in Section 2.3). Furthermore, six years of abundant 

funding liquidity may have masked the depth of the 

deterioration in trading liquidity.

• Measures of contagion risk have moderated.

Market-based measures of joint distress of major U.S. 

financial institutions are at post-crisis lows. (However, it 

is worth noting that market-implied contagion measures 

tend to show weak forward-looking properties. Such 

measures are more effective at identifying peaks in the 

midst of a crisis.) Direct measures of interconnectedness, such as financial institutions’ holdings of sovereign debt and foreign claims of U.S. banks, have also 

declined over the past year. Conservative balance sheets 

have made banks more resilient to financial shocks. 

Measures of cross-asset correlation are at modest levels. 

CONSTRUCTION OF THE MONITOR

The OFR’s Financial Stability Monitor does not predict 

when or how future systemic shocks could materialize. It 

would be difficult, if not impossible, to pinpoint the probability, magnitude, or timing of shocks that might trigger 

a systemic financial event. The monitor instead attempts 

to isolate and analyze vulnerabilities in the financial system 

that could be exposed by shocks, such as incentives to take 

excessive risks.

The Financial Stability Monitor considers five categories of 

risk: macroeconomic, market, credit, funding and liquidity, 

and contagion. These categories closely align with the tasks 

of a well-functioning financial system — credit allocation, 

maturity transformation, risk transfer, liquidity intermediation, and a smoothly operating system for payments (see 

Figure 2-6).

In selecting the monitor’s underlying indicators, we focused 

on ones with high frequency and history over multiple 

business cycles and periods of financial distress. Most start 

in 1990 and have a daily or monthly frequency. 
13 Analyzing Threats to Financial Stability

unprecedented events, although post-crisis forensic 

analysis may still be informative. 

We are seeking ways to make the monitor more forwardlooking — not to try to predict shocks with precision, but 

to signal that trouble might be on the horizon. We are also 

working to increasingly take into account the complexity 

and interconnectedness of institutions and markets, as well 

as the channels that can transmit or amplify shocks.

Since the OFR introduced the Financial Stability Monitor 

one year ago, we have substantially refined our framework. 

First, we expanded the monitor’s breadth and depth, and 

acquired data to populate the underlying metrics. Second, we 

conducted performance tests on the underlying indicators 

to gauge each one’s reliability. We identified indicators that 

can provide early warnings, identify critical thresholds, and 

challenge to develop metrics to monitor cyber-related 

vulnerabilities, assess their systemic impacts, and 

identify gaps in oversight. 

• A number of the indicators in the monitor are nearcoincidental. More work is needed to make it more 

forward-looking. The difficulty in providing early 

warnings or reliable turning points may not allow 

policymakers time to take action to avoid a crisis or 

substantially dampen its effects.

• The Financial Stability Monitor does not take into 

account systemwide feedback effects, changes in 

historical relationships, or different phases in the 

business, credit, and monetary policy cycles.

• Because the Financial Stability Monitor is calculated 

relative to historical norms, it does not account for 

INDICATOR

TYPE

MEASUREMENT

METHOD SECTORS ECONOMIC

GEOGRAPHIES

MODEL-BASED SURVEY CORPORATIONS

ADVANCED PRICE DIRECT

OBSERVATION

NONBANK

FINANCIALS

EMERGING

QUANTITY BANKS

HOUSEHOLDS

 GOV’T

GLOBAL

U.S.

Figure 2-7. Composition (A) and Construction (B) of the Financial Stability Monitor

A. The Financial Stability Monitor cuts across different geographies, sectors, and measurement methods.

Indicator weighted average 

(z-score 0-20)

SUBCATEGORIES INDICATORS

Subcategory 

average

Normalize data across 

individual indicators

0 10 20

19.6

7.7

9.5

RISK CATEGORY

Volatility

Interest Rate Risk

Positioning

MARKET

Market Volatility 

 (Index of VIX, CVIX, and MOVE)

Volatility Surface 

 (Spread of 1m10y and 1y10y Swaption Implied Volatility)

Barclays U.S. Modied Duration Index

State Street Investor Condence Index

Net Speculative Positioning 

 (Weighted average of 10y Treasury, 3m Eurodollar, WTI, euro, and yen futures)

Emerging Market Fund Flows

B. An illustration of how the Financial Stability Monitor is constructed.

Source: OFR analysis

Color conversion scale
2014 OFR Annual Report 14

The Financial Stability Monitor was designed 

based on historical experiences that inform 

our understanding of the preconditions that 

lead to crises. To assess the quality of the 

underlying indicators in the monitor, we tested 

each indicator for its ability to capture extreme 

events such as market peaks and troughs, identify 

turning points, and give early warning signals of 

stress at a reasonable horizon during prior crises 

(see Arsov and others, 2013; Dattels and others, 

2010). 

We looked at selected historical crisis episodes to assess 

how the range of indicators performed. If a particular 

indicator performed poorly on all three tests, we removed it 

from the monitor. Indicators that performed well on the tests 

were weighted more heavily. 

The tests are summarized as follows:

EARLY WARNING SIGNAL

The early warning signal tested an indicator’s ability to signal 

stress far enough in advance to give policymakers time to Identifying measures that provide consistent early warning 

signals is a challenge for policymakers. Our initial findings 

showed few indicators performed well in identifying early 

warning signals. However, a large number performed better 

in the extreme event study. A mix of market- and institution-based indicators across all five risk categories included 

in the Financial Stability Monitor showed some ability to 

identify and capture stress events. Measurements that estimate the impact of broader financial distress on a specific 

bank’s market value or insurance premium performed well 

in all the tests, and so did measures of volatility, corporate 

credit pricing, and asset return correlation.

implement contingency measures. We tested each underlying indicator against a proxy for systemic risk, the Financial 

Stress Index published by the Federal Reserve Bank of 

Cleveland (FRB Cleveland). 

We applied a Granger causality test, a statistical technique 

that tests if one time series precedes (or “Granger-causes”) 

another. We used F-tests to test whether lagged information 

on a variable in the monitor provided statistically significant 

information about the FRB Cleveland stress index. If it did 

not, we gave it a low score on its early warning characteristics. A final early warning score was computed by taking 

a simple average of the measure’s performance on the 

Granger causality and lag-length regression tests.

EXTREME EVENT SIGNAL

The extreme event tested an indicator’s ability to forecast 

extreme stress events such as market peaks and troughs 

with reasonable accuracy. We used a statistical technique 

known as logistic regression to test lags of each indicator 

against periods of high financial stress. We defined a period 

of high stress occurring when the FRB Cleveland stress 

index exceeded its 75th percentile on a historical basis. 

The extreme event score was computed by taking a simple 

average of the measure’s performance based on statistical 

significance and model fit.

TURNING POINT SIGNAL

The turning point tested an indicator’s ability to capture the 

timing of critical turning points during a stress event. We 

examined how each indicator performed ahead of sharp and 

sustained equity market selloffs during recent crises, such as 

the January 1991 savings and loan crisis, the July 1997 Asian 

financial crisis, the August 2000 bursting of the U.S. Internet 

bubble, and the July 2007 global credit crisis. To reach our 

turning point conclusions, we asked four questions:

1. Did the indicator fall outside the threshold (defined as 

plus or minus one standard deviation from the mean) 

six months ahead of the stress event, providing an early 

warning signal?

2. Did the indicator fall outside the threshold during or 

within six months after the stress event, capturing its 

severity?

3. Did the indicator signal a stress event that never materialized over the next 12 months? If so, we classified it as 

a false positive error.

4. Did the indicator fail to signal a stress event before or 

during a stress event that actually materialized? If so, we 

classified it as a false negative error.

The turning point exercise produced mixed results in testing 

for errors. While more than 90 percent of the measures were 

able to capture the stress events, many signaled false positives. A policymaker might worry less about false positives 

and prefer to err on the side of caution rather than overlooking a stress event entirely. In any case, follow-up analysis 

and judgment will be needed prior to taking any action. 

The indicators we selected seem to successfully capture 

mounting risks ahead of selected crisis events and to help us 

evaluate their depth. But it is only one tool in our toolkit. We 

continue to identify and develop forward-looking indicators 

that may help improve our ability to monitor vulnerabilities.

Back-testing the Financial Stability Monitor
15 Analyzing Threats to Financial Stability

accurately mark the severity of a crisis (see Back-testing 

the Financial Stability Monitor). Third, we shifted from 

an equal-weighting scheme to one that gives more weight to 

indicators that generate a higher-quality signal.

OTHER MONITORING ACTIVITIES

The Financial Stability Monitor is just one tool policymakers 

can use to monitor the health of the financial system. It is a 

starting point to highlight potential weak links that require 

further investigation. It should be accompanied by rigorous 

and robust quantitative assessments, such as stress tests and 

macroprudential surveillance. 

The OFR is building a suite of monitoring tools to assess 

risks to the financial system (see Building Tools for 

Financial Stability Monitoring). Some will appear as 

analytical tools or as working papers, including one that 

describes our Financial Stability Monitor in greater detail. 

We will also publish research briefs about some of the current threats in this chapter.

Macroprudential surveillance, a complement to this toolkit, 

contains five critical elements:

1. Robust technology and granular data to monitor 

price fluctuations, transactions, and investor 

positioning across assets.

2. Real-time market intelligence-gathering from a 

broad range of sources.

3. Flexibility to update surveillance techniques as 

financial instruments and markets evolve.

4. Collaboration and routine information-sharing with 

regulators in the United States and internationally. 

5. Ability to communicate concerns promptly to 

authorities when action may be needed.

Central banks, regulators, and supervisors collect large 

amounts of data and intelligence about financial institutions 

and markets (see Watkins, 2008).Authorities have made 

significant progress since the financial crisis, with greater 

access to detailed financial data, enhanced monitoring 

efforts, and expanded sources of information through interagency briefings, surveys, and interactions with industry and 

market participants.

But more is needed. There is no integrated platform to analyze large amounts of data across asset classes, nor are there 

sufficiently detailed data to detect real-time vulnerabilities in 

major asset markets or key participants. Surveillance efforts 

Building Tools for Financial 

Stability Monitoring

The tools we are developing to assess 

risks to the financial system include:

MONITORING PRODUCTS

Financial Markets Monitor for the public that summarizes major developments and emerging trends 

in global capital markets. A version is currently 

distributed or presented roughly twice a month to 

the Council and its Systemic Risk Committee. 

Interactive money market fund monitor that 

examines holdings of individual funds and the 

industry as a whole on the basis of credit, interest 

rate, and liquidity risk. 

Credit default swap monitor that provides analytics 

on various financial stability metrics in the credit 

default swap (CDS) market, such as market concentration and interconnectivity. 

Liquidity library to monitor market liquidity conditions on a broad, high-frequency basis.

Hedge fund monitor based on regulatory and 

commercial data to provide insights into this traditionally opaque alternative asset class.

MONITORING-RELATED PAPERS

Methodology paper on the Financial Stability 

Monitor. 

Reference guide on U.S. repurchase agreement 

(repo) and securities lending markets. The guide 

will examine how dealers and their clients use these 

markets, building on the Office’s ongoing research 

on the sources and uses of short-term funding, and 

will identify potential vulnerabilities and data gaps. 

Short, nontechnical papers analyzing potential 

emerging threats to financial stability in our new 

OFR Briefs series (see Section 4.5).
16 2014 OFR Annual Report

2.3 Potential Threats to Financial 

Stability

Although overall financial stability risks are not particularly elevated, some vulnerabilities have intensified. In this 

section, we take a closer look at the risks that either signal 

further deterioration or suggest caution is warranted. We 

highlight cyclical vulnerabilities, many of which are interrelated. For example, low interest rates combined with low 

market volatility may have reinforced excessive risk-taking 

related to credit and liquidity. A reversal in these conditions 

could interact with underlying vulnerabilities to pose a threat 

to financial stability. We also highlight structural vulnerabilities in market liquidity, run risk in short-term wholesale 

funding markets, market infrastructure, and risk migration. 

Cyclical Risks

INTEREST RATE RISKS 

The risk associated with keeping long-term rates low for a 

protracted period and the challenge of managing a smooth 

exit from extraordinary monetary policy remains a recurring 

theme. Although accommodative policy supports a robust 

economic recovery, there are increasing signs that the Federal 

Reserve’s low interest rate policy may be encouraging excessive risk-taking in some asset classes, increasing the potential 

for adverse market outcomes. These signs include an increase 

in corporate credit risk (for example, low long-term borrowing costs have encouraged nonfinancial corporates to 

increase leverage to peak levels prevailing in 2005-07); carry 

trades and other trading strategies that are contingent on 

volatility remaining low; and a shift into higher-yielding but 

less liquid assets. 

There is a tradeoff between mitigating excessive risk-taking 

and promoting the mandated macroeconomic objectives 

of the Federal Reserve. While the purpose of quantitative 

easing is to encourage risk-taking in an effort to spur economic growth, there may be a point at which it could also 

increase the vulnerability of the financial system to a future 

shock. This illustrates the often-necessary complementarity 

between monetary policy, which addresses risks in the real 

economy, and macroprudential policy, which addresses 

potential financial stability concerns using a separate set of 

tools. In recent years, while monetary policy deliberately 

sought to stimulate economic risk-taking by boosting risky 

asset values, macroprudential policy narrowly targeted 

are overly dependent on potentially unobjective market 

participants to provide intelligence and data.

The Securities and Exchange Commission’s (SEC’s) Market 

Information Data Analytics System and the Financial 

Industry Regulatory Authority’s Order Audit Trail System 

and planned Consolidated Audit Trail are steps in the right 

direction (see Section 6.3). But they were not designed to 

support the analysis of financial stability and they cover only 

public markets. Large gaps in coverage remain and the process for collecting critical data and developing sophisticated 

technology can be slow.

Monitoring tools need to be improved. For instance, natural 

language processing tools could be more widely used to 

identify and monitor emerging trends and themes (see 

Leskovec and others, 2009), to develop more timely and 

accurate metrics for monitoring and forecasting macroeconomic variables (see Choi and Varian, 2009; Antenucci and 

others, 2014), and to understand the impact of disparate 

information sources on markets such as how markets interpret negative and positive economic news (see Sinha, 2010).

While monitoring tools provide insight into the buildup of 

vulnerabilities, they reveal little about the amplification and 

propagation of shocks. For those insights, we need other 

forensic analytical tools. For example, we can apply agentbased models to financial markets to run dynamic simulations and observe how an individual agent’s behavior can 

transform a crisis by withdrawing funding or selling assets 

(see Section 4.2). Unlike existing monitoring tools, dynamic 

agent-based simulations can help explain complex situations 

in which the relationships among variables do not necessarily follow historical patterns. 

Although accommodative policy 

supports a robust economic recovery, 

there are increasing signs that the 

Federal Reserve’s low interest rate 

policy may be encouraging excessive 

risk-taking in some asset classes, 

increasing the potential for adverse 

market outcomes.
17 Analyzing Threats to Financial Stability

financial risk-taking in order to mitigate potential vulnerabilities (see Chapter 3). 

Despite the recent market dislocation, market pricing and 

investment positions continue to suggest market participants expect interest rates to remain low, followed by a 

gradual rise when the Federal Reserve begins to tighten 

monetary policy. This expectation is reflected, for instance, 

in: (1) low near-term market-implied interest rates and low 

nominal growth expectations relative to the forecasts of the 

Federal Open Market Committee (FOMC); (2) depressed 

medium-term interest rate and inflation expectations relative 

to prior monetary policy cycles, as implied by yields on fiveyear U.S. Treasury notes five years ahead (see Figure 2-8); 

and (3) elevated duration in bond portfolios. 

There are a few important potential risks associated with 

the policy normalization process. First, there is the risk 

that the adjustment occurs more quickly than some market 

participants expect — this could have adverse results, for 

example, investors could experience large portfolio losses 

on longer-dated assets, and bank depositors could shift 

large amounts from banks to alternative, higher-yielding 

investments. Second, there is the risk that financial markets 

may overreact to a change in policy, causing destabilizing 

volatility and conditions to tighten more quickly than policymakers would otherwise like, both domestically and abroad.

When we published our 2013 annual report, the duration 

of a typical U.S. fixed-income portfolio was high, suggesting that investors were not particularly concerned about 

an imminent rise in long-term interest rates. (Duration 

measures the vulnerability of a portfolio of assets to a rise 

in interest rates.) Portfolio duration is even higher today. 

An immediate 100 basis point shock to interest rates would 

result in an estimated $212 billion (unhedged) loss to U.S. 

bond mutual funds, or 5.6 percent on average across funds 

— well above estimated losses at this point in previous 

monetary policy cycles (see Figure 2-9). Losses from a 

given change in interest rates would be larger than in the 

past.

Outsized losses may be difficult for some market participants to absorb in the event of an unanticipated increase 

in long-term rates. Of course, the impact of such losses 

depends on the distribution, time frame, hedging activity, 

and other conditions. For instance, losses that are concentrated in entities with large unhedged positions or with 

mismatches between assets and liabilities would likely be 

more difficult to absorb.

Figure 2-8. Five-Year/Five-Year Forward Rates During 

Federal Reserve Tightening Cycles (percent)

Long-term interest rate and inflation expectations are low 

compared to historical tightening episodes

Figure 2-9. Estimated Loss to U.S. Bond Funds 

Following a 100 Basis Point Shock to Interest Rates 

($ billions and percent)

Extended duration increases vulnerability to interest rate 

shocks

Note: Data are based on prior periods of U.S. monetary policy 

tightening starting in February 1994, July 1999, and July 2004. For 

the 1994-95 cycle, bond convexity is set to zero, because convexity data for that period are unavailable. The Barclays Capital 

U.S. Aggregate Float Adjusted Index which excludes the Federal 

Reserve’s holdings of MBS and Treasuries, was used as a proxy for 

duration of an average fixed-income portfolio. Modified duration 

stood at 5.6 years during the month of September 2014, a longterm peak. 

Sources: Bloomberg L.P., Haver Analytics, OFR analysis

0

50

100

150

200

250

2

4

6

8

1994-95 1999-00 2004-06 July

2013

Sept

2014

Percent loss (left axis)

Ination-adjusted loss (right axis)

Note: Data are based on prior periods U.S. monetary policy tightening starting in February 1994, July 1999, and July 2004. Hypothetical interest rate tightening in the current cycle is assumed to 

begin in June 2015, as implied by interest rate futures markets. The 

horizontal axis represents the number of trading days before and 

after the first interest rate hike (which occurs at time t=0). 

Sources: Bloomberg L.P., OFR analysis

0

2

4

6

8

10

-500 -250 0 250

Current cycle

Average of past three cycles
18 2014 OFR Annual Report

Interest rate risk extends beyond nonfinancial bond portfolios. On the liability side, a reversal of banking system 

deposits is a potential risk once interest rates rise. U.S. banks 

have seen dramatic growth in their non-interest-bearing 

deposits relative to total banking system liabilities. There is a 

non-negligible risk that these deposits could shift to alternative, higher-yielding investments as rates rise. The lack of 

historical data on deposit behavior at near-zero rates, coupled 

with structural changes post-crisis, make it difficult to quantify potential deposit outflows if rates were to normalize. 

Additional institutional deposit outflows may also occur 

as a result of a reallocation by eligible participants to the 

Federal Reserve’s Overnight Fixed-Rate Reverse Repurchase 

Agreement Operational Exercise.

More broadly, the Federal Reserve’s monetary policy trajectory could have spillover effects on global interest rates, 

currencies, and capital flows, tightening domestic or global 

financial conditions more quickly than expected. Historically, 

tightening in U.S. monetary policy has been accompanied by 

a rise in interest rate volatility, as markets adjust to the shift 

in stance. (There are, of course, exceptions: in 2004, for 

instance, long-term rates and volatility rose only marginally 

in reaction to monetary policy tightening.) 

During the past year, global monetary policies became 

less synchronized. Central banks in the United States and 

the United Kingdom took the first steps toward slowing 

the pace of policy accommodation, while central banks in 

Japan and the eurozone have further eased policy. Even if 

central banks can control short-term rates through forward 

guidance, there is a risk that normalization of the U.S. term 

premium could exert upward pressure on long-term rates 

in other countries. The sharp but short-lived selloff in 

emerging markets during the so-called Taper Tantrum in 

mid-2013 illustrates this risk. 

The process of departing from a policy of low interest 

rates may also pose operational and market challenges. The 

FOMC’s current strategy for normalizing monetary policy, 

when economic conditions permit, contains the following 

elements: (1) raise the target range for the federal funds 

rate and (2) reduce the Federal Reserve’s securities holdings, 

primarily by ceasing to reinvest repayments of principal on 

securities held in its long-term securities portfolio. 

To implement this strategy, the Federal Reserve has said it 

will use the interest rate it pays on excess reserve balances 

as the primary tool to guide the federal funds rate into the 

target range, while relying on other tools, like the reverse 

Figure 2-10. Three-Month Eurodollar Futures 

(percent)

Market-implied interest rate expectations are more dovish 

than Federal Open Market Committee (FOMC) projections

Note: Expectations are based on the Federal Reserve’s September 

17, 2014 FOMC meeting. The federal funds target rate is the policy 

rate determined by the FOMC. Whiskers represent the range of 

FOMC projections and dots represent median FOMC projections.

Sources: Bloomberg L.P., Board of Governors, OFR analysis

0

1

2

3

4

Jun

2014

Dec

2014

Jun

2015

Dec

2015

Jun

2016

October 17, 2014

As of:

July 17, 2014

April 17, 2014

Federal funds target rate

expectations by FOMC 
19 Analyzing Threats to Financial Stability

Figure 2-11. Federal Reserve Policy Rates and Shortterm Market Interest Rates (basis points)

The exit strategy may pose challenges guiding short-term 

market rates

Notes: The overnight reverse repo facility began on September 23, 

2013. 

Sources: Bloomberg L.P., Board of Governors, Depository Trust & 

Clearing Corp., Federal Reserve Bank of New York

0

10

20

30

40

Jan

2013

Apr

2013

Jul

2013

Oct

2013

Jan

2014

Apr

2014

Jul

2014

Oct

2014

Effective federal funds

GCF Treasury repo

Overnight reverse repo

Interest on excess reserves

repo and the term deposit facilities, to augment the process, 

if needed. This strategy is contingent on the evolution of 

economic and financial developments.

The more immediate challenges associated with the 

mechanics of the exit strategy include the lack of experience 

with new monetary policy tools, the difficulty of managing 

short-term interest rates in the normalization process, and 

reliance on a larger set of counterparties, all against the 

backdrop of ongoing regulatory reforms. Specifically, those 

challenges include:

• Communication challenges. Enhancing its communications strategy has been a long-term priority 

for the Federal Reserve. There has been considerable improvement in transparency (see Yellen, 2012; 

Jeremy Stein, 2014). However, the Federal Reserve 

has never implemented such a complex exit strategy. 

There is noticeable skepticism among investors, as 

indicated by a gap between market-implied expectations of the future path of interest rates and the 

FOMC’s projections (see Figure 2-10). Uncertainty 

could result if market participants misinterpret the 

plans or if the Federal Reserve’s actions are perceived 

as incomplete or still evolving in response to changes 

in financial or economic conditions. 

• Inability to guide short-term rates. In September 

2013, the Federal Reserve authorized overnight 

reverse repo auctions to help control the federal 

funds rate. The reverse repo facility appears able to 

support the floor for overnight interest rates at the 

level paid by the facility. However, demand for the 

facility increases at the end of each quarter, at which 

time short-term market interest rates decline below 

the facility rate (see Figure 2-11). This dynamic 

suggests a potential challenge for the Federal Reserve 

to maintain the target for the effective fed funds rate. 

To mitigate the risk of significant shifts in shortterm rates, the Federal Reserve plans to vary the rate 

offered on its overnight facility, adjust the facility cap, 

and provide term reverse repo operations.

• Disintermediation and run risk. Because the 

Federal Reserve may need to drain a large amount 

of its reserves to control the federal funds rate, 

the central bank expanded its list of authorized 

counterparties for the reverse repo facility beyond 

primary dealers. The Federal Reserve is now the 

largest counterparty in the triparty repo market, 

Figure 2-12. Federal Reserve Reverse Repo Facility 

Demand and Utilization ($ billions) 

Utilization spikes at the end of each quarter reflect increased 

demand from money market funds

Note: Money market fund utilization data are available on a monthly 

basis. 

Sources: Federal Reserve Bank of New York, SEC Form N-MFP, OFR 

analysis

0

100

200

300

400

Sept

2013

Dec

2013

Mar

2014

Jun

2014

Sept

2014

Total demand

Utilization by money market funds
20 2014 OFR Annual Report

and its role is likely to remain substantial, subject to 

a cap. As new nonbank counterparties rely on the 

reverse repo facility as an overnight cash investment, the Federal Reserve may potentially supplant 

other funding intermediaries, the consequences 

of which are difficult to fully project (see Figure 

2-12). This risk of disintermediation should be 

mostly contained by limiting the size of the facility 

and by phasing it out when it is no longer required 

for monetary policy normalization purposes. 

CREDIT RISKS

Vulnerabilities to credit-related risks highlighted in our 

annual report last year persist; if anything, they have 

increased. 

The traditional credit cycle goes through four phases: 

• Repair (balance-sheet cleansing),

• Recovery (restructuring),

• Expansion (increasing leverage, weakening lending 

conditions, diminishing cash buffers), followed by

• Downturn (rising defaults, falling asset prices, 

increasing funding pressure). 

Figure 2-13. Where Are We in the Credit Cycle? 

Late-cycle behavior is becoming increasingly pronounced

REPAIR RECOVERY EXPANSION DOWNTURN

Credit conditions

Valuation

Banks

Households

• Restructuring

• Rising free cash ow

• Increasing margins

• Falling leverage

• Funding pressures

• Falling asset prices

• Increased defaults

Asset quality

• Cleansing balance 

sheets

Strong cash positions, interest 

coverage, valuations, and 

margins signal expansion.

Loan losses fall and 

margins rise, but market 

valuations still lag.

Defaults remain low 

while speculative-grade 

credit issuance expands.

Household debt and 

delinquencies decline.

Signs of overheating 

in credit growth and 

lending standards 

ease.

Yields approach historical 

lows and property prices 

show improvement.

• Balance-sheet relevering

• Increasing volatility and 

speculation

• Rising LBOs, M&A, 

capital expenditure

Corporate fundamentals*

Note: Metric includes credit growth, lending conditions, leverage, interest coverage, capital expenditures, EBITDA margins, bond yields, 

housing prices, default rates, non-performing loans, price-to-book ratio, gross debt, foreclosures, and delinquencies. The current value of 

each credit metric was compared to the range of values in each phase for the last credit cycle ending around 2007-08 and placed accordingly. 

EBITDA is an indicator of a company’s operating performance and refers to earnings before interest, tax, depreciation, and amortization.

* Corporate fundamentals data are through Q1 2014.

Sources: Bloomberg L.P., Haver Analytics, Morgan Stanley, OFR analysis

Figure 2-13 illustrates where we are in the credit cycle. 

Most nonfinancial corporate credit market indicators 

show the United States is currently somewhere between 

the expansion and downturn phases, while the household 

and banking sectors are still recovering from the financial 

crisis. Nonfinancial corporate balance-sheet leverage is still 

rising, underwriting standards continue to weaken, and an 

increasing share of corporate credit risk is being distributed 

through market-based financing vehicles that are exposed 

to redemption and refinancing risk. Credit spreads remain 

tight and risk premiums low, even after the repricing that 

occurred in September and October 2014.

Earlier in the cycle, corporations issued long-term debt to 

replace short-term debt and to finance capital expenditures, 

but the share of proceeds allocated to these activities has 

diminished. Proceeds are instead being used in ways that 

increase leverage such as through stock buybacks, dividend 

increases, mergers and acquisitions, and leveraged buyouts, 

rather than to support business growth. 

Low interest rates and looser bank lending standards have 

encouraged a rapid expansion in corporate credit. These 

conditions have enabled corporations to reduce debt 

servicing costs and lock in a low cost of funding. But easy 
21 Analyzing Threats to Financial Stability

Figure 2-14. New Issue Covenant-Lite and HighlyLeveraged Loan Volumes ($ billions)

New issuance reflects weak covenant structures and higher 

deal leverage

Figure 2-15. High-Yield Corporate Leverage and 

Bond Yield

Rise in leverage has not translated into a higher cost of 

credit

0

200

400

600

800 Highly-leveraged

Covenant-lite

Note: 2014 data are based on annualized data through September 

30, 2014. Highly-leveraged loans are defined as loans with a spread 

of London Interbank Offered Rate (LIBOR) + 225 basis points or 

more. 

Sources: Standard & Poor’s, OFR analysis

0

6

12

18

24

30

0

2

4

6

8

10

2008 2009 2010 2011 2012 2013 2014

debt/EBITDA percent

Gross leverage (left axis)

Net leverage (left axis)

High-yield corporate 

bond yield (right axis)

Note: EBITDA is an indicator of a company’s operating performance 

and refers to earnings before interest, tax, depreciation, and amortization. Gross leverage is the ratio of total debt to EBITDA. Net 

leverage is the ratio of net debt to EBITDA, where net debt is total 

debt less cash and short-term investments.

Sources: Haver Analytics, JPMorgan Chase & Co., OFR analysis

credit conditions have also allowed companies to lever up, 

perhaps taking on more debt than they can service. The 

ratio of debt to earnings before interest, taxes, depreciation, 

and amortization for the most highly leveraged loans rose 

to 7.7 in October 2014, up from a low of 5.5 in 2009, and 

is approaching the peak registered in 2007. Even an average 

rate of default could lead to outsized losses once interest 

rates normalize, given the expansion in corporate debt. 

The quality of new debt issued by companies has also been 

weaker than in previous cycles. High-yield debt accounts 

for 24 percent of total corporate debt issued since 2008, 

compared with 14 percent during past cycles, and low-rated 

credits dominated new issuance volumes over the past 

year. Companies have also taken advantage of looser bank 

lending standards. Two-thirds of loans to companies during 

this cycle have been covenant-lite (lacking strict legal covenants), compared with 33 percent during previous cycles 

(see Figure 2-14). In addition, there has been a trend of 

eroding debt cushions for covenant-lite loans and a significant increase in bank debt-only structures. These attributes 

are likely to lead to lower recovery rates on defaulted credit 

instruments once the credit cycle turns.

The combined issuance of collateralized loan obligations 

— securities backed by pools of corporate loans — and 

leveraged loans, which are higher-risk bank loans often sold 

to institutional investors, has exceeded the peak levels of 

the last credit cycle. Bank regulators are clearly aware of the 

buildup in credit risk and have responded with guidance and 

exhortations to banks (see Section 3.5). 

Leveraged loans are often viewed as a hedge for investors 

against a rise in interest rates because they tend to carry 

floating rates. However, interest rate risk may be higher 

than perceived, because recent deals have been sold with 

high interest rate floors. Were the Federal Reserve to tighten 

monetary policy, causing market rates to rise, the value of 

those floors would decline initially and prices on leveraged 

loans would fall.

For now, still-strong retained earnings and better liability 

management help companies mitigate potential refinancing 

risk. But as the cycle turns from expansion to downturn, 

the buildup of past excesses will eventually lead to future 

defaults and losses.

Some investors remain undeterred by the deterioration 

in corporate credit fundamentals and rising debt levels. 

Although corporate credit spreads are not excessively tight 
22 2014 OFR Annual Report

relative to the historical trend, yields on leveraged loans and 

high-yield bonds, and spreads per unit of leverage, are at 

historic lows. Current low rates indicate investors are not 

being compensated for the incremental increase in corporate leverage (see Figure 2-15). Mispricing is also evident 

from the fair value estimates we calculated based on a set of 

fundamental determinants of high-yield bonds. High-yield 

bond spreads are roughly 50 basis points rich based on our 

estimates (see Figure 2-16). Credit, liquidity, and volatility 

risk have contributed to the mispricing, and all three risks 

tend to rise simultaneously during periods of stress. 

Product innovation has also increased in corporate credit 

markets, a hallmark of late-stage credit cycles. Recent issues 

have provided broader, cheaper access to credit such as 

exchange-traded, high-yield, and leveraged loan funds; total 

return swaps on leveraged loans; and synthetic collateralized 

debt obligations. This development contrasts with limited 

innovation elsewhere in the financial system.

Nonbank lenders have increased their credit exposure 

significantly since the financial crisis (see Figure 2-17) and 

engage in riskier deals than banks because of low interest 

rates (see Aramonte, Jung, and Stebunovs, 2014). The 

composition of investors in the corporate bond market has 

also changed since 2007. Insurance companies and pension 

funds collectively own about one quarter of outstanding 

corporate bonds, but mutual funds and exchange-traded 

funds (ETFs) are rapidly catching up. Investments in corporate bonds by mutual funds and ETFs have increased by 500 

percent to $622 billion since the end of 2008, with almost 

half allocated to high-yield bonds. Short-duration funds, 

which invest in leveraged loans, have shown the most significant growth. Assets under management have increased 

ten-fold over the last five years, driven by a search for yield 

and a hedge against an eventual rise in interest rates. In sum, 

much of the recent growth in credit risk-taking is concentrated in nonbank entities that are not directly regulated by 

banking supervisors.

Figure 2-17. Primary Market for Highly Leveraged 

Loans: Banks versus Nonbanks (percent)

VOLATILITY RISKS 

In our last annual report, we discussed the volatility paradox — the increased potential for excessive leverage or 

risk-taking during periods of low volatility. Expectations of 

low volatility and continued benign conditions paradoxically 

incentivize market participants to extend risk positions, 

sowing the seeds of financial stress and high volatility when 

excesses unwind. This section analyzes broad developments 

in volatility markets, documenting where volatility has 

Figure 2-16. Fair Value of U.S. High-Yield Bond 

Spread (basis points)

Depressed volatility and liquidity risk premia contribute to 

underpricing in high-yield bonds

Credit risk is increasingly being distributed via nonbanks

Note: Nonbanks include institutional investors, insurance companies, and finance companies. Data for 2014 are through June 30, 

2014. Highly-leveraged loans are defined as loans with a spread of 

LIBOR + 225 basis points or more.

Source: Standard & Poor’s

Note: Credit losses are defined as the expected loss from default 

(using Moody’s actual and forecast high-yield default rates and 

assuming a constant 30 percent recovery rate). Volatility is based 

on an average of the normalized VIX and MOVE indices. The cost 

of liquidity incorporates two components: cost of trading (defined 

as high-yield bond turnover multiplied by bid-ask spreads), and the 

opportunity cost of cash (defined as mutual fund liquidity). 

Sources: Bloomberg L.P., Haver Analytics, Moody’s Analytics, OFR 

analysis

0

25

50

75

100

1998 2002 2006 2010 2014

Banks and securities rms

Nonbanks

-500

0

500

1,000

1,500

2,000

2,500

Apr

2007

Apr

2008

Apr

2009

Apr

2010

Apr

2011

Apr

2012

Apr

2013

Apr

2014

Regression constant

Credit losses

Total cost of liquidity

Volatility

Actual high-yield spread
23 Analyzing Threats to Financial Stability

positive for the first eight months of 2014, they have declined 

from those registered between 2004 and 2007 (see Figure 

2-18). The rally in the U.S. dollar has wiped out most of the 

year-to-date returns for dollar-funded carry trades, and there 

is some evidence that fewer leveraged investors have been 

involved in currency carry trades.

The CFTC reports the positioning of speculative traders in 

foreign exchange futures contracts on the Chicago Mercantile 

Exchange. While a useful guide the data do not differentiate 

carry trades from other trades, and forward markets are not 

captured. The flow of funds across borders is another potential proxy for activity. However, gross portfolio debt inflows 

do not show a particularly strong link with the carry-to-risk 

ratio over a long period. 

Another important metric is the likelihood of carry trades to 

unwind. For instance, the Barclays Capital Carry Unwind Risk 

Index measures the probability of a decline in carry trades 

based on volatility, swap spreads, speculative positioning, 

and an estimated price of risk (see Figure 2-19). That index 

showed an increase in the probability of a broad selloff as 

volatility temporarily rose in October.

To monitor carry trade activity, we need better data. The 

Depository Trust & Clearing Corporation (DTCC) has been 

collecting data on derivatives positions and could expand the 

granularity of such data to identify investors who simultaneously hold short positions in low-yielding currencies and long 

positions in high-yielding currencies. Similarly, the CFTC could 

provide more disaggregated classification data to include a 

similar identifier.

Investors’ expectations of low volatility have 

increased the popularity of carry trades. 

In its broadest sense, a carry trade is a trade that takes advantage of the difference between the income stream earned on 

a financial contract or asset and the cost of funding to hold 

the asset. Carry trades are most predominant in currency, 

fixed-income, volatility, and derivatives markets. 

In a simple currency carry trade, an investor borrows a 

currency with low interest rates to finance the purchase of 

a higher-yielding currency. The trade generates an income 

stream, or carry, while the investor holds the asset. The 

final returns on the investment depend on the difference 

in interest rates and the movements of the exchange rate 

between the two currencies. A depreciation of the higher-yielding currency can offset the returns from the interest 

rate differentials. 

Carry trades have implications for financial stability (see 

FSOC, 2014). A crowded carry trade position — when many 

investors use similar trading strategies — may contribute 

to excessive volatility during a market selloff as investors 

liquidate positions at the same time. The inherent leverage 

can amplify losses that stem from higher funding costs and 

reduced returns on long positions. 

Market conditions that support the activity and performance 

of carry trades are relatively easy to track. Carry trade performance is gauged by the returns on portfolios with long 

positions in high-yielding assets and short positions in lowyielding assets. This measure shows that while returns were 

Carry Trades Rise When Volatility Is Low

Figure 2-18. Foreign Exchange Carry Returns and 

Implied Volatility (Index January 2, 2004 = 100)

Low volatility has supported carry trade returns

Figure 2-19. Risk of Carry Unwind Leads Carry Index

The risk of a reversal in carry trades increase with a rise in 

volatility 

0.0040

0.0045

0.0050

0.0055

0

30

60

90

120

2008 2009 2010 2011 2012 2013 2014

Barclays Capital carry 

unwind risk index 

(left axis)

Inverted Goldman 

Sachs carry index 

(right axis)

0

50

100

150

200

250

2004 2006 2008 2010 2012 2014

G10 carry index

EM carry index

G10 FX volatility (inverted)

EM FX volatility (inverted)

Note: The G10 carry index is the Barclays Intelligent Carry Index. 

The EM carry index is the Citi Foreign Exchange Emerging Markets 

Carry Beta Index. G10 FX volatility (inverted) is the Deutsche Bank 

Currency Volatility Index. EM FX volatility (inverted) is the J.P. Morgan Emerging Market Volatility Index.

Note: A decrease in the Barclays Carry Unwind Risk Index represents a decrease in the risk of carry unwinds. A decrease in the 

inverted Goldman Sachs Carry Index indicates the index is increasing in value.

Sources (both charts): Bloomberg L.P., OFR analysis
2014 OFR Annual Report 24

Figure 2-20. Implied Volatility Indexes (normalized to 

Z-scores)

Implied volatility is unusually low across all major assets...

Figure 2-21. One-Year Swap Rate Volatility Term 

Structure (basis points)

...although steeper curves reflect uncertainty in the

longer run

0

30

60

90

120

150

1M3M6M

1Y5Y

10Y15Y20Y25Y

30Y

October 17, 2014

As of:

October 31, 2013

December 31, 2006

Note: The x-axis indicates option expiry dates. 

Source: Bloomberg L.P. 

Note: Simple average of the standardized Chicago Board Options 

Exchange Equity Market Volatility Index (VIX)®, the three-month 

Deutsche Bank Currency Volatility Index (CVIX), and the Merrill 

Option Volatility Estimate Index (MOVE) based on data since January 1991. 

Sources: Bloomberg L.P., OFR analysis

-2

0

2

4

6

1994 1998 2002 2006 2010 2014

declined and the factors keeping it low. This type of analysis 

is essential to complement market-based measures that tend 

to reflect low risk when volatility is low.

Option-implied volatility has been unusually low across most 

major asset markets during the post-financial crisis period, 

with brief interruptions in mid-2013 when the Federal 

Reserve indicated it might reduce asset purchases sooner 

than investors expected and in October 2014 as investors 

reassessed their expectations for global growth (see Figure 

2-20). Similarly, a measure of the attractiveness of selling 

volatility — the difference between implied and trailing 

one-month realized volatility — is also at low levels. Since 

September 2014, there has been some evidence that risk sentiment is beginning to shift. The slope of the volatility curve 

has steepened somewhat, reflecting increased concern about 

a rise in volatility in the intermediate term (see Figure 2-21), 

and demand for downside protection has increased in some 

markets, notably foreign exchange and equities. 

Option-implied volatility has rarely been so low over this 

length of time across such a wide range of assets. Low 

volatility creates moral hazard by reducing investors’ perception of risk. A sustained period of low volatility can lead 

investors to an increased use of carry trades and willingness 

to use leverage inexpensively through options to amplify 

returns (see Carry Trades Rise When Volatility is Low). 

This in turn sows the seeds for a market reversal, as market 

participants become too levered and reduce their buffers 

against adverse shocks. 

Several factors have contributed to the decline in volatility:

• Less uncertainty about future monetary policy.

The dispersion of risk-neutral option-implied expectations has narrowed for the federal funds rate, the

interest rate set by the Federal Reserve for a bank to

lend overnight funds to another bank. That suggests

convergence across investors on the central bank’s

monetary policy plans. To be clear: there is reduced

uncertainty amongst market participants on the

timing and pace of interest rate hikes, but still high

uncertainty on the mechanism of exiting from unconventional policies.

• Less uncertainty about the economic outlook.

Lower volatility in asset prices is linked to lower volatility in forecasts about inflation and economic growth.

• Reduced signs of financial stress. Periods of significant financial stress can generate market volatility.

Option-implied volatility has rarely been 

so low over this length of time across 

such a wide range of assets.
2 Analyzing Threats to Financial Stability 5

The OFR Financial Stress Index 

Sources (both): Bloomberg L.P., Federal Reserve banks of Cleveland, 

Kansas City, and St. Louis, Haver Analytics, JPMorgan Chase & Co., OFR 

analysis

Figure 2-22. OFR Financial Stress Index

The recent rise in stress reflects broad sources of instability

Our Financial Stress Index (FSI) is a real-time 

snapshot of global risk appetite. When it is 

low or declining, it signals risk appetite is 

high or increasing. Its purpose is to distill 

information embedded in daily market pricing 

into a measurement of global financial stress, 

which can be further decomposed into various 

dimensions of stress. 

While the Financial Stability Monitor signals the buildup of 

vulnerabilities across different dimensions of risk, the OFR’s 

FSI shows the overall risk appetite of market participants. 

The markets included in the FSI are based on their responsiveness to factors associated with financial stress. They 

include equities, high-yield debt, emerging market assets, 

volatility, and other traditional risk assets 

(see Figure 2-1 in Section 2.2). 

The index uses a statistical technique called principal component analysis, which identifies the unique factors or components that influence the total variation across the sample. 

The FSI uses a rolling window on the set of inputs in order to 

take into account changes in market conditions. 

To construct the index on a given date, we first use a subset 

of the data from the previous 500 trading days. Each of the 

series in this subset is standardized to have zero mean and 

variance equal to one. A principal components analysis is run 

on these standardized series and the first principal component is extracted. This first principal component, which 

by construction reflects the extent that the variables move 

together, is interpreted to reflect the degree of financial 

stress. The value of the FSI on the given date is then the projection of the actual data on the date along the first principal 

component.

A higher value of the index reflects greater financial stress 

and lower risk tolerance among investors. In contrast to 

financial stress indexes published by the Federal Reserve 

banks of Cleveland, Kansas City, and St. Louis, our FSI uses 

a statistical technique that allows the relationships among its 

risk factors to change over time. (We use a rolling principal 

component window rather than a static window, which 

allows us to capture relationships dynamically.) In addition, 

while other financial stress indexes focus on signals derived 

from U.S. capital markets, our FSI has a more global scope. 

This is intended to reflect the potential for contagion to 

transmit risk across national borders. 

Figure 2-22 shows our FSI’s performance over the past 

decade, including the contributions of five key risk factors. 

Since January 2013, the index suggests that until recently, 

financial stress remained subdued, due primarily to low 

stress in short-term funding markets, credit markets, and 

volatility. While further investigation is needed, our initial 

findings suggest that the innovative construction and the 

broader reach appear to improve the responsiveness of the 

FSI relative to other indices. Note, for instance, that the 

index signaled increased stress — rising more steeply and 

sooner than other FSIs — prior to the beginning of the financial crisis in 2007, and again, during the more recent dislocation in September and October 2014 (see Figure 2-23).

-10

0

10

20

30

2006 2008 2010 2012 2014

OFR FSI

Equity valuation

Funding liquidity

Volatility

Credit

Flight to safety

-10

0

10

20

30

-3

0

3

6

9

2006 2008 2010 2012 2014

Cleveland FSI (left axis) OFR FSI (right axis)

Kansas City FSI (left axis)

St. Louis FSI (left axis)

Figure 2-23. Comparison of the OFR and Federal 

Reserve Banks’ Financial Stress Indexes 
26 2014 OFR Annual Report

Such stress occurred during the global financial crisis 

in 2007-09 and during the eurozone crisis in 2010-12. 

Since then, financial stress has receded (see The OFR 

Financial Stress Index). 

• Supply and demand in volatility markets have 

changed. Greater willingness to sell volatility has 

also suppressed its price, driven by two developments. 

First, asset managers, high-yield funds, and leveraged 

loan funds have increased their short volatility positions, indicating they expect volatility to remain at a 

low level, at least in the near term. Second, a contraction in the balance sheets of the government-sponsored enterprises, Fannie Mae and Freddie Mac, led to 

reduced demand to hedge negative convexity risk and 

reduced demand for interest rate volatility protection. 

We assessed the relative weight of these factors in explaining 

the current low levels of implied interest rate volatility (see 

Figure 2-24). 

We found both structural and cyclical factors help to explain 

a reasonable amount of the overall level of implied volatility in interest rates. Contrary to expectations, there was no 

evidence that reduced demand to hedge mortgage-related 

securities has contributed to changes in volatility. The results 

confirm that greater clarity about the macroeconomic and 

monetary policy outlooks, in particular, was an important 

driver of implied volatility (see Figure 2-25). Any significant change in these factors (assuming coefficients remain 

stable) could lead to a reversal in the current low volatility 

environment. 

Figure 2-25. Contributors to Interest Rate Volatility 

(basis points)

EMERGING MARKETS

Low interest rates, stronger growth prospects, and greater 

risk appetite have attracted large capital flows into emerging 

market assets and enabled borrowers in emerging market 

economies to increase leverage. This section examines the 

growing investment in emerging markets and attempts to 

assess the importance of domestic and external factors as 

drivers of local currency emerging market bonds.

The same vulnerabilities highlighted in last year’s annual 

report remain, including the risk of a reversal in capital 

inflows, the buildup of corporate debt, and the potential 

for a policy misstep as a result of diminished policy buffers. 

Despite the rise in idiosyncratic risks over the last year — 

such as Argentina’s currency devaluation and default, the 

crisis in Ukraine (see Russian and Eastern European 

Developments), tensions in the Middle East, and concerns 

about an economic slowdown in China — spillovers to 

Macroeconomic and monetary policy clarity have lowered 

interest rate volatility

Figure 2-24. Explanatory Variables

Area of Focus Variables

Unconventional 

Monetary Policy

Zero-lower bound dummy

Dispersion of Market 

Expectations

CPI forecasts 

GDP forecasts

Volatility of federal funds rate

Volatility of U.S. Economic Surprise 

Index

Lack of Financial 

Stress

TED Spread

Reduced MortgageRelated Hedging 

Activity

Size of Federal Reserve balance 

sheet

Size of Fannie Mae and Freddie 

Mac retained portfolios

Note: The TED spread is the difference between the three-month 

U.S. Treasury bill interest rate and the London Interbank Offered Rate.

Sources: Bloomberg L.P., Federal Reserve Bank of Philadelphia, Haver 

Analytics, OFR analysis

Sources: Bloomberg L.P., Federal Reserve Bank of Philadelphia, OFR 

analysis

-20

30

80

130

180

230

2002 2005 2008 2011 2014

GDP forecast dispersion

CPI forecast dispersion

TED spread

Economic surprise volatility

Fed funds rate volatility

Zero interest rate policy
27 Analyzing Threats to Financial Stability

An escalation in political tensions could lead to more 

aggressive sanctions and potentially more counter actions 

by Russia. Foreign financial institutions could be affected 

if Russia seized foreign assets or if the creditworthiness of 

Russian assets declined.

Since the OFR’s last annual report, tensions in 

Ukraine and Russia have increased, exposing 

regional financial markets to potential risks. 

In late 2013, the Ukrainian government came under pressure from Russia not to sign a historic European Union (EU) 

deal. Russian militants began to take control of the Crimean 

peninsula and in mid-March 2014, Crimean voters passed a 

referendum to join Russia. After the referendum, the United 

States and the EU imposed sanctions against Russian individuals and companies, including travel bans, asset freezes, 

and restricted access to capital markets. Russia responded 

with retaliatory travel and trade sanctions. Violence continues in parts of eastern Ukraine.

Price declines have been limited to assets with significant 

exposure to Russia and Ukraine. Russian equities, bonds, 

and currencies sold off significantly after the United States 

and EU imposed several rounds of sanctions. The ruble is 20 

percent weaker against the U.S. dollar, and external sovereign bond spreads are 135 basis points wider (see Figures 

2-26 and 2-27). Spillovers have been limited in other 

regional asset classes.

TRANSMISSION CHANNELS

Escalation of the crisis could spread to financial markets in 

the United States and Europe through several channels. The 

first is through total foreign claims on Russia: external debt 

totals roughly $700 billion, of which foreign bank claims 

represented roughly $209 billion as of the first quarter of 

2014. Some European banks are heavily exposed. The direct 

exposure of U.S. banks is a manageable $27 billion (or 0.8 

percent of bank claims), but if other claims such as derivatives, guarantees, and trade credit are included, the combined exposure amounts to 3.4 percent. U.S. banks have 

tried to mitigate these risks by reducing their exposure to 

Russia.

Another direct linkage comes from financial investments. 

Roughly half of Russia’s foreign portfolio assets are held by 

offshore centers, including holdings by investment funds. 

The dominance of these funds increases the vulnerability to 

outflows. Russia registered $85 billion of private capital outflows by nonresidents during the first nine months of 2014, 

an increase in the pace of outflows.

Stress could also be transmitted through energy trade 

and other macroeconomic channels. Russia accounts for 8 

percent of global crude oil imports and the EU imports 25 

to 30 percent of its crude oil and gas from Russia. A decline 

in overall domestic demand in Russia and Ukraine could also 

affect trade among major exporters, especially for nations in 

central Europe and the former Soviet Union.

Russian and Eastern European Developments

Figure 2-26. Performance of Russian Sovereign and 

Corporate Bonds

Russian assets under significant pressure as geopolitical risks 

rise

0

3

6

9

12

0

150

300

450

600

May

2013

Aug

2013

Nov

2013

Feb

2014

May

2014

Aug

2014

basis points percent

CEMBI Russia spread 

(left axis)

EMBIG Russia spread 

(left axis)

GBI-EM Russia yield 

(right axis)

Note: CEMBI is the Corporate Emerging Market Bond Index 

denominated in U.S. dollars. EMBIG is the Emerging Market Bond 

Index Global denominated in hard currency. GBI-EM is Government 

Bond Index-Emerging Markets denominated in local currencies. All 

three are JPMorgan Chase & Co. indexes.

Sources: Bloomberg L.P., JPMorgan Chase & Co., OFR analysis

Figure 2-27. Performance of Russian Ruble and 

Cross-Currency Basis Swap

90

100

110

120

130

5

7

9

11

13

Jan

2014

Mar

2014

May

2014

Jul

2014

Sep

2014

percent index

US$/ruble spot

index (right axis)

Ruble/US$ one-year 

swap rate (left axis)

Note: An increase in the spot rate is associated with a depreciation 

of the ruble. Index: January 2, 2014 = 100.

Sources: Bloomberg L.P., OFR analysis
28 2014 OFR Annual Report

broader emerging market assets have been limited. Even the 

risk-reduction that occurred in September and October 2014 

in response to global growth concerns had limited sustained 

impact on emerging market assets. Emerging market bonds 

are still one of the best performing assets this year. 

Cross-border investment flows into emerging market assets 

have been above their long-term trend in recent years. 

Emerging markets have registered $177 billion in short-term 

equity and debt capital inflows this year, bringing their influx 

since 2010 to $1.3 trillion. Yield-sensitive debt flows account 

for most of the increase. Foreign holdings as a share of 

total outstanding debt are also substantially higher relative 

to the immediate post-crisis period across most emerging 

markets, driven both by a deepening in local capital markets 

as well as by a search for higher-yielding assets (see Figure 

2-28). While foreign participation lowers borrowing costs 

and provides access to a larger, more diverse investor base, 

research shows that cross-border capital flows are more volatile, particularly when they include retail investors. The continued development of a local investor base could mitigate 

the effects of foreign capital outflows in future crises.

Nonfinancial corporations, which took advantage of low 

interest rates and strong demand to increase leverage, are 

especially vulnerable to the curtailing of foreign investment 

(see Figure 2-30). Corporate debt in emerging markets 

has expanded more rapidly than nearly all other credit 

asset classes since the crisis (see Figure 2-29). There are a 

number of countries and sectors with high and rising debt 

levels, which may complicate the adjustment when financial conditions eventually tighten. A sharper-than-expected 

slowdown could lead to increased default rates as revenues 

slow relative to debt service requirements. Indeed, profitability has already decreased in a number of countries where 

leverage is elevated amid slower growth, in turn eroding 

debt servicing capacity (see IMF, 2014b). An increasing 

amount of debt of nonfinancial corporations is also 

denominated in foreign currency. This may increase sensitivity to debt servicing risks and rising losses in the event of 

depreciation in local currencies if corporations do not have 

natural or financial hedges. 

Given the sensitivity of emerging market assets to U.S. 

interest rates during the selloff that occurred in mid-2013, 

we assessed the relative importance of domestic fundamentals and global factors in driving the yields of emerging 

market local currency bonds (see Ebeke and Lu, 2014; 

Miyajima, Mohanty, and Chan, 2012). We regressed nine 

Figure 2-28. Foreign Ownership of Emerging Market 

Local Currency Government Bonds (percent of 

outstanding)

Foreign participation in emerging markets has increased as 

capital markets have deepened

0 15 30 45 60

Egypt

Bulgaria

India

China

Argentina

Ukraine

Colombia

Philippines

Lithuania

Brazil

Thailand

Turkey

Romania

Russia

Malaysia

Indonesia

Poland

Mexico

South Africa

Hungary

Latvia

Peru

Q1 2010

Q4 2013

Q1 2010

average

Q4 2013

average

Sources: International Monetary Fund, OFR analysis
29 Analyzing Threats to Financial Stability

Figure 2-30. Median Corporate Leverage in 

Emerging Markets (GDP-weighted, ratio)

Emerging market corporate leverage is at elevated levels

0

1

2

3

4

5

2007 2008 2009 2010 2011 2012 2013 2014

Gross leverage

Net leverage

Figure 2-31. Fair Value for Emerging Market Local 

Currency Government Bonds (percent)

Bond yields are no longer depressed relative to 

fundamentals, but are sensitive to changes in external 

factors

Note: Domestic factors are proxied by the headline inflation rate, 

domestic policy rate, GDP growth rate, ratio of current account 

balance to GDP, ratio of fiscal balance to GDP, and ratio of external 

debt to GDP. External factors are proxied by the U.S. 10-year Treasury yield, foreign ownership of domestic bonds, and the Chicago 

Board Options Exchange Market Volatility Index, or VIX®.

Sources: Bank of Mexico, Bloomberg L.P., Central Bank of the Republic 

of Turkey, Central Reserve Bank of Peru, Haver Analytics, Hungarian 

National Bank, International Monetary Fund, OFR analysis

Note: Gross leverage is the ratio of total debt to EBITDA and net 

leverage is the ratio of net debt to EBITDA, where net debt is equal 

to total debt less cash and short-term investments. Corporate 

leverage is averaged from the trailing four quarters and is based on 

firms in countries including: Brazil, China, India, Indonesia, Poland, 

Russia, South Africa, South Korea, and Turkey. EBITDA is an indicator of a company’s operating performance and refers to earnings 

before interest, tax, depreciation, and amortization.

Sources: Bloomberg L.P., Morgan Stanley, OFR analysis

4

6

8

10

2009 2010 2011 2012 2013 2014

Actual

Modeled

Figure 2-29. Average Annual Gross Debt Issuance 

($ billions, percent)

Emerging market corporate debt has expanded at a 

fast pace

Note: Data include private placements and publicly-issued bonds. 

2014 data are through August 2014 and annualized.

Sources: Dealogic, OFR analysis

-200

0

200

400

600

0

250

500

750

1,000

1,250

Global

ABS

U.S. Europe U.S. Europe Emerging

market

corporate

2000-07 (left axis)

2009-14 (left axis)

Percent change of selected 

date ranges (right axis)

Corporate

investment

grade

Corporate

high-yield
30 2014 OFR Annual Report

domestic and global factors against the level of yields on 16 

emerging market bonds, using quarterly data from the first 

quarter of 2009 through the second quarter of 2014. The 

analysis leads to the following conclusions:

• Local currency valuations in emerging markets 

appear appropriately priced. Despite earlier outsized inflows and demand for higher yielding assets, 

the selloffs in mid-2013 and again in 2014 appears 

to have curtailed some of the excesses in emerging 

market debt pricing (see Figure 2-31).

• However, yields appear vulnerable to future 

changes in global factors. A one standard deviation 

increase in U.S. long-term yields is associated with 

an increase of roughly 60 basis points in emerging 

market local yields, while a one standard deviation 

increase in the VIX® is associated with a 25 basis 

point increase in yields. Meanwhile, domestic fundamentals, such as local policy rates and external debt, 

have been weaker drivers of emerging market bond 

yields over the past few years.

Although valuations of bonds in emerging markets do not 

currently appear high, greater sensitivity to global forces, the 

rapid growth in short-term capital flows across borders, and 

a weakening in corporate balance sheets increase vulnerability to external shocks. An abrupt flight of investment 

would be especially challenging for countries where policy 

buffers — or the capability to cushion a shock — are weak 

or have diminished such as in Argentina, Russia, South 

Africa, and Turkey (see Figure 2-32).

Structural Vulnerabilities

MARKET LIQUIDITY RISKS

Various developments since the crisis have led to changes 

in market liquidity, such as changes in broker-dealer risk 

preferences, changes in the investor base, financial product 

innovation, and regulatory changes. This section discusses 

how changes in the provision of liquidity could disrupt 

market conditions and impair financial stability. We also discuss potential liquidity-related risks with mutual funds and 

exchange-traded funds (ETFs) that invest in illiquid assets 

(see Bank Loan Funds and Liquidity Mismatches). 

Market liquidity is essential for markets to operate efficiently. Whereas funding liquidity relates to conditions that 

affect the liabilities of institutions (that is, the availability of 

wholesale funding), market liquidity applies to the trading 

activity that takes place in capital markets. The two are 

Figure 2-32. Policy Buffers in Emerging Markets

Diminished policy buffers reduce the ability to cushion an 

external shock

Argentina

Brazil

Chile

China

Colombia

Czech Republic

Hungary

India

Indonesia

Korea

Malaysia

Mexico

Peru

Philippines

Poland

Russia

South Africa

Thailand

Turkey

Fiscal Monetary External

Capacity to Absorb Shock

Low Moderate High 

2013 2014 2013 2014 2013 2014

Note: Color-coding is determined as follows: 

Fiscal Buffer: Green if gross financing needs are less than 5% of gross domestic product (GDP) and overall balance is less negative than -2% of GDP. Red if 

gross financing needs are greater than 10% of GDP or overall balance is more 

negative than -3% of GDP. Yellow otherwise. 

Monetary Buffer: For inflation targeters, green if the policy rate is at least 

4% and projected CPI inflation is at least 1 percentage point lower than the 

(upper bound of the) inflation target (range); red if the policy rate is lower 

than 2% or projected inflation is above the (upper end of the) inflation target 

(range); and yellow otherwise. For noninflation targeters, green if the policy 

rate is at least 4% and projected inflation is 3% or lower. Red if the policy rate 

is lower than 2% or projected inflation is above 6%. Yellow otherwise. Countries operating under currency board regimes are coded as red. 

External Buffer: Green if current account balance is greater than 6% of GDP 

and foreign exchange reserves are greater than 6% of GDP; or if current 

account balance is less negative than -5% of GDP and foreign exchange 

reserves are greater than 40% of GDP. Red if current account balance is more 

negative than -5% of GDP or foreign exchange reserves are smaller than 6% 

of GDP. Yellow otherwise.

Sources: Bloomberg L.P., Haver Analytics, International Monetary Fund, 

OFR analysis
31 Analyzing Threats to Financial Stability 31

Bank loans, which are mostly floating-rate 

products, have attracted investor interest as 

a hedge for rising interest rates and for yield 

enhancement. However, investments in mutual 

funds and ETFs that invest in bank loans carry 

risks from a mismatch of liquidity as a result 

of different settlement practices and liquidity 

between fund shares and the underlying bank 

loan assets. 

ETF shares are traded on an exchange throughout the day 

at market-determined prices, unlike mutual funds, whose 

shares can only be traded at the fund’s net asset value that 

is calculated at the end of each business day. ETFs were 

initially created to provide retail investors with intraday 

liquidity in actively-traded asset classes. Market-makers 

facilitate trading and profit from a small margin they earn 

between the purchase and sale price of ETF shares. 

• 

ETFs have been marketed to a broad range of investors as 

a way to diversify exposure into less liquid asset classes, 

including high-yield corporate bonds and emerging market 

assets. In October 2014, assets under management of U.S. 

ETFs and exchange-traded products stood at a record high 

of nearly $1.9 trillion. • 

Demand for instruments to hedge interest rate risk has 

led to growth in floating-rate bank loan ETFs. At present,

the outstanding size of bank loan ETFs is relatively small at 

around $8 billion, while bank loan mutual funds are larger 

with more than $70 billion in assets. 

The following scenario illustrates underappreciated liquidity 

risks in bank loan ETFs. It shows how a mismatch in liquidity 

may fuel a self-reinforcing cycle of price declines in ETF 

shares and underlying assets. While the same liquidity 

mismatch is also present in mutual funds investing in bank 

loans, ETFs could be more vulnerable because of investors’ 

expectations of intraday liquidity. 

• Bank loan prices drop modestly due to an isolated 

credit event, change in market expectations, or other 

development. The decline leads some investors to exit 

the market by selling their ETF shares in the secondary 

market.

• Investors in bank loan ETFs attempt to limit losses by 

selling shares. Because an ETF is explicitly marketed 

as a product with better liquidity than the underlying 

assets or a similar mutual fund portfolio, ETFs attract 

investors who are more likely to trade in response to a 

large intraday price movement.

• ETF market-makers now hold substantial inventories of 

ETF shares. To limit the selling pressure, market-makers 

cut the price at which they are willing to buy ETF shares 

in the secondary market. This accelerates the price 

decline for ETF investors. 

• ETF market-makers (through authorized participants) 

turn to ETF portfolios to redeem shares purchased in 

the secondary market, although the majority of ETF 

share trading occurs in the secondary market and does 

not affect ETF portfolio liquidity (see ICI, 2014). 

However, liquidity risk management practices may vary. 

If the ETF portfolio does not have sufficient liquid assets 

to meet redemptions, its manager may attempt to raise 

cash by selling portfolio assets — bank loans — in the 

secondary market. Because the settlement period for 

bank loans is longer than for ETF shares, the ETF portfolio is unable to meet requests for cash redemptions. 

ETF shares are normally settled within three to seven 

days, while the settlement time of bank loans is generally longer than seven days (see Moody’s, 2014). Mutual 

funds investing in bank loans are also susceptible to the 

settlement mismatch.

As available liquidity in bank loan funds evaporates 

and selling pressure from investors builds, ETF market-makers may refrain from buying additional ETF 

shares altogether if they reach their balance-sheet 

capacity. 

This illustrative scenario shows how demand for liquidity 

from ETF investors ultimately translates into demand for 

liquidity in the ETF’s underlying assets, affecting other types 

of funds with similar investments. 

Not all ETFs are vulnerable to a self-reinforcing cycle of 

liquidity-induced price declines. For example, ETFs tracking 

equities — which represent the bulk of the ETF universe 

— do not exhibit the same price opacity and potential 

settlement delays as bank loan ETFs. But investors may not 

appreciate the liquidity differences of the underlying markets for bank loan ETFs and equity ETFs. 

At present, mutual funds and ETFs that invest in illiquid 

assets such as bank loans represent a small part of total 

mutual fund and ETF assets. However, the continued search 

for yield and demand for secondary market liquidity could 

spur growth. Under normal market conditions, these investment alternatives work as intended. But funds that reference 

assets with weak market liquidity may give investors a false 

sense of security about liquidity during stressful episodes. 

Bank Loan Funds and Liquidity Mismatches
32 2014 OFR Annual Report

related because the ease with which an asset is traded is 

contingent on the ease with which funding can be obtained. 

Financial crises often include sharp reductions in liquidity 

and downward cascades in prices, as large losses force asset 

sales and risk aversion increases the hoarding of cash.

Several developments since the financial crisis have altered 

the amount of liquidity available in the financial system and 

the ways investors redeem holdings to get cash. Regulations 

requiring banks to hold more capital and more restrictive 

constraints on leverage have increased the cost of securities financing activities and reduced incentives to maintain 

them. Changes in the investor base, securities markets, 

financial products, and risk appetite have also contributed 

importantly to the decline in liquidity. Some changes are 

cyclical, such as a decline in available collateral (see Figure 

2-33) from slow-to-recover securitization markets and the 

removal of a sizable portion of Treasury securities, agency 

mortgage-backed securities (MBS), and agency debt from 

the market as a result of the Federal Reserve’s asset purchase program. Other changes are structural, such as regulatory changes that introduce longer-term balance-sheet 

constraints and an evident reduction in broker-dealer willingness to put capital at risk (see Adrian and others, 2013).

Traditional indicators do not show excessive concern about 

market liquidity. But signs are emerging that market liquidity 

has become more fragmented in a few markets since the 

crisis. These signs include the following, to varying degrees:

• Large broker-dealer inventories have shrunk, inventories have grown more concentrated in high-quality 

liquid assets, and dealer willingness to buffer periods 

of intense selling pressure has been more limited;

• Trading volumes have declined despite increased 

inflows (for instance, in emerging market sovereign bonds and U.S. corporate bonds), leading to 

depressed turnover in secondary markets;

• Trading activity is concentrated in the primary new 

issue market or in a small number of credits in U.S. 

and emerging market corporate bonds, suggesting 

reduced market depth;

• The size of an average trade has declined in both highyield and investment grade corporate bond markets;

• Spreads for newly issued bonds have widened relative 

to older benchmark bonds for the same maturity in 

some asset classes; and

Figure 2-33. Net Issuance of Fixed-Income Securities

($ billions)

Lack of available collateral may have contributed to changes 

in trading liquidity

Figure 2-34. Primary Dealer Settlement Fails to 

Deliver U.S. Treasury Securities ($ billions)

U.S. Treasury securities show a structural increase in trades 

that failed to settle as scheduled

Note: Other includes agency debt, municipal bonds, emerging 

market sovereign bonds, and corporates. 2014 data are estimated 

annual figures. 

Sources: Haver Analytics, JPMorgan Chase & Co., OFR analysis

Sources: Bloomberg L.P., Federal Reserve Bank of New York, OFR 

analysis

-1,000

-500

0

500

1,000

1,500

2,000

2,500

3,000

2004 2006 2008 2010 2012 2014

Treasuries

Corporates

U.S. securitized products

Other

Total

Total after Federal 

Reserve asset purchases

0

100

200

300

400

2009 2010 2011 2012 2013 2014

Introduction of fails

charge of 3% 

S&P downgrades 

U.S. sovereign credit rating
33 Analyzing Threats to Financial Stability

• Even traditionally liquid assets are failing to settle as 

scheduled as broker-dealer inventories and securities 

lending portfolios have diminished (see Figure 2-34).

The fragile nature of liquidity was especially evident during 

the selloff in fixed-income markets in mid-2013 and during 

the market dislocations in September and October of 2014. 

Neither development was widespread or severe enough to 

lead to outsized price declines and forced deleveraging. 

To some extent, factors such as changes in the investor 

base and low volatility have helped prevent mild liquidity 

shortages from becoming more severe and sparking 

selloffs. The cycle is self-reinforcing. An increase in traditional buy-and-hold investors results in less turnover, 

which suppresses volatility. Low volatility enhances returns 

and reinforces trading strategies such as carry trades that 

assume volatility will remain low. Positive returns attract 

more investment to higher risk assets that are less liquid, 

which perpetuates the cycle.

Many market participants acknowledge the risk that market 

liquidity may decline once interest rates begin to rise. 

However, investors have no obvious hedge to manage 

liquidity shocks, aside from holding more cash or cash 

equivalents or securing committed liquidity facilities. Since 

the crisis, investors have not been subject to a true test of 

the market’s resilience to provide liquidity, particularly in 

newer niche markets. 

Tracking market liquidity is a further challenge because asset 

markets are numerous and diverse. The OFR is developing 

a set of measures to monitor liquidity across core markets. 

Our measures are organized into broad categories based on 

the aspect of liquidity they address: depth, breadth, resilience, quality, and immediacy (see Figure 2-36). This exercise will help develop a knowledge base on the use of the 

various measurement methodologies. Monitoring liquidity 

on an asset basis and across different dimensions may also 

provide insights into how liquidity shocks are transmitted 

across markets.

RUN RISKS AND ASSET FIRE SALE RISKS

Short-term funding markets are critical to market functioning as an efficient source of financing, but may create 

systemic vulnerabilities. We remain concerned about risks 

related to short-term wholesale funding markets, given 

that incentives still exist for runs and asset fire sales during 

periods of stress.

Figure 2-35. Funding in the Fed Funds and Repo 

Markets ($ billions)

The largest borrowers in short-term wholesale funding 

markets

The repo market, the largest shortterm funding market, has undergone 

substantial change in recent years. 

However, it still remains susceptible 

to asset fire sales and runs when a 

borrower cannot roll over or renew 

short-term funding backed by collateral.

The repo market, the largest short-term funding market, 

has undergone substantial change in recent years. However, 

it still remains susceptible to asset fire sales and runs when 

a borrower cannot roll over or renew short-term funding 

backed by collateral.

Borrowers in the repo market obtain funding from repo 

dealers by posting collateral; repo dealers, in turn, often 

borrow cash from cash-rich lenders. The Federal Reserve, 

U.S.-based foreign banks, U.S. banks and broker-dealers, and 

mortgage real estate investment trusts (mREITs) are significant participants in repo markets (see Figure 2-35). Repo 

markets are vulnerable to runs for several reasons. Repo 

contracts tend to be short-term. In a market disruption, 

firms relying on short-term repos could quickly lose access 

to their funding sources when existing contracts expire and 

new ones become hard to obtain. 

0

250

500

750

1,000

1,250

2008 2009 2010 2011 2012 2013 2014

Q2

Federal Reserve

U.S. banks

Foreign banks

Broker-dealers (net nancing)

mREITs

Sources: Board of Governors, OFR analysis
34 2014 OFR Annual Report

Figure 2-36. Market Liquidity Indicators

Sources: Gabrielsen, Marzo, and Zagaglia (2011), OFR analysis

Indicator Data 

Requirements

Aspect of Liquidity 

Measured

Strengths and Weaknesses Reference

Breadth

Trading 

volume

Volume Higher volume implies more 

trading, suggesting more 

opportunities to buy or sell at 

a given price level.

Strengths: Readily available across multiple 

asset classes. Simple to update and 

understand.

Weaknesses: Higher volumes may not imply 

higher liquidity, due to concomitant increased 

volatility. Double-counting of trades is 

frequently a problem in practice.

Blume, Easley 

and O’Hara 

(1994)

Turnover Volume, shares 

outstanding

Measures the pace of trading 

relative to the total amount 

of a security outstanding. 

Higher turnover suggests 

greater availability of 

possible buyers.

Strengths: Data are readily available across 

multiple asset classes. Simple to calculate and 

understand.

Weaknesses: Frequently underestimates 

market depth, because some willing buyers 

and sellers do not participate in actual trades. 

Structural changes in markets can lead to false 

signals.

Amihud and 

Mendelson 

(1986); Datar, 

Naik, and 

Radcliffe (1998)

Conventional 

liquidity ratio

Price, volume Price change (impact) per 

dollar volume traded.

Strengths: Simple to calculate; data typically 

readily available.

Weaknesses: Does not adjust for firm size and 

ignore daily lows and daily highs.

Gabrielsen, 

Marzo, and 

Zagaglia (2011)

Martin’s 

liquidity index

Price, volume Price change per unit of 

volume traded. The higher 

the index, the higher the 

price dispersion relative to 

volume, and the lower the 

liquidity.

Strengths: Simple to calculate; data typically 

readily available. Flexibility to run across 

various asset classes and time horizons.

Martin (1975)

Depth

Average 

rolling 

differential 

Volume, daily 

high/low 

prices, shares 

outstanding

Average of rolling 5-day 

windows of absolute 

percentage price change 

(from lowest daily low to 

highest daily high of 5 days) 

per unit volume, adjusted for 

market capitalization.

Strengths: Captures the full extent of price 

fluctuations by incorporating daily low and 

daily highs. Adjusts for market capitalization. 

Uses responsive five-day estimation periods.

Weaknesses: Five days may still be too 

long for the index to detect certain market 

anomalies, because asset prices can adjust 

quickly to liquidity problems. Daily low/high 

prices are often unavailable.

Hui and Heubel 

(1984)

Average daily 

share price 

impact 

Return, volume Captures the average of the 

daily price impacts over a 

given sample period. 

Strengths: Applicable in markets where the 

bid-ask spread is not available.

Weaknesses: Ignoring the bid-ask spread 

introduces minor imprecision. Volumes are 

not price-weighted.

Amihud (2002)

Average 

trade price 

impact 

Price, number of 

trades

Captures average absolute 

percentage of price change 

across all trades.

Strengths: Simple intuition – many small 

transactions imply more liquidity than one 

large transaction. Not affected by firm size. 

Suitable for both dealer and auction markets.

Weaknesses: Ignores volume.

Marsh and Rock 

(1986)

Microstructure 

invariants

Price, volume, 

volatility

Measures the impact of a 

standardized quantity of 

order flow in a consistent way 

across markets.

Strengths: Price impact is normalized in a 

way that makes it directly comparable across 

markets and over time. Rationale for stable 

percentage price impact has a theoretical 

foundation.

Weaknesses: Relatively new measure; needs 

broader testing.

Kyle and 

Obizhaeva 

(2014)

continued on next page
35 Analyzing Threats to Financial Stability

Figure 2-36. Market Liquidity Indicators (continued)

Sources: Gabrielsen, Marzo, and Zagaglia (2011), OFR analysis

Indicator Data 

Requirements

Aspect of Liquidity 

Measured

Strengths and Weaknesses Reference

 Resiliency

Variance ratio Price Measures the impact of 

execution costs on price 

volatility over short horizons. 

Higher liquidity reduces 

variance of transaction prices 

around the equilibrium price.

Strengths: Applicable to contexts indirectly 

connected with market liquidity, such as 

volatility and intraday effects. 

Weaknesses: Can be sensitive to the time 

interval chosen for calculating the returns 

variance. Assumes unobservable equilibrium 

prices, but is measured from observed 

transaction prices.

Hasbrouck 

and Schwartz 

(1988)

High 

frequency 

time series 

econometrics

Model dependent Often using VAR and 

cointegration techniques, 

these methods provide 

insight to price discovery 

mechanisms and other 

microstructure aspects of 

liquidity.

Strengths: These methods allow for 

highly sophisticated analysis of market 

microstructure aspects of liquidity.

Weaknesses: Complicated to conduct 

and require rigorous analysis to ensure 

appropriate interpretation.

Hasbrouck 

(1993); Chung, 

Han, and 

Tse (1996); 

Hasbrouck 

(2002); Harris, 

McInish, and 

Wood (2000)

Quality

Published 

bid-ask 

spread

Bid and ask 

quotes

Measures costs that 

market participants pay for 

"immediacy." 

Strengths: Simple to calculate and interpret. 

Data are available for most exchange-traded 

instruments. 

Weaknesses: Innovations in electronic trading 

have reduced the spread in many cases to 

the minimal tick size, removing much of the 

information value.

Huang and 

Stoll (1996); 

Amihud and 

Mendelson 

(1991)

Implied 

bid-ask 

spread

Price and/or 

return

Infers the effective spread 

from the autocorrelation 

that arises as an artifact of 

prices “bouncing” randomly 

between bids and asks. 

Strengths: Simple, reduced-form equation; 

data are readily available; flexibility to run 

across various asset classes.

Weaknesses: No insights on factors affecting 

the estimated spread. Fails to capture 

asymmetric information effects. Assumes 

no informed traders and homogeneous 

information across traders.

Roll (1984)

Bid-ask spread 

decomposition

Bid/ask quotes, 

prices, source of 

order flow (buy vs. 

sell side)

Decomposes the bid-ask 

spread into order processing, 

inventory, and adverse 

information costs. 

Strengths: Data requirements are modest 

(source of order flow can be imputed from 

bid/ask quotes and transacted prices). 

Provides attribution for source of transaction 

costs.

Weaknesses: Bid-ask spreads are often tick 

constrained. Detailed transaction information 

is often unavailable. Appears to reveal little 

about overall market liquidity.

Stoll (1989); 

Huang and 

Stoll (1997)

Immediacy

Short-term 

reversals

Daily returns Uses returns on a contrarian 

long-short strategy to 

estimate the cumulative 

impact of short-term price 

reversals due to noise 

traders’ transitory effect on 

dealer inventories.

Strengths: Data requirements are very 

modest and model implementation is 

straightforward.

Weaknesses: Initial implementation limited 

to equities. 

Rinne and 

Suominen 

(2010)

Quantity 

structure of 

immediacy

Daily risk-free 

interest rates, 

volatility of daily 

returns, trades 

and bid-ask 

quotations

Estimates immediacy costs 

separately for purchase 

and sale orders as the 

price deviation needed to 

induce a dealer to transact 

immediately for the full 

amount of an order.

Strengths: Data requirements are modest 

(source of order flow can be imputed from 

bid/ask quotes and transacted prices). 

Weaknesses: Model assumption of a 

monopolistic dealer may be inappropriate for 

some markets.

Chacko, Jurek, 

and Stafford 

(2008)
36 2014 OFR Annual Report

Repo contracts allow borrowers to boost returns by combining leverage with maturity mismatches, which contributes 

to contagion and fire sale risk. If a market shock leads to 

concerns about risky counterparties, a repo lender may 

demand higher margins or terminate the counterparty 

exposure altogether (see Copeland, Martin, and Walker, 

forthcoming). The demand for higher margins could force 

a highly leveraged counterparty to sell some of its assets 

to meet the new requirements, leading to fire sales. The 

downward spiral could accelerate if many firms sell assets 

simultaneously. The resulting drop in prices would lower the 

value of the collateral, causing counterparties to demand 

more collateral or raise haircuts. A haircut is an additional 

buffer of collateral that is held to protect against declines 

in the assets’ market value over the life of the transaction. 

Chapter 3 describes policy changes to address vulnerabilities 

in wholesale funding markets.

Agency mREITs are one, but not the only, example of a 

leveraged financing vehicle that is vulnerable to run risk and 

asset fire sales. Agency mREITs borrow in short-term repo 

markets to purchase longer-dated real estate assets, typically 

MBS issued by Fannie Mae or Freddie Mac. Repo funding 

represents about 90 percent of their liabilities and the 

weighted average maturity of repo funding is relatively short 

compared to the duration of their assets, because repo borrowing is a cost effective way to obtain leverage. This makes 

agency mREITs vulnerable to runs by repo investors. Given 

the agency mREITs’ relative importance in the repo market, 

any instability among large agency mREITs could aggravate 

broader short-term funding markets. 

Their large MBS holdings also expose agency mREITs to 

risk from interest rate fluctuations, which they hedge using 

U.S. Treasury bonds, interest rate swaps, or similar instruments. However, mortgage bonds are negatively convex 

— as interest rates rise, their projected lives also extend, 

because fewer homeowners are likely to refinance — and 

agency mREITs must adjust their hedges as interest rates 

change. This hedging strategy exposes agency mREITs to 

the risk of fire sales (see FSOC, 2013). These exposures 

increased concern among investors and regulators following 

the rise in interest rates and volatility in mid-2013, leading 

agency mREITs’ share prices and equity book values to 

decline. Some agency mREITs sold a portion of their MBS 

holdings, which exacerbated the widening in agency MBS 

spreads, or shifted into short-duration assets (see OFR, 

2013a).

Figure 2-37. Total mREIT Assets and Average 

Leverage ($ billions and percent)

Agency mREITs have delevered but still are susceptible to 

shocks

Embedded within the U.S. market 

structure are numerous economic 

incentives that influence where and 

how brokers route client orders. These 

incentives have implications for liquidity 

provisioning.

Note: Leverage is total assets divided by total equity of 14 mREITs. 

Not all mREITs existed in all time periods in the figure.

Sources: SNL Financial LC, OFR analysis

0

5

10

15

20

0

125

250

375

500

2000 2002 2004 2006 2008 2010 2012 2014

Total assets 

(left axis)

Average leverage 

(right axis)
37 Analyzing Threats to Financial Stability

impact on the highly leveraged group was more extreme. 

Capital fell by a total of 52 percent and did not stabilize for 

eight quarters.

However, it is important to emphasize that these results are 

not forecasts. The strength of agent-based modeling lies not 

in the magnitude of the results but in its ability to compare 

scenarios — in this case, the loss was nearly twice as much 

for the more leveraged mREIT compared to the less leveraged mREIT.

The model also showed how the actions of one firm affect 

another. Our simulations showed a strong correlation and 

dependence between the two groups of agency mREITs. If 

one group was forced to sell assets, the resulting price drop 

forced the other group to sell during the following quarter. 

In most of our simulated scenarios, all the mREIT firms experienced significant losses but survived. In other instances, 

capital fell significantly — to a point where solvency was at 

risk. This divergence reflects the fact that in the majority of 

simulations, the imposed shock did not trigger any fire sales. 

But when the model produced fire sales, they lasted for a 

prolonged period and resulted in a dramatic reduction in 

capital.

We used an agent-based model to assess how 

vulnerable agency mREITs are to a withdrawal 

of funding or an asset price shock. (See Section 

4.2 for the mechanics of the model.) The goal 

of this exercise was to incorporate both funding 

and interest rate risks to see how interest rate 

shocks reverberate through the system and to 

determine how capital levels react. 

In our model, we used two groups of agency mREITs — one 

with high leverage and another with low leverage. Both had 

the same business objectives: to achieve a target amount of 

leverage and maintain a stable interest rate exposure. Both 

also hedged the interest rate risk of their MBS portfolios by 

shorting U.S. Treasury bonds, that is, by selling borrowed 

bonds with the expectation they can buy them back when 

prices have fallen. Adjusting the size of their MBS and U.S. 

Treasury bond positions allowed the mREITs to maintain a 

constant interest rate exposure. They met leverage targets 

by borrowing funds to purchase assets.

The scenario that we modeled began with a price shock 

that cut the value of MBS by 7 percent (a similar magnitude to the price decline that occurred in mid-2013), which 

reduced the agency mREITs’ equity value and increased 

their leverage. As leverage rose above the target, both lowleverage and high-leverage agency mREITs were forced to 

delever and sell assets at a time when prices were already 

declining. The forced sales reduced MBS prices further, 

creating an adverse feedback loop.

Next, our model assumed that sharp movements in the MBS 

market would create uncertainty and increase risk aversion, 

raising concerns about counterparty and collateral risks 

that also affect the agency mREITs’ funding. We introduced 

a funding shock by increasing the haircut substantially (by 

7 percentage points to a cumulative 14 percent) on MBS 

assets pledged as collateral to obtain repo funding (see

Figure 2-38). We ran the simulation 10,000 times to allow 

for some random variation in prices and capital stocks held 

prior to the shock. 

On average, the simulations showed the capital of the 

agency mREITs with low leverage fell by 35 percent, followed by an additional reduction of up to 10 percent over 

the next two quarters. In the third quarter, the funding shock 

occurred and reduced capital again by up to 6 percent. The 

Assessing the Vulnerability of Agency mREITs

Figure 2-38. Illustrative Stress Test of Agency 

mREITs (number of simulations and percent) 

Highly leveraged agency mREITs are more negatively 

affected by asset price and funding shocks

0

100

200

300

400

500

-90 -80 -70 -60 -50 -40 -30 -20 -10

Percent Reduction in Capital

High

leverage

Low

leverage

Note: The bimodal distribution represents the fact that if a fire sale 

occurs in the first period, a larger number of fire sales is likely to 

occur over the subsequent periods.

Sources: Bloomberg L.P., SNL Financial LC, OFR analysis
38 2014 OFR Annual Report

Policymakers are well aware of the adverse effects of 

market structure vulnerabilities in U.S. equity markets. The 

Financial Stability Oversight Council (Council), in its 2014 

annual report, cited weakness in the financial infrastructure 

as a potential risk to financial stability. The SEC outlined 

several key initiatives the agency is taking to enhance 

market structure. Further, the Financial Industry Regulatory 

Authority (FINRA) implemented a new rule to improve 

transparency of off-exchange trading volumes. Congress 

has also conducted hearings on structural vulnerabilities in 

equity markets.

Financial markets are complex systems. Complex 

market systems are assembled from several subsystems that 

are independently controlled and managed without an overarching authority. Trading systems for many asset classes fit 

this description. Automated algorithm-based trading strategies and technologies for routing orders are layered on top 

of these trading systems and add to the complexity. Given 

the rapid speed at which trades are executed, mitigating 

errors when automated controls fail is a challenge.

These vulnerabilities are not unique to equity markets. 

Similar fragilities are also evident in other types of markets. 

For instance, high-frequency trading and algorithm trading 

are widely used in futures, options, foreign exchange, and 

Since then, agency mREITs have reduced their total assets, 

obtained longer-term funding, cut dividend payouts, and 

reduced their leverage (see Figure 2-37). However, the 

duration of their assets has extended by more than their 

liabilities, resulting in larger duration gaps compared to 

a year ago. Agency mREITS are still sensitive to a rise in 

interest rates or liquidity risks because of their reliance on 

repo funding and a sizeable duration gap. Their portfolios 

are also highly concentrated, increasing their vulnerability 

to an outsized price shock in the agency MBS or real estate 

market. Raising new equity has been difficult for agency 

mREITs because of depressed book values. To boost 

returns, agency mREITs may seek to increase leverage 

or increase the riskiness of assets they are accumulating 

(see Assessing the Vulnerability of Agency mREITs). 

Currently, agency mREITs are not as tightly supervised as 

other financial entities that are thought to pose systemic 

risks (see Pellerin, Sabol, and Walter, 2013).

MARKET INFRASTRUCTURE RISK

This section focuses on two characteristics of U.S. equity 

markets — complexity and fragmentation — that could 

contribute to systemic risk. Future efforts will extend this 

analysis to other markets, with an emphasis on the transmission of risks across market structures.

Figure 2-39. Routing Practices Differ Based on Investor Type and Order Type

Note: A limit order is an order to execute a securities trade only at a specified price (the limit) or better. A market order is an order to execute 

at the best available price. Wholesalers are dealers who execute trades on behalf of clients introduced by retail brokers. Internalization refers 

to trades in which dealers fill orders from their existing inventories. An upstairs market is an off-exchange market for large securities transactions. Dark pools are private electronic trading venues where traders anonymously buy and sell securities.

Source: OFR analysis

Institutional

investor

(informed)

Retail 

investor

(uninformed)

“Upstairs” Market

Dark

Pool

(internal)

Dark

Pool

(external)

Wholesaler 

Internalization/Dark Pool

Consolidated tape

Clearing and settlement

Exchange

Exchange

Market 

order

Limit

order

Limit

order

high

touch

low

touch

FINRA trade reporting mechanism

Broker-Dealer

ORDER TYPE TRADING VENUE REPORTING CLOSE
39 Analyzing Threats to Financial Stability

some fixed-income markets. Regulators need to cooperate 

to monitor and understand interconnections across markets 

(see Kara Stein, 2014).

Complexity and fragmentation are two key attributes 

of equity markets. Complexity arises from several factors, 

including the diverse needs of market participants, technological advancements that enable more complex trading 

strategies and faster speeds, and regulatory rules that may 

stem from good intentions but occasionally may bring 

unintended consequences. For instance, the proliferation of 

trading venues is largely attributed to the SEC’s Regulation 

National Market System (Reg NMS) and Regulation 

Alternative Trading Systems (Reg ATS), implemented in 

2007 and 1999, respectively. Both regulations fostered 

significant competition among trading centers and benefited 

investors through lower trading costs. However, they also 

increased complexity and market fragmentation.

Complexity is underscored by the highly segmented process 

by which an equity order is routed through the system (see 

Figure 2-39). Reg NMS requires brokers to seek the best 

price (the national best bid offer) for each client trade. 

Brokers must connect and route orders to a number of 

trading venues to ensure best execution for client orders and 

connecting and accessing multiple venues adds significant 

complexity to order routing practices. Economic incentives 

embedded in order-routing practices have important implications for liquidity provisioning.

Another important element of complexity is the large 

number of order types. Each exchange and off-exchange 

trading venue offers numerous specialized order types, 

many of which are very complex. Order complexity feeds 

into a number of problems, including broker conflicts of 

interest, slower trade execution during times of stress, and 

an uneven playing field. The large number of venues where 

trading may occur on exchanges and off exchanges via dark 

pools, broker-dealer internal inventories (internalization), 

and electronic communication networks, further adds to the 

complexity (see Figure 2-40).

Complexity and fragmentation may constrain the normal 

functioning of markets by limiting the financial system’s 

ability to provide basic services. Price discovery and providing liquidity are key services of effective markets. More 

often than not, equity markets function in a highly efficient 

and effective manner, with price discovery and liquidity 

provision effectively serving the interests of all investors. 

Figure 2-40. Share of Trading Volume by Venue 

(percent of shares)

Off-exchange trading has increased for market orders and 

limit orders

Source: Rosenblatt Securities Inc.

However, complexity and fragmentation can impair these 

key services.

Price discovery. Unlike “lit markets,” such as exchanges, 

where publicly displayed quotes determine the national best 

bid and offer, trades conducted in dark pools or through 

internalization are transacted at prices that are a derivative 

of those determined in lit markets. These off-exchange 

dark trades offer important benefits to investors (such as a 

reduction in information leakage, ability to conduct trades 

anonymously, and minimization of market price impact 

costs). But they also raise important questions regarding 

price discovery. In June 2014, SEC Chair Mary Jo White 

publicly expressed concerns about dark pools, noting that 

“dark trading can sometimes detract from market quality, 

including the information efficiency of prices” (see White, 

2014). Other factors that may impede price discovery 

include “single points of failure,” which bring trading to a 

halt when critical infrastructure components malfunction.

Liquidity provisioning. Market liquidity refers to the 

ability to trade a substantial amount of stock at close to the 

current market price. This liquidity is supplied by market 

intermediaries. During normal market environments, 

liquidity provisioning enables buyers and sellers to interact 

with one another efficiently. However, this mechanism can 

break down during times of market stress.

Market prices may be more sensitive to liquidity shocks 

in fragmented markets, resulting in more extreme price 

changes during periods of stress (Madhavan, 2011). 

0

25

50

75

100

2008 2009 2010 2011 2012 2013 2014

Exchange and electronic

communication networks

Off-exchange dark pools

Off-exchange internalization
40 2014 OFR Annual Report

the combined interests of end investors who interact in the 

market via intermediaries.

MIGRATION OF ACTIVITIES AND FINANCIAL 

INNOVATION

Financial activities and risks are constantly evolving in 

response to market forces, regulatory developments, and 

technological innovation. Financial innovation can make 

the system more effective and efficient, provide value to 

customers, and promote economic growth. But it can 

also create, transfer, or amplify risks in ways that are not 

apparent. This section describes recent examples of activities shifting from banks to nonbank institutions, among 

subsidiaries of the same firm, and from firms to markets. 

This migration is sometimes driven by firms’ desire to circumvent regulations, known as regulatory arbitrage.

Historically, improvements in technology, changes in 

competition, and new regulations have triggered financial 

innovations and other changes that lead to a migration in 

activity from one sector to another. Some of these changes 

are benign from a financial stability standpoint. For example, 

shifting a cash investment from an uninsured deposit to a 

money market fund may not increase the likelihood of a run 

on the investment. 

But migration could increase risks to financial stability if 

the new activities are not subject to prudential regulation. If 

the regulatory playing field is not level across the financial 

system, the shift of certain activities to more lightly regulated sectors could increase risk-taking and reduce transparency in market practices. For example, financial institutions 

may try to reduce their regulatory capital requirements by 

shifting activities out of subsidiaries with relatively high 

capital requirements, such as banks and insurance companies, to subsidiaries, special purpose vehicles, or third parties 

that are subject to less stringent regulatory requirements. 

Financial innovation that creates new products without regulatory precedent also could introduce unforeseen risks that 

are poorly understood. 

For these reasons, the migration of financial activities 

and financial innovation requires close monitoring. The 

remainder of this section discusses examples of migration and innovation that may require monitoring for their 

potential to pose financial stability risks including captive 

reinsurance, nonbank mortgage servicers, and single-family 

rental securitizations. 

Historically, stock markets relied on intermediaries known 

as market-makers and specialists who are expected to 

buy and sell a particular stock at a publicly quoted price 

to maintain fair and orderly markets. Today, their role has 

significantly diminished as newer market participants, using 

high-frequency trading strategies, have emerged. Firms using 

high-frequency trading strategies are an important liquidity 

source under normal conditions, but do not have an explicit 

obligation to provide liquidity during times of stress. 

The so-called flash crash in equity securities on May 6, 2010 

is one such example. As prices of many U.S.-based equity 

products fell sharply and suddenly that day, market-makers 

acted in their own best interests and withdrew from the 

market, leading to a brief liquidity shock. By the end of the 

day, stocks had recovered most or all of their losses. A joint 

SEC and Commodity Futures Trading Commission (CFTC) 

investigation later concluded the crash was triggered when 

a fund sold a large number of stock index futures that 

exhausted available buyers, and then was exacerbated by 

aggressive selling by other computer-driven traders. That 

example shows how a future flash crash occurring at the 

end of a trading session could severely disrupt the close and 

the pricing of index derivative products, with effects spilling 

into overseas markets and subsequent trading sessions. 

Embedded within the U.S. market structure are numerous 

economic incentives that influence where and how brokers route client orders. These incentives have implications 

for liquidity provisioning. Retail market orders are almost 

entirely routed to wholesalers. Meanwhile institutional 

orders may route through any number of dark and lit 

venues prior to execution, potentially exposing these orders 

to other traders which results in information leakage and 

adversely impacts the institutional investor. Complex order 

routing decisions have implications for all market participants, including those that supply liquidity on exchanges. 

Specifically, brokers may be disincentivized to post orders 

on a particular exchange because it offers lower liquidity 

rebates than other exchanges, even though that exchange 

offers the best possibility of order execution (see Battalio, 

Corwin, and Jennings, 2014). 

Investor confidence deteriorates when price discovery and 

liquidity do not operate efficiently and effectively. To an 

extent, liquidity shocks are inherent in market structure as 

market intermediaries are not constant sources of liquidity. 

Liquidity and price discovery are ultimately determined by 
41 Analyzing Threats to Financial Stability

Captive Reinsurers

Captive reinsurance companies are affiliates of insurers not 

subject to the same prudential reserve and capital requirements as a primary insurer. Captive reinsurance companies 

are created for the purpose of assuming insurance risk 

transferred from a regulated insurance affiliate.

Life insurers’ use of captive reinsurance has expanded dramatically in the past decade. By transferring (“ceding”) life 

insurance and annuity risk to captive reinsurance companies, 

life insurers reduce their reserve and capital requirements. 

Use of captives has grown rapidly since 2000, when the 

National Association of Insurance Commissioners (NAIC) 

passed its Valuation of Life Insurance Policies Regulation. 

The regulation, which most states have adopted, requires 

insurers to hold higher reserves on newly issued term and 

universal life insurance. Reserves ceded through captive 

reinsurance grew from $11 billion in 2002 to $364 billion 

in 2012 and now have expanded to include risk-sharing 

on products such as annuities that are not covered by the 

regulation. 

Risk-Based Capital 10-Year Default Probability

Reported Adjusted for captive 

reinsurance

Difference Reported Adjusted for captive 

reinsurance

Ratio

2002 160% 150% -10% 0.8% 1.4% 1.8

2012 208% 155% -53% 0.9% 3.3% 3.5

Figure 2-41. Captive Reinsurers Can Affect Capital and Default Risk

Source: Koijen and Yogo (2014)

Figure 2-42. Relatively Risky Assets Are Sometimes Used to Capitalize Captive Reinsurers

Type of Asset Description of Asset

“Hollow assets” A letter of credit from a bank is backed by a parental guarantee and recorded as an asset on the 

books of the captive reinsurance company. Unlike other assets such as cash or bonds, this does not 

provide for specific assets that can be used to support reinsurance recoverables. 

Naked parental 

guarantee The parent company promises to indemnify potential losses of the captive reinsurance company 

without setting aside dedicated resources. The asset is referred to as a “naked” parental guarantee 

because it does not involve the use of a letter of credit obtained from a bank. 

Conditional letter 

of credit The bank places a restriction on the letter of credit, such as making the letter of credit the last 

available fund before a drawdown can be initiated. 

Sources: New York State Department of Financial Services, OFR analysis

The adoption of the regulation may have spurred the 

growth of captive reinsurance. As of 2012, the reported 

risk-based capital ratio for the average life insurer ceding 

risk to captives would have been 53 percentage points 

lower and the reported default probability more than three 

times higher without the use of reinsurance transactions, 

according to one study (see Koijen and Yogo, 2014; also see 

Figure 2-41). 

U.S. life insurance companies now use captive affiliates 

more than they use nonaffiliated or third-party reinsurers 

(see Koijen and Yogo, 2014). NAIC, the Council, and the 

Federal Insurance Office have raised concerns about the 

solvency of captives and the potential that losses at captives 

could negatively affect their holding companies.

In 2013, the New York Department of Financial Services 

called attention to the use of risky assets to capitalize 

captive reinsurance companies (see Figure 2-42) and 

found that some insurers used the reserves freed via the 

use of captive reinsurance to boost risk-based capital (see 

NYSDFS, 2013). No other state regulators have publicly 

issued reviews of reinsurance practices. Although the NAIC 

has strengthened public disclosure around the quantity of 
42 2014 OFR Annual Report

Figure 2-43 Use of Captive Reinsurance Varies across U.S. Life Insurers ($ billions and percent)

Consolidated data for life insurers that wrote more than $2 billion of direct premiums in 2013

SNL Top-Tier Entity Life 

Insurance in 

Force ($B)

Total Ceded 

to Captives 

($B)

Ceded 

to U.S. 

Captives 

($B)

Ceded to 

Non-U.S. 

Captives 

($B)

As Percent of Total Life 

Insurance in Force

Total 

ceded to 

captives

Ceded 

to U.S. 

captives

Ceded 

to 

non-U.S. 

captives

MetLife Inc. 4,388.45 1,614.30 99.89 1,514.41 36.79 2.28 34.51

Prudential Financial Inc. 3,724.81 606.89 606.89 0.00 16.29 16.29 0.00

AEGON NV 1,506.49 447.26 241.72 205.54 29.69 16.04 13.64

Voya Financial Inc. 1,506.87 393.66 393.66 0.00 26.12 26.12 0.00

Protective Life Corp. 829.45 187.24 187.24 0.00 22.57 22.57 0.00

Lincoln National Corp. 1,277.66 126.04 126.04 0.00 9.87 9.87 0.00

Manulife Financial Corp. 642.76 94.53 0.00 94.53 14.71 0.00 14.71

AXA 541.20 71.64 71.64 0.00 13.24 13.24 0.00

Sammons Enterprises Inc. 236.81 52.21 52.21 0.00 22.05 22.05 0.00

Nationwide Mutual Group 243.24 35.64 35.64 0.00 14.65 14.65 0.00

Primerica Inc. 589.04 13.89 13.89 0.00 2.36 2.36 0.00

American International 

Group Inc.*

 920.14 0.01 0.01 0.00 0.00 0.00 0.00

Northwestern Mutual Life 

Insurance Co.

 1,462.93 0.00 0.00 0.00 0.00 0.00 0.00

New York Life Insurance 

Group

 1,253.50 0.00 0.00 0.00 0.00 0.00 0.00

Massachusetts Mutual Life 

Insurance Co.

 508.76 0.00 0.00 0.00 0.00 0.00 0.00

Aflac Inc. 157.02 0.00 0.00 0.00 0.00 0.00 0.00

State Farm Mutual 

Automobile Insurance Co.

 798.97 0.00 0.00 0.00 0.00 0.00 0.00

Guardian Life Insurance 

Co. of America

 494.19 0.00 0.00 0.00 0.00 0.00 0.00

Securian Financial Group 978.04 0.00 0.00 0.00 0.00 0.00 0.00

Hartford Financial 

Services Group Inc.

 951.84 0.00 0.00 0.00 0.00 0.00 0.00

Pacific Mutual Holding 

Co.

 299.26 0.00 0.00 0.00 0.00 0.00 0.00

TOTAL* 23,311.43 3,643.31 1,828.83 1,814.48 15.63 7.85 7.78

INDUSTRY* 43,627.36 6,390.10 4,426.26 1,963.84 14.65 10.15 4.50

Note: Data as of September 11, 2014, are from annual filings to the National Association of Insurance Commissioners (NAIC) and may 

include business directly written outside the United States if reported in NAIC statements. Direct premiums represent a consolidation of 

credit life insurance, group life insurance, ordinary life insurance, and industrial life insurance.

*Data for American International Group, Inc. include adjustments for intercompany reinsurance.

Source: SNL Financial LC
43 Analyzing Threats to Financial Stability

More so than most mortgage assets, MSRs are highly 

sensitive to interest rates and mortgage defaults. They drop 

in value when interest rates fall, because of the risk that borrowers will prepay and refinance their mortgages. Mortgage 

servicers face additional risks because they must continue 

to service loans and advance payments to investors after a 

borrower defaults, although they can recoup servicing fees 

and payments advanced only after foreclosure and sale of 

the home. As a result, servicers must finance activities that 

produce no revenue during tight funding environments that 

typically accompany periods of rising mortgage defaults. 

Mortgage servicing can also carry the risk of litigation 

arising from operational failures, another potential expense 

that nonbank servicers may not be prepared for. Issues with 

transferring mortgage servicing, even without a firm failure, 

are an ongoing problem in the industry (see CFPB, 2013). 

If a large servicer fails, shifting its activities to another 

servicer while ensuring the continued transfer of timely payments to investors could prove difficult, particularly given 

the risk that nonbank servicers would experience stress 

simultaneously. 

One potential mitigant to these risks would be for state regulators to define prudential standards for nonbank servicers, 

including standards for capital, liquidity, and operating 

practices, a recommendation the Council made in its 2014 

annual report. State regulators, through the Conference of 

State Bank Supervisors, have launched an initiative to evaluate potential prudential standards.

Two additional reforms would also be helpful. First, 

reforming the model for servicer compensation could align 

the interests of servicers more closely with those of investors and borrowers. Second, establishing industrywide standards for transferring servicing files would make it easier to 

transfer servicing rights if a servicer failed. 

Single-Family Rental Securitizations

Since the crisis, investors have purchased large numbers of 

single-family homes with distressed mortgages and converted them into rental properties, betting on combined 

returns from rental income and home price appreciation. 

This practice was concentrated in a handful of metropolitan areas where home prices had declined sharply during 

the crisis. More recently, home prices in these areas have 

increased more than the national average. 

reinsurance that firms obtain from captives, more public 

data about the quality and quantity of captives’ capital 

would be useful to evaluate risk migration.

The practice of using captive reinsurance is not uniform 

across the life insurance industry and some risk is being 

ceded to offshore captive reinsurance affiliates, which are 

not subject to U.S. regulatory oversight (see Figure 2-43). 

While the growing use of captive reinsurance could be 

driven by factors such as differences in tax and regulatory 

regimes, it remains difficult not only for policyholders and 

investors, but also in some cases for state regulators, to 

determine the capital adequacy and financial strength of 

captive reinsurers (see Section 6.2). 

Mortgage Servicers

Mortgage servicers collect payments from borrowers; set 

aside escrows and insurance payments; forward principal 

and interest to the mortgage owners, including payments to 

investors in agency MBS; and handle tasks such as foreclosing. Rights to service mortgages for fees — mortgage 

servicing rights (MSRs) — are assets that can be bought and 

sold.

When federal bank regulators set standards for bank capital 

under the Basel III international accord, they limited how 

much MSR assets could count toward bank capital. MSRs 

now cannot count as more than 10 percent of a bank’s Tier 

1 common equity capital (or 15 percent when deferred tax 

assets are taken into account), reflecting the difficulty in 

valuing these assets. Beyond that level, excess holdings of 

MSRs must have dollar-for-dollar capital allocated to them. 

Additionally, regulators increased risk weights for the portion of MSRs included in capital from 100 to 250 percent.

These regulatory changes have created incentives for banks 

to sell MSRs to nonbanks. Over the past two years, the top 

five nonbank servicers alone have increased their share 

of servicing nationwide from 5 percent to 14 percent, and 

further growth is likely. Mortgage servicing activity and the 

accompanying risks appear to be migrating to sectors of the 

financial system that do not have comparable prudential 

supervision or capital standards and that rely on debt or 

securitization to finance servicing. Additionally, these firms 

have much less diversified revenue streams than banks and 

generally do not hold mortgages on their balance sheets, 

which can act to hedge MSR risk. 
44 2014 OFR Annual Report

Several investment firms have recently issued floating-rate 

securities backed by pools of single-family rental properties 

they purchased. As of September 2014, there have been 

nine of these deals, totaling close to $5 billion in par value 

outstanding. At this point, there is no evidence of an impact 

on financial stability from this practice, but the activity 

should be monitored.

The structures and risks of these rental securitization bonds 

are more similar to commercial mortgage-backed securities 

than residential MBS in two ways: (1) they rely on medium-term debt to fund long-term assets, and (2) there is 

little or no amortization of principal. These features create 

maturity mismatch, which means the securities must be 

refinanced at regular intervals, creating risk for investors and 

sponsors.

These bonds are structured through special purpose 

vehicles, which relieve deal sponsors of any legal obligation to bail out a failing entity. Although not required, a 

sponsor may bail out a failing vehicle to prevent damage 

to the sponsor’s reputation, which happened with similar 

products many times during the recent crisis and in earlier 

episodes (see Gorton and Souleles, 2007). 

However, in severe stress, concerns about possible failure 

could trump reputation risk. Sponsors retain equity stakes in 

these vehicles, but bankruptcy protections create an incentive for deal sponsors to let the vehicles default if home 

prices drop significantly. Such a price drop could spur largescale sales of investment properties, losses for investors, and 

possible spillover to households. In addition, deal sponsors 

have some incentive to sell properties that increase in value, 

although this risk is mitigated by deal provisions. 

Although financial stability risks from these deals are 

currently limited, how much of a threat they might pose 

in the future depends on how large the market becomes, 

whether standard practices develop, and how rental-backed 

securities are used as collateral elsewhere in the financial 

system. The market is expected to grow — perhaps rapidly. 

Analysts disagree on the potential size of the market, with 

estimates ranging from $20 billion or less (see Goodman, 

2014) to as much as $900 billion (see Rahmani, George, and 

O’Steen, 2013). To grow beyond the $20 billion estimate, 

the market — which currently finances mainly rental properties held by large institutional investors — would need to 

expand to finance small investors who own only a few rental 

properties.
45

3

Evaluating Macroprudential Policy Tools

Macroprudential policies are essential to increase the resilience 

of the financial system and address emerging vulnerabilities. 

Since the crisis, bank regulators have taken important steps intended 

to shore up capital, liquidity, and risk management standards at large 

banking organizations. Routine stress testing and resolution planning 

have further changed the regulatory approach to these companies. 

Regulators have also developed some new tools to strengthen nonbank 

financial institutions and financial markets. However, implementation at 

this new frontier has so far been limited. Additional measures would 

help prevent a significant migration of financial activity to institutions 

and markets subject to less prudential regulation. Also, several countries 

are experimenting with policy tools to address cyclical excesses, such as 

housing booms; assessments of their effectiveness are preliminary.

3.1 Framework for Policy Tool Evaluation

Macroprudential policy aims to make the financial system more resilient to shocks by addressing cyclical 

and structural vulnerabilities. To formulate macroprudential policy and to evaluate its effectiveness, we 

need to: (1) monitor financial developments for potential weaknesses, 

keeping an eye on innovation and the movement of financial activities 

to less regulated institutions, markets, and products; (2) identify the 

best tool for the job, through quantitative limits, buffers, or incentives that increase the price of risk-taking; (3) examine how tools may 

interact with each other and with microprudential oversight and monetary policy; and (4) understand how these tools may limit the transmission or amplification of shocks across national borders. 

Macroprudential policies that address the financial stability risks 

described in Chapter 2 can be cyclical or structural (see Figure 3-1). 

Most of the policies discussed in this chapter address structural vulnerabilities. There are also many potential tools to address the cyclical risks 

discussed in Chapter 2, such as rapid credit growth and deteriorating 

underwriting standards in leveraged lending; “time-varying” tools — which can be used to respond to a 

cyclical buildup of risks — are being developed and used outside the United States. How those tools are 

used should shed light on their effectiveness and utility. 

Macroprudential policies 

are, by definition, intended 

to address vulnerabilities 

across the financial system. 

But financial regulation is 

traditionally implemented 

through oversight of 

financial institutions or 

particular markets.

EVALUATING 

MACROPRUDENTIAL 

POLICY TOOLS
46 2014 OFR Annual Report

Macroprudential policies are, by definition, intended to 

address vulnerabilities across the financial system. But 

financial regulation is traditionally implemented through 

oversight of financial institutions or particular markets. 

Consequently, a robust policy response to financial stability risks will likely need to include prudential and market 

oversight and to span more than one regulatory jurisdiction. 

Post-crisis regulations have begun to recognize that reality. 

For example, the Dodd-Frank Act widened the Federal 

Reserve’s supervisory umbrella to include designated nonbank financial institutions. But much remains to be done.

Structural Policies to Promote Resilience in Financial 

Institutions. To address risks posed by the largest financial 

firms, regulators have tightened standards for bank capital, 

liquidity, and risk management (see Section 3.2). These 

regulations are intended to increase banks’ buffers against 

shocks, which the financial crisis revealed to be insufficient. 

However, some of the specific provisions of these regulations may have unintended consequences, which could pose 

financial stability concerns.

Monitor activities

Evaluate risks

Identify

potential shocks

Credit allocation

Maturity 

transformation

Liquidity provision

Price discovery

Risk transfer

Payments

Macroeconomic

Market

Credit

Funding and 

liquidity

Contagion

Failure of a 

large rm

Disruption of a 

market utility

Breakdown of 

a major market

Structural policies to 

promote:

Financial rm resilience

Infrastructure resilience

Major market resilience

Financial System

Policies to 

address cyclical 

excesses

Figure 3-1. Financial System Activities, Risks, Potential Shocks, and Policy Responses

Source: OFR analysis

The use of stress testing as a core supervisory tool for U.S. 

bank regulators is an important innovation; a number of 

agencies on the Council are also developing stress testing 

regimes for nonbank financial companies. Another innovation is the requirement that large, complex financial institutions file resolution plans, or “living wills,” that describe 

how they could be resolved in a rapid and orderly manner 

under bankruptcy laws to limit possible spillovers from a 

failure. The creation of new legal authorities to wind down 

or resolve such firms in an orderly way is also new. 

The Council has designated three large nonbank financial 

institutions and eight financial market utilities for heightened 

supervision, and regulators are working to develop supervisory and regulatory frameworks tailored to the businesses of 

those companies. 

Structural Policies to Promote Market Resilience.

Regulators have also taken or proposed measures intended 

to address potential risks posed by certain market activities 

(see Section 3.3). They have taken steps to address run risks 
47 Evaluating Macroprudential Policy Tools

in money market funds, suggested remedies to reduce the 

likelihood of fire sales in repo markets, and proposed new 

standards for mortgage markets. However, there remains 

more work to do to promote market resilience.

Structural Policies to Promote Resilience in Clearing 

Infrastructure. The Dodd-Frank Act directed all standardized swaps to be cleared through a central counterparty (see 

Section 3.4). To address risks posed by these central counterparties, the Act also gave regulators, through the Council, 

the power to designate them for heightened supervision. To 

date, the Council has designated eight financial market utilities, including two central counterparties that clear swaps. 

Policies to Address Cyclical Financial Excesses. Both 

through-the-cycle and time-varying macroprudential policies 

can help mitigate cyclical excesses, such as credit, leverage, 

or liquidity transformation, that could lead to financial 

instability (see Section 3.5). Although supervisors have 

firm-specific tools, such as enforcement actions and supervisory rating downgrades, they have few systemwide tools 

to address market and credit excesses during the current 

extended period of low interest rates. 

An important test case is the regulators’ response to 

increased risk-taking in leveraged lending markets. Although 

bank regulators have issued updated guidance intended to 

strengthen banks’ risk management in leveraged lending 

activities, it is not clear whether the guidance or other supervisory actions are curbing those activities or merely encouraging the activity to continue to move to products offered 

by asset managers, such as high-yield bond funds, exchangetraded funds (ETFs), hedge funds, and other private funds. 

In the OFR’s 2013 Annual Report, we introduced an analytical framework for evaluating potential structural and 

cyclical macroprudential policy tools (see Figure 3-2). 

Current vulnerabilities influence decisions about appropriate policy options. Policymakers need to define the 

toolkit in advance; prepare to evaluate the effectiveness 

of options, including potential drawbacks and unintended 

consequences; and then pick the right tool for the job. 

Policymakers must also be vigilant about whether macroprudential policy, traditional microprudential regulatory 

tools, and monetary policy complement or conflict with 

each other.

The macroprudential toolkit is far from complete and will 

be a moving target. Financial innovation and the migration 

DEFINE

the toolkit

EVALUATE

effectiveness

SELECT

the right tool

Ongoing risk

assessment

Cyclical trends

Structural 

weaknesses

Macroprudential 

policy

Microprudential 

supervision

Market conduct 

regulation

Monetary policy

Intended 

results?

Drawbacks?

Unintended 

consequences?

Who is affected?

What risks are 

addressed?

When to use?

How used?

Figure 3-2. Macroprudential Policy Framework

Source: OFR analysis

Although supervisors have firm-specific 

tools...they have few systemwide tools 

to address market and credit excesses.
48 2014 OFR Annual Report

Reserve used to provide hundreds of billions of dollars in 

loans to banks and their broker-dealer affiliates. 

The Basel Committee on Banking Supervision and U.S. 

bank supervisors introduced reforms to capital regulation 

and new quantitative liquidity metrics to force firms to rely 

less on public support and limit leverage and maturity transformation. Since early 2013, bank regulators have issued 

final rules implementing Basel III capital requirements, 

including the countercyclical capital buffer (see Section 3.5) 

and capital conservation buffer (July 2013), supplementary 

leverage ratio (October 2013), enhanced supplementary 

leverage ratio (May 2014), and liquidity coverage ratio 

(September 2014). A rule modifying the calculation of the 

supplementary and enhanced supplementary leverage ratios 

also was issued in September 2014. Regulators have also 

begun to discuss another proposal that would introduce 

a new type of loss-bearing liability instrument to support 

recapitalization during the resolution of a large bank; 

together with existing capital requirements, this has been 

called the Total Loss Absorbing Capacity. 

With these new measures, regulators have sought to 

strengthen banks’ ability to weather stress. However, analysis 

of the economic and financial stability impacts of the 

regulations is warranted. For example, it is possible the new 

capital and liquidity standards could reduce banks’ ability 

to lend. Large banks’ loan growth has been slow relative to 

the growth in gross domestic product in recent years (see

Figure 3-3). That could (1) interfere with the credit channel 

and efforts by the central bank to stimulate economic 

activity, and (2) shift lending activity from banks to capital 

markets and other forms of nonbank financing that do 

not have a federal government backstop and generally are 

subject to less prudential oversight. While regulators have 

tried to lessen these effects through long phase-in periods 

for revised or new prudential regulations, large banks have 

worked towards early compliance. 

RISK-BASED CAPITAL REQUIREMENTS 

In Basel III, regulators here and abroad agreed on several 

new global risk-based capital requirements. First, regulators 

increased the quality and quantity of capital and introduced 

a common equity Tier 1 capital requirement for all banks 

and bank holding companies. Banks and bank holding 

companies will also phase in a capital conservation buffer 

that will limit dividends and discretionary bonuses paid 

when a bank’s total capital ratio is less than 250 basis points 

above the regulatory minimum of 10.5 percent. For bank 

of activities will create new vulnerabilities, and new structural and cyclical challenges will continue to emerge as 

institutions, markets, and products evolve. By assessing 

vulnerabilities in the context of the basic functions of the 

financial system, as detailed in our first annual report in 

2012, policymakers can make progress towards a better and 

more complete financial stability toolkit.

3.2 Structural Policies to Promote 

Resilience in Financial Institutions 

This section discusses changes to the existing supervisory 

framework that address the risk of a large financial firm 

failing. These policies can play a crucial role in reducing 

risks to financial stability. However, these policies can also 

have unintended consequences that could conflict with 

financial stability.

The failure of a large, complex, and interconnected financial institution could have a negative impact on the real 

economy, shrinking credit to households, initiating a cascade 

of losses at other financial companies, or limiting access 

to payment and settlement services, if few substitutes are 

available. 

This section describes key tools regulators have introduced 

or significantly changed since the crisis to promote the 

resilience of financial firms, such as stronger bank capital 

and liquidity standards, stress tests, resolution planning, and 

designation of nonbank financial companies. These tools 

are inherently microprudential — that is, they are used by 

regulators to ensure the safety and soundness of individual 

financial companies. But they also have implications for 

macroprudential policy because they affect large financial 

institutions’ risk-taking.

Bank Capital and Liquidity Standards

Stronger regulatory capital rules and new liquidity standards 

for banks have been central to post-crisis regulatory reform. 

The financial crisis of 2007-09 revealed that a number of 

the largest U.S. banks lacked sufficient high-quality capital 

to weather a severe economic downturn without government financial assistance, such as the Troubled Asset Relief 

Program. The crisis also revealed material liquidity risk at 

U.S. banks and bank holding companies, as evidenced by 

large-scale Federal Reserve discount window lending and 

the introduction of special liquidity programs the Federal 
49 Evaluating Macroprudential Policy Tools

holding companies that regulators have identified as globally 

systemically important banks (G-SIBs), there will be an 

additional capital surcharge of 100 to 250 basis points of 

risk-weighted assets. 

The forthcoming U.S. G-SIB proposal will use the G-SIB 

framework developed by the Basel Committee as a starting 

point. However, the Federal Reserve is considering implementing standards beyond the Basel framework in two 

areas: (1) the surcharge levels for U.S. G-SIBs would be 

higher than those finalized in the Basel framework, and (2) 

the surcharge formula would directly take into account each 

U.S. G-SIB’s reliance on short-term wholesale funding.

The advanced approach may also increase the procyclicality 

of bank lending, because internal risk-based models tend to 

base risk calculations on the recent historical performance 

of each asset class, resulting in higher capital requirements 

during a downturn and lower requirements during a boom 

(see Andersen, 2011). Some companies and regulators have 

tried to reduce this problem by developing models that use 

longer historical data series to estimate potential loss.

SUPPLEMENTARY AND ENHANCED SUPPLEMENTARY 

LEVERAGE RATIO STANDARDS

U.S. bank regulators have also strengthened the leverage 

ratio, a simpler capital standard based on total exposures, 

to include a broader definition of off-balance-sheet items. 

In the leverage ratio, the measure of total assets includes 

exposures with no risk weights, as well as off-balance-sheet 

exposures, such as derivatives and repos. Some regulators 

have even argued that advanced approaches to risk-based 

capital should be discarded in favor of greater reliance on 

Figure 3-3. Large Bank Holding Company Loan 

Growth, Excluding Residential Real Estate Loans 

(percent change)

Annual loan growth at large banks remains weak relative to 

growth in GDP

0

-1 Risk-based capital standards are based on the risk of each 

asset and exposure. Smaller, less complex banks may use 

a set of standard risk weights for assets defined by regulators, simplifying compliance. Larger banks may determine 

their own capital requirements based on internal risk-based 

models (reviewed by regulators) that can be complex and 

diverse across banks. The Basel Committee’s fundamental 

review of the trading book shows that regulators are 

concerned about the accuracy and rigor of these models, 

because many parameters are needed to estimate a single 

firm’s capital requirement. In the United States, a section 

of the Dodd-Frank Act requires that the standardized 

approach should serve as the minimum for all U.S. banks’ 

risk-based capital requirements. 

-2

Note: Large bank holding company (BHC) loan growth is calculated 

using the median rate for bank holding companies with assets 

greater than $50 billion.

Source: Bureau of Economic Analysis, Federal Reserve Form Y-9C

0

0

10

20

1997 2001 2005 2009 2013

Large BHC loan growth (ex-residential real estate)

Nominal GDP growth

The advanced approach may also 

increase the procyclicality of bank 

lending, because internal risk-based 

models tend to base risk calculations 

on the recent historical performance 

of each asset class, resulting in higher 

capital requirements during a downturn 

and lower requirements during a boom.
50 2014 OFR Annual Report

supervisory stress tests, the leverage ratio, and the U.S. statutory floor (see Tarullo, 2014). Figure 3-4. Large U.S. Bank Balance-Sheet Trends 

($ billions)

Based on large banks’ public reporting, it appears that the 

enhanced supplementary leverage ratio is likely to prove 

more of a constraint than risk-based standards for some 

large banks and may lead to unintended consequences. 

Leverage ratios tend to encourage banks to hold higher-yielding but riskier assets relative to low-risk assets, such 

as excess central bank reserves and repurchase agreement 

(repo) transactions backed by government securities. Also, 

in a stressed environment, the leverage ratio may create an 

incentive to sell securities rather than finance them in the 

repo market, potentially promoting asset fire sales. This 

type of risk is poorly understood but could be assessed in 

stress tests. 

LIQUIDITY COVERAGE RATIO 

The liquidity coverage ratio (LCR) requires certain companies to maintain sufficient high-quality liquid assets (HQLA) 

to cover potential net cash outflows over a 30-day stress 

period. The ratio applies to consolidated bank holding companies with assets greater than $250 billion or foreign exposures greater than $10 billion and affiliated banks with assets 

greater than $10 billion. The Federal Reserve also adopted 

a separate modified LCR rule for bank holding companies 

with assets greater than $50 billion, which in practice allows 

these firms to hold HQLA sufficient to meet 70 percent of 

anticipated net cash outflows. 

The requirement is aimed at the perceived runoff risk of 

different types of liabilities, drawdown rates on commitments, and contractually scheduled cash flows over a 30-day 

period. Although capital and liquidity have long been given 

equal weight in assigning U.S. banks’ supervisory ratings, 

supervisory assessments of liquidity have lacked a standardized quantitative metric. For this reason, the LCR metric 

The strongest area of bank balance-sheet growth since 2010 

is high quality liquid assets (HQLA) U.S. supervisors also adopted stronger requirements for 

U.S. bank holding companies with $700 billion or more 

in assets. These firms must begin to report an enhanced 

supplementary leverage ratio in January 2015 and must 

meet a minimum 5 percent enhanced supplemental leverage 

requirement beginning in January 2018. (Currently, eight 

bank holding companies would qualify.) Their affiliated 

banks must meet a minimum ratio of 6 percent. Both 

covered bank holding companies and banks must meet the 

required ratios to be free from restrictions on capital distributions and discretionary bonuses to executives. 

Note: Growth is measured from June 30, 2010, to June 30, 2014. 

High-quality liquid assets include cash, U.S. Treasuries, agency 

debt, and agency mortgage-backed securities.

Source: Federal Reserve Form Y-9C

HQLA

Nonresidential 

real estate loans

Residential real 

estate loans

-$164

$708

$853

Figure 3-5. Weighted Average Maturity of Banks’ 

Bonds Outstanding (years)

Some large banks are reducing weighted average maturity

Sources: Bloomberg L.P., OFR analysis

BB&T

Capital One

Fifth Third

Huntington

U.S. Bancorp

Comerica

SunTrust

Bank of NY

Mellon

PNC

State Street

Regions

M&T

Northern Trust

0 5 10 15

Q4 2010

Q2 2014
51 Evaluating Macroprudential Policy Tools

is a useful common yardstick, but it also represents new 

territory for supervisors. 

Banks can comply with the LCR by increasing holdings of 

liquid assets, changing the maturity or composition of their 

liabilities to reduce projected cash outflows, or shortening 

the maturity of assets to increase projected cash inflows. 

Banks have been getting ready for the LCR’s 2015 phase-in 

mostly by acquiring liquid assets (see Figure 3-4). 

Just as capital standards promote some types of assets as 

less risky than others, the LCR promotes some types of 

liabilities over others. For example, the LCR requires banks 

to hold HQLA to cover the risk of heightened withdrawals 

by depositors, but only a fractional amount for bonds 

and other debt with a maturity of 30 days or more. Banks 

may be responding to the approach of the LCR by issuing 

more bonds and using the proceeds to acquire HQLA. In 

some cases, they also have shortened the weighted average 

maturity of their outstanding bonds to reduce their interest 

expense, offsetting the lower interest income on HQLA 

(see Figure 3-5).

In anticipation of the phase-in of the LCR, four of the 

largest banks increased their use of financing from the 

Federal Home Loan Banks (FHLBs) by 150 percent 

between March 2012 and December 2013 (see FHFA, 

2014). Much of this funding was used to acquire highquality liquid assets that can include the debt of the FHLBs 

and other government-sponsored enterprises (GSEs) under 

the rule. However, this development could heighten the risk 

of contagion through an increase in interconnectedness 

between banks and FHLBs (see Figure 3-6). The LCR 

also assumes that banks can roll over 75 percent of FHLB 

borrowing due within 30 days, which is more favorable 

than other forms of wholesale funding. If bond markets 

were to freeze up and FHLB issuance became difficult, it is 

not clear if the FHLBs hold sufficient liquidity to roll over 

maturing bank borrowings.

Does the LCR also increase the cost of bank lending? The 

answer depends. When banks make loans, deposits increase 

in the banking system, requiring banks to acquire liquid 

assets or otherwise build their LCRs. If deposits increase 

enough to make banks subject to the LCR noncompliant 

with the minimum requirement, the banks would have to 

acquire liquid assets or otherwise build LCR, which would 

increase net funding costs. Banks can absorb the cost and 

reduce profitability or take measures to offset the increased 

regulatory costs, such as raising interest rates or fees, 

Figure 3-6. Large U.S. Banks’ FHLB Borrowings ($ 

billions)

The largest banks are ramping up FHLB borrowing to 

acquire buffer assets to comply with LCR

Note: The four largest bank holding companies (BHCs) are Bank of 

America Corporation, Citigroup Inc., JPMorgan Chase & Co., and 

Wells Fargo & Company. Other BHCs include all other bank holding 

companies with total assets greater than $50 billion.

Source: Federal Reserve Form Y-9C

Q4 2012

Q4 2013

Top four

BHCs

$53

Other large

BHCs

$67

Top four

BHCs

$148

Other large

BHCs

$76

In anticipation of the phase-in of the 

LCR, four of the largest banks increased 

their use of financing from the Federal 

Home Loan Banks (FHLBs) by 150 

percent between March 2012 and 

December 2013...the risk of contagion 

could be heightened through an 

increase in interconnectedness between 

banks and FHLBs.
52 2014 OFR Annual Report

However, publicly listed banks might be hesitant to allow 

LCRs to fall below the regulatory minimum during a crisis 

if they were required to report breaches to comply with 

disclosure regulations, because that could signal weakness 

to their investors. The final rule does not provide a clear 

mechanism, such as a reduction or waiver of the LCR 

requirement by U.S. supervisors, to allow banks to use their 

liquidity buffers during a systemic stress without potentially 

triggering disclosure issues. Such a mechanism could allow 

supervisors to use the LCR as a countercyclical macroprudential tool to address liquidity shocks (see van den End and 

Kruidhof, 2013).

NET STABLE FUNDING RATIO

The net stable funding ratio (NSFR), which was finalized 

in October by the Basel Committee for implementation 

by January 2018, is a structural balance-sheet measure to 

address liquidity risk beyond the LCR’s 30-day horizon. 

Unlike the LCR, which is a measure of a bank’s short-term 

cash flow profile under stressed conditions, the NSFR is 

intended to address more normal market conditions. The 

investing in riskier assets with higher returns, or cutting 

expenses. 

Studies estimate the effects of the LCR requirement could 

be significant, with lending declining 3 to 5 percent and 

interest rates rising 15 to 30 basis points (see Figure 3-7). 

However, those studies date from 2011 and consider an 

early version of the LCR, which the Basel Committee subsequently made less stringent. The final U.S. rule is a stronger 

requirement than the final Basel rule. Although it is early to 

analyze the economic impact of the LCR in practice, these 

studies provide some preliminary analysis of the question. 

It remains unclear whether the LCR will work as a buffer 

that banks can draw down during times of financial stress 

(see BCBS, 2012), one of the key intended benefits of the 

regulation. In the final rule, regulators say they “should 

not discourage or deter a banking organization from 

using [high-quality liquid assets] when necessary to meet 

unforeseen liquidity needs arising from financial stress that 

exceeds normal business fluctuations” (see OCC, Board of 

Governors, and FDIC, 2014).

Study LCR compliance gap size 

and closure method

Sample Lending spread 

increase

Lending 

volume 

decline

Impact on GDP

BIS-MAG

(2010)

25% increase in liquid 

assets/total assets

International 14.9 basis points 

(bps) (median 

country estimate in 

18 quarters)

-3.2% 

(median 

country 

estimate)

-11 to 14 bps 

Standard macro 

and DSGE model 

(median country)

Interagency Study 

(2011)

$1.2 trillion bank by bank 

LCR gap closure for US. 

individual banks using 

estimate of “least cost” 

mechanism

U.S. 15 to 27 bps

(median estimate 

for different loan 

sectors)

n/a -2-33 bps decline 

in level of nominal 

GDP

EBA LCR impact 

study (2013)

EUR 264 billion compliance 

gap from supervisory data

E.U. 6.9 bps 

(short-term)

3.6 bps 

(steady state)

n/a -3 to 7 bps impact 

on level of real 

GDP in transition

IIF (2011) Banks issue term debt to 

buy buffer assets to close 

$1.8 trillion LCR gap (but 

also includes impact of 

higher capital standards and 

Dodd Frank)

U.S. 

(all countries)

468 bps (364 bps) -4.6% (-3.2%) 300 bps decline in 

real GDP

Covas/Driscoll 

(2011)

Banks cut loans and increase 

liquid asset holdings by 20%

U.S. 20 bps 

(long-run estimate)

-5% -70 bps change in 

level of output

BIS-MAG = Bank of International Settlements-Macroeconomic Assessment Group

EBA = European Banking Authority

IIF = Institute of International Finance

Source: Office of the Comptroller of the Currency (2014b)

Figure 3-7. Results of Selected Studies on Liquidity Coverage Ratio Economic Impacts
53 Evaluating Macroprudential Policy Tools

adverse scenarios for the tests. The Federal Reserve uses the 

results from its own models and the company-run models 

in its comprehensive capital analysis and review process 

(CCAR), which assesses capital adequacy and the strength of 

each company’s capital planning. 

The Federal Reserve’s 2014 test estimated total losses of 

$501 billion for the 30 companies under the severely adverse 

scenario, which included a deep recession in the United 

States, Europe, and Japan; sharp declines in asset prices; and 

an economic slowdown in developing Asia. Despite those 

steep losses, banks’ capital ratios remained about the level 

experienced during the financial crisis. In its CCAR results, 

the Federal Reserve again noted qualitative issues with the 

capital planning processes at several banks. 

COMPANY-RUN STRESS TESTS

The Dodd-Frank Act also mandated company-run stress 

testing be performed by certain financial companies. The 

law requires semiannual company-run stress tests for bank 

holding companies with assets greater than $50 billion, as 

well as Council-designated nonbank financial companies. 

In one semiannual cycle, the firms use supervisor-prescribed 

scenarios and their results are compared to those of the 

Federal Reserve’s CCAR model. In the other cycle, firms 

provide to supervisors stress test results based on their own 

internally generated scenarios. 

Dodd-Frank also required annual company-run stress 

tests for any financial company with assets greater than 

$10 billion and regulated by a primary federal financial 

regulatory agency. The Federal Reserve, OCC, FDIC, and 

Federal Housing Finance Agency (FHFA) have finalized 

rules implementing this requirement. The SEC and CFTC 

have not yet proposed rules. The National Credit Union 

Administration approved a proposed rule last year requiring 

annual stress tests at any credit union with more than $10 

billion in assets, although it was not required to do so under 

the Dodd-Frank Act. 

A number of companies recently held their first company-run stress test under the Dodd-Frank Act, including 

Fannie Mae, Freddie Mac, and banks and bank holding 

companies supervised by the Federal Reserve, OCC, and 

FDIC with assets in the $10 billion to $50 billion range. 

Large insurance companies are not subject to the DoddFrank Act company-run stress testing, unless designated 

by the Council for heightened prudential supervision. 

Basel framework for the NSFR states that available stable 

funding must equal or exceed required stable funding (see 

BCBS, 2014b). 

The Basel framework’s calculation of available stable 

funding is weighted by the perceived stability of each 

liability — higher for more stable funding, such as time 

deposits or equity, and lower for less stable funding, such 

as short-term loans from another bank. The calculation of 

required stable funding is weighted by the perceived liquidity 

of a bank’s assets and off-balance-sheet exposures — lower 

for liquid assets (zero for cash and 5 percent for unencumbered U.S. Treasuries) and higher for loans and other longterm assets.

Stress Tests

The Dodd-Frank Act introduced significant new capital stress testing requirements for financial companies, 

including Federal Reserve-run supervisory stress tests and 

company-run stress tests with supervisor-prescribed scenarios to complement firms’ internal stress test processes. 

Supervisors give companies three economic scenarios to 

use for their stress tests: (1) a baseline scenario that reflects 

the consensus view of the U.S. economy, (2) an adverse 

scenario that reflects a decline in economic activity and 

other risks, and (3) a severely adverse scenario that reflects 

a significant decline in the U.S. economy. 

Both the adverse and severely adverse scenarios include a 

trading shock. Stress testing is an important macroprudential tool because it allows supervisors to evaluate financial 

institutions’ resilience under various stress scenarios, which 

supervisors can tailor to address perceived systemwide 

threats. 

The three federal bank supervisors — the Federal Reserve, 

the Office of the Comptroller of the Currency (OCC), and 

the Federal Deposit Insurance Corporation (FDIC) — collaborate on common scenarios and have examination teams 

review stress-testing model governance and capital planning, 

a critical but resource-intensive component of the process. 

FEDERAL RESERVE STRESS TESTS 

The Federal Reserve this year performed supervisory stress 

tests on the 30 largest bank holding companies. In the 

future the three nonbank financial companies the Council 

has designated for heightened prudential supervision will 

be included in this stress test, although not in 2015. The 

Federal Reserve specifies baseline, adverse, and severely 
54 2014 OFR Annual Report

State insurance regulators traditionally have required 

stress testing only in certain industry sectors; for example, 

asset-liability testing by life insurers. State insurance regulators, acting through the National Association of Insurance 

Commissioners, recently adopted a model and supporting 

guidance that, if adopted by states, would require larger 

insurers and insurance groups to annually perform an “Own 

Risk and Solvency Assessment” including a prospective 

solvency assessment in both normal and stressed environments. The new model and guidance do not prescribe 

a specific degree of stress or a specific methodology of 

application. 

STRESS TESTS AND FINANCIAL STABILITY

OFR staff ’s initial work in this area suggests that there 

remains room for further enhancements to the current 

stress test framework (see Bookstaber and others, 2014). For 

example, bank supervisors provide 28 high-level, national 

and international variables in the supervisor-prescribed 

stress scenarios. Although agencies also provide a description of the macrofinancial scenario, to estimate losses banks 

must extrapolate those variables into several hundred more 

variables. The results may be neither consistent with the 

scenario nor comparable across firms, which could affect 

company-run stress test results. Stress tests also could be 

more valuable if they were flexible enough to consider a 

broader range of possible supervisor-prescribed stress scenarios, although this could be difficult to implement in the 

current process. This is particularly important as the range 

of sizes and business models of banks subject to stress tests 

has grown and become more diverse.

Supervisory stress tests currently assume credit losses are 

the driver that will pressure capital. It is also possible that 

the driver of a bank’s stress is not a macroeconomic shock, 

but a funding stress that arises inside the financial sector 

or a credit shock that could be magnified by funding stress. 

Potential liquidity and solvency interactions receive little 

consideration. For example, banks have increased securities 

holdings in held-to-maturity portfolios by nearly half a trillion dollars since the crisis — in part, this reflects new capital standards that require advanced approach banks to take 

a capital charge for unrealized losses on available-for-sale 

securities, but not held-to-maturity securities (see Figure 

3-8). Under stress, banks may need to use these securities to 

generate liquidity, either by selling them (which could result 

in a capital loss) or by financing them through repos (which 

could reduce a bank’s leverage ratio and LCR).

Figure 3-8. Held-to-Maturity Securities Portfolios at 

U.S. Banks ($ billions)

Revised capital standards are contributing to rapid growth

Source: Federal Financial Institutions Examination Council Call Reports 

for all FDIC-insured banks

0

150

300

450

600

2002 2004 2006 2008 2010 2012 2014

0% Risk-weighted

20% Risk-weighted

50% Risk-weighted

100% Risk-weighted

Stress tests also could be more valuable 

if they were flexible enough to consider 

a broader range of possible supervisorprescribed stress scenarios.
55 Evaluating Macroprudential Policy Tools

After reviewing the revised 2013 plans of the 11 largest, 

most complex banking organizations, the Federal Reserve 

and FDIC jointly directed the companies to address shortcomings and demonstrate they are taking actions to be able 

to be resolved under the Bankruptcy Code (see Board of 

Governors and FDIC, 2014). Directed actions included 

rationalizing corporate structures, amending financial contracts to stay, or suspend, certain early termination rights of 

external counterparties, and taking action to ensure continuation of critical services. Firms are expected to respond to 

regulatory feedback in their 2015 resolution plans.

Resolving a large, complex insolvent financial firm raises 

many challenges under the Bankruptcy Code. These include 

the risk of multiple, competing insolvency proceedings 

in different jurisdictions, domestic and international; the 

threatened discontinuity of critical operations; and potential 

systemic consequences of counterparty actions. 

In addition, in some cases, a diversified, global company 

may be resolved under the strategy called “multiplepoint-of-entry,” which means its subsidiaries would enter 

resolution under different bankruptcy regimes. Lack of convergence in insolvency law makes this exercise complicated. 

Title II gave the FDIC the back-up authority to resolve a 

financial company if it is determined that the firm cannot 

be resolved through bankruptcy without serious, adverse 

effects on financial stability. After the Federal Reserve and 

either the FDIC, SEC, or Federal Insurance Office make 

a recommendation, the Treasury Secretary, in consultation 

with the President, must make a determination to begin 

a Title II proceeding. Under Title II, the FDIC would be 

appointed as receiver, succeeding to all rights and title to the 

company’s assets, and would manage the insolvency process. 

The Dodd-Frank Act provided for an Orderly Liquidation 

Fund, subject to certain parameters, as a backup source of 

liquidity support. 

An alternative is the single-point-of-entry strategy, which 

may provide for a more rapid and orderly resolution under 

the bankruptcy code. The FDIC has proposed this approach 

as one of several possible strategies available for implementing its Title II back-up authority (see FDIC, 2013). 

Under the single-point-of-entry proposal, the FDIC would 

be appointed receiver only of the top-tier U.S. holding company, while subsidiaries would remain open and continue 

operating. The FDIC would organize a bridge financial 

company that would receive the failed parent company’s 

assets, primarily investments in and loans to its subsidiaries. 

Losses would be apportioned first to the equity holders and 

then to other claimants of the failed company according to 

the order of statutory priority. In theory, the bridge financial 

company could be created quickly, possibly over a weekend, 

potentially allowing for continuation of subsidiaries’ critical 

operations with minimal disruption.

However, the single-point-of-entry strategy does not solve 

all concerns with a Title II resolution. A key requirement 

of the single-point-of-entry strategy is that bank holding 

companies must have sufficient long-term, unsecured debt 

(“bail-in debt”), so losses could be covered by the claimants 

of the parent company and a new company or companies 

Resolution Plans and Orderly Liquidation 

Authority 

Despite the recent enhancements in prudential standards for 

the largest banks and bank holding companies, the risk of 

a systemically important bank failure cannot be reduced to 

zero. The financial crisis illustrated that the U.S. Bankruptcy 

Code is not always able to handle a rapid and orderly resolution of a large, complex insolvent financial institution. 

However, the resolution of such a financial firm should 

occur efficiently and quickly to minimize market disruption 

and potential systemic consequences. Title I and Title II of 

the Dodd-Frank Act introduced a new regulatory approach 

to help expedite the orderly resolution of large firms. 

Title I required certain companies to prepare resolution 

plans to demonstrate how they could be resolved in a rapid 

and orderly manner under the Bankruptcy Code, without 

extraordinary government assistance, in the event of their 

material financial distress or failure. The Federal Reserve 

Board and the FDIC have joint authority to review and set 

information requirements for the plans, in addition to the 

information requirements set out in the Dodd-Frank Act.

Title I required periodic submission of resolution plans by 

the largest bank holding companies and designated nonbank 

financial companies to the FDIC and the Federal Reserve 

(see Board of Governors and FDIC, 2013). Eleven companies submitted plans in 2012 and revised them in 2013 and 

2014. About 120 institutions submitted their first plans in 

2013 and have submitted revised plans in 2014. The three 

nonbank financial companies designated by the Council 

(American International Group, Inc., General Electric 

Capital Corporation and Prudential Financial, Inc.) submitted initial resolution plans in 2014.
56 2014 OFR Annual Report

transmitted to other business lines in the same company 

but outside of regulators’ authority. And it is intended 

to address “too-big to-fail” risk — that large, complex 

institutions might benefit from an implicit government 

backstop — by requiring that the risks posed by the largest, 

most complex financial institutions are prudently managed 

and subject to adequate oversight. The Federal Reserve is 

working to develop ways to supervise and regulate designated nonbank financial companies to ensure that standards 

and oversight are appropriate given the companies’ business 

mixes, models, and practices. 

There are also potential challenges associated with designation. The most important of these is the difficulty of 

developing and implementing appropriate prudential oversight for financial companies with diverse business models 

and mixes, because what is appropriate for some businesses 

may not work for others. In addition, regulatory costs for 

designated firms will rise on designation, and that could 

promote a migration of businesses or activities to other 

parts of the financial system. 

Given those potential benefits and challenges, it is premature to assess the net effects of designation at this early 

stage. Some academic researchers have analyzed market 

pricing to evaluate whether market participants adjust their 

views of insurance companies identified under a separate 

international process by the Financial Stability Board. This 

research argues that there may be some erosion in market 

discipline (see Dewenter and Riddick, 2014). 

Recognizing that tools other than firm-specific designation 

may be appropriate remedies for risks in some types of 

nonbank financial companies, the Council directed member 

agency staff to undertake a more focused analysis of 

industry-wide products and activities to assess risks associated with the asset management industry. For example, 

some industrywide activities that could introduce risk, such 

as investment in certain types of derivatives, may be more 

appropriately addressed through market-based or industrywide regulation.

could be capitalized when they break away from the bridge 

company. The reaction of foreign regulators is also uncertain. For example, the single-point-of-entry strategy may not 

work if foreign regulators ring-fence assets, that is, prevent 

the assets of a failing firm from leaving their jurisdiction. 

Another concern is that a one-business-day stay on a counterparty’s rights to terminate qualified financial contracts 

such as derivatives will not bind counterparties overseas or 

those with contracts governed by foreign law. This concern, 

however, is reduced by a new protocol developed by the 

International Swaps and Derivatives Association (ISDA), 

providing for temporary stays on certain default and early 

termination rights within standard ISDA derivatives contracts. So far, 18 large banking organizations have agreed to 

sign on to the protocol.

Firms have also been considering the possibility of using 

the single-point-of-entry approach under the Bankruptcy 

Code as a potential resolution strategy under Title I. Under 

a Title I resolution using the single-point-of-entry approach, 

the creation of a bridge company could be initiated by the 

firm itself or by the firm’s primary regulator. Regulators 

have discussed possible changes to the Bankruptcy Code to 

allow the single-point-of-entry approach.

Designation of Nonbank Firms for 

Heightened Oversight

The financial crisis illustrated that the potential impact of 

the failure of a financial firm is related not only to the size 

of the institution but also to its business mix and the nature 

and extent of its connections to other market participants. 

The Dodd-Frank Act gave the Council the authority to designate nonbank financial companies that could pose a threat 

to financial stability for enhanced prudential standards and 

supervision by the Federal Reserve. The Council designated 

three companies in 2013 — AIG, General Electric Capital 

Corporation, and Prudential. Under a separate Dodd-Frank 

Act authority, the Council in 2012 designated eight financial 

market utilities, which are companies that manage or operate 

systems for transferring, clearing, or settling financial 

transactions. 

The designation of nonbank financial companies has 

potential benefits for financial stability. The most important 

potential benefit is that it provides for consolidated supervision of the largest, most complex firms. Evaluating risks 

across a firm’s businesses by a single supervisor reduces the 

likelihood that risky activities in one business line could be 
57 Evaluating Macroprudential Policy Tools

3.3 Structural Policies to Promote 

Market Resilience 

This section discusses policies that address the risk of a 

breakdown of a major market, which could include asset 

fire sales, runs on short-term liabilities, and a sudden loss 

of market liquidity.

Even as new measures strengthen regulatory requirements 

on banks and expand the universe of institutions subject 

to consolidated supervision, the credit intermediation of 

other types of financial institutions is growing. For example, 

the asset holdings of registered funds are now greater than 

bank assets (see Figure 3-9). As the OFR concluded in its 

2013 study on the asset management industry, regulators 

need to consider potential financial stability risks associated 

with asset management activities, in addition to individual 

companies (see OFR, 2013b). 

This section describes three types of microprudential tools 

that focus on activities and can promote market resilience: 

redemption policies and regulation, limits on haircuts and 

collateral, and risk retention rules. Registered investment 

funds have some built-in safeguards to protect investors, 

such as restrictions on exposure, leverage, and illiquidity, 

and requirements related to reporting and governance. 

Unregistered funds and other types of nonbank market 

participants have fewer built-in safeguards. 

Redemption Policies and Regulation 

Investors in collective investment vehicles are exposed to 

market risk and may have an incentive to redeem ahead of 

other investors in market downturns to reduce their losses. 

Individual fund managers can impose redemption policies 

to mitigate the impact of redemption risk on the fund and 

to improve its resilience in the event of widespread redemptions. Managed funds’ redemption policies collectively can 

affect the spread of contagion during a systemic event based 

on how many assets funds are forced to sell to meet 

redemption obligations.

Each fund’s redemption policy exerts an effect on its peers. 

If fund managers are allowed to compete for investors 

through their redemption policies, they may individually 

construct policies that in aggregate make the industry undesirably fragile. Funds with liquidity mismatches compared 

to the stated redemption policies can create market fragility. Although this is not a concern for funds invested in 

Figure 3-9. Financial Assets in Registered Funds and 

Depository Institutions ($ trillions)

Asset holdings of registered funds are now greater than 

bank assets

Source: Haver Analytics

0

4

8

12

16

20

1984 1989 1994 1999 2004 2009 2014

Depository institutions

Registered funds

If fund managers are allowed to 

compete for investors through their 

redemption policy, they may individually 

construct policies that in aggregate 

make the industry undesirably fragile. 
58 2014 OFR Annual Report

assets, might also impose a “lock-up period,” often one 

or two years from the initial investment, during which an 

investor cannot withdraw funds without penalty. 

Mutual funds are generally able to meet redemption 

requests within seven days, as required by current regulation. However, some mutual funds and exchange-traded 

funds (ETFs) offer exposures to less liquid asset classes, 

such as emerging markets, high-yield bonds, or syndicated 

bank loans (see Exchange-Traded Funds and Liquidity 

Mismatches in Section 2.3). Managing liquidity for mutual 

fund or ETF portfolios invested in less liquid asset classes 

may require additional risk management and regulatory tools.

Redemption restrictions should take into account the 

likelihood that market liquidity will become impaired during 

market stress and serve as a mechanism to limit systemic 

spillovers at those times. Under normal circumstances, 

money market funds offer same-day redemption because 

they invest in highly liquid assets. However, recent money 

highly liquid assets, such as large-cap equities or Treasuries, 

some funds offer exposure to less liquid assets, such as 

emerging markets, high-yield bonds, or syndicated bank 

loans. Prudential regulation could overcome this coordination problem for less liquid funds. For this reason, recent 

reforms to the valuation and redemption of money market 

fund shares are an area of interest and ongoing analysis for 

the OFR. 

Redemption policies vary primarily in terms of speed (how 

quickly investors can liquidate) and cost (whether fees are 

associated with investor withdrawals) (see Figure 3-10).

SPEED LIMITS

Managers of private funds can suspend redemptions for 

a certain period of time using redemption restrictions, or 

“gates.” Practices vary in the hedge fund industry. Hedge 

funds can limit redemption requests to only once per month 

or once per quarter and may require as much as six months 

advance notice. Hedge funds, which may invest in illiquid 

Figure 3-10. Redemption Policies 

Fund 

Types

Earliest Redemption Cost (ex-distribution fees and broker 

commissions)

Current Practice Regulation Current Practice Regulation

Money 

Market 

Funds

Pricing: End of day 

Settlement: End of day

7 days or fewer 

depending on the 

prospectus 

If money market fund 

share is about to fall 

below its par value, 

board can suspend 

all redemptions and 

liquidate the fund. In 

2016, discretionary 

redemption gates if 

weekly liquid assets fall 

below 30%.

None In 2016, default liquidity fee; 

1% liquidity fee when the 

fund’s weekly liquid assets 

are less than 10% unless the 

board decides a fee is not in 

the best interest of the fund 

Discretionary liquidity fee; 

not exceeding 2% when the 

fund’s weekly liquid assets 

are less than 30%, unless the 

board decides a fee is not in 

the best interest of the fund

Mutual Funds Pricing: End of day 

Settlement: 

Transaction plus 1 day

7 days or fewer 

depending on the 

prospectus

Some charge 0.5% to 

2% redemption fees 

against fund withdrawals 

too soon after fund 

purchase to discourage 

opportunistic trading

None

ExchangeTraded Funds

Pricing: End of day 

Settlement: 

Transaction plus 3 days

3 days for retail investors None None

Hedge Funds Pricing and settlement 

determined by 

offering; that is oneto-two year “lock-up;” 

infrequent redemption 

once per month or 

once per quarter. 

None Some funds allow “soft 

lock; ” early withdrawal 

with 2% to 10% penalty 

fee

None

Source: OFR analysis
59 Evaluating Macroprudential Policy Tools

Money Market Fund Reform

The financial crisis illustrated the 

vulnerability of money market funds to mass 

redemptions and prompted regulators to 

implement a series of reforms in this market.

Under reforms implemented in 2010, the SEC requires 

at least 10 percent of money market fund assets to be 

cash, U.S. Treasuries, or other securities that can be 

converted into cash within a day; 30 percent must be 

able to be converted into cash within a week. The SEC’s 

reforms also limited the maturity and credit risk in fund 

portfolios and introduced Form N-MFP, which requires 

detailed monthly disclosures (see SEC, 2010).

In November 2012, the Council issued for public 

comment a proposed recommendation that the SEC 

require either a floating or a fixed net asset value with 

significant new safeguards. This represents the first 

time the Council used its authority under Section 120 

of the Dodd-Frank Act, which authorized the Council to 

recommend that a primary federal regulator apply new 

or heightened standards to address a risk to financial 

stability (see FSOC, 2012).

The SEC announced reforms in July 2014 addressing 

those concerns (see SEC, 2014b). Its rule requires 

institutional prime money market funds to implement 

a combination of floating net asset value, redemption 

restrictions, and liquidity fees by October 2016. The rule 

also made enhancements to the SEC’s existing stress 

testing regime by requiring a fund to test its ability to 

maintain weekly liquid assets of at least 10 percent and 

to minimize principal volatility in response to certain 

specified hypothetical stress scenarios. 

With the implementation of floating net asset values, 

institutional investors may leave prime money market 

funds for government money market funds, which invest 

mostly in government securities, cash, or repurchase 

agreements backed by government securities. Under 

the SEC rule, these funds retain their ability to transact 

at a stable net asset value. Investors may also increase 

their bank deposits and holdings of other cash products 

offered by banks. Some large institutional investors 

might also switch to separately managed cash accounts, 

which could be harder for supervisors to monitor (see 

Chapter 6). 

The rule also sought to address potential preemptive 

runs by allowing boards of directors of funds the discretion to apply redemption restrictions. Redemption 

restrictions, often referred to as “gates,” have been 

adopted in many overseas markets to limit fund 

outflows during financial crises (see Axenov, 2014). 

However, in Europe these gates are always in place as 

described in fund offering documents, but only rarely 

used. Some research has suggested shareholders might 

run preemptively if they feared redemption restrictions 

would be imposed (see Cipriani and others, 2014), 

meaning discretionary gates could worsen a run. Others 

have argued that liquidity fees may exacerbate institutional investor runs during a crisis (see Fecht and 

Wedow, 2014). 

The benefit of the new rule cannot be evaluated until 

the money market industry again faces strain and run 

risks. The ongoing concentrations of money market 

fund assets within a few large asset managers may raise 

more systemic stability concerns and require enhanced 

monitoring of potential cash reallocation. 
60 2014 OFR Annual Report

market fund reforms introduced discretionary redemption 

restrictions to better manage heavy redemptions if market 

liquidity is impaired. These reforms provide for discretionary gates by allowing the fund’s board of directors to 

suspend redemptions under certain circumstances for up 

to 10 days (see Money Market Fund Reform). However, 

other mutual funds are not permitted to impose redemption 

gates under SEC rules.

COST

Insured banks have become net cash providers in the repo 

market for the first time

Funds can impose redemption fees on the dollar amount 

of shares an investor requests to sell. Some mutual funds 

impose fees only on redemptions by investors who move 

frequently into and out of the same fund to discourage 

opportunistic trading. Redemption fees can help force 

redeeming shareholders to bear the cost of liquidation. 

Research shows that liquidity fees can reduce the volatility 

of fund flows during periods of market illiquidity, when 

increasing redemptions might otherwise hurt shareholders 

who do not redeem shares (see Greene, Hodges, and 

Rakowski, 2007). However, it is a challenge to calibrate fees 

so that they reduce volatility, while still providing for a viable 

investment vehicle. 

Haircuts and Collateral Requirements 

Regulators have several options to address the risk of asset 

fire sales and other forms of contagion in the event of a 

market shock. First, firm-focused policies aim to reduce 

the reliance of individual banks and dealers on short-term 

funding to make it less likely they will sell assets preemptively to raise liquidity in a crisis. Banks’ use of short-term 

secured funding now carries a higher FDIC deposit insurance assessment and higher capital and liquidity requirements under Basel III. Partly due to these reforms, repo 

liabilities for U.S. banks have declined sharply. Banks’ repo 

assets have also contracted, but they have not fallen as 

sharply. As a result, U.S. banks now are net providers of 

repo funding to nonbanks (see Figure 3-11).

Market-focused policies address a specific type of fire sale 

risk, the risk of a broader fire sale of assets by repo investors, who keep securities collateral after a dealer defaults (see 

Begalle and others, 2013). These include requirements on 

the quality of collateral in repo transactions and floors on 

haircuts to limit the buildup of leverage and mitigate potential losses in an event of a fire sale. 

Figure 3-11. Use of U.S. Banks’ Repo and Reverse 

Repos ($ billions)

Sources: Federal Financial Institutions Examination Council Call Reports 

for all FDIC-insured banks, OFR analysis

-400

0

400

800

1,200

2002 2005 2008 2011 Q2

2014

Repurchase agreements purchased

Reverse purchase agreements

Net cash provided

In October 2014, the Financial Stability 

Board (FSB) published a regulatory 

framework on minimum haircuts on 

securities-financing transactions that 

are not centrally cleared.
61 Evaluating Macroprudential Policy Tools

In October 2014, the Financial Stability Board (FSB) 

published a regulatory framework on minimum haircuts 

on securities-financing transactions that are not centrally 

cleared (see FSB, 2014c). The framework sets standards for 

haircut calculation methods, as well as minimum haircuts for 

some assets that are not government securities. 

For some countries, the framework is a step forward. By 

raising the cost of short-term secured funding, haircut 

floors may encourage borrowers to extend the maturity of 

their liabilities. However, the FSB’s haircut floors are below 

levels currently prevailing in the U.S. triparty repo market, 

which are published by the Federal Reserve Bank of New 

York on a monthly basis and used by many market participants to gauge their risk management practices.

Although not binding today, minimum haircuts could reduce 

procyclicality in haircuts during credit expansions. Under the 

FSB framework, market participants still would be expected 

to conduct their own analysis in setting haircuts, taking into 

account counterparty and collateral characteristics.

Large haircuts may be needed on assets where the collateral 

is illiquid or its price is volatile, but excessive haircuts could 

also exacerbate asset fire sales. This is because a holder of 

collateral subject to a large haircut could have little incentive 

to liquidate collateral in an orderly fashion. Instead, the large 

haircut allows the collateral holder to effectively pass losses 

incurred during an asset fire sale on to the pledging institution, an unfortunate incentive that could have negative 

systemic implications (see Duffie, 2014). 

At a recent workshop on wholesale funding risks conducted 

by the Federal Reserve banks of New York and Boston, 

participants discussed the possibility of eliminating preferential treatment of repos backed by nongovernment 

securities in bankruptcy to help prevent collateral fire sales 

and limit spillovers to the broad market. However, possible 

unintended consequences may include a rapid contraction 

of the repo market and a reduction in the availability of 

credit. Domestic repo market participants also could have 

an incentive to migrate their repo funding overseas, where 

they would be able to access their collateral without having 

it delayed by a lengthy bankruptcy resolution process. 

Addressing Risks in Securitization Markets 

Today, because the vast majority of residential mortgage 

securitizations are originated by the government-sponsored housing enterprises (GSEs), risk in private residential r

mortgage securitizations is not a current financial stability 

issue. As housing markets recover, private securitization may 

revive and require careful assessment of potential risks to 

financial stability.

Flaws in the securitization process and loan underwriting 

standards, especially in mortgage lending, contributed to a 

buildup of risks in securitized products before the financial 

crisis. Reforms have sought to address investors’ over-reliance on credit ratings of asset-backed securities and to 

improve disclosures by securitization issuers. But part of 

the problem may also have been that the issuers of securitized products lacked sufficient incentives to scrutinize the 

products they created (see FCIC, 2011; and FSOC, 2011). 

Risk retention rules mandated by the Dodd-Frank Act seek 

to correct this by requiring securities issuers to have “skin 

in the game” by retaining unhedged exposures equal to at 

least 5 percent of the value of the collateral underlying any 

issuance.

Regulators issued the final credit risk retention rule in 

October 2014 (see OCC and others, 2014b). For a number 

of types of securitizations (for example, collateralized loan 

obligations) this rule may help align banks’ incentives as 

loan originators with those of investors in securitized products. In the case of residential mortgage-backed securities, 

the Dodd-Frank Act required regulators to define a qualified 

residential mortgage (QRM) as a loan with relatively low 

expected default risk that would be exempt from risk retention. In an earlier QRM definition proposed by regulators, 

a mortgage would not qualify for the exemption if, among 

other things, the loan-to-value (LTV) ratio was above 80 

percent, the borrower was currently delinquent on other 

obligations, or the borrower had recently been seriously 

delinquent or bankrupt (see OCC and others, 2011). 

In addition, the Dodd-Frank Act required that the definition of a QRM may be no broader than that for a qualified 

mortgage (QM), which is a standard set by the Consumer 

Financial Protection Bureau (CFPB) to provide a lender safe 

harbor from the borrower’s-ability-to-repay requirement 

under the Truth in Lending Act. The final QM definition 

excludes the types of mortgage products with the worst 

performance during the crisis, such as interest-only loans, 

negative-amortization loans, and hybrid adjustable-rate 

mortgages underwritten with low initial “teaser” rates.

Under the final risk retention rule, the additional credit 

standards on LTV and borrower creditworthiness were 

emoved, and QRM was defined as equal to the definition 
62 2014 OFR Annual Report

Regulatory measures taken since the crisis will partly 

mitigate these concerns by helping to ensure that investors 

have sufficient information to evaluate securitizations. In 

particular, the QM rule included income documentation 

of QM. Due to the QM’s debt-to-income ratio test and 

product feature requirements, QM-qualifying mortgages 

should have lower expected default rates on average than 

non-QM loans. However, the QM rule is an ability-to-pay 

standard designed to protect consumers; it is not a broader 

credit risk standard designed to protect lenders or investors 

(see Cordray, 2013).

with different LTV ratios and borrower credit scores can 

be dramatic. OFR calculations, which use more than half a 

million privately-securitized loans that met an approximation of the QM standard, show that loans originated before 

the crisis with LTVs over 80 percent and FICO scores 

below 640 defaulted during the crisis more than four times 

as often as loans that had LTVs less than 80 percent and 

FICO scores higher than 640. Under the final risk retention 

rule, future securitizations of loans similar to those in Figure 

3-12 would all be exempt from risk retention, despite the 

wide variation in historical default rates. 

Historical default rates can provide some insight into the 

impact of setting the QRM standard equal to QM. Figure 

3-12 shows default rates for securitized loans made before 

the crisis that roughly met the QM standard. The figure 

illustrates with historical data that LTV and borrower credit 

history (represented by Fair Isaac Corporation, or FICO®, 

scores) differentiate between high-quality and high-risk 

mortgage loans. The differences in default rates for loans 

Figure 3-12. Historical Mortgage Defaults by LTV and Credit Score

Loan-to-Value Ratio

<40 40 50 60 70 80 90 100+

FICO 

Score

<580 22.2% 22.3% 24.8% 27.4% 32.7% 39.9% 46.2% 66.2%

580 16.7% 19.9% 20.3% 26.4% 31.7% 42.9% 41.8% 59.3%

590 17.6% 17.5% 20.3% 22.5% 28.5% 40.7% 40.1% 53.9%

600 15.6% 19.9% 19.3% 23.6% 28.2% 39.2% 37.9% 45.9%

610 15.3% 15.5% 18.8% 21.5% 28.0% 36.9% 34.2% 43.4%

620 14.5% 17.6% 16.0% 20.8% 25.6% 34.5% 33.1% 38.8%

630 9.9% 15.2% 19.1% 20.5% 24.4% 31.7% 31.6% 37.8%

640 10.2% 12.2% 16.4% 18.2% 22.4% 29.5% 30.3% 32.1%

650 11.9% 10.8% 13.3% 17.8% 20.8% 28.4% 29.2% 28.3%

660 10.8% 11.7% 12.1% 15.3% 18.5% 26.3% 29.4% 29.8%

670 6.6% 7.6% 10.9% 12.6% 17.1% 25.3% 27.7% 24.5%

680 6.5% 5.2% 9.6% 12.2% 15.3% 23.6% 27.5% 23.2%

690 5.5% 4.1% 5.5% 10.3% 14.1% 21.7% 26.7% 22.5%

700 2.6% 4.2% 7.2% 9.6% 11.6% 18.8% 25.7% 18.9%

710 6.4% 3.2% 5.7% 6.1% 10.7% 18.2% 24.9% 19.8%

720 5.8% 3.5% 5.3% 7.2% 10.3% 16.3% 22.6% 19.2%

730 3.2% 4.5% 3.3% 5.1% 9.2% 14.0% 24.8% 17.5%

740 1.0% 2.2% 2.6% 4.3% 8.1% 11.4% 23.1% 13.6%

750 1.1% 2.0% 1.9% 3.5% 5.2% 10.2% 18.3% 11.8%

760 1.1% 1.0% 1.8% 2.3% 5.1% 8.3% 18.2% 14.0%

770 0.2% 1.3% 1.3% 2.8% 4.6% 7.0% 18.5% 15.1%

780 0.6% 0.2% 2.1% 2.0% 3.7% 6.0% 14.8% 12.1%

790 0.2% 0.7% 0.9% 2.3% 2.7% 6.3% 15.2% 16.5%

800+ 1.4% 2.0% 2.1% 2.5% 4.2% 6.5% 12.2% 18.4%

Note: This sample consists of first-lien mortgages on single-family homes originated in the years 2003-06 and included in pools backing private-label mortgage-backed securities. Qualified mortgage (QM) eligibility is represented here by excluding interest-only and negative-amortization loans and including only full-documentation loans that fully amortize over a term of 360 or fewer months and have at origination a 

back-end debt-service-to-income ratio of 43 percent or less. Default is defined here as a loan being 90 or more days delinquent, written off, 

or sold in a foreclosure sale, real estate owned sale, or short sale. To be conservative in assumptions, loan-to-value (LTV) is defined as firstlien LTV, excluding pledged assets. Missing second-lien data in any case make comprehensive calculations difficult. LTV ratios and Fair Isaac 

Corporation, or FICO®, scores are rounded down; for example a 749 FICO® score is categorized here as a 740.

Source: CoreLogic, Inc.
63 Evaluating Macroprudential Policy Tools

3.4 Structural Policies to 

Promote Resilience in Clearing 

Infrastructure

The Dodd-Frank Act mandated that over-the-counter derivative trading and clearing shift from an opaque and complex 

network of bilateral trading to organized trading platforms 

and centralized clearing mechanisms subject to supervision. 

As the industry adjusts to this new infrastructure, supervisors should monitor potential risk implications arising from 

the implementation of new regulatory requirements. 

Over-the counter (OTC) derivatives can allow firms to 

manage economic and financial risk, but also may create 

risks for financial institutions and for financial stability in 

some cases. Firms are unable to observe the risk concentrations of their trading counterparties’ derivatives positions 

and might not properly evaluate the risk a counterparty 

poses. Derivatives also allow entities to take on leveraged 

exposures. These risks were realized during the financial 

crisis, when counterparties to insurer AIG stood to lose 

billions of dollars had AIG failed to meet payments due on 

credit default swaps written by a subsidiary. 

The Dodd Frank Act introduced key changes in the OTC 

derivatives market, including: (1) mandated central clearing 

through central counterparties (CCPs), (2) an organized 

trading platform, (3) required reporting to a trade repository, (4) new margin requirements for both cleared and 

uncleared swaps, and (5) heightened supervision and regulation of financial market infrastructures, such as CCPs the 

Council designates as systemically important.

The New Framework

Mandatory central clearing of swaps under the Dodd-Frank 

Act began in the U.S. when the CFTC implemented its 

rule for OTC derivatives under its jurisdiction (referred to 

as “swaps”) in 2013. The CFTC initially mandated central clearing for a narrow range of interest rate derivatives 

and credit default swap index products for most market 

participants but will likely extend the clearing mandate to 

additional products. The SEC has regulatory authority over 

security-based swaps, which are defined as swaps based on 

a single security or loan, a narrow-based group or index 

of securities, or events relating to a single issuer or issuers 

of securities in a narrow-based security index. The timeline 

for an SEC final rule phasing in central clearing for security-based swaps is uncertain.

requirements, and the SEC’s Regulation AB II required 

loan-level disclosure requirements for registered public 

offerings of mortgage-backed securities. However, these 

loan-level disclosure requirements do not apply to nonpublic offerings to institutional investors, which comprise 

the bulk of non-GSE issuance. 

Both theory and empirical evidence in the academic and 

policy literature support the notion that “risk retention, if 

properly structured, can address some of [the risks in securitization revealed in the crisis] by requiring an originator 

or securitizer to have ongoing exposure to the credit risk 

of the underlying assets” (see FSOC, 2011, p. 3). The risk 

retention rules have only recently been finalized and have 

not yet gone into effect, and private residential mortgage 

securitization activity is dormant, limiting the ability to 

evaluate the impact of the rules on securitization markets or 

on financial stability. However, in the event that this activity 

picks up, it will be important to monitor volumes, pricing, 

and the risk embedded in securitizations, especially for 

mortgages because of their importance in the capital markets. Looking across the chain of mortgage activity from 

origination to securitization, it will also be important to 

study the alignment of incentives for borrowers, investors, 

loan originators, and securitizers.

Regulators and the OFR will be monitoring whether or not 

a robust non-QM eligible market develops and what that 

implies for QRM. The rule requires the agencies to review 

the QRM definition within four years after it is implemented, and every five years thereafter. This could result in 

revisions to the rule if all six agencies responsible for the 

rule agreed that changes were needed.

If non-QM loans remain a small part of the market, the 

risk retention requirement will not apply for nearly all 

mortgages, including many that performed poorly during 

the crisis. 
64 2014 OFR Annual Report

multilateral netting is still limited by lack of netting across 

different product lines and different CCPs. Eventually, 

cross-margining across CCPs may provide further opportunities for netting, but it will have to be supported with 

adequate margin, taking into account cross-product correlad tions to protect against risks of default.

ORGANIZED TRADING PLATFORM 

To provide greater transparency and foster efficient markets, 

the Dodd Frank Act also created a new type of marketplace 

called a swap execution facility. 

Through swap execution facilities, multiple participants have 

the ability to trade swaps by accepting bids and offers made 

by multiple participants via multilateral execution methods 

such as request-for-quote and central limit order book. The 

CFTC has temporarily registered 22 swap execution facilities 

since it introduced rules in October 2013. Under these rules, 

swaps mandated for central clearing, offered for trading on 

a swap execution facility, and determined to be appropriate 

for organized platform trading by the CFTC, must be traded 

through a swap execution facility. 

Swap execution facilities, central counterparties, and swap 

dealers are required to submit trade data to swap data 

repositories for access by regulators and the public (see 

Chapter 6).

ENHANCED MARGIN REQUIREMENTS

Margin requirements are an important part of the new 

regulatory framework. Under the Dodd-Frank Act, margin 

is required to be posted both for cleared and uncleared 

swaps. Specifically, the Dodd-Frank Act required central 

REDUCED COUNTERPARTY RISK THROUGH CENTRAL 

CLEARING 

Before the Dodd-Frank Act, most derivatives in overthe-counter swap markets were bilaterally netted. Central 

clearing was standard primarily in exchange-traded derivatives markets, such as futures and options, but only occurre

with certain interest rate swap and credit default swap 

products on a voluntary basis. 

Bilateral markets create substantial risks that are complex 

to manage. Every dealer, for instance, interacts directly with 

every other counterparty (another dealer or client), incurring 

market risk on the open position and credit exposure to 

the counterparty. A single firm’s failure could have systemic 

impacts if a large number of bilateral swaps form a complex, interlinked network of counterparties.

But in centrally cleared markets, central counterparties stand 

between the counterparties to every contract, becoming 

the buyer to every seller and seller to every buyer. The CCP 

guarantees settlement for both parties, so each is no longer 

exposed to the other’s default. But central clearing is not a 

solution to the problem of a firm building up an excessive 

market risk concentration in derivatives, as occurred with 

AIG. Monitoring firms’ derivative positions both within and 

across CCPs can help identify when a firm has developed a 

risk concentration. 

The CCP nets or clears transactions between members on 

a multilateral basis, resulting in much smaller net exposure than bilateral netting. In bilateral netting, parties can 

only net transactions with the same counterparty. Despite 

its advantages over bilateral netting, the effectiveness of 

Figure 3-13. Default Waterfalls in the Event of a Member Default

Default waterfalls are designed to strengthen CCP resilience, but they can transmit risk to nondefaulting members 

Source: OFR analysis

Default fund contribution

Initial margin

CCP

CCP

CCP

CCP

CCP

CCP

1st tranche 

2nd tranche

Default fund contribution

Rights of assessment

Non-Defaulting Member A

Defaulting Member

1

2

3

5

4

6

Default fund contribution

Rights of assessment

Non-Defaulting Member B 

4

6

Default fund contribution

Rights of assessment

Non-Defaulting Member C

4

6
65 Evaluating Macroprudential Policy Tools

regulation, including capital and liquidity requirements. 

To date, the Council has designated eight financial market 

utilities, five of which are CCPs. Two of these companies 

centrally clear OTC derivatives, ICE Clear Credit and the 

Chicago Mercantile Exchange. CCPs must register with the 

CFTC or the SEC or both, depending on the type of products they clear.

The Dodd-Frank Act required regulators to take into 

account relevant international standards when setting 

rules for designated CCPs. Subsequently, international 

standards were issued in April 2012 by a joint committee 

of global regulators as the Principles for Financial Market 

Infrastructures. The international standards were supplemented by consultative reports that focused on resolution 

and recovery issues specific to these companies (see CPSSIOSCO, 2012b; CPSS-IOSCO, 2012c; CPSS-IOSCO, 2013). 

In November 2013, the CFTC issued a final rule establishing enhanced risk management standards for designated 

derivatives clearing organizations, consistent with those 

principles. 

Banks and some bank regulators have called for more 

meaningful levels of capital at the CCP as an incentive 

to strengthen risk management, because there has been 

no proof of the ability to request additional funds from 

clearing members in the event of a member default. Banks 

and bank regulators have expressed concern that CCPs 

facing a member default can transmit large quantities of 

risk to CCP members that do not default. It is challenging 

for banks and bank supervisors, given available data, to 

determine the resiliency of CCPs, which is an issue relevant 

to monitoring and managing banks’ CCP exposure (see 

Clearing House, 2012). 

The new central clearing system concentrates risks in a smal

number of large central counterparties, transforming the 

network to a hub-and-spoke system that can better manage 

a larger number of dealer failures but is highly vulnerable 

to the failure of a CCP that can transmit risk to all members. Because of that vulnerability, the Dodd-Frank Act 

authorized the Council to designate certain central clearing 

counterparties as systemically important financial market 

utilities, subject to heightened prudential supervision and 

Remaining Challenges 

The transition to central counterparties has several unresolved issues important for financial stability. Most major 

dealers are subsidiaries of bank holding companies and also 

clearing members of multiple CCPs. Basel III encourages 

banks to use central clearing by assigning a relatively low 

counterparties to collect margin from clearing members 

to protect against potential price movements and member 

default. Similarly, the Dodd-Frank Act imposed margin 

requirements on uncleared swaps to reduce counterparty 

risk in those markets and promote use of standardized 

swaps that can be centrally cleared. 

The CFTC and SEC have issued proposed rules for margin 

and capital requirements that would apply to swap entities 

not regulated by another regulator (see CFTC, 2014; SEC, 

2012). In September 2014, the CFTC and banking regulators released revised proposed rules on margin requirements 

for swaps not centrally cleared (see CFTC, 2014; OCC and 

others, 2014a). The rules outline specific collateral eligible to 

be used to satisfy initial margin requirements and limit variation margin payments to cash. Under the proposed rules, 

a swap entity’s transactions with nonfinancial firms, municipalities, or sovereigns do not require initial and variation 

margin; only those with financial firms do.

DEFAULT WATERFALL

In the event of a member default, central counterparties 

manage their obligations to each party to a swap by tapping a predetermined sequence of resources known as the 

default waterfall (see Figure 3-13). These waterfalls are 

broadly similar across CCPs, although the details vary. 

Funds collected in advance from the defaulting party in the 

form of initial margin are the first to be drawn in the event 

of a default. The initial margin is used to offset losses for 

the CCP when it unwinds the swaps or auctions off the 

defaulting party’s swaps to other members. 

An optimal waterfall structure would balance the incentives 

for CCPs to manage their own risks and for clearing members to monitor the credit risk of their counterparties, even A critical benchmark in CCP risk management is the ability 

of the CCP to cover the default of its two largest counterparties, which is called the “cover 2” standard. A recent 

Bank of England working paper illustrated that where the 

risks are distributed more uniformly among clearing members, the cover 2 standard may not be sufficiently prudent l 

(see Murphy and Nahai-Williamson, 2014). 

when transacting through a CCP. 

Heightened Supervision and Regulation of 

Central Counterparties
66 2014 OFR Annual Report

product, which could also be a source of liquidity demands. 

To guard against this risk, financial institutions and supervisors should analyze firms’ ability to handle variation margin 

calls. A shock to initial or variation margin requirements 

could alter participants’ willingness to enter into new derivative transactions and, in turn, affect derivative prices. 

Margin requirements for CCPs vary across jurisdictions 

and countries, increasing the incentive for companies to 

move their trading to jurisdictions with weaker standards. 

Companies that are unable to meet strict margin requirements may decide to transact business through CCPs with 

weaker requirements, concentrating risk in those CCPs least 

able to bear the risk. Concentration may pose a financial 

stability risk in the event of a participant’s failure. These 

issues highlight the need for continued coordination among 

domestic and international regulators as new CCPs are 

established. 

capital risk weight of 2 percent for swaps cleared through 

qualifying CCPs and a relatively high risk weight for bilaterally cleared swaps for counterparty risk (see CFTC, 2013). 

Although central clearing reduces risks for clearing members, some bank regulators have expressed concerns about 

the concentration of counterparty credit risk and potential 

contagion risks, since the largest banks are members of multiple CCPs (see OCC, 2014a). Banks could face significant 

losses if a CCP experienced losses and transmitted them 

to clearing members. In addition, some U.S. banks are also 

members of foreign CCPs, where less may be known about 

risk management practices.

Additionally, margin requirements for swaps enhance 

financial stability by reducing counterparty risk, but they can 

increase liquidity demands on market participants. As prices 

fluctuate, a party to a swap subject to margining may need to 

quickly post additional cash or other high-quality collateral, 

which is known as “variation margin.” CCPs themselves 

can also decide to increase margins for a specific firm or 

Regulatory Policy Countries Impact

Reserve requirements 3 OECD

18 non-OECD including 

the BRIC group

Results are mixed.

Differentiated or 

time-varying capital 

requirements or risk 

weights

11 OECD

9 non-OECD

Some countries have sizeable slowdowns in credit growth rates, 

although this decline is often followed by a reversal to higher rates. 

Generally seen to improve banks’ capital positions.

Liquidity requirements 4 OECD

5 non-OECD

Generally seen to have improved liquidity positions; not a clear 

impact on credit growth.

Dynamic or increased 

provisioning

3 OECD

9 non-OECD

Some countries have shown a limited effect on credit growth rates. 

Generally seen to improve banks’ capital positions.

Limits on credit growth 

or new loans

5 OECD

11 non-OECD

Effects seen as muted, as lending shifted to foreign banks or lessregulated financial intermediaries.

Limits on loan-to-value 

ratios or debt-toincome ratios

11 OECD

10 non-OECD

Asian countries have curtailed real estate price appreciation and 

reduced defaults, although the evidence is less clear on these tools’ 

ability to control leverage by households and banks. Evidence is 

limited in other countries where implementation is more recent.

Limits on exposures, 

credit concentrations, 

net open positions, or 

maturity mismatch 

10 OECD

16 non-OECD

Direct impact on aggregate credit growth rate is difficult to detect, 

but positive effect on the resilience of financial institutions seems to 

exist. Circumvention problems have been reported, especially in the 

case of exposure or credit concentration limits.

Figure 3-14. Examples of Cyclical Macroprudential Policy Tools in Other Countries

OECD = Organisation for Economic Co-operation and Development

BRIC = Brazil, Russia, India, China

Source: Crowe and others (2011)
67 Evaluating Macroprudential Policy Tools

put in place a cyclical macroprudential policy regime since 

the financial crisis (see Elliott, Feldberg, and Lehnert, 2013). 

Countercyclical Capital Buffer 

So far, the only new cyclical tool the United States has introduced since the crisis is the countercyclical capital buffer for 

large and internationally active banks, part of the Basel III 

risk-based capital standard discussed in Section 3.2. 

Under Basel III, bank regulators can require banks to hold 

an additional capital buffer up to 250 basis points of riskweighted assets during booms to protect against losses 

and limit credit excesses in specific markets. The easing of 

a countercyclical capital buffer could, by contrast, boost 

lending and economic activity during a downturn. 

Since 2010, at least 12 countries have implemented a version 

of the buffer and as many as 25 countries will likely have 

a rule implemented by the end of 2015. Several countries’ 

regulators have taken first steps in determining not only the 

triggers, but also a timeframe. Three have activated it (see 

International Experience with Countercyclical Capital 

Buffers). 

U.S. regulators have not announced specific metrics or 

thresholds that could trigger the activation of a countercyclical capital buffer. Under the final rule, regulators may 

activate the buffer based on a “range of macroeconomic, 

financial, and supervisory information indicating an increase 

in systemic risk” (see OCC and Board of Governors, 2013). 

Federal Reserve officials have mentioned the possibility of 

using the buffer to target overheating sectors, although the 

final rule did not specifically mention that (see Yellen, 2014). 

An unresolved question is whether the countercyclical 

buffer would affect lending as intended. A recent Bank 

of England working paper found that microprudential 

changes in capital requirements do affect bank lending 

(see Bridges and others, 2014). The authors found that this 

3.5 Policies to Address Cyclical 

Financial Excesses 

Countercyclical macroprudential policy tools, or simply, 

cyclical policy tools, are designed to address potential 

cyclical excesses in credit growth, leverage, and maturity 

transformation or liquidity transformation. This section 

describes the countercyclical capital buffer, discusses how 

regulators could use existing policy levers to moderate a 

hypothetical residential housing boom, and analyzes the 

supervisory response to the current boom in leveraged 

lending.

Central banks may face difficult choices if they attempt to 

use monetary policy alone to achieve price and financial stability, because the two objectives may conflict. For example, 

when inflation is low, achieving price stability may require 

monetary policy settings that encourage excessive risk-taking 

and foster future financial instability. In addition, monetary 

policy affects credit provision across the economy and is too 

blunt an instrument to address excesses in specific credit 

markets (see Stein, 2013; Yellen, 2014). 

In such circumstances, either policymakers must trade off 

current and future objectives or draw on additional tools to 

help. As noted in our 2013 annual report, effective policymaking requires at least as many tools as objectives. Cyclical 

macroprudential tools may be needed to limit credit booms 

or excessive risk-taking. 

In 2013, the Federal Reserve and OCC implemented the 

buffer only for advanced approach banks and bank holding 

companies (see OCC and Board of Governors, 2013). 

The countercyclical buffer also enhances banks’ safety and 

soundness, but it is clearly a cyclical tool. For example, if the 

primary purpose of the buffer were to enhance safety and 

soundness, then all insured U.S. banks would be required to 

hold additional capital buffers during periods when supervisors determined risks of credit shocks could be high. 

Many countries have experimented with cyclical macroprudential policies in recent years to address perceived excesses 

in specific sectors (see Figure 3-14). Studies have produced 

mixed results on the success of such policies. The effectiveness of cyclical macroprudential policy may be blunted as 

a result of its interaction with monetary policy (see Wang 

and Sun, 2013) and it may “leak” as financial activities shift 

to institutions and markets not directly affected (see Aiyar, 

Calomiris, and Wieladek, 2014; Ono and others, 2014; Bank 

of England, 2014). 

Macroprudential policies are sometimes difficult to implement, because one of their purposes can be to restrict 

credit to less creditworthy borrowers. As with monetary 

policy, market participants may disagree with policymakers 

about potential long-term risks when asset prices are rising 

and credit risk seems low (see Fischer, 2014). Although the 

United States has a long history with such tools, it has not 
68

International Experience with Countercyclical Capital Buffers

Three of the 25 countries that have made 

the countercyclical buffer part of their 

macroprudential toolkit have already 

activated it in response to perceived market 

excesses. Their experiences provide the 

first evidence about the potential for this 

cyclical macroprudential tool. In every case, 

policymakers said they were using the buffer 

primarily to promote bank resilience to a 

downturn, not to “pop” a credit “bubble.”

SWITZERLAND

Switzerland was the first country to activate a countercyclical 

capital buffer under Basel III. Between 2008 and 2013, Swiss 

housing prices rose more than 35 percent and mortgage volumes increased by 23 percent. In February 2013, the Swiss 

National Bank said it would activate a countercyclical capital 

buffer of 100 basis points of risk-weighted assets — but only 

for exposures to residential mortgages. 

The central bank gave banks nine months to comply with the 

higher capital requirement. Mortgage markets continued to 

boom and in January 2014, the Swiss National Bank raised 

the buffer to 200 basis points and gave banks five months to 

comply (see Figure 3-15).

NORWAY

Norway’s Ministry of Finance activated a countercyclical capital buffer of 100 basis points in December 2013 on the recommendation of the Norwegian central bank, responding to 

rapidly growing residential and commercial property prices 

and the rising ratio of private sector debt to gross domestic 

product (GDP) (see Figure 3-16). High private sector credit–

to-GDP ratios are a concern for regulators, because high 

debt burden increase the likelihood of loan defaults. Banks 

have 18 months to adjust their balance sheets. Unlike in 

Switzerland, the Norwegians placed a capital surcharge on 

all types of bank loans, not just one sector. The ministry said 

it expected the buffer to help prepare banks for high future 

loan losses. 

SWEDEN

Figure 3-15. Swiss Mortgage Rates (percent)

The first Swiss CCB activation had little effect on mortgage 

rates

1

2

3

4

Jan

2012

Jul

2012

Jan

2013

Jul

2013

Jan

2014

Variable rate (mean)

Variable rate (median)

Fixed rate to swap rate    

Countercyclical capital

 buffer announced

Increase

in buffer

Buffer goes

into effect

   

Note: The fixed rate is the 10-year fixed mortgage rate, and the 

swap rate is the 10-year Swiss swap rate.

Sources: Haver Analytics, Swiss National Bank

Figure 3-16. Norway Private Sector Debt to GDP 

(percent)

Norwegian private sector debt-to-GDP is high

60

80

100

120

140

160

180

200

1978 1982 1986 1990 1994 1998 2002 2006 2010 2014

Average

Note: Gray bars signify recessions in Norway.

Sources: Center for Economic Policy Research, NorgesBank

After Sweden’s Stability Council expressed concern about 

historically high household debt, the Swedish Financial 

Supervisory Authority (FSA) announced in May 2014 that 

it would activate a countercyclical capital buffer to address 

credit growth in the residential mortgage market. The FSA 

announced in September 2014 the buffer will be 100 basis 

points and take effect in summer 2015. The Swedish regulator also said it will increase the capital risk weight floor for 

Swedish mortgages from 15 percent to 25 percent.

2014 OFR Annual Report
69 Evaluating Macroprudential Policy Tools

response varied based on the lending sector, with secured 

household loans decreasing relatively less and commercial 

real estate loans decreasing more. 

Another question is whether nonbanks not affected by the 

capital buffer requirement could merely serve as alternative 

sources of credit to overheating sectors. Basten and Koch 

(2014) did not find evidence of macroprudential policy 

leakage after the activation of the buffer in Switzerland 

— banks subject to the buffer raised mortgage rates, but 

insurance companies unaffected by the regulation raised 

mortgage rates by an even greater amount. OFR will continue to study other countries’ experiences with the countercyclical capital buffer and other cyclical macroprudential 

policy tools. 

Cyclical Macroprudential Policy in Housing 

How would the United States respond to another housing 

boom? None of the cyclical tools described in Figure 3-14 

have been adopted in the United States. The creation of a 

new policy tool could take a long time if a rulemaking were 

needed. In theory, in the face of unexpected housing market 

excesses, it might be easier for regulators to vary existing 

fees to change the incentives of market participants and 

influence the rate of credit growth. 

This section focuses on two existing tools that could be 

used this way in the United States: (1) the assessment rates 

that FDIC-insured banks pay for deposit insurance; and (2) 

the guarantee fees the GSEs charge lenders to guarantee 

loans, currently set by the FHFA. In both cases, policymakers could adjust the pricing of guarantees to influence 

borrowing costs in the housing market. This discussion is 

hypothetical and the results of OFR researchers’ analysis it 

is based on are preliminary.

For example, the FDIC could increase the weights of 

housing-related assets in its deposit insurance assessments 

in response to signs of housing market excess. Facing 

higher weights, banks would have an incentive to reduce 

their exposure to housing credit or face higher assessments. 

Although the FDIC already varies these risk weights across 

assets, this approach would allow the FDIC to also vary 

them cyclically.

Similarly, the FHFA could adjust guarantee fees, called 

G-fees, in response to financial conditions. A key advantage of using G-fees is that the impact on mortgage prices 

would be relatively transparent, because an increase in 

Figure 3-17. Proposed and Historical G-Fees 

(percent)

Both alternative countercyclical G-fee rules would suggest 

current G-fees are too high

Sources: Federal Housing Finance Agency, OFR analysis

-1.0

-0.5

0.0

0.5

1.0

1994 1998 2002 2006 2010 2014

Constrained rule

Unconstrained rule

Historical G-fee

borrowing costs can have a direct and material effect on 

borrowing. Studies have shown that an increase of just 1 

percent in interest rates — in other words, a 4 basis point 

increase based on current rates of about 4 percent — would 

reduce loan demand by roughly 2.5 percent (see Gross and 

Souleles, 2002; Čihák, Iossifov, and Shanghavi, 2008). This 

relationship suggests a 25 basis point reduction in G-fees 

would result in a 15 percent increase in lending at current 

mortgage rates, assuming G-fees changes fully pass through 

to interest rates. Green (2013) estimates that for every basis 

point increase in G-fees, mortgages rates increase by 2.5 

basis points, in which case the effects would be even larger. 

A forthcoming OFR paper develops a housing conditions 

index and proposes a policy rule for setting G-fees to 

implement countercyclical macroprudential housing policy. 

The index, which combines a large number of housing data 

series, is normalized to a value of 100 in March 2003, when 

housing finance conditions were relatively stable and healthy.

OFR staff members calculated how countercyclical changes 

to G-fees might have been adjusted historically to achieve 

conditions similar to those in 2003 in housing finance. For 

example, in the early 1990s, this countercyclical rule suggests the G-fee could have been lower to stimulate housing 

finance. But in the mid-2000s, countercyclical G-fees should 

have been significantly higher to offset excessively easy 

credit conditions in housing. To illustrate, the middle line 

(in dark blue) in Figure 3-17 depicts this proposed method, 

and the line to the upper right (in green) reflects G-fees 

from 2007 to 2013. The difference in G-fees between the 
70 2014 OFR Annual Report

historical and proposed policies is roughly 25 to 50 basis 

points over this timeframe. 

EFFECTS 

OFR staff estimate that this method would have increased 

lending by $640 billion between 1992 and 2003. After that, 

as the market heated up, lending would have been $1.1 

trillion lower through the middle of 2008. Since then, the 

method would have stimulated $1.8 trillion in additional 

borrowing to assist in the recovery. This would have represented a substantial increase relative to the current total 

outstanding conforming mortgages of about $5 trillion (see 

Board of Governors, 2014).

Figure 3-18 presents estimates of the results of following a 

G-fee countercyclical policy on conforming loan issuance. 

The lower line (light blue) reflects monthly estimates of the 

changes in loans. The upper line (in dark blue) reflects estimates of the cumulative effects of following such a policy.

Based on this analysis, the current policy of raising G-fees 

to invigorate private mortgage markets appears to lessen 

housing credit. Specifically, the 10 basis point increase mandated in the Temporary Payroll Tax Cut Continuation Act of 

2011 appears to have reduced mortgage demand by about 6 

percent. Because G-fees were already 6 basis points higher 

in 2011 than 2007, reducing G-fees back to 2007 levels 

could have increased mortgage demand by about 9 percent. 

The total effect of varying the weights of risk factors in 

FDIC assessment rates could be much greater than varying 

G-fees. G-fees affect only new mortgages, but FDIC 

assessment rates are levied on banks’ total balance sheets, 

including all new and existing holdings. However, the G-fee 

method is more targeted on new housing credit and would 

also apply to nonbank mortgages sold to the GSEs. For that 

reason, it is likely to have fewer unintended consequences.

Responses to Leveraged Lending 

Banking regulatory agencies that are members of the 

Council have launched a coordinated response to identified 

excesses in leveraged lending, which is lending to corporations that already carry considerable debt. The response 

includes updated, more aggressive supervisory guidance and 

escalating actions to curtail risk-taking by banks. Concern 

centers on the deteriorating credit profiles of borrowers and 

the capital and liquidity implications for banks if a sudden 

stop in the leveraged lending market forced banks to hold 

the leveraged loans they originated to distribute.

Figure 3-18. Estimated Effects of the Proposed 

G-Fee Policy Rule ($ billions)

The proposed G-fee policy could reduce the boom-bust 

cycle in housing credit

Sources: Federal Housing Finance Agency, Freddie Mac, Mortgage 

Bankers Association, OFR analysis

-40

-20

0

20

40

60

80

-1,000

-500

0

500

1,000

1,500

2,000

1994 1998 2002 2006 2010 2014

Cumulative volume change (left axis)

Volume change (right axis)

...a 25 basis point reduction in G-fees 

would result in a 15 percent increase in 

lending at current mortgage rates...
71 Evaluating Macroprudential Policy Tools

Although bank regulators have taken action, a significant 

amount of this risk continues to migrate to asset management products, such as high-yield bond funds, exchangetraded funds, hedge funds and other private funds, and 

collateralized loan obligations (CLOs). Regulators have 

limited ability to stop this migration and there is no consensus that they should. The new risk retention rules for 

securitizations could dampen CLO origination activity. Still, 

the growing role of asset management products in funding 

leveraged lending adds urgency to discussions about structural vulnerabilities, such as redemption, fire sale, and maturity transformation risks in credit funds, and whether and to 

what extent they can contribute to financial stability risks. 

SUPERVISORY ACTIONS

The Federal Reserve, FDIC, and OCC issued updated guidance in March 2013 to banks and bank holding companies 

intended to reduce risk in leveraged loans, both for those 

retained on banks’ balance sheets as well as those repackaged for sale to other parties (see Board of Governors, 

FDIC, and OCC, 2013). The guidance recommended banks 

follow heightened risk management when the borrower’s 

debt exceeds six times its earnings before deducting interest 

expenses, taxes, depreciation, and amortization, commonly 

referred to as EBITDA. 

Unlike the agencies’ previous leveraged lending guidance 

in 2001, the inclusion of the EBITDA ratio in the 2013 

guidance introduced a specific risk metric that would raise 

supervisory concerns. The guidance also recommended 

risk management measures, such as periodic stress tests 

conducted by banks on their leveraged loan portfolios, and 

noted systemic concerns when banks sell leveraged loans to 

other banks or asset managers. The guidance noted that “a 

poorly underwritten leveraged loan that is pooled with other 

loans or is participated with other institutions may generate 

risks for the financial system” (see Board of Governors, 

FDIC, and OCC, 2013). 

The guidance does not appear to have curbed banks’ risktaking in this sector. Underwriting standards have continued 

to deteriorate and the volume of leveraged loans has risen. 

Before the guidance was issued, new large corporate loans 

with leverage higher than six times EBITDA accounted for 

about 15 percent of total issuance. So far in 2014, new loans 

with higher leverage have made up one-third of corporate 

bank loans (see Figure 3-19). 

Figure 3-19. New Leveraged Loan Deals with Total 

Leverage Greater than 6 Percent (percent)

Leveraged loans above the supervisory metric are rising

Note: Includes large corporate transactions, which are defined 

as issuers with earnings before interest, taxes, depreciation, and 

amortization (EBITDA) greater than $50 million. Data for 2014 are 

through June 20, 2014.

Source: Standard & Poor’s

0

10

20

30

1998 2002 2006 2010 2014

The results of the Shared National 

Credit program, an annual interagency 

review of large syndicated corporate 

loans, showed gaps between industry 

practices and expectations in the 2013 

guidance.
72 2014 OFR Annual Report

Supervisors have followed up the 2013 guidance with supe

visory actions at individual institutions and more frequent 

and intensive reviews of leveraged lending activities at the 

largest banks. The results of the Shared National Credit 

program, an annual interagency review of large syndicated 

corporate loans, showed gaps between industry practices 

and the expectations articulated in the 2013 guidance (see 

Board of Governors, FDIC, and OCC, 2014). Thirty-one 

percent of leveraged transactions originated within the pas

year exhibited structures that were cited as weak, up from 

24 percent last year and 13 percent five years ago. The thre

bank regulators also released a frequently asked questions 

document in early November to clarify how banks should 

interpret the 2013 guidance. 

rt 

e 

NONBANK ACTIVITIES 

Asset managers are purchasing an increasing share of leveraged loans on behalf of investors in hedge funds, highyield bond mutual funds, and collateralized loan obligations. 

Many banks reduced their holdings of CLO securities after 

regulators announced the final rule in December 2013 

implementing the Volcker Rule, which restricts proprietary 

trading by banks and limits their role in private funds. 

In an example of risk migration, as banks stepped away, 

asset managers and pension funds stepped in. One result 

of this movement is a decline in the ability of regulators to 

address reaching for yield and herding behavior. There is 

debate about whether and how best to influence investment 

behaviors, particularly whether policy guidance to banks is 

more effective than measures aimed at addressing structural 

vulnerabilities in asset management products. 

Increasing investment in corporate bond funds — more 

than $1 trillion by retail investors since 2009 — may pose a 

threat to financial stability, because investors expect liquidity 

within one day, even though it might take fund managers 

longer to liquidate assets.

One way to counter this threat is to impose withdrawal 

fees on certain types of funds to discourage sudden mass 

redemptions, or runs, by investors. Officials at the Federal 

Reserve discussed this possibility earlier this year. Former 

Federal Reserve Governor Jeremy C. Stein noted that corporate bond funds are “bank like” because their assets are 

illiquid but they offer investors the same quick redemption 

as a typical mutual fund (see Braithwaite and others, 2014). 

The liquidity mismatch would be particularly worrisome 

during a crisis. The SEC has jurisdiction to decide whether 

to require withdrawal or exit fees. In reviewing such policies, 

it would have to consider the cost to retail investors.

Meanwhile, the final risk retention rule, issued in October 

2014, required that unhedged exposures of at least 5 percent be held by the CLO manager or the lead arranger of 

the underlying loans. Because the rule could mean banks 

have to retain some economic exposure, it may moderate 

banks’ leveraged lending originations and temper credit 

excesses in this sector.
73

RESEARCH ON 

FINANCIAL STABILITY 4

Research on Financial Stability

This chapter highlights the OFR’s research, which aims to create a solid 

foundation for our financial stability policy analysis and monitoring 

work. The chapter summarizes the range of research we published in the 

past year, including a new series introduced in 2014. 

4.1 Fundamental Research Agenda

The OFR’s fundamental research agenda supports our mandate to: (1) develop financial stability metrics, 

(2) assess the causes and consequences of financial instability, (3) evaluate policies related to financial stability and risk management practices, and (4) improve the quality and scope of financial data. 

Chapter 4 focuses on three multiyear research projects. The first uses agent-based models to understand 

contagion in financial networks, which will help analyze how shocks can be transmitted across the financial system. The second project investigates visualization techniques to support financial stability monitoring, and the third analyzes risks in credit markets using credit default swap data.

A final section summarizes our 2014 research agenda and discusses

research published since our last annual report. It also describes one

new research product we introduced in 2014, OFR Staff Discussion 

Papers, and another product forthcoming, OFR Briefs. OFR Briefs 

are designed to reach a broad audience. OFR Staff Discussion Papers 

are a venue for OFR staff members to produce academic papers that 

contribute to our understanding of financial markets, financial data, 

and financial institution risks — topics that are the building blocks of 

financial stability analysis.

Our fundamental research activities focus on the following four areas: 

1. Developing tools and metrics to support our monitoring and analysis of the financial 

system. Our fundamental research supports our financial stability monitoring activities, 

described in Chapter 2. Research projects include: (1) the preliminary Financial Stress Index, (2) 

a project to contribute to the understanding of market liquidity, and (3) the visualization project 

described in Section 4.3.

2. Assessing the causes and consequences of financial instability. Projects include: (1) network 

analysis that explains how financial contagion can spread through the financial system, (2) 

mapping projects that describe the funding durability of broker-dealers (see Aguiar, Bookstaber, 

and Wipf, 2014) and the movement of funds through the shadow banking system (see Pozsar, 

2014), and (3) the agent-based modeling project described in Section 4.2. 

3. Analyzing policies related to financial stability and risk management practices. Our 

fundamental research supports our analysis of macroprudential policy, described in Chapter 3. 

Research projects include: (1) a program to promote a macroprudential approach to stress testing 

This chapter focuses on 

three research products: 

agent-based models to 

understand contagion; 

visualization techniques; 

and using credit default 

swap data to analyze 

credit market risk.
74 2014 OFR Annual Report

Agent-based models (ABMs) have the potential to complement supervisory stress testing and address these issues. 

ABMs follow the dynamics of agents (market participants), 

assessing their reactions to events period-by-period and 

updating system variables accordingly. Unlike in typical 

economic analysis, agents’ reactions in ABMs can be based 

on heuristics, or rules of thumb, rather than on calculations 

designed to maximize their own utility.

A key OFR i

vulnerabilitie

system as a w

al

re

al nitiative employs agent-based models to assess 

s in the financial system and resilience of the 

hole. Parts of the project include developing 

models to: (1) study fire sales, (2) measure the market impact 

of large liquidity events, and (3) assess how the configuration of a financial network can affect financial stability.

These three related parts of the project illustrate ways to 

assess the resilience of the financial system by looking at 

its components and the transmission and amplification of 

shocks among them. For example, if an agent (such as a 

broker-dealer) experiences losses that affect its capital, it 

may be forced to sell assets quickly, potentially causing a 

fire sale. If those sales are large enough, they could have an 

impact on the availability of liquid assets, resulting in a large 

liquidity event, and the drop in liquidity could spread to 

other agents linked directly or indirectly through the market 

network. These dynamics might then lead to more losses 

and price drops, creating a feedback loop that worsens the 

impact on financial stability. 

Scientists have been using ABMs for more than a decade to 

explain how the behaviors of individual agents can affect 

complex phenomena such as traffic jams and the spread of 

epidemics. But the use of ABMs is relatively new in finance 

and economics. An early OFR working paper discussed the 

use of agent-based modeling to assess financial vulnerabilities (see Bookstaber, 2012). Academics have proposed 

broader use of ABMs in financial and economic modeling 

(see Farmer and others, 2012) and described the potential 

advantages of ABMs compared to standard economic 

models that attempt to show what conditions lead to market 

balance (see Farmer and Geanakoplos, 2009).

Agent-based models are at the core of several European 

initiatives for evaluating crisis risk, most prominently the 

Complexity Research Initiative for Systemic Instabilities, 

or CRISIS, a consortium of universities and policymakers 

sponsored by the European Commission.

(see Bookstaber and others, 2014), (2) research to 

analyze the design and implementation of regulatory 

policy (see Glasserman and Kang, 2014), and (3) an 

assessment of the historical use and effectiveness 

of macroprudential policy in the United States (see 

Elliott, Feldberg, and Lehnert, 2013).

4. Identifying and filling gaps in data for financi

stability analysis and helping to ensure data a

usable to support assessment and monitoring

of threats to financial stability. Our fundament

research also supports our efforts to address 

data gaps and promote financial data standards, 

described in chapters 5 and 6. Recent projects 

include: (1) analyzing the costs and benefits of 

standard identifiers — for example, see McCormic

and Calahan (2013), highlighting the need for a 

unique mortgage identifier and describing how it 

would improve aggregation, comparability, and 

analysis in the mortgage industry, while protecting 

individual privacy, and (2) analyzing new datasets 

and assessing their use for financial stability 

analysis, such as the derivatives data described in 

Section 4.4.

k 

4.2 Agent-Based Models 

Financial crises involve chains of complex events, with 

multiple transmission channels across diverse market participants. They often include widespread losses, sharp declines 

in asset prices and liquidity, a rapid loss in market confidence, breakdowns in financial services, and consequent 

disruption of economic activity. However, every crisis is different because financial instruments and institutions change 

and vulnerabilities evolve. These changes make predicting 

and managing financial crises extraordinarily difficult. The 

OFR is using agent-based models as a means of addressing 

such changes to explore the dynamics and key transmission 

channels of financial crises. Such modeling helps us take 

an overall view of the dynamic interactions of agents in 

the financial system by considering the different roles each 

agent plays. 

Stress tests are valuable for identifying potential vulnerabilities of individual institutions to the impacts of financial 

crises, at least as currently employed. But today’s stress 

testing methodology doesn’t reveal financial crisis dynamics 

and feedback effects. 
75 Research on Financial Stability

as funding moves from the cash providers through prime 

brokers to the hedge funds. Some assets, such as mortgages, are structured into more targeted debt instruments. 

Market making (the service of matching buyers and sellers) 

enhances liquidity.

Example of the Fire Sale Model

Figure 4-2 shows the progression of one simulation of the 

agent-based model over time. The simulation is based on 

1,000 runs of the model. The figure shows a simplified 

setup with two bank/dealers, two hedge funds, three types 

of assets, and one cash provider (denoted in the figure as 

CP). In Figure 4-2, Bank/Dealer 1 (B1) and Hedge Fund 1 

(HF1) hold equal weights in Asset 1 (A1) and Asset 2 (A2) 

and Bank/Dealer 2 (B2) and Hedge Fund 2 (HF2) hold 

equal weights in Asset 2 (A2) and Asset 3 (A3).

In the agent-based modeling of these relationships, the 

network structure changes period-by-period as the agents’ 

actions change the environment and the agents adapt 

accordingly. In the progression, the dark outline for each of 

the nodes shows the agents’ relative size through the course 

of the scenario. The shrinking of the solid area within the 

node is proportionate to the decline in capital in the case of 

the hedge funds and bank/dealers, the reduction in funding 

in the case of the cash provider, and the drop in prices 

in the case of the assets. If the node is empty, then that 

agent has defaulted. Similarly, the width of the edge shows 

the cumulative effect of transmission from one node to 

another. For example, as the selling of HF1 leads to more 

and more of a decline in the price of A1, the thickness of 

Assessing the Dynamics of Fire Sales

Agent-based modeling was the subject of an OFR working 

paper released in July 2014 (see Bookstaber, Paddrik, and 

Tivnan, 2014), which focused on three types of agents operating in asset and funding markets:

1. Cash providers that act as funding sources by 

pooling investors’ assets; 

2. Banks and dealers (bank/dealers) that provide 

funding to hedge funds and other bank/dealers 

and participate in asset markets through several 

subagents, such as the prime brokerage and the 

finance desk; and

3. Hedge funds and other asset managers that 

participate in asset markets and may require 

funding.

Figure 4-1 shows the components of the bank/dealer and 

its links to borrowers and lenders. The figure is a simplified 

version of the funding map presented in a recent OFR 

working paper (see Aguiar, Bookstaber, and Wipf, 2014).

Figure 4-1 depicts the connections among a bank/dealer, 

hedge funds, and cash providers. In reality, the network is 

much broader. A complete picture would require a larger 

number of each type of agent.

As funding, collateral, and securities flow through the 

system, they are not simply shuffled from one institution 

to another — the institutions take the flows and transform 

them in various ways. For example, credit quality changes 

Figure 4-1. Model Relationship Diagram ASSET MARKETS

BANK/DEALERS

Flow of Funding

Asset-based Fire Sale Funding-based Fire Sale 

Flow of Collateral

Institutions/ 

Other Bank/ 

Dealers

Institutions/ 

Other Bank/ 

Dealers

Hedge 

Funds

Cash 

Providers

Trading

Derivatives

Treasury

Prime

Brokerage Financing

INVESTORS

Source: OFR analysis
76 2014 OFR Annual Report

Figure 4-2. Network Shock Propagation

Description Network Graph of Three Assets, Two Hedge Funds, 

Two Bank/Dealers, and One Cash Provider

Period 0

Asset 1’s (A1’s) price declines sharply. 

Because Bank/Dealer 1 (B1) and Hedge 

Fund 1 (HF1) hold A1, they face losses due 

to the shock. The Cash Provider (CP) is also 

affected because the value of collateral 

declines. In a static stress test, the analysis 

ends at this point.

Period 2 

B1 and HF1 must sell assets to cover losses 

on A1. They sell A1 and A2, causing a drop 

in the price of A2. This in turn affects other 

agents with holdings in A2, in particular, B2 

and HF2. CP1 is affected because it holds 

collateral in A2 as well as in A1.

Period 4

The propagation from the shock leads to a 

default of HF1 and B1. Credit exposure that 

B2 has to B1 spreads problems through the 

credit channel. The drop in A2 affects HF2, 

and its forced sale spreads the shock to A3. 

Note that no firms holding A3 also hold the 

asset that was originally shocked, A1, but 

they have losses because of contagion. CP 

markedly reduces its funding due to the 

drop in the value of its collateral.

Period 6

The system finally settles down with funding 

all but shut off, and both hedge funds and 

B1 in default. A2 ultimately has a greater 

price drop than A1, the shocked asset.

Source: O

A1 = Asset 1 B1 = Bank/Dealer 1 HF1 = Hedge Fund 1 

A2 = Asset 2 B2 = Bank/Dealer 2 HF2 = Hedge Fund 2

A3 = Asset 3 CP = Cash Provider

FR analysis
77 Research on Financial Stability

A financial network is dynamic. Although a snapshot at 

any point in time can give a sense of a network’s current stability, what matters is how the network evolves. 

Interconnections can diversify risk, but can also be pathways 

for shocks. The common-sense view that diversification 

reduces risk may be correct when failures are infrequent, 

but there may be a tipping point. If failures move above 

some threshold, a highly interconnected and diversified 

system may actually be a more fragile system (see Acemoglu, 

Ozdaglar, and Tahbaz-Salehi, forthcoming).

We are using ABMs in our research to follow the evolution 

of financial networks over time and assess the resilience 

of those networks to shocks. ABMs work well because 

each period of an ABM simulation depicts a network, 

which can change as each agent’s actions affect the network 

environment.

the edge will increase. The color of the edge in the figure to market signals than those who are demanding liquidity 

(see Bookstaber, Foley, and Tivnan, forthcoming). We also 

used the fire sale model to analyze how important it is that 

investors have different decision cycles — in other words, a 

hedge fund manager may make many buy and sell orders in 

the course of a few minutes, while a pension fund manager 

may take days or weeks to adjust positions.

shows the intensity of the interaction in the current period 

— a darker color means greater intensity or change in the 

system relative to other runs and periods.

Stress tests have become a standard tool in the macroprudential toolkit, but they do not address the follow-up effects 

of a stress event — that is, how the losses incurred by the 

individual banks might feed through the financial system 

in the face of forced selling and withdrawn liquidity. The 

ABM fire sale model extends stress testing by analyzing the 

pathways for the initial stress to spread through the financial 

system. Network diagrams such as Figure 4-2 visualize the 

severity and the sequencing of this dynamic. The model is 

designed to be applied to a wide range of stress scenarios. 

The triggering event in the figure is a price shock, but 

the model also allows for shocks based on a reduction in 

funding by the cash provider, a drop in the creditworthiness 

of the bank/dealer, or a sudden increase in redemptions by 

the hedge fund clients.

Evaluating the Stability of Financial 

Networks

The role of financial interconnections among market participants in the 2008 crisis has prompted a surge in network-related financial system research. Network depictions can give 

a startling visualization of the magnitude of interrelationships. But one problem with the network approach is that 

it does not capture the dynamics of how the nodes transform the flows, how the flows carry risk from one node to 

another, and how the nodes in turn change the structure of 

the network.

Measuring the Market Impact of Large 

Liquidity Shocks

The OFR is also using ABMs to gauge the market impact of 

large asset liquidations. Academic research and risk monitoring often focus on analyzing the day-to-day functioning 

of market microstructure and related liquidity measures, 

such as bid-offer spread and daily volume. But these 

analyses yield limited insights into the market effects of 

large-scale liquidations. During periods of sudden, outsized 

liquidity demand, normally sufficient liquidity suppliers 

may be overwhelmed. Deep pockets of liquidity beyond 

the short-term suppliers may be slow to respond and might 

even head to the sidelines after sudden, large price drops. 

Two recent OFR working papers used ABMs to help 

measure the market impact of large asset liquidations that 

occur during forced selling and financial crises. One paper In one working paper, we looked at how various agents — 

the nodes of a network — react to changes in the network 

(see Bookstaber and Monin, forthcoming). The agents 

collect data from other agents and those data improve their 

success. However, relying on connections to other agents 

reduces resilience if an agent drops out of the network. The 

result is 

a system that can generate boom-bust cycles. As agents 

create a broader network, it becomes increasingly successful 

over time but then suffers a greater loss when a shock 

causes some connections to fail. The paper looked at the 

demonstrated the application of ABMs in a market with 

an electronic order book. Using actual order flow data with 

user identifications provided by the CFTC, the authors 

analyzed the stability of the order book after sharp price 

changes. They examined the trade-offs of different levels of 

microstructure data and the ability to predict sudden price 

changes (see Paddrik and others, 2014a).

A second working paper focused on an aspect of market 

behavior first discussed in Duffie (2010) — the market 

impact when liquidity suppliers are slower in responding 
78 2014 OFR Annual Report

Figure 4-3. Examples of Financial Data Visualizations

Small-multiple Layouts

Source: IMF (2014b)

Tree Maps

Source: MarketWatch (2014)

Radial Plots

Source: Lei and Zhang (2011)

Repeated variations of similar plots 

differing in isolated ways, with the small 

plots organized into a larger grid for 

easy comparison

Other example: Schreck and others 

(2007 and 2009)

Hierarchical data, shown as nested 

rectangles, with color, size, and category 

representing key data attributes 

Other examples: Rank and Filed (2014); 

Merino and others (2006); Jungmeister 

and Turo (1992)

Data depicted in polar coordinates 

or a circular arrangement, often 

to accommodate data points by 

placing densely populated clusters of 

observations near the exterior of the 

circle

Other examples: Wu and Phillips 

(2010); Sawant (2009)

according to its own objectives and interacting in an 

environment that changes largely because of the agents’ 

actions. Individual agents can act prudently, only to have the 

combined effect of their actions cascade to create instability 

for the system as a whole. The system can manifest stability 

during typical day-to-day levels of risk only to careen out 

of control when a shock reaches some critical threshold. 

Because agent-based modeling has the ability to incorporate 

the behavior of varied, dynamic, and interacting agents, it is 

well-suited for assessing these vulnerabilities.

4.3 Visual Tools for 

Understanding Financial Stability 

Financial stability analysts face a daunting challenge to make 

sense of a seemingly infinite stream of data. The OFR is 

experimenting with visualization techniques to reveal trends 

and relationships in data and transform massive raw data 

streams into useful information for analysis. The recent 

crisis demonstrated that need.

Good visualizations reveal key patterns and connections 

in complex data. Typically, visual attributes such as distances, areas, and color intensities correspond to attributes 

of the data. However, poorly crafted visualizations can be 

To assess vulnerabilities, we have to consider the financial system as a wide and varied set of agents, each acting 

characteristics that mitigate the cycles and evaluated metrics 

to assess the stability of the financial system.

In another forthcoming paper, we apply techniques used by 

chemical plant managers to the assessment of risks in the 

financial system (see Bookstaber and others, forthcoming). 

Process hazard analysis (the standard risk assessment 

tool in the chemical engineering industry) can help identify loops of interactions within the financial system that 

might be subject to positive feedback and instability. From 

a systemic risk standpoint, the network characteristics and 

stability concerns are surprisingly similar. The plumbing of 

a chemical plant allows flows in and out between processes 

that transform inflows into outflows, often based on complex, nonlinear interactions — in other words, the output 

is difficult to predict because it is not proportional to the 

input. Although the processes of the financial system are 

different from those of a chemical plant (maturity, liquidity, 

and credit transformations, for example), the complexity of 

interconnections and the potential for propagation due to 

leverage and liquidity lead to striking similarities from the 

standpoint of risk control. 
79 Research on Financial Stability

confusing, distracting, and even misleading (see Lemieux, 

Fisher, and Dang, 2013; Sarlin, 2013; and Sarlin, 2014). 

Researchers and graphics experts at the OFR are exploring 

whether innovative visualizations can improve on the standard bar charts and time-series plots that tend to dominate 

presentations of financial data.

Choose the Right Tool

Visualization encompasses a range of techniques with 

varying strengths and weaknesses. Choosing the right tool 

for the task is important (for an overview of approaches, 

see Plaisant, 2004; and Munzner, 2009).

Selecting an appropriate graphic should include: 

• Identifying the task. Who will see the visualization 

and what tasks are they performing?

• Selecting the data. Which particular data should the 

visualization depict to support the task?

• Choosing visual forms. How should the visualization render or represent the data?

Each of these considerations can involve a large range of 

possibilities. Creativity, judgment, and consideration for 

whether users are viewing the analysis in print or on a computer screen should help guide the choice of a particular 

visualization. Figure 4-3 illustrates a few possibilities.

Visualization researchers have documented various aspects 

of such analysis. For example, Wilkinson (2005) presents a 

framework for assembling most of the common scientific 

graphics from modular visual building blocks. Ware (2012) 

discusses how to craft images that people can understand, 

given the significant strengths and weaknesses of human 

vision. Tufte (2001) sets out graphic design principles for 

well-crafted scientific visualizations. Lemieux, Fisher, and 

Dang (2014) survey the use of visualization tools in the 

financial domain.

Figure 4-4, which originally appeared in the OFR 2012 

Annual Report, shows the lead-up to the collapse of MF 

Global Holdings Ltd. (see OFR, 2012, pp. 66-67). This figure 

is a narrative visualization, a technique that tells the story of 

an interconnected sequence of decisions and events over 

Figure 4-4. The Lead-Up to the Collapse of MF Global Holdings Ltd.

Narrative visualizations relate a sequence of unfolding events

2008 2009

02 04 12 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03

2010 2011 2012

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

7

6

5

4

3

2

1

 Exposure 

(Billions of US$)

CDS SOVX 

European Index 

Spread

FEB 2008

In a failure of 

oversight, the 

company suffers 

an “unauthorized 

trading incident” 

and loses 

US$141 million.

APR 2009

CFTC warns MF Global 

of major compliance 

issues, which delay its 

acceptance as a 

primary dealer until 

February 2011.

DEC 2009

CFTC imposes a US$10 million ne on MF 

Global for “signicant supervision violations” 

arising from rogue trading and orders MF 

Global to enhance its internal controls.

LATE 2009

Increase in MF Global’s 

balance sheet leverage. 

Moody’s assigns a negative 

outlook to Baa2 ratings.

APR 2010

Interest rates on bonds issued 

by Greece, Portugal, and Ireland 

rise amid euro area worries. 

Increases in rates on Spanish 

and Italian bonds follow in 

November 2010.

JUN 2010

Chief Risk Ofcer (CRO) 

agrees to adjust the 

European sovereign 

investment limit to 

US$1.0 billion total 

gross notional across 

sovereigns.

SEP 2010

MF Global begins 

investing in sovereign 

bonds of Belgium, 

Italy, Ireland, Portugal 

and Spain.

MID SEP 2010

Positions and limits 

increase to 

US$1.5-2.0 billion.

OCT 2010

Positions increase to US$3.5–4.0 

billion and sovereign limit to 

US$4.75 billion.

NOV 2010

Disagreement about 

liquidity risk at board 

meeting. S&P downgrades 

MF Global to BBBbecause of deteriorating 

liquidity and increased 

leverage.

2011

As the euro area 

crisis escalates, 

MF Global bets 

against the market.

MAY 2011

MF Global discloses 

off-balance-sheet exposure 

of US$6.3 billion to European 

Sovereign debt.

AUG 2011

Client money 

accounts shrink by 

US$1.5 billion. 

Another US$1.8 

billion is withdrawn in 

2 months.

OCT 2011

Counterparties call for 

more collateral. MF 

Global announces 

quarterly loss of 

US$191.6 million, its 

biggest ever.

OCT 2011

MF Global les for 

bankruptcy, the fth 

largest for a nancial 

institution in U.S. 

history.

NOV 2011

At least US$1.6 billion 

in customer funds 

missing and 

apparently 

misappropriated.

JUL 2011

Portugal 

Downgraded

APR 2011

Ireland 

Downgraded

OCT 2011

Spain 

Downgraded

NOV 2011

Belgium 

Downgraded

SEP 2011

Italy

Downgraded

JAN - MAR 2011

CRO replaced. New CRO reports to the

COO instead of the CEO.

Exposure to European Sovereign Debt

Markit CDS SOVX 5 Year Daily European 

Index Spread (Western Europe Region) Series 

4,5,6

Sources: Markit Group Ltd., Haver Analytics, Congressional hearings, news reports, OFR analysis
80 2014 OFR Annual Report

time (see Segel and Heer, 2010). The depiction illustrates the 

story line by placing events on a horizontal timeline, accompanied by renderings of two key time series, credit default 

swap (CDS) spreads on European sovereign debt and MF 

Global’s exposures, measured against separate vertical axes.

Narrative visualizations like Figure 4-4 are well suited for 

case studies or forensic timelines, where the sequence of 

individual decisions, actions, and events plays a central role. 

These visualizations are less useful in supporting unbiased 

supervisory decision-making, because they emphasize the 

roles of particular firms and people in the sequence of 

events. Visualizations that support policymaking should 

focus viewers’ attention on the broader goals and principles 

that underlie policy choices.

Task Orientation

The foundation for an effective visualization is a clear identification of the needs and tasks of the intended audience.

Figure 4-5 illustrates the importance of tailoring the visualization to the context of its use. A recent OFR working 

paper identified four high-level tasks for visualizations 

that support financial stability monitoring (see Flood and 

others, 2014):

• Sense making. Integrating noisy perceptions into a 

coherent understanding (making sense) of a situation.

• Decision making. Choosing from a set of available 

options.

• Rule making. Creating formal processes or boundaries to constrain behavior.

• Transparency. Sharing information with others in an 

accessible way.

Figure 4-5 shows two different perspectives on equity 

markets, highlighting the distinction between decision 

making (on the left) and sense making (on the right). As a 

rule, visualizations to support decision making should avoid 

suggesting narratives that might bias a decision one way or 

another. For example, the left panel shows an excerpt from 

a briefing book for a meeting at the Federal Reserve Board 

to determine monetary policy (see Board of Governors, 

2008). The chart is a familiar time-series plot — concise, 

smoothed, and uncluttered — of the recent behavior of a 

single equity-market index. The uncertain future is shaded 

in gray, and the shift in projections since the last meeting 

provides context.

In contrast, the right panel of Figure 4-5 is a parallel-coordinates plot over eight years summarizing hundreds of 

thousands of monthly observations on roughly the same set 

of stocks as in the line graph on the left side of the figure

(see Alsakran, Zhao, and Zhao, 2010; and Inselberg, 2008). 

Each observation is plotted on five dimensions, represented 

as five parallel vertical axes: return, price, volume, shares 

outstanding, and industrial classification. For example, the 

distribution of volume (the middle axis) is highly skewed, 

with a large cluster of the low-volume stocks (the bright 

green patch at the bottom) and a long tail of higher volume 

stocks spreading upward. Many of the low-volume stocks 

are clustered in three industry categories, indicated by the 

three green pathways extending to the rightmost axis.

A standard parallel-coordinates plot would connect each 

data point across the five parallel axes with a distinct line 

(see Figure 4-7C). In the right panel of Figure 4-5, however, the number of observations is much higher, making 

the display of information extremely dense. For that reason, 

Figure 4-5. Examples of Task-Oriented Visualization 

Decision making:

A familiar time-series plot

Sense making:

A large number of multidimensional data points in a single picture

Sources: Board of Governors (left); Alsakran, Zhao, and Zhao, 2010 (right)
81 Research on Financial Stability

the authors chose a custom mosaic coloring scheme to 

convey the varying density of the histogram rather than 

rendering individual data points. The intense information 

density of the graph is typical of sense-making plots, which 

are often not immediately intuitive as a result. This is not a 

shortcoming, but simply a by-product of capturing as much 

information as possible in a single image.

Data Selection

Selecting information that supports the identified task can 

be a challenge for financial stability analysis because data 

gaps may prevent direct observation of emerging problems. 

For example, historical context may be lacking for new 

financial products or trading venues.

The researcher must choose the appropriate observation 

frequency, level of aggregation, and data attributes or 

dimensions for the scope of analysis — for example, the B

Figure 4-6. Different Perspectives on One Market

Visualizations A and B show who trades with whom and who 

owns certain risks. The hive plot (C) incorporates both types 

of risk into a single visualization

A. Top CDS Participants

. Connecting Dealers to Risks

C. Interacting Risk Networks

Dealer A

Dealer B

Dealer C

Dealer D

Dealer E

Dealer F

Dealer G

Dealer H

Dealer I

Dealer J

COUNTERPARTIES

Contract A

Contract B

Contract C

Contract D

Contract E

Contract F

Contract G

Contract H

Contract I

Contract J

REFERENCE ENTITIES

Nondealer

Reference 

Entity

Dealers

Note: These figures do not use actual market data and are for representative purposes.

Source: OFR analysis

set of firms or transactions to consider. Analyzing systemic 

threats to treat one component of the system in isolation 

is not likely to yield sound results. The analyst needs to 

understand the relationships between that component 

and the other parts of the system. Getting all of this right 

may require many versions of data selection and charting 

choices.

For example, OFR researchers are experimenting with interactive visual analytics to illuminate the workings of the CDS 

market (see Haynes, Paddrik, and Rajan, forthcoming).

Figure 4-6 shows three views of CDS risk exposures. The 

top panel, Figure 4-6A, is a traditional node-link diagram 

(see Ghoniem, Fekete, and Castagliola, 2005) depicting the 

network of bilateral trade, using link thickness to indicate 

the proportion of bilateral trading volume and color (green 

or red) for dealers’ net long or short activity. Although this 

overview gives a clear sense of the central nodes in the network, it does not reveal the details of the contracts traded. 

The center panel, Figure 4-6B, is a bipartite (two-part) 

diagram (see Brandes, Raab, and Wagner, 2001) depicting 

connections from buyers and sellers on the left to the 

reference entities of the CDS contracts they trade on the 

right. The thickness of the lines indicates the net position a 

buyer or seller has against a specific reference entity; green 

entities have net long exposures and red entities have net 

short exposures. Here only the largest few exposures for 

the selected broker-dealers are included, prioritizing the 

holdings likely to have the largest risk impact. Although this 
82 2014 OFR Annual Report

overview illustrates exposures to specific underlying credits, 

unlike the node diagram it does not expose the details of 

whom they were traded with.

Much like network metrics, these visualizations focus 

primarily on a single type of risk, though more comprehensively than in individual metrics. The node-link diagram 

depicts existing counterparty risk between entities and the 

bipartite diagram summarizes held reference entity risk. 

Both visuals efficiently capture and usefully communicate 

relative and absolute importance to a user. Because the 

market for credit contracts involves a diverse set of risk 

sources, visuals can be even more effective than simple 

numerical metrics in communicating the intricate relationships involved. 

Figure 4-6C displays detailed interactions among different 

types of participants for a set of CDS transactions on a particular reference entity. This type of network visualization is 

called a “hive plot” (see Krzywinski and others, 2012). The 

hive plot includes two elements of traditional network diagrams, vertices and edges, representing firms and financial 

transactions. However, additional information is included 

in the hive diagram, which highlights some of the peculiar 

aspects of the CDS transactions.

The hive plot groups vertices on four separate axes, two 

of which are paired. The north axis includes all nondealer institutions which participate in the credit market. 

Positioning along the axis is determined by the entity’s net 

outstanding CDS exposures. The southeast axis includes all 

credit reference entities, distributed along the axis relative 

to the entity’s total gross outstanding CDS exposure. Note 

that some reference entities may overlap with financial firms 

located on the other three axes. Finally, the two southwest 

axes include all of the active dealers. Each dealer is included 

twice — once on each of the paired axes.

The division of financial entities into three axes, two 

of which are equivalent, tries to mirror traditional credit 

transaction activity. As in the previous network diagrams, 

transactions are usually executed first between nondealer 

entities and dealers and then between dealers. 

The hive diagram attempts to reflect this two-stage level of 

credit intermediation, allowing a user to concentrate on the 

first or the second set of transactions, or institutions, with 

little distraction. It also broadens the scope of interest from 

an individual reference entity to a whole suite of entities to 

summarize exposure or concentration across sectors. This 

allows for a much fuller, and much more product specific, 

depiction of risk transfer between and across entities that 

should not all be considered the same.

Visual Rendering

Visual renderings of the data should convey with appropriate visual emphasis the scope (type and number), 

granularity (specific attributes and level of detail) and interconnections (relationships and their attributes) for the data 

points. The possibilities for combining visual elements into 

a coherent graphic are infinite, and a deep understanding of 

the data and the tasks to be performed is essential.

Figure 4-7 illustrates some of the possibilities for visual 

rendering in the context of financial stability maps. The top 

panel, Figure 4-7A, is a “heat map” from the OFR’s 2013 

Annual Report (see Figure 2-6 in Section 2.2 for a description of the five main categories here). 

In Figure 4-7B, distances from the center correspond to 

measured attributes. It is a type of radial chart known as a 

spider chart, taken from the International Monetary Fund’s 

Global Financial Stability Report (see IMF 2014b; Dattels and 

others, 2010). The two loops — red and green — each 

represent the state of financial stability as of the publication 

of an issue of the IMF stability report. 

The three examples in Figure 4-7 capture the multifaceted 

nature of threats to financial stability by simultaneously 

depicting high-level measures in multiple dimensions. The 

charts group the dimensions in higher-level categories — 

five in the heat map, two in the spider chart, and five in the 

parallel coordinates plot.

Other techniques, such as Sarlin’s (2013) “self-organizing 

financial stability map,” avoid presenting all the data and 

dimensions simultaneously (not shown). He clusters many 

data points into a smaller number of representative clusters 

and projects high-dimensional data (data with many variables) into a two-dimensional plane. The result is abstract 

but condenses a large amount of information into a single 

two-dimensional picture.

Next Steps in Visualization Research

One direction for our future research involves tailoring 

new visualizations to specific use cases in macroprudential 

analysis. The OFR paper by Haynes, Paddrik, and Rajan 

(forthcoming) is one example. In that work, we combine 
83 Research on Financial Stability

Figure 4-7. Examples of Financial Stability Maps 

A. Heat map indicates risk by color

B. Spider charts indicate risk by distance from center

C. Parallel coordinates plot indicates risk by the position on 

each vertical axis

y

idity

s

Less Risk More Risk

2013 2014 2013 2014

Funding Stress

Market Liquidity

Balance-Sheet Liquidity

Intermediation

Corporate Sector

Households

Banking Sector

Joint Distress

Asset Market Interdependence

Interconnectedness

Volatility

Interest Rate Risk

Positioning

Economic Activity

Sovereign Risk

Ination Uncertainty

External Sector

SUBCATEGORIES

(underlying indicators)

RISK CATEGORY

MACRO

CONTAGION

MARKET

FUNDING/

LIQUIDITY

CREDIT

MACRO

Economic ActivitySovereign RiskInation UncertaintExternal Sector

MARKET Volatility Interest Rate Risk PostioningCREDIT Corporate Sector Households Banking SectorFUNDING/

LIQUIDITY

Funding StressMarket LiquidityBalance Sheet LiquIntermediation

CONTAGION Joint Distress Asset Market Interdependence Interconnectednes

High Low Risk Level

2014 2013 2011 2007 2005

experience in visualization with access to an important confidential data source from the CDS markets.

USABILITY TESTING

Usability testing is an important step in developing new 

visualizations (see Plaisant, 2004).

For example, in response to research on visual perception, 

we are experimenting with alternate renderings of the financial stability data shown in Figure 4-7. These charts present 

key facts in a concise and attractive way, but improvements 

are possible. The use of color to convey magnitudes in heat 

maps can be problematic, in part because of differences in 

viewers’ perceptions, for example, as a result of color blindness (see Ware, 2012, chapter 4).

In addition, recent research indicates that users’ perceptions 

of magnitudes are less accurate and slower when data are 

shown radially, in contrast to the standard layout, which 

uses perpendicular coordinates. For an overview of radial 

visualization, see Draper, Livnat, and Riesenfeld (2009). For 

an analysis of potential weaknesses, see Diehl, Beck, and 

Burch (2010).

INTERACTIVE VISUALIZATION

Another area where the OFR is exploring new possibilities 

is interactive visualization. A number of organizations publish interactive online tools for exploring data of macroprudential interest (see IMF, 2014a; World Bank, 2014; FRB-St. 

Louis, 2014; ECB, 2014). One of the simplest forms of 

interactivity allows the user to select dates and filter out (or 

in) particular data. Another common type of interactivity 

gives the user “details on demand” (see Shneiderman, 1996) 

in the form of temporary pop-up information triggered by 

mouse-overs or similar user actions. The hive-plot visualizations of Haynes, Paddrik, and Rajan (forthcoming) incorporate this feature.

A recent OFR working paper by Flood and others (2014) 

discusses the other extreme of interactivity, known as 

“visual analytics.” In visual analytics, a software application 

recalculates and redisplays new derived results in response 

to user choices. The recalculation must occur quickly 

to avoid distractions in user attention. The OFR is also 

developing a “RiskMapper” prototype (Lemieux and others, 

forthcoming) to allow analysts to explore the interactions 

of a range of systemic risk measures with different rules for 

portfolio selection.

Sources (top to bottom): OFR analysis, IMF (2014b), OFR analysis
84 2014 OFR Annual Report

Figure 4-8. Central Counterparty Share of 

Transactional Volumes

CCPs’ market share has grown, measured by transactions

Sources: Depository Trust & Clearing Corp., OFR analysis

4.4 Credit Markets

OFR’s financial stability research on CDS markets has four 

goals: 

1. understanding market characteristics, 

2. identifying risks, 

3. alyzing risk management practices, and 

4. 

This section describes that research and then focuses in 

detail on what we have learned about the growing role that 

central counterparties play in these markets.

Market participants use credit default swaps to buy and sell 

exposure to the default of underlying reference entities, 

which could be governments, corporations, or credit-linked 

securities. The amount of the exposure in a CDS contract 

is called its notional value. Since the financial crisis, reforms 

have sought to make these markets more transparent and to 

require standardized CDS contracts to clear through central 

counterparties (see Section 3.5).

Central counterparty (CCP) clearing activity continued to 

grow in 2014 and is nearing two-thirds of transactional 

volume in CDS markets on a notional basis (see Figure 

4-8), reflecting regulatory objectives and market concerns 

following the financial crisis.

CDS Research Agenda

The OFR is working with data from the Depository Trust 

& Clearing Corp. (DTCC)’s Trade Information Warehouse 

on CDS positions and transactions to understand the risks 

these products may pose and to analyze the evolving role of 

CCPs. DTCC data provide insight into market concentration, size, and distribution of exposures by reference entity, 

counterparty, and date. A clear understanding of participants, their characteristics, and their behavior is necessary 

for evaluating the effectiveness of policy tools and monitoring potential risks.

UNDERSTANDING THE ROLE OF INFORMATION IN CDS 

MARKETS

OFR researchers are interested in understanding the flow 

of information in markets and how this flow drives the 

behavior of market participants. We consider sources of 

information available to participants and the relationship 

between information, market characteristics, participants’ 

behavior, and the effects on markets. Sinha and Dong 

0.00

0.25

0.50

0.75

1.00

2010 2011 2012 2013 2014

Smoothed moving average ratio

Gross ratio

OFR researchers are interested in 

understanding the flow of information 

in markets and how this flow drives 

the behavior of market participants. 

We consider sources of information 

available to participants and the 

relationship between information, 

market characteristics, participants’ 

behavior, and the effects on markets.
85 Research on Financial Stability

(2011) demonstrated the different roles played by options about the implications for financial stability? Does the 

volatility of hedging portfolios threaten the solvency of 

clearing members and the CCPs they constitute? If so, 

how should policymakers balance clearing member solvency with inclination to hedge risks? OFR researchers are 

using data supplied by DTCC on counterparty exposures 

between clearing members and with CCPs, along with price 

histories of cleared reference entities, to try to answer these 

questions.

versus equity markets in price discovery. Understanding the 

role of CDS markets relative to others (for example, bonds, 

equities, options) during different market conditions will 

help drive a better understanding of the market’s functions 

and susceptibilities.

Using DTCC data going back to 2010, OFR researchers are 

studying how transaction sizes affect prices and liquidity 

under different market conditions. The 2012 so-called 

London Whale incident, in which an investment bank lost 

billions on large positions in the CDS market, demonstrated 

the effect that large transaction sizes and information flow 

can have on shifting market values of CDS (see U.S. Senate, 

2011).

We are also looking at how external factors, such as regulations and news about firms, affect CDS prices and market 

depth. In addition, we are examining tools that will help us 

understand how different market participants originate and 

react to information. For example, we are asking whether 

customers of smaller broker-dealers routinely trade after 

customers of larger firms. We are also looking for patterns 

in the timing of market-moving trades (such as between 

morning and afternoon) that could interest policymakers.

ANALYZING THE RISKS OF CLEARING-MEMBER 

PORTFOLIO SELECTION

Hedging of credit exposures is increasingly cleared through 

CCPs, supported by financial regulatory reform. This development leads to research questions: How might dealers’ 

outside exposures result in centrally cleared hedging activities? What are the implications of this connection?

OFR researchers and collaborators are analyzing the 

dynamics of clearing members’ portfolio selection within 

CDS markets. OFR researchers are asking how individual 

clearing members’ hedging portfolio values evolve and how 

the growth in central clearing presents new considerations 

for risk management. Central clearing changes the risk 

profile of cleared hedging portfolios and presents a possibly 

greater level of risk for clearing members. This research 

borrows from foundational work of Eisenberg and Noe 

(2001), which specifies systemic risk in interbank networks, 

but also proposes specific linkages and considers the feedback effect of negative shocks in the spirit of Acemoglu, 

Ozdaglar, and Tahbaz-Salehi (forthcoming).

As central clearing volumes increase and fulfill the intentions of financial reform, should policymakers be concerned 

IDENTIFICATION OF COUNTERPARTY RISK 

MANAGEMENT

Counterparty risk management is important in derivatives 

markets because in periods of crisis, parties to financial contracts may come under stresses that jeopardize their ability 

to deliver on contractual obligations. Counterparty failure 

can threaten financial stability when hedges fail and market 

participants take losses. Consequently, counterparties must 

manage their exposures to each other by adjusting prices 

to account for contractual risk, setting aside risk buffers to 

offset costs of counterparty loss, and imposing limits on 

exposure to risky counterparties.

Counterparty risk management is a concern across a 

number of asset classes and markets. OFR researchers are 

studying counterparty risk within credit derivatives markets, 

where buyers and sellers face each other in bilateral CDS 

contracts. Buyers of CDS protection are concerned about 

default of the protection seller over the life of the contract. Sellers of CDS protection are concerned with contractual failure of protection buyers to pay predetermined 

premiums.

Recent academic work on counterparty risk has focused on 

how buyers and sellers price protection as a way to account 

for counterparty risk. Arora, Gandhi, and Longstaff, (2012) 

studied prices at which dealers are willing to sell protection 

and whether the prices varied with dealers’ credit risk. More 

specifically, they found that the price at which dealers are 

willing to sell protection falls as the market perceptions of 

dealer default increase. Although the relationship was small, 

it was significant. 

OFR researchers are studying whether transactional history 

corroborates the academic findings in indicative quotes 

and whether other mechanisms are employed in counterparty risk management. If counterparty risk is a commonly 

shared concern in OTC markets, what other ways besides 

pricing are used to manage contractual risk? Do protection 

buyers reduce the initial margins they pay to protection 
86 2014 OFR Annual Report

sellers when sellers are more risky? And do buyers set risk 

limits based on notional or mark-to-market exposures with 

protection sellers?

DEVELOPMENT OF MONITORING TOOLS

OFR researchers are also developing monitoring tools to 

make complex data on exposures and transactions accessible 

to policymakers. During the past year, we have focused on 

understanding developments in counterparty risk, distribution of credit risk, and overlap of counterparty and credit 

risk.

Our ongoing research highlights counterparty risk and 

the risks of a central counterparty clearinghouse, which 

helps clear and settle market transactions. This role is a 

recent development in credit derivatives markets and lent 

momentum by the Dodd-Frank Act. Central clearing puts 

in place clearing requirements similar to those of futures 

markets where exchanges are connected to a proprietary 

central counterparty.

Monitoring margin requirements is critical to understanding 

how central counterparties manage risk (see Adrian, Covitz, 

and Liang, 2013). Identification of CCP counterparty 

concentration is also important, because failure of a clearing 

member may pose a systemic risk. Are central counterparty clearinghouses exposed to certain clearing members 

disproportionately by CDS product type, transactional size, 

settlement currency, or status as an end-user or dealer?

The OFR is developing market monitoring systems that 

policymakers can use to study these considerations interactively, to assess the transition to central clearing. We have 

developed additional visualization tools to understand credit 

markets at large without exposing confidential position or 

transaction information. One example is a tree map to illustrate changes in the constitution of the CDS market over 

time. The market is increasingly a reflection on sovereign 

default risk and proportionately less on corporate default 

risk, as it was before the financial crisis. Figure 4-9 illustrates 

the increase in the proportion of the market related to 

sovereign and governmental credit risk, from 9.4 percent in 

January 2010 to 18.6 percent in August 2014. At the same 

time, indexes that were issued during the financial crisis 

remain significant, in terms of notional exposures, and may 

pose liquidity risk in a period of credit deterioration.

Figure 4-9. Composition of Risks Traded in CDS 

Markets

Sovereign default risk emerges as a major market focus

January 2010

August 2014

Sources: Depository Trust & Clearing Corp., OFR analysis

FINANCIALS

CONSUMER SERVICES

INDUSTRIALS

TELECOM ENERGY TECH

BASIC MATERIALS

GOVERNMENT

9.4% OF TOTAL

UTILITIES HEALTH

CONSUMER 

GOODS

FINANCIALS CONSUMER SERVICES

CONSUMER 

GOODS INDUSTRIALS

TECH TELECOM ENERGY

BASIC MATERIALS

GOVERNMENT

18.6% OF TOTAL UTILITIES HEALTH
87 Research on Financial Stability

Figure 4-10. Cleared Positions as a Percentage of All 

Outstanding CDS Positions

CCPs’ market share has grown, measured by outstanding 

positions

Source: Depository Trust & Clearing Corp., OFR analysis

The Evolving Role of CCPs

The Dodd-Frank Act mandated that clearing of the most 

liquid over-the-counter (OTC) derivatives contracts would 

migrate to central counterparty clearinghouses. CCPs are 

expected to reduce systemic risk through centralized netting 

of exposures and separation of portfolio risk management 

from counterparty risk management. However, some policymakers and market participants are concerned about the 

potential concentration of risk in CCPs (see Chapter 3).

A CCP should always be able to net risk more effectively 

than dealers can in the bilateral over-the-counter market. For 

example, assume that three dealers transact business in the 

bilateral market with no central counterparty. There is one 

type of CDS contract on Company A. Each dealer sells to 

the other two dealers $10 million of protection on Company 

A in the CDS market. After three periods, each dealer is a 

buyer and seller of credit risk — in other words, they all have

a “net zero” risk exposure to Company A. But counterparty 

exposures remain. If Company A defaults, each dealer is 

obliged to deliver on its protection sale and expects to be 

paid on its protection purchase. For any dealer that fails, 

another dealer purchasing protection is at a loss. This market 

structure creates a chain of dependencies, which when not 

met, can spread losses throughout the system. 

When a CCP is involved, the clearinghouse acts as counterparty to every market participant. When each dealer adds up 

its contingent assets and liabilities, it counts them against the 

same contractual entity, the CCP. The CCP intermediates 

the risk for each dealer-to-dealer transaction, so by the third 

period, all the dealers have a net zero risk exposure on the 

underlying risk and to their counterparties.

CCPs net the risk of counterparties, but they are at the same 

time exposed to the risk of any counterparty’s failure. The 

CCP assumes the net liabilities of the counterparty. If the 

counterparty representing a large proportion of the CCP’s 

liabilities fails, the CCP may be unable to pay out on cleared 

CDS contracts. This risk is a potential threat to financial 

stability.

As of October 2014, U.S.-based CCPs are counterparty to 

approximately $4 trillion in notional exposure, compared to 

the roughly $12.5 trillion of gross notional exposure outstanding in the bilateral market. Because significant netting 

occurs in the cleared market, the fraction of exposures CCPs 

are counterparty to is less than the fraction of transactional 

0

10

20

30

40

2010 2 2011 012 2013 2014

0

20

40

60

2010 2011 2012 2013 2014

ICE Clear Credit

Dealers

Figure 4-11. Top Five Counterparties for All 

Outstanding CDS Positions

ICE Clear Credit has been one of the top five CDS

counterparties since 2011

Source: Depository Trust & Clearing Corp., OFR analysis
88 2014 OFR Annual Report

sector risk. As of May 2014, 21 dealers held exposures to 65 

financial reference entities through 12 cleared indexes that 

ranged from $2 billion in protection purchased to $3 billion 

in protection sold. Market participants could come to question the creditworthiness of several dealers concurrently in a 

crisis, which would present a challenge for the CCP, because 

the guarantee fund may not be large enough to settle losses 

arising from the default of several dealers at once. CCPs 

could require larger guarantee fund commitments from 

clearing members to improve safeguards in a crisis, but 

financing those buffers also increases the costs of central 

clearing in normal times. 

Clearing member exposures to financial entities also introduce wrong-way risk. For example, clearing members may 

not be sound protection sellers at a time when the financial 

sector as a whole is under stress. For that reason, CCPs’ 

protection purchases from clearing members would be least 

reliable when they are needed most. 

Clearing member risk exposure to CCPs will continue to 

grow as the movement to central clearing proceeds. CCPs 

now account more than 15 percent of gross exposure in 

the CDS market, and that share has increased during a time 

when volumes in credit derivatives fell. As the importance 

of CCPs in the credit derivatives markets grows, policymakers must pay close attention to the counterparties these 

institutions transact with and the risks they clear.

4.5 Recent and Forthcoming 

OFR Research 

The OFR has three research publication series: Working 

Papers, Briefs, and Staff Discussion Papers. These publications are designed to trigger lively discussion among 

researchers, market participants, and the regulatory community, and generate feedback that can help us achieve our 

statutory mission.

OFR Working Papers

The OFR launched the Working Paper Series in January 

2012 for staff researchers to collaborate with outside 

research experts, expanding our virtual research community and leveraging the expertise of our staff. The OFR 

Working Paper Series has sparked interest and discussion in 

the academic and regulatory communities. The papers have 

been presented at conferences and cited in the press and 

volume they clear. Nonetheless, gross notional exposures 

involving CCPs have risen rapidly (see Figure 4-10).

In 2010, dealers accounted for the top five counterparties 

and represented 55 percent of exposures by total volume 

in the CDS markets the OFR observes (see Figure 4-11). 

However, by 2011 ICE Clear Credit, LLC, had emerged 

to displace one among these top five entities. ICE Clear 

Credit’s fraction of the market has only grown since 2011. 

By 2014, ICE Clear Credit had become the largest CDS 

counterparty and has reached more than 15 percent of 

total notional exposures. The relative proportion of the 

remaining four dealer-participants has diminished. CCPs 

already constitute the greatest source of counterparty concentration in the CDS market.

One major concern for policymakers that is also a potential 

threat to financial stability is the possibility of joint default 

of a CCP and one or more clearing members. Clearing 

member default can arise in several ways, one of which is 

stresses on its credit portfolio. Some stresses may result 

from the default of reference entities clearing members are 

exposed to and which the CCP clears. 

Figure 4-12 shows that the most recent versions of the U.S. 

and European investment-grade credit indexes constitute 

the largest risk exposures among reference entities that are 

centrally cleared. These indexes are broad, but closer inspection reveals a degree of risk concentration that may concern 

policymakers. Credit indexes include reference entities from 

a variety of credit-sensitive sectors in the economy. When 

the reference entities contained in these indexes are categorized into sectors, financials emerge as the sector with the 

second highest exposure. A total of $182 billion resides in 

financials.

Policymakers are concerned about possible scenarios in 

which multiple stressed clearing members could strain the 

CCP because of common dealer exposure to financial 

Figure 4-12. Top Five Reference Entities for Cleared 

CDS Indexes, January 2010 to May 2014

Source: Depository Trust & Clearing Corp.

CDX.NA.IG.9 $179,113,684,096 

CDX.NA.IG.19 $123,208,324,318 

ITRAXX EUROPE SERIES 20 $106,620,317,497 

CDX.NA.IG.22 $96,953,694,900 

ITRAXX EUROPE SERIES 21 $92,919,341,812 
89 Research on Financial Stability

types of asset managers: (1) cash pools searching for 

safety through collateralized cash investments, and 

(2) levered portfolio managers searching for yield 

through funded securities portfolios and derivatives. 

• A Map of Funding Durability and Risk by Aguiar, 

Bookstaber, and Wipf (2014) presented a funding 

map to illustrate the primary business activities 

and funding sources of a typical bank/dealer. The 

authors used the map to trace the paths of risk 

through four financial institutions during historical 

crises and to identify gaps in data needed for financial stability monitoring. They also introduced the 

concept of “funding durability,” defined as the effective term of funding amid signaling and reputational 

considerations during periods of stress.

• The Application of Visual Analytics to Financial Stability 

Monitoring by Flood and others (2014) presented an 

overview of visual analytics — the science of analytical reasoning enhanced by interactive visualizations 

produced by data analytics software — and discussed 

its potential benefits for monitoring financial stability 

(see Section 4.3).

• Competition in Lending and Credit Ratings by Ahmed 

(2014) related corporate credit rating quality to competition in lending between the public bond market 

and banks. The author showed that the quality of 

credit ratings plays an important role in financial stability because strategic behavior by the rating agency 

in an issuer-pays setting dampens the influence of 

macroeconomic shocks. The paper also explained 

the use of informative unsolicited credit ratings to 

prevent unrated bond issues, particularly during 

good times.

OFR Briefs

OFR Briefs are less academic than working papers and 

allow us to describe our research to a broader audience. The 

first brief to be published later in 2014 is Systemic Importance 

Indicators for Large U.S. Bank Holding Companies: An Overview of 

Recent Data by Allahrakha, Glasserman, and Young (forthcoming) which uses a new dataset collected by the Federal 

Reserve to evaluate the systemic importance of the largest 

U.S. bank holding companies. The authors compared the 

banks’ scores on several measures of systemic importance 

and showed that a financial connectivity index introduced in 

an earlier OFR working paper can be useful for measuring 

and monitoring interconnectedness. Overall, their analysis 

academic literature. About half have also been published in 

academic journals.

Through November 2014, the Office had published 21 

working papers, including the following nine since our last 

annual report:

• Effects of Limit Order Book Information Level on Market 

Stability Metrics by Paddrik and others (2014a) used 

an agent-based model of the limit order book to 

explore how the levels of information available to 

participants, exchanges, and regulators can be used 

to improve our understanding of the stability and 

resiliency of a market (see Section 4.2).

• Hedging Market Risk in Optimal Liquidation by Monin 

(2014) explored the optimal strategy for a financial 

institution seeking to sell a large block of securities. 

In these situations, an institution would attempt to 

minimize the price impact of the large sell order by 

spreading it out over time. The paper describes the 

optimal strategy for hedging the resulting market 

risk.

• Structural GARCH: The Volatility-Leverage Connection

by Engle and Siriwardane (2014) proposed a new 

model of volatility in which financial leverage 

amplifies equity volatility by what the authors call the 

“leverage multiplier.” GARCH stands for “generalized autoregressive conditional heteroscedasticity.” 

The model estimates daily asset returns and asset 

volatility.

• Design of Risk Weights by Glasserman and Kang (2014) 

investigated the design of risk weights used to set 

minimum levels of regulatory capital for banks and 

introduced a formula for regulators to set weights by 

analyzing banks’ portfolios.

• An Agent-Based Model for Financial Vulnerability by 

Bookstaber, Paddrik, and Tivnan (2014) developed 

an agent-based model that uses a map of funding and 

collateral flows to analyze the vulnerability of the 

financial system to fire sales and runs (see Section 

4.2).

• Shadow Banking: The Money View by Pozsar (2014) 

presented an accounting framework for measuring 

the sources and uses of short-term funding in the 

global financial system. The paper also introduced a 

dynamic map of global funding flows to show how 

dealer banks emerged as intermediaries between two 
90 2014 OFR Annual Report

shows the need for monitoring multiple aspects of systemic 

importance.

Future briefs may:

• profile specific financial stability metrics and monitoring tools; 

• provide technical primers or reference guides on 

key topics, such as secured finance transactions and 

high-frequency trading; 

• offer a digest for a broader audience of the OFR’s 

published academic research; 

• summarize ongoing research programs; 

• describe the OFR’s progress in addressing gaps in 

data for financial stability monitoring; and

• describe and promote progress on the implementation of financial data standards.

OFR Staff Discussion Papers

OFR Staff Discussion Papers are academic papers by the 

OFR research staff that contribute to our understanding 

of financial markets, financial data, and financial institution risks. These topics are the building blocks of financial 

stability analysis. The papers may be preliminary versions 

of work intended for the OFR Working Paper Series 

or research papers intended for submission to external 

academic publications in economics or finance. Staff 

Discussion Papers in 2014 included the following:

• Clustering Techniques and their Effect on Portfolio Formation 

and Risk Analysis by Lemieux and others (forthcoming) illustrated how the choice of a clustering 

technique — the method used to group similar data 

objects into clusters — in a large financial dataset 

can affect analysts’ perceptions of the riskiness of 

different asset portfolios. The authors argued that a 

poor choice of technique could result in misinterpretations of the data and adversely affect the quality of 

financial stability analysis.

• Trade Credit and Cross-Country Predictable Firm Returns

by Albuquerque, Ramadorai, and Watugala (2014) 

investigated whether trade credit links between firms 

are an important factor in predicting returns in international equity markets. The authors found that the 

propagation of shocks across borders from customers 

to suppliers via this mechanism is stronger when the 

availability of credit is lacking, such as during financial crises.

• A Flexible and Extensible Contract Aggregation Framework 

(CAF) for Financial Data Stream Analytics by Ball and 

others (2014) presented a framework that uses the 

financial contract as the common denominator to 

enable financial data integration and aggregation 

from a wide range of sources to support financial 

stability monitoring.

• The Role of Visual Analysis in the Regulation of Electronic 

Order Book Markets by Paddrik and others (2014b) 

described visualization techniques to help financial 

stability analysts understand investor behavior in electronic markets (building on the agent-based model of 

the order book described in Section 4.3). The authors 

argue that the prevalence of automated trading and 

the growing incidence of “flash crashes” highlight 

the need to understand not just completed trades but 

the underlying details of order flow and the evolving 

order book. The paper proposed visualizations to 

help with surveillance and enforcement and also to 

help academics interpret the data in a manner that can 

be conveyed to nonexperts.

• On the Optimal Wealth Process in a Log-Normal 

Market: Applications to Risk Management by Monin 

and Zariphopoulou (2014) described a technique for 

evaluating individual investors’ risk preferences based 

on their stated willingness to lose specific amounts, 

as expressed through the value-at-risk and expected 

shortfall measures. Such models provide a direct link 

between risk management and the dynamics of the 

financial system.
91

5

Advancing Data Standards

ADVANCING 

DATA STANDARDS 

The OFR has a mandate to promote and develop financial data 

standards that are critical for improving the quality and usability of 

those data. To fulfill this mandate, we are encouraging regulators around 

the world to require the use of existing standards, such as the Legal 

Entity Identifier (LEI), in regulatory reporting. We are also leading in the 

development and implementation of new standards, such as identifiers 

for products and transactions.

5.1 Data Standards Agenda

Data standards are basic building blocks for creating quality financial data needed for accurate reporting, 

quality analysis, and performance assessment. They define precisely who is involved in a financial transaction, what securities or other products are traded, how market participants report their transactions, 

and how listed companies report their earnings and balance sheets (see What are Financial Data 

Standards?). 

The financial crisis illustrated what can happen when standards are 

weak or nonexistent and investors are unable to track losses, for 

example, from the mortgage market to their mortgage-linked securities

or calculate their exposures to failing counterparties.

Without appropriate data standards, the quality of financial data will 

suffer, market participants and policymakers will be misinformed, and 

markets will function less efficiently. Data standards are essential to 

create consistent, comparable, and reliable data. Standards help companies share data with investors, investors compare data across companies, and regulators combine and aggregate data to track market trends 

and monitor financial stability.

Why have standards not become ubiquitous? Standards are often a 

classic public good, with costs borne by a few and benefits accruing over time for many. To solve the 

collective action problems created by these disincentives, government organizations such as the OFR must 

take a leadership role.

That is why Congress mandated the OFR to standardize data reported and collected on behalf of the 

Council and the public. The OFR can play any of three roles in a standards project: lead, collaborate 

with a regulatory agency, or participate in organizations that work through consensus (see Figure 5-1).

This chapter examines trends in information standards and then discusses the four important data standards initiatives in which the OFR is playing one or more of those roles:

We provide support and 

, leadership in developing, 

using, and integrating 

data standards that 

help investors and 

regulators by reducing 

data collection costs 

and facilitating data 

aggregation analysis.
92 2014 OFR Annual Report

What Are Financial Data 

Standards?

Financial markets rely on data standards 

to function smoothly. 

Entity identifiers identify specific legal entities 

such as parent companies, subsidiaries, and off-balance-sheet vehicles.

Instrument identifiers identify financial instruments 

like stocks, bonds, and loans. For example, there is 

the International Organization for Standardization 

(ISO) standard for individual securities known as 

the International Securities Identification Number. 

The project to create a universal loan identifier is 

another example.

Product identifiers provide commonly accepted

definitions of products like “equities” and “swaps.”

Standards for financial and business reporting 

describe information reported by companies to 

the public on financial disclosures and regulatory 

reports. An important initiative is XBRL, or eXtensible Business Reporting Language, which enables 

free and open exchange of business and financial 

information. 

Transaction standards describe information used in 

financial transactions. For example, the Mortgage 

Industry Standards Maintenance Organization 

developed a language that enables consistency in 

describing mortgage transactions.

Legal Entity Identifier (LEI). The LEI project to 

precisely identify each legal entity involved in a financial 

transaction reached two important milestones in 2014. 

First, approximately 300,000 LEIs had been issued as of 

September 30, 2014, triple the count a year ago (see FSB, 

2012a). Second, the Global LEI Foundation and its new 

board began to assume operational management of the LEI 

system in June 2014. The foundation oversees 19 local operating units authorized to issue LEIs, up from five in 2013.

But widespread use of the LEI by both the public and 

private sectors will be the true measure of its success (see

Section 5.3). In the United States and Europe, LEI use has 

been driven primarily by swaps regulation. The OFR urges 

all financial regulators to mandate use of the LEI in all 

regulatory reporting, beginning with large, complex financial 

companies and market participants.

Standards for Derivatives Markets. The OFR is working 

with the CFTC and other regulators to improve financial 

reporting standards for swap data repositories (SDRs). 

SDRs were mandated by the Dodd-Frank Act to promote 

transparency in over-the-counter derivatives markets (see

Section 5.4). 

Internationally, the OFR is assisting the CFTC and its 

global peers in developing shared taxonomies for categorizing derivatives products for analysis and regulatory action. We have developed a set of principles and 

requirements for derivatives product identifiers, which we 

continue to fine-tune in collaboration with international 

regulators. The OFR also contributed to a report on how 

to aggregate data on derivatives, released in 2014 by the 

Financial Stability Board, an international group of finance 

ministries, market regulators, and central banks. We continue to work on these initiatives.

Universal Loan Identifiers. The OFR is providing technical support to the CFPB and other regulators to create a 

universal mortgage loan identifier to promote transparency, 

data aggregation, comparability, and analysis in the home 

mortgage market. The Dodd-Frank Act authorized the 

CFPB to collect more data about individual mortgage loans 

and to mandate that entities reporting data under the Home 

Mortgage Disclosure Act (HMDA) provide a universal loan 

identifier for each loan or application that they are required 

to report. The OFR published a working paper on this 

subject in late 2013 (see McCormick and Calahan, 2013) 

and the CFPB issued a proposal in July 2014 to require a 
93 Advancing Data Standards

5.2 Trends in Data Standards

There is growing acceptance among regulators and market 

participants about the need for financial data standards. 

Our standardization work has shown that early collaboration with industry to develop standards is a key step before 

launching new data collections. Regulators should also 

identify and adopt best practices to manage datasets that are 

rapidly growing in size and complexity.

As the volume of financial data increases, so does the need 

for data standards.

Data standards can be driven by industry, government, or 

both. Private companies and industry groups often reach 

consensus on standards without government involvement 

when benefits are clear.

However, consensus on standards may be difficult to 

achieve when costly upfront work is required or where 

proprietary interests exist. In many cases, regulators work 

with industry groups on voluntary standards that build 

on existing industry practices and reflect industry input. 

Early collaboration with industry can be critical for success. 

Regardless of who creates a data standard, the industry will 

encourage its use if it gives them a tangible benefit.

universal loan identifier in data reported under HMDA (see

Section 5.5).

Reference Databases. The Dodd-Frank Act requires 

the OFR to prepare and publish reference databases for 

financial entities and financial instruments. The global LEI 

system will meet the requirement for an entity reference 

database (see Section 5.6). The OFR is examining ways 

that open-source algorithms, symbologies, and collections 

of codified background knowledge could be used to build 

a cost-effective and useful reference database for financial 

instruments.

The diversity and complexity of data reporting mandates 

among U.S. financial regulators makes agreement critical on 

standards for data collections. As technologies improve, regulators increasingly seek to collect raw data that can be analyzed more easily than data reported in text, spreadsheets, 

and document formats. The OFR works with other financial 

regulators to develop standards whenever appropriate.

Identifiers

Universal 

Loan (ULI)

✔

✔

✔

✔

✔

✔

✔

✔

✔

✔

Unique 

Product (UPI)

✔

✔

✔

✔

✔

✔

✔

✔

✔

✔

Unique 

Transaction 

(UTI)

✔

✔

✔

✔

✔

✔

✔

✔

✔

✔

Legal Entity 

(LEI) 

✔

✔

✔

✔

✔

✔

✔

✔

✔

✔

Improve Council data collection 

Support financial stability analysis

Promote regulatory data sharing 

Promote financial stability

Uniform characteristics

Large volume of data to exchange

Sufficient info about data

Multilateral exchange

Ease of execution

Leader

Partner

Advisor

Is it within the

OFR’s mission 

and scope?

Does it meet 

standardization 

criteria?

What is the 

OFR’s strategic 

role?

Figure 5-1. How the OFR Sets Data Standards Priorities 

The OFR considers three issues when setting data standards priorities 

Source: OFR analysis
94 2014 OFR Annual Report

be difficult to analyze. For example, does “4/6/2010” mean 

April 6 or June 4 in 2010?

Without a standard format, some companies may report 

their entire address in one text field, while others report 

street address, city, state, and ZIP code in separate fields. 

When planning a data collection, regulators should examine 

the standards available, evaluate potential uses of the data, 

and choose the most appropriate standards.

Standardized Content is information produced by creating 

a finite list of acceptable data choices that can be entered in 

a particular field. This requirement reduces the data cleaning 

needed to remove inaccurate, corrupt, or inconsistent information from a dataset and facilitates comparison among 

datasets. For example, requiring a state name to be entered 

as a two-digit postal code eliminates the use of unstructured 

text such as “Calif ” or “California.”

Standardization also makes combining datasets easier. In the 

postal code example, standard content creates a one-to-one 

link in which “CA” always equals “California.” Without that 

standard, combining datasets requires matching a variety of 

state abbreviations and spellings to “CA.”

5.3 Legal Entity Identifier

The OFR is a leader in the global initiative to develop, 

implement, and encourage industry adoption of a unique 

Legal Entity Identifier for financial market participants. The 

LEI — a 20-digit alphanumeric code that precisely identifies parties to financial transactions — will help market 

participants and regulators in many ways. Regulators around 

the world are using a combination of regulatory mandates, 

international regulatory coordination, and consensus standard setting to promote the use of the LEI.

The need for a common global entity identifier became 

apparent in 2008, when market participants and their regulators were unable to gauge exposures to Lehman Brothers 

and its many legal entities. The LEI, once fully implemented 

and adopted worldwide, can help address these problems by 

acting as a common reference point — a unique, universally recognized code for every party in financial markets, 

including every legally distinct subsidiary or affiliate (see 

Evaluating the Benefits and Costs of the LEI). The 

private sector and international regulators have also collaborated to create a standard format for LEI data files so the 

issuers of LEIs — known as local operating units (LOUs) 

— can easily share and compare LEI data.

Financial data standards and collections must keep pace 

with technology and market developments. Data requirements are continually becoming more demanding in scope, 

size, and complexity. For example, some high-volume, 

high-velocity data, such as data generated by high-frequency 

trading, are based on market orders that are executed in 

fractions of seconds. These types of very large and complex 

datasets might be described as “big data,” but even datasets 

that are merely large can pose technical and organizational 

challenges.

As financial transactions become more complex, market 

participants often recognize the need to agree on precise 

and consistent definitions in a contract. An example is 

the Financial products Markup Language (FpML) used in 

derivatives markets. Different technologies are advancing 

to address the need for common meanings for financial 

terms. For example, XBRL is being used to document 

accounting definitions, while the Internet standard Resource

Description Framework (RDF) is also being used to document terms in a variety of industries, including finance.

Users and owners of data standards must also keep the 

standards up to date as financial markets change. Revision 

cycles need to match the speed of financial and technological innovation. A standard that falls behind and no longer 

meets the needs of its users is likely to be abandoned.

Implications for Data Collection

Financial data standards contain specific definitions, formats, and content that lead to accurate and consistent data 

understood by all users.

Standardized Definitions used across the public and 

private sectors improve the value of data for analysis. When 

key terms are not clearly defined, financial analysts are 

unable to accurately interpret and compare data, resulting 

in a lack of confidence in the results. Standards are particularly needed when data include a common term that can be 

understood in various ways. Ambiguity in simple terms such 

as “delivered” or “annual” can be particularly troublesome.

Standardized Formats help analysts aggregate and compare data, and automate processes for storing, reporting, 

and processing data. It is important to consider how data 

may be used and to apply a standard format, even to routine 

information. If calendar dates are entered in a free-form 

text field rather than in a consistent date format, they can 
Advancing Data Standards 95

Evaluating the Benefits and Costs of the LEI

The LEI has earned support from regulators 

and market participants in a relatively short 

time for two reasons: the financial crisis plai

illustrated the need for a universal identifier,

and there are no viable alternatives.

BENEFITS

nly 

The LEI will eventually allow analysts to combine and analyze 

multiple public and proprietary datasets (see Section 6.4). 

Currently, companies face a costly, labor-intensive and 

mistake-prone process to accurately align and maintain 

different identification systems as companies and relationships change. This may include manually cross-referencing 

identifiers issued by vendors, private companies, and regulators; validating legal entity information; and maintaining an 

internal system of legal entity hierarchies and networks. If all 

companies were required to report LEIs, analysts could link 

related entity reports, aiding both company risk management and government oversight.

The LEI will also help reduce or eliminate confusion about 

counterparty identification, one of the most common reasons for errors and failures in derivatives trades (see ISDA, 

2013). An International Swaps and Derivatives Association 

survey found an error rate above 10 percent in 2012 for the 

two largest categories of swaps — interest rates and foreign 

currencies (see Figure 5-2). The LEI allows financial market 

participants to know the identity of every counterparty 

throughout the life of their transactions.

the benefits of the LEI system will accrue disproportionately 

to large and complex companies facing many counterparties 

in derivatives and other markets. To address these concerns, 

a phased approach may be appropriate, with larger companies required to adopt the LEI before smaller companies.

It is premature to estimate how much the financial industry 

will save by adopting the global LEI system. Industry estimates of the annual savings range from $300 million to $10 

billion (see Chan and Milne, 2013). COSTS

To obtain an LEI in any country, a company pays an initial 

registration fee of approximately $200 (or equivalent), followed by an annual maintenance fee of approximately $100 

(or equivalent). All fees are paid to the local operating unit 

(LOU) that issued the identifier to cover its operating costs. 

Each LOU is required to share a portion of those fees with 

the Global LEI Foundation, the nonprofit group that is now 

taking over management of the global LEI system.

Some have argued that the costs of obtaining LEIs, in addition to new regulatory requirements since the financial crisis, 

represent an unfair burden for small firms and subsidiaries 

that are relatively inactive in financial markets. They contend 

In general, the LEI system is expected to improve industry 

efficiencies and reduce costs for data collection, cleaning, 

and aggregation; transaction processing; data management; 

business operations; compliance monitoring; regulatory 

reporting; research and analysis; information sharing; and 

intra- and inter-organization communication.

Another benefit of the LEI once it is more broadly adopted 

by Council authorities will be a reduction in reporting 

burden. Companies spend significant time and resources 

managing their identification systems for reporting purposes, for themselves and their counterparties. Benefits of 

the LEI will grow rapidly as more companies get one.

Figure 5-2. Trading Errors by Swap Category

(percent)

Swap traders surveyed from 2003 to 2012 ranked wrong 

names as a common error

Sources: International Swaps and Derivatives Association Operational 

Benchmarking Surveys, 2003-2012; OFR analysis. The 2013 survey did 

not include swap trading error rates.

0

2003 2006 2009 2012

6

12

18

24

30 Interest rate

Credit

Currency

Equity

Commodity
96 2014 OFR Annual Report

The global LEI initiative has made extraordinary progress 

since 2010, when the OFR issued a policy statement calling 

for the LEI and noting the potential benefits if regulators 

required its use (see OFR, 2010). The LEI Regulatory 

Oversight Committee, currently chaired by the OFR’s Chief 

Counsel, ensures that the LEI system works for the public 

good. It consists of more than 60 international members 

representing market and prudential regulators as well as 

international organizations.

In June 2014, the new Global LEI Foundation held its first 

board of directors meeting. The Swiss-based foundation 

is assuming operational management of the global LEI 

system and overseeing construction of the LEI system’s 

technology infrastructure, under the oversight of the 

Regulatory Oversight Committee. The foundation will also 

be responsible for ensuring adherence to LEI governing 

principles and standards, including the reliability, quality, and 

uniqueness of LEIs (see FSB, 2012a). The foundation plans 

a central database of LEIs that will be free to the public 

and all market participants, although privately-sponsored 

databases already exist.

The global reach of the LEI system significantly expanded 

in 2014. Approximately 300,000 LEIs had been issued to 

entities in more than 190 jurisdictions as of September 30, 

2014, up from 100,000 LEIs at the time of our last annual 

report. The number of LOUs authorized to issue identifiers 

rose from five last year to 19 on September 30, 2014. Eleven 

others were in earlier planning stages (see Figure 5-3). 

Dutch Chamber 

of Commerce

Netherlands

NordLEI

Norway

Central Securities 

Depository of Poland

Poland

Irish Stock Exchange

Ireland

Palestine Securities 

Exchange

Palestinian Authority

Clearing 

Corporation of 

India Limited

India

Saudi Credit 

Bureau

Saudi Arabia

National 

Settlement 

Depository

Russia

Federal Gazette 

Germany APIR 

Systems 

Limited

Australia

Japan Exchange 

Group/Tokyo 

Stock Exchange

Japan

Korea

Securities 

Depository

South Korea

China Financial 

Standardization 

Technical Committee

China

Takasbank 

Turkey London Stock

Exchange

United Kingdom

Central Bank of Argentina

Argentina

Institute of Registries 

and Notaries, IP

Portugal

Central Mercantile 

Register

Spain

Central Securities 

Depository of Prague

Czech Republic Strate Ltd 

South Africa

Financial Services 

Commission 

Mauritius

Central Securities 

Clearing System 

Plc 

Nigeria

Central Securities 

Depository of the 

Slovak Republic

Slovakia

Italian Union of 

Chambers of Commerce, 

Industry, Handicraft, 

and Agriculture

Italy

National Institute 

of Statistics and 

Economic Studies

France

DTCC/SWIFT

(Global Markets Entity Identifier)

United States

Clearing and Depository 

Services, Inc. 

Canada

WM Datenservice

Germany

National Board 

for Patents and 

Registration

Finland

LuxCSD

Luxembourg

Countries with LOUs

operating/pending 

Figure 5-3. Issuers of Legal Entity Identifiers

As of September 30, 2014, 19 local operating units were issuing LEIs and 11 others were preparing to do so.

Sources: LEI Regulatory Oversight Committee, LEI local operating units
97 Advancing Data Standards

Regulator Effective Date Purpose

Required by 

Regulator

U.S. Federal Reserve System Nov. 3, 2014 Annual reports of domestic and foreign 

holding companies on Forms Y-6, 7, and 10

European Insurance and 

Occupational Pensions Authority

Dec. 31, 2014 Annual financial report and reports filed with 

regulators

Canadian Securities 

Administrators

Oct. 31, 2014 Swap transactions reported to trade 

repositories

European Securities and Markets 

Authority

Feb. 12, 2014 Swap transactions reported to trade 

repositories

Jan. 1, 2014 Alternative investment fund reports

Monetary Authority of Singapore Oct. 31, 2013 Swap transactions reported to trade 

repositories

Australian Securities and 

Investments Commission

Oct. 1, 2013 Swap transactions reported to trade 

repositories

National Association of Insurance 

Commissioners

March 31, 2013 Annual and quarterly investment reports filed 

by insurance firms

U.S. Commodity Futures Trading 

Commission

March 13, 2012 Swap transactions reported to data 

repositories

Recommended 

or Listed as 

an Option by 

Regulator

U.S. Securities and Exchange 

Commission

June 15, 2015 Credit rating firms’ disclosures of issuer ratings

Oct. 14, 2014 Monthly Form N-MFP reports filed by money 

market funds

U.S. Municipal Securities 

Rulemaking Board

Aug. 10, 2014 Registration Form A-12 filed by municipal 

securities dealers and advisors

U.S. Commodity Futures Trading 

Commission

Feb. 18, 2014 Ownership Form 102 filed by futures clearing 

merchants, clearing members, and foreign 

brokers

European Banking Authority Jan. 29, 2014 All regulatory reports filed by EU banks

U.S. Commodity Futures Trading 

Commission

June 26, 2012 Annual Form TO filed by counterparties to 

unreported trade options

March 31, 2012 Annual and quarterly Form PQR reports filed 

by private fund managers

U.S. Securities and Exchange 

Commission

March 31, 2012 Annual and quarterly Form PF reports filed by 

private fund managers

Sept. 19, 2011 Annual Form ADV reports filed by investment 

advisors

n/a Effective date not yet set for Rule 613 

requiring Financial Industry Regulatory 

Authority to maintain a consolidated audit trail

Pending Proposals 

by Regulators to 

Require LEI

European Securities and Markets 

Authority

n/a Credit rating firms’ reports of issuer ratings

Market trading data reports

U.S. Consumer Financial 

Protection Bureau

n/a Home Mortgage Disclosure Act submissions

U.S. Securities and Exchange 

Commission

n/a Swap transactions reported to data 

repositories under Regulation SBSR

Figure 5-4. Where Is the LEI Required for Regulatory Reporting?

n/a = Not applicable

Sources: Regulators in United States, Europe, Canada, Hong Kong, Singapore, and Australia; OFR analysis
98 2014 OFR Annual Report

LOUs can be organized by public or private sector organizations and must meet certain requirements set by the global 

LEI system.

The LEI system’s data are updated regularly. Currently, 

individual LOU websites provide information on which 

companies have obtained LEIs. Third-party websites are 

also available that combine this information globally.

So far, derivatives regulators have driven LEI adoption 

across the world. The CFTC has required use of the LEI 

for reporting derivatives transactions to swap data repositories since 2012 (see CFTC, 2012). Swap regulators in 

Europe, Canada, Australia, and Singapore also now require 

companies to use the LEI (see European Commission, 

2012).

In the United States, required use of the LEI is expanding 

beyond the initial focus on swap transactions. The Federal 

Reserve announced that bank holding companies which 

have already acquired an LEI will be required to report it on 

several annual forms after October 31, 2014.

In other sectors, regulations recommend or allow the LEI 

to be used in data submitted to the government, but stop 

short of requiring it. For example, the LEI is now optional 

for reporting by private funds that file annual reports to the 

SEC and municipal advisors that register with the Municipal 

Securities Rulemaking Board (see Figure 5-4).

The OFR is encouraging Council member agencies and regulators around the world to require the LEI in all new data 

collections. The Securities Industry and Financial Markets 

Association has called for “more fulsome adoption and 

use” by U.S. regulators (see SIFMA, 2014b). In July 2014, 

the CFPB proposed requiring lenders to use the LEI in data 

reported under the Home Mortgage Disclosure Act (see 

CFPB, 2014). 

Voluntary use was understandable when the LEI was in 

its formative stages but mandating that reporting entities 

obtain an LEI will be far more effective in propagating the 

LEI and helping it become ubiquitous in the long run. The 

OFR has also argued that several key datasets — including 

call reports for banks and securities financial reports and 

offering materials (including those for asset-backed securities) — should be modified to require use of the LEI.

Using LEIs to Map Corporate Hierarchies

As the global LEI system expands, one of its most 

important uses is to help regulators and market participants 

understand and document complex corporate structures or 

hierarchies. Some of the largest multinational banks have 

thousands of legal entities, many with similar names, operating around the globe. Data about the relationships among 

corporations’ legal entities can show networks of control, 

ownership, liability, and risks, giving financial regulators 

deeper insights into how financial market participants are 

connected to each other.

The OFR is helping a working group established by the LEI 

Regulatory Oversight Committee examine ways to add corporate hierarchy information to the global LEI database.

5.4 Standards for Derivatives 

Market Data

An important global regulatory objective is to make derivatives transactions more transparent by requiring market participants to report them to swap data repositories, whether 

or not those transactions are centrally cleared. The OFR 

is assisting domestic and international efforts to promote 

data standards at these trade repositories. Use of the LEI in 

these markets is a crucial first step, but standards will also be 

needed for derivatives products, transactions, and reporting.

Global regulators agreed after the financial crisis to make 

derivatives markets more transparent, a step requiring 

high-quality and comprehensive post-trade data that can 

be aggregated, compared, and analyzed. The United States, 

Europe, and a growing number of countries now require 

companies to report over-the-counter derivatives data to 

trade repositories, also known in the United States as swap 

data repositories (SDRs) (see Figure 5-5). The reporting 

covers most of the global derivatives market, as measured 

by notional (face value) amounts outstanding. Mandatory 

reporting of accurate and well-defined data will help regulators and market participants assess counterparty exposures 

and other risks.

Because of the global nature of 

derivatives markets, the OFR is working 

with foreign regulators to promote 

international consistency in SDR and 

trade repository reporting.
99 Advancing Data Standards

Figure 5-5. Trade Repositories for Swap Data

As of September 30, 2014, 23 trade repositories were operating or planned.

Repository

Name 

Parent 

Company

Country Regulator Types of Swap Reported

Commodities Credit Equities Foreign 

Exchange

Interest 

Rates

Banco de Mexico n/a Mexico Banco de Mexico

Bank of Korea n/a South 

Korea

Financial Services 

Commission

BM&F Bovespa S.A. BM&F Bovespa 

S.A.

Brazil Banco Central do Brasil

BSDR LLC Bloomberg LP U.S. Commodity Futures 

Trading Commission

Cetip S.A. Cetip S.A. Brazil Banco Central do Brasil

Clearing Corporation 

of India 

n/a India Reserve Bank of India

CME European Trade 

Repository Ltd

CME Group Inc. U.K. European Securities 

and Markets Authority

CME Swap Data 

Repositorya

CME Group Inc. U.S. Commodity Futures 

Trading Commission

DTCC Data Repository 

(Japan) KKb

Depository Trust & 

Clearing Corp.

Japan Japan Financial 

Services Agency

DTCC Data Repository 

(Singapore) PTE Ltdb

Depository Trust & 

Clearing Corp.

Singapore Monetary Authority of 

Singapore

DTCC Data Repository 

(U.S.) LLCa

Depository Trust & 

Clearing Corp.

U.S. Commodity Futures 

Trading Commission

DTCC Derivatives 

Repository Ltd.b

Depository Trust & 

Clearing Corp.

U.K. European Securities 

and Markets Authority

Financial Supervisory 

Service

n/a South 

Korea

Financial Services 

Commission

Hong Kong Trade 

Repositoryb

n/a Hong 

Kong

Hong Kong Monetary 

Authority

ICE Trade Vault 

Europe Ltd.

Intercontinental 

Exchange, Inc.

U.K. European Securities 

and Markets Authority

ICE Trade Vault LLCa Intercontinental 

Exchange, Inc.

U.S. Commodity Futures 

Trading Commission

KDPW Trade 

Repository

Central Securities 

Depository of 

Poland (KDPW)

Poland European Securities 

and Markets Authority

Korea Exchange Korea Exchange South 

Korea

Korea Financial 

Services Commission

National Settlement 

Depository CJSC

Moscow Exchange 

Group

Russia Bank of Russia

OJSC Saint Petersburg 

Exchange 

Moscow Exchange 

Group

Russia Bank of Russia

REGIS-TR S.A. Deutsche Borse 

Group and Bolsas 

y Mercados 

Espanoles

LuxembourgEuropean Securities 

and Markets Authority 

SAMA Trade 

Repository

n/a Saudi 

Arabia

Saudi Arabian 

Monetary Agency

UnaVista Limitedb London Stock 

Exchange Group

U.K. European Securities 

and Markets Authority

n/a Not applicable

a

 Also authorized to operate in Australia and Canada

b Also authorized to operate in Australia

Sources: Financial Stability Board, trade repositories
100 2014 OFR Annual Report

Establishing a unique transaction identifier is particularly important to help prevent the inadvertent duplicate 

counting of over-the-counter derivatives transactions. With 

data repositories around the world and evolving reporting 

requirements, parties in an over-the-counter derivatives 

deal may be required to report a transaction to more than 

one repository, or both parties may report the swap to the 

same repository. In Europe, for example, where regulations require both counterparties in a transaction to report 

the same data, a unique transaction identifier is critical to 

prevent double counting. Without a transaction identifier, 

regulators will face difficulty in spotting duplicate transactions, raising questions about the quality of aggregated data.

The FSB study group in September 2014 recommended the 

FSB launch a formal project to develop global product and 

transaction identifiers. It also recommended authorities and 

SDRs collaborate to harmonize data elements for aggregation. Developing a long-term aggregation solution will 

require a combination of technology, changes in business 

processes, and protocols for sharing data across national 

borders, according to the study group (see FSB, 2014a). In 

the near term, the group urged regulators to assess the costs 

and governance structures required to aggregate data from 

multiple countries and propose solutions to potential legislative and regulatory challenges, especially those regarding 

data security and privacy, among others. The group also 

urged regulators to continue developing bilateral agreements 

to share data. 

Collaboration with the CFTC

In the United States, the CFTC requires regulated platforms 

for swap trading, known as swap execution facilities, to 

report data about transactions and prices to an SDR, which 

must publicly disseminate those data in real time. Three 

SDRs began reporting data to the CFTC in 2013, joined by 

a fourth in 2014. The CFTC aggregates regulatory data with 

the SDRs’ real-time transaction-level data and publishes a 

weekly summary on its website.

However, the lack of shared standards in defining and collecting the swap data means market participants have submitted fragmented and inconsistent data to the SDRs. The 

four SDRs in the United States have different system architectures and technologies that result in data being reported 

differently. The differences hinder efforts by U.S. regulators 

to accurately aggregate and compare data. Inconsistent data 

reporting is also occurring in other jurisdictions.

In the United States, mandatory reporting to SDRs for 

transactions regulated by the CFTC began January 1, 2013. 

The CFTC has primary jurisdiction over interest rate swaps, 

credit index swaps, foreign exchange swaps, and commodities swaps. The smaller market in security-based swaps 

is regulated by the SEC, which has not finalized its rules 

for reporting those instruments to a swap data repository. 

Mixed swaps that involve both commodities and security-based components are regulated jointly. In Europe, overthe-counter derivatives reporting to trade repositories began 

on February 12, 2014, as required by the European Market 

Infrastructure Regulation.

We are also assisting the CFTC in efforts to improve data 

quality in SDRs in the United States.

Successfully converting data from the SDRs and foreign 

trade repositories into useful information for regulators 

depends on new or enhanced data standards, including 

unique identifiers for entities, products, and transactions, as 

well as mapping the relationships among corporate subsidiaries, and reporting standards.

Although over-the-counter derivatives regulators in the 

United States, Europe, Canada, Australia, and Japan have 

mandated use of the global LEI system, other important 

steps are necessary. Regulators and market participants need 

to develop and use product taxonomies to define product 

categories, such as cross-currency swaps or interest rate 

options, and to use unique transaction identifiers to efficiently match counterparties and prevent double-counting 

of transactions.

International Collaboration

International adoption of data standards is essential for 

over-the-counter derivatives data to be aggregated accurately across jurisdictions to monitor exposures in financial

products (see CPSS-IOSCO, 2012a). Consistent reporting 

standards are also needed so market participants operating 

in multiple financial markets around the world can use the 

same set of reference data and processes, reducing errors 

and regulatory reporting requirements.

Since July 2013, the OFR has participated in the FSB’s 

Aggregation Feasibility Study Group, which has recommended continued work to reach international agreement 

on unique product identifiers and transaction identifiers, 

among other things.
101 Advancing Data Standards

The CFTC has taken several steps to improve SDR data 

quality during the past year. In January 2014, the agency asked 

for public comments on how to improve the SDR data and 

announced the formation of a new internal working group 

to review certain swaps reporting provisions. On March 31, 

2014, the CFTC and the OFR announced a plan to assess 

the quality of data submitted to the CFTC and jointly pursue 

solutions to improve it (see U.S. Treasury, 2014). 

Our partnership with the CFTC consists of three initiatives:

1. Assess and improve the quality of data 

collected. Members of the CFTC Technology 

Advisory Committee concluded at a meeting 

on February 10, 2014, that missing, incomplete, 

and inaccurate data made SDR data unfit to use 

in regulatory oversight. The committee said the 

CFTC’s definitions for SDR reporting were not 

sufficiently precise and that standards must be 

applied when data are collected instead of trying to

harmonize data later in the process. The OFR and 

the CFTC are collaborating to address these data 

quality issues with the data already collected.

2. Develop unique product identifiers and unique 

transaction identifiers. To address data quality 

concerns on a prospective basis, the CFTC and 

the OFR are examining benefits, complexities, 

and possible solutions for identifiers of products 

and transactions. Standards exist for both, and 

work is underway to determine if these standards 

can be extended to meet SDRs’ current and future 

reporting needs. A unique product identifier 

is essential for each category of swaps, such as 

fixed-for-floating interest rate swaps, so data can 

be aggregated. CFTC regulations allow SDRs to 

use their own internal identifiers until the CFTC 

approves a unique product identifier (see CFTC, 

2012). The first step in creating unique product 

identifiers is to develop a product taxonomy 

showing the relationships among complex swap 

instruments (see What are Taxonomies?). Unique 

transaction identifiers are also needed, and the 

OFR has begun discussions with the CFTC and 

European regulators to implement international 

standards in this area.

3. Develop other swap data standards. The 

OFR is also helping the CFTC develop precise 

definitions of swaps-related terms, conditions, 

What Are Taxonomies?

A taxonomy is a way to classify and 

organize elements in a hierarchy.

One well-known taxonomy is the “Tree of Life,” 

which shows the diversity of plants, animals, and 

other living organisms. It organizes species by 

shared characteristics. The class of mammals, for 

example, includes only animals that have hair, give 

milk, and bear live young. Within the class of mammals, canines can be distinguished from felines by 

characteristics they do not share.

The same principle of shared characteristics can 

be applied to financial instruments. The choice of 

taxonomy can vary, depending on the purpose. Fo

example, convertible corporate bonds — which 

have characteristics of both debt and equity securities — could be grouped with debt instruments 

for one purpose, and with equity instruments for 

another.

r

A unique product identifier is essential 

for each category of swaps, such as 

fixed-for-floating interest rate swaps, so 

data can be aggregated.
102 2014 OFR Annual Report

and relationships that can be updated as markets 

change in the future. Private sector groups have 

already been working for several years to develop 

such a dictionary for financial instruments, 

business entities, and analytic tools. Clear, agreedupon definitions are the foundation for accurately 

collecting and comparing data.

5.5 Universal Loan Identifiers

We are applying the best practices learned from our LEI 

work to other areas, such as universal loan identifiers for the 

mortgage industry.

Because of the complex and fragmented nature of the U.S. 

mortgage system, a universal identifier for every mortgage 

would greatly benefit regulators and financial market participants. We published a working paper in 2013 explaining 

how a universal loan identifier would improve data aggregation, comparisons, and analysis about the mortgage industry 

(see McCormick and Calahan, 2013). The CFPB proposed a 

rule in July 2014 requiring a universal loan identifier for each 

loan or application reported under HMDA, as authorized 

by the Dodd-Frank Act, and the OFR continues to support 

this effort (see CFPB, 2014). A critical component of a universal loan identifier is that it must not include any information that could be used to directly identify the applicant or 

borrower, such as a Social Security number or date of birth. 

At the same time, each universal identifier must provide 

traceability of the loan through its life cycle.

A mortgage loan typically passes through multiple companies as it is originated, sold, packaged for securitization, and 

serviced. Issuing a universal loan identifier when a mortgage 

is originated would help protect a borrower’s personal information during the life of the loan because the personal information would be isolated from other loan data. A universal 

loan identifier code would travel with the loan as it is sold 

and resold.

Expanding Mortgage Standards

The Mortgage Industry Standards Maintenance 

Organization (MISMO), a nonprofit subsidiary of the 

Mortgage Bankers Association, is leading efforts to apply its 

voluntary standards to mortgage origination, underwriting, 

service administration, and other events in a loan’s life cycle. 

Unlike many data standards, the MISMO standards are 

exchange standards that not only match data terms with 

their definitions, but also facilitate mapping an organization’s 

internal data structures to a consensus standard for mortgage businesses and mortgage participants. Use of the 

MISMO standards has increased since the financial crisis, 

largely because of the work of the FHFA, which regulates 

Fannie Mae, Freddie Mac, and the 12 Federal Home Loan 

Banks.

The OFR is working with the CFPB, FHFA, and MISMO 

to advance data-sharing standards. These include the 

CFPB’s Loan Estimate and Closing Disclosure and National 

Servicing Rule, FHFA’s scorecard initiatives for mortgage 

servicing and securities issuance, and loan-level disclosures 

for both guaranteed and nonguaranteed securities issuance. 

The OFR also helped relaunch the MISMO Government 

Forum, which encourages regulators and industry to share 

information on mortgage data standards.

MISMO has begun several long-term projects to encourage 

the adoption of standard industry practices in loan origination and servicing. MISMO also supports mortgage 

research and publication on subjects from universal identifiers to data reporting templates. MISMO workgroups 

have published papers calling for regulators and industry 

to collaborate in developing universal identifiers for loans 

and parcels of property, as well as a universal document 

identifier. The OFR has worked with MISMO so that its 

standard includes the capability to use the LEI to identify 

each financial firm involved in the origination and servicing 

of loans. Embedding this information brings the industry 

a step closer to market transparency and the ability to track 

mortgage information through the loan lifecycle.

5.6 Reference Databases

The Dodd-Frank Act requires the OFR to prepare and publish reference databases for financial entities and financial 

instruments. Neither database may contain any confidential 

data. The Global LEI Foundation plans to create an entity 

database and offer it to the public for free. The OFR will 

create an instruments database for the public by using opensource components.

In financial markets, the term “reference data” often refers 

to the data describing instruments and entities. Some reference data rarely change, such as a company’s stock symbol 

and headquarters location. Other reference data may change 

frequently, such as daily opening and closing stock prices. 

Still other data change periodically because of corporate 

actions or events.
103 Advancing Data Standards

commercial paper, options, futures, swaps, and other structured products. 

It is impractical for the OFR to assign a financial identifier 

and metadata to every known financial instrument. The private sector has established successful proprietary identifiers 

for some financial products, and those identifiers are already 

deeply embedded in market operations. In addition, maintaining a new government database would require frequent 

and expensive updates to keep up with new products and 

could potentially compete with private vendors that already 

sell reference data. Finally, much of the data that would be 

needed for a new government database are available only 

from primary or proprietary sources. As a result, regulators 

An ontology can be thought of as a combined dictionary and thesaurus to precisely describe data concepts, their 

roles, and their relationships. It is a powerful tool to use with datasets because it also contains rules of inference to 

make logical deductions based on definitions and characteristics entered into the ontology. This inference ability 

adds more knowledge to the ontology and greatly expands its usefulness.

Unlike a taxonomy, which simply lists or classifies elements within a hierarchy, an ontology can express multiple 

kinds of roles and relationships at the same time. 

Figure 5-6 shows how an ontology can describe a simple family tree. On the left is the ontology’s class knowledge, 

showing classes of data in the ontology, the relationships a person can have, and what kind of logical rules can be 

applied. On the right is specific knowledge inferred about a particular child’s ancestors based on the ontology’s 

logical rules.

To fulfill the OFR’s statutory requirement to establish a reference database of financial industry legal entities, the global 

LEI system will help by providing data on companies that 

have acquired an LEI. These data will embody the globally-accepted standards to precisely identify parties to financial 

transactions around the world.

The OFR has been exploring how to create a reference 

database for all financial instruments with the greatest 

benefit and lowest cost to the public and private sectors. 

Creating this database is a challenging project because 

so many tradable instruments exist — stocks, sovereign 

bonds, corporate bonds, commodities, asset-backed securities, loans, exchange-traded funds, foreign currencies, 

What Is an Ontology?

Figure 5-6. Ontologies Capture Different Kinds of Knowledge

Class knowledge, for example, can create new instance knowledge

Instance Knowledge

transitive

property

inferred

property

Class Knowledge

has Ancestor

has Ancestor

has Parent

has Parent

has Parent

person child

parent

grandparent

Source: OFR analysis
104 2014 OFR Annual Report

would have to begin collecting extensive data from industry 

at significant cost to the industry and the government.

In consideration of the law and in light of feedback from, 

among others, our advisory committee, the OFR prefers 

a more efficient and less costly approach that fulfills the 

societal need. Through various channels, we have invited 

the industry to suggest open-source or free-to-use components or to contribute components for building a reference 

database for financial instruments. Regulators, the financial 

industry, academics, and the public could use the database 

to calculate the value of an instrument, compare a group of 

instruments, or link instruments to other datasets that use 

the same instrument identification.

Our approach recognizes three essential components to 

create a financial instrument reference database:

1. Ontology. An ontology precisely defines terms, 

conditions, characteristics, and relationships of each 

instrument in a database. In addition to providing 

a common language for all users, an ontology also 

creates a conceptual framework for organizing data 

(see What is an Ontology?).

2. Identifiers and Metadata. Identifier systems and 

comprehensive descriptive data, or metadata, form 

for each financial instrument a unique identifier tha

can be mapped to proprietary identifiers widely use

in the market.

t 

d 

3. Valuation and Analytical Tools. Analytical 

software will allow users to query, browse, 

compare, and model financial instrument data. For 

example, mathematical algorithms could be created 

to accurately represent each instrument in the 

database. An algorithm is a process or procedure 

a computer follows to solve problems or complete 

calculations. Each algorithm would link daily 

changes in an instrument’s credit risk, market risk, 

and other risk factors to the instrument’s cash flow 

obligations.

Private sector initiatives are already under way in each of the 

three component categories. Those initiatives will help the 

OFR identify the standards — and open-source intellectual 

property components — useful for constructing a financial instrument reference database. The OFR has begun 

outlining the acceptance criteria for the components needed 

to publish a financial instrument reference database. Our 

objective is to set general criteria, not to preselect or endorse 

any particular contributor’s solution.

We are still in the early planning stages, and expect to 

develop specific criteria for each category of component. 

However, we expect that there will be broad criteria that will 

apply to all components and to the way that components are 

expected to work together. We expect contributors to agree 

to provide web access and a high level of availability, so 

that information will be accessible to the public. We would 

expect access to be free of charge, at some defined service 

level. And, we would expect the contributor to use standard interfaces, which the OFR would specify, to allow the 

components to share data. These, and other criteria to be 

determined, would drive the acceptance process.

The OFR plans to hold a workshop to share our proposed 

approach with industry stakeholders and gather feedback. 

We believe that setting acceptance criteria for each of these 

three content categories and publishing use cases to validate 

interoperability is the most direct approach to creating an 

important public good.

A significant hole in both existing open source and proprietary identifiers is instrument identification for innovative 

new instruments. Our approach to a reference database 

would accommodate financial instruments at the leading 

edge of market innovation. These bespoke or exotic instruments have unique, highly customized contract terms. An 

ontology can ensure a common understanding of the metadata that describe the instrument. For example, if a bespoke 

instrument description refers to a coupon, the ontology 

can be consulted for the precise meaning of that word in 

relation to the instrument.

Instruments in the reference database could also be defined 

by their functions. For example, a swap is the exchange 

of cash flows by two parties to transfer maturities or risks. 

Common functions and cash flows can be used to organize 

instruments into groups for analyzing the properties of 

instruments within and across different groups.
105

ADDRESSING DATA GAPS 6

Addressing Data Gaps

Policymakers and market participants have far more detailed, highquality financial data available to them than before the financial crisis 

began in 2007. But significant gaps remain, and the OFR has a mandate 

to fill them. This chapter reviews our progress in 2014 in fulfilling that 

mandate. We are making it a high priority to fill data gaps in secured 

funding markets and asset management. More broadly, the chapter 

discusses how we and financial regulators work to identify, analyze, and 

fill data gaps that impede financial stability analysis and monitoring.

6.1 Data Gaps Agenda 

Improving the quality and scope of financial data on behalf of the regulatory community and the public 

is the OFR’s signature mandate. Those data must be comprehensive, timely, sufficiently detailed, suited for 

their intended purpose, and available to support in-depth analysis. To implement our data gaps agenda, 

we seek to: (1) understand the data needed for financial stability analysis, (2) analyze available data and 

determine where gaps exist, (3) identify the causes of gaps and how those gaps might be filled, and (4) 

prioritize the needs and determine the feasibility of obtaining the needed data.

Our job is to identify, prioritize, and fill data gaps. As this is an ongoing 

and iterative process, we work to address the underlying issues that 

cause gaps in data (see Figure 6-1). Our work also includes promoting 

more efficient data collections through the greater use of standardized 

protocols, enhancing collaboration and sharing among regulators, and 

promoting use of data standards, as discussed in Chapter 5.

Data gaps occur for many reasons. As we develop new tools of financial stability analysis, we realize the need for more and better financial 

data. Some data might exist but may not be collected systematically by 

regulators. Other data might be collected, but security needs or laws might restrict sharing. Still others might 

be difficult to link, to compare with other data, or to aggregate. Existing data may also be unusable if they 

are not electronically accessible, lack standards, or have had standards inconsistently applied. In addition, 

financial innovation and regulatory arbitrage (exploiting regulatory loopholes) constantly create needs for 

new analysis and new data. Consequently, filling data gaps will always be a moving target.

This chapter describes:

Data Gaps Initiatives. Data available to regulators are not currently sufficient to evaluate many of the 

key risks and policy issues discussed in Chapters 2 and 3.

Data gaps occur for many 

reasons. As we develop 

new tools of financial 

stability analysis, we 

realize the need for more 

and better financial data.
106 2014 OFR Annual Report

Collaboration and Sharing Initiatives. Many financial 

markets and major financial institutions are global. For a 

clear view of interconnections and exposures, financial 

supervisors and regulators often need data representing 

activity beyond their jurisdiction. Data collections are driven 

by initial business and regulatory needs, but greater domestic 

and cross-border collaboration and secure data sharing 

would help meet the need for regulators to develop a global 

view and minimize reporting burdens. The OFR collaborates broadly when possible to share data. 

Although obstacles remain to sharing data, the OFR has 

initiatives underway, as described in Section 6.4, to promote 

more extensive collaboration and secure sharing among 

financial regulatory agencies. During 2014, the OFR and 

FDIC co-chaired a working group of the Council’s Data 

Committee to enhance the Interagency Data Inventory, 

which lists data that Council member agencies collect from 

industry or purchase from vendors. 

The inventory, first released in 2013 and described in our 

2013 annual report, facilitated several initiatives, including 

a project to link datasets by connecting (or “mapping”) 

unique identifiers in those datasets. Another OFR pilot 

project uses the inventory to explore potential overlaps 

in regulatory reporting to gain insights on the nature and 

extent of reporting burdens. 

The data inventory and related projects are key ingredients 

in our efforts to meet the Dodd-Frank Act mandate to 

provide the public with useful data to help increase market 

Filling data gaps in secured funding markets is a high priority (see Section 6.2). The OFR, working with the Federal 

Reserve Board of Governors and the Federal Reserve Bank 

of New York, is planning a joint pilot data collection to 

improve our understanding of bilateral repurchase agreement (repo) activities. The OFR also participates in the 

Financial Stability Board (FSB) shadow banking workstream 

on securities lending and repo that is working to address 

data gaps in these markets across international borders. We 

highlighted these activities and their risks in past annual 

reports, and they remain a key focus of our research and 

analysis.

In addition to these initiatives, the OFR will continue to 

explore data gaps in areas such as captive reinsurers, mortgage and other markets, and activities engaged in by the 

asset management industry.

Regulatory Collections. In Section 6.3, we describe new 

and enhanced data collections by member agencies of the 

Council and other domestic and international regulators 

that have already improved our ability to conduct financial 

stability analysis. We highlight data collections about hedge 

fund and other private fund activities, systemic risk indicators for large bank holding companies, home mortgage data 

from banks, and trading activity in off-exchange markets 

and derivatives markets. We also illustrate how data from the 

new Form PF can be used to analyze hedge fund leverage 

across investment strategies.

TACTICAL GOAL 

• Fill data gaps

STRATEGIC GOALS 

• More efcient data

 collections

• Enhanced 

 collaboration and 

 data sharing

• More effective use 

 of standards

EVALUATE AND

REASSESS NEEDS

UNDERSTAND

CAUSE

ANALYZE

AND

DETERMINE

GAPS

PRIORITIZE

AND

FILL GAPS

NEW GAPS 

EXIST

• Market 

 Innovation

• Research and 

 Monitoring

Figure 6-1. How the OFR Addresses Data Gaps

Source: OFR analysis
107 Addressing Data Gaps

standards and processes for data collection and aggregation 

at the global level and to ensure consistent data collection 

by national authorities. The OFR participates in this effort, 

bringing expertise based on our research on data gaps in 

short-term funding markets and ideas based on our agenda 

for improvements in data collections and effective use of 

standards. 

As part of the domestic efforts on this front, the OFR, 

the Federal Reserve Board of Governors, and the Federal 

Reserve Bank of New York are planning a joint pilot data 

collection based on these templates to improve our understanding of bilateral repo activities. These agencies have 

solicited voluntary participation and feedback on a proposed 

template from firms that are large participants in the repo 

market, with an aim to finalize this data template by the end 

of 2014. We anticipate a voluntary data collection focused 

on bilateral repo activity will begin in the first half of 2015. 

These data will also be shared with the SEC. Further work 

will be done on a securities lending data collection in 2015. 

Certain trade and settlement-level data are necessary for 

analyzing risks in the repo and securities lending markets 

(see Figure 6-2). Trade-level data show the dependence of 

individual repo market participants on short-term funding, 

counterparty exposure for repo and securities lending 

market participants, and interconnections among participants. Settlement-level data show types, loan maturities, haircuts (percentage discounts on collateral value), and quality 

transparency and facilitate research on financial stability. We 

expect the inventory will be useful for identifying data gaps 

and for establishing more effective data sharing arrangements among Council member agencies to support financial 

stability analysis.

6.2 Data Gaps Initiatives

The OFR identifies and prioritizes data gaps through our 

research and monitoring of financial markets and through 

collaboration with the Council and its member agencies. 

Addressing data gaps in the repo and securities lending 

markets is a top priority for the OFR. This section describes 

these activities and also describes gaps in data related to 

other secured funding markets, asset management activities, 

and emerging areas such as captive reinsurance.

Repo and Securities Lending

Availability of data about repo and securities lending activities has improved since the crisis. But much of the available 

data is not collected in a consistent manner, which would 

allow for comparison and aggregation, and most is not 

available to the public. In addition, there are still segments 

of these markets not covered by existing data collections.

In 2012, the FSB published a consultative document recommending improvements in market transparency in securities 

lending and repos (see FSB, 2012b). As part of this effort, 

the FSB set up a data experts group to develop proposed 

Level Bilateral Repo Securities Lending

Trade • Identity of dealer and counterparty

• Clearing entity

• Trade and settlement date

• Principal and currency

• Type of collateral

• Transaction term 

• Interest rate

• Identity of dealer, securities owner, and lending agent

• Clearing entity

• Trade and settlement date

• Principal and currency

• Type of collateral (cash or securities)

• Transaction term

Settlement • Allocated collateral security

• Haircut

• For cash collateral:

• Description of reinvestment by quality and maturity

• Reinvestment income and rebate rate

• Haircut

• For securities collateral:

• Allocated collateral security

• Lending rate

• Haircut

Figure 6-2. Critical Elements to Close Data Gaps in Repo and Securities Lending Markets

Source: OFR analysis
108 2014 OFR Annual Report

enables dealers, who are required to be netting members 

of Fixed Income Clearing Corporation, to trade general 

collateral repos based on rate, term, and collateral type 

throughout the day.

The Federal Reserve Bank of New York publishes summary information about outstanding repo transactions in 

the GCF repo market for one day each month (see Federal 

Reserve Bank of New York, 2014b). In addition, DTCC 

publishes a repo index, the DTCC GCF Repo Service 

Index, which reflects daily funding costs for dealers in the 

GCF repo market.

By contrast, there are limited market data available about 

repo trades that dealers settle bilaterally outside the triparty 

clearing banks. Anecdotal and survey evidence indicates this 

repo market suffered distress during the financial crisis.

The 22 U.S. primary dealers that serve as trading counterparties to the Federal Reserve Bank of New York account 

for most trading in the U.S. repo market (see Federal 

Reserve Bank of New York, 2014c). These dealers confidentially report their market activities weekly to the Federal 

Reserve on Form FR 2004. This form collects information 

including position, transaction, financing, and fails data in 

U.S. government securities and other selected fixed-income 

securities. The Federal Reserve Bank of New York publishes on its website every week consolidated information 

about primary dealer positions based on Form FR 2004 

data.

However, Form FR 2004 does not cover activities of broker-dealers that are not U.S. primary dealers, and it does 

not differentiate triparty from bilateral trades. In addition, 

the form does not include important information such as 

of the securities used as collateral, as well as exposures of 

market participants to specific types of securities, market 

sectors, and geographies.

The OFR plans to publish a working paper in the near 

future that will serve as a primer on securities financing 

markets — describing how these markets function, the vulnerabilities and data gaps, and measures that would increase 

transparency.

REPO MARKETS

The degree of transparency about repo transactions and 

positions from a market-wide perspective depends on 

whether trades are settled centrally (triparty) or bilaterally. 

The triparty market is the most transparent part of the 

market. These trades are settled using the triparty repo settlement platforms at the clearing banks. Currently only two 

banks — Bank of New York Mellon Corp. and JPMorgan 

Chase & Co.. — provide triparty repo services.

The Federal Reserve Bank of New York collects data about 

trading activities in triparty repo markets from the two 

clearing banks, identifying the dealers, investors, and collateral by asset class. The Federal Reserve Bank uses this information for its own monitoring and analysis and publishes 

monthly triparty repo summary statistics on its website (see 

Federal Reserve Bank of New York, 2014a).

The General Collateral Finance (GCF) market is an anonymous wholesale market that is centrally cleared and netted 

by Depository Trust & Clearing Corporation’s (DTCC) 

Fixed Income Clearing Corporation with almost exclusively 

dealer-to-dealer transactions. Trades are settled on the books 

of the triparty clearing banks. The GCF Repo Service 

Figure 6-3. Data Collection Gaps and Overlaps in Repo Markets

Total Bilateral

Market

Triparty Market

Non-GCF GCF Federal Reserve 

reverse repo facility

Primary

Dealers Collectively reported in 

Form FR2004

Not collected

Aggregated data 

publicly available

Nonprimary

Dealers Not systematically 

collected (not eligible)

GAP GAP OVERLAPS

Included in triparty 

repo data collection

GCF = General Collateral Finance

Source: OFR analysis
109 Addressing Data Gaps

brokers, dealers, and investors about securities lending. The 

SEC is still accepting comments and has not yet issued a 

proposal in this area (see SEC, 2014a).

Enhanced Financial Accounts

The OFR has begun to assist the Federal Reserve Board in 

its long-term project to enhance the Financial Accounts of 

the United States, formerly known as the Flow of Funds 

Accounts (see Gallin and Smith, 2014).

The Federal Reserve has long used the Financial Accounts 

to measure credit growth and wealth dynamics in the 

economy and in parts of the financial system. But the recent 

financial crisis illustrated that policymakers need much more 

detailed information about such activity in financial markets 

and about risk-taking — who is borrowing and lending, how 

financial market participants are changing the aggregate risk 

profile of the system through derivatives and other products, and where risk concentrations are emerging.

The objectives of the project are to: (1) link the quarterly, 

highly aggregated data to more detailed (granular) and frequent source data, where available; (2) increase the coverage 

of financial activity represented in the accounts to include 

off-balance-sheet and noncash activity; and (3) explore new 

measures of the flow of collateral and the flow of risks 

across the financial system.

A number of datasets could be helpful for this project. 

Those include derivatives data available through swap data 

repositories and the new data filed by hedge funds and other 

private funds on Form PF.

A recent OFR working paper suggested the need for 

additional information similar to that envisioned by the 

Enhanced Financial Accounts project, and proposed an 

accounting framework as a first step (see Pozsar, 2014). A 

key next step will be to document the data gaps identified 

by the project. We can use that knowledge to help inform 

our future data collection efforts. The ultimate goal will be 

to use the information for financial stability monitoring and 

analysis. That will be possible only when the pilot accounts 

are established, populated with data, and any remaining gaps 

identified.

haircuts, rates, and the identity of the counterparty, and 

the information it does contain is highly aggregated. As a 

result, significant data gaps exist at the transaction level of 

these trades, particularly in the repo activities of dealers who 

are not primary dealers and the repo activities that are not 

settled on the clearing banks’ triparty settlement platforms 

(see Figure 6-3). We can only produce rough estimates of 

the size of the bilateral repo market based on these data (see 

Federal Reserve Bank of New York, 2014d).

Transaction-level data would not only offer insights into 

the dispersion of pricing across counterparties and asset 

classes, but with proper identifiers, such as the Legal Entity 

Identifier (LEI), transaction data can be consolidated with 

other data sources. This detail is critical for fundamental 

research projects that attempt to measure the market impact 

of a large liquidity shock and assess subsequent fire sales, 

such as our agent-based models described in Section 4.2. 

More information on repo transactions would also be valuable for our financial stability monitoring and analysis so we 

can better understand how the markets function, how they 

interact with one another, and how risks can build and shift 

in these markets.

SECURITIES LENDING MARKETS

Securities lenders, including mutual funds, exchange-traded 

funds, insurance companies, and other investors, lend securities in their portfolios to earn additional income. Securities 

borrowers, mainly broker-dealers, generally relend the securities to their clients for short selling and other permitted 

purposes.

A substantial amount of data is available about securities 

lending markets, but those data belong to private vendors. 

For example, the two largest securities lending data vendors 

have accumulated substantial databases covering more than 

$13 trillion of global securities and millions of single-day 

transactions involving more than 45,000 securities lenders. 

Industry practitioners, including custodians, prime brokers, 

asset managers, and hedge funds, provided the information.

However, these data collections are voluntary and do 

not include essential data elements about counterparties 

or collateral management. No systematic, targeted data 

collection is conducted for the benefit of regulators or the 

investing public. For that reason, these data are not necessarily complete or comparable for analysis. Section 984(b) 

of the Dodd-Frank Act mandates that the SEC adopt rules 

to increase the transparency of information available to 
110 2014 OFR Annual Report

Asset Management

SEPARATELY MANAGED ACCOUNTS

Separately managed accounts (or separate accounts) are 

a type of customized investment product that asset management firms offer to large institutional investors under 

terms defined in an investment management agreement. 

The OFR’s 2013 Annual Report pointed out the lack of 

publicly available and standardized information on separate accounts, and those data gaps prevent regulators from 

gauging how much risk separate accounts may represent in 

financial markets.

In response to the identified data gaps, the Securities 

Industry and Financial Markets Association (SIFMA) conducted a voluntary survey of its members in early 2014 on 

the separate accounts they manage (see SIFMA, 2014a). The 

survey provided valuable information and confirmed there 

is a need to improve transparency on separate accounts.

Nine firms with $11.2 trillion in total assets under management and about $4 trillion in separate accounts responded 

to the survey. The survey found the majority of separate 

accounts (97 percent) are long-only portfolios. The firms 

said they invest in securities such as stocks and bonds — 

not derivatives — and they do not sell securities short. Only 

1.7 percent of the separate accounts surveyed said they use 

leverage. Separately managed accounts that invest only in 

short-term assets collectively have about $330 billion under 

management in 347 accounts, according to the survey. None 

was involved in securities lending.

Without access to market-level data, regulators cannot 

evaluate shifts in activities and their impacts on broader 

markets. For example, a potential shift of cash management 

away from money market funds in response to regulatory 

reform could accelerate the growth of separately managed 

cash accounts. Separate cash accounts could also become 

significant investors in certain types of securities and accumulate large exposures to certain entities or regions. These 

market developments cannot be effectively understood and 

monitored so long as gaps remain.

CORPORATE CASH INVESTMENT ACTIVITIES

Nonfinancial corporations had about $1.8 trillion in cash at 

the end of the first quarter of 2014, a record according to 

available data. But regulators have limited information about 

how nonfinancial corporations invest their cash. This represents an important gap in our ability to understand what 

Figure 6-4. Current Percentage of Short-Term 

Portfolio Allocated to Specific Investment Vehicles

Approximately 52 percent of corporate cash is invested in 

bank deposits

0

20

40

60

80

100

2006 2008 2010 2012 2014

Bank deposits, money market funds, and Treasury bills

Bank deposits only

Source: 2014 Association for Financial Professionals Liquidity Survey

Regulators have limited information 

about how nonfinancial corporations 

invest their cash. This represents 

an important gap in our ability to 

understand what drives growth and 

risk in short-term wholesale funding 

markets. Specifically, we need data to 

help us analyze the sources and uses of 

funds in these markets. 
111 Addressing Data Gaps

The Federal Insurance Office recommended in 2013 that 

states adopt consistent disclosures and oversight standards for captives, including public disclosure of financial 

statements (see FIO, 2013). The National Association of 

Insurance Commissioners (NAIC) is considering broadening its definition of a multistate insurer, which would 

subject captive reinsurers to the same oversight and transparency requirements as other insurance companies.

Last year, the NAIC adopted requirements for insurance 

companies to report in their required annual statements 

the amount of reinsurance transferred to affiliated captives 

and to offshore affiliated captives. These requirements will 

provide greater insight into the level and growth of captive 

reinsurance activities by U.S. insurance companies. However, 

these changes still may not give regulators a complete picture of the level of captive reinsurance.

Any filing requirements for captive reinsurers would not 

extend to offshore affiliated captives. If overseas jurisdictions show more leniency than U.S. jurisdictions on either 

filing or substantive requirements, insurance companies 

might respond by increasing their use of offshore captives 

instead of domestic captives. 

Mortgage Servicing Information

The most comprehensive information about mortgages is 

available from servicers. Mortgage servicers have extensive 

information about mortgages because they handle borrowers’ payments and provide services over the life of the 

loan. However, it is difficult to get a comprehensive view 

of the mortgage industry from servicers because they are 

subject to different regulatory frameworks.

For example, the OCC collects mortgage data from large 

national bank servicers and uses that data to generate the 

quarterly Mortgage Metrics Report. However, mortgage 

servicing activities are migrating to nonbank companies not 

subject to OCC oversight (see Section 2.3). Although some 

nonbank mortgage servicers are publicly traded and fall 

under the oversight of the SEC, their financial statements 

do not contain detailed data about the mortgages they 

service.

drives growth and risk in short-term wholesale funding markets. Specifically, we need data to help us analyze both the 

sources and uses of funds in these markets, as discussed in 

the OFR’s 2013 Annual Report and in a recent OFR working 

paper (see OFR, 2013a, and Pozsar, 2014). 

information about guarantees, and the department considered only half of those disclosures sufficient (see NYSDFS, 

2013).

In a recent survey by the Association for Financial 

Professionals, 81 percent said they expect their cash and 

short-term investment balances to grow or remain at the 

current level (see AFP, 2014). Corporations keep cash 

balances for a variety of reasons, including operating 

costs, dividend payments, share buybacks, and acquisitions. 

Interruptions in market access during the financial crisis 

may also have led some corporate treasurers to keep more 

liquidity available.

The substantial cash balances have made nonfinancial corporations important investors in banks and the short-term 

markets (see Figure 6-4).

Such surveys are currently the only available data source 

of corporate cash investments. No complete standardized 

dataset on corporate investments of financial assets is available to aid regulators and policymakers in monitoring any 

potential shifts in corporate investment preferences.

Captive Reinsurers

Regulators and market participants need better information 

about captive reinsurance to evaluate the financial solvency 

of captive reinsurers and the potential risks to holding 

companies (see Section 2.3). Captive reinsurers are licensed 

insurance companies created to assume insurance risk transferred from a corporate affiliate.

Captive reinsurance transactions must be approved by state 

regulators. Captive reinsurers do not always have the same 

filing requirements as traditional insurance companies, 

which submit large amounts of data to regulators and the 

public. Financial statements are publicly available for traditional insurance companies, but not for captives.

In a 2013 report, the New York State Department of 

Financial Services criticized the disclosure practices of 

some life insurance companies and their affiliates regarding 

captives. The state found that New York insurance companies and affiliates outside New York did not disclose in 

their SEC filings nearly 80 percent of captive reinsurance 

companies’ reserve collateral secured by parental guarantees. Only 10 of the 17 insurers in the survey disclosed any 
112 2014 OFR Annual Report

us to map in unprecedented detail the interconnections 

of those historical interbank networks, which banks rely 

on for liquidity. Although banks in the same location may 

have faced common shocks, the degree of distress differed 

depending on the location and financial condition of the 

banks to which they were connected. However, to benefit 

from this information, we would need to convert the data 

into electronic formats.

Several researchers at the OFR are collecting detailed data 

on interbank connections. The first project is to create a 

map of interbank relationships for banks in Pennsylvania 

from 1870 to 1897. The second project is to create a dataset 

on state chartered banks and trust companies in Illinois 

to gain insight into the strength of interbank relationships 

and the effects on bank panics, using detailed information 

on the amount of deposits at each connected bank. These 

states were selected because of the availability of data.

d

6.3 Regulatory Collections to 

Address Data Gaps

Since the financial crisis, regulators have collected new data 

from previously less-regulated areas of financial markets 

and expanded existing collections to include more detailed 

information on financial market activity. Regulators are also 

using technology and data standards to improve the quality 

and timeliness of collected data. This section highlights 

some new and enhanced data for financial stability analysis.

New and Updated U.S. Regulatory Data 

Collections

PRIVATE FUND DATA: FORM PF

The SEC issued a rule in July 2014 with amendments to 

the liquidity fund section of Form PF, the primary form 

for collecting data about private funds (see SEC, 2014b). 

These amendments align the reporting about liquidity funds 

in Form PF with the information that money market funds 

report on the SEC’s Form N-MFP and which banks report 

To address these issues, the Conference of State Bank 

Supervisors introduced the quarterly Mortgage Call Report 

in 2011 to standardize the collection of data on financial 

condition and mortgage origination through its Nationwide 

Mortgage Licensing System. The Mortgage Call Report 

collects and aggregates data from state-licensed mortgage 

companies and nonbanks that use state-licensed mortgage 

originators. However, these data provide only high-level 

information for a subset of states and territories.

Industry participants have attempted to standardize mortgage data by establishing the Mortgage Industry Standards 

Maintenance Organization reference model, which allows 

adopters to more readily transmit servicing data between 

entities using an open-source, XML architecture (a markup 

language that provides a flexible way to create and share 

information and format). However, adoption is voluntary 

and, for some companies, cost prohibitive.

Analysis of potential threats to financial stability would be 

better supported by a standardized collection of mortgage 

data from bank and nonbank servicers, reported consistently over time as mortgages are transferred and sold. Such 

a collection could improve data quality and mapping and 

help identify risks building in areas with little or no regulatory oversight.

Our researchers also are creating a dataset on debit and 

credit payments of the New York Clearing House and 

haircuts applied to banks during banking panics to help us 

understand the flow of liquidity during financial crises.

These projects will broaden our understanding of systematic shocks during financial crises of the past — an

possibly of the future.

Historical Data Gaps Analysis

It is essential for the OFR to understand past financial 

crises as we analyze potential policies to reduce systemic 

risk because the next crisis may not be the same as the mos

recent one. There have been 17 major banking crises in the

United States over the past two centuries (see Reinhart and

Rogoff, 2011). It makes sense to take advantage of inform

tion about these crises to identify patterns. A broad historical focus also permits researchers to respond to available 

data.

t

aFor some periods and circumstances, data on interbank 

connections are better and more informative than data 

available today. For example, in the 19th century, regulators 

frequently checked details on interbank deposits — deposits 

that banks held with correspondent banks — in order to 

verify that banks were meeting reserve requirements. 

But historical data are often locked in static, paper forms 

and not available for electronic analysis. If made available, 

historical resources — such as bank directories and bank 

examination reports from an earlier era — could allow 
113 Addressing Data Gaps

about their short-term investment funds to the OCC. This 

realignment will improve the comparability of data collected through the forms, permitting the SEC, OFR, and 

others to simultaneously evaluate risks in money market 

funds and private liquidity funds. Firms must comply with 

the new requirements by April 14, 2016 (see Hedge Fund 

Leverage and Strategy).

MONEY MARKET FUND DATA: N-MFP AND N-CR

The SEC’s new rule on money market funds, adopted in 

July, seeks to reduce the risk of runs on money market 

funds and includes significant updates to requirements 

related to disclosure and data collection (see Section 3.4). 

Starting in April 2016, the 60-day lag on public availability 

of information filed on Form N-MFP will be eliminated. 

Money market funds will be required to disclose detailed 

information on their websites daily, including net asset 

values rounded to the fourth decimal place, daily liquid 

assets, weekly liquid assets, net inflows and outflows, imposition of fees and gates, and any use of affiliate sponsor 

support. In addition, the SEC will introduce a new form, 

Form N-CR, for reporting material fund events.

SYSTEMIC RISK INDICATOR DATA: FR Y-15

This year, for the first time, data from the Federal Reserve’s 

FR Y-15 data collection on 33 bank holding companies is 

publicly available, except certain line items and data related 

to the liquidity coverage ratio. This collection, which began 

with data as of December 31, 2012, provides insight into 

the structure of financial networks and the interconnectedness of large financial institutions. Over the past year, minor 

revisions have been made, including revisions to align the 

FR Y-15 more closely with the assessment methodology 

of the Basel Committee on Banking Supervision for global 

systemically important banks, or G-SIBs.

The FR Y-15 collects information on the Basel Committee’s 

systemic importance categories of size, interconnectedness, 

cross-jurisdictional activities, substitutability, and complexity 

from bank holding companies with assets of more than $50 

billion. The data are used to help monitor financial stability 

risks posed by bank holding companies subject to enhanced 

prudential standards and to determine capital surcharges 

for G-SIBs.

A forthcoming OFR Brief will present the data used in 

determining U.S. bank holding companies’ systemic importance scores and apply an OFR financial connectivity 

index to the data (see Allahrakha, Glasserman, and Young, 

Figure 6-5. U.S. G-SIBs Vary in Use and Provision of 

Funding

Companies below the line are net lenders to other financial 

institutions

JPMorgan

Bank of

America State

Street 

Wells

Fargo

Citibank

Morgan

Stanley 

Goldman

Sachs 

BoNY

Mellon

0

100

200

300

400

500

600

700

800

0 100 200 300 400 500 600 700

Intranancial system assets (horizontal axis)

Intranancial system liabilities (vertical axis)

45-degree line 

separates net

borrowers vs.

net lenders 

Note: Bubble size shows total exposures in billions.

Source: Federal Reserve Form Y-15

The FR Y-15 collects information on the 

Basel Committee’s systemic importance 

categories of size, interconnectedness, 

cross-jurisdictional activities, 

substitutability, and complexity from 

bank holding companies with assets 

of more than $50 billion. The data are 

used to help monitor financial stability 

risks posed by bank holding companies 

subject to enhanced prudential 

standards and to determine capital 

surcharges for G-SIBs.
2014 OFR Annual Report 114

* CTA = Commodities Trading Advisory 

Note: All filings within a reporting period are aggregated on the 

period end date. Funds with 70 percent or more of their strategy 

allocation in one strategy are labeled with that strategy. 

Sources: SEC Form PF, OFR analysis

Figure 6-6. Qualified Hedge Fund Gross/Net Asset 

Ratios

Relative value funds have reduced leverage

0

1

2

3

4

5

6

7

8

9

Jun

2012

Sep

2012

Dec

2012

Mar

2013

Jun

2013

Sep

2013

Dec

2013

Mar

2014

Credit

Equity

Macro

Relative value

Managed futures/

CTA

Event driven

Multistrategy

Other

Hedge Fund Leverage and Strategy

In 2012, the SEC began collecting confidential 

data on hedge funds on Form PF, the primary 

form for collecting data about private funds. 

This analysis considers leverage levels across 

different hedge fund strategies. Hedge funds 

with higher leverage or debt are typically more 

vulnerable to adverse events, if all other factor

are equal.

s 

Every new data collection initiative has growing pains, and 

Form PF is no exception. Filling data gaps begins with data 

collection, but ensuring complete and accurate data takes 

time and requires an ongoing assessment of data quality. 

Because Form PF collection is still new, caution is important 

in interpreting the information collected. 

As of May 2014, about 7,800 hedge funds report data 

through Form PF. Of these, approximately 1,300 are 

qualifying funds, or funds with net assets of at least $500 

million. These funds, which manage more than 80 percent 

of hedge fund assets, are required to file data quarterly 

through Form PF. 

The analysis presented here is based on data from qualifying hedge funds, which report more detailed information 

about borrowing and derivative exposures than other funds. 

Although there are various ways to calculate fund leverage, 

a commonly used metric is the ratio of gross assets (assets 

under management based on the current market value of 

assets and uncalled commitments) to net assets (gross assets 

under management minus outstanding indebtedness or 

other accrued but unpaid liabilities). 

Figure 6-6 shows this ratio for qualifying hedge funds by 

strategy type from the beginning of Form PF reporting in 

June 2012 through March 31, 2014. The figure shows that 

this ratio has been higher in relative-value funds than in 

other funds but has been declining since 2012. During the 

period, leverage levels in macro funds and multistrategy 
115 Addressing Data Gaps

Figure 6-8. Qualified Hedge Fund Borrowing by 

Source ($ billions)

Most borrowing is secured, with the largest percentage 

sourced from prime brokers 

0

300

600

900

1,200

Jun

2012

Sep

2012

Dec

2012

Mar

2013

Jun

2013

Sep

2013

Dec

2013

Mar

2014

Secured- prime

Secured- reverse repo

Secured- other

Unsecured

* CTA = Commodities Trading Advisory 

Note: All filings within a reporting period are aggregated on the 

period end date. Funds with 70 percent or more of their strategy 

allocation in one strategy are labeled with that strategy. 

Sources: SEC Form PF, OFR analysis

Figure 6-7. Qualified Hedge Fund Borrowing by 

Strategy Type ($ billions)

Multistrategy and equity funds borrow the most 

0

200

400

600

800

1,000

Jun

2012

Sep

2012

Dec

2012

Mar

2013

Jun

2013

Sep

2013

Dec

2013

Mar

2014

Credit

Equity

Macro

Relative value

Managed futures/

CTA

Event driven

Multistrategy

Other

funds have increased slightly. Gross assets were $4.2 trillion 

and net assets were $2.1 trillion for all qualifying hedge 

funds as of March 31, 2014.

Funds typically build leverage through borrowing or use of 

derivatives. Figure 6-7 shows fund borrowing by strategy 

type. Borrowing decreased notably in funds after June 2013. 

Borrowing increased steadily for multistrategy and equity 

funds between 2012 and 2014. 

Figure 6-8 shows that most fund borrowing is obtained 

through prime brokers, followed by repos and other secured 

borrowing. Very little hedge fund borrowing is unsecured.

Gross notional exposure is another common measure of 

leverage. Gross notional exposure represents the sum of the 

values of long and short positions in a portfolio, including 

notional values (face values) of derivatives. (The dollar value 

of interest rate derivatives are based on 10-year bond 

equivalent value). Multistrategy funds and macro funds managed roughly 60 percent of the gross notional exposures 

of qualifying hedge funds, on March 31, 2014, largely due 

to the use of derivatives. The notional values of derivatives 

represented more than half of the gross notional exposure 

of qualifying hedge funds. Gross notional exposure for all 

qualifying hedge funds was more than $14 trillion.

Hedge fund strategies can change rapidly in response to 

market factors. Leverage levels, even within hedge fund 

strategy types, can vary significantly over time, depending 

on individual funds’ investment decisions. Monitoring ways 

hedge funds combine borrowing and derivatives to obtain 

leverage is essential to identifying potential risks in asset 

markets.
116 2014 OFR Annual Report

• a list of cross-guarantees and major counterparties, as well as the attribution of collateral to those 

counterparties; 

• a list of international operations; 

• an explanation of the potential liquidity needs of 

various unwinding strategies; and 

• perspective on the filers’ strategies for managing technology, collateral, capitalization, and liquidity.

However, the data would need to be more structured to 

support macroprudential analysis. Structured data may 

include standardized formats and metadata tagging and 

might also incorporate additional detailed forms to capture 

data consistently.

HMDA DATA

The CFPB is implementing the Dodd-Frank Act changes to 

the Home Mortgage Disclosure Act (HMDA) and has proposed to change the information banks must provide about 

home mortgages under Regulation C to include a range of 

new data variables, including the length of the loan, total 

points and fees, the length of any teaser or introductory 

interest rates, and the applicant’s or borrower’s age and 

credit score.

In addition, the CFPB is considering expanding disclosures 

about interest rates, total origination charges, and total discount points for each loan. These changes will help public 

officials distribute public sector investment, determine 

whether financial institutions are serving the housing needs 

of their communities, and identify possible discriminatory 

lending patterns and enforce antidiscrimination statutes. 

They also will improve our understanding of threats to 

financial stability from housing finance because HMDA 

data are a powerful tool for understanding applications and 

completed mortgage loans. As these public data become 

available, they can reach many more researchers than proprietary products from private vendors.

FINRA DATA COLLECTION PROGRAMS

More than 35 percent of the volume of stock trades occurs 

outside of traditional exchanges. A portion of this off-exchange trading volume occurs in “dark pools,” which are 

a type of alternative trading system. Dark pools comprise 

approximately 16 percent of overall trade volume (see 

Schack, Kemmsies, and Upward, 2014). Relatively opaque 

alternative trading systems raise concerns about the transparency of pricing and the impact of high frequency 

trading.

forthcoming). For example, two of the interconnectedness 

indicators — intrafinancial system assets and intrafinancial 

system liabilities — attempt to measure the amount U.S. 

bank holding companies lend to and borrow from other 

financial institutions. As Figure 6-5 shows, companies above 

the 45 degree line have intrafinancial liabilities greater than 

intrafinancial assets; these banks use the financial system as 

a source of funding. In the figure, the size of each company 

in terms of total exposures is reflected by the area of each 

circle. The data indicate that size alone does not determine 

the connectivity of a bank holding company with other 

financial institution. Data about the interconnectedness 

of these institutions can help identify potential knock-on 

effects if one of these companies were to fail. 

The FSB and Basel Committee use FR Y-15 data to update 

the list of firms identified as G-SIBs. In the future, the 

Federal Reserve may use the data to identify domestic systemically important banks.

RESOLUTION PLAN DATA

The Federal Reserve Board and FDIC continue to receive 

resolution plans, informally called “living wills,” from all 

bank holding companies and foreign banking organizations 

with global assets in excess of $50 billion, as well as other 

institutions the Council has designated for supervision by 

the Federal Reserve Board (see Section 3.2). Pursuant to 

the Dodd-Frank Act, resolution plans are contingency plans 

for orderly resolution in the event of a failure. Currently, 

more than 120 institutions submit plans each year.

The current form of these documents (often more than 

1,000 pages in length) is unwieldy, reflecting the complexity 

and unique nature of the information companies provide. 

The plans are not reported in a consistent format and data 

are presented in an unstructured fashion. Although this 

approach supports supervisors’ evaluation of the plans for 

resolving business operations, greater standardization of 

this data collection in areas of commonality between the 

filers could make it easier for regulators to use the information for financial stability analysis. 

Some of the information in resolution plans could be useful 

for monitoring and analyzing threats to financial stability, 

especially during crises. For example, resolution plans 

include:

• a map of a bank’s ownership structure under its 

holding company; 
117 Addressing Data Gaps

Although MIDAS is not a regulatory data collection — it 

draws exclusively from commercial sources — it demonstrates the use of technology and standards to make existing 

data more useful and closer to real-time, closing data gaps 

that impede timely analysis and monitoring. 

SWAP DATA REPOSITORY DATA

Swap data are now available through swap data repositories (SDRs) to the public and to regulators, principally the 

CFTC. However, SDRs have different system architectures, 

and the data are reported differently to each SDR. The lack 

of reporting standards across SDRs has created significant 

data gaps.

The CFTC has begun to address these issues on many levels 

by, among other things, requesting comments related to 

swap data reporting. The OFR has also been assisting the 

CFTC to improve the quality of SDR data (see Section 

5.4). The SEC has proposed swap reporting rules but they 

have yet to be adopted. In the meantime, the OFR has 

access to credit default swap data from the Depository Trust 

& Clearing Corporation, which is a market utility supervised 

by the SEC (see Section 4.4).

CYBERSECURITY

Concerns over cybersecurity have grown over the past 

decade because large-scale data breaches have become more 

common, reflecting the growing volume of data stored 

electronically and the increasing technical sophistication of 

cyberattackers. The increased frequency of cyber-attacks 

has prompted attention from regulators and raises concerns 

about potential financial stability risks. 

The SEC requires public companies to disclose cybersecurity breaches, and many firms note breaches in their public 

regulatory filings. However, there remains a significant data 

gap, because firms are reluctant to provide details about the 

size or impact of cybersecurity breaches due to concerns 

over potential damage to the confidence of clients and 

business partners. 

Separately, this year the Federal Financial Institutions 

Examination Council (FFIEC) announced a pilot program 

to assess the management of cybersecurity risks by community financial institutions. It is undertaking other initiatives 

to raise awareness of those risks across the financial sector. 

To better understand trading taking place in these systems, 

the SEC and the Financial Industry Regulatory Authority 

(FINRA) have launched several initiatives, and with these 

initiatives have demonstrated a shift towards more automated, frequent, detailed (account-level), and structured data 

collections. These new programs use formats and standards 

for better timeliness, clarity, and machine readability of 

the data. The data collected will create opportunities for 

research on systemic issues, such as market liquidity, concentration, and interconnectedness.

FINRA Rule 4552 requires SEC-registered alternative 

trading systems to report aggregated transaction data on 

volume and number of trades for each traded equity security on a weekly basis. For data integration, FINRA requires 

each alternative trading system to use a unique market participant identifier. FINRA began collecting the data in May 

2014 and posts the data on its website after a wait period of 

several weeks.

FINRA is also developing the Comprehensive Automated 

Risk Data System (CARDS). In addition, self-regulatory 

organizations are working to implement the SEC’s Rule 

613, Consolidated Audit Trail (CAT). CARDS will automate the collection of business conduct information with 

data on account activity (balance, margin, for example) and 

customer investment profiles from all supervised institutions. CAT will allow regulators to track stock trading at the 

account level for forensic purposes.

Combined, CARDS and CAT will provide an almost 

complete and continuous account-level picture of market 

activity and status. FINRA has announced plans to have 

CARDS and CAT online in 2015.

TRADING EXCHANGE DATA: MIDAS

The SEC’s Market Information Data Analytics System 

(MIDAS) provides staff at the SEC with an analytics and 

data platform geared towards research on equity and equity 

options market structure. MIDAS joins data feeds from the 

11 domestic stock exchanges, as well as the consolidated 

tapes for equities and options, to report information for 

exchange-based posted orders, modifications, cancellations, 

and off-exchange executions. 

MIDAS is available to the SEC in near real-time through 

cloud computing. The SEC uses this information to inform 

research on equity and equity options market structure and 

also publicly provides quarterly data metrics and analysis on 

its website. 
118 2014 OFR Annual Report

is substantially different from the U.S. repo market. The 

majority of European repos are conducted in the electronic, 

anonymous interbank market, which relies on a central 

counterparty. All European repo market participants have 

access to the European Central Bank’s refinancing facilities, 

which substantially reduces the risk of asset fire sales due 

to counterparty default. Because of bank participation in 

the repo market, the European Central Bank has collected 

a substantial database of repo and reverse repo activities, 

among other data elements related to monetary statistics 

and bank operations. Aggregated data are publicly available 

on the European Central Bank’s Statistical Data Warehouse 

website.

The OFR is closely following developments in Europe’s 

Common Reporting (COREP) and Financial Reporting 

(FINREP) regimes. These are reporting frameworks mandated by the European Banking Authority to harmonize 

supervisory reporting standards across Europe. FINREP 

provides templates for detailed data about income statements, balance sheets, and other areas to improve consistency of scope, granularity, and definition of data elements. 

This standardized data collection effort leverages new 

technologies and promises to provide regulators data that 

can be easily compared, analyzed, and aggregated in a timely 

manner.

OFR INTERNATIONAL PARTICIPATION

The OFR participates in international efforts because crossborder cooperation is essential for us to serve the Council, 

promote data standards, fill data gaps, and promote secure 

international data sharing.

In 2014, we participated in the FSB’s Feasibility Study on 

Approaches to Aggregate OTC Derivatives Data, published in 

September, which studied issues and options for harmonizing derivatives data reporting across jurisdictions (see 

FSB, 2014a). During the crisis, the complexity and lack of 

transparency in derivatives trading and markets were debilitating for firms and impaired regulators’ ability to understand interconnections and the spread of the crisis.

The OFR also participates in the FSB Workstream on 

Securities Lending and Repos. We focused on efforts to 

identify data gaps and develop protocols and standards for 

collecting data for cross-jurisdictional comparability and 

aggregation. We provided expertise from user and data management perspectives drawing on our research and monitoring priorities on short-term funding markets and our data 

policy agenda for more efficient data collections and use of 

International Data Collection Efforts

The global nature of the financial crisis underscored that 

data gaps, problems in data quality, and a lack of data 

standards were international problems, not just issues of 

domestic concern. To resolve cross-border issues, regulators 

collaborate through international financial forums such as 

the Financial Stability Board and the Basel Committee on 

Banking Supervision.

International data-sharing is essential to afford regulators a 

complete view of financial risks. The OFR recognizes that 

security concerns can be an obstacle to data sharing, but 

appropriately constructed and shared security frameworks 

can lessen those concerns (see Section 6.4).

Over the past year, the G-20 Data Gaps Initiative has 

made progress in expanding data collection on concentration risk and interconnections among G-SIBs, to include 

data on G-SIB funding dependencies. (The G-20, or Group 

of 20, is a forum of the world’s largest advanced and 

emerging economies.) 

The initiative will continue work during the next year to 

collect G-SIBs’ consolidated balance-sheet data broken 

down by risk exposures such as sector, instrument, and 

maturity. These data collections will be valuable for financial 

stability analysis because they provide an unprecedented set 

of comparable, detailed data about some of the largest and 

most complex financial institutions.

The G-20 uses a data hub to pool collected data and 

provide secure sharing arrangements for analytical reports 

based on the data. This is an example of how international regulators can cooperate to pool highly confidential 

cross-jurisdictional data and link and aggregate those data 

for financial stability analysis.

The Basel Committee on Banking Supervision continues 

to review issues that surface as jurisdictions implement 

Basel III. This year, the committee focused on the Basel III 

leverage ratio to ensure comparability and accuracy of the 

data (see BCBS, 2014a). Although Basel III was finalized 

in 2010, problems that emerged during implementation 

highlight the ongoing regulatory challenge, particularly for 

internationally agreed standards of data definitions, quality, 

and comparability in data collections.

Another challenge for international data efforts is the divergence of market infrastructure and its institutional design 

across jurisdictions. For example, the repo market in Europe 
119 Addressing Data Gaps

• We are exploring adding new information, for 

example, by the legislation or regulation that required 

the collection, and the dates the data collection began 

and ended. Other information being considered 

includes the financial market subsector or instrument 

of primary focus, based on a classification system we 

are developing.

• We reconciled inventory records with the Office of 

Management and Budget’s public record of data collections. This marked an important step in improving 

the accuracy and comprehensiveness of the inventory.

• We developed a visualization tool for use by Council 

member agencies for easy search and analysis by one 

or more criteria.

The Data Committee and the OFR will review the inventory 

on an ongoing basis to evaluate its usefulness and identify 

ways it can be used and improved for Council member 

agencies and the public. The public portion of the inventory 

is on the OFR website (see OFR, 2014).

Reporting Burden and Efficiency Project

Before the crisis, financial activity had grown substantially 

in less transparent or less regulated markets. In response, 

Congress has expanded regulatory jurisdictions, and 

Congress and the agencies have increased data reporting 

requirements, domestically and internationally. As the 

depth and type of data collected have expanded, so has the 

potential for overlapping requests among agencies and their 

reporting forms.

The OFR has begun a project to better understand potential 

data reporting overlaps. The experience gained will provide 

insights for the ongoing dialogue between regulators and 

market participants concerning reporting efficiency and 

burden.

For the initial project, we focused on a proposed new collection because the largest expense for firms and regulators in 

data collection is during startup. We looked for overlaps in 

the second portion of the FSB’s new international collection 

of data from G-SIBs. Phase 1 of the collection started in 

2013 and focused on companies’ counterparty exposures 

and other major risk dimensions. Phase 2 in 2014 added 

information about institution-to-institution liabilities, large 

bank and nonbank funding providers, and their sources of 

funding. Phase 3 will include more detailed balance-sheet 

data (see FSB, 2014b).

data standards. We hope these data can be more readily and 

securely shared across borders. Domestic efforts to fill data 

gaps in repo and securities lending are linked to this work 

(see Section 6.2).

6.4 Collaboration and Data 

Sharing Initiatives

Sharing information is essential to make the best use of 

data for effective and comprehensive financial stability 

analysis, as no regulator has access to all of the data that 

might help monitor risks across the financial system. It can 

also help minimize regulatory burden on financial entities 

whose activities we seek to understand. Of course, sharing 

information must be done in a secure manner to protect the 

confidentiality and security of the data being shared.

Because of our mandate to support the Council in its 

analysis of threats to financial stability, we work to improve 

data sharing among Council member organizations. We 

also sponsor research and undertake projects related to 

collaboration and sharing. This section describes three: the 

Interagency Data Inventory, sponsored by the Council’s 

Data Committee, a project the OFR has begun to learn 

about reporting overlaps in new data collections, and a 

project that the OFR has begun to connect datasets that 

use different identifiers. It also describes our framework for 

protecting data that others share and entrust with us.

Data Inventory

The Council’s Data Committee sponsored an initiative to 

develop a catalog of datasets that Council member agencies have available through industry filings or commercially 

purchased data.

This Interagency Data Inventory does not hold the actual 

datasets, but rather is a list of the metadata, or information, 

about the datasets. The metadata include a brief description of each dataset and categorizes the dataset as financial, 

supervisory, application, complaints, structure, or other (see 

OFR, 2013a). Basic information, including the collecting 

agency, name of the form used to collect the data, and form 

number, are also included.

Over the past year, the OFR and Council member agencies 

began to transform the inventory from a preliminary catalog 

into a more searchable, accessible, and information-rich 

resource, in the following ways:
120 2014 OFR Annual Report

We began our analysis by reviewing the Interagency Data 

Inventory to identify reports that might have areas of 

overlap with the FSB Phase 2 data collection template. 

An overlap occurs if some companies have been asked to 

report the same type of data in multiple forms. We compared the purposes, scope, organizational frameworks, 

data definitions, line items, and data fields of the Phase 2 

template and existing reports. We also considered whether 

a filer could have fulfilled a data request in Phase 2 with an 

existing report, either an exact match or through a small 

modification of an existing data field. Such modifications 

could include adding subfields for more detail or minor 

alterations in data definitions.

This FSB data collection on G-SIBs will form an unprecedented set of detailed and comparable data about some 

of the largest and most complex financial institutions in 

the world — an invaluable resource for financial stability 

analysis. For this and most new collections, exploring for • 

and understanding potential overlaps in regulatory datasets 

and addressing reporting burden are complex tasks. We 

continue to work through our findings, but have identified 

the following general issues:

• Variations in Data Definitions and Concepts.

Even a simple concept can have complex dimensions. For example, the Federal Reserve’s Form 

Y-9 collects information on deposits broken 

down into two categories: interest-bearing and 

noninterest-bearing. The FSB project’s Phase 2 template requests data on nonmaturity deposits. These 

categories are different.

• Challenges of Disaggregation and 

Reaggregation. Reporting forms may rely on the 

same definitions, but differ in their requests for detail. 

For example, Phase 2 requests data on bond holdings. 

But there is no such item on the Y-9, which instead 

includes bond holdings in more than one line item, 

including “Line 2. Securities” and “Line 5. Trading 

Assets” on Schedule HC, on the Consolidated 

Balance Sheet. To compare the two forms, an analyst 

would have to disaggregate, or separate, these and 

other line items to identify the bond subcomponents 

(not currently possible with Y-9 data), and then reaggregate them to calculate a total comparable to the 

Phase 2 data entry.

Variations in Collection Frequency. Collection 

schedules can be expensive to change for regulators 

and companies because of the cost of systems and 

processes. The Y-9 is reported quarterly, but the 

Phase 2 data are to be reported monthly, with a lag of 

five working days. The goal is to report Phase 2 data 

weekly with a lag of three working days.

Standardization of data definitions and formats can 

address many of these differences (see Chapter 5). 

Another way to promote reporting efficiency is to collect 

Figure 6-9. Matching a Firm Across Datasets

LEI

Dataset + + FFIEC Federal ReserveNIC = Identifier Call Report Map

LEI LEI

A ENTITY

Legal Name Legal Name Legal Name Legal Name

Other IDs CERT CERT CERT ...

... Other IDs ID_RSSD ID_RSSD

...

... Tax ID Tax ID

CUSIP CUSIP

Other IDs Other IDs ... ... ... ...

...

1. Match the 2. Use CERT to 3. Pick up other 4. Add all IDs 

company name match FFIEC and IDs from Federal associated with 

in LEI and FFIEC Federal Reserve Reserve’s that company 

datasets (direct datasets (cross dataset into the Identifier 

matching) referencing) Map dataset

Source: OFR analysis
121 Addressing Data Gaps

data, not forms — or in other words, to transmit data 

using existing standard transmission protocols such as 

SDMX, XBRL, and ISO 2022, which provide definitions 

for the transmission and meaning of the data.

Working together, regulators should be able to collect 

the information they need and minimize the burden for 

reporting firms. Regulators that identify a need for a 

new collection could determine first whether the needed 

information already lies in existing collections that can be 

accessed through regulatory collaboration and data sharing, 

or modest reworking of the standards or definitions in 

existing collections.

Identifier Mapping

Analysts need connected sets of information about companies, industries, and markets to conduct financial stability 

analysis and monitoring.

Datasets from regulatory and commercial sources frequently 

use proprietary unique identifiers to identify firms, so 

matching firms to compare or link information about them 

across datasets can be difficult or even impossible. The 

Legal Entity Identifier (LEI), a unique identifier for firms in 

financial transactions, offers a solution to this problem, but 

companies and regulators need time to adopt and implement it fully (see Section 5.3). Until then, matching firms 

across datasets is a time- and resource-intensive undertaking.

To address this need, the OFR is creating an “identifier 

map” to enable research and analysis that may not otherwise 

be feasible. We intend to make it publicly available once 

complete. The identifier map will match firms and link their 

identifiers across regulatory and private vendor datasets, so 

the same firm in one dataset can be definitively identified in 

other datasets. It will enable information about a firm to be 

combined across multiple sources of data, allowing insights 

not possible by looking at each dataset in isolation.

The map is an example of a project made possible by the 

Interagency Data Inventory, showing how valuable collaboration among Council member agencies can be.

IDENTIFIER MAPPING PILOT

In 2014, the OFR’s pilot project mapped the LEI with: 1) 

the FDIC’s entity identifier, the certificate number (CERT), 

used in the FFIEC Consolidated Reports of Condition 

and Income (commonly known as the commercial bank 

Call Report), and 2) the Federal Reserve’s entity identifier 

(ID_RSSD), used in its National Information Center (NIC) 

database. 

The pilot used two techniques: 

1. Direct matching seeks an exact match of one or 

more fields between datasets. If any variation exists 

in the fields to be matched, it is not considered 

a match. We found matches between the “Legal 

Figure 6-10. Complexities of Creating an Identifier Map

Source: OFR analysis

LEI

Legal Name 

and Address

Federal Reserve

ID_RSSD

FDIC CERT

Key

Identifiers

Identifier 

Map

Other Unique

Identifiers

(i.e. CIK, CUSIP)

Potential 

Mappings

LEI

Legal Name

Address

ID_RSSD

CERT

Other Unique 

Identifiers

Entity A

Entity B

Entity ZZZ

Entity A

Entity B

Entity ZZZ

Entity A

Entity B

Entity ZZZ

Entity A

Entity B

Entity ZZZ

Other

Datasets

LEI

Federal 

Reserve 

NIC 

FFIEC

Call

Report

Datasets
122 2014 OFR Annual Report

The benefits of sharing data are largely understood. Still, 

there are obstacles to doing so appropriately. Regulatory 

agencies must maintain the security of confidential data, 

financial institutions need strong comfort on the appropriate regulatory sharing of their data, and regulatory agencies receiving shared data must provide assurances that the 

data will be appropriately interpreted. The OFR collaborates 

with Council member agencies to overcome these obstacles. 

We also collaborate with researchers and research organizations in analysis and monitoring.

INTERAGENCY FRAMEWORK FOR INFORMATION 

SECURITY CATEGORIZATION

In 2013, OFR co-chaired a working group of the Council 

Data Committee that recommended a common information security categorization framework for communicating 

and ensuring the security of shared data among Council 

member agencies. The goal is to ease data sharing and 

ensure the receiver of shared data keeps them at least as 

secure as the data provider does.

The framework sets out principles for data sharing and 

responsibilities between agencies. It also establishes procedures that emphasize joint communication and control 

and required documentation of the data request process. 

The framework leverages existing information security 

guidance issued by the National Institute of Standards and 

Technology (NIST) for federal agencies. 

The framework is voluntary and agencies may choose 

whether or not to adopt it. We believe, if adopted, it will 

improve secure information sharing. The OFR follows this 

framework for information sharing.

OFR’S SECURING DATA FOR COLLABORATION

To appropriately protect data that others share and entrust 

with us, the OFR takes legal and technical steps to secure 

the data and protect its confidentiality. For example, we and 

Council member agencies have signed a memorandum of 

understanding (MOU) governing the treatment of nonpublic data. We continue to enter into other nondisclosure 

agreements and additional MOUs as necessary to protect 

and preserve data confidentiality and outline the responsibilities of data users. We also work to ensure the proper 

handling of data through education and training, written 

policies, securing files at rest and in transit, encrypting files 

for transmission, and signed confidentiality agreements.

The OFR purchases data from commercial vendors and 

is bound by licensing agreements. These licenses provide 

Name” fields of the LEI dataset and the call report. 

Matches between the “Legal Name” fields allowed 

for company matches between the two datasets and 

mapping of their identifiers (LEI and CERT).

2. Cross-referencing verifies the results of the direct 

match and adds commonly shared identifiers to the 

final merged identifier map. The FDIC’s certificate 

number is in the FFIEC’s call report and the NIC 

database. We used the certificate number for further

verification of the firms mapped through the “Legal 

Name” fields. The cross referencing also allowed us 

to add identifiers from the NIC database, such as 

the Federal Reserve’s entity identifier (ID_RSSD), 

the tax ID number (an entity identifier used by the 

Internal Revenue Service), and the CUSIP (a widely 

used entity identifier used in financial transactions), 

making the identifier map a more information-rich 

resource.

Figure 6-10 shows the connections between the datasets we mapped and the complexity of the process and 

relationships.

Figure 6-9 shows the process for matching a single company 

across the LEI, FFIEC call report, and NIC database, and 

creation of the identifier map. The pilot showed us that, 

although algorithms exist to aid the mapping process, mapping identifiers is still a heavily manual process.

We believe the identifier mapping project will prove valuable 

to the OFR, the Council, other researchers, and the public. 

We intend to expand the scope of the project and release 

results as they develop.

Once the LEI has been fully adopted and embedded in 

industry and regulatory datasets, it will allow datasets that 

contain LEIs to be merged based on the company LEI, 

minimizing the need for time-intensive, manual entity resolution and identifier mapping.

Securing Data for Collaboration and Sharing

Data on a broad range of activities and markets across 

the financial system must be available and easily accessible 

for monitoring and analysis. However, no single regulator 

has all needed data, and it is critical to not duplicate data 

collections. Consequently, it is imperative that the OFR and 

Council member agencies collaborate and share data for 

financial stability analysis.
123 Addressing Data Gaps

access to data and restrict some types of data sharing. 

However, OFR can often use such data to create work products which can subsequently be shared. In other cases, the 

OFR establishes agreements with noncommercial providers, 

such as financial market utilities, for use of their data for our 

research and analysis.

OFR INFORMATION SECURITY PROGRAM

Our information security program employs a variety of 

technologies to safeguard data security through transmission, storage, access control, and dissemination or 

publication.

Transmission. Our data sharing agreements set standards 

for handling highly sensitive data, using the information 

security categorization framework as a minimum standard. The OFR also enters into interconnection security 

agreements, which go a step beyond MOUs to outline 

expected behaviors for incident handling and notification 

procedures. The agreements are developed in accordance 

with NIST Special Publication (SP) 800-47, Security Guide for 

Interconnecting Information Technology Systems (see NIST, 2002).

When the OFR requests data, we work closely with the data 

provider to address any unique security requirements. Once 

we receive the data, we continuously monitor for any anomalous activities and potential signatures that may indicate a 

cyberattack or an unauthorized access attempt.

Storage and Access Control. To protect data stored and 

accessed by OFR employees, we constructed a new analytic environment and adopted a continuous monitoring 

approach, with proactive security measures to prevent, 

detect, and respond to potential attacks and attempts to gain 

unauthorized access. We completed an independent security 

assessment and authorization of the analytic environment in 

accordance with standards from NIST SP 800-53 Revision 

4, Security and Privacy Controls for Federal Information Systems 

and Organizations (see NIST, 2013). We also completed an 

external penetration test conducted by an independent third 

party. The analytic environment achieved full accreditation, 

which verifies that an information technology system has 

passed a host of rigorous security checks based on NIST 

guidelines.

To guard against potential access by unauthorized individuals, we conduct routine access reviews and security posture 

assessments, and require multiple levels of approval before 

granting staff members access to data. We developed an 

access control and management application and employ a 

role-based access control model that incorporates physical 

access controls, technical controls over network connections, 

and frequent internal audits. The access control model also 

allows security groups and policies to be applied at a detailed 

level, ensuring a high degree of oversight and control.

Dissemination and Publication. The OFR is also evaluating technologies to promote collaboration and dissemination of data, while ensuring that data remain secure. One 

technology uses virtual computing platforms that allow 

users (such as researchers working outside our offices) to 

work collaboratively without affecting the overall security of 

the environment.

Another collaboration mechanism uses secure peer-to-peer 

technologies that meet the security requirements of FIPS 

140-2, Security Requirements for Cryptographic Modules (see NIST, 

2001) and allows secure file sharing among federal agencies.

OFR RESEARCH ON SHARING AND SECURITY

In 2014, our researchers continued to explore a cryptographic method called “noise addition,” which is related 

to common data anonymization and masking techniques. 

Noise addition adds statistical noise to a dataset, so no 

information is initially available. Over time, the variance 

of the added noise is reduced, gradually revealing more 

information. This technique might allow us to secure data 

to collaborate with other regulators and appropriately share 

data with agencies, researchers, and potentially the public 

while protecting confidentiality.

OFR researchers also continue to explore other cryptographic methods that could help with safe and effective 

sharing of data, as described in a 2013 OFR working paper 

(see Flood and others, 2013). The purpose of this work is to 

improve access to data that would otherwise be confidential, 

closing data gaps for market participants and the public. 

Tension exists between making data available and protecting 

those data, and this research focuses on easing that tension.

125

7 OUTREACH, 

COLLABORATION, AND

 INFRASTRUCTURE

Outreach, Collaboration, and Infrastructure

Collaboration is central to the mission of the OFR and includes 

outreach to Congress, Council member agencies, international 

financial regulators, academic researchers, industry groups, and the 

public. Since 2010, we have assembled a virtual research community of 

global financial stability experts and, at the same time, built a highly 

skilled OFR workforce and cost-effective operational systems.

7.1 Collaboration and Outreach Drive the OFR’s Work

In FY 2014, the OFR partnered with the CFTC to help improve how swap data repositories collect and 

standardize data about derivatives trades. The OFR also collaborated with other regulators and industry 

internationally and in the United States as the Office played a key role in developing, launching, and rolling 

out the global Legal Entity Identifier (LEI) system to help map connections in the financial system that 

will cut industry costs for cleaning, combining (aggregating), and reporting data.

The OFR regularly reaches out to academic and financial industry 

groups for input about emerging issues in financial stability and 

financial data standards. In FY 2014, we cosponsored two conferences 

that featured leading experts on monitoring and measuring risks to the 

financial system.

• In January 2014, the OFR and the Council cosponsored their 

third joint conference. The event, entitled “Mapping and 

Monitoring the Financial System: Liquidity, Funding, and 

Plumbing,” explored the interconnectedness among firms and 

markets as well as other vulnerabilities in the financial system.

• 

We also chaired the interagency planning committee of the annual Regulatory Data Workshop in August 

2014. More than 300 employees of Council member agencies and regional Federal Reserve banks attended 

to share potential solutions to common financial data-related problems. The government-only event 

discussed ways to improve financial data standards and governance, strategies to visually represent data 

for analysis, techniques to securely collect and store data, and protocols to safely share data with other 

regulators.

We collaborate with 

others to maximize 

our resources, support 

financial stability research, 

and promote standards 

that will help produce 

reliable, high-quality 

data about the financial 

system. In June 2014, we joined the Consortium for Systemic Risk 

Analytics and the Massachusetts Institute of Technology’s 

Laboratory for Financial Engineering in sponsoring the “Conference on Systemic Risk” in 

Cambridge, Massachusetts. The event explored four broad aspects of the measurement of financial 

stability: risk data, stress testing, market-based measures, and measurement of financial networks.
126 2014 OFR Annual Report

Our director and senior managers also speak frequently at 

industry and academic events to explain the OFR’s projects and discuss potential threats to financial stability. The 

following speeches during the 2014 fiscal year are published 

on our website:

• Director Richard Berner, at the Money Marketeers of 

New York University, “Financial Stability: Progress 

and Challenges,” October 16, 2014;

• Director Berner at the Joint Conference of the Cente

for Financial Policy at the Robert H. Smith School 

of Business at the University of Maryland and The 

Clearing House, “The Financial Industry in a PostCrisis World Symposium,” July 10, 2014;

r 

• Chief Data Officer Linda Powell at the Object 

Management Group Technical Meeting, March 26, 

2014;

• Chief Data Officer Powell at the GS1 Global Forum 

2014, February 18, 2014; and, 

• Director Berner at the Exchequer Club of 

Washington, October 16, 2013.

We welcome groups or companies that want to share information with the OFR on issues related to financial stability 

or data standards. Because of the international nature of 

financial markets, we are building relationships with authorities in other countries to discuss research and analysis and 

promote information sharing and the use of data standards 

around the world.

Financial Research Advisory Committee

Our work is enhanced by the insights and expertise of the 

OFR’s Financial Research Advisory Committee, a group of 

31 distinguished professionals in economics, data management, risk management, information technology, and other 

fields. The committee, established in November 2012, meets 

approximately twice each year. It has three subcommittees 

that meet more frequently and are focused on research; data 

and technology; and financial services and risk management.

In 2014, the committee welcomed 11 new members to 

replace members whose terms expired. Current members 

include a former vice chairman of the Board of Governors 

of the Federal Reserve, a former chief economist of the 

Securities and Exchange Commission, and senior data and 

risk management executives at major financial services 

companies.

OFR Responsibilities Under 

the Dodd-Frank Act

The Dodd-Frank Wall Street Reform and Consumer 

Protection Act of 2010 established the OFR and 

its Data Center and Research and Analysis Center. 

The law requires the OFR to support the Financial 

Stability Oversight Council and to carry out the 

following responsibilities:

DATA CENTER

• Collect data on behalf of the Council and the 

public;

• Assist Council member agencies in determining the types and formats of data 

authorized by the Act to be collected;

• Standardize the types and formats of data 

reported and collected;

• Prepare and publish a financial company 

reference database and a financial instrument 

reference database; and,

• Perform other related services.

RESEARCH AND ANALYSIS CENTER

• Develop and maintain metrics and reporting 

systems for risks to U.S. financial stability;

• Monitor, investigate, and report on changes in 

systemwide risk to the Council and 

to Congress;

• Conduct, coordinate, and sponsor research 

to improve regulation of financial entities and 

markets;

• Evaluate and report on stress tests or other 

stability-related evaluations of financial entities 

overseen by Council member agencies;

• Maintain the expertise necessary to support 

specific requests for assistance from financial 

regulators;

• Investigate disruptions and failures in the 

financial markets, report findings, and make 

recommendations to the Council;

• Conduct studies and provide advice on the 

impact of policies related to systemic 

risk; and

• Promote best practices for financial risk 

management.
127 Outreach, Collaboration, and Infrastructure

information collected from financial contracts can be used 

to determine economic risk exposures. A third grant of 

$250,000 over two years went to a University of Michigan 

researcher to study the stability of dynamic credit networks 

by applying recent developments in economics and computer science to risk analysis.

Throughout the year, we invited experts from financial regulatory agencies, universities, and industry who are exploring 

new financial stability theories to discuss their work with our 

staff in a collaborative forum where ideas can be tested and 

expanded. This series of in-house research seminars is just 

one of the ways the OFR leverages the expertise of staff 

members and promotes the continuing exploration of issues 

related to financial stability.

Public Transparency

The OFR is committed to sharing nonconfidential financial 

stability information with the public on our website.

In FY 2014, we posted the public portion of the 

Interagency Data Inventory, which catalogs the data that 

Council member agencies buy from vendors, collect from 

industry, or derive from other data. This portion of the 

inventory included more than 300 items that U.S. regulators 

collect from industry, typically on a monthly, quarterly, or 

annual basis.

OFR’s financial stability conferences and advisory committee meetings are also accessible to the public through 

webcasts. Although much of the OFR’s work products 

cannot be made public because of confidential and market-sensitive data, we strive to be transparent whenever 

possible. Traffic has steadily increased on the OFR’s website 

and total visitors rose more than 35 percent in FY 2014 

from the previous year. When we post significant content 

on our website, we send an alert to website subscribers. At 

the end of FY 2014, we had nearly 6,000 subscribers signed 

up to receive e-mail alerts, more than double from the end 

of the previous year.

OFR speeches, Congressional testimony, press releases, and 

information about public conferences and events are posted 

online at www.treasury.gov/ofr.

Congressional Affairs

We meet frequently with Members of Congress and their 

staffs to keep lawmakers informed of our work and to discuss financial stability issues. OFR Director Berner testified 

At the committee’s most recent meeting in July 2014, 

members addressed two discussion topics suggested by the 

OFR: 1) the impact of reduced liquidity on financial markets during stress events, and 2) the metrics regulators need 

to analyze and aggregate data about the swap market. The 

committee also adopted and presented to the OFR a recommendation for the Office to begin a project to evaluate how 

standardized definitions (known collectively as ontologies) 

could help the CFTC and OFR improve data quality in swap 

data repositories.

Working Papers, Grants, and Seminars

The OFR Working Paper Series is an important tool for 

disseminating OFR research and informing the process of 

assessing, measuring, monitoring, and mitigating threats 

to financial stability. The series is also a significant collaboration tool because papers are frequently coauthored by 

OFR staff researchers with outside experts from academia, industry, and other federal agencies. At the end 

of November 2014, the OFR had published a total of 21 

working papers on subjects ranging from risk management 

to stress tests to shadow banking.

The OFR launched a new research product in FY 2014, the 

OFR Staff Discussion Paper Series, for our staff researchers 

to share more of their work with the academic community 

and the public. Three discussion papers were published as 

part of the new series that contributes to our understanding 

of financial markets, financial data, and financial institution 

risks. In the coming year, the OFR will continue fine-tuning 

and improving our Financial Stability Monitor to keep the 

public informed about vulnerabilities. The monitor, initially 

released in our 2013 annual report and updated in this 

report, tracks financial distress based on a mix of economic 

indicators, market indexes, and measurements calculated by 

the OFR.

To supplement the work of our staff members in the OFR’s 

Research and Analysis Center, we brought outside experts 

on board for fellowships and other temporary employment. 

We also awarded three grants in FY 2014 to promote financial stability research. A University of Maryland researcher 

received a grant of approximately $300,000 over two years 

to research whether information in companies’ 10-K annual 

filings with the SEC can be extracted, aggregated, and 

interpreted to help identify emerging risks to financial stability. Another grant of the same size went to a University 

of South Florida researcher to explore whether detailed 
128 2014 OFR Annual Report

OFR’s Vision, Mission, and 

Goals for FY 2015-19

Vision: A transparent, efficient, and stable financial system.

The OFR’s mission is to promote financial stability 

by delivering high-quality financial data, standards, 

and analysis for the Council and the public.

Goal: The OFR is an essential source of data 

and analysis for monitoring threats to financial 

stability. 

• The OFR’s monitoring tools and analyses are 

widely used and critical to assessing financial 

stability.

• Data used to monitor financial stability 

are comprehensive, reliable, and 

accessible to policy makers and the 

public through the OFR.

• Data providers and the public recognize that 

OFR data are protected and secure.

Goal: Standards that improve the quality 

and utility of financial data are identified and 

adopted. 

• Industry and policy makers recognize the need 

for standards.

• The OFR is the source of expert 

knowledge needed to develop and 

implement types and formats of data reported 

and collected.

• Financial data standards that create 

efficiencies and facilitate analysis are widely 

used.

Goal: Leading edge research improves financial 

stability monitoring and the scope and quality 

of financial data, and informs policy and risk 

management. 

• The OFR is the recognized center for 

objective, innovative research on 

financial stability.

• OFR research is widely cited and used to 

improve policy making, risk management, 

financial stability, and the scope and quality of 

financial data collections. 

in January 2014 before the Senate Banking Subcommittee 

on Economic Policy and in February 2014 before the 

House Financial Services Subcommittee on Oversight and 

Investigations.

At both hearings, the Director described the Office’s accomplishments, priorities, and efforts to enhance transparency 

and accountability. In addition, the Director gave a presentation on Capitol Hill for Congressional staff members 

in April 2014 for the Capital Markets 101 Distinguished 

Speakers Series. During the presentation, the Director 

discussed the OFR’s mission and highlighted some of the 

Office’s significant work.

7.2 OFR Vision, Mission, and 

Operations 

The OFR’s mission is to promote financial stability by delivering high-quality financial data, standards, and analysis for 

the Council and the public. Our strategic plan will guide our 

goals and objectives in fiscal years 2015-19. Through our 

work, we aim to move toward the vision of a “transparent, 

efficient, and stable financial system.”

Strategic Plan for FY 2015-19

The statutory mandates in the Dodd-Frank Act and the 

OFR’s strategic plan are the foundation for our mission, 

vision, goals, objectives, and strategies. The FY 2015-19 plan 

builds upon the strategic framework covering FY 2012-14, 

and guides our next stage of development. The new plan 

sets three strategic goals, each with specific objectives and 

strategies, to fulfill our Congressional mandate and produce 

value for stakeholders (see OFR’s Vision, Mission, and 

Goals for FY 2015-19). The strategic plan, which will be 

released shortly, also serves as a guide for us in setting priorities and allocating resources over the next five years.

The financial system will continue to change over the life 

of this strategic plan and will require the OFR to continually review its strategies for effectiveness in achieving the 

organization’s goals and objectives to deliver on its mission. 

As a result, strategies and tactics may change related to data 

management, financial data standards, research that the 

organization pursues, and initiatives aimed at building institutional capabilities. All these efforts will focus on delivering 

ever-increasing value for our stakeholders. 
129 Outreach, Collaboration, and Infrastructure

Performance

The OFR established initial performance measures in 2012 

tied to its then-current strategic goals. The measures were 

designed to track our performance in achieving the OFR’s 

goals and objectives. The measures are continually reviewed 

for effectiveness in assessing the performance of the 

organization, and changes are implemented as appropriate. 

These measures are shared annually with the public in the 

President’s Budget.

Tracking performance measures is only one way to manage 

performance. We will also use qualitative information 

to assess our performance, and we will continue to use 

comprehensive performance reviews to check progress and 

make appropriate course adjustments.

Budget

The Dodd-Frank Act directed the Department of the 

Treasury to establish a schedule to collect assessments to 

pay the expenses of the Office, the Council, and certain 

expenses for the implementation of the FDIC’s orderly 

liquidation authority.

Treasury finalized a rule in 2012 that enabled the Office to 

collect semiannual assessments from bank holding companies with total consolidated assets of $50 billion or greater 

and nonbank financial companies supervised by the Board 

of Governors of the Federal Reserve. The initial assessment 

on July 20, 2012, was based on a fee rate of about $7,700 

per $1 billion of assets held by the assessed companies. The 

semiannual fee rate has since declined to about $3,000 per 

$1 billion of assets held by the assessed companies.

In FY 2014, the OFR spent about $82.7 million. Our 

estimated budget for FY 2015 is $99.5 million. Details are 

provided annually in the President’s Budget.

Our budgets are developed following the Office of 

Management and Budget’s Budget Circular A-11 guidance. 

The OFR’s financial management process follows Treasury 

policies, and our financial activities and controls are 

reviewed as part of the Department’s consolidated audit. To

strengthen our stewardship of the funds entrusted with us, 

we have also developed additional rigorous internal controls,

project review mechanisms, and decision-making protocols 

to monitor spending effectively. Treasury’s Office of the 

Inspector General and the Government Accountability 

Figure 7-1. Current and Target Workforce

Research and

Analysis Center

Data

Center

Ofce of the Director

and Support Ofces 85

150

67

60

108

44

Source: OFR analysis

Because of the international nature 

of financial markets, we are building 

relationships with authorities in other 

countries to discuss research and 

analysis and promote information 

sharing and the use of data standards 

around the world.

Office periodically audit OFR governance, processes, procedures, and activities.

Information Technology

The Dodd-Frank Act requires the OFR to maintain 

adequate information technology systems for data acquisition, management, analysis, and dissemination, applying 

strict rules for security and data sharing. In FY 2014, we 

completed the installation of a robust, long-term analytical 

environment for storing and handling large amounts of data 

to support complex financial models, computations, and 

analysis. The Office has continued to install a wide range of 

security tools and components to strengthen its already high 

security protections. 
130 2014 OFR Annual Report

7.3 OFR Organization and 

Workforce

The OFR continues to hire specialized and highly qualified 

employees for our Research and Analysis Center, Data 

Center, and other operations.

The Dodd-Frank Act requires that a Director lead the OFR 

and oversee its two major components: the Data Center and 

the Research and Analysis Center. One of our priorities is to 

recruit and hire specialized and highly trained employees to 

fulfill our mission. We continue to build an organization and 

culture of trust, accountability, and urgency to attract and 

retain talented individuals.

The OFR staff has increased from 30 in FY 2011 to 224 at 

the end of FY 2014. Our target for FY 2015 is to reach a 

total workforce of approximately 300, including permanent, 

reimbursable, and detailed staff members (see Figure 7-1).

When fully staffed, the OFR will have about 50 percent of its workforce in the Data Center. The Research 

and Analysis Center will constitute about 22 percent 

of the OFR’s staff, complemented by a broad network 

of resources through work arrangements with outside researchers and collaboration with other Council 

members. The Director’s office and support functions 

(Counsel, External Affairs, and Operations) will constitute the remaining 28 percent of the OFR workforce (see 

Figure 7-2).

Most of our staff members are located at the OFR’s 

Washington, D.C., headquarters. We maintain a small office 

in New York City to support our research and data initiatives and to facilitate regular contact with regulators, data 

providers, academics, and financial market participants. The 

OFR also has a small number of work arrangements with 

contributors outside Washington, D.C., and New York to 

support research collaboration with academics.

During FY 2014, the OFR focused on developing a highcaliber workforce and producing research and data products. 

We recruited to fill vacant positions, aligned functions and 

resources within the Office to meet stakeholder 

and staff needs, and developed current staff members to 

address critical skills gaps.

In FY 2015, we will continue to review, refine, and expand 

our human capital strategies, while ensuring alignment with 

the OFR’s FY 2015-19 strategic plan.

Through our work, we aim to move 

toward the vision of a “transparent, 

efficient, and stable financial system.”

Ofce of 

the Director 

and Support 

Ofces

28%

Research 

and Analysis

Center

22%

Data 

Center:

Technology

23%

Data 

Center:

Data

27%

Source: OFR analysis

Figure 7-2. Planned Workforce Distribution
8

131 Agenda Ahead

AGENDA AHEAD

We will soon publish a detailed, five-year strategic plan about our 

strategic goals and their implications for our direction in coming 

years. The three goals in the plan will also drive the OFR’s research and 

data agenda for 2015, as follows: 

• We will provide critical data and analysis for monitoring threats to financial stability. A top priority 

is our joint project with the Federal Reserve to collect repo data from firms on a voluntary basis. 

We will also improve our Financial Stability Monitor, publish a Financial Markets Monitor, and 

expand the suite of dashboards, monitors, metrics, and other tools that we offer the Council.

• We will help develop and promote standards that improve the quality and utility of financial data. 

We will work to further integrate the Legal Entity Identifier (LEI) in regulatory reporting and 

business practices, collaborate with the CFTC to promote standards in derivatives markets, and 

create prototype entity and instrument reference databases to promote market transparency.

• We will conduct and publish leading edge research to improve financial stability monitoring and 

inform policy and risk management. Key projects will focus on macroprudential policy, stress tests, 

agent-based models, and innovative tools that can promote financial stability analysis. We will also 

conduct and publish research related to short-term wholesale funding, credit default swaps, hedge 

funds, and other important financial activities.

Research Priorities

Our research activities encompass financial stability monitoring, macroprudential policy evaluation and 

analysis, and basic research to contribute to our understanding of vulnerabilities in the financial system.

We will broaden our monitoring framework tools in 2015. When possible, we will make our analysis 

available to the public, using appropriate techniques to ensure the security and confidentiality of nonpublic data. In the near term, we plan to share with the public a version of our monthly Financial Markets 

Monitor, which we currently present to the Council. We will develop additional monitoring products 

focused on money market funds, hedge funds, and credit default swap markets. We will also publish 

working papers describing in greater detail how we produce our Financial Stability Monitor and our 

Financial Stress Index, as well as a series of shorter, less technical papers on emerging threats to financial 

stability. 

We will expand our contribution to the analysis and debate about the macroprudential policy toolkit in the 

coming year. The Office has an important mandate under the Dodd-Frank Act to conduct studies and 

research on regulation, conduct studies and provide advice on the impact of financial stability policy, and 

evaluate and report on stress tests and best practices in risk management. We are in a position to objectively 

evaluate and study such policies because we do not make policy. Our contribution may include research on 

what tools are available, how they work, and how they complement or conflict with other policy goals. We 

continue to build our policy analysis team to broaden our expertise across financial institutions and markets, 

with a focus on banking, insurance, and asset management. A key debate for 2015 will revolve around the 
132 2014 OFR Annual Report

current cyclical excesses in some markets and the effectiveness of the tools that policymakers now have to address 

them (see Chapter 3).

The OFR’s basic research supports our monitoring and 

policy analysis work. We will publish several papers in 2015 

resulting from the three research streams described in 

Chapter 4 — visualization, agent-based modeling, and the 

analysis of credit derivatives markets. Our research program 

on agent-based modeling has resulted in two working papers 

so far. In this annual report, we described a preliminary 

use of the tool to analyze the risk of contagion in a specific market segment (see Assessing the Vulnerability of 

Agency mREITs in Chapter 2). More research on agentbased models will come in 2015. We will also publish several 

papers on short-term wholesale funding markets, including a 

reference guide on repurchase agreement (repo) and securities lending markets.

Data Priorities

In the coming year, we will advance two key aspects of our 

data agenda: 

1. to develop and promote financial data standards, 

and 

2. to identify and fill gaps in the data that analysts 

need to monitor and evaluate threats to financial 

stability.

The Legal Entity Identifier (LEI) system is now up and 

running and growing quickly. To realize its full benefits, it is 

essential that the LEI become widespread in business practices. The OFR and the Council have called on regulators 

to require use of the LEI in regulatory reporting, beginning 

with large, complex financial companies and market participants. We note in Section 5.3 several examples of regulatory 

reports in which agencies have already adopted or proposed 

the use of the LEI. The LEI system generates efficiencies 

for financial companies in internal reporting, risk management, and in collecting, cleaning, and aggregating data. We 

believe it will decrease overlap and duplication in regulatory 

reporting, reducing the reporting burden for companies. 

Another priority is our work with the CFTC and other 

regulators to promote the use of data standards in trade 

repository reporting. Our joint project with the CFTC seeks 

to enhance the quality, types, and formats of data collected 

from registered swap data repositories. In 2015, OFR and 

the CFTC will make progress in establishing standards and 

data harmonization. 

The Dodd-Frank Act mandate for the OFR to prepare a

publish reference databases for financial entities and fina

cial instruments is a top priority. The global LEI system

e

nd 

nwill meet the requirement for an entity reference databas . 

In 2015, we will build a prototype reference database for

financial instruments.

The repo data collection pilot is a signature project for the 

OFR (see Section 6.2). The pilot marks the first time we 

are going directly to financial companies to collect data. 

Participation is voluntary. Companies that participate will be 

asked for input on what data should be collected. We expect 

to begin collecting data early next year. We intend to publish 

aggregated data from the survey to provide greater transparency into the bilateral repo market for participants and 

policymakers. We expect the project will be a template for 

future data collection efforts.

The OFR will continue to collaborate broadly to share data 

and to design and implement financial data standards that 

give U.S. supervisors and their foreign counterparts a more 

accurate and global picture of the financial risks assumed by 

the entities they oversee.

Institutional Priorities

In 2015, we plan to further align our efforts to meet the 

three strategic goals in our new strategic plan (see Chapter 

7). The strategic plan will help guide our long-term investment in people, processes, and technologies, while effective 

strategy management will ensure we remain responsive to 

changes in technology, stakeholder needs, and the financial 

system. 

In 2014, the OFR completed the installation of the secure 

analytic environment needed to collect, process, store, 

manage, administer, and analyze large and complex datasets. 

In 2015, we plan to deploy the initial release of the new 

OFR public website. We will also build an improved intranet 

for the OFR staff, expand the capabilities of our virtual 

research community offerings, and continue automating 

business processes. 

We will continue in 2015 to identify and fill critical staffing 

gaps, based on the capabilities and competencies needed to 

achieve OFR strategic goals and objectives through 2019 

and consistent with our forthcoming FY 2015-19 strategic 
133 Agenda Ahead

plan. Recruiting and training are also essential to build the 

capabilities needed to analyze risks to financial stability, fill 

data gaps, and promote financial data standards, and we 

will continue to allocate resources toward these essential 

activities.

We will continue to build strong relationships with our 

stakeholders. We will work closely with the Council and 

its member agencies and we will engage with Congress to 

ensure that Congress is apprised of our activites. We will 

accelerate our outreach and collaboration through our 

network of outside researchers, academics, industry experts, 

and others. We continue to receive valuable recommendations from our external Financial Research Advisory 

Committee. We will expand the grants program, in collaboration with the National Science Foundation, and will 

continue to sponsor conferences and research on financial 

stability and related topics.

A.1

A

Glossary

GLOSSARY

Accommodation Expansionary monetary policy in which a central bank seeks to lower borrowing 

costs for businesses and households to make credit more easily available.

Agency Mortgage-Backed 

Securities

A mortgage-backed security issued or guaranteed by federal agencies or 

government-sponsored enterprises.

Advanced Approaches Under Basel III, the standard that U.S. banks with $250 billion or more in 

consolidated assets, or $10 billion or more in foreign exposures, must use to 

calculate risk-weighted assets. The advanced approaches require models based upon 

a bank’s experience with its internal rating grades. Smaller banks use a standardized 

approach that sets risk weights for asset classes. 

Bank for International 

Settlements (BIS)

An international financial organization that serves central banks in their pursuit of 

monetary and financial stability, helps to foster international cooperation, and acts 

as a bank for central banks.

Bank Holding Company (BHC) Any company that has direct or indirect control of one or more banks and is 

regulated and supervised by the Federal Reserve under the Bank Holding Company 

Act of 1956. BHCs may also own nonbanking subsidiaries such as broker-dealers 

and asset managers.

Basel Committee on 

Banking Supervision (BCBS)

An international forum for bank supervisors that aims to improve banking 

supervision worldwide. The BCBS develops guidelines and supervisory standards 

such as standards on capital adequacy, the core principles for effective banking 

supervision, and recommendations for cross-border banking supervision. 

Basel III A comprehensive set of global regulatory standards for bank capital adequacy 

and liquidity. The reform measures, published in 2010 by the Basel Committee on 

Banking Supervision, introduced a leverage ratio along with two liquidity standards: 

the liquidity coverage ratio and the net stable funding ratio.

Call Report A quarterly report of a bank’s financial condition and income that all federally 

insured U.S. depository institutions must file.

Capital Requirement The amount of capital a bank must hold to act as a cushion to absorb unanticipated 

losses and declines in asset values that could otherwise cause a bank to fail. U.S. 

banking regulators require banks to hold more high-quality, or Tier 1, capital 

against total risk-weighted assets under the Basel III international accord. Banks are 

classified as well capitalized, adequately capitalized, undercapitalized, significantly 

undercapitalized, or critically undercapitalized based on regulators’ capital and 

leverage calculations.

Captive Reinsurance Company A subsidiary entity that provides insurance for its parent company.

Carry Trade An investment strategy involving borrowing at low interest rates to purchase assets 

that yield higher returns.

Central Clearing A settlement system in which securities or derivatives of a specific type are cleared 

by one entity, a clearinghouse or central counterparty, which guarantees the trades. 

It is an alternative to bilateral or over-the-counter trading (see Over-the-Counter 

Derivatives).

Photo credit: © iStockphoto/polygraphus
2014 OFR Annual Report A.2

Central Counterparty (CCP) An entity that interposes itself between counterparties to contracts traded in one or 

more financial markets. A CCP becomes the buyer to every seller and the seller to 

every buyer to help ensure the performance of open contracts.

Clearing Bank A commercial bank that facilitates payment and settlement of financial transactions, 

such as check clearing or matching trades between the sellers and buyers of 

securities and other financial instruments or contracts.

Clearing Member A member of, or a direct participant in, a central counterparty (CCP) that is entitled 

to enter into a transaction with the CCP.

Clearing A system that facilitates the transfer of ownership of securities after they are 

traded. 

Clearinghouse See Central Counterparty.

Collateral Any asset pledged by a borrower to guarantee payment of a debt.

Collateralized Loan Obligation 

(CLO)

Securities that hold pools of corporate loans and are sold to investors in tranches 

with varying levels of risk.

Commercial Paper (CP) Short-term (maturity of up to 270 days), unsecured corporate debt.

Comprehensive Capital 

Analysis and Review (CCAR)

The Federal Reserve’s annual exercise to ensure that the largest U.S. bank holding 

companies have robust, forward-looking capital planning processes that account for 

their unique risks and sufficient capital for times of financial and economic stress. 

The exercise also evaluates the banks’ individual plans to make capital distributions 

such as dividend payments or stock repurchases. 

Concentration Risk Any single exposure or group of exposures with the potential to produce losses 

large enough to threaten a financial institution’s ability to maintain its core 

operations.

Conditional Value at Risk 

(CoVaR)

A measure of the value at risk of the financial system conditional on distress at a 

single financial institution, from Adrian and Brunnermeier (2011).

Correlation Risk The risk that the value of two or more assets will move in tandem, increasing a 

portfolio’s volatility and potentially leading to large, simultaneous losses. Correlation 

risk is typically mitigated through hedging.

Countercyclical The movement of a financial or macroeconomic variable in the opposite direction 

of the business or credit cycle (see Procyclical).

Countercyclical Capital Buffer A policy requiring banks to build capital buffers during favorable economic periods 

that can be used to absorb losses in unfavorable periods.

Counterparty Risk The risk that the party on the other side of a contract, trade, or investment will 

default.

Covenant-lite Loans Loans that do not include typical covenants to protect lenders, such as requiring the 

borrower to deliver annual reports or restricting loan-to-value ratios.

Credit Default Swap (CDS) A bilateral contract protecting against the risk of default by a borrower. The buyer 

of CDS protection makes periodic payments to the seller and in return receives a 

payoff if the borrower defaults, similar to an insurance contract. The protection 

buyer does not need to own the loan covered by the swap.

Credit Risk The risk that a borrower may default on its obligations.

Credit Spread The difference in yield between a security and an otherwise similar security of 

higher quality.
A.3 Glossary

Cyclical Risk Any financial or economic risk that is closely tied to the business cycle.

Dark Pools Private electronic trading venues, also referred to as alternative trading systems, that 

allow institutional investors to anonymously buy and sell securities, primarily stocks. 

Unlike stock exchanges, dark pools do not publish pretrade prices for offers to buy 

and sell, and report transactions to regulators after a trade is executed.

Derivative A financial contract whose value is derived from the performance of underlying 

assets or market factors such as interest rates, currency exchange rates, and 

commodity, credit, and equity prices. Derivative transactions include structured debt 

obligations, swaps, futures, options, caps, floors, collars and forwards.

Distressed Insurance Premium 

(DIP)

An indicator of a firm’s vulnerability to systemic instability. DIP uses information 

from credit default swap spreads and equity prices to measure the implied cost of 

insuring a given firm against broader financial distress.

Dodd-Frank Act Short name for the Dodd-Frank Wall Street Reform and Consumer Protection 

Act of 2010, the most comprehensive financial reform legislation in the United 

States since the Great Depression. The Dodd-Frank Act seeks to promote financial 

stability by improving accountability in the financial system, adding transparency 

about

over-the-counter (OTC) derivative markets, and protecting consumers from abusive 

financial services practices. 

Duration Risk The risk associated with the sensitivity of the prices of bonds and other fixedincome securities to changes in the level of interest rates.

Emerging Markets (EM) Developing countries where investments are often associated with both higher 

returns and higher risk. EM countries fall between developed markets such as the 

United States and frontier markets that are more speculative.

Eurozone A group of 18 European Union countries that have adopted the euro as their 

currency.

Exchanged-Traded Fund An investment fund whose shares are traded on an exchange. Because ETFs are 

exchange-traded products, their shares are continuously priced unlike mutual funds 

which offer only end-of-day pricing. ETFs are often designed to track an index or a 

portfolio of assets.

Fair Value Models Models for determining the value of an asset based on the price at which the asset 

could be bought or sold between two willing parties.

Federal Financial Institutions 

Examination Council (FFIEC)

An interagency body that prescribes uniform principles, standards, and report 

forms for the federal examination of financial institutions. The FFIEC makes 

recommendations to promote uniformity in banking supervision. Members 

include the Federal Reserve, the FDIC, the NCUA, the OCC, the CFPB, and a 

representative of state financial supervisors.

Financial Contagion A scenario in which financial or economic shocks initially affect only a few financial 

market participants then spread to other financial sectors and countries in a manner 

similar to the transmission of a medical disease. Financial contagion can happen at 

both the international level and the domestic level.

Financial Intermediation Any financial service in which a third party or intermediary matches lenders 

and investors with entrepreneurs and other borrowers in need of capital. 

Often investors and borrowers do not have precisely matching needs, and the 

intermediary’s capital is put at risk to transform the credit risk and maturity of the 

liabilities to meet the needs of investors.
2014 OFR Annual Report A.4

Financial Stability The condition in which the financial system is sufficiently functioning to provide its 

basic tasks for the economy, even under stress.

Financial Stability Board (FSB) An international coordinating body that monitors financial system developments on 

behalf of the G-20 nations. The FSB was established in 2009 and is the successor 

to the earlier Financial Stability Forum.

Fire Sale The disorderly liquidation of assets to meet margin requirements or other urgent 

cash needs. Such a sudden selloff can drive prices below their fair value. The 

quantities sold are large relative to the typical volume of transactions.

Fiscal Risk Risk stemming from deviations in fiscal policy from expectations.

Form N-MFP A monthly disclosure of portfolio holdings submitted by money market funds 

to the SEC, which makes the information publicly available. SEC Rule 30b1-7 

established the technical and legal details of N-MFP filings.

Form PF A periodic report of portfolio holdings, leverage, and risk management submitted 

by hedge funds, private equity funds, and related entities. The report is filed with 

the SEC and CFTC, which keep the information confidential. The Dodd-Frank Act 

mandated the reporting to help the Council monitor financial stability risks. 

Funding Liquidity The availability of credit to finance the purchase of financial assets.

General Collateral Finance 

(GCF) 

An interdealer repurchase agreement (repo) market in which the Fixed Income 

Clearing Corporation plays the role of intraday central counterparty. Trades are 

netted at the end of each day and settled at the triparty clearing banks (see Triparty 

Repo).

Global Systemically Important 

Banks (G-SIBs)

Banks annually designated by the Basel Committee on Banking Supervision for 

having the potential to disrupt international financial markets. The designations 

are based on banks’ size, interconnectedness, complexity, dominance in certain 

businesses, and global scope.

Haircut The discount at which an asset is pledged as collateral. For example, a $1 million 

bond with a 5 percent haircut would collateralize a $950,000 loan.

Hedge Fund A pooled investment vehicle available to accredited investors such as wealthy 

individuals, banks, insurance companies, and trusts. Hedge funds can charge a 

performance fee on unrealized gains, borrow more than one half of their net asset 

value, short sell assets they expect to fall in value, and trade complex derivative 

instruments that cannot be traded by mutual funds.

Hedging An investment strategy to offset the risk of a potential change in the value of 

assets, liabilities, or services. An example of hedging is buying an offsetting futures 

position in a stock, interest rate, or foreign currency.

High-Quality Liquid Assets 

(HQLA)

Assets such as central bank reserves, government bonds, and corporate debt that 

can be quickly and easily converted to cash during a stress period. U.S. banking 

regulators require large banks to hold HQLA to comply with the Liquidity 

Coverage Ratio.

High-Yield Bonds Instruments rated below investment grade that pay a higher interest rate than

investment-grade securities because of the perceived credit risk.

Implied Volatility The market’s estimate of the volatility of the price of an underlying asset. The 

current market price of an option contract can be used in a mathematical pricing 

model to calculate the level of volatility that market participants expect. 
A.5 Glossary

Initial Margin A percentage of the total market value of securities that an investor must pay to 

purchase securities with borrowed funds.

Interest Rate Swap A swap in which two parties swap interest rate cash flows, typically between a fixed 

rate and a floating rate (see Swap).

International Monetary Fund An international organization created at the end of World War II to stabilize 

(IMF) exchange rates and support international payment systems. The IMF provides 

credit to developing nations and those in economic distress, typically conditional on 

economic and financial reforms.

International Organization for The world’s largest developer of voluntary international standards in products, 

Standardization (ISO) services, and practices.

International Swaps and An industry association of over-the-counter derivative market participants. The 

Derivatives Association (ISDA) ISDA Master Agreement standardized derivative terms for counterparties to 

simplify netting and reduce legal risks.

Investment-Grade Bonds Securities that credit rating agencies determine carry less credit risk. Noninvestment grade securities have lower ratings and a greater risk of default. 

Legal Entity Identifier (LEI) A unique 20-digit alphanumeric code to identify each legal entity within a company 

that participates in global financial markets. 

Leverage The use of borrowed money to finance investments or conduct financial activities.

Leverage Ratio The Tier 1 (highest quality) capital of a bank divided by its total exposure to 

derivatives, securities financing transactions, and on- and off-balance-sheet 

exposures. The Basel III bank capital standards set a minimum leverage ratio 

of 3 percent, but the Federal Reserve said it will require the largest U.S. banks to 

maintain a leverage ratio above 5 percent beginning in 2018.

Liquidity See Funding Liquidity and Market Liquidity.

Liquidity Coverage Ratio (LCR) A Basel III standard to ensure that a bank maintains enough high-quality liquid 

assets to meet its anticipated liquidity needs for a 30-day stress period. The ratio 

applies to banks with $250 billion or more in total consolidated assets, or $10 billion 

or more in on-balance-sheet foreign exposure. A less-strict ratio is required of 

banks with $50 billion or more in total assets (see High-Quality Liquid Assets).

Liquidity Risk The risk that a firm will not be able to meet its current and future cash flow and 

collateral needs, both expected and unexpected, without materially affecting its daily 

operations or overall financial condition.

Living Wills Annual resolution plans required of U.S. banks with $50 billion or more in total 

consolidated assets and nonbank financial companies designated by the Council 

for supervision by the Federal Reserve. Each living will must describe how the 

company could be dismantled in a rapid, orderly way in the event of failure. 

Loan-to-Value (LTV) Ratio The ratio of the amount of a loan to the value of an asset, typically expressed as a 

percentage. This is a key metric in the financing of a mortgage.

Local Operating Unit (LOU) Private- or public-sector group authorized by the Global Legal Entity Identifier 

Foundation to register and issue LEIs. LOUs also validate and maintain reference 

data, and protect information that must be stored locally. Some jurisdictions may 

have multiple LOUs. 

Macroeconomic Risk Risk from changes in the economy or macroeconomic policy.
2014 OFR Annual Report A.6

Macroprudential Supervision Supervision to promote the stability of the financial system as a whole (see 

Microprudential Supervision).

Margin Call A requirement by a broker that a borrower increase the collateral pledged against a 

loan in response to changes in the collateral’s value.

Margin Requirement Rules governing the necessary collateral for a derivative, loan, or related security 

required to cover, in whole or in part, the credit risk one party poses to another.

Market Depth The ability of a market to absorb excess demand to buy or sell a security without 

affecting the price quoted for subsequent trades. In a deep market, a large number 

of shares or other financial instruments can be purchased with little impact on 

prices.

Market Liquidity The ability of market participants to sell large positions with limited price impact 

and low transaction costs.

Market Microstructure In economics, the study of the process and outcomes of exchanging assets under 

explicit trading rules. Microstructure theory focuses on how specific trading 

mechanisms affect the price formation process.

Market Risk The risk that an asset’s value will change due to unanticipated movements in market 

prices.

Market-Making The process in which an individual or firm stands ready to buy and sell a particular 

stock, security, or other asset on a regular and continuous basis at a publicly quoted 

price. Market-makers usually hold inventories of the securities in which they make 

markets. Market-making helps to keep financial markets efficient.

Maturity Mismatch The difference between the maturities of an investor’s assets and liabilities. A 

mismatch affects the investor’s ability to survive a period of stress that may limit 

its access to funding and to withstand shocks in the yield curve. For example, if a 

company relies on short-term funding to finance longer-term positions, it will be 

subject to significant refunding risk that may force it to sell assets at low market 

prices or potentially suffer through significant margin pressure.

Maturity Transformation Funding long-term assets with short-term liabilities. This creates a maturity 

mismatch that can pose risks when short-term funding markets are constrained.

Metadata Data that provide information about the structure, format, or organization of other 

data.

Microprudential Supervision Supervision of the activities of a bank, financial firm, or other components of a 

financial system (see Macroprudential Supervision).

Money Market Fund (MMF) A fund that typically invests in government securities, certificates of deposit, 

commercial paper, or other highly liquid and low-risk securities. Some MMFs are 

governed by the SEC’s Rule 2a-7.

Mortgage Call Report A quarterly report of mortgage activity and company information created by 

state regulators and administered electronically through the Nationwide Mortgage 

Licensing System & Registry (NMLS).

Mortgage Servicing Rights The right to service and collect loan payments and fees on a mortgage.

(MSRs)

mREITS Real estate investment trusts that borrow short-term funds in repo markets and 

invest in real estate, mortgages, and mortgage-backed securities.

Mutual Fund A pooled investment vehicle, regulated by the SEC, that can invest in stocks, bonds, 

money market instruments, other securities, or cash. 
A.7 Glossary

Net Asset Value The value of an entity’s assets minus its liabilities. For example, a mutual fund 

calculates its NAV daily by dividing the fund’s net value by the number of 

outstanding shares.

Net Stable Funding Ratio A Basel III standard to ensure that a bank holds sufficient available stable funding 

(NSFR) to limit its funding risk from maturity mismatches between assets and liabilities. 

Available stable funding is the portion of a bank’s capital and liabilities expected to 

be reliable for at least one year.

Network A model consisting of a set of nodes, or financial institutions, and a set of payment 

obligations linking them, to show how financial interconnections can amplify 

market movements.

Operational Risk Risks occurring during the normal operation of a business, including, for example, 

failed internal processes, legal risk, and environmental risk.

Option A financial contract granting the holder the right, but not the obligation, to engage 

in a future transaction on an underlying security or real asset. For example, an 

equity call option provides the right, but not the obligation, for a fixed period to 

buy a block of shares at a fixed price. 

Order Book A list of bids and offers a trading venue uses to match buyers and sellers. A limit 

order book is a record of unexecuted limit orders (an order to buy a stock at or 

below a specified price, or to sell a stock at or above a specified price) that are 

treated equally with other orders in terms of priority of execution. A central limit 

order book is a centralized database for all limit orders received by specialists and 

market-makers for different types of securities. 

Originate To extend credit after processing a loan application. Banks, for example, originate 

mortgage loans and either hold them until maturity or distribute them to other 

financial market participants. The distribution can include a direct sale or a 

securitization of a portion of the credit at the time of origination or later.

Over-the-Counter (OTC) Deals negotiated privately between two parties rather than traded on a formal 

Derivatives securities exchange. Unlike standard exchange-traded products, OTC derivatives 

can be tailored to fit specific needs, such as the effect of a foreign exchange rate or 

commodity price over a given period. 

Parallel-Coordinates Plot A figure used to visualize and analyze multiple financial, economic, and other 

variables simultaneously.

Price Discovery The process of determining the prices of assets in the market place through the 

interactions of buyers and sellers.

Primary Dealer Banks and securities broker-dealers designated by the Federal Reserve Bank of 

New York to serve as trading counterparties when the FRBNY is carrying out U.S. 

monetary policy. Among other things, primary dealers are required to participate in 

all auctions of U.S. government debt and to make markets for the FRBNY when 

it transacts on behalf of its foreign official accountholders. A primary dealer buys 

government securities directly and can sell them to other market participants.

Procyclical Financial or economic indicators that tend to move in the same direction as the 

overall economy (see Countercyclical).

Qualified Mortgage (QM) Under the Dodd-Frank Act, a mortgage loan that meets certain underwriting 

criteria set by the CFPB. The originator of a QM has certain protections from 

borrower lawsuits alleging the originator failed to make a good faith and reasonable 

determination of the borrower's ability to repay the loan.
2014 OFR Annual Report A.8

Qualified Residential Mortgage Under the Dodd-Frank Act, a mortgage loan exempt from the requirement that 

(QRM) sponsors of asset-backed securities must retain at least 5 percent of the credit risk 

of the assets collateralizing the securities.

Quantitative Easing (QE) An unconventional monetary policy to stimulate growth when policy rates are close 

to zero by purchasing government or other securities from private institutions.

Refinancing Risk The risk that a borrower will face liquidity problems if unable to roll over existing 

debt.

Reinsurance The risk management practice of insurers to transfer some of their policy risk to 

other insurers. A second insurer, for example, could assume the portion of liability 

in return for a proportional amount of the premium income. 

Repo Run A situation in which repurchase agreement (repo) investors lose confidence in the 

market due to concerns about counterparties, collateral, or both, and respond by 

pulling back their funding or demanding larger haircuts. 

Repurchase Agreement A transaction in which one party sells a security to another party and agrees to 

(Repo) repurchase it at a certain date in the future at an agreed price. Banks often do this 

on an overnight basis as a form of liquidity that is similar to a collateralized loan.

Resolution Plans See Living Wills.

Risk Management The business and regulatory practice of identifying and measuring risks and 

developing strategies and procedures to limit them. Categories of risk include 

credit, market, liquidity, operations, model, and regulatory.

Risk Retention Under the Dodd-Frank Act, a requirement that issuers of asset-backed securities 

must retain at least 5 percent of the credit risk of the assets collateralizing the 

securities. The regulation also prohibits a securitizer from directly or indirectly 

hedging the credit risk (see Qualified Residential Mortgage).

Run Risk The risk that investors lose confidence in a market participant due to concerns 

about counterparties, collateral, solvency, or related issues and respond by pulling 

back their funding or demanding more margin or collateral.

Search for Yield (Reach for The practice of accepting greater risks in hopes of earning higher than average 

Yield) returns.

Securities Financing The transfer or lending of securities from one party to another. A borrower of 

securities puts up collateral in the form of shares, bonds, or cash, and is obliged to 

return the securities on demand. These transactions provide liquidity in the market.

Securities Lending/Borrowing The temporary transfer of securities from one party to another for a specified fee 

and time period in exchange for collateral in the form of cash or securities.

Settlement The process by which securities are transferred and settled by book entry according 

to a set of exchange rules. Some settlement systems can include institutional 

arrangements for confirmation, clearance, and settlement of securities trades and 

safekeeping of securities.

Shadow Banking System Credit intermediation outside the insured depository system, involving leverage, 

maturity transformation, and the creation of money-like liabilities. 

Short-Term Wholesale Funding Funding instruments typically issued to institutional investors to raise large amounts 

of funding for short periods. Examples include large time deposits, commercial 

paper, and repurchase agreements.
A.9 Glossary

Single-Family Rental A structured security backed by mortgage loans on pools of single-family rental 

Securitizations (SFR) properties. 

Spread The difference in yields between various private debt instruments and government 

securities of comparable maturity. The spread can be used as one of many 

indicators of financial stability. 

Stress Test An exercise that shocks asset prices by a pre-specified amount, sometimes along 

with other financial and economic variables, to observe the effect on financial 

institutions or markets. Under the Dodd-Frank Act, banking regulators run annual 

stress tests of the biggest U.S. bank holding companies. 

Supplemental Leverage Ratio Under Basel III, the ratio of a bank’s Tier 1 (high quality) capital to its total leverage 

exposure, which includes all on-balance-sheet assets and many off-balance-sheet 

exposures. U.S. regulators require a 3 percent ratio for most banks with $250 billion 

or more in consolidated assets or $10 billion or more in foreign exposures. The 

eight large U.S. banks designated as global systemically important banks by the 

Financial Stability Board must maintain a ratio of 5 percent. 

Swap An exchange of cash flows agreed by two parties with defined terms over a fixed 

period.

Swap Data Repository (SDR) A central recordkeeping facility that collects and maintains a database of swap 

transaction terms, conditions, and other information. In some countries, SDRs are 

referred to as trade repositories. 

Swap Execution Facility Under the Dodd-Frank Act, a trading platform market participants use to execute 

and trade swaps by accepting bids and offers made by other participants.

Systemic Expected Shortfall A systemic risk indicator that estimates the extent to which the market value equity 

(SES) of a financial firm would be depleted by a decline in equity prices. 

Tail Risk The low-probability risk of an extreme event moving an asset price.

Tier 1 Capital Ratio and Tier 1 Two measurements comparing a bank’s capital to its risk-weighted assets to show its 

Common Capital Ratio ability to absorb unexpected losses. Tier 1 capital includes common stock, preferred 

stock, and retained earnings. Tier 1 common capital excludes preferred stock. 

Triparty Repo A repurchase agreement in which a third party, such as a clearing bank, acts as an 

intermediary for the exchange of cash and collateral between two counterparties. In 

addition to providing operational services to participants, agents in the U.S. triparty 

repo market extend intraday credit to facilitate settlement of triparty repos.

Volatility Risk The risk in the value of a portfolio from unpredictable changes in the volatility of a 

risk factor or underlying asset.

Volcker Rule A provision of the Dodd-Frank Act that generally prohibits a bank from certain 

investment activities that are not directly related to trading for customers or for 

market-making. The provision also limits insured depository institutions from 

owning or sponsoring hedge funds or private equity funds.

XBRL (eXtensible Business A common computer language for the electronic communication of business and 

Reporting Language) financial data. Regulators can use XBRL as an efficient way to obtain information 

from companies.

XML (eXtensible Markup A common computer language that defines a set of rules for the semantic markup 

Language) of documents. 

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