OFR OFFICE OF FINANCIAL RESEARCH 

2012

ANNUAL

RepoRt

Office of Financial Research i

The Office of Financial Research

Establishment of the Office

The Dodd-Frank Wall Street Reform and Consumer Protection Act (Dodd-Frank Act) 

established the Office of Financial Research (OFR) within the Treasury Department.

Section 153(a) of the Dodd-Frank Act charges the OFR with supporting 

the Financial Stability Oversight Council (Council) and member agencies by:

1. collecting data on behalf of the Council, and providing such data to the 

Council and member agencies;

2. standardizing the types and formats of data reported and collected;

3. performing applied research and essential long-term research;

4. developing tools for risk measurement and monitoring;

5. performing other related services;

6. making the results of the activities of the Office available to financial 

regulatory agencies; and

7. assisting such member agencies in determining the types and formats of 

data authorized by this Act to be collected by such member agencies.

Organizational Structure and Responsibilities 

of the Primary Programmatic Units

Section 154 of the Dodd-Frank Act establishes within the Office, to carry out 

the programmatic responsibilities of the Office, the Data Center and the 

Research and Analysis Center. 

The Data Center, on behalf of the Council, shall collect, validate, and 

maintain all data necessary to carry out the duties of the Data Center. The 

data assembled shall be obtained from member agencies, commercial data 

providers, publicly available data sources, and financial entities under certain 

statutory authority detailed in the law. 

The Research and Analysis Center, on behalf of the Council, shall develop 

and maintain independent analytical capabilities and computing resources—

A. to develop and maintain metrics and reporting systems for risks to the 

financial stability of the United States;

B. to monitor, investigate, and report on changes in systemwide risk levels 

and patterns to the Council and Congress;

C. to conduct, coordinate, and sponsor research to support and improve 

regulation of financial entities and markets;
2012 OFR // Annual Report II

D. to evaluate and report on stress tests or other stability-related 

evaluations of financial entities overseen by the member agencies;

E. to maintain expertise in such areas as may be necessary to support 

specific requests for advice and assistance from financial regulators;

F. to investigate disruptions and failures in the financial markets, report 

findings, and make recommendations to the Council based on those 

findings;

G. to conduct studies and provide advice on the impact of policies related 

to systemic risk; and

H. to promote best practices for financial risk management. 

Statutory Requirements for the Annual Report

Section 154(d)(2) of the Dodd-Frank Act requires an Annual Report that 

assesses the state of the United States financial system including:

A. an analysis of any threats to the financial stability of the United States;

B. the status of the efforts of the Office in meeting the mission of the 

Office; and 

C. key findings from the research and analysis of the financial system by 

the Office.

Abbreviations for Federal Agencies and

Offices Referred to in the Report

•	 Department of the Treasury (Treasury)

•	 Financial Stability Oversight Council (FSOC or Council)

•	 Board of Governors of the Federal Reserve System 

(FRB or Federal Reserve)

•	 Office of the Comptroller of the Currency (OCC)

•	 Consumer Financial Protection Bureau (CFPB)

•	 Securities and Exchange Commission (SEC)

•	 Federal Deposit Insurance Corporation (FDIC)

•	 Commodity Futures Trading Commission (CFTC)

•	 Federal Housing Finance Agency (FHFA)

•	 National Credit Union Administration Board (NCUA)

•	 Federal Insurance Office (FIO)
Preface III

Preface

The creation of the Office of Financial Research 

represents a milestone in the efforts to strengthen 

America’s financial system.

The financial crisis made clear that the understanding of the financial system 

was deficient in many respects. Market participants and regulators broadly 

misperceived the extent of leverage and maturity transformation. They did not 

see the migration of such activities to unregulated or lightly regulated financial 

companies and markets in the so-called shadow banking system. And they 

collectively underestimated how disruptions could spread horizontally across 

interconnected companies and markets and impair the functioning of the 

financial system, with severe consequences for the economy. 

Likewise, the crisis revealed significant deficiencies in the data available to monitor 

the financial system. Financial data collected were too aggregated, too limited 

in scope, too out of date, or otherwise incomplete. The crisis demonstrated the 

need to reform the data collection and validation process and to strengthen data 

standards in order to improve the utility of data both for regulators and for market 

participants. In contrast with the local jurisdictions of regulators, the global nature 

of financial markets and institutions complicated that process. 

Through the Dodd-Frank Act, Congress addressed many of these shortcomings. 

It created the Financial Stability Oversight Council (FSOC or Council), consisting 

of the federal financial regulators and others with oversight responsibilities, to 

identify threats to the financial stability of the United States, to respond to those 

threats, and to promote market discipline. It also created the Office of Financial 

Research (OFR or Office) to serve the needs of the Council and member agencies, 

to collect and standardize financial data, to perform essential research, and to 

develop new tools for measuring and monitoring risk in the financial system. 

This inaugural Annual Report describes how the OFR is working to satisfy its 

statutory mandates and mission in four areas:

•	 To analyze threats to financial stability. The Office is developing and 

implementing metrics for measuring risks to financial stability. The Office 

is evaluating methods in current use, considering their effectiveness both 

for individual firms and systemwide. It is our statutory mandate to monitor, 

investigate, and report to Congress and the Council on changes in those 
2012 OFR // Annual Report IV

risks. Gaps in analysis, data, and data standards represent threats to financial 

stability, and helping to fill those gaps is also part of the OFR’s mandate.

•	 To conduct research on financial stability. The Office is conducting research to 

enable monitoring and investigation of threats to financial stability; to analyze 

regulation of financial entities or markets; to evaluate and report on stress 

tests or other stability-related evaluations of financial entities; to investigate 

and report on disruptions and failures in financial markets; and to conduct 

studies about and provide advice on the impact of policies related to financial 

stability. The Office is also collaborating with regulators, market participants, 

and academics to promote best practices in financial risk management.

•	 To address data gaps. The Office is beginning to help ensure that 

policymakers as well as market participants, where appropriate, have access 

to reliable, high-quality financial data in order to understand the state of the 

financial system, how it is developing and transforming risks, and the nature 

of its vulnerabilities. The Office is helping the Council and member agencies 

determine data needs, assess gaps in the scope or quality of available data, and 

help prioritize and fill those gaps. The Office may require financial companies 

to submit data, including transaction and position data, as necessary to 

improve the analysis of financial stability, and will take all necessary and 

appropriate precautions to ensure that any data so submitted will be stored 

and used safely and securely. 

•	 To promote data standards. The Office is leading efforts to improve financial 

data and information standards, working closely with financial regulators 

and the industry. Standardization is essential to improve the quality and 

transparency of financial data. It will help risk managers and supervisors 

compare, aggregate, link, and analyze data about financial entities, 

instruments, and markets, and the threats they pose to financial stability. 

The Office may promulgate regulations to standardize the types and formats 

of data reported and collected on behalf of the regulatory agencies. The 

Office will also produce and maintain catalogues of reference entities and 

instruments. The first ongoing standardization priority is to establish a Legal 

Entity Identifier (LEI), a unique, global standard for identifying parties to 

financial transactions. The LEI initiative has made important progress in 

2012 with strong support from both the private sector and the international 

regulatory community.

A key component of the OFR’s mission is to support the Council and its members. 

The OFR is supporting the Council by providing data and analysis related to the 

Council’s evaluation of nonbank financial companies for potential designation 

for Federal Reserve supervision and enhanced prudential standards. Much of the 

OFR’s work analyzing financial stability, conducting research, collecting data, and 

promoting data standards has involved collaboration with Council members.

Unlike the other federal financial agencies in the Council, however, the OFR 

has no supervisory responsibilities; we are focused purely on research, data, and 

analysis. That is important for two reasons. First, significant gaps remain in the 
Preface V

analytical work done by the Council member agencies because none of them 

individually has authority to look across the entire financial system—in other 

words, to implement a macroprudential approach to research and data activities. In 

part, those gaps arise in financial activities that take place in less-regulated markets 

and across national boundaries. Congress created the OFR as a separate office 

to produce the “connective tissue” filling gaps in both information and analytics. 

That is a key rationale for the Office’s separate research and data mission.

A second reason why the Office’s separate research and data mission matters 

is that it permits the Office to offer a different perspective, one that should 

enhance the Council’s peripheral vision. That perspective is critical in identifying 

threats to financial stability and in evaluating stress tests and other tools in the 

macroprudential toolkit individually and taken together. It also can help the 

regulatory community avoid accepting the conventional wisdom when a skeptical 

look is needed. 

Both factors are critical in assessing the OFR’s mandates. In particular, they help 

explain why the OFR should conduct and sponsor financial stability research even 

when others in the Council are engaged in similar work. In addition, new data 

collected by the OFR will permit research that previously was difficult to conduct. 

Meanwhile, with our broad mandates come significant responsibilities. 

Three stand out: accountability to Congress and the public; the need to be 

thoughtful and judicious in collecting data; and a resolute commitment to 

information security. 

The OFR is accountable through several channels. Congress has oversight 

authority over the OFR: the Dodd-Frank Act requires the Director to testify 

regularly before Congress, and the OFR provides the Congress with this first 

Annual Report on its activities to be consistent with requirements under that 

statute. The Office will also provide a second annual report on its human 

resources practices later this year. It also published its initial strategic framework 

earlier this year. In addition, the OFR publishes its budget as part of the 

President’s budget and it is included as part of the Treasury Department’s 

consolidated audit. At the same time, the Dodd-Frank Act provides authority for 

Treasury’s Inspector General, the Government Accountability Office, and the 

Council of Inspectors General on Financial Oversight to oversee the activities of 

the OFR. 

The OFR will not collect data for collection’s sake. Indeed, the Dodd-Frank Act 

requires that the OFR not duplicate others’ data collection efforts. The OFR is 

working with the federal financial supervisors to inventory the data they already 

collect and to improve data-sharing among them—creating economies of scale, 

lowering costs, and reducing regulatory burden. While the opportunities are 

immense for improving financial data available both to supervisors and to 

financial companies themselves, the Office is sensitive to the potential costs. And, 

of course, the Dodd-Frank Act does not contemplate collecting and the OFR will 

not collect any information from consumers. 
VI 2012 OFR // Annual Report

Data security is the highest priority for the OFR. As an office of the Department of 

the Treasury, the OFR utilizes Treasury’s sophisticated security systems to protect 

sensitive data. The OFR is also implementing additional controls for OFR-specific 

systems, including a secure data enclave within Treasury’s IT infrastructure. Access 

to confidential information will only be granted to personnel that require it to 

perform specific functions, and the OFR will regularly monitor and verify its use to 

protect against unauthorized access. In addition, the OFR is collaborating with other 

Council members to develop a mapping among data classification structures and 

tools to support secure collaboration and data-sharing. Such tools include a data 

transmission protocol currently used by other Council members that will enable 

interagency data exchange and a secure collaboration tool for sharing documents.

The discussions in this Annual Report should make clear the importance, breadth, 

and scope of the OFR’s mandates. They should not obscure the need for humility 

in pursuing those mandates, because the precise causes of financial crises are 

fundamentally uncertain. And they should underscore two other facts. First, 

better analysis and data can help reduce this uncertainty and inform the design 

of the shock absorbers and guardrails needed to make the financial system more 

resilient and less prone to shocks. Second, while the OFR’s work is well under 

way, it will take time and resources to build its capabilities in each of these areas. 

Building a technological infrastructure must be done with care to ensure a secure 

environment in which confidential data are always protected. 
Contents
 

Preface................................................................................................................III
 

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

2 Analyzing Threats to Financial Stability ................................................... 7
 

2.1 Research Agenda..................................................................................... 7
 

BOX A. Knowns and Unknowns in 2005 ........................................................... 8
 

BOX B. Shadow Banking—It’s Not a New Story ..............................................14
 

2.2 Current Threats to Financial Stability .................................................... 19
 

3 Research on Financial Stability................................................................33
 

3.1 Cataloguing Indicators and Models of Risks to Financial Stability ......33
 

3.2 Stress Testing as a Macroprudential Tool ............................................. 46
 

BOX C.	 Using Agent-Based Models to Analyze Threats 


to Financial Stability............................................................................ 50
 

BOX D.	 Reverse Stress Testing ....................................................................... 52
 

3.3 Counterparty Risk Management: Best Practices
 

and Unmet Challenges........................................................................... 59
 

BOX E. Best Practices in Risk Management................................................... 60
 

BOX F.	 Lessons from the Collapse of MF Global ........................................... 64
 

4 Addressing Data Gaps...............................................................................83
 

4.1 Data Agenda...........................................................................................83
 

BOX G. Building a Secure Infrastructure......................................................... 88
 

4.2 Examples of Financial Stability Data Gaps............................................89
 

BOX H. Improvements in Financial System Monitoring .................................. 92
 

5 Promoting Data Standards ..................................................................... 107
 

5.1 What Are Data Standards? .................................................................. 107
 

BOX I. The Growth of Financial Market Data..................................................110
 

BOX J. Case Studies in the Adoption of Data Standards 


by U.S. Regulators .............................................................................114
 

5.2 Benefits of Data Standards...................................................................115
 

5.3 The LEI Initiative ....................................................................................117
 

6 The Agenda Ahead ................................................................................... 121
 

BOX K.	 Improving Research and Data on Repo Markets...............................124
 

Contents VII 
Appendix A: Progress in Institution Building ............................................. 127
 

Appendix B: OFR Activities and Collaboration .......................................... 129
 

Glossary .......................................................................................................... 131
 

Abbreviations ................................................................................................. 139
 

Notes on the Data .......................................................................................... 143
 

VIII 2012 OFR // Annual Report 
Executive Summary 1

1 Executive Summary

This inaugural OFR Annual Report details the Office’s 

progress in meeting its mission and statutory requirements. 

The report must assess the state of the U.S. financial system, 

including: (1) An analysis of any threats to the financial 

stability of the United States; (2) The status of the efforts of 

the Office in meeting its mission; and (3) Key findings from the 

research and analysis of the financial system by the Office.1

Chapters 2 and 3 describe the Office’s approach to analyzing threats to financial 

stability and conducting essential research. Chapters 4 and 5 describe the Office’s 

agenda for addressing data gaps through three types of strategy: (1) Helping to 

better organize existing data, (2) Promoting data standards, and, (3) Collecting 

data that are otherwise not available to the OFR and other FSOC members. 

Chapter 5 focuses on the Office’s agenda for promoting data standards, which 

began with the widely supported initiative to create a global Legal Entity 

Identifier (LEI). 

The OFR’s four agendas—analyzing threats to financial stability, conducting 

research, addressing data gaps, and promoting data standards—are highly 

complementary. In pursuing them, key principles will guide the Office: 

Accountability, an emphasis on a cost-effective approach to meeting our mandates, 

and an unwavering commitment to information security.

Analyzing Threats to Financial Stability

There is an emerging consensus that policymakers need to understand the 

functioning of the entire financial system—of institutions and markets—in order 

to assess and monitor threats to financial stability; to appreciate how those threats 

propagate from one institution to many, or from one market to others; and to 

evaluate mitigants to address those risks. 

“Financial stability” means that the financial system is operating sufficiently to 

provide its basic functions for the economy even under stress. Our framework 

includes an analysis of the six basic functions of the financial system—credit 

allocation and leverage, maturity transformation, risk transfer, price discovery, 

liquidity provision, and facilitation of payments—and an assessment of how threats 

may disrupt their functioning. In our taxonomy, such threats to financial stability 
2 2012 OFR // Annual Report

can emerge from within or outside the financial system, and they can be either 

cyclical or structural. Similarly, the FSOC Annual Report describes a taxonomy of 

internal or external shocks that may interact with structural vulnerabilities in the 

financial system to disrupt financial stability. Thus both taxonomies are aligned 

with the important point that, even absent external shocks, financial activity 

can generate threats to financial stability. Typically those occur when market 

participants have incentives to take on excessive risks due to a lack of market 

discipline, opaque (mis)pricing of risk, or other flaws in the policy guardrails 

that should curb those incentives. 

The U.S. economy and financial system today are still recovering from the 

financial crisis and recession that began in 2007. The slow recovery in both 

owes importantly to the legacy of the crisis that has also left two key cyclical 

risks in the financial system—the ongoing weakness in housing finance and the 

historically low levels of interest rates. Housing finance remains challenged due 

to still-dysfunctional securitization markets and the consequent lack of private 

market interest, which has been partly filled by an outsized role for government. 

Low interest rates, while beneficial to the economy in the near-term, may have 

long-term adverse consequences if large numbers of market participants take on 

excessive credit risk or duration risk. Supervisors have expressed concerns in 

both areas. 

Structural vulnerabilities within the financial system remain in short-term funding 

and derivatives markets. While important reforms are now under way in the 

derivatives markets, largely in response to Dodd-Frank mandates, short-term 

funding markets remain subject to run risk, and more needs to be done. External 

to the financial system, financial stability concerns are focused prominently on the 

euro area sovereign debt crisis and on the U.S. fiscal outlook. Policymakers have 

taken measures to promote a housing recovery but the market remains depressed.

Another risk is that the mitigants in place to promote stability in the financial 

system won’t work as intended. Those mitigants include both supervisory 

monitoring of financial markets and internal risk management within financial 

companies. Both need to constantly update their approach, including their use 

of mathematical models, to keep up with rapidly changing business practices. 

Most important from the perspective of the OFR’s mandate, the lack of highquality data and weaknesses in data standards still represent a critical potential 

source of risks to the financial system as, more than ever, both supervisors and 

company risk managers rely upon such data to carry out their responsibilities.

Conducting Research on Financial Stability

The OFR is required by statute to perform essential research on risks to financial 

stability and to evaluate responses to those risks. This report provides analysis 

in three specific priority areas identified in Dodd-Frank: To develop metrics for 

measuring risks to financial stability; to evaluate stress tests that can aid in financial 

stability analysis; and to promote best practices in financial institution risk 
Executive Summary 3

management. Such a focus must and will be a hallmark of the OFR’s research and 

data efforts going forward.

The academic community has proposed hundreds of financial stability measures 

since the financial crisis. Some of these measures seek to provide forward-looking 

indicators of financial stress, for example, by revealing cyclical upswings in the 

use of leverage or in the level of asset prices; others seek to explain current 

financial conditions and the vulnerabilities of the system to a shock, for example, 

by revealing possible sources of contagion in a crisis through connections among 

financial market participants. The OFR’s first published working paper surveyed 

31 of these measures in some depth (Bisias and others, 2012). Section 3.1 in 

this report represents a preliminary effort to take that analysis a step further. 

We evaluate 11 of these measures by comparing their performance during four 

historical financial crises—1929, 1987, 1998, and 2008. This exercise illustrates 

the strengths and weaknesses of different approaches. An overriding message 

is that gaps in financial data constrain the current generation of measures. 

Looking ahead, the OFR will work to fill these data gaps in order to improve these 

analytical tools and will take a careful, thoughtful approach to this work. 

Another important development following the financial crisis is the increasing 

use of stress tests as a financial stability tool. The Federal Reserve, working with 

other U.S. supervisors, carried out stress test exercises in 2009, 2011, and 2012 

that subjected large financial firms to similar theoretical shocks, in some cases 

revealing capital gaps that companies were required to address. Section 3.2 

describes how supervisors can use stress tests to evaluate the vulnerability of the 

financial system to shocks and how stress tests can be extended to incorporate 

feedback from the financial system to the economy and contagion effects within 

financial markets. It also discusses two methodologies that could be useful in 

the ongoing development of stress tests: agent-based models, which simulate the 

behaviors of market participants to garner insights into market dynamics during 

crises, and reverse stress tests, which evaluate the types of scenarios that might 

produce adverse outcomes.

A third area of OFR research lies in the quality of risk management at large 

financial institutions, which, of course, has always been a central focus for 

supervisors. This was the topic of the OFR’s second working paper (Flannery and 

others, 2012). Section 3.3 discusses the evolution of best practices and challenges 

in counterparty risk management, an important area both for individual firm 

risk managers and for those concerned with limiting financial contagion during 

periods of market uncertainty. Counterparty risk arises in any transaction in which 

firms make commitments to each other—derivatives, short-term funding markets, 

credit guarantees—and can be an important source of contagion when market 

participants doubt each other’s soundness. 

Addressing Data Gaps

The OFR was created in large part to fill knowledge gaps and reduce inefficiencies 

in supervisors’ collection and use of data. The OFR follows a three-step process in 
4 2012 OFR // Annual Report

setting its data agenda: (1) Identify financial stability data needs; (2) Determine 

data gaps and weaknesses; and, (3) Prioritize and fill these gaps by better organizing 

existing data, promoting data standards, and collecting new data where necessary. 

Chapter 4 describes how the OFR will execute its data agenda and ensure a secure 

data environment. It describes how the Office is working to identify and address 

data needs and data gaps among regulators and to define best practices that can 

be leveraged across Council member agencies. The OFR is building a descriptive 

inventory of data that the Council member agencies already purchase or collect. 

This inventory will help avoid duplication, trim costs, minimize the regulatory 

burden, and take advantage of existing data sources to the extent possible. 

The Office identifies and prioritizes data gaps in the course of its own research 

and monitoring, at the behest of the Council, or through interaction with 

Council agencies and other stakeholders. Section 4.2 examines factors affecting 

financial stability and the need for more and better data on leverage, liquidity, and 

interconnectedness, with a focus on derivatives and short-term funding markets. 

In our judgment, these data are still of low quality, and the policymaking and riskmonitoring payoffs from improving them would be substantial. 

Promoting Data Standards 

Data standards provide common, clear definitions for financial entities, 

instruments, positions, and transactions. Common definitions promote 

comparability, which means that information can be reliably combined from 

different sources and systems and that terms and definitions mean the same thing 

regardless of where the data come from. 

The lack of high-quality, consistent, and accessible data was a key source of 

risk during the recent financial crisis. As concerns spread about certain assets, 

particularly those related to subprime mortgages, financial companies often 

were unable to aggregate their own exposures or evaluate the exposures of 

their counterparties to those assets. Supervisors were similarly challenged. Even 

in the ordinary course of business, gaps and overlaps in data standards create 

unnecessary burdens for managers of financial institutions and their supervisors. 

It would be difficult or impossible for the OFR to conduct its essential financial 

stability monitoring function without better data standards in the financial world. 

Standardization will allow more consistent and complete reporting, so data 

available to supervisors will be more accurate, more comparable across firms and 

industries, and easier to use. It will improve the ability of regulators to respond 

quickly as needed to new developments that could affect financial stability and to 

resolve troubled financial institutions. 

Better data standards will be equally valuable to risk managers at financial 

companies. Standards allow risk managers to aggregate individual transactions 

and positions into a complete and accurate picture of the enterprise. By making 

it easier to link and aggregate information, standards enable firms to use the 

same basic data both for reporting to regulators and for managing their business. 
Executive Summary 5

Standardization will also improve market discipline by giving market participants a 

more transparent picture of firms’ activities.

Chapter 5 describes the OFR’s approach to promoting improvements in data 

standards used in the financial industry. The OFR’s goals for data standards 

include the need for data integrity and quality control, data security, datasharing protocols, and data management. The OFR’s first priority is to support 

domestic and global efforts to establish an LEI for the benefit of macroprudential 

regulators and of researchers and financial market participants. These efforts 

have made important progress in 2012 with the publication of a new International 

Organization for Standardization standard and the endorsement by the Group 

of Twenty Finance Ministers and Central Bank Governors (G20) of a March 2013 

deadline for global implementation.

The Agenda Ahead 

Our priorities for the coming year include work to develop more robust analytical 

frameworks for analysis to assess and monitor threats to financial stability, to 

evaluate mitigants to those threats, and to improve the scope and quality of 

financial data required for that work. Accordingly, we will focus on the forces that 

promote the migration of financial activities, including maturity transformation 

and the creation of money-like liabilities, into unregulated or lightly regulated 

markets—the so-called shadow banking system—and we will investigate in depth 

the behavior of short-term funding markets and collect better data on repo 

markets. We will build on the work on the three topics outlined in this report—

indicators of threats to financial stability, stress testing, and risk management. We 

will employ network analysis and new data to research interconnectedness among 

financial institutions. Our data agenda is tied closely to our research agenda and 

includes further work on data standards to improve the quality of existing and 

new information.

Endnote

1. This report complements the Annual Report of the Financial Stability Oversight Council, to which 

the OFR contributes in fulfillment of its mandate. The Council report provides a broad overview 

of the financial system, its risks and vulnerabilities, and the policy recommendations and 

priorities of the regulatory agencies. The OFR’s report more narrowly describes the work of one 

Office and its in-depth analysis of the financial system and potential threats to stability.

References for Chapter 1

Bisias, Dimitrios, Mark Flood, Andrew W. Lo, and Stavros Valavanis. “A Survey of Systemic Risk 

Analytics.” Working Paper #0001, Washington, DC: Office of Financial Research, January 15, 2012.

Flannery, Mark J., Paul Glasserman, David K.A. Mordecai, and Cliff Rossi. “Forging Best Practices in 

Risk Management.” Working Paper #0002, Washington, DC: Office of Financial Research, March 26, 

2012.
6 2012 OFR // Annual Report
Analyzing Threats to Financial Stability 7

2 Analyzing Threats to Financial Stability 

Our approach to promoting financial stability focuses on analyzing 

disruptions to the basic services provided by the financial system. Given 

the dynamic nature of markets and the propensity for market participants 

to move risky activities out of the view of regulators, supervisors, and 

investors, our analysis must be dynamic and flexible. The first section of 

this chapter describes the OFR’s approach to financial stability research 

and monitoring; the second section applies that approach to the financial 

system in the summer of 2012. 

2.1 Research Agenda

Three broad themes drive the OFR’s research agenda: (1) Understand how the financial system is evolving in its provision of basic financial services; (2) Assess emerging risks and vulnerabilities; and (3) 

Evaluate mitigants, such as risk management, disclosure, supervision, and macroprudential policy. 

The financial crisis revealed significant gaps 

in the analytical and empirical understanding 

of the financial system, its interaction with 

the economy, and the role of the financial 

regulatory structure. Today, it is better 

understood that the financial system was 

evolving in ways that changed the behavior 

of institutions and markets. Financial activity 

had moved substantially outside the focus of 

supervisors with responsibilities over specific 

markets and institutions into the unregulated 

or lightly regulated shadow banking system—

that is, credit intermediation by unregulated 

financial institutions in combination with the 

creation of money-like liabilities, involving 

leverage and maturity transformation, in 

opaque markets (Pozsar and others, 2012). 

Regulatory reporting systems had not kept up 

with the increased interconnections among 

financial institutions, the heightened reliance 

on leverage, and the dramatic increase in the 

variety, complexity, and volume of financial 

activity. Following the crisis, it is now much more 

clearly understood that the financial system is 

prone to instability and that weak links in the 

chain of intermediation must be strengthened. 

Financial supervisors have begun to address 

these problems in important ways. There is a 

new consensus that policymakers need to have 

a comprehensive understanding of how the 

financial system is arranged and connected, 

how it performs its key functions, and how those 

functions are being transformed through the 

activities of market participants, including the 

development of new products and markets. 

Likewise, it is also more clearly understood 

that policymakers must adopt a macroprudential

approach to their analysis and policy tools, one 

that looks across the entire financial system to 

assess and deal with threats to financial stability. 

Had the regulatory community known in 2005 

what we now know, would the outcome have 

been different? We think the answer is yes, but 

humility is essential. Our knowledge today is 

far from complete, and the constant evolution 
8 2012 OFR // Annual Report

BOX A. KNOWNS AND UNKNOWNS IN 2005

What types of data or analysis might have helped policymakers identify the risks and 

vulnerabilities of the financial system as the seeds of the financial crisis were being sown?

Congress created the Office both to analyze 

the financial system and to conduct forensic 

analyses following financial disruptions. Based 

on those mandates, it is appropriate and 

essential for the OFR to ask what was known 

and not known before the crisis; what could 

have been done to develop a clearer picture of 

the potential for disaster; what information to 

look for during such a crisis; and how to learn 

from the crisis in the aftermath. 

This analysis could focus on mid-2005, two 

years before the first liquidity phase of the 

financial crisis. At that time, some but not all of 

the key elements that made the financial crisis 

so devastating were already in place. 

At that time, there was a broad public debate 

about whether the nation was in the midst of a 

housing bubble. Policymakers had expressed 

concerns about underwriting standards and 

about the potential for economic pain when 

growth in housing prices inevitably slowed. But 

the consensus was that this adjustment would 

be moderate, largely because securitization and 

other market innovations appeared to transfer 

credit risks and liquidity risks from the regulated 

and insured banks to other financial institutions 

that were presumably better able to bear them. 

However, few had done the work to follow the 

risks to their ultimate bearers—and those riskbearers were too removed from the information 

to determine the nature of their own risks. It 

was not well known that American International 

Group (AIG), the largest insurance company, 

had already taken significant exposures to the 

mortgage market, largely through derivatives 

and the securities lending market, and that 

several of the largest commercial banks and 

investment banks had begun to take similar 

positions. It was not known that the investors 

in short-term funding markets (asset-backed 

commercial paper or ABCP, repurchase 

agreements or repos, and securities lending), 

who had helped finance mortgage-backed 

securities (MBS) and other markets, might panic 

and pull their money. The nature of leverage in 

certain markets, particularly collateralized debt 

obligations (CDOs), derivatives, and repos, was 

not understood.

An appropriate role for an OFR in 2005 would 

have been to ask broad questions about how 

the financial system was conducting its basic 

tasks—credit allocation and leverage, maturity 

transformation, price discovery, risk transfer, 

liquidity provision, and facilitation of payments—

and what the risks and vulnerabilities were. 

Although some data may have been available 

to explore these questions, an agency with a 

macroprudential perspective may have realized 

that more data were needed.

An OFR in 2005 might have focused on how 

new products and markets were affecting these 

basic financial tasks. How was credit risk being 

allocated—were concentrations developing, 

were new credit products distorting incentives, 

were risk takers sufficiently capitalized? How 

was maturity transformation being done—were 

there excessive maturity mismatches, what 

types of products or entities could be subject 

to run risk? These questions would have been 

as important in 2005 as they would have been 

in 1925, when the regulatory framework of 
Analyzing Threats to Financial Stability 9

a different era similarly found itself unable to 

cope with imbalances across regulated and 

unregulated markets. 

For 2005, these questions could have led, 

for example, to requests for more information 

about CDOs that were taking the riskiest parts 

of MBS—both by buying those securities 

and by selling protection on those securities 

through credit derivatives—and about who 

was buying the different types of CDOs. 

Large holdings of CDO securities contributed 

significantly to the losses for AIG, Citigroup, 

and other large financial institutions—and 

these holdings were highly leveraged, meaning 

the institutions had set little capital aside to 

back those investments. In an early 2005 

report, international supervisors noted the 

possibility that CDOs and credit derivatives 

could concentrate credit risks in a small number 

of institutions but did not recognize the role 

these products were beginning to play in the 

mortgage market; they noted as reassurance 

that “such firms are subjected to regulatory, 

rating agency, and market scrutiny” (BCBS, 

2005). The Financial Crisis Inquiry Commission 

later reported that CDOs and credit derivatives 

had stimulated demand for MBS and distorted 

incentives in the mortgage market, contributing 

both to the excesses of the boom and the 

severity of the bust (FCIC, 2011).

These questions could have also led to 

requests for more information about short-term 

funding markets that were providing funding 

for CDO and MBS securities, specifically the 

repo, securities lending, and ABCP markets. 

Each of these markets would suffer a crisis 

of confidence in 2007 and 2008 as investors 

became concerned about the ability of 

borrowers to make good on their mortgages. 

Again, AIG, Citigroup, and other financial 

institutions would suffer significant losses 

because of implicit or explicit support they 

had provided to ABCP programs. The nature, 

pricing, and risk of those commitments could 

have been better analyzed. In retrospect, it is 

better understood that these firms were taking 

“tail risk”—their losses in these markets, like 

their losses on CDOs, would only occur in a 

systemwide crisis. Macroprudential analysis 

requires a particular focus on the incentives to 

take this type of risk, particularly at the largest 

financial institutions. 

Since the crisis, financial supervisors have 

begun to take a more comprehensive approach 

to monitoring and addressing threats to 

financial stability. That approach includes an 

emphasis on continually updating policymakers’ 

understanding of activities outside or on 

the edge of the regulated periphery. As 

shown during the crisis, derivatives and 

short-term funding markets are of particular 

concern because of their ability to shift 

risk in unexpected ways and because they 

create leverage, counterparty risk, and other 

interconnections among market participants. 

The new approach includes a renewed emphasis 

on large financial institutions whose failure could 

have systemic implications. It also emphasizes 

continual improvement in firms’ own risk 

management practices and rigorous stress 

testing to better understand connections and 

exposures within the financial system and the 

potential for contagion in the event of a shock. 

As described in this report, the OFR has begun 

to play an important role in implementing this 

approach, helping to make sure supervisors and 

market participants have the data they need to 

understand the financial system and its risk and 

vulnerabilities and contributing to the evolution 

of stress testing and risk management.
10 2012 OFR // Annual Report

Chart 2.1.1 Goals of OFR Research

Understand 

the Financial System

Assess Risk and 

Vulnerabilities

Evaluate

Mitigants

•	 Analyze the basic functions and 

services provided by the financial 

system

•	 Analyze market developments, 

particularly in shadow banking 

and derivatives

•	 Analyze new products and 

markets

•	 Assess gaps in analytics and 

data

•	 Evaluate measures of threats to 

financial stability

•	 Evaluate stress tests

•	 Monitor financial stability

•	 Promote best practices in risk 

management

•	 Promote data standards

•	 Conduct forensic analysis of 

market disruptions

•	 Analyze macroprudential policy

in financial markets will make it elusive (Box A: 

Knowns and Unknowns in 2005). 

That is why the OFR and other agencies 

charged with monitoring financial stability 

must always ask the same questions: How is 

the financial system changing? Where are risks 

accumulating? What are the forces driving 

risk-taking activities and what is the interplay 

among them? And, do policymakers have 

sufficient data and information to answer these 

questions? This report describes supervisors’ 

efforts to address these questions and the roles 

the OFR has begun to play to support those 

efforts (Chart 2.1.1).

Of course, we know more now than before the 

crisis. But there will always be a fundamental 

uncertainty about the sources and severity 

of threats to financial stability, so we must 

be modest about our ability to judge them. 

Financial innovation aimed at improving 

efficiency and promoting better risk-sharing 

potentially can morph into excessive risk-taking, 

and knowing when, why, and how healthy 

activity crosses the line to creating systemwide 

threats is difficult. But better data and analysis 

can help market participants identify and assess 

their own risks and make appropriate decisions 

about them. 

Better data and analysis can also help 

policymakers evaluate and promote mitigants, 

that is, financial shock absorbers and guardrails, 

to reduce the risk of crises. Mitigants include 

indicators of threats to financial stability, 

risk management systems, and stress tests; 

macroprudential policy tools that seek to reduce 

both the structural vulnerabilities and cyclical 

excesses in the financial system; strong data 

standards to promote sound analysis; and crisis 

management and forensic analysis, to mitigate 

the effects of crises that occur and help draw 

lessons for the future. 

2.1.1 Understand the Financial System

The OFR’s financial stability monitoring efforts 

are driven by the principle that financial 

activities and risks are constantly shifting. In 

a short time, an entire market can develop 

out of a new way to perform an old financial 

function, as technologies and products evolve 

and companies experiment with new business 

models. Such innovation can make the financial 

system more effective and efficient and can 

promote economic growth; at the same time, it 

can create unexpected and hard-to-detect risks. 

For that reason, financial stability analysts must 

always be asking how the financial system is 

conducting its basic tasks. While those basic 

tasks can be characterized in various ways, one 

framework would consist of the following six:

•	 Credit allocation and leverage. A dynamic 

economy needs a mechanism for making 

funds available to borrowers with projects 

or goods that need to be financed. Because 

of information asymmetries, the financial 

system provides a valuable service matching 
Analyzing Threats to Financial Stability 11

lenders with borrowers. For investors, 

leverage magnifies financial returns. 

•	 Maturity transformation. Many investors 

wish to commit themselves for only a short 

period of time, while many borrowers need 

to finance their investments over a longer 

period. Responding to those needs, banks 

and other financial institutions provide 

maturity transformation, for example, 

when they accept short-term deposits and 

invest in long-term loans. But that maturity 

transformation service is fundamentally 

unstable because short-term depositors may 

demand their money on short notice. Since 

the advent of the FDIC, insured deposits 

at banks are no longer susceptible to 

rapid outflows amidst a loss of confidence. 

However, other forms of market-based 

maturity transformation became prevalent 

in the past decade—money market funds, 

asset-backed commercial paper, repo 

markets—and also proved susceptible to a 

sudden loss in funding when investors lost 

confidence in their underlying assets or in 

the strength of the financial institutions that 

backed them.

•	 Risk transfer. Investors may wish to hold 

relatively safe claims while borrowers 

are often in the business of taking risks. 

Financial intermediaries assess borrowers’ 

risks and provide their own capital cushions 

to transform risky individual loans into 

lower-risk, diversified portfolios that can 

offer reliable payoffs. Credit risk transfer 

became increasingly complex and opaque 

in the past decade with the advent of credit 

derivatives and complex structured credit 

products, and these innovations contributed 

importantly both to the mortgage market 

excesses of the 2000s and to the severity of 

the ensuing financial crisis.

•	 Price discovery. Through the interaction 

of buyers and sellers, markets perform a 

valuable social function by determining 

fair market prices for financial assets. 

This mechanism is essential for the 

efficient allocation of credit, maturity 

transformation, and risk transfer in the 

financial system. 

•	 Liquidity provision. The willingness of 

investors, borrowers, and lenders to 

participate in the financial system depends 

on their ability to execute transactions in 

a timely fashion. Markets and financial 

institutions provide the liquidity necessary 

to fulfill many of the financial system’s 

other roles.

•	 Facilitation of payments. All activities in 

the financial system depend on the smooth 

operation of a complex infrastructure for 

processing transactions and payments. 

That infrastructure enables market 

participants to clear and settle transactions 

and provides documentation for risk 

monitoring and risk management.

For consumers of financial services, institutions 

like banks or insurance companies offer the 

most tangible examples of the value of these 

financial services. A traditional bank offers 

low-risk deposits to savers and uses the funds 

to make riskier loans to borrowers. Risk 

transformation is accomplished through a 

combination of diversification and an equity 

cushion that shelters depositors from default 

risks. A bank provides maturity transformation 

and liquidity by allowing investors or depositors 

to withdraw their funds on short notice, even 

though the loans they fund are relatively illiquid 

and mature later. However, nonbanking markets 

also perform similar functions. For example, 

securitization has split up the traditional lending 

process into separate stages of loan origination, 

pooling, and market funding. 

The execution of these six financial system 

functions is always evolving. Financial 

services swing between traditional banks and 

nonbanking markets in response to forces 

that drive market incentives and behaviors: 

financial innovations and other competitive 

forces, industry’s perennial efforts to arbitrage 

official supervision and regulation, and other 
12 2012 OFR // Annual Report

policies, such as fiscal policy, monetary policy, 

and government guarantees such as deposit 

insurance. In recent decades, as noted above, 

government supervision focused on the primary 

providers of intermediation services, while many 

of the same services were provided through 

the shadow banking system (Box B: Shadow 

Banking—It’s Not a New Story). 

2.1.2 Assess Risks and Vulnerabilities

“Financial stability” simply means that the 

financial system is sufficiently functioning to 

provide those six basic tasks for the economy 

even under stress; in short, the system is resilient 

to the inevitable shocks and breakdowns in 

market confidence.1 A breakdown in any one 

of these basic tasks can be very dangerous for 

the economy. Also, the provision of these tasks 

can be a double-edged sword. For example, 

although credit allocation and leverage, 

maturity transformation, and risk transfer 

are essential to the financial system and the 

economy, they can also pose risks to financial 

stability if taken to extreme. 

Financial stability does not imply the absence 

of price volatility or failures of firms in the 

financial system; rather, it implies that markets 

continue to function despite such shocks. These 

shocks can take many forms, but in general can 

be transmitted through one of three channels: 

(1) A default by one or more major market 

participants; (2) A sudden loss of market 

confidence, which could be caused by new 

information about a particular type of asset and 

which could be expressed, for example, through 

freezing of liquidity or a sudden change in 

prices; or (3) A disruption in the market 

infrastructure—the so-called “plumbing” of the 

financial system, such as systems for payment, 

clearing, and settling of transactions.

Financial stability is essential for sustainable 

economic growth and efficient allocation of 

resources in the economy and the financial 

system. A stable financial system promotes 

economic growth and increasing wealth, while 

an unstable system can be both an independent 

source of shocks and a source of vulnerabilities 

to outside shocks. 

Prior to the financial crisis, mainstream 

economic analysis did not routinely incorporate 

a framework explaining how risks could emerge 

from within the financial system. To be sure, 

some theorists had argued that relatively calm 

periods in financial markets could create an 

environment in which risk-taking and leverage 

would build, reinforcing the severity of business 

cycles and aggravating downturns (Minsky, 

1992). But these were not mainstream views. 

Across the profession, there was a widespread 

belief in the self-correcting nature of markets 

and the inherent stability of financial activity. 

Under this view, risks flowed in one direction, 

from the economy to the financial system. It 

was widely believed that the so-called Great 

Moderation—a period of low inflation and 

remarkably steady economic growth—had 

engendered financial stability. If monetary 

policy could achieve price stability and steady 

economic growth, financial stability would 

naturally result. It remained possible that a 

severe recession would strain balance sheets 

and cause widespread defaults, but successful 

monetary policy made this seem unlikely. 

Moreover, private incentives for diversification 

would, it was believed, serve to limit the systemic 

effects of financial intermediaries’ difficulties.

The recent crisis served as a painful reminder 

that the financial system is prone to internal 

instability resulting from a buildup of leverage, 

maturity mismatch, and mispriced credit and 

liquidity risks. The benign economic conditions 

seen in the Great Moderation created the 

illusion of financial stability as threats to 

financial stability proliferated in a climate of 

complacency and excessive risk-taking. 

Traditionally, macroeconomists used analytical 

frameworks or models that precluded the 

analysis of such risk buildups, because of 

simplifying assumptions, for example, that 

all market participants were identical. Both 

traditional macroeconomic models and socalled Dynamic Stochastic General Equilibrium 
Analyzing Threats to Financial Stability 13

models, which describe how forces of supply 

and demand can achieve balance in the 

economy, ignore financial complications such as 

the possibility of default. Assuming away default 

and the risks created by leverage is equivalent 

to assuming that the value of an individual firm 

is unrelated to the extent of debt or equity 

financing—that is, to its leverage (Modigliani 

and Miller, 1958).

But, from a financial stability perspective, 

debt and equity are not and should not be 

considered equivalent. Equity or capital 

provided by investors who are able to bear 

losses acts as a shock absorber that self-insures 

lenders against loss, helps contain leverage, and 

limits contagion in a crisis. In contrast, excessive 

credit or leverage promotes contagion; during a 

crisis, defaults tend to exceed the expectations 

of credit providers. The recent crisis is Exhibit 

A: It was fueled by badly managed credit and 

excessive leverage that led to the bankruptcy of 

individuals and the failure of firms, threatening 

financial stability as a whole. In the absence 

of significant capital buffers, the losses from 

defaults triggered deleveraging and balance 

sheet contraction. 

Macroeconomists have been working to 

incorporate more robust assumptions about the 

financial system into their models to explain 

such internal buildups of risk.2

 Far from being 

a sterile intellectual exercise, adopting a more 

realistic analytical framework helps policymakers 

understand why the downside of credit cycles 

unfolds much faster than the upside. Equally, 

such tools help explain how tail risks in stress 

scenarios manifest themselves. 

Taxonomies of Risks and Vulnerabilities

Risks to financial stability can be internal or 

external relative to the financial system. An 

example of an internal threat is the excessive 

risk-taking, fueled by relatively cheap credit and 

liquidity, which promoted the unsustainable 

housing price boom in the 2000s. Examples 

of external threats are a sovereign debt crisis 

overseas, a pandemic or other natural disaster, 

and an international political crisis. Financial 

stability analysis focuses on (1) the propensity 

of the financial system to generate risks—

in particular, on procyclicality, which is the 

tendency of swings in financial activity, especially 

downswings, to magnify the business cycle and 

possibly trigger financial instability—and (2) 

the vulnerabilities or resilience of the financial 

system in the event of a shock.3

Vulnerabilities in the financial system have 

both cyclical and structural components. The 

buildup of leverage in a credit cycle is an 

example of how risks can accumulate both in a 

given financial institution and in the financial 

system as a whole. Alternatively, crowded trades 

in an interconnected system are an example of 

how the structure of the financial system itself 

exacerbates and transmits risks across investors 

within the system.

This taxonomy is similar to the approach in the 

FSOC Annual Report, which describes internal 

or external shocks interacting with structural 

vulnerabilities to disrupt financial stability 

(FSOC, 2012). In both taxonomies, a key point 

is that financial activity itself can generate 

threats to financial stability, as periods of calm 

lead to excess; not all potential threats are 

external to the financial system.

Cyclical Vulnerabilities. Leading up to the crisis, 

credit and leverage both grew significantly 

on the balance sheets of households and 

financial institutions and in financial markets. 

Some of the largest investment banks and 

commercial banks took increasing risks in their 

trading activities in pursuit of higher returns 

or incidentally in the course of providing 

services for their clients. Often those positions 

involved the use of leverage embedded in or 

created by financial innovations in derivatives, 

collateralized debt obligations (CDOs), and 

asset-backed commercial paper (ABCP) 

programs. In turn, that leverage amplified price 

bubbles, particularly in the housing market. 

In retrospect, it is clear that the management 

of these companies did not understand the 

risks they were taking and the dangers of the 

leverage they were using. Although recent 
14 2012 OFR // Annual Report

BOX B. SHADOW BANKING—IT’S NOT A NEW STORY

The term “shadow banking” has gained currency since the financial crisis to describe 

the provision of bank-like services—in particular, credit intermediation, maturity 

transformation, and the creation of money-like liabilities—by companies and 

markets other than banks. These activities are generally subject to less supervision 

and regulation than banks and do not have the benefit of federal deposit insurance. 

Nonbank banking has been central to the financial system and to financial booms and 

busts for more than a century. In general, market participants will seek to conduct 

their activities wherever the regulatory environment is most conducive. 

The Panic of 1907 emanated from the call loan 

market, in which banks and nonbanks invested 

excess cash on a short-term basis to fund 

brokers’ loans backed by stocks and bonds. 

The panic consisted largely of a run on the 

trusts, which were nonbanking institutions that 

performed services similar to banks without 

being subject to similar government supervision. 

Responding to that crisis, Congress created 

the Federal Reserve System in large part to 

hold banks’ reserves as an alternative to a 

nationwide system that channeled much of the 

banking system’s reserves into the New York 

City call loan market. 

Chart B.1 Nonbank Lending Boom of the 1920s

DEC

1938

DEC

1934

DEC

1930

DEC

1926

DEC

1922

DEC

1918

0

50

100

150

200

250

300

350

400

0

1

2

3

4

5

6

7

8

9

Billions of US$ DJIA

Dow Jones Industrial Average (DJIA)

Loans to Brokers by New York Banks (Billions of US$)

Loans to Brokers by Outside Banks (Billions of US$)

Loans to Brokers by Nonbanks (Billions of US$)

B.1 Nonbank Lending Boom of the 1920s

Source: Board of Governors (1943), FRED, FRASER

Chart B.2 Securitized Credit Boom of the 2000s

Q4

2011

Q4

2007

Q4

2003

Q4

1999

Q4

1995

Q4

1991

Trillions of US$ FHFA House Price Index

0

0.5

1.0

1.5

2.0

2.5

3.0

160

200

240

280

320

360

400

FHFA House Price Index (1980=100) 

Private-Label MBS Outstanding (Trillions of US$) 

B.2 Securitized Credit Boom of the 2000s

Source: FHFA, FRB, Haver Analytics
Analyzing Threats to Financial Stability 15

The call loan market was also central in 

the stock market boom in the 1920s. This 

time, most of the financing was provided by 

nonfinancial corporations with excess cash (the 

blue area in Chart B.1), which were attracted 

to the relatively high returns and the secured 

nature of the market. This situation is analogous 

to the role of the securitized credit markets in 

the housing boom of the 2000s (Chart B.2). 

The subsequent crash was made worse by the 

flight of these investors. Even before the crash, 

the role played by nonfinancial corporations 

in fueling the stock market credit boom had 

been derided as “bootleg banking,” and the 

Federal Reserve had sought in vain to limit 

the practice indirectly through moral suasion 

and by attempting to limit access to the 

discount window for banks that lent to those 

corporations (Harrison, 1931).

Building and loan associations, precursors to 

the savings and loan industry that collapsed 

during the 1980s, led the mortgage market 

boom during the same period; there was also 

an early form of private mortgage-backed 

securities in which mortgages were packaged 

and sold to investors as securities—clearly an 

activity that falls within the current generally 

accepted definition of “shadow banking.”

The modern trend toward market-based or 

shadow banking has its roots in the late 1960s, 

when caps on deposit interest rates and other 

regulations encouraged financial markets to 

develop deposit-like products paying higher 

interest rates. The commercial paper market 

grew rapidly, connecting corporate borrowers 

with large cash investors. By the late 1970s, 

much of the demand in this market came from 

money market funds. The U.S. securitization 

process also began in the 1970s with prime 

mortgage loans. It expanded to a wide range of 

other asset types, including auto loans, credit 

Chart B.3 Shadow Banking vs. Traditional 

 Banking Liabilities

1960 1970 1980 1990 2000 2010

Trillions of US$

Q1 2008 = US$ 20.1 Trillion

0

2

4

6

8

10

12

14

16

18

20

22

Traditional Banking Liabilities

Shadow Banking Liabilities

Source: Pozsar and others (2012), Flow of Funds, 

Haver Analytics, OFR calculations

Note: Traditional bank liabilities refer to total liabilities of U.S. 

chartered depository institutions, foreign banking offices in 

the U.S., holding companies, banks in U.S. affiliated areas, 

and credit unions. Shadow bank liabilities (netted from 

overlaps) refer to the sum of total outstanding open market 

paper, total repo liabilities, net securities loaned, total GSE 

liabilities and pool securities, total liabilities of ABS issuers, 

and total shares outstanding of money market funds.

B.3 Shadow Banking vs. Traditional Banking Liabilities

card receivables, lease payments, and, finally, 

subprime mortgages. 

The risks of these markets displacing or 

“disintermediating” the banking sector were widely 

discussed throughout this period. Academics 

and policymakers debated whether banks were 

“dead” (markets could do everything banks could 

do) or “special” (they provided essential payments 

services; they continued to innovate; and, in fact, 

the largest banks tended to be facilitators or 

financiers in the new markets) (Corrigan, 1983; 

Boyd and Gertler, 1994). Regulatory changes 

generally encouraged these developments, 

as evidenced by the rapid growth of shadow 

banking liabilities that began at the turn of the 

last decade (Chart B.3). The share of total credit 
16 2012 OFR // Annual Report

BOX B. SHADOW BANKING—IT’S NOT A NEW STORY - CONTINUED

Chart B.4 Origin of Private Nonfinancial Debt 

 Outstanding

BankBasedMarketBased

Q4 2007 Q4 2011

BankBasedMarketBased

0

MAR

2012

MAR

2004

MAR

1996

MAR

1988

MAR

1980

Percent

25

30

35

40

45

50

55

60

65

Share Intermediated Through Depository Institutions

Share Intermediated Through Securities Markets

Source: Morgan Stanley calculations based on Flow of 

Funds

Chart B.5 Bank vs. Market Intermediated Credit 

 Outstanding

Trillions of US$

5

10

15

20

25

GSEs

Finance Companies

Broker Dealers

Depository Institutions

ABS Issuers

Source: Flow of Funds, OFR calculations

intermediated through securities markets rather 

than banks has grown over the past 30 years, 

although that trend slowed since the onset of the 

crisis as some important market-based channels 

were disrupted (Charts B.4 and B.5).

The lesson from history is that the provision 

of basic financial services constantly evolves. 

An important role for the OFR is to keep track 

of those changes, particularly when activities 

increase in markets and institutions that are not 

being monitored by microprudential supervisors. 

The OFR’s efforts to identify and address data 

gaps in these types of markets are described 

further in Chapter 4.

American experience had suggested that 

mortgages contained only modest credit risk, 

when the bubble burst, the combination of poor 

underwriting, faulty risk transfer, and excessive 

leverage created extraordinary defaults and a 

cascade of systemic deleveraging. 

In the past, the breadth, depth, and liquidity 

of the U.S. capital markets generally enhanced 

the resilience of the overall financial system by 

enabling companies to finance their operations 

efficiently and promoting the low-cost provision 

of essential maturity transformation and 

payments services. They allow investors and 

savers to have confidence in their ability to 

access their capital at the time of their choosing 

without significant cost. Indeed, strong capital 

markets were widely viewed as a backup pillar of 

support or a “spare tire” for the financial system 

in times of banking stress.4

However, easy liquidity—and the expectation 

that it would continue indefinitely—helped 

make credit available on relatively generous 
Analyzing Threats to Financial Stability 17

terms in the 2000s and contributed to what in 

retrospect was a mispricing of both liquidity 

risk and credit risk among market participants 

and traditional lenders. In 2007, credit losses 

triggered a deleveraging in housing finance 

markets and market participants suddenly lost 

confidence in the creditworthiness of mortgagebacked securities and related derivatives, CDOs, 

and ABCP. Previously cheap short-term funding 

and liquidity became dear. Fire sales resulted: 

as individual firms rushed to improve their cash 

positions, they sold both troubled assets and 

other assets that were easier to sell, magnifying 

and reinforcing the effects of the deleveraging 

on housing, the economy, credit availability, risk 

appetite, and the very liquidity that had started 

the cycle. 

Structural Vulnerabilities. The financial 

crisis also provides case studies in structural 

vulnerabilities. For example, through credit 

guarantees, structured credit products, and 

credit derivatives, a small number of large 

financial institutions sold or provided billions 

of dollars in protection against losses in the 

housing market. Yet, structural weaknesses 

in the global derivatives markets—poor 

regulation and opacity that undermined 

market confidence in times of stress—made 

them poor vehicles for risk transfer. Ad hoc 

trade execution and risk management meant 

that users could not gauge product risk, market 

risk, and counterparty risk, particularly given 

the proliferation of customized or bespoke 

transactions. As a result, risk managers who 

believed that they were appropriately hedging 

their risks were often crowded into the same 

risk management strategy, buying protection 

via credit default swaps. This concentration 

of credit risk was generally not understood as 

a threat to financial stability because of the 

high credit ratings and apparent financial 

soundness of those financial institutions that 

provided backstops and guarantees. However, 

in the housing market meltdown, the losses 

faced by these institutions posed serious risks 

to the financial system when investors and 

counterparties lost confidence and pulled their 

funding or demanded more collateral.

While these categories provide a useful 

framework for thinking about threats to 

financial stability, there is no bright line 

separating them. For instance, the housing 

boom at the heart of the financial crisis was 

amplified by both forces internal to the 

financial system (liquid securitization markets, 

for example) and those external to it (strong 

housing demand and the belief that home 

prices would not fall). Of course, the cyclical 

extremes of the boom were fueled by the 

interplay between them—easy credit promoted 

strong housing demand and rising prices, 

while the price gains encouraged leveraging on 

attractive terms. 

2.1.3 Evaluate Mitigants

Macroprudential regulation should mitigate 

threats to financial stability by limiting 

the internal buildup of risk, reducing 

vulnerabilities, and promoting resilience to 

shocks. Mitigants can be described as guardrails, 

which set limits or controls on the activities 

of financial institutions and help to restore 

market discipline, and shock absorbers, which 

prevent shocks from disrupting the financial 

system’s performance.

Such regulation should counter procyclicality, 

which is, as noted, the tendency of swings in 

financial activity to magnify the business cycle. 

For example, it should lean against the tendency 

during good times for firms to take excessive 

risks with thin capital and liquidity buffers 

and, during bad times, to act in ways that seem 

rational for a single firm but that have negative 

systemic consequences when many firms act 

similarly—for example, by selling assets in fire 

sales or by reducing lending rather than raising 

new capital (Hanson, Kashyap, and Stein, 2011). 

Best practices for macroprudential policy dictate 

that policymakers cannot deliver financial 

stability without a tool to counter each source 

of financial instability. They also dictate that 

policymakers should assign to each target the 

right tool for the job—the one that has the 

biggest influence on the policy objective—

following the “assignment principle” (Mundell, 
18 2012 OFR // Annual Report

1962). For example, if policymakers want to 

combat three targets—excessive leverage, 

insufficient liquidity, and procyclicality—a 

satisfactory toolkit must contain tools to address 

each of the three—such as capital, liquidity, 

and margin regulations (Kashyap, Berner, and 

Goodhart, 2011).

Macroprudential policy must also take 

into account that this toolkit will have 

macroeconomic consequences, just as monetary 

and fiscal policy—typically macroeconomic 

tools—may have macroprudential 

consequences. While the assignment principle 

suggests that these spillovers and potential 

conflicts do not preclude effective policy 

implementation, both financial instability and 

macroprudential tools to combat it may alter 

the transmission mechanism for monetary and 

fiscal policies in ways that policymakers need 

to recognize (Carney, 2009). Encouragingly, 

flexible inflation targeting likely gives central 

banks the flexibility to deploy macroprudential 

tools both in crises and in periods of stability 

(Carney, 2012). However, if a financial crisis 

impairs the traditional policy transmission 

mechanisms, the assignment principle may 

indicate that macroprudential tools be used 

to restore their functioning and achieve 

macroeconomic goals.

Mitigants are largely complementary from a 

microprudential and macroprudential point 

of view. From a microprudential point of view, 

the guardrails in the financial system include 

firm risk management, market discipline and 

information intermediaries (for example, 

rating agencies and data providers), and 

microprudential supervision and regulation; 

shock absorbers include capital and liquidity 

standards. From a macroprudential point of 

view, guardrails include stress tests, living wills 

for certain financial institutions, and central 

clearing for swaps and derivatives; systemwide 

shock absorbers include an orderly liquidation 

authority, deposit insurance and emergency 

liquidity provision by the central bank.

The Dodd-Frank Act introduces several 

measures aimed at strengthening guardrails. 

For example, it subjects large, complex financial 

companies to more stringent supervision 

by the Federal Reserve and it creates a 

new regulatory framework for derivatives, 

requiring most derivatives to clear through 

central counterparties. To strengthen shock 

absorbers, Dodd-Frank regulations and the 

Basel III agreement among international 

supervisors have improved capital standards, 

and Basel III introduced the first international 

liquidity standard (BCBS, 2010a; BCBS, 

2010b). Dodd-Frank also introduces a new 

resolution regime that creates a process for 

breaking up and winding down failing large 

financial companies, which aims to contain the 

systemic repercussions of such events. These 

new guardrails and shock absorbers will also 

help restore market discipline by reducing the 

expectation that taxpayers will bail out failing 

companies.

The OFR must contribute to these mitigants by 

fulfilling its analytical mandates, which include 

identifying and filling data gaps; developing 

and maintaining metrics and reporting systems 

for risks to financial stability; monitoring, 

investigating, and reporting to Congress and 

the Council on changes in systemwide risks; 

conducting, coordinating, and sponsoring 

research to support and improve regulation of 

financial companies; assessing and reporting 

on stress tests and other stability-related 

evaluations of financial companies; conducting 

forensic analyses of market disruptions; 

conducting studies and providing advice on 

macroprudential policies; and promoting best 

practices in firm risk management.
Analyzing Threats to Financial Stability 19

2.2 Current Threats to Financial Stability 

Cyclical threats to financial stability today include the lingering weakness in the housing finance market; the extremely low level of interest rates; the possibility of a deterioration in lending standards; and 

the potential impacts of the euro area sovereign debt crisis on the U.S. financial system and economy. 

Structural threats remain in the prevalence of data gaps and inadequate data standards in the financial sector; the ongoing challenges to risk management posed by complex trading activities, particularly 

at the largest financial institutions; and the run risk for money market funds and other short-term 

funding markets.

To analyze threats to financial stability, we 

look to the analytical framework laid out 

in the first part of this chapter. The six 

basic tasks—credit allocation and leverage, 

maturity transformation, risk transfer, price 

discovery, liquidity provision, and facilitation 

of payments—are all fundamental to the 

functioning of modern financial systems. The 

financial system is stable as long as it can provide 

these basic services for the economy, even under 

stress. By the same token, shocks can disrupt 

these basic functions. 

As noted, two taxonomies shed further light on 

the sources of potential shocks. The first draws 

a distinction between internal and external 

risks. Internal risks—those arising from within 

the financial system—include failures of the 

mitigants described above, such as inadequate 

risk management among financial firms or 

insufficient regulatory shock absorbers or 

guardrails, while external risks include potential 

contagion from the European sovereign debt 

crisis. The second taxonomy distinguishes 

between cyclical and structural risks; although 

some risks represent a mix of both. Cyclical 

risks involve the familiar buildup of risks over 

the credit and business cycle, for example, 

excessive credit growth and leverage. Structural 

risks involve risks across the financial system 

at a point in time, for example, the fixed net 

asset value that promotes the risk of a run in 

money-market funds, intraday credit risks in 

tri-party repo, the continuing presence of large 

institutions that are perceived as too big to fail, 

or the forces that promote the migration of 

activity into unregulated or lightly regulated 

markets. The European crisis represents a 

combination of structural risks (a currency 

union without a fiscal union) and cyclical risks 

(the current recession).

The intersection of these two taxonomies can 

be helpful in categorizing the major potential 

threats that the OFR sees in the financial system 

today (Chart 2.2.1).

2.2.1 Internal Risks 

Cyclical Concerns

Among risks that might arise within the 

financial system, the current credit environment 

poses two types of cyclical concerns: weaknesses 

in credit intermediation in the housing market 

and the possibility of excesses in credit markets 

fueled in part by historically low interest rates. 

Consumer Finance. Although housing prices 

may be at or near the bottom, even seven years 

past their peak, a key financial imbalance—that 

is, one that is internal to the financial system—

continues to restrain the housing sector. 

The legacy of the mortgage bust lingers on 

household and lender balance sheets, weighing 

on mortgage markets and the availability of 

mortgage credit. Twelve million homeowners 

have outstanding balances on their mortgages 

exceeding the current market value of their 

homes. Nationally, that so-called “negative 

equity” has been estimated at $717 billion 

(CoreLogic, 2012). Lenders continue to work 

through nonperforming loans originated before 

the crisis, while continuing to be exposed to 

the risk of “put backs” and the uncertainty 

surrounding proposed risk retention rules. 

Mortgage originations remain depressed, 

mortgage debt continues to contract, and even 

creditworthy potential homebuyers continue to 

have difficulty obtaining financing. As a result, 
20 2012 OFR // Annual Report

Chart 2.2.1 Current Potential Threats to Financial Stability

Internal External

Cyclical •	 Sluggish growth and dependence on 

government support in housing finance

•	 Low interest rate environment encourages 

“reaching for yield”

•	 Some evidence of deterioration in underwriting 

standards, e.g., “covenant-lite” loans

•	 European sovereign debt crisis

•	 Possible slowdown 

in emerging markets

Structural •	 Data and analytical gaps and lack of data 

standards

•	 Breakdowns in risk management and other 

guardrails

•	 Short-term funding and derivatives markets

•	 Too big to fail

•	 Domestic fiscal policy

•	 Cyber attacks

the government continues to play an outsized 

role in the market through the Federal Housing 

Administration and the two governmentsponsored enterprises (GSEs), which have 

been operating under conservatorship by the 

government since 2008. 

Meanwhile, Americans owe $1 trillion on 

student loans that are predominantly held 

by the government. While this debt does not 

present direct risks to financial institutions, 

consumers with large student debt burdens may 

spend less and are more likely to have difficulty 

securing a mortgage. These factors could 

significantly depress demand for mortgage 

credit and dampen consumption. 

Low Interest Rates. The second major cyclical 

issue today is the extraordinarily low level of 

interest rates. In early June, the 10-year Treasury 

yield fell below 1.5 percent for the first time. 

Although low rates have made an important 

contribution to the economic recovery, a 

low-rate environment creates potential stress 

for some investors, particularly financial 

institutions, such as life insurers and pension 

funds, that need to earn a certain return on 

their fixed-income portfolios to cover a fixed 

stream of liabilities. Low interest rates can 

therefore lead some investors to reach for yield 

by taking on additional credit risk to enhance 

their expected earnings. 

Financial crises, including the most recent one, 

often emerge after long periods of low rates 

during which lending standards deteriorate. 

Council member agencies are concerned about 

the potential for such excesses in markets 

other than housing finance. They have noted 

recently the increased issuance of “covenantlite” loans, which are loans that waive the 

typical restrictions on commercial borrowers 

with respect to collateral, income, or payment 

terms. Second, banks could be exposed to losses 

if they do not hedge their balance sheets to 

protect against higher rates in the future. The 

federal banking agencies issued guidance to 

supervised institutions about interest rate risk 

management in January 2010 and followed up 

with clarifications in January 2012 (Board of 

Governors and others, 2010; OCC and 

others, 2012).

The MF Global bankruptcy and JPMorgan’s 

recent trading losses suggest that, against 

the backdrop of low returns on equity, some 

companies continue to take significant risks in 

their trading operations. The role of financial 

stability analysis in such situations is to consider 

the potential systemic impacts—for example, 

to determine whether the strategies on which 

those companies experienced losses were 

common in the market, whether other firms 

faced similar control problems, and what the 
Analyzing Threats to Financial Stability 21

implications might be for the market if they 

were to fail.

Structural Concerns

Structural concerns today include the remaining 

intraday credit risk in the tri-party repo market 

and the risk of runs on money market funds. 

Pairing these risks together and with others is 

important. Structural weaknesses in the tri-party 

repo market may increase the risks of using 

short-term funding for illiquid assets. Moreover, 

the practice of money market funds maintaining 

a one-dollar net asset value can magnify this 

type of instability. If investors believed that the 

one-dollar-per-share value exceeded the true 

liquidation value of a fund, they would have an 

incentive to pull out their money before other 

fund investors.

Short-Term Funding Markets. Before the crisis, 

secured and unsecured short-term funding 

markets provided major sources of financing 

for the portfolio holdings and securities 

inventories of broker-dealers and other market 

participants. This ability to mismatch maturities 

provided a major source of the returns to 

securitization. The use of short-term wholesale 

funding has decreased since the crisis but it is 

still used substantially by large bank holding 

companies, including those with large brokerdealer operations.

Short-term funding markets are key focal 

points for the emergence of excessive 

leverage, liquidity risk, and new forms of 

interconnectivity among financial institutions, 

the three vulnerabilities of the financial system 

highlighted in Section 4.2. Short-term funding, 

obtained through repos, commercial paper, 

and prime broker lending, can be a source 

of instability if lenders, worried about the 

value of collateral or counterparty risks, make 

it difficult for borrowers to roll over their 

maturing short-term debt on economically 

viable terms. Those terms include the rate and 

tenor and, for secured funding, the haircuts on 

collateral. Under stress, lenders shorten tenor 

and increase haircuts, significantly raising the 

cost of funding. Swings in repo haircuts add 

procyclicality to the financial system. Under 

extreme stress, borrowers funding long-duration 

illiquid assets with wholesale funding could be 

forced to sell assets under fire sale conditions. 

Pressure on asset prices, in turn, reinforces the 

downward spiral. Short-term funding markets 

also face run risk in a crisis because they do 

not benefit from official backstops in the form 

of federal deposit insurance or the Federal 

Reserve’s discount window. 

Some progress has been made in addressing 

risks in the tri-party repo market and money 

market funds since the crisis. An industry task 

force on tri-party repo reform disbanded after 

some success, but the problem of intraday credit 

remains. The Federal Reserve is now taking a 

more direct supervisory approach to making 

the necessary changes (Tarullo, 2012). Similarly, 

the SEC made important reforms in money 

market fund regulation in 2010, including more 

stringent liquidity requirements, but the risk 

of runs remains due to the combination of a 

promised stable net asset value and investments 

in securities that can default or lose value 

precipitously (SEC, 2010). The SEC is reviewing 

further policy options for money market 

fund reform. Among the options are a capital 

requirement, in which money market funds 

would be required to hold a layer of equity 

that would absorb losses before investors incur 

losses; restrictions on redemption; and a move 

away from a fixed net asset value.

Market Integrity. For the financial system to 

perform its price discovery function through 

the interactions of buyers and sellers, markets 

must be transparent, fair to all participants, 

and not subject to manipulation. U.S. and U.K. 

regulators recently announced that Barclays 

Bank PLC, a London-based financial institution, 

will pay close to half a billion dollars in penalties 

to resolve violations arising from alleged 

manipulations of the London Interbank Offered 

Rate (LIBOR) and the Euro Interbank Offered 

Rate (EURIBOR). Market participants, risk 

managers, and regulators have relied on these 

rates for many years as benchmarks for the cost 
22 2012 OFR // Annual Report

of short-term, unsecured funding that in large 

part reflect counterparty risks.

LIBOR and EURIBOR are calculated as an 

average of the rates that major banks submit 

each day. Each bank is supposed to contribute 

rates that reflect its estimated cost of funds in 

the unsecured interbank market. An agreement 

between Barclays and the U.S. Department of 

Justice (DOJ) stated that Barclays had submitted 

bids that took into account trading positions 

of its own derivative traders or reputational 

concerns about Barclays itself. Regulators have 

an ongoing investigation into other banks’ 

activities in the market (DOJ, 2012). 

LIBOR and EURIBOR are benchmark interest 

rates that market participants use as the basis 

for pricing trillions of dollars worth of loans and 

securities. As the preeminent benchmarks for 

unsecured transactions, these rates also provide 

important market signals about counterparty 

credit risk. This type of manipulation—resulting 

from an opaque and closed process that allows 

a small number of firms to have significant 

influence—poses significant risks to market 

integrity and investor trust, and will require 

continuing regulatory focus. 

Collateral in Secured Lending Transactions.

Securitization involves a chain of activities 

ranging from origination at one end to 

financing at the other and is an important 

part of the shadow banking system. Securities 

lending and repo financing are two key activities 

in this chain. Lenders of securities, primarily 

institutional investors, offer their holdings to 

banks and broker-dealers who need to borrow 

them in order to hedge or outright short them. 

As collateral for the loan, the broker-dealers 

offer the lenders cash resulting from the 

short. Broker-dealers finance their securities 

inventory with repo, done for example with a 

mutual fund, using the securities as collateral. 

Leveraged investment funds and other providers 

of repo financing also play important roles. 

These activities pose potential threats to 

financial stability through three channels:

Maturity transformation and credit risk through 

cash collateral reinvestment. Securities lenders 

can reinvest the cash collateral they receive 

from securities lending transactions and 

engage in credit and maturity transformation, 

taking on credit and liquidity risks. If asset 

prices fall and those investments turn illiquid, 

and if the borrowers ask for their cash 

collateral to be returned, these companies can 

lose their access to market funding. Such banklike activities create bank-like risks without the 

safeguards banks enjoy. 

Procyclicality of systemwide leverage and 

interconnectedness. Securities lenders may also 

obtain leverage that is sensitive both to asset 

prices and their own counterparty risk, creating 

procyclicality in securities financing markets. 

This procyclicality depends importantly on the 

changes in haircuts applied to those collateral 

securities, and the extent to which collateral 

is used more than once (collateral velocity). 

Haircuts rise with credit and counterparty 

risk, raising the cost of credit and prompting 

deleveraging in a downswing. Extensive 

collateral re-use, or rehypothecation, coupled 

with leverage, maturity transformation, and 

interconnectedness among firms, could create 

several threats to financial stability, including 

fire sales of less liquid assets. While it appears 

that such risk-seeking has diminished in 

the wake of the crisis, the current low level 

of returns may create pressures to stretch 

for yield. And the current fraught state of 

unsecured funding markets has intensified an 

already high demand for collateral in secured 

funding markets, one that may intensify 

this procyclicality. 

Lack of transparency. Securities financing 

markets are often opaque because they 

are complex and rapidly evolving, and the 

transactions are usually bilateral. Better 

data are essential to understand the risks in 

such activities. More disclosure by market 

participants would also help, including 
Analyzing Threats to Financial Stability 23

disclosures about transactions that are 

typically “looked through” for the purposes of 

financial reporting. Better risk reporting by 

intermediaries to their clients would help them 

understand the counterparty risk and cash 

collateral reinvestment risk of their securities 

lending programs. 

Derivatives. When properly managed, 

derivatives provide value to market 

participants by allowing them to hedge risks 

or to gain exposures to real assets without 

having to hold those assets. Derivatives also 

offer a relatively cheap way to leverage market 

positions. But those characteristics—risk 

transfer and leverage—also make derivatives 

markets potentially an important source of 

threats to financial stability, particularly when 

poorly managed. 

The crisis illustrated the dangers of poorly 

understood derivatives markets, particularly 

credit default swaps, which shift the credit 

risk related to a reference entity—such as a 

corporation, a country, or a specific bond—

from a protection buyer to a protection seller. 

Between 2004 and 2007, credit default swaps 

referencing different tranches of mortgagebacked securities (MBS) became ubiquitous. 

CDOs that invested in MBS and synthetic 

CDOs that took long positions in credit 

defaults swaps that referenced MBS or other 

CDO securities also proliferated. These new 

financial innovations facilitated complex 

trading strategies that distorted incentives in the 

mortgage market. They also made it possible for 

a small number of large financial institutions—

prominently, AIG, the nation’s largest insurance 

company, and Citigroup, a commercial bank 

and investment bank holding company—to take 

outsized positions in the mortgage market.

The crisis also revealed serious structural 

problems in the derivatives market. The lack of 

transparency, limited regulation, and poor risk 

management created uncertainty during the 

financial crisis as market participants could not 

gauge market risk and counterparty risk. In the 

absence of margin requirements, AIG was able 

to take on those positions in mortgage-related 

derivatives without posting margin. The lack 

of transparency in the markets contributed to 

the uncertainty during the crisis, as financial 

institutions tried to understand their exposures 

to specific counterparties and regulators tried 

to understand the potential contagion effects of 

the failure of a large firm. 

Regulators and the industry itself have taken 

important measures to make derivatives markets 

more transparent and robust. The Group of 

Twenty Finance Ministers and Central Bank 

Governors (G20) at the 2009 Pittsburgh Summit 

agreed that standardized derivative contracts 

should be traded on exchanges or electronic 

platforms, as appropriate, and cleared through 

central counterparties—meaning there is a 

separate institution that intermediates between 

the parties to a swap, which improves the 

management of credit risk. The G20 leaders 

also agreed that all over-the-counter (OTC) 

derivative transactions should be reported 

to trade repositories, which collect and store 

information about derivatives trades. The DoddFrank Act establishes those requirements in 

the U.S. The U.S. is also working with foreign 

regulators to introduce global margin standards 

for OTC derivative contracts that are not 

centrally cleared.

Since the crisis, there has been a substantial 

increase in the volume of swaps that are 

centrally cleared. Trade repositories have 

expanded, providing previously unavailable 

transparency for regulators into derivatives 

exposures. Also significantly improving 

transparency, the Dodd-Frank Act requires many 

types of swaps to be traded on a swap execution 

facility (SEF), defined in the Act as a trading 

platform that market participants must use to 

execute swap transactions. The SEC and CFTC 

have proposed rules for regulating SEFs (SEC, 

2012; CFTC, 2012a).

In the spring of 2012, the OFR began to 

collect data on credit default swaps from 

a private-sector data repository. The data 

will allow the OFR and supervisors access to 
24 2012 OFR // Annual Report

information about positions that are taken by 

U.S.-based entities or that reference U.S.-based 

entities. The OFR anticipates using this data, 

in cooperation with the agencies, to analyze 

the aggregate exposures that different types 

of firms are taking in credit derivatives, to 

investigate whether undue concentrations of 

the AIG type are developing, and to respond 

to specific queries such as the extent to which 

U.S. entities have sold protection on troubled 

entities or markets. 

Too Big to Fail. Capital injections to save 

our financial system have led some market 

participants to believe that some large 

institutions carry an implicit government 

guarantee, which could lead to competitive 

inequities within the financial system. 

Supervisors have taken important steps to 

reduce the risks posed by the potential failure of 

large, complex financial institutions. 

Steps taken since the crisis include higher 

capital standards, particularly for trading 

activities, a proposed new global liquidity 

standard, and a tougher supervisory regime 

for large, complex financial institutions. DoddFrank also makes it easier for regulators to 

liquidate or resolve large, troubled financial 

companies in a way that minimizes the impact 

on the rest of the financial system. Title II of 

Dodd-Frank, which establishes the Orderly 

Liquidation Authority, also requires that 

the shareholders and creditors of the failed 

company and, if necessary, the industry—

rather than the taxpayers—cover the cost of 

these failures. These measures limit the ability 

of the government to provide extraordinary 

support to these companies in a crisis. As 

such, these measures seek to restore market 

discipline by reducing the implicit government 

guarantee and the incentive it may create for 

the management of these companies to take 

asymmetric risks. The credit rating agencies 

recently reduced the “uplift” they incorporate 

in the long-term credit ratings of several large 

financial institutions to reflect these measures.

2.2.2 External Risks

Cyclical Concerns

The U.S. financial system presently confronts 

two external risks that are also cyclical: Europe 

and the domestic housing market.

Europe. While we have classified the European 

economic crisis, now more than two years old, 

among cyclical concerns, in reality it is a good 

example of a risk that has both structural and 

cyclical elements. The situation resulted from 

the interplay of three forces. 

First, the launch of the common currency 

in 1999 in the face of significant economic 

disparities across the euro area sowed the 

seeds for the development of large economic 

imbalances. The euro brought about a 

significant reduction in borrowing costs for 

the southern European members of the 

monetary union on the assumption that 

fiscal policies would converge to the norms 

established by the core countries. Put simply, 

the markets misjudged euro area sovereign 

risk. Mispriced debt allowed the peripheral 

countries—many of which came into the EU 

and monetary union with rigid labor markets 

and poor competitiveness—to borrow heavily 

at increasingly cheap rates in the run up to the 

crisis. That borrowing occurred in the public 

domain in Greece, but in Ireland, Spain and 

Portugal, the bulk of the borrowing occurred 

in the private sector, which supported import 

consumption booms generally and in Spain and 

Ireland fueled domestic property bubbles. Italy 

is an outlier—its public debt burden is a legacy 

from the 1970s and 1980s, which it had some 

success addressing, having run primary budget 

surpluses for 17 years until 2008. Its low level of 

private sector indebtedness also contrasts with 

other countries in the periphery. 

Second, the euro area lacked the necessary 

institutions to facilitate adjustment of economic 

imbalances, which became necessary in the 

wake of the global crisis and the end to private 

and public sector borrowing binges. Locked 

into a common currency, the periphery could 

not deploy monetary policy or benefit from 
Analyzing Threats to Financial Stability 25

exchange rate flexibility to help cushion the 

downturn. A loss of wholesale funding and 

deleveraging among banks worsened the 

resulting squeeze on credit. Crucially, the euro 

area did not have a system of fiscal transfers to 

offset the recession-related costs in the harderhit regions of the currency union. In addition, 

the absence of centralized bank supervision 

and support meant that national budgets were 

tapped for bank cleanup costs. Thus, banking 

sector weaknesses reinforced sovereign credit 

problems and vice versa. 

Finally, the euro area’s governance structures, 

which often require unanimity and must 

constantly balance widely varying national 

interests, have made it difficult to develop 

an effective crisis response and stay ahead 

of markets. Importantly, however, there has 

been notable progress to strengthen euro 

area governance and to develop a robust crisis 

fighting toolkit. Country fiscal and structural 

reform efforts are backed by significantly 

strengthened monitoring mechanisms and 

supported by resources from European 

financing mechanisms developed since 

2010. There is recognition that more work 

is needed to develop institutions that enable 

a more comprehensive crisis response that 

taps the monetary union’s unified strength, 

fosters greater fiscal and financial integration, 

and finds a better balance between fiscal 

consolidation and growth. 

Spillovers from Europe to the U.S. financial 

system can occur through five channels. 

First, U.S. financial institutions have direct 

exposures to European banks, mainly through 

unsecured bank loans and secured repo loans, 

although these are relatively small. Second, 

the downturn in European economies will 

weaken U.S. growth. Exports to Europe 

account for 25 percent of total U.S. exports, 

or roughly 3 percent of our gross domestic 

product (GDP). Third, Europe accounts for 

half of U.S. overseas corporate profits and 

about one-sixth of overall U.S. corporate 

profits. Fourth, a weaker euro (and stronger 

dollar) will further blunt U.S. exports and the 

Chart 2.2.2 Shadow Inventory in the Housing Market

Q1

2012

Q3

2011

Q1

2011

Q3

2010

Q1

2010

Q3

2009

Q1

2009

Loan Count (Millions)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Real Estate Owned by Lenders

> 12 Months Delinquent and in Foreclosure

Loans Sold

Source: Amherst Securities Estimate using CoreLogic 

Prime Serving Database, CoreLogic Securitized Loan 

Database, FDIC, Fannie Mae, Freddie Mac, FHA, 

1010Data

2.2.2 Shadow Inventory in the Housing Market

translation into dollars from profits abroad, 

and boost U.S. imports, although the near-term 

effects on trade likely will be small. Finally and 

most importantly, the flight from risky assets in

Europe and the efforts of European banks to 

reduce the leverage on their balance sheets will 

raise risk premiums in U.S. markets.

Real Estate Supply and Demand. A key legacy of 

the crisis is that the housing market continues to 

face a supply overhang. Although the inventory 

of for-sale homes is not high by historical 

standards, there is a potentially much bigger 

“shadow” inventory consisting of properties 

that are stuck in the foreclosure process (Chart 

2.2.2). Because foreclosed properties tend to 

sell at a discount to owner-occupied ones, the 

backlog of delinquent and foreclosable homes 

gives buyers an incentive to delay purchases. 

The anticipation of this discounted shadow 

inventory depresses prices today. Estimates of 

this shadow inventory vary significantly, with 

press reports quoting estimates between 1.6 

and 10.3 million homes (Whelan, 2011). A 

third type of overhang comes from discouraged 

homeowners who would like to sell but are 

discouraged by weak demand. Improving 
26 2012 OFR // Annual Report

Chart 2.2.3 U.S. House Prices

JAN

2012

JAN

2010

JAN

2008

JAN

2006

JAN

2004

JAN

2002

JAN

2000

75

100

125

150

175

200

225

Index (January 2000=100)

Source: FHFA, S&P/Case-Shiller, CoreLogic, Haver Analytics

2.2.3 U.S. House Prices

FHFA House Price Index (Purchase Only, U.S.)

S&P/Case-Shiller Home Price Index (Composite 20)

CoreLogic National House Price Index

(Distressed Sales Included)

CoreLogic National House Price Index

(Distressed Sales Excluded)

market conditions may well be met with these 

sellers returning to markets and limiting house 

price appreciation. 

While housing prices may have stabilized in 

recent months (Chart 2.2.3), these overhangs 

of excess supply may continue to contribute 

to generally depressed home prices and 

limited new construction activity. The interplay 

between the financial (internal) and realeconomy (external) imbalances has created a 

vicious cycle in the past. Low home prices and 

negative equity escalate defaults, which in turn 

induce losses among lenders and deleveraging 

by households. 

Policy interventions to halt this vicious cycle 

have included fiscal and monetary policy as well 

as interventions to support both borrowers and 

lenders. The primary fiscal intervention was 

the Federal Home Buyer Credit of 2010, which 

provided a tax credit to first-time home buyers 

in an attempt to stimulate housing demand. The 

Fed’s monetary policy—including federal funds 

rate changes and quantitative easing—have 

lowered nominal rates significantly, reducing 

the mortgage burden for households with 

adjustable rate mortgages and lowering the 

monthly costs for borrowers who refinance an 

existing home or purchase a new one. 

There have also been interventions to 

reduce homeowner debts or payments. The 

largest policy initiative, the Home Affordable 

Modification Program, attempts to identify 

needy and non-strategic defaulters for principal 

reductions and term and rate changes by 

compensating the lenders for a fraction of 

the reduced debts. Interventions that support 

creditors to promote lending have included 

capital injections to increase bank capital, 

the government takeover of Freddie Mac 

and Fannie Mae, the purchase of securitized 

mortgages, and low- or no-downpayment loans 

through the Federal Housing Administration 

and the Department of Veterans Affairs.

Structural Concerns

U.S. Fiscal Policy Outlook. Sovereign credit risks 

for U.S. Treasuries seem remote. Real (inflationadjusted) yields and term premiums on 10-year 

U.S. Treasuries have been negative for nearly a 

year, reflecting a flight to Treasuries from riskier 

assets and the portfolio effects of the Fed’s large 

scale asset purchases (Chart 2.2.4). However, if 

officials do not come to agreement on policies 

to deal with the nation’s long-term, structural 

fiscal challenges or on near-term fiscal policy 

measures, such risk premiums and those on 

risky assets could rise, potentially weighing on 

financial and economic stability.

Many policymakers agree that a credible plan 

for fiscal sustainability is essential to our longterm prosperity. Agreement on such a 

plan could boost risk appetite, improving 

prospects both for economic growth and for 

financial stability. 

Before the details of a long-term fiscal 

consolidation plan can be worked out, however, 

several fiscal policy issues must be addressed 

around the end of the year. The Budget Control 

Act of 2011 mandates significant and automatic 

fiscal restraint on January 1, 2013. In addition, a 
Analyzing Threats to Financial Stability 27

number of tax policies are set to expire. Under 

current law, the Congressional Budget Office 

estimates that tax increases and the budget 

sequestration would swing the government 

budget toward austerity by about 4 percent of 

GDP, threatening the economy with renewed 

recession (CBO, 2012).

A number of outcomes are possible. On the 

one hand, policymakers could come to an 

agreement that would balance the need for 

near-term support for the economic recovery 

with longer-term fiscal consolidation. Less 

favorably, policymakers could choose a path that 

either puts in place significant consolidation in 

the near-term at the temporary cost of economic 

growth or alternatively a path that would fail to 

make a significant step toward consolidation. 

The outcome could have a significant bearing 

on risk premiums and the cost of borrowing. 

Risk of Cyber Attack. The Council, in its 2012 

Annual Report, identifies the threat of cyber 

attacks on the financial infrastructure—for 

example, attempts to gain unauthorized access 

into networks and systems—as a significant and 

increasing threat to financial stability (FSOC, 

2012). Attacks have become more frequent, 

more targeted on specific aspects of financial 

institutions’ infrastructure, and more disruptive. 

They pose particularly significant risks through 

the potential disruption of transaction, 

payment, clearing, and settlement systems. 

2.2.3 Challenges to Mitigants

Financial stability mitigants share the common 

challenge of keeping up as technologies evolve, 

transactions accelerate, financial activities shift 

to new products and markets or overseas, and 

traders develop ever-more complex ways to 

shift risk through derivatives and other opaque 

products. Financial activities have a natural 

tendency to move to markets or jurisdictions 

that are subject to relatively less supervision and 

regulation. A comprehensive, macroprudential 

approach to supervision and regulation could 

limit such regulatory arbitrage.

Chart 2.2.4 U.S. Treasury Term Premium: 

Calculated for a 10-Year Zero Coupon 

Note

JAN 

2012

JAN

2008

JAN 

2004

JAN

2000

JAN

1996

JAN

1992

Basis Points

0

-150

-100

-50

50

100

150

200

250

300

350

Source: FRB, Kim and Wright (2005)

2.2.4 U.S. Treasury Term Premium:

 Calculated for a 10-Year Zero Coupon Note

Gaps and Weaknesses in Data. Leading up 

to and during the financial crisis, market 

participants and their supervisors did not have 

critical information necessary for assessing the 

buildup of leverage and liquidity and the nature 

and extent of interconnections among financial 

entities. As described in Chapter 4, an important 

role for the OFR is to help address gaps and 

weaknesses in the data that financial supervisors 

use to monitor those risks. 

Supervisors have already made substantial 

progress in improving their ability to monitor 

the financial system. Regulated institutions, 

such as banks and thrifts, now file a great deal 

more information than they did before the 

crisis about their activities and exposures, both 

in confidential reports to supervisors and in 

public regulatory reports. Hedge funds and 

money market funds are now required to file 

Form PF and Form N-MFP, respectively, with 

regulators (CFTC and SEC, 2011; SEC, 2010). 

Supervisors are also working in conjunction with 

the International Monetary Fund and Financial 

Stability Board to promote improvements in 

data available for monitoring international 

financial developments.
28 2012 OFR // Annual Report

It will be an ongoing challenge for the OFR 

and the supervisors to ensure that their sources 

of data on threats to financial stability keep 

pace with constantly evolving financial markets, 

particularly with respect to derivatives markets 

and other activities that have traditionally 

resided outside the regulatory sphere.

Need for Data Standards and Better Data 

Management. The crisis also revealed deep 

problems with the infrastructure upon 

which financial institutions depend for the 

smooth delivery of services, the functioning 

of markets, and the management of risk. For 

years, financial sector investment in the back 

office—where trades are processed and risks are 

managed—has not kept up with investment in 

the front office. It is too easy for the managers 

of financial firms to view the front office as 

the profit center and the back office as the 

cost center. Meanwhile, after years of mergers 

and acquisitions, the presence of redundant 

computer systems performing similar tasks, but 

with data that may be dissimilar, is common 

across the industry. Simply, the quality of data 

standards and data management in the financial 

sector has lagged significantly behind where 

they need to be.

The result is that communication is difficult 

within firms, among firms, and with regulators. 

Financial companies lack standards for basic 

data elements and terms, an acute problem 

during the crisis. When uncertainty grew in 

2007 and 2008 about the risks of mortgagerelated derivatives and CDOs, many financial 

managers were unable to measure and 

address risks across different desks and legal 

entities within their own organizations. 

They could not answer seemingly simple 

questions, such as their aggregate exposure 

to the housing downturn or their exposure to 

specific troubled counterparties. Regulators 

were unable to understand exposures within 

individual firms, compare exposures across 

firms, or analyze the systemic impacts as losses 

spread from firm to firm. 

Currently, a lack of standardization hinders 

comparison between mortgage datasets at 

large financial institutions and the GSEs, and 

the lack of a uniform nationwide system of 

property identifiers makes matching of liens 

more difficult. If unresolved, these problems 

will, among other consequences, continue to 

affect the willingness of investors to purchase 

securitized mortgage products.

A critical mandate for the OFR, as described 

in Chapter 5, is to work with domestic and 

global financial regulators to promote the 

use of consistent standards so that financial 

institutions and their regulators will be able 

to understand and analyze these types of 

issues in real-time. The establishment of a 

global Legal Entity Identifier (LEI), which 

will help precisely identify parties to financial 

transactions, is a natural and essential priority in 

pursuit of that mandate. The LEI project made 

historic progress in the past year, including the 

International Organization for Standardization’s 

publication of an LEI standard and the G20 

leaders’ call for implementation of a global 

LEI by March 2013 (ISO, 2012; FSB, 2012). 

The CFTC has already established the CFTC 

Interim Compliant Identifier (CICI) to identify 

counterparties in swap transactions (CFTC, 

2012b). The OFR continues to promote an LEI, 

working with domestic and foreign regulators 

and standard-setters. 

Need for Constant Improvements in Risk 

Management and Stress Testing. Improving data 

is essential as firms and their regulators seek to 

modernize the approach to risk management 

and stress testing in the wake of the financial 

crisis. Supervisors were quick to identify lessons 

learned with respect to corporate governance, 

credit risk and liquidity risk management, and 

the use and misuse of derivatives and other 

complex financial products (SSG, 2008; SSG, 

2009). But these lessons are still not being 

applied consistently, as shown by the MF 

Global failure (Box F: Lessons from the Collapse 

of MF Global). Financial managers and their 

supervisors need to be vigilant as financial 

markets evolve, creating new risks and new 
Analyzing Threats to Financial Stability 29

challenges for risk management. Stress testing, 

discussed in Section 3.2, suffered from a failure 

of imagination before the crisis. Supervisors 

now subject the largest firms to unprecedented 

annual stress tests. Firms that fail those tests 

can, among other things, be subject to dividend 

restrictions or be required to raise new capital. 

The OFR has an important statutory mandate 

to promote best practice in these areas by 

combining new insights about macroprudential 

oversight with traditional concerns about 

firms’ solvency.

Model Risk. The financial crisis also illustrated 

the dangers to financial stability posed by 

the failure to account for the limitations and 

uncertainties associated with financial risk 

models. For example, two types of models in 

particular were implicated. 

First, the market for CDOs, which in turn 

contributed to the bubble in MBS, relied on 

models that dramatically under-estimated the 

correlation in the performance of similar MBS, 

with disastrous results: nearly all CDO securities 

backed by MBS were ultimately downgraded 

by the rating agencies. The rating agencies 

that had used these models did not have 

strong empirical support for their correlation 

assumptions and they have since improved their 

disclosure of the limitations of these models 

(Heitfield, 2010).

Second, risk managers at financial firms and 

supervisory capital standards relied on value at 

risk (VaR) models that measured the potential 

losses from trading portfolios based on recent 

historical experience, which was relatively 

benign, encouraging balance sheet expansion. 

As the period of financial calm lengthened 

up to 2007, however, these models provided 

no indication that market relationships might 

change in fundamental ways. More recently, the 

trading losses that JPMorgan Chase announced 

earlier this year occurred after the company 

revised its VaR model, a revision that reduced 

the reported risk by half (Keoun, 2012). 

These examples illustrate the challenges that 

complex models pose to risk management 

and corporate governance. Going forward, 

the tendency of financial markets to develop 

complex new products is likely to result in 

new types of model risk. Risk managers and 

supervisors have heightened their attention to 

model risk, and this will be an important focal 

point for the OFR. 

Endnotes

1. Similar definitions are provided by Rosengren (2011) 

and Tucker (2011).

2. The Basel Committee on Banking Supervision, a 

global committee of bank supervisors, issued two 

relevant working papers in May 2012 that discuss links 

between the real economy and the financial sector and 

address ways to improve financial stability monitoring 

and the identification of potential threats (BCBS, 

2012a; BCBS, 2012b).

3. “A common explanation for the procyclicality of 

the financial system has its roots in information 

asymmetries between borrowers and lenders. When 

economic conditions are depressed and collateral 

values are low, information asymmetries can mean 

that even borrowers with profitable projects find it 

difficult to obtain funding. When economic conditions 

improve and collateral values rise, these firms are able 

to gain access to external finance and this adds to 

the economic stimulus. This explanation of economic 

and financial cycles is often known as the ‘financial 

accelerator’. [This analysis] has a long history.” (Borio, 

Furfine, and Lowe, 2001). The authors also point to 

the regulatory structure itself—as in the Basel II capital 

regime—as a source of procyclicality.

4. “Experience tells us that alternatives within an 

economy for the process of financial intermediation 

can protect that economy when one of those financial 

sectors undergoes a shock” (Greenspan, 1999).

References for Chapter 2

Basel Committee on Banking Supervision. Credit Risk 

Transfer. Joint Forum. Bank for International Settlements. 

Basel: BCBS, 2005. 

Basel Committee on Banking Supervision. Basel III: A 

Global Regulatory Framework for More Resilient Banks 

and Banking Systems. Bank for International Settlements. 

Basel: BCBS, 2010a.

Basel Committee on Banking Supervision. Basel III: 

International Framework for Liquidity Risk Measurement, 

Standards and Monitoring. Bank for International 

Settlements. Basel: BCBS, 2010b.
30 2012 OFR // Annual Report

Basel Committee on Banking Supervision. The Policy 

Implications of Transmission Channels between the 

Financial System and the Real Economy. Bank for 

International Settlements. Basel: BCBS, 2012a.

Basel Committee on Banking Supervision. Models and 

Tools for Macroprudential Analysis. Bank for International 

Settlements. Basel: BCBS, 2012b.

Board of Governors of the Federal Reserve System. 

Banking and Monetary Statistics 1914–1941. Washington, 

DC: Board of Governors, 1943. 

Board of Governors of the Federal Reserve System, 

Federal Deposit Insurance Corporation, National Credit 

Union Administration, Office of the Comptroller of the 

Currency, and FFIEC State Liaison Committee. Advisory on 

Interest Rate Risk Management. Washington, DC: Board of 

Governors and others, January 6, 2010. 

Borio, Claudio, Craig Furfine, and Philip Lowe. 

“Procyclicality of the Financial System and Financial 

Stability: Issues and Policy Options.” BIS Papers no. 1, 

Basel: Bank for International Settlements, 2001. 

Boyd, John H., and Mark Gertler. “Are Banks Dead? Or Are 

the Reports Greatly Exaggerated?” Federal Reserve Bank 

of Minneapolis Quarterly Review 18, no. 3 (Summer 1994). 

Carney, Mark. “Some Considerations on Using Monetary 

Policy to Stabilize Economic Activity.” Speech at the 

Symposium Sponsored by the Federal Reserve Bank of 

Kansas City, Jackson Hole, WY, August 22, 2009. 

Carney, Mark. “A Monetary Policy Framework for All 

Seasons.” Speech at the 2012 U.S. Monetary Policy 

Forum, New York City, NY, February 24, 2012. 

Commodity Futures Trading Commission. Swap Data 

Recordkeeping and Reporting Requirements. Final Rule, 

Federal Register 77, no. 9, January 13. Washington, DC: 

CFTC, 2012a, 2136–2224. 

Commodity Futures Trading Commission. “CFTC 

Announces Process to Designate the Provider of CFTC 

Interim Compliant Identifiers.” Press release, March 9, 

2012. CFTC, 2012b. http://www.cftc.gov/PressRoom/

PressReleases/pr6200–12 (accessed July 3, 2012). 

Commodity Futures Trading Commission and Securities 

and Exchange Commission. Reporting by Investment 

Advisers to Private Funds and Certain Commodity Pool 

Operators and Commodity Trading Advisors on Form PF. 

Joint Final Rules, Federal Register 76, no. 221, November 

16. Washington, DC: CFTC and SEC, 2011, 71128–71239. 

Congressional Budget Office. Economic Effects of 

Reducing Fiscal Restraint That Is Scheduled to Occur in 

2013. Report, May 22, 2012. CBO, 2012. http://www.cbo.

gov/publication/43262 (accessed July 3, 2012).

CoreLogic. “CoreLogic Reports Negative Equity Increase 

in Q4 2011.” Press release, March 1, 2012. http://www.

corelogic.com/about-us/news/corelogic-reports-negativeequity-increase-in-q4-2011.aspx (accessed July 3, 2012). 

Corrigan, Gerald E. “Are Banks Special?” Annual 

Report. Minneapolis, MN: Federal Reserve Bank of 

Minneapolis, 1983. 

Department of Justice. “Barclays Bank PLC Admits 

Misconduct Related to Submissions for the London 

Interbank Offered Rate and the Euro Interbank Offered 

Rate and Agrees to Pay $160 Million Penalty.” Press 

release, June 27, 2012. DOJ, 2012. http://www.justice.gov/

opa/pr/2012/June/12-crm-815.html (accessed 

July 3, 2012).

Financial Crisis Inquiry Commission. The Financial Crisis 

Inquiry Report. Washington, DC: FCIC, 2011. 

Financial Stability Board. A Global Legal Entity Identifier 

for Financial Markets. FSB Report to the G20. FSB, June 

8, 2012. 

Financial Stability Oversight Council. Annual Report. 

Washington, DC: FSOC, 2012. 

Greenspan, Alan. “Do Efficient Financial Markets Mitigate 

Financial Crisis?” Remarks before the 1999 Financial 

Markets Conference of the Federal Reserve Bank of 

Atlanta, Sea Island, GA, October 19, 1999. 

Hanson, Samuel G., Anil K. Kashyap, and Jeremy C. Stein. 

“A Macroprudential Approach to Financial Regulation.” 

Journal of Economic Perspectives 25, no. 1 (2011): 3–28. 

Harrison, George L. Responding to questioning in 

Operation of the National and Federal Reserve Banking 

Systems, Hearings before a Subcommittee of the 

Committee on Banking and Currency. U.S. Senate, 

January 22, 1931. 71 Cong. Washington, DC: Government 

Printing Office, 1931, 66. 

Heitfield, Erik. “Lessons from the Crisis in MortgageBacked Structured Securities: Where Did Credit Ratings 

Go Wrong.” In Rethinking Risk Measurement and 

Reporting: Volume II, edited by Klaus Bocker. 

Risk Books, 2010. 

International Organization for Standardization. “ISO 

17442:2012. Financial services–Legal Entity Identifier 

(LEI).” ISO, 2012. http://www.iso.org/iso/catalogue_

detail?csnumber=59771 (accessed July 3, 2012).
Analyzing Threats to Financial Stability 31

Kashyap, Anil, Richard Berner, and Charles A.E. Goodhart. 

“The Macroprudential Toolkit.” IMF Economic Review 59, 

no. 2 (2011): 145–61. 

Keoun, Bradley. “JPMorgan’s Iksil Said to Take Big Risks 

Long Before Loss.” Bloomberg, June 1, 2012. http://www.

bloomberg.com/news/2012-06-01/jpmorgan-s-iksil-said-totake-big-risks-long-before-loss.html (accessed July 3, 2012). 

Kim, Don H. and Jonathan H. Wright. “An ArbitrageFree Three-Factor Term Structure Model and the Recent 

Behavior of Long-Term Yields and Distant-Horizon Forward 

Rates.” Finance and Economics Discussion Series 2005–

33, Washington, DC: Board of Governors of the Federal 

Reserve System, August 2005.

Minsky, Hyman P. “The Financial Instability Hypothesis.” 

Working Paper no. 74, Annandale-on-Hudson, NY: Levy 

Economics Institute of Bard College, May 1992. 

Modigliani, F., and M. Miller. “The Cost of Capital, 

Corporation Finance and the Theory of Investment.” 

American Economic Review 48, no. 3 (1958): 261–97. 

Mundell, Robert A. “Appropriate Use of Monetary 

and Fiscal Policy for Internal and External Stability.” 

International Monetary Fund Staff Papers 9, no. 1 (1962): 

70–79. 

Office of the Comptroller of the Currency, Board of 

Governors of the Federal Reserve System, Federal 

Deposit Insurance Corporation, National Credit Union 

Administration, and FFIEC State Liaison Committee. 

Interagency Advisory on Interest Rate Risk Management: 

Frequently Asked Questions. Washington, DC: OCC and 

others, January 12, 2012.

Pozsar, Zoltan, Tobias Adrian, Adam Ashcraft, and Hayley 

Boesky. “Shadow Banking.” Staff Report no. 458, New 

York, NY: Federal Reserve Bank of New York, July 2010, 

Revised February 2012. 

Rosengren, Eric S. “Defining Financial Stability, and Some 

Policy Implications of Applying the Definition.” Keynote 

Remarks at the Stanford Finance Forum, Stanford, CA, 

June 3, 2011. 

Securities and Exchange Commission. Money Market Fund 

Reform. Final Rule, Federal Register 75, no. 42, March 4. 

Washington, DC: SEC, 2010, 10060–10120. 

Securities and Exchange Commission. Exemptions 

for Security-Based Swaps Issued by Certain Clearing 

Agencies. Final Rule, Federal Register 77, no. 66, April 5. 

Washington, DC: SEC, 2012, 20536–20549.

Senior Supervisors Group. Observations on Risk 

Management Practices during the Recent Market 

Turbulence. SSG, March 6, 2008. 

Senior Supervisors Group. Risk Management Lessons from 

the Global Banking Crisis of 2008. SSG, October 21, 2009. 

Tarullo, Daniel K. “Regulatory Reform since the Financial 

Crisis.” Speech at the Council on Foreign Relations C. 

Peter McColough Series on International Economics, New 

York, NY, May 2, 2012. 

Tucker, Paul. “Macroprudential Policy – Building Financial 

Stability Institutions.” Remarks at the 20th Annual Hyman 

P. Minsky Conference, New York, NY, April 14, 2011. 

Whelan, Robbie. “How Many Homes Are in Trouble?” 

Wall Street Journal, November 11, 2011. http://blogs.wsj.

com/developments/2011/11/11/how-many-homes-are-introuble/ (accessed July 3, 2012). 
32 2012 OFR // Annual Report
Research on Financial Stability 33

3 Research on Financial Stability

The sections in this chapter survey three areas that are central to the 

OFR’s research agenda: (1) Efforts to develop indicators of threats to 

financial stability as tools for policymakers; (2) The use of stress tests as 

a macroprudential tool; and (3) Counterparty risk management, an aspect 

of firms’ internal risk management that is particularly relevant to containing 

threats to financial stability. 

3.1 Cataloguing Indicators and Models of Risks to Financial Stability 

The OFR has a mandate to develop and maintain metrics and reporting systems for risks to financial 

stability. The Office’s first working paper catalogued the state of the art in this field (Bisias and others, 

2012). Measures proposed to-date seek to provide insights about an aspect of financial instability, generally informed by the recent crisis: for example, the tendency of asset-price bubbles to emerge, the transmission of financial shocks during a crisis, and the risks posed by rapid systemwide growth in liquidity or 

leverage. However, these measures tend to be limited by the public availability of data. An important 

goal of the OFR’s work will be to identify data needed to improve these measures. This section describes 

11 illustrative examples of specific risk metrics and provides evidence on what they would have shown 

during four prior crises.

Since the crisis, interest in measuring risks 

to the financial system—as opposed to risks 

faced by individual institutions—has grown 

dramatically. In general, such metrics can have 

three types of value for policymakers: 

•	 Predictive or ex ante measures may be 

able to provide early warnings of a future 

crisis, for example, by identifying specific 

vulnerabilities in the structure of the system 

that may demand a preventive policy, or by 

identifying potential shocks to the financial 

system, such as those arising from asset 

price misalignments;

•	 Contemporaneous measures can alert 

policymakers on a real-time basis to the level 

of risks and vulnerabilities, for example, by 

identifying individual institutions that pose 

outsized threats to financial stability, or by 

helping policymakers understand events as 

a crisis unfolds; and,

•	 Ex post measures support forensic analysis 

of crises after they occur and can help 

supervisors in the orderly liquidation of 

financial institutions that have failed. 

This section categorizes into four groups the 

financial stability measures that analysts have 

developed since the crisis: (1) Macroeconomic 

measures, (2) Measures of the vulnerability of 

individual firms to a shock, (3) Measures of the 

vulnerability of the financial system to a shock, 

and, (4) Measures of the interconnections 

among financial institutions. 
34 2012 OFR // Annual Report

We evaluate 11 specific measures as illustrative 

examples. Because the next financial crisis 

will not be identical to the last, it is crucial 

to understand how these models behave in a 

variety of conditions. To this end, we compare 

their performance during four historical 

financial crises, including the 2008 event. The 

selected measures represent only a small sample 

from a literature that has grown to hundreds of 

papers since the crisis. 

The 11 measures may reveal structural 

vulnerabilities but they are less effective at 

providing early warnings of impending crises—

similar to an automobile’s speedometer, which 

does not predict crashes but is still a useful 

risk indicator. Any systemic risk measure is 

also limited by a reliance on realized events; 

false alarms and failures to alert are only 

identifiable after the fact. On the other hand, 

all of the measures illuminate some facet of 

a complicated system and may play a useful 

role in informing macroprudential policy 

and decisions. Analysts should use a range of 

measures. One of the goals of OFR research will 

be to develop robust software implementations 

of the most promising models and document 

their strengths, weaknesses, and appropriate 

range of application. 

The most important lesson of this exercise is 

the need for better data. The first generation 

of systemic risk measures to emerge from the 

recent crisis relied, by necessity, on existing 

data. But today’s data and information systems 

were not built to monitor threats to overall 

financial stability. Academic authors are also 

limited to what is publicly available; heavy use 

is made of market prices. Neither accounting 

data nor market data provide information 

directly on financial interconnections. 

Accounting respects the boundaries of the 

firm, and a market price is only one attribute 

of a transaction in which the counterparties are 

typically not publicly identified. 

The OFR has an important role in gathering 

new data where necessary to improve this 

analysis. The OFR will also standardize such 

data to facilitate systemwide integration 

and comparisons.

3.1.1 Summary of Measures

As noted, risks to financial stability can be 

cyclical (particularly with respect to liquidity, 

leverage, and asset pricing), or structural 

(meaning, for example, that risky activities 

may be concentrated in a small number of 

firms). Currently available measures reflect 

this diversity of potential sources of threats to 

financial stability. We group them here into four 

broad categories:

(1) Macroeconomic Measures, Using Aggregate 

Data. These measures approach threats 

to financial stability from the top down: Is 

aggregate credit growing too fast? Are credit 

underwriting standards falling? Are asset 

prices too high relative to fundamentals? In 

an internal boom-bust cycle, an initial market 

upswing entices new investors and rising prices 

until additional capital or investors’ nerves are 

exhausted (Evanoff, Kaufman, and Malliaris, 

2012). This process can be amplified by capital 

rules that encourage banks to increase leverage 

when the economy is expanding and loan losses 

are low (Hanson, Kashyap, and Stein, 2011). In 

the ensuing bust, a credit crunch can occur as 

participants switch from lending too much to 

lending too little (Brunnermeier, 2009). 

A selling point for some macroeconomic 

measures is their early-warning potential, 

which derives from the view that large-scale 

systemic imbalances should be visible in 

appropriately constructed aggregate measures 

(Alessi and Detken, 2011). For example, the 

Basel Committee proposed an increase in 

banks’ capital requirements when the ratio of 

a country’s total credit outstanding to its GDP 

rises above historic norms (BCBS, 2010). 

(2) Measures of Firm-Level Exposures, Using 

Portfolio Details. These measures use granular 

information about individual firms’ positions 

and portfolios to estimate cash flows at 

different times in the future and under varying 

circumstances, particularly in the complex world 
Research on Financial Stability 35

of derivatives and structured products. Measures 

in this category include portfolio stress tests 

and value at risk (VaR) models that assess the 

losses expected on a given market position over 

a certain period of time, based on the historical 

distribution of price movements. 

Forward-looking metrics that exploit detailed 

information about positions and portfolios can 

help focus regulatory scrutiny on emerging risks 

and exposures before they begin to appear in 

financial statements. For example, a put option 

with a large notional value that is deep out of 

the money may have the same present value as 

an option with a smaller notional value when 

the underlying security is trading close to the 

exercise price. Yet the two options have very 

different payoff profiles and risk implications; 

this fact is difficult to judge based on price 

alone, without access to the contractual terms 

and conditions that define the notional amounts 

and exercise prices. 

(3) Measures of Market Dynamics, Using 

Sensitivity Data. These measures go beyond 

static exposures to gauge the dynamic behavior 

of market participants, especially in stressful 

situations when liquidity may be tight.1 For 

example, in a crisis, customers may withdraw 

deposits, and wholesale lenders may refuse to 

renew their funding. As market participants 

rush to sell assets to raise liquidity, prices may 

move precipitously, and the range of possible 

portfolio adjustments can change markedly. 

Leverage also magnifies the risk of insolvency. 

From a systemic perspective, it is insufficient 

to consider only firm-by-firm maturity 

transformation or leverage. Excessive maturity 

transformation and leverage can arise from 

within the financial system as investors borrow 

to profit from rising asset prices, creating a 

familiar boom-bust pattern. Counterparty 

exposures also constrain the ability of 

institutions to adjust their portfolios in a crisis. 

Ideally, measurement of these dynamic issues 

would rely on a diverse array of information, 

including bid-ask spreads, transaction volumes, 

order flows, and the details of collateral, 

margin, and netting arrangements. However, 

such details are not always readily available.

(4) Measures of Interconnectedness, Using 

Relationship Data. These measures take a 

network approach to the financial system.2

To date, these measures have had to make 

do with traditional data sources, inferring 

the underlying connections by observing 

co-movements in market prices. The data 

requirements for a fully detailed counterparty 

network model are potentially extensive. A 

key policy development related to models of 

interconnectedness is the requirement in the 

Dodd-Frank Act for large financial institutions 

to create resolution plans, also known as living 

wills. These plans must include details on 

firms’ ownership structures, assets, liabilities, 

contractual obligations, cross-guarantees, 

collateral pledges, major counterparties, and 

significant credit exposures. An example of what 

is possible going forward appears in Chart 3.1.1, 

which depicts the connections of the largest 

money market funds to the institutional issuers 

whose securities they hold. These data only 

became available in 2010 through the SEC’s 

new Form N-MFP. They can illuminate systemic 

fragility by revealing which issuers might face 

funding liquidity issues if a given money market 

fund experienced a run, or which money 

market funds would be harmed if an issuer were 

to default.

These four categories are not discrete; some 

measures may have characteristics of more than 

one. They are listed roughly in order of the 

difficulty of data acquisition. Macroeconomic 

measures generally use readily available, 

public data; at the other extreme, the most 

effective measures of interconnectedness 

would be informed by confidential information 

about firms’ specific positions, exposures, 

and counterparty relationships. Chart 3.1.2

shows examples of approaches based on these 

categories and on the event horizon—that is, 

whether the value of each measure is ex ante, 

contemporaneous, or ex post. 
36 2012 OFR // Annual Report

Chart 3.1.1 The Money Fund Network: Top 10 Issuers and Top 10 Funds, as of January 31, 2012

Fund 1 Holding A

Holding B

Holding C

Holding D

Holding E

Holding F

Holding G

Holding H

Holding I

Holding J

Fund 4

Fund 7

Fund 8

Fund 9

Fund 10

Fund 2

Fund 3

Fund 5

Fund 6

3.1.1 The Money Fund Network: Top 10 Issuers and Top 10 Funds, as of January 31, 2012

Source: SEC Form N-MFP; OFR calculations

Funds Holdings

US$10 billion
Research on Financial Stability 37

Chart 3.1.2 Examples of Risk Tools by Event Horizon and Data Requirements

Ex Ante Contemporaneous Ex Post

Macroeconomic 

Measures, Using 

Aggregate Data

Macro stress 

testing

Alessi and Detken, 

2011

Borio, Drehmann, 

and Tsatsaronis, 

2012

Monitoring global funding risk

Fender and McGuire, 2010

Accountability for 

macroprudential 

regulators

Borio, 2010 

Caruana, 2010

Measures of FirmLevel Exposures, 

Using Portfolio 

Details

Traditional, firmlevel stress testing

Hirtle, Schuermann, 

and Stiroh, 2009

Credit value adjustment,

Basel III liquidity rules

Gray and Jobst, 2010

Orderly resolution 

and living wills

Measures of Market 

Dynamics, Using 

Sensitivity Data

Monitoring serial 

correlation and 

illiquidity

Getmansky, Lo, and 

Makarov, 2004

Monitoring financial 

turbulence

Kritzman and Li, 2010

Kritzman and others, 2011

Khandani and Lo, 2011

Addressing fire sales 

in mark-to-market 

accounting

Laux and Leuz, 2010

Measures of 

Interconnectedness, 

Using Relationship 

Data

Network 

monitoring, 

10x10x10

Duffie, 2011

Systemic risk contributions, 

CoVaR 

Billio and others, 2010

Adrian and Brunnermeier, 2011

Forensic analysis

Brunnermeier, Gorton, 

and Krishnamurthy, 

2011

Efforts to develop these models have already 

provided some important insights. First, the 

problem of measuring threats to financial 

stability is strikingly multifaceted. While crises 

may play out in a mix of fire sales, institutional 

defaults, and liquidity crunches, these are 

typically the final chapter in a longer story. 

Systemic risk measures tend to focus on the 

various structural vulnerabilities that may lead 

to a crisis event, for example, complex network 

connections among financial institutions, 

information asymmetries among market 

participants, asset price bubbles and rapid 

leverage growth, concentrated or correlated 

risk exposures, moral hazard and too big 

to fail institutions, volatility, and external 

macroeconomic shocks. Financial stability 

analysts will need to follow a large number 

of measures. There will never be a single, 

“bottom-line” index covering everything we 

need to know.

Second, the recent crisis is a natural focus of 

attention but is only one data point in a longer 

history. Measures designed to understand this 

event may not work as well in others and may 

produce an undesirable number of false alarms 

if put to practical use. By applying the measures 

forensically to a range of historical episodes, we 

can learn more about both the nature of crises 

and the measures themselves.

Third, the financial sector and broader 

economy are complicated, noisy, and 

continuously evolving; simple aggregates 

cannot describe the full state of the system. 

The systemic risk measures described here 

exploit the structure of the financial system 

and provide a more detailed understanding of 

its vulnerabilities. 
38 2012 OFR // Annual Report

3.1.2 Evaluation of Measures

For this evaluation, we selected 11 measures 

that have been proposed by researchers and 

policymakers and compared their performance. 

While the analysis is in its early stages and 

conclusions are preliminary, we can draw 

some initial lessons. The OFR will publish 

more detailed analyses, including software 

implementations, as that research is completed.3

The systemic risk measures analyzed are:

•	 Five macroeconomic measures: a set of

financial stress indexes (FSIs), which use 

financial market prices to evaluate the level 

of stress in the financial system at a point 

in time, and a GDP stress test, which tracks 

large deviations of realized GDP from the 

forecast level.

•	 Four measures of systemic vulnerability: a

financial turbulence measure, which measures 

deviations of stock returns from their joint 

historical patterns of behavior; an absorption 

ratio, which simplifies the analysis of comovements in the stock price performance 

of different financial institutions; and two 

measures of market depth, which estimate the 

ability of a market to absorb large buy or sell 

orders without affecting the price quoted 

for subsequent trades.

•	 Two indirect measures of

interconnectedness: the Conditional Value 

at Risk (CoVaR) measure, which estimates 

the risk to the system posed by individual 

institutions that have a large market 

footprint, and the systemic expected shortfall 

(SES), which measures an individual firm’s 

tendency to be undercapitalized during 

episodes when the financial system overall 

is undercapitalized.

Every financial crisis has unique causes, yet most 

current crisis measures, including some of those 

considered here, were first estimated with the 

2008 event in mind. By testing these measures 

against a range of historical events, we aim to 

glean some understanding of their sensitivities, 

forecasting power, reliability, and recommended 

domain of application. 

We analyze each of these measures in four 

systemic episodes: (1) The 1929 stock market 

crash that marked the start of the Great 

Depression; (2) The 1987 stock market crash, 

an extraordinary shock that had little impact on 

the economy or financial stability; (3) The 1998 

Russian bond default, which contributed to 

the failure of Long Term Capital Management 

(LTCM), a large hedge fund, through network 

connections; and (4) The 2007–2009 crisis, 

which was marked by excessive leverage, poor 

underwriting, asymmetric information, network 

complexity, liquidity crunches, and fire sales.

Chart 3.1.3 shows the results. Chart 3.1.4

summarizes our evaluation of the individual 

measures, which are described in greater 

detail below.

Macroeconomic Measures: FSIs

We first consider the financial stress indexes 

produced by several Federal Reserve Banks, 

outlined in Chart 3.1.5. These measures have 

several advantages. First, they measure financial 

markets directly, rather than extrapolating 

from GDP forecasts. With the exception of 

the National Financial Conditions Index, they 

are derived exclusively from financial market 

prices. Second, they are higher frequency (daily, 

weekly or monthly, rather than quarterly), thus 

providing a more timely signal. Even a few extra 

days’ head start may be enormously valuable in 

the context of crisis intervention by policymakers. 

Developers of FSIs sometimes claim these 

tools can provide early warnings of financial 

disruptions. For example, the Cleveland FSI was 

“flashing red” prior to the Bear Stearns failure 

in March 2008. The FSIs clearly detected the 

1998 and 1987 events. However, even under 

the best of circumstances, the FSIs cannot be 

a panacea because they measure the system at 

an aggregate level. Considerable additional 

information would be needed to pinpoint the 

sources of financial stress and to move from an 
Research on Financial Stability 39

FSI warning to interventions in specific markets 

and institutions. 

Macroeconomic Measures: GDP Stress Tests

Economy-wide aggregates have two clear 

advantages. First, many macroeconomic time 

series are available internationally. Second, 

while some subtleties are lost in aggregation, 

systemically threatening imbalances are likely to 

be large enough to emerge in aggregate data. 

For that reason, these measures could serve a 

valuable early warning function. 

The aggregate measure we consider is based 

loosely on the GDP stress test of Alfaro and 

Drehmann (2009), which seeks to identify large 

deviations of realized GDP from the forecast 

level. We consider these GDP surprises as a 

potential crisis monitoring tool. But there are 

several reasons not to be hopeful. First, Alfaro 

and Drehmann note the strong reverse causality 

as the effects of financial crises feed back to 

disrupt the real economy. It may be easier to 

forecast a recession after seeing a financial crisis 

than to predict a financial crisis after seeing a 

recession. Second, forecasters typically do not 

set out to project financial crises at all; rather, 

they more commonly forecast the mean future 

level of GDP. Third, forecasting macroeconomic 

activity—especially turning points such as a 

financial crisis—is notoriously difficult. For 

example, in June 2008, after the Bear Stearns 

failure and just before the failures of Fannie 

Mae, Freddie Mac and Lehman Brothers, the 

Federal Reserve’s econometric models projected 

real 2009 GDP growth between 2.0 and 2.8 

percent (FOMC, 2008) while the realized value 

for 2009 turned out to be negative 3.5 percent 

(BEA, 2012). 

The first row of Chart 3.1.3 compares GDP 

growth forecasts by professional forecasters 

with actual GDP growth for the three most 

recent financial crises. Alfaro and Drehmann 

average forecast data across 43 crises over 

many years. Because macroprudential monitors 

will not have the noise-reducing benefits of 

averaging over events, we consider only one 

country and episode at a time. Professional 

forecasters under-predicted GDP growth before 

the arrival of recession late in the 1987 and 

1998 episodes. Notably, these both turned out 

to be largely financial-sector events, with little 

fallout for the real economy, so it is reasonable 

that forecasters might not predict real-sector 

implications. For September 2008, forecasts 

were more accurate leading up to the crisis, 

but both the timing and magnitude of the 

GDP shock surprised forecasters, even though 

the National Bureau of Economic Research 

would later backdate the start of the recession 

to the beginning of 2008. The data are much 

rougher for 1929—professional forecasts are 

unavailable and, since GDP itself had not 

yet been defined, GDP has been imputed as 

an annual number after the fact. With these 

caveats, we see very high GDP growth in 1929, 

followed by a sharp collapse in 1930. With 

hindsight, it is easy for us now to interpret this 

growth as a reflection of imbalances building 

up in an overheating economy.

Overall, repurposing GDP forecasts to serve 

as a financial stability indicator is probably the 

wrong tool for the job. The same aggregation 

and averaging that reduces noise, eliminates 

too much of the nuance and detail necessary 

for macroprudential risk management. More 

granular measures are required.

Systemic Vulnerability: Financial Turbulence

The “financial turbulence” measure defined 

by Kritzman and Li (2010) seeks to identify 

extraordinary market moves. Specifically, they 

look for highly unusual combinations of daily 

asset class returns. When this method is used, 

simultaneous “big movers” are more likely than 

isolated outliers to cause a given trading month 

to register as “turbulent.” The blue and gray 

bars in the charts in the second row of Chart 

3.1.3 show the results for monthly returns on 

a diverse set of domestic and international 

indexes of stocks, bonds, and commodities. 

With the confidence interval set at 75 percent, 

approximately 25 percent of the bars represent 

turbulent months. There is a clustering of 

turbulence around the crisis date. The limited 

evidence in the figures suggests that this measure 
40 2012 OFR // Annual Report

Chart 3.1.3 Behavior of the Measures in Four Crises

1929: The Great Crash of 1929* 1987: Black Monday

- 20

0

80

40

60

20

100

Percent

1986 1987 1988 1989 1990

SES**

3,500

3,000

2,500

1,500

500

1,000

2,000

0 0

0.0020

0.0015

0.0010

0.0005

0.0025

0.0030

0.0035

1986 1987 1988 1989 1990

Market

Liquidity

Microstructure 

Cost

0

0.6

0.4

0.2

0.8

1.0

1986 1987 1988 1989 1990

1986 1987 1988 1989 1990

FSI Value

-10

0

5

-5

10 6

4

2

-2

-4

0

-6

GDP Quarterly 

Percent Change

0

0.002

0.004

80,000

60,000

40,000

20,000

0

0.006

1928 1929 1930 1931 1932

Market

Liquidity

Microstructure 

Cost

0

0.6

0.4

0.2

0.8

1.0

0

1928 1929 1930 1931 1932

GDP Annual Percent Change

-10

0

5

-5

10

1928 1929 1930 1931 1932

Normalized Value Normalized Value

ROW A—

Macroeconomic

Measures

•	Federal

Reserve FSIs

•	GDP Stress Tests

ROW B—

Systemic 

Vulnerability:

Co-Movement

•	Financial

Turbulence

•	Absorption

Ratio

ROW C—

Systemic 

Vulnerability:

Market Depth

•	Equity Market

Liquidity

•	Microstructure

Invariants

ROW D—

Indirect Measures of 

Interconnectedness

•	Conditional Value

at Risk (CoVaR)

•	Systemic Expected

Shortfall (SES)

* Notes for 1929: Row A: GDP growth is an imputed rearcast. Row B: a full set of 10 Standard Industrial 

Classification categories was unavailable. Row C: note the difference in scales.

**BHC leverage data unavailable prior to Q3 1986.
Research on Financial Stability 41

1998: LTCM and Russian Bond Default 2008: Lehman Brothers Bankruptcy

Percent 

-20

20

40

0

60

100

80

2007 2008 2009 2010 2011

SES

CoVaR

3,500

3,000

2,500

1,500

500

1,000

2,000

0

Market

Liquidity

Microstructure 

Cost

2007 2008 2009 2010 2011

0

0.0020

0.0015

0.0010

0.0005

0.0025

0.0030

0.0035

0

0.6

0.4

0.2

0.8

1.0

2007 2008 2009 2010 2011

Normalized Value

FSI Value

6

4

2

-2

-4

0

-10 -6

0

5

-5

10

2007 2008 2009 2010 2011

-20

20

40

0

60

100

80

Percent

SES

CoVaR

1997 1998 1999 2000 2001

3,500

3,000

2,500

1,500

500

1,000

2,000

0

1997 1998 1999 2000 2001

Market

Liquidity

Microstructure 

Cost

0

0.0020

0.0015

0.0010

0.0005

0.0025

0.0030

0.0035

0

0.6

0.4

0.2

0.8

1.0

1997 1998 1999 2000 2001

Normalized Value

FSI Value

-10

0

5

-5

10 6

4

2

-2

-4

0

-6

1997 1998 1999 2000 2001

GDP Quarterly 

Percent Change

GDP Quarterly 

Percent Change

C

NBER	Recession

Event

Real	GDP	Growth

GDP	Growth	Forecast	

One	Year	Ahead

GDP	Growth	Forecast	

One	Quarter	Ahead

CHI	 KC

FSIs

STL	 CLE

NBER	Recession

Event

Quiet	Day

Turbulent	Day

S&P	500	Index

Absorption	Ratio

NBER	Recession

Event	

Market	Liquidity:	

Large	Firms

Market	Liquidity:	

Broad	Market

Microstructure		

Invariants:		

Large	Firms

Microstructure	

Invariants:	

Broad	Market

NBER	Recession

Event	

SES 15	largest	BHCs	

each	quarter

(positive	values)	

VaR 15	largest	

BHCs	as	of	

the	event	date

(negative	values)

o

Sources: Letters [A, B, C, D] indicate table row: Maddison (2003) [A]; Federal Reserve Bank of

Chicago [A,D]; Federal Reserve Bank of Cleveland [A]; Federal Reserve Bank of Kansas City [A];

Federal Reserve Bank of Philadelphia [A]; Federal Reserve Bank of St. Louis [A,B,C,D];

CRSP [B,C,D]; Bloomberg [B,D]
42 2012 OFR // Annual Report

Chart 3.1.4 Evaluation of the Measures

Description Evaluation of the 

Model Output

Macroeconomic 

Measures

Federal Reserve

Financial Stress

Indexes (FSIs)

Intended to measure

accumulating “stress” in

the financial system, these

are often put forward as

early warning indicators of

financial crises.

Calculation frequency varies

across the FSIs, from daily

(Cleveland Fed) to monthly

(Kansas City Fed); daily

observations are more conducive

to contemporaneous monitoring.

Actual crises tend to be much

rarer than the stresses identified

by the FSIs, so there is a

tendency to over-predict.

GDP Stress Tests Macro aggregates can reveal

system-level imbalances.

Original study emphasizes

that actual crises tend to

be much more severe than

plausible ex ante forecasts.

Original study averaged the

measures across a range of

historical crisis events; this is

not possible for ongoing

monitoring. GDP observations

are quarterly, making real-time

monitoring difficult.

Systemic 

Vulnerability:

Co-movement

Financial

Turbulence

Daily indicator of unusually

turbulent episodes in

market prices, emphasizing

simultaneous large moves in

multiple markets.

Exhibits clustering of turbulence

over time. Not promising as

an early warning measure,

but may be useful for

contemporaneous monitoring.

Absorption

Ratio (AR)

A measure of co-movement

in market prices. Unlike

correlation, which tracks comovement between bilateral

pairings, AR tracks aggregate

co-movement in a full system

of return series.

Based on our analysis of four

events, AR tends to drift

upward ahead of the crisis and

then jump abruptly on the event

date, persisting at the new higher

level afterward.

Systemic 

Vulnerability:

Market Depth

Equity

Market Liquidity

A measure of the stock

market’s ability to absorb

large one-sided order flow

without a large impact on

prices. This measure is

estimated on a monthly basis.

Price impacts of large orders

increase in crises. The magnitude

of price impact also varies

markedly across crises.

Microstructure

Invariants

A higher frequency measure

of the price impact of

large order flows. Posits

a statistical rule for price

impact that works across

markets and time.

The magnitude of price impact

varies strongly across crises.

Moving to daily data increases

volatility and heavy tails of

the measure, with occasional

one-day spikes.

Indirect 

Measures of 

Interconnectedness

Conditional Value

at Risk (CoVaR)

A measure of distress

afflicting a bank

and the system

simultaneously. Offered

as an indirect measure of

interconnectedness.

Relies on public equity returns

data on bank stocks and therefore

on market perceptions of actual

risks. Highly sensitive in 2008

but comparatively non-responsive

in 1998, which involved banks

less directly.

Systemic

Expected

Shortfall (SES)

A measure of the

propensity of a bank to be

undercapitalized when the

system as a whole

is undercapitalized.

Like CoVaR, relies on bank

stock return data, but

incorporates leverage more

explicitly. SES is sensitive to

the 2008 event and relatively

insensitive to 1998 and 1987.
Research on Financial Stability 43

Chart 3.1.5 Federal Reserve Bank Financial Stress Indexes (FSIs)

Chart 3.1.6

Measure Description References

St. Louis Financial 

Stress Index (STLFSI)

First principal component in a set of

18 weekly financial time series derived

from market prices

Federal Reserve Bank

of St. Louis, 2010

Cleveland Financial 

Stress Index (CFSI)

Daily weighted average of 11 financial

time series derived from market prices

Oet and others, 2011

Bianco, Oet, and Ong, 2012

Kansas City Financial 

Stress Index (KCFSI)

First principal component in a set

of 11 monthly financial time series

derived from market prices

Hakkio and Keeton, 2009

National Financial 

Conditions Index 

(NFCI)

Weekly weighted average of 100

measures of financial activity

Federal Reserve Bank of Chicago, 2012

would be weak as an early warning tool but may 

be valuable in contemporaneous monitoring.

Systemic Vulnerability: Absorption Ratio

It is widely recognized that correlations between 

returns tend to spike during financial crises 

as market participants respond in concert to 

unusually large common shocks. This behavior 

can be amplified if one firm’s forced liquidation 

of positions depresses asset prices, provoking 

mark-to-market margin calls that affect market 

participants more generally. From a modeling 

perspective, bilateral correlation measures the 

co-movement among a pair of return series; 

the absorption ratio (AR) of Kritzman and 

others (2011) essentially collapses the matrix of 

bilateral correlations down to a single measure 

of the more general co-movement of returns. 

The AR is the proportion of the variance in 

the system explained or “absorbed” by a fixed 

number of factors. A higher AR reveals more 

tightly coupled markets, suggesting that shocks 

may propagate through the system more 

quickly. For the 1998 crisis, in which tight 

coupling of other markets to the Russian bond 

market caught LTCM by surprise, there was a 

gradual increase in the AR before the event 

and a gradual decrease after. Similarly, the AR 

rose gradually up to September 2008 but then 

jumped abruptly by more than 10 percent and 

remained elevated for two years. There was a 

similar pattern in 1929. Although the sample of 

four crises is small, the tendency for the AR to 

rise in advance of a crisis event suggests some 

promise as an early warning measure.

Systemic Vulnerability: Market Depth

Market depth metrics measure the liquidity of 

a marketplace, as opposed to the liquidity of a 

firm or portfolio, by estimating the ability of a 

market to absorb one-sided order flow (buying 

or selling) without affecting the price quoted for 

subsequent trades. The measures we consider 

here relate back to Kyle’s (1985) “lambda,” 

which measures the trading volume required 

to move the price of a security by one dollar. 

Khandani and Lo (2011) measure equity market 

liquidity by calculating a linear regression 

of daily returns on the product of price and 

volume, which are then averaged across a crosssection of firms to calculate marketwide lambda. 

Kyle and Obizhaeva (2011) adjust the data to 

account for the higher order arrival rates that 

typically characterize fast-moving markets. Their 

metric is microstructure invariant, meaning that 

the method works for a variety of asset classes, 

not just equities, and over a variety of historical 

episodes. Market depth is relatively easy to 

implement because it can be updated using 

daily or intraday data on prices and volumes.
44 2012 OFR // Annual Report

The third row of Chart 3.1.3 presents these 

measures applied to U.S. stock prices for all four 

historical episodes. For each crisis episode, we 

run both measures on two subsamples of the full 

universe of daily returns from the Center for 

Research in Security Prices (CRSP): the largest 5 

percent of firms (by market capitalization) and 

the largest 20 percent of firms. The stocks of 

larger firms are more liquid—that is, they show 

less price impact on any given date. The two 

measures track each other closely. The equity 

market liquidity metric is monthly because 

the lambdas are estimated from monthly 

regressions. The microstructure invariant 

metric is presented as a daily time series to 

illustrate the fundamental tension between 

signals (significant shocks or other market 

phenomena) and noise (occasional statistical 

flukes in the data). Distinguishing signals from 

noise is likely to be difficult for traditional linear 

statistical models. Although we present the 

measures as applied to the overall stock market, 

they can be applied to other asset classes and 

narrower market segments. Overall, these 

metrics demonstrate the benefits from tailoring 

measurement to more granular details of 

individual securities and markets, and focusing 

on a narrow risk type, in this case liquidity. 

Interconnectedness: CoVaR and SES

CoVaR and SES attempt to measure the 

connection of individual firms to the larger 

financial system. As such, they measure 

interconnectedness. However, lacking direct 

observation of the individual exposures that 

create these connections, both use forms of 

correlation in traded equity prices as an indirect 

measure. Because they are driven by market 

prices, both measures can be updated day to 

day and minute to minute, which has obvious 

advantages in a crisis. 

Value at risk (VaR) measures the smallest 

expected loss on a portfolio for a given time 

horizon and confidence level. Adrian and 

Brunnermeier (2011) propose to extend the 

VaR methodology to measure a firm’s conditional

value at risk (CoVaR), defined as the VaR of 

the financial system as a whole, conditional 

on the firm in question being in distress. The 

institution’s contribution to systemic risk is 

in turn defined as the difference between its 

CoVaR conditional on being in distress and 

its CoVaR in more “normal” times. CoVaR can 

indicate risks posed by large, complex financial 

institutions, as well as by smaller institutions 

acting in concert. 

Systemic expected shortfall represents the 

propensity of a financial institution to be 

undercapitalized when the system as a whole 

is undercapitalized (Acharya and others, 

2010). But SES is a theoretical construct that 

cannot be measured directly; researchers must 

use proxies. One proposed proxy for SES 

uses the decline in equity valuations of large 

financial firms during a crisis, as measured 

by their cumulative equity returns. Leading 

indicators of SES, such as leverage, can then 

track ex ante risk. 

A comparison of the 1998 and 2008 events 

is instructive. Both measures register much 

more weakly for 1998, reflecting the fact that 

banks and their leverage were less centrally 

involved. SES is similarly insensitive for 1987. 

This underscores that different measures 

highlight different facets of the system and 

that some recently proposed measures have 

been calibrated especially to improve our 

understanding of the 2008 crisis.

Both CoVaR and SES illustrate the information 

limitations that afflict most of the firstgeneration systemic risk measures to appear 

since 2008. Reliance on market prices in a crisis 

situation is likely to create false alarms (as well 

as alarms that fail to sound), because market 

valuations can be contaminated by fire sale 

effects, spiking uncertainty and risk aversion, 

and valuation models that were not calibrated 

for crisis environments. Ultimately, marketbased measures must be supplemented with 

other measures, including direct measures of 

interconnectedness based on the position and 

transaction data that the OFR and other FSOC 

agencies are beginning to assemble.
Research on Financial Stability 45

Data availability also limits our ability to test 

these models out of sample by applying them 

to earlier crises. For example, many of the 

variables used as controls in the Adrian and 

Brunnermeier (2011) CoVaR estimation did 

not exist in the 1980s. Where feasible, efforts 

should be made to fill these historical gaps, 

for example, by identifying and collecting a 

historical database of robust control variables to 

support CoVaR estimation.

3.1.3 Conclusions

The measures evaluated here represent the 

first generation of financial stability models to 

emerge since the recent crisis. As such, they 

show what is possible with legacy information 

and technology resources available to scholars 

and policymakers. These legacy data collections 

rely heavily on market prices, especially equity 

prices, and firm-level accounting data. Notably 

lacking are data from over-the-counter markets 

such as swaps, bonds, and structured products. 

Also lacking are direct measures of the 

insurance industry, which is an important locus 

of contingent exposures. These gaps underscore 

once again the need for a more comprehensive 

picture of the financial system. The failure of 

supervisors to foresee the 2007–2009 crisis, 

despite an elaborate combination of aggregate 

analysis, regular examinations, and continuous 

monitoring at the largest commercial and 

investment banks, illustrates the need for 

further investment and research to improve the 

information sources that they have available to 

monitor financial stability. 

For example, leverage can be a key factor in 

crisis dynamics. Traditional accounting gives 

us a measure of firm-level leverage, and the 

Basel capital standards have made this a focal 

point of banking regulation. Unfortunately, 

traditional capital is not well suited for 

buffering against concentrated contingent 

exposures, which continue to expand through 

the growth in derivatives markets and the 

structuring and fragmentation of contractual 

exposures. Stress testing can be one way 

to assess contingent exposures. Ultimately, 

proper understanding of contingent exposures 

requires additional details about specific 

positions and contractual terms that can have a 

significant impact on net cash flows. 

The OFR is well positioned to advance the state 

of the art in financial stability metrics because 

of its mandate to track the fragility of the 

financial system. In part, this mandate motivates 

our focus on identifying legal entities and the 

connections between them as necessary building 

blocks to understanding the financial system as 

something greater than a simple aggregation of 

individual institutions.

A key focus of the OFR’s research will be to 

identify data needed to improve the value of 

measures of threats to financial stability, and to 

collect them if they are not otherwise available. 

Such data could, for example, make use of 

more granular, albeit confidential, information 

about the credit exposures that large financial 

firms have to each other.4

 In collecting data to 

support systemic risk metrics, the OFR will seek 

to minimize duplication and the burden or cost 

to the private sector. 
46 2012 OFR // Annual Report

3.2 Stress Testing as a Macroprudential Tool 

The Dodd-Frank Act requires the OFR to “evaluate and report on stress tests or other stability-related 

evaluations of financial entities overseen by the [Council] member agencies.” To fulfill this role, the 

Office can: (1) Help to ensure that the necessary data are available; (2) Help to advance the state-of-theart in stress test methodologies to move from a microprudential to a macroprudential approach; and (3) 

Contribute to the development and evaluation of quantitative tools that are used to analyze how a stress 

scenario will affect the financial system.

Stress tests can provide valuable insights into 

the vulnerabilities and resilience of financial 

institutions, markets, and even the financial 

system as a whole. Recent supervisory stress tests 

have helped supervisors and firms evaluate and 

improve the adequacy of capital and the quality 

of risk management processes at individual 

institutions. For that reason, stress testing has 

become a valuable microprudential tool. 

Macroprudential stress tests should go beyond 

the scope of microprudential supervisory 

analysis. They should aim to determine whether 

the financial system as a whole has the balance 

sheet capacity to support a normal path of 

economic activity. Such tests should focus not 

just on capital adequacy to buffer loan losses, 

but also on the individual and collective ability 

of large, complex financial institutions to 

fund their activities under stress. A key goal 

is to develop tools that will help avoid runs in 

wholesale funding markets and fire sales on 

securities, which could promote a credit crunch 

and disrupt the economy. 

To be sure, the results of supervisory stress 

tests have been aggregated to serve a 

macroprudential purpose. For example, the 

Supervisory Capital Assessment Program 

(SCAP) subjected the nation’s largest banks—

accounting for the majority of lending and 

market-making activities—to identical shocks. 

These results were used in the spring of 2009 

to gauge the capital buffers needed to keep 

banks well capitalized and able to lend across a 

range of economic scenarios, including adverse 

scenarios. Complemented by government 

backstops like the Temporary Liquidity 

Guarantee Program implemented in the fall of 

2008, the SCAP helped backstop the stability 

of the system. The program aimed to reduce 

uncertainty among investors regarding future 

losses and capital needs and thereby to help 

improve the banking system’s access to private 

capital. The program also added to the market’s 

understanding of the potential risks to financial 

stability that could be transmitted through these 

banks (Board of Governors, 2009). 

However, such an approach, while useful, was 

not completely macroprudential. An important 

challenge going forward will be to increase the 

macroprudential value of supervisory stress 

testing by, for example, incorporating feedback 

from the financial system to the economy and 

enhancing the models to allow for runs and fire 

sales. Ultimately, a macroprudential stress test 

would ask whether the system as a whole has the 

capital and liquidity to support lending and to 

be resilient to shocks. 

3.2.1 Macroprudential Objectives of 

Stress Testing

Typically, microprudential goals for stress testing 

are defined for individual institutions in isolation, 

whereas macroprudential goals are defined 

based on the effects of distress in institutions and 

markets on each other, as well as on the system as 

a whole. For example, from a macroprudential 

standpoint, banks have to be sufficiently 

capitalized to avoid significantly contributing to 

contagion from a shock. This may entail higher 

capital than is necessary for the bank when 

considered on a stand-alone basis.5

Macroprudential stress scenarios should 

consider both sides of the balance sheet—both 

assets and liabilities—and take into account the 

possibility of fire sales. And, because fire sales 

are liquidity-induced, liquidity rules should be 
Research on Financial Stability 47

added to capital requirements as part of the 

overall framework of macroprudential oversight. 

The design and objectives of stress tests vary 

based on the roles of the entities applying the 

tests. Financial firms have used stress tests since 

the late 1980s to evaluate the risk of losses in 

their trading operations; in many cases, the risk 

of complex trading positions can be illuminated 

only by applying discrete shocks to specific 

risk factors. The widespread losses following 

the 1998 Russian debt crisis led to a marked 

increase in firms’ use of stress tests to evaluate 

more comprehensively their exposures to 

specific shocks.

Financial supervisors have also increasingly 

used stress tests to evaluate capital sufficiency 

and risk management practices at individual 

firms. In some cases, they have looked at the 

aggregation of those individual stress test results 

to gain an understanding of the vulnerabilities 

of the financial system as a whole. In particular, 

the results generated by institution-specific 

stress testing, in combination with the scenarios 

themselves, can be used to generate inferences 

regarding the way risks are amplified through 

links between entities in the financial system 

and how they propagate through the network 

via feedback cycles.

Stress tests can provide the following benefits 

to macroprudential supervision, listed in 

approximate order from the most easily 

accomplished to the most difficult:

Create an analytical framework for assessing 

threats to financial stability. Stress tests can 

help provide a common understanding 

about issues related to financial stability by 

bringing together the various stakeholders—

macroeconomists, market and credit risk 

managers, and prudential supervisors.

Develop policy tools. Stress tests can help 

policymakers gauge the effects of potential 

policy actions on the financial system. 

Identify vulnerabilities and evaluate crisis 

management and resolution tools. Stress tests 

can provide policymakers with insights about 

the likelihood and form of potential crises so 

that they can consider alternative responses in 

advance of an actual event. 

Serve as an early warning system. Stress tests 

can add to the identification of vulnerabilities 

the anticipation of shocks that might occur, a 

task that in itself is fraught with uncertainty, and 

is made more complex because the policy and 

market responses cannot always be anticipated. 

3.2.2 Elements of Traditional 

Stress Tests

Analogous to the practice in engineering, a 

stress test generally starts with identifying a set 

of risk factors to be stressed and developing 

the scenario of shocks to be applied to those 

factors. The selection of risk factors partly 

depends on the objective of the stress test. For 

financial firms, stresses are applied to loan 

defaults and market factors such as interest 

rates, equity prices, and credit spreads. In 

the case of microprudential supervision, 

in which supervisors are concerned about 

an institution’s ability to withstand adverse 

macroeconomic conditions, the risk factors 

tend to be macroeconomic variables related 

to an economic downturn, such as GDP, 

housing and commercial real estate prices, 

consumer spending, and unemployment, 

as well as certain financial variables such as 

equities, currency rates, and interest rates.6

When it comes to macroprudential supervision, 

which relates to the broader financial system, 

supervisors are likely to focus on market 

factors, such as interest rates and equity prices, 

and on factors that reflect the condition of the 

institutions such as counterparty risk, leverage, 

liquidity, and net capital.

Once a comprehensive set of risk factors is 

identified, the stress test proceeds to shock 

these factors based on a stress scenario that 

hypothesizes a large change in their value 

that might contribute to an adverse or even 

catastrophic event. Thus, a stress scenario is 
48 2012 OFR // Annual Report

not representative of an expected path for the 

economy or the financial system but rather is a 

thought experiment, an exercise intended to 

depict events that are improbable but plausible. 

The selection of shocks is only a first step in the 

stress test. The shocks must be tied to a model 

of how the shocks feed through to the markets 

and the financial institutions of concern. For 

a firm-specific stress test, shocks generate 

implications for capital requirements, funding 

sources, and patterns of customer activity in 

the entities being tested. For a stress test of 

the overall financial system, shocks simulate 

outcomes for the entire network of institutions. 

For a macroeconomic stress test, the model 

should delve into real economic effects, for 

example, on capital formation, credit extension, 

and consumption. 

3.2.3 Limitations of Traditional 

Stress Tests

Two key limitations of traditional stress tests are: 

(1) The models don’t capture fire sales or runs, 

so the shocks required to produce scenarios with 

realistically fat tails—aberrations from historical 

patterns—are unrealistically large, and (2) The 

shocks themselves are external to the financial 

system. In turn, external shocks by design mean 

that the exercise cannot capture the buildup 

of internal threats to financial stability or 

feedback loops such as the feedback from losses 

to balance sheet shrinkage to reduced credit 

availability. To be sure, stress tests do not answer 

every question a macroprudential supervisor 

might have. Federal Reserve Governor Daniel 

Tarullo noted recently that “stress testing is no 

more a panacea for the supervision of large 

financial institutions than capital requirements 

themselves, or any other regulatory device. By 

design, the stress tests to date have not covered 

other sources of stress, such as funding and 

interest rate risks, which are the subjects of 

other supervisory exercises.” (Tarullo, 2012).

However narrow or broad the objective of a 

stress test, its quality depends on the definition 

of stress scenarios. A commonly used approach 

in specifying scenarios is to draw on historical 

episodes. Scenarios might simply replicate 

historical events or they might be expressed 

as multiples of standard deviations from a 

historical distribution. History can provide some 

insights about the market environment during 

a crisis because most crises have the same 

directional effects on the critical risk factors and 

asset classes. For example, crises tend to include 

a flight to both quality and liquidity in which 

equity prices drop, credit spreads widen, shortterm rates increase, volatility increases, and 

correlations among similar assets increase. 

But, as implied above, there are limitations 

to relying on historical scenarios. The world 

changes both in terms of market structure 

and regulation, so no past event is likely to 

repeat itself. Any number of changes in risk 

factors can be applied in stress tests, but the 

ones that are relevant after the fact might 

be considered implausible before the fact. 

Economic relationships change during times 

of stress: an unexpected shock creates dynamic 

behavior among diverse market participants, 

comparable to what is observed with traffic jams 

or the panic of crowds, and shocks can have a 

complex and hard-to-predict impact on preexisting vulnerabilities in the financial system, 

such as excessive leverage or funding fragility. 

While modelers tend to think of a crisis as just a 

bad draw or a fat-tailed event, an alternative view 

would consider whether a model that relies only 

on historical events is appropriate. 

Financial innovations complicate the task 

of designing stress tests. To project the 

complexities of the 2008 crisis, for example, 

a modeler would have had to fully identify 

the interconnectedness and risk of contagion 

caused by new financial products such as credit 

derivatives, structured credit products, and 

certain types of short-term funding, particularly 

repos and asset-backed commercial paper 

backed by nontraditional assets. However, 

the prevailing view prior to the crisis was that 

these innovations were strictly beneficial to the 

financial system, promoting market liquidity 

and shifting risk to entities that were better 

able and willing to bear it. The temptation to 
Research on Financial Stability 49

argue that “this time is different” is especially 

strong during times of extensive financial 

innovation because financial innovation 

often holds out the promise of a much better 

management of risks. Any argument to 

the contrary is hard to justify because little 

historical data exists for new products, and if 

an innovation grows rapidly enough to pose 

a threat to the system, it almost certainly has 

been performing well. So innovations can mask 

critical financial vulnerabilities. 

For these reasons, the design of stress test 

scenarios has understandably been subject to 

some fundamental rethinking since the 2008 

crisis and has moved away from an historical 

approach. With the benefit of hindsight, it 

is clear that stress tests prior to 2008 did not 

anticipate the extreme shocks that occurred 

during the crisis, failed to shed light on 

some of the sectors and risk factors that were 

instrumental in the development of the crisis, 

and ignored the dynamics among the sectors 

that were ultimately affected.7

 The boxes 

accompanying this section explore agent-based 

models (ABMs), which provide a methodology 

to address the dynamic nature of financial 

crises (Box C: Using Agent-Based Models to Analyze 

Threats to Financial Stability), and reverse stress 

tests, which provide an alternative to historical 

scenarios (Box D: Reverse Stress Testing).

More fundamentally, the crisis has promoted 

a reevaluation of the models used to conduct 

stress tests, reflecting three considerations 

discussed above. First, models that allow for 

default, fire sales, and runs are needed. Second, 

on a related note, models that capture the 

internal buildup of risks in the financial system 

are much more likely to help policymakers 

understand the tail risks and vulnerabilities 

in the system in response to the external 

shocks imposed on it in stress tests. Finally, 

models that capture the cross-sectional or 

structural vulnerabilities and that look at the 

interconnectedness of institutions and markets 

are much more likely to reveal the effects of 

those shocks on the financial system as a whole 

(Greenlaw and others, 2012). 

3.2.4 The Evolution of Supervisory 

Stress Tests

Supervisory stress tests have three components: 

(1) A specification of the stress scenario, 

including both macroeconomic and financial 

market disruptions; (2) An analysis of the 

impacts of the stress scenario on earnings, 

capital, and liquidity of individual financial 

institutions and the financial system overall; 

and, (3) A supervisory follow-up, which 

can include public disclosure of the results; 

requirements that firms raise capital, improve 

their capital or risk management practices, or 

adjust their business models; and potentially 

other supervisory actions.

In the U.S., supervisory stress testing began with 

the Federal Housing Enterprises Financial Safety 

and Soundness Act of 1992, which required 

the regulator of the government-sponsored 

enterprises (GSEs) to employ a risk-based 

capital test to determine the capital required in 

the event of specified shocks to property values, 

credit losses, and interest rates; however, the 

regulator was not allowed under the legislation 

to vary the details of the stress events, limiting 

the usefulness of the exercise. The Basel II 

Accord of 2004, though not giving an explicit 

definition of stress testing, required banks to 

perform stress tests for credit risk, market risk, 

and liquidity risk.8 Here the objective remained 

microprudential: to encourage sound risk 

management practices.

The Federal Reserve’s SCAP exercise in 2009 

used stress testing as a tool to determine 

capital sufficiency during a crisis, as opposed 

to evaluating the financial landscape during 

stable times. The Federal Reserve followed up 

with the Comprehensive Capital Analysis and 

Review (CCAR) program that uses stress tests 

as a tool to help evaluate, improve, and give a 

forward-looking perspective into the internal 

capital planning processes for large, complex 

bank holding companies. Similar programs 

are employed by the International Monetary 

Fund (IMF), which uses stress tests in a menu of 

approaches to examine the soundness of banks 

and the financial sector in its Financial Sector 
5050 2012 OFR // Annual Report

BOX C. USING AGENT-BASED MODELS TO ANALYZE THREATS TO 

FINANCIAL STABILITY

Scientists use agent-based models (ABMs) to explain how the behaviors of individual 

agents can affect outcomes in complex systems such as the emergence of traffic jams, 

the patterns of flocks of birds in flight, and the spread of epidemics. These concepts 

may also improve the modeling of financial stability. 

Traditional economic and financial models

share certain weaknesses: they take a

top-down approach, they assume market

participants are homogeneous, and they are

guided by history. For example, two traditional

risk management techniques, value at risk

(VaR) and stress testing, estimate potential

losses by replicating historical events or by

expressing extreme “tail events” based on an

historical pattern. Typical economic models

assume equilibrium in supply and demand

for specific assets based on the expected

behaviors of individuals in markets during

normal, non-crisis periods; importantly,

they assume representative homogeneous

individuals who operate rationally.

But traditional models miss critical points

about financial crises. Crises tend to emerge

from the unleashing of a new dynamic when

economic relationships among individuals

can change in diverse and complex ways.

Historical patterns are not always relevant, and

individuals are heterogeneous.

To address these characteristics, an agentbased model analyzes the actions of

autonomous agents to predict the “macro”

behavior of the system as a whole.

ABMs specify rules that dictate how individual

agents will act based on various factors. The

rules can vary from one agent to the next and

can allow for less-than-optimal behavior. Once

the model has specified the initial conditions

and the agents’ rules, the “world” is let loose

and the subsequent events are driven by

interactions among agents. The agents are free

to act within their computational world, just as

their counterparts do in the real world.

Economists have begun to use ABMs to

explain components of the financial system

based on the expected behavior of diverse

market participants. Gilbert, Hawksworth, and

Swinney (2009) use an ABM to investigate

shocks in the English housing market by

simulating interactions among buyers, realtors,

and sellers. Thurner (2011) uses an ABM to

explore how excessive leverage can both

emerge and dissipate within a financial system.

In a boom, individual banks may lend with

declining collateral requirements (that is, at

higher and higher leverage) as they feel safer

about asset valuations; in a bust, as banks

get more nervous about rising uncertainty

in the world, they may stiffen their collateral

requirements, reducing leverage. Rarely are

banks able to take into account that they all

may be behaving similarly and that, as a result,

they could actually create the catastrophe they

are each trying to avoid.

The Bank of England pioneered the use of

ABMs to analyze payment systems, which

handle billions of transactions every day and

can pose serious threats to financial stability

if they break down (Galbiati and Soramäki,
Research on Financial Stability 51

2008). The Bank of Italy introduced an ABM in

which banks operating in the midst of a crisis

are unable to perform operations such as

payments and interbank loan requests over a

given timeframe (Arciero and others, 2009).

The characteristics of an ABM directed toward

threats to financial stability might include:

Key Agents. The key agents for analyzing

threats to financial stability are those that

provide funding, those on the other side that

use leverage, and those that provide liquidity.

The first of these can be represented by

money market funds and banks lending in the

repo market. The second can be represented

by hedge funds. The third can be longer

term, unleveraged investors, such as asset

managers and pension funds. One valuable

feature of an ABM is that the agents can

represent actual entities in the financial system,

delving into their policies and procedures for

responding to various shocks (for example,

how banks alter their haircuts in the face of

higher volatility in the collateral) and describing

each agent’s financial condition (for example,

capital, positions, and counterparties).

Policy Levers. These include minimum haircuts,

margin requirements, and capital and liquidity

ratios for banks. If a model is extended to the

housing sector, the levers would include loanto-value ratios. Policy levers might also include

“circuit breakers” that operate to slow down

any liquidity and funding demand to a pace

closer to that of the decision process for key

liquidity and funding providers.

Shocks and Vulnerabilities. The model should

allow for the range of shocks that are typical

in causing a crisis. These include a seizing

up of liquidity; a fire sale in the face of forced

deleveraging with the subsequent funding and

liquidity effects; a sudden funding impairment,

which is often brought on by a shock to real or

perceived creditworthiness or liquidity; and, in

the extreme case, the failure of a firm.

Policy Applications. Policymakers can use

ABMs to explore major policy changes that

diverge far from current policy settings. An

ABM with adapting, heterogeneous agents

provides a virtual policy experiment, exploring

the importance of behavioral adjustments

in a given situation. And the features of

ABMs make them particularly well suited for

analyzing an economy in extreme situations

where standard empirical models are likely

to fail. ABMs can help analyze issues such

as leverage, market crowding, modes of

intervention during a crisis, and even the type

of data and risk metrics that will be of greatest

value in evaluating market vulnerabilities.

Although ABMs have shown value in other

fields, particularly for modeling emergent

phenomena such as crowd stampedes

or epidemics, it can be, as Axelrod (2006)

pointed out, a “hard sell” in the community of

academic economists, in which mathematical

techniques are more common than computer

simulations. The OFR is actively engaged

with the research and policy communities to

understand whether this method can be useful

to the OFR and others with responsibility for

modeling vulnerabilities of the financial system

(Bookstaber, forthcoming).
5252 2012 OFR // Annual Report

BOX	D.	 REVERSE STRESS TESTING

A standard stress test sets a scenario and measures the consequences. In contrast, a 

reverse stress test poses an adverse outcome and identifies the scenarios that lead to 

that outcome. OFR research is developing methodologies to implement this approach.

A standard stress test might ask: How much

would a money market fund lose under a

hypothetical combination of rate and spread

movements? A reverse stress test would ask

instead: How much would rates and spreads

have to move for the fund to “break the buck”

and drop below its fixed net asset value of one

dollar? Similarly, reverse stress tests could ask:

What would make a firm insolvent or breach

capital requirements?

Reverse stress testing, which originates

from industry practice, focuses efforts on

scenarios of key importance to a specific

portfolio, institution, or set of institutions. It

offers potential advantages for interpretation;

although the relevance of a hypothetical stress

scenario is often open to debate, all parties

can agree on the significance of an adverse

outcome. The results of a reverse stress test

are also potentially more actionable precisely

because they spotlight specific vulnerabilities.

Scenario selection is an integral part of all

stress testing; for reverse stress testing, it

entails identifying the scenarios that lead to

a specified adverse outcome. For both types

of stress tests, it is useful to think in terms

of “factors”—market rates and economic

variables, for example—that drive gains and

losses. A stress scenario is then defined by a

shock to the factors or possibly a sequence of

shocks. Scenario selection is the process of

choosing factors and shocks.

The directional effect of a shock is often

clear. A house price decline will adversely

affect a mortgage lender and a stock market

decline will generate losses for a stock

portfolio. But for portfolios using derivatives,

embedded optionality, or hedging, the

directional impact may be obscured. A bank

that partially hedges its interest rate risk

might be insensitive to a modest increase or

decrease in rates and yet be vulnerable to

large changes in either direction. Flood and

Korenko (forthcoming) develop a method

that avoids making assumptions about

which directions lead to adverse outcomes

and instead seeks to explore directions of

potential risk comprehensively.

When the available information is sufficient,

interest centers on the most likely scenarios

leading to a specified adverse outcome.

Glasserman, Kang, and Kang (forthcoming)

develop a method for estimating the most

likely combinations of factor shocks leading to

a given outcome and for identifying important

sets of factor shocks, rather than a single

scenario. Many different combinations of

movements in market factors could produce

equally large losses, but historical data may

make some combinations more plausible than

others. Getting the relative severity of various

shocks right is important in determining the

proper response to vulnerabilities identified by

a stress test.
Research on Financial Stability 53

Chart 3.2.1 Sample Stress Variables Used in CCAR 2012

Variable Stress Case Peak-to-Trough for 2008 Crisis

Real GDP -5.2% (Q3 2011–Q3 2012) -5.1% (Q4 2007–Q2 2009)

Unemployment Rate Maximum: 13.0% (Q2 2013) Maximum: 10.0% (Q4 2009)

Chicago Board

Options Exchange

Market Volatility

Index (VIX)

Maximum: 90.50 (Q1 2012) Maximum: 80.86 (Q4 2008)

Dow Jones US Total

Stock Market Index

(DWCF)

-51.8% (Q3 2011–Q4 2012) -47.2% (Q3 2007–Q1 2009)

CoreLogic House

Price Index (HPI)

-21.0% (Q3 2011–Q1 2014) -33.2% (Q4 2006–Q1 2012)*

Source: Board of Governors (2012), OFR calculations

*To present; trough not yet established.

Assessment Program (FSAP), and the European 

Banking Authority (EBA), which applied a stress 

test on macroeconomic variables against all 

countries in the European Union. 

SCAP was a one-time supervisory stress 

test. The supervisors specified the adverse 

scenario and determined the resulting loss 

and revenue estimates on a standardized basis 

using information submitted by each firm. Its 

purpose was to restore confidence in large U.S. 

banks during a time of great market turmoil by 

measuring how much capital the banks would 

need in an even more stressed environment 

and then forcing these banks to increase 

capital accordingly. In contrast, the CCAR is 

an ongoing program which has already run 

through two cycles in 2011 and 2012. The CCAR 

employs stress tests with scenarios specified by 

the Federal Reserve Board but run by the banks 

to fulfill a secondary objective of assessing the 

banks’ internal risk management capabilities 

and capital planning processes. Chart 3.2.1

presents the stress scenario for several of the key 

variables in the 2012 test along with the peakto-trough change in these variables during the 

2008 crisis. 

The stress tests of the European Banking 

Authority, the IMF, and CCAR all provide 

insight into the resilience of the financial 

system, though differing in the specifics of the 

scenarios, the data available for the tests, the 

components of the testing done by the entities 

versus the regulators, and the regulatory targets 

(for example, capital and leverage ratios). 

These programs also use similar methods 

for determining scenarios, and they base the 

scenarios on market and economic variables 

(Chart 3.2.2).

The recent financial crisis has prompted a 

critical reassessment of these methods because 

stress tests before the crisis missed important 

sources of instability, most notably the effects of 

liquidity risk and credit risk—both particularly 

manifest in the banks’ exposures to the real 

estate market and off-balance-sheet risks—and 

the availability of funding to support the banks’ 

leverage. The crisis also dramatically illustrated 

the force of contagion and related fire sales and 

thus the importance of following the path of a 

shock through the financial system.
54 2012 OFR // Annual Report

Chart 3.2.2 Comparison of Key Stress Test Parameters

U.S. CCAR 2012 U.S. FSAP (IMF) EU EBA Stress Test

Date March 2012 July 2010 July 2011

Scope 31 U.S. BHCs with at least

US$50 billion in assets,

including the 19 that were

subject to the SCAP

53 largest BHCs,

representing 85 percent of

aggregate BHC assets

90 banks in 21 countries,

representing approximately

2/3 of total banking assets

Process Stress tests for the top 19

were conducted by each

bank under the Federal

Reserve's adverse scenario,

and the Federal Reserve

conducted its own tests

of the banks under both

its baseline and severe

stress scenarios

A balance sheet-based

macroprudential analysis,

without detailed supervisory

data, a distress-dependency

model using CDS data,

and a contingent claims

analysis to estimate

potential government

contingent liabilities

Stress tests for all banks

were conducted by

each bank based on the

established stress scenarios

and methodology, and were

verified by home country

supervisors and EBA staff

Target 

Capital Ratio

5% Tier 1 Common (FRB

rules); 4% Tier 1; 8% Total

6% Tier 1 Common 5% Core Tier 1

(EU Capital Requirements

Directive)

Key 

Parameters

Unemployment rate

increases approximately 4

percentage points to a peak

of 13 percent; equity prices

drop by approximately 50

percent; housing prices

decline by an additional 20

percent from Q3 2011 levels

Unemployment rate rises

1.1 percentage point to

10 percent; commercial

property prices fall by 8

percent; residential property

prices decline by 6.6 percent

For each country:

unemployment rate

increases 3.2 percentage

points; commercial

property prices decline by

20–40 percent; residential

property prices decline by

5–30 percent

Disclosure Disclosed on aggregate and

bank-level basis using a

common template

Many results released on

a bank-level basis for this

test, with consent of U.S.

authorities

Disclosed on aggregate and

bank-level basis using a

common template

Note: The Tier 1 capital ratios used in the various tests are not directly comparable.

Source: Board of Governors (2012), IMF (2010), EBA (2011)

3.2.5 Stress Test Disclosure

Following the SCAP stress test, the Federal 

Reserve disclosed details of the results on a 

company-by-company basis using a common 

template to ensure comparability across 

bank holding companies. This disclosure of 

supervisory data was unprecedented but was 

regarded by the policymakers as essential for 

the credibility of the exercise among market 

participants. Dodd-Frank required the Federal 

Reserve Board to disclose summary results of 

supervisory stress tests for large banks.9

 The EBA 

discloses results of Europe-wide stress tests on 

both the aggregate and bank level, again with 

a common template for bank-level results.10

For comparison, the results of the stress tests 

performed under the IMF’s FSAP are disclosed 

in a manner to preserve the anonymity of the 

individual banks or are presented only on an 

aggregate basis. 
Research on Financial Stability 55

making the stress tests far less informative 

(Goldstein and Sapra, 2012; Tarullo, 2012). 

3.2.6 The Future of Stress Testing

The next generation of supervisory stress 

tests may improve on the current generation 

by: (1) Introducing new stress factors, (2) 

Taking account of financial innovations, (3) 

Incorporating the dynamics of crisis events and 

the related feedback cycles and non-linearities, 

(4) Recognizing the variability in the objectives 

and behavior of financial agents, and (5) 

Addressing specific market vulnerabilities, 

such as the potential for fire sales and runs 

in wholesale funding markets, which requires 

stressing both sides of the balance sheet. 

(1) Stress Factors

The recent crisis has shown that stress scenarios 

have to include credit risk and liquidity risk, 

along with the interaction between the two, 

for example, when solvency concerns cause a 

shock to systemic liquidity.11 Funding can dry up 

because of increased concern about the risk of 

default; a drop in asset prices, perhaps due to a 

leverage-induced fire sale that affects the margin 

requirements for the banking system, thereby 

increasing funding costs; or a drop in funding 

liquidity, because uncertainty over counterparty 

risk and lower asset valuations induce banks and 

investors to hoard liquidity.

Shocks to individual banks can lead to 

marketwide reductions in liquidity by increasing 

counterparty risk or through “liquidity 

hoarding,” in which banks do not extend credit 

even to high-quality counterparties in order to 

stay liquid “just in case” during periods of great 

uncertainty, or sell high-quality assets to meet 

liquidity needs.12 

(2) Innovations and Structural Change

Innovations can lead to changes in market 

relationships and thus may require updates to 

models employed in stress tests. Innovations 

also impose difficulties in terms of data. By 

definition, limited data are available for new 

products and, further, they are unlikely to 

span a crisis period, so understanding the 

There are several key issues that underlie the 

disclosure decision:

Comparability of stress tests across institutions.

This has been accomplished for both the CCAR 

and the EBA stress tests by adopting a common 

disclosure template. 

Consistency in the stress tests demanded 

by various supervisory agencies. As stress 

tests become more common, there may be 

conflicting disclosures and confusion if different 

stress factors and scenarios are applied. 

Focus on the extreme scenarios. The present 

supervisory stress tests focus on extreme 

scenarios because they share the objectives of 

determining capital adequacy and financial 

stability in the face of market stresses. One 

concern is that stress tests might turn into an 

earnings forecasting exercise if they lose their 

focus on extreme scenarios. 

Consideration of the appropriate level of 

disclosure during normal versus crisis periods.

Normal times may not require the same degree 

of transparency as is needed in times of crisis, 

and indeed the same level of disclosure may 

not be desirable. With the uncertainty about 

the banking system that can arise during times 

of crisis, there is an immediate benefit to the 

supervisors’ ready assessment of the health 

of individual banks and to the ability of the 

market to better differentiate the healthy banks 

from the weaker ones. During normal times, 

more consideration can be given to the effect 

of disclosure on the behavior of banks and the 

market generally. Along with the benefits of 

increased market transparency and discipline 

that come from bank-level disclosure also come 

potential costs: banks may make poor portfolio 

choices in order to increase their chances 

of passing the test (in other words, window 

dressing); market participants may place too 

much weight on the public information of stress 

test disclosure; or the incentive to produce and 

analyze other information about the banks may 

diminish. This behavior might lead to gaming 

the tests to conform to a particular set of rules, 
3.2.3 Fire Sales (Liquidity Cycle)

 

Chart 3.2.3 Fire Sales (Liquidity Cycle) 

A fire sale often begins with news that prompts a repricing of assets, combined with a concentration of leveraged funds 

that are forced to sell to meet margin requirements. As the forced selling sustains downward pressure on prices, margin 

calls feed back to magnify the effects, forcing additional rounds of selling. 

Market Price 

Time 

5) Liquidity dries up as 

investors flee the 

market, leading to 

forced selling in 

other markets 

1) Initial market 

shock 

2) Highly leveraged funds 

are forced to sell 

3) Less leveraged funds 

are forced to sell 

4) Banks raise margins 

as collateral values 

drop and volatility 

increases 

“true” statistical properties is difficult, if not 

impossible, before the fact. Even less prior 

information exists about the effect of regulation 

on new products and markets because of lags in 

the initiation of regulatory oversight. 

(3) Dynamics and Feedback 

The current approach to stress testing employs 

models based on the risk models of banks. 

Risks are taken as external factors and there 

is no feedback when the actions of banks and 

others create secondary, ripple effects, or when 

the initial shocks to the risk factors themselves 

trigger chain reactions that affect factors outside 

of the initial set. Feedback effects are difficult 

to capture because of the granularity of data 

required, the diversity of behaviors of various 

market participants, and because current 

models have difficulty handling their non-linear 

time-dependent nature. 

One important example of feedback relates 

to simultaneous deleveraging by financial 

institutions. An attempt by a large number of 

intermediaries to reduce leverage may backfire 

as asset prices plunge. Further, financial 

institutions may have trouble rolling over their 

short-term funding when the value of their 

collateral declines, triggering forced sales. 

These dynamics can lead to contagion, 

both across markets and across institutions 

holding similar assets. And, when liquidation 

is no longer possible within similar markets, 

institutions in need of cash may seek to sell 

assets in unrelated markets, creating the same 

feedback effects in those markets. These 

paths for contagion are difficult to anticipate 

within a conventional framework because the 

affected markets may not have been correlated 

historically. Charts 3.2.3 and 3.2.4 illustrate the 

multi-stage feedback dynamics that can occur 

56 2012 OFR // Annual Report 
Chart 3.2.4 Runs (Funding Cycle) 3.2.4 Runs (Funding Cycle)

A run often begins with concerns about counterparty creditworthiness and a drying up of liquidity, which boost funding 

costs, placing strains on vulnerable firms. The rise in funding costs promotes further concerns about counterparty risk 

and ever-wider funding spreads. 

1) Initial funding 

shock 

2) Firms with low 

credit quality face 

funding shortfalls 

3) Increased funding 

uncertainty affects 

higher quality firms 

4) Banks hoard assets in 

the face of the credit 

and funding risk 

5) Funding sources dry up 

as retail and institutional 

investors withdraw assets 

6) Banks reduce 

leverage to increase 

liquidity, resulting in 

a drop in asset 

prices and thus in 

the value of capital 

and collateral 

Time 

Funding Cost 

in the case of a liquidity shock and a funding 

shock, respectively.13 

(4) Heterogeneous, Disaggregated Agents 

Agents in the financial system operate according 

to their own governance procedures, risk 

management structures, and business cultures. 

Consequently, their reactions to any event will 

not be uniform and are unlikely to conform 

to abstract notions of optimal behavior. Such 

behaviors are complex, and current supervisory 

models do not attempt to predict them.14 

Instead, today’s models generally are estimated 

at an aggregate level, or use firm-level data and 

are estimated uniformly across entities. 

For issues of financial stability, however, the task 

of incorporating heterogeneous behaviors into 

stress tests is not as daunting as it may appear. 

Such an analysis would have to consider only 

a small number of representative financial 

entities, and their “rules of market engagement” 

can be determined through an analysis of 

governance structures and interviews with key 

decision makers. It therefore may be possible to 

overcome some of the limitations of the paucity 

of historical data about market participants’ 

behavior during shocks, especially shocks that 

fall outside the range of recent history. 

The heterogeneity of banks can pose problems 

for aggregating and comparing stress test 

results. Each bank applies its own models to 

the exercise, so there is no common frame of 

reference. One proposal to facilitate an applesto-apples comparison of stress tests is to have 

each bank perform a stress test on a standard 

portfolio that has the sorts of assets the banks 

tend to hold, and then report the results for 

the overall portfolio and for segments of the 

portfolio. The variation of the banks’ results 

for this standard stress test will provide some 

Research on Financial Stability 57 
58 2012 OFR // Annual Report

transparency into their internal methodology. 

If a bank shows a loss for this stress test that is 

small relative to those of other banks, then its 

other stress test results are likely to be relatively 

optimistic as well. The standardized test will 

provide the banks with essential information for 

moving toward a consistent, comparable set of 

methodologies (Pandit, 2011).

(5) Stress Scenarios

Stress test scenarios face the difficulty of being 

most important when complacency is highest. 

Before the 2008 crisis, few would have taken 

seriously a test that assumed that credit default 

swap spreads would rise by as much as a factor of 

10. Adverse scenarios thus have to extend beyond 

the market’s comfort zone while at the same 

time not doing so arbitrarily. An outsized 300 

basis point swap spread does not happen without 

context. The stress test must take into account 

the broader context and general nature of the 

current environment—the level of leverage, the 

areas of crowding, and the sources of funding—

and then at a fundamental, structural level show 

how the interactions of market participants 

could, due to vulnerabilities in that environment, 

lead to an unanticipated market result. 

3.2.7 Conclusion

It is crucial to enhance stress-testing 

methodologies to incorporate the modeling of 

feedback and secondary effects following initial 

shocks. A shock in one market may propagate 

into a second, seemingly unrelated market if 

the institutions with significant exposure in 

the first market also are heavily positioned in 

the second. These interactions can lead to an 

overall effect that is more than the sum of the 

individual shocks. 

Improving stress tests requires a deep 

understanding of the exposures of various 

financial entities and the potential for changes in 

their access to market funding. Simply put, stress 

tests need to be improved to more accurately 

capture crisis dynamics. Doing so also requires 

something more than the partial equilibrium 

framework of the current generation of models. 

One line of research involves macroeconomic 

models that embed a more explicit financial 

system, including banks and capital markets 

and allowing for default, fire sales, and runs 

(Goodhart and others, 2012). Another promising 

area lies in the application of agent-based 

models, as described in Box C.

The objectives of stress testing for the OFR 

represent a second step after the CCAR. CCAR 

applies a basic set of assumptions about changes 

in the banks’ business lines when confronted 

with a shock, assuming, for example, no 

changes in the loan portfolio or in the position 

on the trading book. A stress test to expose 

vulnerabilities in the financial system as a whole 

requires the modeling of interactions. The 

enhanced stress-testing methodology should 

account for a host of potential channels of risk 

propagation, including interdependence among 

financial firms through clearing and settlement 

systems, common exposures, collective patterns 

of behavior, and broader market failures, such 

as externalities and moral hazard, all of which 

have the potential to amplify shocks and spill 

over into the real economy. This is the focus of 

the OFR’s thinking and work.
3.3 Counterparty Risk Management: Best Practices and Unmet Challenges 

The Dodd-Frank Act charges the OFR with promoting best practices in risk management, and counterparty risk management is especially important to that mission. Strong counterparty risk management by 

individual financial institutions provides a stabilizing buffer against the propagation of shocks through 

the financial system; poor counterparty risk management can turn the failure of an individual firm into 

a systemic event. As part of its work in this area, the OFR is also contributing significantly to the improvement of counterparty risk management by helping establish a global Legal Entity Identifier (LEI). 

The financial system depends on companies 

honoring their commitments to each other 

in market transactions. Failure of a company 

to do so can cause significant losses to its own 

counterparties and can threaten a chain reaction 

among interconnected market participants. 

This section highlights lessons learned from 

the financial crisis and ongoing changes in the 

measurement and management of counterparty 

risk. This overview covers current trends in this 

critical area and highlights an evolving focus 

on credit value adjustment (CVA) as a tool for 

market participants and financial regulators 

in quantifying counterparty risk. The financial 

system and its oversight both stand to benefit 

from best practices in the implementation and 

application of this important tool. 

Counterparty risk is sometimes construed 

narrowly to refer to exposures in over-thecounter (OTC) derivatives trading. The 

discussion here takes a much broader view 

that includes many other types of counterparty 

relationships. Domestic prime brokers, for 

example, are generally prohibited from 

transacting in OTC derivatives, but their 

relationships with their hedge fund clients 

expose both parties to risk. Money market 

funds face counterparty risk through their 

exposures to bank deposits, repurchase 

agreements, and other debt instruments, and 

through the third-party guarantees that are 

sometimes attached to these instruments. 

Failures in any of these and in many other 

types of counterparty relationships can have 

ripple effects on financial stability. 

A crucial prerequisite to counterparty risk 

measurement is the proper identification 

of counterparties. The OFR’s work to help 

establish a global LEI is thus an essential 

component of the OFR’s risk management 

mandate. While this section focuses on 

counterparty risk, Box E highlights the OFR’s 

broader mission to promote best practices in 

risk management. 

3.3.1 Historical Context 

Counterparty risk is not a new phenomenon. 

The collapse of Long-Term Capital Management 

(LTCM) in 1998 heightened awareness of the 

risks that market participants face through 

their exposure to counterparties. LTCM’s 

counterparties were particularly exposed to the 

hedge fund through OTC derivatives positions. 

LTCM’s failure led to the formation of the 

Counterparty Risk Management Policy Group, 

a consortium of commercial and investment 

banks, which issued its first report in 1999. 

The principles put forward in that report— 

on transparency, risk assessment, reporting, 

documentation, collateral management, and the 

integration of market and credit risk—remain 

as relevant today. A second report, published 

in 2005, put particular weight on improving 

the operation of the credit default swap (CDS) 

market, which had grown since the first report 

from insignificance to become a $10 trillion 

market. International bank supervisors, working 

through the Basel Committee on Banking 

Supervision, introduced a series of requirements 

for banks to hold extra capital as a buffer 

against the counterparty risks arising from OTC 

derivatives, repo transactions, securities lending, 

and margin lending. Basel III substantially 

increases these capital requirements. 

The recent financial crisis served as a 

reminder of the importance of counterparty 

Research on Financial Stability 59
60 2012 OFR // Annual Report

BOX	E.	 BEST PRACTICES IN RISK MANAGEMENT

The Dodd-Frank Act charges the OFR with promoting best practices in risk 

management, a mission the OFR pursues through its research and through 

discussions with industry participants and academic experts.

Effective risk management relies on a

combination of quantitative tools, data

management, and governance procedures.

This box highlights some critical dimensions

of best practices in risk management. These

topics are further explored in an OFR working

paper (Flannery and others, 2012).

Risk Governance and Incentives

A strong culture of risk governance is

a necessary ingredient of effective risk

management. Key elements of a strong risk

culture include adequate resources and

independence for the risk function; a board

of directors with the proper information and

expertise to understand the firm’s risk-taking;

and compensation schemes that align the risks

taken by individual units with the long-term

objectives of the firm. Despite some progress,

the overall performance of the financial industry

on these dimensions needs improvement.

Liquidity Risk Management

Excessive reliance on short-term funding

amplifies shocks to the financial system.

Repo markets, money market funds, assetbacked commercial paper, securities lending,

and rehypothecation—the reuse of collateral

by a broker to borrow for its own use—all

came under stress during the financial crisis,

and firms with the greatest reliance on these

funding sources were among those at greatest

risk. Avoiding similar errors in the future will

require regulatory changes and improvements

in firms’ management of their funding sources,

with appropriate contingencies to function

through times of market stress.

risk management. A central episode was the 

liquidity squeeze experienced by the major 

investment banks when their counterparties, 

including hedge funds that were their prime 

brokerage clients, suddenly demanded their 

funds. Prime brokers provide a range of services 

to hedge funds and traditionally earn fees by 

rehypothecating fund assets held as collateral—

that is, re-using the collateral for securities 

lending or as collateral for the broker’s 

borrowing. During the crisis, investment banks 

had trouble meeting the large number of 

requests by hedge funds for cash and collateral. 

Thus, while the LTCM failure focused attention 

on the risks that investment banks face in the 

event of a hedge fund failure, the events of 

2008 illustrated the risks that hedge funds face 

in their dealings with investment banks. The 

financial industry has responded to heightened 

concern for counterparty risk with greater use 

of custody accounts to hold fund assets, tighter 

controls on rehypothecation, and diversification 

across multiple prime brokers. 

The near-failure of American International 

Group (AIG) also illustrated the threat 

to financial stability that can result from 

inadequate counterparty risk management. 

Through a combination of over-reliance on 

credit rating agencies, market opacity, and 

weak supervision, AIG was able to take on 

enormous positions by selling credit protection, 
Research on Financial Stability 61 61

Data and Information Technology 

The financial crisis has highlighted the varied

level of integration that firms have achieved

in their risk management infrastructure. Some

of the firms that fared best had developed

a firmwide view of their risks, aggregated

across diverse lines of business. Most large

complex financial institutions have not yet fully

developed this capability.

Market Risk and Credit Risk

These are the traditional focal areas of risk

management and in many respects they are

the best developed aspects of the field. An

important lesson of the financial crisis is the

need to build longer horizons into market risk

and credit risk measurement to capture the

behavior of financial markets under a range of

business conditions.

Operational Risk

As highlighted in the 2012 FSOC annual

report, strong cybersecurity is a key element

of protecting financial stability and an ongoing

challenge for financial institutions. The Flash

Crash on May 6, 2010—when the Dow Jones

Industrial Average plunged nine percent and

then recovered within minutes—pointed to

the new types of operational risk that emerge

from high-speed trading and highlighted the

importance of a sound infrastructure. Ensuring

the prevention of unauthorized trading

and fraud also should remain a priority for

operational risk management.

The Micro-Macro Interface

Firm-level risk management focuses on

risks to a single institution. But actions that

a single institution may take to mitigate its

risks—withdrawing funding, selling impaired

assets, or exiting a market—can amplify risks

in the system as a whole when undertaken

simultaneously by many firms, as in the case of

a classic bank run. Indeed, risk management

practices that may seem sound in isolation can

have procyclical effects when widely adopted.

Because of its broader mandate, the OFR has

a particular interest in the macroprudential

implications of firm-level practices.

and its counterparties apparently failed to 

recognize the magnitude of the accumulating 

risk until it was too late. This led to a cliff effect 

as downgrades of AIG triggered collateral 

requirements it could not meet. The DoddFrank Act seeks to prevent the accumulation 

of risk into such concentrated exposures; in 

particular, it provides a process for regulators 

to designate a large financial institution for 

additional financial supervision based on the 

systemic implications of its potential failure.

Despite the decade of attention to counterparty 

risk that passed between the failures of LTCM 

and Lehman Brothers, the recent crisis changed 

the market’s assessment of counterparty risk, 

as reflected in market prices and practices. 

The failures and near-failures of large financial 

institutions led market participants to demand 

greater compensation for bearing the risk of 

potential failures of their counterparties. 

This pattern is evident, for example, in the 

LIBOR-OIS spread, a measure of counterparty 

risk in the banking system (Chart 3.3.1). The 

OIS (overnight indexed swap) rate is tied to 

the overnight federal funds rate; the swap 

involves an exchange of interest payments 

only, not principal, and thus reflects minimal 

credit risk. In contrast, three-month LIBOR 

(the London Interbank Offered Rate) embeds 

the risk banks face in lending to each other 
62 2012 OFR // Annual Report

Chart 3.3.1 Three-Month USD LIBOR-OIS Spread

JAN

2002

JAN

2004

JAN

2006

JAN

2008

JAN

2010

JAN

2012

0

50

100

150

200

250

300

350

400

Basis Points

Source: Bloomberg

3.3.1 Three-Month USD LIBOR-OIS Spread

Chart 3.3.2 USD LIBOR Basis Swap: Three-Month 

versus Six-Month

JAN

2002

JAN

2004

JAN

2006

JAN

2008

JAN

2010

JAN

2012

-10

0

10

20

30

40

50

3.3.2 USD LIBOR Basis Swap: Three-Month versus 

 Six-Month 

Basis Points

Source: Bloomberg

for three months. The difference is thus widely 

viewed as a measure of the premium for bank 

credit risk. The spread was small and stable 

until the summer of 2007, skyrocketed in 2008, 

and continues to be larger and more volatile 

than it has been historically. A similar pattern is 

evident in the basis swap rate between six-month 

LIBOR and three-month LIBOR (Chart 3.3.2). 

This spread measures the risk that a bank—an 

average bank from the LIBOR panel—will be 

unable to roll over its short-term debt because of 

a decline in its credit quality; as noted in Section 

2.2, the integrity of the LIBOR setting process 

has recently come into question.

More recently, the market’s new assessment of 

counterparty risk can be seen in sovereign credit 

default swap spreads (Chart 3.3.3)—historically 

very low, but now elevated even for developed 

economies. Sovereigns have traditionally been 

free from collateral requirements in their swaps 

with banks, but the debt management offices of 

Hungary, Ireland, Portugal, and Sweden have 

agreed to two-way collateral agreements with 

banks in the past year, and other countries may 

follow. This trend reflects both a general push 

toward expanded use of collateral and a change 

in the market’s perception of sovereign risk.

These changes in market prices and practices 

reflect some of the lessons learned about 

sources of counterparty risk through the 

financial crisis. We next describe measures 

to manage this risk, articulating the OFR’s 

initial areas of focus as we begin to discharge 

our mandate to develop best practices for risk 

management and to supply analytical support 

to policymakers considering choices for 

improving the rigor of market participants’ risk 

management activities.

3.3.2 Mitigants to Counterparty Risk

Financial institutions mitigate counterparty risk 

through a combination of firm-specific practices, 

market structure, and financial transactions. This 

subsection discusses specific practices, current 

developments, and issues requiring further 

attention by firms and regulators. 

Internal Controls 

Effective counterparty risk management, like all 

effective risk management, begins with internal 

procedures, proper controls, and strong risk 

governance. For counterparty risk, this entails 

rigorous monitoring of counterparty credit, a 

thorough procedure for setting and enforcing 

risk limits, and proper controls for managing 

collateral and complying with all terms of 

credit support agreements with counterparties. 

Achieving these objectives presents data 

management challenges for large diversified 
Research on Financial Stability 63

financial firms that may face the same 

counterparty or affiliated counterparties across 

many lines of business. Establishing an LEI will 

facilitate the process of integrating counterparty 

risk from multiple affiliates and subsidiaries and 

will help firms monitor their exposures more 

consistently and comprehensively.

Credit analysis is a core risk management 

function of nearly all financial intermediation. 

In eliminating regulatory reliance on credit 

rating agencies, Section 939A of the Dodd-Frank 

Act also put greater responsibility on firms to 

take ownership of credit risk assessment. The 

financial regulatory agencies issued a series 

of proposed and final rules in 2011 to address 

the removal of credit ratings from regulations; 

a thrust of these rules is to avoid using credit 

ratings as seals of approval and thus to put 

greater weight on due diligence. Credit analysis 

is too critical to be outsourced.

Risk management ultimately relies on proper 

incentives and governance, a point stressed in 

a recent OFR working paper (Flannery and 

others, 2012). The independence, influence, 

and incentives afforded the chief risk officer 

matter as much as the methodologies employed. 

Recent supervisory guidance on counterparty 

credit risk management begins by detailing 

the responsibilities of the board of directors, 

senior managers, the risk management function, 

and independent auditors in ensuring the 

effectiveness of firm-level risk management 

(OCC and others, 2011). Box F highlights some 

significant failures of risk governance at MF 

Global that contributed to its collapse and the 

loss of funds by its customers.

Netting and Collateral

Regular participants in the OTC derivatives 

market often accumulate a large number of 

transactions with a single counterparty, and 

some of these transactions may partially offset 

each other. Under a netting agreement, one 

party pays the other the net amount owed on 

a portfolio of derivatives, instead of each party 

making a gross payment to the other. Netting 

Chart 3.3.3 Sovereign 5-Year CDS Spreads

JUN

2005

JUN

2004

JUN

2006

JUN

2007

JUN

2008

JUN

2009

JUN

2010

JUN

2011

JUN

2012

Basis Points

Belgium France Japan Sweden

50

100

150

200

250

300

350

400

450

0

3.3.3 Sovereign 5-Year CDS Spreads

Source: Bloomberg

thus reduces the size of the exposure each party 

faces from a potential default of the other party. 

Chart 3.3.4 illustrates the idea. Party A owes 

Party B $100 on one swap, and Party B owes 

Party A $80 on another swap. Under a netting 

agreement, the payments would be offset and 

Party A would pay just the difference of $20 to 

Party B. This reduces B’s exposure to A from 

$100 to $20, and it eliminates A’s exposure to B.

Collateral agreements also protect creditors 

from loss in the event of default of an obligor. 

When a swap (or a portfolio of derivatives) is 

collateralized, the parties to the swap agree 

to exchange collateral as the market value of 

the swap moves in favor of one party or the 

other. With more frequent updating of the 

collateral level, the payments to be exchanged 

are typically smaller, and this reduces each 

party’s exposure to a default of the other. Title 

VII of the Dodd-Frank Act and the OCC’s 

proposed margin rules mandate collateral 

for most swaps that are exempt from central 

clearing and call for stricter rules on segregation 

and rehypothecation of collateral to ensure 

its availability as a buffer against the spread of 

shocks through the financial system. 
64 2012 OFR // Annual Report

BOX	F.	 LESSONS FROM THE COLLAPSE OF MF GLOBAL

On October 31, 2011, MF Global Holdings Ltd., the parent of the broker-dealer and 

futures commission merchant MF Global Inc., filed for bankruptcy—the fifth largest 

failure of a financial institution in U.S. history. The company had placed large bets 

on European sovereign debt, increasing its exposure as market prices continued to 

fall. Long after incurring losses, the company was unable to account for $1.6 billion 

in customers’ funds in the aftermath of its failure. 

MF Global’s high-profile collapse offers lessons

in several areas: (1) Compliance and corporate

governance, with the company’s deficiencies

culminating in its failure to safeguard customer

funds; (2) Liquidity management, in the use of

short-term funds to finance bets on Europe;

and (3) Macroprudential analysis, as the

incident provides an opportunity to evaluate

what helps prevent the failure of a large firm

from becoming a threat to financial stability.

Compliance and Corporate Governance

Following its failure, MF Global was unable

to account for over $1.6 billion in customer

funds amid allegations that the firm used

customer assets to cover its losses. The

apparent failure to properly segregate

customer funds followed a pattern of lapses in

compliance and governance.

According to Congressional testimony, as the

firm raised its limits on European sovereign

debt exposure from $1 billion to $4.75 billion

between September 2010 and January 2011,

the chief risk officer (CRO) voiced concerns

to the chief executive and the board of

directors. His concerns went unheeded and

he was replaced by a new CRO in January

2011 (Roseman, 2012a). The position was

effectively demoted, as the new CRO reported

to the chief operating officer rather than to

the chief executive, and projects to enhance

risk management were shelved (Stockman,

2012). All of this should have been a red

flag, signaling a culture in which the CRO

position was not sufficiently independent and

empowered to restrain decisions by senior

management that put the firm at risk.

There had been earlier signals. In 2008, the

company incurred a $141 million loss due to

unsupervised trading by a single employee.

In 2009, the CFTC, its regulator, imposed a

$10 million fine on the firm for “significant

supervision violations.” The commission said

that between 2003 and 2008 it had warned MF

Global about major compliance issues, noting,

“MF Global failed in four separate instances to

ensure that its risk management, supervision

and compliance programs comported with its

obligations to supervise diligently its business

as a CFTC registrant.” (CFTC, 2009). Repeated

incidents of poor internal controls delayed

the firm’s acceptance as a primary dealer.

According to data compiled by the National

Futures Association, fines imposed by the

CFTC and various exchanges made MF Global

one of the highest fined firms among its peers

(Beyers, 2011).

Taken together, these and related incidents

indicate an environment with a weak culture of
Research on Financial Stability 65

compliance and risk management. A firm with

better internal controls and governance could

have avoided MF Global’s fate and protected

customer assets. Better management is a

necessary element of proper risk control.

Liquidity Management

MF Global’s losses and ultimate collapse

resulted from leveraged bets. The firm

borrowed to invest in European sovereign

debt, financing its purchases through repo

agreements. It reportedly used “repo-tomaturity” agreements through which MF Global

was able to borrow funds, using the bonds

as collateral, until the bonds would come

due. Accounting rules permitted these repo

transactions to be treated as sales, obscuring

the firm’s leverage, but ultimately leading to a

revision of capital charges in the summer of

2011. In Congressional testimony, the first CRO

said the firm engaged in window dressing—

presenting the firm favorably in its public

financial statements—by reducing leverage at

reporting dates, and he noted that this became

more difficult as the firm took on less liquid

positions (Roseman, 2012b). As the market

value of the bonds declined amid continuing

concerns about deteriorating circumstances in

Europe, the firm received margin calls it could

not meet.

Different investors can reasonably have

different views on whether to buy particular

assets, in this case European bonds. But

sound risk management requires anticipating

the liquidity needed to sustain an investment

strategy and avoiding excessive and opaque

leverage. MF Global was ultimately undone by

poor liquidity management of a concentrated

bet on European sovereign debt.

Macroprudential Analysis: 

Not a Systemic Event

MF Global’s customers have paid a high price

for the firm’s errors. Nevertheless, it is worth

reflecting on why this failure of a major financial

firm did not have the systemic repercussions

associated with the failures of 2008.

Size is undoubtedly an important factor—at

$41 billion in assets, MF Global was roughly

a tenth the size of Bear Stearns, which was

in turn about two-thirds the size of Lehman

Brothers. But concentration of risk is also

significant. In 2007-2008, markets grew

increasingly aware and concerned about

the scope of transactions tied to real estate,

particularly subprime lending, and no financial

institution seemed safe.

Had many other dealers or hedge funds held

large positions similar to MF Global’s, the

firm’s collapse might have resulted in greater

spillover effects. Indeed, Jefferies, a firm of

roughly similar size and services, suffered a

loss of market confidence and a 20 percent

drop in its share price in intraday trading as

investors feared—without justification, it turned

out—that the firm might be engaging in similar

activities. Such fears were contained as MF

Global’s leveraged exposure did not reflect the

position of U.S. financial institutions generally.

This episode is an important reminder

that greater transparency and effective

counterparty risk management are essential

principles to counter the threat of contagion

and the risk that the consequences of a failure

would ripple throughout the financial system.

Better data management and data standards

would support these principles.
66 2012 O F R // Annual Report 

F.1 The Lead-Up to the Collapse of MF Global

BOX F.	 lessons FroM the collAPse oF MF gloBAl —CONTINuEd 

Chart F.1 The Lead-Up to the Collapse of MF Global 

SEP 2010 

MF Global begins investing 

in sovereign bonds of 

Belgium, Italy, Ireland, 

Portugal and Spain. 

Exposure 

(Billions of US$) 

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. 

JUN 2010 

Chief Risk Officer (CRO) agrees 

to adjust the European 

sovereign investment limit to 

US$1.0 billion total gross 

notional across sovereigns. 

7 

6 

5 

4 

02 04 12 01 02 03 04 05 06 07 08 09 

2008 2009 2010 

3 

2 

1 

LATE 2009 

Increase in MF Global’s balance 

sheet leverage. Moody’s assigns a 

negative outlook to Baa2 ratings. 

DEC 2009 

CFTC imposes a US$10 million fine on MF 

Global for “significant supervision violations” 

arising from rogue trading and orders MF 

Global to enhance its internal controls. 

MID SEP 2010 

Positions and limits increase 

to US$1.5-2.0 billion. 

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. 

Exposure to European Sovereign Debt 

Markit CDS SOVX 5 Year Daily European Index 

Spread (Western Europe Region) Series 4,5,6 

Source: Markit, Haver Analytics, Congressional Hearings, and news reports 
Research on Financial Stability 67

10 

CDS SOVX European 

Index Spread 

NOV 2010 

Disagreement about liquidity risk at 

board meeting. S&P downgrades 

MF Global to BBB- because of 

deteriorating liquidity and 

increased leverage. 

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. 

JAN - MAR 2011 

CRO replaced. New CRO reports 

to the COO instead of the CEO. 

4.5 

4.0 

3.5 

3.0 

2.5 

2.0 

1.5 

1.0 

0.5 

2011 APR 2011 JUL 2011 SEP 2011 NOV 2011 2012 

Portugal Italy Belgium 

Downgraded 

Ireland 

11 12 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 

Downgraded Downgraded Downgraded 

MAY 2011 OCT 2011 

MF Global discloses Spain 


off-balance-sheet exposure Downgraded
 

of US$6.3 billion to 


European Sovereign debt.
 

2011 OCT 2011 NOV 2011 

As the euro area crisis MF Global files for bankruptcy, At least US$1.6 billion in 

escalates, MF Global bets the fifth largest for a financial customer funds missing and 

against the market. institution in U.S. history. apparently misappropriated. 

OCT 2010 

Positions increase to US$3.5–4.0 billion and 

sovereign limit to US$4.75 billion. 
68 2012 OFR // Annual Report

Chart 3.3.4 Illustration of Payment Netting Between 

Parties A and B

A B

A B

$100

$80

Net $20

Netting

3.3.4 Illustration of Payment Netting Between

 Parties A and B

Chart 3.3.5 Total Collateral Outstanding in OTC 

Derivatives Markets

1999 2001 2003 2005 2007 2009 2011

Trillions of US$

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Source: ISDA

3.3.5 Total Collateral Outstanding in OTC 

 Derivatives Markets

Reported Estimated

Chart 3.3.5 shows total collateral outstanding 

in OTC derivatives markets as reported to the 

International Swaps and Derivatives Association 

(ISDA) and as estimated by ISDA to correct 

for nonparticipation in the ISDA survey. The 

decline following 2008 is due to several factors: 

the shrinking of the market, counterparty 

consolidation, and greater use of central 

clearing. These trends generally enhance 

financial stability.

Properly managed collateral offers one of the 

most effective ways to mitigate counterparty 

risk. However, it also introduces greater liquidity 

needs for swap participants. The collapse of MF 

Global was due, at least in part, to the firm’s 

failure to maintain a liquidity buffer to meet 

collateral needs, for which it allegedly raided 

customer accounts. The near-demise of AIG 

in 2008 was accelerated by a cycle of collateral 

calls by AIG’s counterparties triggering 

rating downgrades, thereby prompting 

further collateral calls. Thus, collateral can 

convert counterparty risk to liquidity risk, 

and the market’s evolution toward greater 

use of collateral must be accompanied by a 

corresponding focus on new liquidity needs 

and liquidity risk management. Demands 

for collateral are also potentially procyclical, 

reducing the availability of credit in times of 

elevated market stress.

Central Clearing

The vast OTC swap market is the aggregation 

of bilateral exposures that are largely opaque 

to outsiders. Charts 3.3.6 and 3.3.7 show the 

evolution of the gross market value and notional 

amounts of OTC derivatives outstanding, 

respectively, as reported by the Bank for 

International Settlements. These exposures 

are opaque because participants do not know 

the counterparties their own counterparties 

are exposed to, or how those exposures are 

managed. Title VII of the Dodd-Frank Act 

requires that eligible swaps be cleared through 

central counterparties (CCPs), and the CFTC 

and SEC are formulating rules for swaps and 

security-based swaps (CFTC, 2011; SEC, 2010). 

Some customized or bespoke derivatives will 

continue to trade over-the-counter but these will 

be subject to higher capital requirements and 

margin requirements. 

With central clearing, a single trade between 

two counterparties is replaced by a pair of 

trades through the CCP. The CCP’s positions 

offset each other, and the two original 

counterparties face the CCP rather than each 

other (Chart 3.3.8). This mechanism is similar 

to the approach that futures and options 

exchanges have long taken to guarantee 

trades through margin requirements, default 

fund contributions from members, and their 

own capital. In combination with additional 
Research on Financial Stability 69

reporting requirements to regulators and 

market participants, the move toward central 

clearing brings greater transparency to the 

derivatives market and reduces the direct 

exposures among the dealers that dominate 

the OTC market. Enhanced price transparency 

helps level the playing field for end users of 

derivatives and will enable the OFR to develop 

a more comprehensive map of potential risks in 

the financial system.

Central clearing reduces bilateral exposures 

and bilateral risk, but it also may concentrate 

risk in clearinghouses. The failure of a major 

clearinghouse—one to which large financial 

institutions have significant exposures—is 

potentially disruptive to the functioning of 

the financial system. To mitigate counterparty 

risk, a CCP must be well-capitalized and 

must have effective operations for pricing, 

margining, collateral management, and 

default management. The success of central 

clearing will depend, in part, on the right 

mix and distribution of CCPs. Economies of 

scale and expanded netting opportunities 

argue for fewer CCPs. On the other hand, a 

market structure based on a small number of 

CCPs would present questions about implicit 

guarantees or moral hazard and would limit 

the potential benefits to market participants of 

competition and diversification. Views differ on 

whether each CCP should specialize in a single 

product category, like credit default swaps, or 

provide clearing for a broad range of products 

(Duffie, 2012). 

Jurisdictional concerns may trump economics 

and lead to a proliferation of central 

counterparties internationally; central 

banks may insist on oversight of derivatives 

denominated in their currencies for fear that 

they may be called upon to provide liquidity 

to a CCP. The move to central clearing 

will continue to require the focus of risk 

managers and regulators as these competing 

considerations are resolved. To give impetus to 

a global infrastructure for central clearing, the 

Financial Stability Board (FSB) has developed 

four safeguards that are necessary conditions to 

Chart 3.3.6 Gross Market Value of OTC Derivatives 

Outstanding

DEC

2011

DEC

2009

DEC

2007

DEC

2005

DEC

2003

DEC

2001

DEC

1999

Trillions of US$

0

5

10

15

20

25

30

35

40

Foreign Exchange Contracts

Commodity Contracts

Equity-Linked Contracts

Interest Rate Contracts

Unallocated

Credit Default Swaps

Source: BIS

3.3.6 Gross Market Value of OTC

Derivatives Outstanding

Chart 3.3.7 Notional Amounts of OTC 

Derivatives Outstanding

DEC

2011

DEC

2009

DEC

2007

DEC

2005

DEC

2003

DEC

2001

DEC

1999

0

Trillions of US$

100

200

300

400

500

600

700

800

Foreign Exchange Contracts

Commodity Contracts

Equity-Linked Contracts

Interest Rate Contracts

Unallocated

Credit Default Swaps

Source: BIS

3.3.7 Notional Amounts of OTC Derivatives Outstanding
70 2012 OFR // Annual Report

Chart 3.3.8 Credit Default Swap OTC and Through 

a Central Counterparty

A B

Payment

Protection

A B

C

Payment

Protection

Payment

Protection

3.3.8 Credit Default Swap OTC and Through 

 a Central Counterparty

Figure 2: Credit Default Swap Through Central 

Counterparty C

Figure 1: OTC Credit Default Swap Between Parties A and B

Chart 3.3.9 Spread Between Italian and German 

Bond Yields

JAN

2012

JAN

2011

JAN

2010

JAN

2009

JAN

2008

JAN

2007

JAN

2006

JAN

2005

-100

0

100

200

300

400

500

600

700

800

Basis Points

Credit-Risk Adjusted Unadjusted

3.3.9 Spread Between Italian and German Bond Yields

Source: Bloomberg, OFR calculations

strengthen global CCPs in the areas of access, 

oversight, resolution, and liquidity. The G20 

Leaders endorsed the progress of the FSB at 

their summit in Los Cabos in June 2012.

Two closely related areas of focus for the OFR 

are the operation of the tri-party repo market, 

which is concentrated in two clearing banks, 

and the securities lending market, which relies 

on a relatively small number of lending agents. 

In both cases, the concentration of counterparty 

risk introduces vulnerabilities.

Hedging

Besides collateral requirements, the main tools 

for hedging counterparty risk, including issuer 

risk, are various types of third-party guarantees, 

including bond insurance, lines of credit, 

mortgage insurance, and credit default swaps. 

Each of these mechanisms provides a creditor 

with protection against the default of an obligor; 

however, each also introduces new counterparty 

risk through exposure to the guarantor. 

Some have argued that sovereign CDS have 

lost their hedging effectiveness due to political 

pressures to restructure debt without triggering 

CDS payouts. Such pressures were alleged in 

the course of negotiations about Greek debt 

in 2011 and early 2012, although an ISDA 

determinations committee ultimately declared 

an event of default in that case. Uncertainty 

about the conditions that will trigger a payout 

reduces the hedging effectiveness of CDS and 

is likely to result in higher interest costs for the 

affected sovereigns. Charts 3.3.9 and 3.3.10 show 

the credit-risk-adjusted spread, a measure of the 

market’s view of CDS effectiveness. If market 

participants expect that CDS on Italy are likely 

to pay out in an event of default, for example, 

the credit-risk-adjusted spread between Italian 

and German bonds should be close to zero. The 

movement in the spread in recent years reflects 

market concern that a future Italian default 

could be structured to avoid triggering CDS.15

A simple CDS contract has limited value 

in hedging the counterparty risk in a swap 

portfolio because the exposure in the portfolio 

changes with market rates whereas the payout of 

the CDS contract in the event of default remains 

fixed. This issue is addressed by a contingent 

CDS contract, in which the payout varies 

with the exposure being hedged. Financial 

Accounting Standard FAS 157 requires this type 

of alignment for hedge accounting, and ISDA 

issued new documentation and procedures for 
Research on Financial Stability 71

Chart 3.3.10 Spread Between Credit-Risk-Adjusted Italian and German Bond Yields

JUN

2012

MAY

2012

APR

2012

MAR

2012

FEB

2012

JAN

2012

DEC

2011

NOV

2011

OCT

2011

SEP

2011

AUG

2011

JUL

2011

JUN

2011

MAY

2011

APR

2011

MAR

2011

FEB

2011

JAN

2011

Basis 

Points

-50

0

50

100

150

200

250

300

3.3.10 Spread Between Credit-Risk-Adjusted Italian and German Bond Yields

JUN 2011

ECB chiefs reportedly 

"determined to ensure" that 

Greek debt restructuring will 

not trigger CDS.

JUL 2011

EU leaders agree to a bailout 

plan for Greece, later 

abandoned, designed to not 

trigger payouts on CDS.

AUG 2011

ECB engages in 

large-scale 

purchases of 

Italian and 

Spanish debt.

NOV 2011

Markets react 

to news of Italian 

PM's impending 

resignation.

NOV 2011

Italian bond auction 

attracts limited interest; 

yields rise to record levels.

JAN 2012

News reports indicate that Greece 

will retroactively introduce 

collective-action clauses in its 

bonds, increasing likelihood of 

CDS being triggered.

FEB 2012

German parliament 

signs off on Greek 

loan package.

DEC 2011

Italian PM announces 

EUR 30 billion in measures 

to cut nation's debt load.

Source: Bloomberg, OFR calculations

contingent CDS transactions in February 2012, 

so this is a market that may have the potential to 

grow. Like many financial innovations, it carries 

both potential benefits and risks. The valuation 

of contingent CDS relies on the joint modeling 

of credit risk and market risk. 

Counterparty Diversification

A simple but important tool in managing 

counterparty risk is spreading transactions 

across counterparties. The prudent number 

of counterparties is heavily dependent on 

context. As noted in Section 3.3.1, hedge funds 

traditionally relied on a single prime broker, 

but since the failures of Bear Stearns and 

Lehman Brothers, they have increasingly spread 

their business. In OTC derivatives markets, a 

handful of major dealers are dominant, making 

counterparty diversification difficult and 

creating a greater role for central clearing.

Chart 3.3.11 illustrates counterparty 

concentration among money market funds. 

The chart shows the vulnerability of funds to 

a default of their counterparties. The most 

vulnerable funds would break the buck—fall 

below the $1 net asset value by more than half 

a cent—if any one of 30 or more counterparties 

defaulted; the less vulnerable funds would break 

the buck if any one of 10 to 19 counterparties 

defaulted. The analysis assumes 40 percent 

recovery on all unsecured lending by the funds 

and full recovery on all repo transactions. Chart 

3.3.12 shows the total exposures of U.S. money 

market funds to different regions. 

These two charts highlight the importance of 

understanding relationships and affiliations. 

Even a fully diversified portfolio could present 

counterparty risks that are not apparent on 

the surface, if it leads back through a web of 
72 2012 OFR // Annual Report

Chart 3.3.11 Prime MMFs’ Vulnerability to Counterparty Failure

107

107

21

84

125

24

93

111

27

88

113

29

84

116

30

84

115

34

86

122

25

82

121

28

97

111

22

88

115

23

92

112

21

110

103

15

121

97

12

121

98

11

113

98

14

120

92

12

121

83

16

114

91

14

APR

2012

MAR

2012

FEB

2012

JAN

2012

DEC

2011

NOV

2011

OCT

2011

SEP

2011

AUG

2011

JUL

2011

JUN

2011

MAY

2011

APR

2011

MAR

2011

FEB

2011

JAN

2011

DEC

2010

NOV

2010

Net Assets of Funds (Trillions of US$)

0

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

10-19 Counterparties 20-29 Counterparties 30+ Counterparties

Number of different counterparties which could each cause fund to break the buck if they failed.

Source: SEC Form N-MFP, OFR calculations

Note: The height of each bar represents net assets; the number in each bar represents the number of funds in each category.

Note: Excludes exposure through repurchase agreements, ABCP, and VRDN guarantees. Prime funds only, assuming a 40 

percent recovery rate, based on the actual net asset value of each fund's stated portfolio.

3.3.11 Prime MMFs' Vulnerability to Counterparty Failure

counterparties to a concentrated set 

of guarantors.

Intangibles: Confidence and 

Implicit Guarantees

The most important element of counterparty risk 

may be the most difficult to quantify—market 

confidence, which can vanish abruptly and 

trigger failure. A loss of confidence may be a 

rational response to new information, and yet it 

can reach a tipping point unpredictably, leading 

to a cascade of adverse consequences. A loss of 

confidence played a role in the failures of Bear 

Stearns, Lehman Brothers, and MF Global, as 

it did historically in bank runs. Advancing best 

practices in risk management and monitoring 

threats to financial stability require developing 

a greater understanding of the dynamics 

of confidence and how greater visibility on 

capital, liquidity, leverage, and interconnections 

can promote confidence and stability. These 

important elements of market psychology fall 

outside of traditional economic modeling. 

Perceived implicit guarantees are, by definition, 

intangible, and they are potentially destabilizing 

because they are unfunded and unpredictable 

in times of stress. The fixed share price offered 

by money market funds has drawn scrutiny as 

a perceived guarantee. Mass redemptions by 

money market fund investors in September 

2008, after the Reserve Fund was unable to 

maintain a fixed share price, prompted the 

creation of the Treasury’s Temporary Money 

Market Fund Guarantee Program, turning an 

implicit guarantee into a government guarantee.

Chart 3.3.13 compares asset levels for money 

market funds that are sponsored by banks and 

funds that are not. The chart begins at the end 

of the Treasury’s program and distinguishes 

share classes with minimum investments of at 

least $100,000 from those with smaller minimum 

investments, which is a rough measure of the 

difference between institutional and retail 

accounts. Among share classes with the smaller 

minimum investment, the outflow from nonbank-sponsored funds is much larger than from 

bank-sponsored funds. In contrast, the share 

classes with a larger minimum investment show 

a large inflow to non-bank-sponsored funds 

between June 2010 and May 2011. Many factors 

influence these flows, including interest rates 

and the performance of other asset classes, but 
Research on Financial Stability 73

the figure suggests the possibility of a perceived 

guarantee at bank-sponsored funds among 

smaller investors. The institutional flows are 

much more volatile and suggest that large 

investors may be more willing to move between 

types of funds as yields and perceived risks vary. 

3.3.3 Credit Value Adjustment

Industry practice and regulatory proposals 

have adopted credit value adjustment as a 

key measure of counterparty risk (BCBS, 

2011; Cesari and others, 2010). CVA seeks to 

price the counterparty credit risk incurred 

by banks and broker-dealers through 

derivatives portfolios. Strong counterparty risk 

management is essential to financial stability, 

and CVA is an important barometer of the level 

of counterparty risk between pairs of market 

participants as viewed by the participants 

themselves. However, the evaluation of CVA 

presents both practical and conceptual 

challenges, and the increasing reliance on 

CVA requires continuing focus on fine-tuning 

the underlying principles and improving data 

management and standards.

The CVA Concept

CVA adjusts the market value of a swap or 

portfolio of swaps to reflect a counterparty’s 

default risk. To illustrate this idea, suppose Party 

A has entered into an interest rate swap with 

Party B. In the absence of any default risk, Party 

A could value the swap off a risk-free yield curve. 

However, the possibility that Party B may default 

at some future date lowers the value of the swap 

to Party A. CVA seeks to quantify this effect.

At inception, an interest rate swap is ordinarily 

designed to have zero net value to both parties. 

As interest rates vary over time, the swap could 

take on a positive value for either party. For 

example, if A is paying a fixed rate and receiving 

a floating rate from B, then an increase in the 

floating rate increases the value of the swap 

to A. The counterparty risk that A faces thus 

results from the combination of two factors: 

the possibility that interest rate moves increase 

the value of the swap to Party A and that Party 

B defaults. If the same interest rate movements 

Chart 3.3.12 Total MMF Exposures by Region

APR

2012

DEC

2011

AUG

2011

APR

2011

DEC

2010

200

300

400

500

600

700

1.20

1.30

1.40

1.50

1.60

1.70

Billions of US$ Trillions of US$

United States (Trillions of US$)

All Others (Billions of US$)

Non-Euro Area Europe (Billions of US$)

Euro Area (Billions of US$)

Source: SEC Form N-MFP, OFR calculations

3.3.12 Total MMF Exposures by Region

Chart 3.3.13 Money Market Fund Net Assets by Share 

Class Category and Sponsor Type

MAY

2012

DEC

2011

JUN

2011

DEC

2010

JUN

2010

DEC

2009

Funds (Billions of US$)

0

100

200

300

400

500

600

700

Non-Bank-Sponsored > $100K Minimum Investment

Bank-Sponsored > $100K Minimum Investment

Non-Bank-Sponsored < $100K Minimum Investment

Bank-Sponsored < $100K Minimum Investment

Source: Morningstar, OFR calculations

3.3.13 Money Market Fund Net Assets by Share Class

 Category and Sponsor Type

Note: Depiction of data starts at September 2009 to 

coincide with the expiration of the U.S. Money Market 

Mutual Fund Guarantee Program (September 18, 2009) and 

the Federal Reserve Money Market Investor Funding Facility 

(October 30, 2009).
74 2012 OFR // Annual Report

that increase the swap value to Party A also make 

it more likely that Party B will fail (perhaps 

because B has a lot of floating-rate debt), then 

the combination produces “wrong-way risk” 

for A. If interest rates moved in the opposite 

direction, turning the swap into a liability for A 

rather than an asset, then A would not face any 

risk from a default by B.

As this example illustrates, a CVA calculation to 

price counterparty risk requires the integration 

of the following elements:

•	 a model of market risk factors (interest rates 

in the example above);

•	 a model of default risk (the risk that B will 

default); and,

•	 a model of the co-movement of market risk 

and credit risk factors (because B’s default 

results in a loss to A only if interest 

rates increase).

The integration of market risk and credit risk—

the last of these three elements—is the greatest 

modeling challenge to effective CVA calculation. 

The scale of the problem for large financial 

institutions with thousands of positions also 

poses a significant computational challenge, 

with CVA calculations often running overnight.

The foregoing discussion has taken the 

perspective of firm A; this is a unilateral CVA 

calculation. If A and B were both dealers, then 

B would have a mirror image perspective on the 

swap. The firms would have to use bilateral CVA 

calculations—incorporating the default risk of 

both parties—in agreeing on a price. This would 

add a fourth element to the CVA modeling 

challenge because of the reliance on the comovement of A’s default risk with B’s default 

risk, as well as with market risk factors.

Centralized Counterparty 

Risk Management and Oversight

An effective CVA calculation aggregates all 

transactions a firm has with a counterparty, 

taking into account netting agreements, 

enforceability of these agreements, and 

collateral requirements to evaluate the path of 

future potential exposures. This leads to a single 

number quantifying the total counterparty risk a 

firm faces with each of its counterparties.

Financial firms with diverse trading activities 

are increasingly using this concept to centralize 

counterparty risk management. Individual 

trading units transacting with multiple 

counterparties “swap” their counterparty risk 

to a central CVA desk, which effectively charges 

them a fee for off-loading their counterparty 

risk. This internalizes the cost of counterparty 

risk at the level of the trading desk and allows 

the CVA desk to manage firmwide counterparty 

risk. The CVA desk may, for example, buy 

CDS protection on counterparties to hedge 

the firmwide CVA for that counterparty (Chart 

3.3.14). This process allows for comprehensive 

firm-level counterparty risk management, but 

it also raises the stakes for the reliability of CVA 

modeling by pinning the pricing and hedging of 

counterparty risk on this number. Overreliance 

on this type of modeling, especially if it contains 

material flaws, could create a false sense of 

comfort, leading to greater risk-taking. 

Because CVA aggregates and centralizes 

counterparty risk, it represents a potentially 

valuable tool for monitoring threats to financial 

stability arising through inter-firm exposures. 

From a systemwide perspective, counterparty 

risk is a network phenomenon—firms are nodes, 

and nodes are connected when they trade 

with each other. CVA measures the exposure 

on each edge of the network as quantified 

by the firms themselves for their internal risk 

management. CVA values could be viewed 

together with gross and net notional exposures 

to give a more nuanced perspective on potential 

buildups of counterparty risk. Gross and net 

exposures measure the sizes of potential losses, 

but CVA seeks to measure the market price for 

offloading exposure. As such, it is more sensitive 

to changes in market conditions as a gauge of 

financial stability. 
Research on Financial Stability 75

Chart 3.3.14 CVA Hedging

OTC

Trades

Trading Floor

Counterparty

Counterparty

Trading

Desk

Trading

Desk

Trading

Desk

CVA

Desk

Open

Market CVA Swaps CVA Hedging

3.3.14 CVA Hedging

Potential Pitfalls

Model Risk. The main obstacle to accurate CVA 

calculation lies in capturing co-movements 

between market risk and credit risk. The 

challenge is magnified by the need to address 

swap portfolios exposed to multiple sources of 

market risk with transactions that may extend 

5 to 30 years. Little information is available to 

quantify the correlation between interest rates, 

exchange rates, and other market factors on one 

hand and the creditworthiness of a counterparty 

on the other hand. Moreover, correlation is a 

limited measure that cannot account for the full 

complexity of dependence between market risk 

factors and credit risk factors.

A weakness in integrating market risk and credit 

risk is that it leaves a firm exposed to “wrongway risk,” the possibility that a counterparty’s 

credit declines just as the exposure to that 

counterparty increases. This scenario can 

occur, for example, when a dealer enters into 

an energy swap with an energy company and 

the dealer’s side of the transaction increases as 

energy prices fall.

These considerations leave CVA exposed 

to a high degree of model risk, that is, 

vulnerability to errors that result from poor 

modeling assumptions. The OFR is developing 

methods for incorporating robustness to 

model uncertainty in risk calculations. These 

techniques identify model elements that 

contribute most to model risk and quantify 

their impact on risk measurement. In the case 

of CVA, a particular vulnerability lies in the 

dependence between a counterparty’s CDS 

spread—a measure of its credit risk—and the 

value of a swap portfolio.

Data Management. Calculating a single CVA 

number for a counterparty requires aggregating 

all the ways a firm is exposed to that entity, 

incorporating all relevant netting agreements, 

and capturing terms and conditions of all 

transactions with the counterparty. This 

presents an enormous data management 

challenge and requires firms to continue to 

invest in technology. An LEI standard and the 

development of instrument identifiers will help 

simplify the CVA process and, more importantly, 

enhance its reliability.

Ambiguities. Although conceptually simple 

as a measure of counterparty risk, CVA poses 

some subtleties in its implementation and 

interpretation. For example, default risk in 

CVA is typically measured through CDS spreads 

but sometimes through internal credit ratings, 

particularly when no liquid CDS spreads or 

bond yields are available for a counterparty. 

CDS spreads reflect a market price for default 

risk whereas ratings ordinarily try to capture 

empirical default frequencies and recovery 

rates. Market risk measures, such as value 

at risk, are ordinarily based on empirical 

observations. Ambiguities also arise in 

measuring exposure at default. The exposure 
76 2012 OFR // Annual Report

is understood to be the replacement value of a 

derivative, but there is no consensus on exactly 

what this means and whether, for example, 

this replacement value should itself reflect 

default risk in the replacement counterparty. 

The handling of alternative events that may 

terminate a swap without a default raises the 

prospect of further discrepancies. It is in the 

financial industry’s interest to resolve these 

ambiguities, and the OFR can help promote 

best practices on these issues.

Incentives. Many media accounts have noted 

instances of banks reporting mark-to-market 

profits as a result of a decline in their credit 

quality through a debt value adjustment (DVA). 

DVA is similar to CVA, but it applies to the price 

of a firm’s own debt. A mark-to-market gain 

from deteriorating credit quality is an inevitable 

consequence of CVA and DVA pricing and fair 

value accounting; it is the mirror image of the 

loss a bank takes when the credit quality of 

its counterparties declines. In October 2011, 

Goldman Sachs reported it was hedging the 

impact of these fluctuations in its own credit 

spreads by selling CDS protection on other, 

presumably correlated financial institutions 

(Moyer and Burne, 2011). The net effect of such 

transactions is to increase overall counterparty 

risk in the financial system solely to modulate 

fluctuations in accounting figures.

Performance in Extremes. Market risk 

capital under Basel III includes a charge for 

counterparty risk tied to a CVA value at risk 

calculated at 99 percent confidence over a oneyear horizon. Indeed, the European Banking 

Authority found the CVA charge to be one of 

the main drivers of increased capital needs 

anticipated for large European banks under 

Basel III (EBA, 2012). In principle, measuring 

the CVA value at risk requires recalculating 

CVA for multiple dates within the one-year 

horizon across a wide range of potential market 

scenarios—wide enough to reliably capture 

the worst 1 percent of potential outcomes. 

The challenges in calculating CVA at a single 

point in time, capturing the risk in a potentially 

large and long-dated portfolio of swaps, are 

thus greatly magnified when the procedure 

is extended to map the potential evolution of 

CVA over a one-year horizon and stressed to the 

worst 1 percent of market and credit scenarios.

Potential Spirals. CDS spreads are used to 

calculate CVA and CDS contracts are used to 

hedge CVA. In the absence of sufficient CDS 

liquidity, this sets up a potential downward 

spiral, with widening CDS spreads (for a 

sovereign, say) increasing the hedging demand, 

and hedging demand widening the spread. 

Myopia. Even if calculated accurately, CVA 

captures, at best, the immediate cost of a 

counterparty’s default. This is a myopic view in 

the sense that it does not capture the potential 

follow-on effects of such a default. One default 

can trigger financial distress at other firms and 

elevate counterparty risk across the financial 

system. A comprehensive macroprudential view 

of counterparty credit risk must incorporate 

these rippling, network effects, as well as the 

direct impact of a default.

3.3.4 Conclusion

The OFR has a mandate to promote best 

practices in risk management. Under 

the broad category of risk management, 

counterparty risk management is of special 

importance in ensuring the resilience of 

the financial system because it addresses the 

linkages of the financial system. Failures of 

counterparty risk management allow losses 

at one institution to propagate to others 

through the interconnections among financial 

intermediaries; strong counterparty risk 

management provides a buffer against threats to 

financial stability.

This chapter has taken a broad view of 

counterparty risk to include not just risks in 

over-the-counter derivatives markets but also 

issuer risk, exposures through third-party 

guarantees, repurchase agreements, and 

relationships between prime brokers and 

hedge funds. These linkages and transactions 

arise to meet the needs of the financial system 

and to enable the financial system to provide 
Research on Financial Stability 77

services to the broader economy. These same 

linkages create potential vulnerabilities if not 

properly managed. The elements of effective 

counterparty risk management include strong 

internal controls and governance, netting 

agreements, collateral, hedging, and central 

clearing. As the challenges and tools of 

counterparty risk management continue to 

evolve, market participants can benefit from 

a coordinated effort to address some of the 

challenges highlighted in this section. The 

OFR’s current work in helping establish a global 

LEI is also an important step in strengthening 

counterparty risk management by standardizing 

the identification of counterparties.

This chapter has highlighted an evolving focus 

on CVA as a key measure of counterparty risk in 

both market practice and financial regulation. 

If evaluated correctly, CVA provides a valuable 

barometer of vulnerabilities in the financial 

system. But important questions remain around 

the principles and practice of CVA calculation. 

Improvements in this area can benefit all market 

participants and enhance supervision. The 

OFR, working with other FSOC members and 

the financial services industry, can help advance 

these efforts.

Endnotes

1. CGFS (2011) examines the macroprudential issues 

surrounding liquidity crises and liquidity management. 

Tirole (2011) surveys the underlying economics.

2. Eisenberg and Noe (2001), Billio and others (2010), 

and Gai, Haldane, and Kapadia (2011) seek to 

understand contagion effects caused by financial firms’ 

contractual relationships, such as counterparty credit 

risk exposures or liquidity guarantees. Cont, Moussa, 

and Santos (2010) and Haldane (2009) also consider 

the ramifications of network complexity as a factor in 

systemic fragility.

3. The historical approach has also been adopted recently 

by others, including Kyle and Obizhaeva (2012), Brave 

and Butters (2012), and Lo and Zhou (2012). CoVaR 

and systemic expected shortfall cannot be applied to 

the 1929 event, because investment banks from the 

period did not trade publicly, while commercial bank 

stock prices are not readily available in digital form.

4. For example, Duffie (2011) proposes asking 10 large 

financial institutions to report their 10 largest net bilateral 

exposures and to stress them under 10 scenarios. 

Brunnermeier, Gorton, and Krishnamurthy (2011) 

propose a two-step “risk topography” analysis. First, 

regulators would accumulate a panel of participants’ 

individual risk exposures (changes in firm value) as well 

as liquidity sensitivities (changes in “effective cash”) 

to various shock scenarios defined on a space of 

external factors. The second step would aggregate 

firms’ individual valuation and liquidity responses into 

a systemwide picture of risks, where those exposures 

diversified in the cross-section of firms should be of less 

concern than systemically concentrated exposures. 

Both proposals combine forward-looking risk analytics 

and network effects and neither can be implemented 

with the data available today. 

5. This also means that from a macroprudential 

standpoint, “even solvent banks may be required to 

refrain from depleting capital if the system as a whole 

does not meet the higher macroprudential criteria. 

For shareholders, one dollar inside the bank should 

be worth more than one dollar in dividends. But, in 

any case, supervisors should consider more than 

just private benefits and costs. Had U.S. supervisors 

suspended dividends in the summer of 2007, $80 

billion of capital could have been retained in the 19 

banks that were subject to the 2009 Supervisory 

Capital Assessment Program. That sum is roughly half 

of the public recapitalization funds that these banks 

received.” (Greenlaw and others, 2012). 

6. In the CCAR, large BHCs must perform four stress tests 

to provide an indication of the effect of the stresses on 

revenues, losses, reserves, and pro forma capital levels: 

a BHC-defined baseline scenario, a supervisory baseline 

scenario specified by the Federal Reserve, a BHCdefined adverse scenario, and a supervisory adverse 

stress scenario specified by the Federal Reserve. The 

Federal Reserve scenarios are defined over 25 variables, 

including measures of economic activity and prices 

(gross domestic product, unemployment, disposable 

income, and inflation), financial factors (two house 

price indexes, an equity index, and a market volatility 

index); interest rates (three-month Treasury bills, 10-

year Treasury notes, 10-year BBB corporate bonds, 

and fixed-rate 30-year mortgages); and international 

measures, each provided for four country blocks 

(change in real GDP, change in inflation, and exchange 

rates) (Board of Governors, 2012).

7. The IMF’s stability assessment for Iceland had stated 

prior to the crisis in that country: “The banking 

system’s reported financial indicators are above 

minimum regulatory requirements and stress tests 

suggest that the system is resilient” (IMF, 2008). 

For evaluations of stress test practices, see Borio, 

Drehmann, and Tsatsaronis (2012) and Alfaro and 

Drehmann (2009).

8. Specifically, the Basel II document states that banks 

should perform “rigorous, forward-looking stress 

testing that identifies possible events or changes in 

market conditions that could adversely impact the 

bank” (BCBS, 2004).

9. The new disclosure regime based on Section 165(i) 

of Dodd-Frank began with CCAR 2012; the first 

CCAR exercise, in 2011, displayed a lower level of 

disclosure than the SCAP, with no bank level results 
78 2012 OFR // Annual Report

being published. The stress scenario disclosure for 

CCAR 2012 included results based on the projections 

made by the Federal Reserve of each bank holding 

company’s losses, revenues, expenses, and capital 

ratios over the planning horizon.

10. See EBA (2011). In addition to the results of the 

stress test under the baseline and adverse scenarios, 

institution-specific disclosures contain information on 

credit exposures and exposure to sovereigns. 

11. Recent research on liquidity risk includes papers 

published by the Bank of England (Aikman and others, 

2009), the Hong Kong Monetary Authority (Wong and 

Hui, 2009), the Dutch National Bank (Van den End 

and Tabbae, 2009), and the IMF (Schmieder, Puhr, 

and Hasan, 2011). Systemic episodes emerging from 

credit risk and funding risk are provided in Gorton 

and Metrick (2009) and Afonso, Kovner, and Schoar 

(2011). Barnhill and Schumacher (2011) observe 

that a systemwide liquidity shock is more likely to 

happen in the presence of a shock to fundamentals 

that depresses asset values and makes the market 

reluctant to fund these assets and the institutions 

holding them.

12. Afonso, Kovner, and Schoar (2011) examine the 

connections between solvency and liquidity during the 

crisis and conclude that counterparty risk played a 

larger role than liquidity hoarding.

13. Brunnermeier and Pedersen (2009) and Diamond and 

Dybvig (1983), respectively, provide models of liquidity 

shocks and funding shocks.

14. “The stress scenario projections do not make explicit 

behavioral assumptions about the possible actions of a 

bank holding company’s creditors and counterparties 

in the scenario, except through the Supervisory Stress 

Scenario’s characterizations of financial asset prices 

and economic activity” (Board of Governors, 2012).

15. The credit-risk-adjusted German or Italian bond yield is 

defined as the yield offered to an investor purchasing 

a five-year German or Italian bond and five-year CDS 

protection on that bond. The credit-risk-adjusted 

spread is the spread between the credit-risk-adjusted 

Italian and German bond yields.

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82 2012 OFR // Annual Report
Addressing Data Gaps 83

4 Addressing Data Gaps

The first section of this chapter describes how the OFR will fill gaps in data 

needed for financial stability analysis and improve the scope and quality of 

those data. The second section describes recent progress and initiatives 

to improve measures of leverage, liquidity risk, and interconnectedness 

among financial institutions. The OFR’s highest immediate data priorities 

lie in short-term funding markets; over-the-counter derivatives, particularly 

credit default swaps; and asset management.

4.1 Data Agenda

Implementing the OFR’s data agenda follows a three-step process: (1) Identify financial stability data 

needs; (2) Determine gaps and weaknesses; and, (3) Prioritize and fill the gaps by better organizing 

existing data, promoting data standards, and sourcing new data where necessary (Chart 4.1.1). To 

avoid duplication, reduce regulatory burden, and take advantage of existing data sources to the extent 

possible, the OFR is building a comprehensive inventory of data that the Council member agencies 

already purchase or collect. To assure data security and confidentiality, the OFR is building a secure 

enclave for data storage and use.

4.1.1 Identify Financial Stability 

Data Needs

Financial stability data needs may arise from 

efforts to answer questions coming from several 

sources. Among them: (1) Work in support of 

the Council; (2) Other data needs identified 

through interactions with Council agencies, 

for example through the Council’s Data 

Committee coordinated by the OFR; with the 

OFR’s Financial Research Advisory Committee; 

or with other stakeholders; (3) The Office’s own 

monitoring and metrics analysis of the financial 

system and its vulnerabilities; and, (4) The 

Office’s risk management, stress testing analysis, 

crisis forensics, and other research activities.

Section 4.2 describes examples of that process 

in analyzing threats to financial stability posed 

by excessive liquidity and leverage and the 

interconnections among financial institutions, 

and discusses some of the key markets that can 

create those risks.

4.1.2 Determine Data Gaps 

The Dodd-Frank Act requires that the OFR rely 

on data already collected by Council member 

agencies, where possible, before requesting 

additional data for financial stability analysis and 

monitoring. To take stock of available data, the 

OFR has developed a comprehensive inventory 

of financial data collected or purchased by 

Council member agencies. Where there are 

gaps in the available data, the Act provides that 

the Office could help fill them by requiring 

financial companies to submit these data, 

including transaction and position data. 

Gaps and weaknesses in financial data arise in 

several ways. First, market participants cannot 

know what specific information will turn out to 
84 2012 OFR // Annual Report

Chart 4.1.1 Addressing Data Gaps

Identify Financial 

Stability Data Needs

Determine 

Data Gaps

Prioritize and

Fill Data Gaps

Work in support of the Council Inventory data available to 

supervisors

Organize data

Other collaborations Search external data sources Promote data standards

OFR monitoring Analyze existing data standards Source data

OFR research Manage data

Ensure security, confidentiality, and privacy of data

be critical for assessing the risks of new products 

or transactions. Second, supervisors cannot 

foresee all uses of the information they collect 

for the analysis of financial stability. Third, the 

data they collect are frequently not comparable, 

which stymies analysis of links among different 

structures and entities. Finally, while financial 

data are dynamic, it is costly and difficult to 

change information systems and collection rules. 

Chart 4.1.2 illustrates sources of such gaps. At 

the most basic level, a financial institution may 

not be collecting and reporting the relevant 

data about terms and conditions of all financial 

contracts. It may be difficult for management 

to keep track of the activities and risk positions 

of its own trades in the case of customized 

transactions—key information on such trades is 

often described in text form on contracts and in 

large text documents rather than in structured 

digital form—or to know the correct amount 

of information required to hold and trace the 

lineage of some products in order to understand 

the risks inherent in new products. 

Even if a firm collects relevant data, it may not 

be able to use them effectively. For example, if 

all of the units in the firm do not use common 

definitions for similar financial exposures, its 

risk managers will have difficulty comparing 

different products, evaluating the firm’s 

aggregate exposures, or making linkages—

such as linking structured products to their 

underlying components. Circa 2006, a large 

financial institution may have had various 

business units that originated and acquired 

mortgages, following waves of corporate 

mergers and acquisitions, but each of those 

units may have had its own definition for a term 

like “subprime.” One unit may have considered 

any mortgage with a FICO score below 620 as 

subprime, while another may have used 660; 

or the definition could have been based on 

income, on the identity of the lender, or on the 

interest rate the borrower paid. Each institution 

also might have stored different amounts of 

details on the transactions, rendering these data 

incomplete across all participants.

Other barriers to comparative analysis would 

be the lack of a common identifier separating 

subprime mortgages from other types of loans 

or insufficient descriptive information about the 

details of the mortgages. Or, the firm may not 

be collecting the data in a standard format—

the various units may use the same definitions 

but may be storing the data on incompatible 

computer platforms. In each case, the financial 

institution has the data but is not collecting 

them in a consistent and comparable way across 

the organization. These examples highlight the 

importance of data and data standards to assist 

with robust risk analysis and development of 

internal systems.
4.1.2 Possible Sources of Data Gaps At the next level, the data may not be available 

for sharing across companies or supervisors. 

Comparing information across companies 

could help supervisors identify crowded trades, 

excessive growth in a particular sector, or other 

systemic issues that are difficult to discern at a 

single institution. Finally, supervisors may be 

collecting data from more than one institution, 

but those datasets cannot be merged with 

datasets from other companies or industries. For 

example, regulatory reports filed by banks and 

securities firms do not have fully comparable 

data categories or nomenclature, which makes 

it difficult to get a picture of the consolidated 

exposures of a complex institution with both 

bank and securities firm subsidiaries. 

Only after all of these potential gaps have been 

addressed can data be consistently aggregated 

for comparative analysis by financial supervisors. 

Interagency Data Inventory 

The data inventory initiative is helping the 

Office identify data sources, data gaps, overlaps, 

and areas where data need to be made more 

consistent and comparable. It covers three types 

of data: purchased data (data procured from 

vendor sources); collected data (data collected 

from financial service firms and other sources 

by supervisors and regulators or directly by the 

OFR); and derived data (data derived from the 

previous two sources, alone or in combination). 

The data inventory will allow the Office and the 

Council agencies to quickly locate data that can 

be used to analyze threats to financial stability, 

ensuring that they can leverage the information 

efficiently as allowed by the contract terms 

for the data. To date, the purchased data 

have been catalogued and metadata—data 

describing the data—have been stored in a 

searchable repository. The OFR is in the process 

of collecting metadata about the agencies’ 

collected data and will follow up with derived 

data, including definitions of these data, to 

complete this inventory. It will be updated on a 

regular basis. 

Chart 4.1.2 Possible Sources of Data Gaps 

Are Data Collected by 

Market Participants? 

No 

Data Gap 

Yes 

Are Data Documented? 

No 

Yes 

Do Data Share a Common 

Definition? 

No 

Yes 

Do Data Share Common 

Identifiers? 

No 

Yes 

Are Data Collected in a 

Standard Format? 

No 

Yes 

No 

Are Data Available for Sharing? 

Yes 

Can Separate Datasets be 

Merged for Analysis? 

No 

Yes 

Data Aggregated 

for Analysis 

Data from the data inventory have already been 

and will continue to be used by researchers in 

the OFR. They will also be made available, where 

possible, to Council member agencies to assist 

in identifying data sources for their research 

or other needs. The OFR expects to provide 

a service to the FSOC agencies in describing 

where information exists before a given agency 

decides to procure it and before requesting new 

information from financial market participants. 

The inventory will promote efficiency and 

data-sharing across the agencies and avoid 

unnecessary burdens on financial service firms. 

As a part of this effort to serve the Council, the 

Office is working with private market vendors, 

where possible, to secure access to identified 

missing data, to simplify the process, to fill the 

gaps, and to increase efficiencies. 

Addressing Data Gaps 85 
86 2012 OFR // Annual Report

This data inventory will also afford the OFR a 

unique perspective on the aggregate reporting 

requirements facing financial institutions. 

Furnished with such information, the OFR can 

work with Council member agencies to reduce 

and eventually eliminate duplication in data 

collection. In addition, in conjunction with 

work on data quality and data standards, this 

information can help the Council to simplify 

and streamline the information collection 

process. Together these initiatives will improve 

the scope and quality of data collected while 

reducing the reporting burden.

4.1.3 Prioritize and Fill Data Gaps 

The OFR has three strategies for filling 

data gaps: 

(1) Help organize and integrate existing data 

gathered by the agencies. The OFR’s approach 

begins with organizing existing data. Organizing 

means not only identifying and cataloguing 

the data, but understanding where the data 

are sourced and in some cases derived; what 

the components of the data really mean in 

the context of the financial contract; and how 

they fit into our understanding of the financial 

system. This activity is central to the Office’s 

ability to monitor and assess the financial system 

because it is crucial to have an up-to-date picture 

of the processes that move capital, ownership, 

control, and risk among investors and financial 

institutions. To understand capital and risk 

flows, the effective macroprudential analyst 

must have the ability to combine data reported 

from different sources and to rely upon 

the data for analysis. For example, the OFR 

receives transaction and pricing data about 

the credit default swap (CDS) market from 

separate sources; a near-term objective is to 

connect and update reliably these two sources 

for monitoring purposes.

(2) Promote data standards. In some cases, 

data gaps can be addressed by improving 

the quality of data already collected through 

standardization. The benefits of standards are 

significant, and they accrue to both market 

participants and supervisors. The OFR will 

seek to enhance the quality and usability of 

existing data to enhance risk management along 

with microprudential and macroprudential 

supervision. The Office will work closely with 

industry, Council member agencies, and 

international bodies to achieve a higher quality 

and standardization of data collections, as 

described in Chapter 5.

(3) Source additional data that are not currently 

available. Where data gaps cannot be closed 

through better use of already collected data or 

through better standards, the OFR will work 

with industry participants and Council member 

agencies to collect the necessary information 

and data to fill those gaps. By coordinating 

with other Council agencies, the Office intends 

to create efficiencies in future collections 

and mitigate burdens on those reporting the 

data. When collecting such data, the Office 

will establish and facilitate standards for those 

collections so that they may be used to their 

fullest potential. 

In pursuing all three of these strategies, the 

overriding concern will be to take an approach 

that considers the burdens associated with 

these activities. 

4.1.4 Manage Data

In many cases, the OFR will acquire, manage, 

and distribute economic statistics; financial 

industry data on companies, products, 

transactions, and positions; and other data 

required for ad hoc FSOC requests and 

forensic analyses. 

Database management demands secure 

processes to extract data from sources, transform 

data for use, and load data into permanent 

storage, such as in a data warehouse and other 

databases optimized by data type and into 

analytic tools for use by researchers. The Office 

will work to achieve appropriate data quality 

on the data it manages so that all data can be 

used without modification or correction by all 

users. Wherever the data come from, the OFR 
Addressing Data Gaps 87

will validate that they are accurate, complete, 

and available for use by researchers. Metrics, 

including data quality and usage metrics, will 

be kept and reviewed on a regular basis. The 

Office has begun a Data Maturity Model project 

to measure data maturity capabilities and define 

best practices that will serve as a benchmark. 

This will help to ensure the highest quality and 

integrity of the data.

The Office will look to increase the transparency 

and accessibility of the data, particularly to 

researchers in the Office and Council, and 

also to member agencies and the public where 

possible. Accessibility will be based on the 

sensitivity of the data and the terms of the 

agreements or data contracts. The Office will 

maintain an electronic library that will contain 

a catalogue of all information collected that 

can be searched and made available based on 

appropriate security and access rights. This 

catalogue will be extended to include research 

papers, data, and other information in order 

to improve efficiency across Council members’ 

organizations, allowing immediate access to the 

data, and to improve the ability to support the 

mission of analyzing threats to financial stability.

4.1.5 Ensure Security, 

Confidentiality, and Privacy 

of Data

Keeping data safe and secure is the highest 

priority for the OFR. One of the biggest 

challenges in analyzing threats to financial 

stability is to collect and store, in a secure 

environment, a significant amount of 

confidential data. Robust security for data 

requires strong technology, governance, 

and processes. 

The OFR currently uses a secure analytic 

environment and is designing a robust data 

and technology infrastructure that will support 

traditional analysis as well as the ability to 

rapidly secure and analyze large amounts of 

data. The analytical environment rests on the 

foundation of technical requirements that are 

well-established by the federal government (Box 

G: Building a Secure Infrastructure). Through 

the use of such techniques as role-based access 

and dual factor authentication, the OFR seeks 

to ensure that the technology supports data 

protection and secure access. The Office has 

several initiatives to ensure security for the data 

it manages: 

•	 As part of the Treasury, the OFR inherits 

and can leverage the secure Treasury 

environment, including its infrastructure 

and policies and procedures. 

•	 The OFR is putting in place additional 

controls for the data in the form of 

hardware, software, policies and procedures, 

and access rights, including granting access 

on a “need-to-know” basis. 

•	 The OFR is in the process of defining 

and will publish a standard data security 

classification in order to map data acquired 

from multiple sources, including the public 

or other agencies. This is needed to ensure 

that the data can be catalogued, tagged, 

and handled properly with the required 

appropriate security measures. In addition, 

access control will be maintained in a 

central location, monitored on a continual 

basis, and updated as needed.

•	 The need to assure data confidentiality 

and security will restrict the scope of 

disaggregated data that can be directly 

shared with external researchers. The OFR 

is investigating sophisticated techniques to 

aggregate, mask, and make data anonymous 

in order to assure the security of the raw 

information while making the derived data 

available to researchers (Abbe, Khandani, 

and Lo, 2012). 

However, even advanced technologies can be 

defeated by poor governance, processes, and 

monitoring. The OFR understands this and is 

developing robust reporting and monitoring 
88 2012 OFR // Annual Report

BOX G. BUILDING A SECURE INFRASTRUCTURE

Essential to the success of the OFR is the protection of the data it receives, derives, 

stores, and transmits. The keystone requirement for the deployment of appropriate 

security controls is the proper categorization of both systems and information and the 

related proper data handling procedures. 

The OFR is developing a comprehensive 

security categorization methodology based on 

National Institute of Standards and Technology 

(NIST) Publication 199 and will adhere to all 

other NIST guidance for the deployment of 

baseline controls (NIST, 2004). The OFR will 

supplement the baseline controls specified by 

NIST with additional handling instructions for 

each category of information. These additional 

handling instructions provide a hardened set of 

security controls. 

This commitment to the protection of data 

is emphasized in the guiding principles of 

our Information Security Program. Those 

principles include:

•	 Strict adherence to a data categorization and 

sensitivity classification methodology based on 

the Federal Information Security Management 

Act of 2002 (FISMA) and NIST Publication 199;

•	 Safeguarding of data that we receive, store, 

and distribute at least as well as they are 

safeguarded by the data’s owner;

•	 Least privileged access, whereby access 

to systems and information is granted on 

an as-needed basis and only to the extent 

necessary for an individual to accomplish his 

or her mission;

•	 A culture of awareness whereby security is a 

primary concern of all personnel;

•	 Compliance with and subordination to higher

office policies and guidance, such as FISMA, 

NIST, and Treasury Departmental Offices;

•	 Leveraging the protections inherited as part of 

the Treasury network and supplementing those 

protections to attain a higher level of protection 

where appropriate;

•	 Supplementing and strengthening the 

policies and controls specified by higher 

offices where appropriate;

•	 Similar security controls that reinforce each 

other in a layered manner; and,

•	 Well-defined roles and responsibilities for 

executing the security program with clear lines 

of accountability, responsibility, and authority.

An example of the OFR’s commitment to 

an enhanced security posture is the recent 

construction of our secure network enclave. 

The OFR shares data center space with and is 

part of the Treasury Departmental Offices local 

area network, and, as such, inherits the high 

degree of protection provided by this network. 

The OFR has built additional security measures 

to segregate its systems from the rest of the 

Departmental Offices.
Addressing Data Gaps 89

policies and procedures. Every member of 

the OFR staff who has access to confidential 

position and transaction data certifies that they 

understand that they are subject to the postemployment restrictions set out by Dodd-Frank. 

Staff are not given access without explicit assent 

from a supervisor. 

4.2 Examples of Financial Stability Data Gaps

To an important extent, the OFR’s research and monitoring activities, in collaboration with the 

regulatory agencies, will inform its identification of data gaps. The OFR views leverage, liquidity, and 

interconnectedness as among the most important factors affecting financial stability. Measuring and 

assessing these three risk factors, among others, will always be important to the OFR’s mission. Excessive 

leverage leaves market participants vulnerable to declines in asset values, creating the potential, in 

a crisis, for distressed asset sales and thus a spread of losses to other asset holders. These effects are 

exacerbated by liquidity mismatches when illiquid long-term assets are funded through short-term 

liabilities. In a crisis, losses may propagate from one institution to another through the many links that 

connect financial institutions in networks, including lending and liquidity provision. These factors 

affecting financial stability are thus interrelated and are a primary focus for the OFR’s research agenda.

This section discusses leverage, liquidity, and 

interconnectedness in turn, emphasizing the 

diverse nature of these factors and identifying 

key information needed for comprehensive 

monitoring. The data needed to measure 

potential threats, like the factors themselves, 

cut across different parts of the financial system. 

Based on these risk factors, the OFR’s highest 

data and research priorities lie in short-term 

funding markets, including money market 

funds, repurchase agreement or repo markets, 

and securities lending; and over-the-counter 

(OTC) derivatives, particularly credit default 

swaps. Structural weaknesses in markets for 

both short-term funding and OTC derivatives 

contributed to the recent financial crisis. Both 

are vast and traditionally opaque. The OFR is 

also interested in addressing data gaps about the 

asset management industry.

Recent reforms have brought greater 

transparency to these markets, but important 

data gaps remain to be filled, and further 

analysis is needed to understand how to 

further enhance the resilience of the rest of 

the financial system to potential vulnerabilities 

in these markets. What follows reflects the 

OFR’s analysis of the data needed to support 

these research priorities. Identification of data 

needs is just the first step in the OFR’s process 

for setting its data agenda; that process also 

includes setting priorities while remaining 

sensitive to the potential costs, considering the 

appropriate form of OFR involvement, and 

defining how to share the data. 

4.2.1 Leverage

Excessive leverage is a frequent precursor 

to financial crises. Just as leverage multiplies 

gains from rising asset values, it also magnifies 

sensitivity to adverse events. Leverage amplifies 

asset bubbles as easy borrowing enables 

speculators to bid up prices. Of course, credit 

also supports economic growth. Because 

leverage can accumulate almost anywhere in 

the financial system, in both regulated and 

unregulated sectors, a comprehensive view is 

needed to identify the proper balance.

Monitoring trends in leverage requires measures 

of both overall leverage and the leverage in 

new transactions, in order to understand 

trends. In real estate, for example, the overall 

loan-to-value ratio of outstanding mortgages 

provides an important measure of total leverage 

in this sector; but the loan-to-value ratio on 

new mortgages provides a better indication of 
90 2012 OFR // Annual Report

Chart 4.2.1 Change in Tier 1 Common Ratios for 19 

 Largest BHCs

Tier 1

Common

(4Q10)

RWA

Change

Impact

Net

Issuance

of Stock

Retained

Earnings

Other

Adjustments

Tier 1

Common

(1Q12)

11

12

10

9.4 0.1 0.3 1.1 (0.0) 10.9

9

8

7

6

Percent of Risk-Weighted Assets

Source: FSOC (2012)

4.2.1 Change in Tier 1 Common Ratios

 for 19 Largest BHCs

Chart 4.2.2 Tier 1 Capital Ratios for Major BHCs

Tier 1 Capital as a Percent of Total Assets

Tier 1 Capital as a Percent of Risk-Weighted Assets

3 5 7 9 11 13

20

18

16

14

12

10

8

6

4

Q3 2007 Q1 2012

Source: FR Y-9C filings via SNL Financial, OFR calculations

Note: Top 20 BHCs by total assets as of Q3 2007 that are 

still in existence. Bubble sizes reflect relative total assets.

4.2.2 Tier 1 Capital Ratios for Major BHCs

current conditions. Geanakoplos and Pedersen 

(2011), for example, report that new leverage 

falls just before a crisis, while a decline in 

total leverage may lag by two years or more. 

As this observation suggests, tracking changes 

in marginal leverage along with total credit 

across many types of lending is an important 

component of macroprudential oversight.

The following subsections discuss drivers 

of leverage and data needed for measuring 

and monitoring leverage. Again, further 

work remains with respect to setting data 

acquisition priorities.

Bank Leverage

Leverage in the banking system is controlled 

through capital requirements and monitored 

by banking supervisors. New capital standards 

set by the federal banking agencies and Basel 

III will increase both the quantity and quality 

of capital by assigning higher risk weights for 

certain asset classes and narrowing the types 

of liabilities that may be counted as capital. 

From the fourth quarter of 2010 to the first 

quarter of 2012, the 19 largest U.S. bank 

holding companies increased their Tier 1 

common ratios, a key measure of capital, from 

9.4 percent to 10.9 percent of risk-weighted 

assets (Chart 4.2.1). In addition, the financial 

crisis demonstrated the importance of limiting 

leverage based on total assets as well as riskweighted assets. Fixed risk weights inevitably 

miss changes in asset riskiness, as they did with 

mortgages and sovereign debt; an overall cap 

on leverage reduces opportunities for banks to 

exploit such gaps. While U.S. banks have been 

subject to a leverage ratio based on total assets 

for two decades, under the proposed Basel III 

rules, large firms would also be subject to an 

international leverage ratio that will capture offbalance-sheet exposure.

Chart 4.2.2 shows ratios of Tier 1 capital to total 

assets and risk-weighted assets for the largest 

U.S. BHCs. The bubble sizes reflect relative total 

assets. Values from 2007 are indicated in blue 

and values from 2012 in brown. The chart shows 
Addressing Data Gaps 91

stronger capital ratios on both measures for the 

more recent period.

Most of a depository institution’s borrowing 

comes from deposits and other forms of debt, 

but banks can also create off-balance-sheet 

leverage through derivatives. Off-balancesheet leverage is more difficult to monitor, 

particularly because the leverage in derivative 

positions is often implicit and can change with 

market prices: swaps can change from assets 

to liabilities, and leverage in options depends 

on the strike price relative to a changing 

spot price. The Federal Reserve’s Form Y-14, 

adopted in 2012, collects more granular 

information than previously available about 

off-balance-sheet exposures at bank holding 

companies, information that can help assemble 

a more comprehensive view of leverage in the 

banking system. This is an important positive 

development; however, the information on 

sensitivities to risk factors collected through 

this form can miss the effects of sudden large 

movements in market prices. For complex 

structured products in particular, sensitivities 

provide an incomplete picture of risk and 

embedded leverage. Detailed information about 

off-balance-sheet exposures thus remains an 

important data gap.

The information from Form Y-14 is a key input 

to the Federal Reserve’s annual Comprehensive 

Capital Analysis and Review (CCAR). This review 

provides an essential perspective for monitoring 

leverage by publicly disclosing information about 

the effectiveness of bank capital and capital 

plans in sustaining losses under stress conditions. 

Information from the CCAR thus goes beyond a 

current snapshot of bank leverage to shed light 

on how leverage might evolve under adverse 

conditions and where additional capital might 

therefore be required. 

Short-term wholesale funding remains a 

potential source of leverage and liquidity risk 

in the banking system. Regulatory reports, 

through the Call Reports filed by banks and the 

Y-9Cs filed by bank holding companies, provide 

limited insights into these risks—they are filed 

Chart 4.2.3 Estimated Value of the Repo Market

JAN

2012

JAN

2010

JAN

2008

JAN

2006

JAN

2004

JAN

2002

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Trillions of US$

Primary Dealer Repo Tri-Party Repo

Source: FSOC (2012)

4.2.3 Estimated Value of the Repo Market

only quarterly and they aggregate overnight 

borrowing with 90-day borrowing, obscuring 

the bank’s exposure to a freeze in short-term 

funding markets. The Federal Reserve and OCC 

have begun to collect more granular information 

about liquidity exposures at the largest bank 

holding companies on a confidential basis 

through the Federal Reserve Bank of New York’s 

daily liquidity monitoring (Box H: Improvements 

in Financial System Monitoring).

Repo and Securities Lending

Repo markets provide a critical channel for 

securitized short-term lending against financial 

assets. Leverage through repo is moderated 

by haircuts or over-collateralization: a larger 

haircut reduces the amount a borrower can 

borrow against the value of an asset posted as 

collateral. As haircuts compress and widen, 

repo leverage expands and contracts. A sharp 

increase in haircuts has the potential to trigger 

a rapid deleveraging. Repo leverage is difficult 

to measure and monitor because bilateral repo 

transactions are widely dispersed, as opposed to 

operating through a single venue or platform. 

Even the size of the repo market is difficult to 

quantify. Chart 4.2.3 shows the scale of repo 
92 2012 OFR // Annual Report

BOX H. IMPROVEMENTS IN FINANCIAL SYSTEM MONITORING

Financial supervisors have taken important steps to expand their knowledge of the 

financial system in recent years. Hedge funds and money market funds are now 

required to file confidential new reporting forms, and thrifts now file the same public 

Call Reports that banks file. Also, a small group of large banks are now required to 

file an extensive confidential financial report as part of the Federal Reserve’s new 

annual capital assessment exercise and stress test. 

Since the crisis, financial supervisors have 

improved their ability to monitor developments 

in the financial system in important ways. 

Those improvements encompass new reporting 

requirements for institutions that are subject to 

federal supervision, such as banks and thrifts. 

In some cases, as required by Congress, 

supervisors have also introduced new reporting 

requirements for institutions that they do not 

supervise directly for safety and soundness, 

such as hedge funds.

Banks and Bank Holding Companies 

Regulatory reports filed by depository 

institutions have been expanded. The Call 

Reports and the comparable forms filed by 

bank holding companies (the Y-9Cs) have 

been expanded to include, for example, more 

granular information about securities holdings, 

derivatives and trading activities, off-balance 

sheet commitments, and nonperforming 

loans. Also, starting with their March 31, 2012 

filings, thrifts now file the same Call Reports 

that banks file. Under the Dodd-Frank Act, 

the Thrift Financial Report was eliminated, and 

thrifts became subject to the same reporting 

requirements as banks. Also, supervisory 

oversight for federal savings associations 

was assumed by the OCC; federal oversight 

for state-chartered savings associations was 

transferred to the FDIC; and oversight of thrift 

holding companies was transferred to the 

Federal Reserve. As a result, these financial 

institutions are now subject to similar regulatory 

regimes as banking organizations. 

The OCC has taken the lead in several largescale projects to collect and aggregate loan-level 

data from large banks reflecting their exposures 

in mortgages, home equity, credit card, and 

commercial real estate loans, often working 

closely with the other federal supervisors. The 

OCC shares that information with the other 

supervisors and, in the case of mortgage data, 

with the public in the quarterly Mortgage Metrics 

Report. The Federal Reserve Board, the OCC, 

and the FDIC are working to modernize the 

Shared National Credit program, a longstanding 

interagency effort that creates and disseminates 

aggregate information about banks’ credit 

exposures to large syndicated loans.

As part of its Comprehensive Capital Analysis 

and Review, the Federal Reserve introduced a 

new regulatory form, the Y-14, to be filed by the 

large banks that participate in the program, to 

support supervisory stress tests, and to improve 

monitoring capabilities. The Y-14 requires 

these companies to provide more data about 

various asset classes—securities risk, retail 

risk, wholesale risk, trading—and categories of 

pre-provision net revenue on a quarterly basis. 
Addressing Data Gaps 93

Unlike most other federal regulatory reports, the 

companies provide the data on a confidential 

basis; however, the Federal Reserve publicly 

releases information about the results of the 

stress tests performed on each bank. 

Similarly, since the financial crisis the Federal 

Reserve Bank of New York has been collecting 

daily information from a small number of large 

bank holding companies about their liquidity 

exposures, including exposures in derivatives 

and short-term funding markets and information 

about counterparties. 

Nonbank Financial Institutions

This year, hedge fund advisers and other 

private fund advisers are filing Form PF for 

the first time with regulators, either the SEC 

for investment advisers with registered private 

funds or the CFTC for certain commodity pool 

operators and commodity trading advisers 

dually registered with the SEC and CFTC. Form 

PF, a confidential reporting form implementing a 

mandate from Congress in the Dodd-Frank Act, 

requires detailed information about assets under 

management, the use of leverage, counterparty 

credit risk exposure, and trading and investment 

exposures. Data from the form should be 

available on a confidential basis to supervisors 

by early 2013.

The OFR monitors money market fund 

holdings through the funds’ monthly SEC 

filings. The filings are required of all 2a-7 

funds—funds covered by Rule 2a-7 of the 

Investment Company Act of 1940. Form 

N-MFP provides valuable disclosure on money 

market fund investments.

International Exposures and Data Gaps

To enhance information on sovereign debt 

exposures, the SEC’s Division of Corporation 

Finance issued in January 2012 disclosure 

guidance on registrants’ direct and indirect 

exposures to European sovereign debt. The 

purpose of the SEC’s guidance was to provide 

investors with greater clarity and comparability 

in substance and presentation between 

registrants. The federal banking agencies’ 

Interagency Country Exposure Review 

Committee (ICERC) is also currently working on 

a project to enhance disclosures on the Country 

Exposure Report (FFIEC 009) that may lead to a 

proposal for comment.

In November 2009, the Financial Stability Board 

and the International Monetary Fund issued 20 

recommendations for international cooperation 

to fill data gaps. They issued progress reports 

in May 2010 and June 2011 (FSB and IMF, 

2009, 2010, and 2011). Their recommendations 

include further investigation of measures of 

systemic risk (similar measures are discussed 

in Section 3.1 of this report); improved data 

on shadow banking (including disclosures on 

asset-backed securities); greater reporting of 

data on derivatives, particularly credit default 

swaps; and a common template for reporting 

by large, complex financial institutions.
94 2012 OFR // Annual Report

transactions conducted by primary dealers and 

through the tri-party repo market. 

The repo market is broadly divided into three 

components. First, the tri-party repo market 

operates through two large clearing banks, 

Bank of New York Mellon and JPMorgan Chase. 

Each participant in this market maintains a cash 

account and a securities account with one of 

these clearing banks. When a repo transaction 

occurs, the clearing bank transfers cash from 

the lender’s cash account to the borrower’s 

and transfers securities (collateral) from the 

borrower’s securities account to the lender’s. 

Second, in the Delivery versus Payment repo 

market, dealers engage in bilateral repo 

transactions with a variety of customers and 

with one another. Third, the General Collateral 

Finance (GCF) market is a blind-brokered 

interdealer market in which the Fixed Income 

Clearing Corporation (FICC) plays the role of 

central counterparty. Trades are netted out each 

day, and FICC reports net clearing amounts to 

the tri-party clearing banks. 

The OFR tracks money market fund 

transactions through data collected monthly 

by the SEC’s Form N-MFP. This is a limited 

but important window into repo markets. The 

concentration of the tri-party repo market in 

two clearing banks provides an opportunity 

for more comprehensive coverage of tri-party 

repo transactions that would allow the OFR 

to monitor potential threats in this critical 

component of short-term funding markets. 

The Federal Reserve Bank of New York currently 

reports aggregate weekly statistics on volumes, 

interest rates, and haircuts for tri-party repo, 

based on information collected directly 

from the clearing banks; these reports do not 

include information on the counterparties to 

the transactions. 

But a complete understanding of the U.S. repo 

market is not feasible without corresponding 

information about bilateral repo transactions 

and the GCF market. Bilateral repos are 

currently included in aggregate data provided 

by primary dealer banks to the Federal Reserve 

in a weekly report (the FR 2004)—although 

supervisors do not know how much bilateral 

repo business takes place outside the primary 

dealers—and the Depository Trust and Clearing 

Corporation makes monthly aggregate data on 

the GCF market available on its website. The 

OFR is engaged in acquiring and analyzing more 

frequent and detailed data on these markets.

Securities lending is similar to repo in that both 

involve a temporary exchange of securities 

and cash, with an interest payment when the 

exchange is reversed. The difference is that 

repo is mainly a mechanism for collateralized 

lending and borrowing, while securities lending 

is driven primarily by demand for holding a 

security temporarily—for example, for purposes 

of short selling. Typical securities borrowers 

are hedge funds and dealers; typical lenders 

are insurance companies, pension funds, and 

investment companies. As it is with repo, the 

over-collateralization required on securities 

lending is an important governor on leverage. 

There is no central information source in the 

securities lending market comparable to the 

two clearing banks in the tri-party repo market. 

Supervisors receive very little information 

about securities lending, and private vendors of 

information about the market rely on voluntary 

reporting by market participants.

A complete picture of these markets requires 

better data on all repo and securities lending 

transactions, including the parties to the 

transaction, collateral, haircuts, and maturities. 

Given the prominence of these markets in the 

financial crisis and their anticipated renewed 

importance as lending rebounds, the absence 

of a complete picture presents a significant 

data gap. The OFR will work closely with FSOC 

agencies to develop strategies to close this gap 

and will set priorities after weighing the relative 

advantages of different strategies.

Derivatives Markets

Derivatives, both exchange-traded and OTC, 

are sources of leverage. Because there is no 

purchase price to enter into a futures contract, 

the initial margin required by an exchange is 
Addressing Data Gaps 95

an important constraint on leverage. Margin 

requirements are public information, but 

there is no comprehensive database that tracks 

margins across exchanges and products; such a 

database is a potentially valuable element of a 

systemwide leverage monitor.

Margins on OTC derivatives are more difficult 

to track. As trading moves to swap execution 

facilities and central clearing, these markets 

will become more transparent and margin 

requirements will be easier to monitor. Indeed, 

Title VII of the Dodd-Frank Act requires that 

the details of swap transactions be reported to 

trade repositories. In addition to margins, total 

exposures through derivatives are indicators 

of overall leverage. Many derivatives—options, 

for example—are effectively equivalent to 

prepackaged trading strategies that use leverage 

to trade in the underlying assets, so increased 

derivatives volume is similar to increased 

leverage. Like leverage, greater use of swaps 

and options can magnify the market’s overall 

sensitivity to fluctuations in underlying prices.

Chart 4.2.4, updating data in Vause (2011), 

illustrates both progress and remaining gaps 

in the available data on OTC derivatives 

transactions. This information has become 

available through international coordination 

to improve reporting. The chart shows net 

credit protection bought by dealers reporting 

to the Bank for International Settlements 

(BIS), broken down by type of counterparty. 

The chart indicates that dealers are net sellers 

of protection to hedge funds and net buyers of 

protection from other counterparties—banks 

and securities firms, insurance and financial 

guaranty firms, special purpose vehicles, other 

financial institutions, and non-financial firms. 

This type of breakdown has become available 

through the BIS only since 2010, in response to 

heightened concerns about the opacity of the 

credit default swap market. At the same time, 

the chart points to remaining data needs—

in particular, the BIS data do not include 

information about counterparty concentrations. 

The risk in the CDS market is often greater 

when protection sellers and reference entities 

Chart 4.2.4 Net Credit Protection Bought by Dealers From

Different Counterparty Groups—December 2011

BSF IFGC SPV OFC NFI HF

-250

250

-200

200

-150

150

-100

100

-50

50

0

Billions of US$

4.2.4 Net Credit Protection Bought by Dealers From

 Different Counterparty Groups—December 2011

Source: BIS, OFR calculations

BSF = Banks and Securities Firms

IFGC = Insurance and Financial Guaranty Companies

SPV = Special Purpose Vehicles

OFC = Other Financial Companies

NFI = Non-Financial Institutions

HF = Hedge Funds 

are in the same sector and when added 

contractual complexity makes CDS difficult 

to value. Granular data on these aspects of the 

CDS market are not available through the 

BIS statistics. 

One of the important historical impediments 

to understanding aggregate counterparty risk 

associated with OTC derivatives was that even 

where transactional data might be available 

to a regulator, each party to the transaction 

might keep records with different identifiers for 

counterparty and reference asset. The global 

effort endorsed by the OFR to establish the 

Legal Entity Identifier (LEI), a single, universal 

naming convention for legal entities, will help 

mitigate the risk that supervisors will miss a 

buildup in these instruments. 

The OFR is in the process of acquiring and 

securing transaction, position, and pricing 

information on CDS contracts in collaboration 

with other FSOC members. Even with these 

data in place, important data gaps will remain, 

including information about collateral 

and netting arrangements between CDS 
96 2012 OFR // Annual Report

Chart 4.2.5 Debit Balances in Margin Accounts at 

Broker-Dealers

APR

2012

APR

2010

APR

2008

APR

2006

APR

2004

APR

2002

APR

2000

Billions of US$

0

50

100

150

200

250

300

350

400

450

Source: NYSE, Haver Analytics

4.2.5 Debit Balances in Margin Accounts 

 at Broker-Dealers

Chart 4.2.6 Ratio of Home Mortgage Liabilities to Real 

 Estate Assets, Household Sector

JAN

1950

JAN

1960

JAN

1970

JAN

1980

JAN

1990

JAN

2000

JAN

2010

JAN

2012

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Ratio

Source: FRED, Flow of Funds, OFR calculations

4.2.6 Ratio of Home Mortgage Liabilities

 to Real Estate Assets, Household Sector

counterparties. As these markets are global, 

the OFR will need to acquire data relating to 

foreign transactions as well, particularly for 

affiliates of U.S. financial institutions. For this 

reason, the OFR is involved in international 

working groups to align data acquisition 

and data-sharing protocols and works closely 

with primary regulators in the U.S. as they 

promulgate their rules.

Hedge Funds

Hedge fund leverage—and the fear of a rapid 

deleveraging—prompted the Federal Reserve 

Bank of New York in 1998 to coordinate the 

response of creditors of Long Term Capital 

Management to prevent a disorderly failure of 

that hedge fund. Fourteen years later, hedge 

funds have grown much larger, yet little more 

is known about the risks they pose to the 

financial system. In early August 2007, stock 

prices dropped sharply in a wave of hedge 

fund deleveraging, contributing to the nascent 

financial crisis.

Chart 4.2.5 shows total debit balances in margin 

accounts at broker-dealers, an aggregated 

indicator of leverage in stock market 

investments. The chart shows a steady buildup 

through mid-2007, followed by a decline 

to early 2009 and then another buildup to 

April 2011. More granular data on lending by 

prime brokers to hedge funds would provide 

regulators with an important tool in monitoring 

hedge fund leverage.

Through a joint effort of the SEC and CFTC, 

Form PF for confidential private fund risk 

reporting, discussed in Box H, now requires fund 

advisers to provide regulators with information 

regarding size, leverage, investor types and 

concentration, liquidity, and fund performance. 

The OFR has provided input into the data 

requirements for these rules and is preparing to 

obtain the data from these forms as they become 

available. This information will significantly 

expand the FSOC’s ability to monitor hedge 

fund leverage and assess its potential impact on 

financial stability. Whether this information will 

be sufficient to monitor threats posed by hedge 
Addressing Data Gaps 97

funds will likely be reassessed by all involved 

once the data have been collected.

Households and Real Estate

Households use debt financing for real estate 

and automobiles while relying on revolving 

credit for a wide range of purposes. Loanto-value ratios, downpayment requirements, 

and credit limits provide measures of leverage 

in this sector. Chart 4.2.6 tracks the ratio of 

home mortgage liabilities to real estate assets 

and shows a dramatic steepening near the 

end of 2005 and building up to the peak of 

the financial crisis. As noted previously, it is 

important to track these types of measures for 

new credit as well as for total credit outstanding. 

Chart 4.2.7 shows the net increase in mortgage 

liabilities dropping earlier and more quickly 

than the house price index. Chart 4.2.8 shows 

credit limits on credit card originations to 

subprime borrowers declining from a peak in 

2006 through 2009 but climbing steadily since 

early 2010. 

The Federal Reserve Bank of Philadelphia, 

with infrastructure support from the Federal 

Reserve Bank of Kansas City, has created an 

extensive warehouse of data on consumer 

credit. This data warehouse helps to improve 

information about securitization. The OFR is 

closely following rulemakings and policy work 

by primary regulators as they consider how to 

collect and aggregate more complete data.

Sovereign Debt

Persistent large government deficits are 

potentially destabilizing. As demonstrated by 

the chain of events set off by fears of a Greek 

default, the consequences of unsustainable 

sovereign debt growth can spread quickly across 

borders. The U.S. continues to enjoy historically 

low funding rates on its debt, but the risk of a 

sharp change in demand for Treasury securities 

must be counted among the potential threats 

to financial stability facing the nation. Net 

purchases of U.S. debt by foreign investors has 

become more volatile, as indicated in Chart 

4.2.9, and a sharp pullback by these investors is 

potentially destabilizing. Chart 4.2.10 compares 

Chart 4.2.7 Net Increase in Mortgage Liabilities: 

Household Sector

1992 1996 2000 2004 2008 2012

Trillions of US$ Home Price Index

0

-0.6

-0.4

-0.2

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0

20

40

60

80

100

120

140

160

180

200

S&P/Case-Shiller National Home Price Index

Net Increase in Mortgage Liabilities (Trillions of US$)

Source: Flow of Funds, S&P/Case-Shiller, Haver Analytics

4.2.7 Net Increase in Mortgage Liabilities: 

 Household Sector

Chart 4.2.8 Aggregate Credit Limit on Bankcard 

Originations to Subprime Borrowers

OCT

2011

OCT

2010

OCT

2009

OCT

2008

OCT

2007

OCT

2006

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Billions of US$

Source: Equifax

4.2.8 Aggregate Credit Limit on Credit Card Originations

 to Subprime Borrowers
4.2.9 Net Purchases of U.S. Treasury Debt

4.2.10 Debt Outstanding as a Percent of GDP

Chart 4.2.9 Net Purchases of U.S. Treasury Debt 

Trillions of US$ 

2.0 

1.5 

1.0 

0.5 

0 

-0.5 

-1.0 

JAN JAN JAN JAN JAN 

2004 2006 2008 2010 2012 

Federal Reserve and Other Government 

Private and Household Total Foreign 

Note: The "Federal Reserve and Other Government" 

category includes purchases by the Federal Reserve, state 

and local governments, government retirement funds, and 

GSEs. 

Source: Flow of Funds, Haver Analytics, OFR calculations 

Chart 4.2.10 Debt Outstanding as a Percent of GDP 

Percent 

130 

120 

110 

100 

90 

80 

70 

60 

50 

40 

JAN JAN JAN JAN JAN JAN JAN 

2000 2002 2004 2006 2008 2010 2012 

U.S. Financial Institutions 

U.S. Households and Nonprofits 

U.S. General Government 

Source: FRB, Haver Analytics 

debt outstanding as a percentage of GDP for 

the U.S. government, financial institutions, and 

household sector. 

Many state and local governments are also 

struggling to balance their budgets. Trouble in 

the municipal debt market could potentially 

spread to or from other sectors through 

linkages between markets. Money market funds 

and municipal issuers have tight and almost 

symbiotic connections; the municipal bond 

market also relies on guarantees and demand 

features provided by third parties—typically 

banks and often European banks. These 

considerations make government debt at all 

levels a necessary element of a comprehensive 

view of leverage. 

Chart 4.2.11 shows CDS spreads for the U.S. and 

six states. The CDS spreads are an indication of 

the market’s perception of credit risk in these 

jurisdictions. The figure suggests linkages that 

require further analysis: the spreads for Florida, 

Massachusetts, New York, and Ohio have moved 

in lock step, and those of California and Illinois 

have followed similar patterns at higher levels. 

For the most part, the state spread movements 

show comparatively little relation to that of U.S. 

sovereign debt. 

A significant data gap in assessing the leverage 

of the public sector results from government 

accounting procedures. Accounting standards 

differ across government entities, and 

governments do not ordinarily provide fair 

value estimates on activities like insurance 

programs, pension benefits, and contingent 

liabilities (Lucas, 2011). The joint report 

of the Financial Stability Board and the 

International Monetary Fund discussed in Box 

H includes recommendations for standardizing 

government finance data and creating a public 

sector debt database. 

4.2.2 Liquidity 

Liquidity has different meanings in different 

contexts. It is particularly important to 

distinguish market liquidity—the ability 

of a market to absorb large and frequent 

98 2012 OFR // Annual Report 
Addressing Data Gaps 99

transactions with limited price impact and low 

transactions costs—from funding liquidity, which 

is the ready availability of credit to finance 

the purchase of financial assets or to redeem 

liabilities. Liquidity risk can refer to the risk that 

an asset may become less liquid and thus harder 

to sell, but it can also refer to the risk of a cash 

shortfall resulting from a mismatch between the 

timing of cash flows generated by an asset and 

the cash needed to fund the asset. 

The discussion in this subsection focuses 

primarily on funding liquidity risk, but the two 

notions of liquidity risk are closely linked. If 

an asset has a high degree of market liquidity, 

it can be sold to avert the consequences of a 

loss of funding liquidity. Conversely, funding 

liquidity is of particular concern in financing 

the purchase of an illiquid asset. Moreover, as 

funding liquidity dries up, it becomes harder 

for leveraged investors to buy the asset, and this 

impairs its market liquidity.

As noted in the discussion of stress tests in 

Section 3.2, the two kinds of liquidity live on the 

two sides of institutions’ balance sheets, with 

market liquidity on the asset side and funding 

liquidity an issue for liability management. 

Collecting data on both sides of the balance 

sheet is thus not just important to reconcile 

the books; it is critical for assessing threats to 

financial stability. 

Short-Term Funding Markets

Short-term funding markets are the primary 

locus of funding and liquidity risk, particularly 

when they are used to finance illiquid long-term 

assets. This risk created by maturity mismatch 

is illustrated by a structured investment 

vehicle (SIV), a type of entity popular before 

the crisis that issued short-term commercial 

paper and medium-term notes and used the 

proceeds to invest in securities such as illiquid 

securitizations. Liquidity risk arose from the 

need to repeatedly roll over the short-term 

funding as it matured. A sudden pullback in 

funding provided by investors, triggered by 

uncertainty about the quality of the underlying 

investments, caused many SIVs to fail in 2007. 

Chart 4.2.11 10-Year CDS Spreads: U.S. and 

 Selected States

APR

2012

FEB

2012

DEC

2011

OCT

2011

AUG

2011

JUN

2011

APR

2011

Individual States U.S.

50

100

150

200

250

300

350

0 0

10

20

30

40

50

60

70

80

90

Ohio

New York

Florida

Massachusetts

California

Illinois

United States

Source: Markit

4.2.11 10-Year Spreads: U.S. and Selected States

Chart 4.2.12 AA Asset-Backed Commercial 

 Paper Issuance

MAY

2012

MAY

2010

MAY

2008

MAY

2006

MAY

2004

MAY

2002

0

Billions of US$ (Monthly Average)

20

40

60

80

100

Source: FRB, Haver Analytics

4.2.12 AA Asset-Backed Commercial Paper Issuance
4.2.13 Taxable Money Market Fund Assets

4.2.14 Margin Requirements for ABS

and CMO Tri-Party Repo Collateral

Chart 4.2.13 Taxable Money Market Fund Assets 

Billions of US$ 

800 

700 

600 

500 

400 

300 

200 

100 

0 

JAN JAN JAN JAN JAN JAN JAN 

2000 2002 2004 2006 2008 2010 2012 

Certificates of Deposit Commercial Paper 

Source: Investment Company Institute, Haver Analytics 

 

Chart 4.2.14 Margin Requirements for ABS and CMO 


Tri-Party Repo Collateral
 

Percent 

16 

14 

12 

10 

8 

6 

4 

2 

0 

MAY SEP JAN MAY SEP JAN MAY 

2010 2010 2011 2011 2011 2012 2012 

CMO Median CMO 10th and 90th Percentiles 

ABS Median ABS 10th and 90th Percentiles 

Note: Some values were calculated by OFR and reflect the 

averages of the margins at a given percentile for investment 

and non-investment grade collateral. 

Source: Tri-Party Repo Infrastructure Reform Task Force, 

OFR calculations 

Investors suffered losses in some cases when 

SIVs sold assets at distressed prices; in other 

cases, SIV sponsors rescued investors despite 

having no prior obligation to do so. 

While the SIV market came to an end in 2007, 

the yield spread generated through this type of 

maturity and liquidity transformation provides 

a persistent incentive for the development 

of products that capture this yield. Tracking 

the growth of financial innovations designed 

for maturity and liquidity transformation is a 

necessary part of monitoring the economy’s 

overall liquidity risk. 

Chart 4.2.12 plots the issuance of AA-rated assetbacked commercial paper, showing a collapse 

in late 2008. Chart 4.2.13 shows the buildup 

and subsequent decline in money market fund 

investments in commercial paper, starting in 

2004. The pullback from commercial paper that 

starts in 2007 is offset in part by an increase in 

certificates of deposits, creating a tighter link— 

and thus greater liquidity risk—between banks 

and money market funds. 

The repo and securities lending markets 

discussed above are key components of shortterm funding markets. Just as the magnitude 

of haircuts provides a measure of leverage, 

the volatility of haircuts—the extent to which 

they fluctuate—provides a measure of liquidity 

risk. With volatile haircuts, the amount a 

borrower can borrow against assets posted 

as collateral can drop sharply, forcing the 

borrower to find an alternative source of funds 

quickly. In 2007–2008, haircuts on asset-backed 

securities widened, triggering rapid sell-offs and 

contributing to falling prices. In quieter times, 

haircuts fluctuate little and therefore provide 

little information about changing sentiments. 

Chart 4.2.14 shows increased volatility in margin 

requirements for repo collateralized by assetbacked securities and collateralized mortgage 

obligations in late 2011. More research is 

needed to understand the dynamics in different 

parts of the repo market that lead to spikes in 

haircuts, to “repo runs,” and thus to sudden 

100 2012 OFR // Annual Report 
Addressing Data Gaps 101

contractions in short-term funding (Martin, 

Skeie, and Von Thadden, 2012).

Money Market Funds 

Money market funds are a major source of 

short-term funding, with over $2.5 trillion in 

assets under management. They also transmit 

liquidity risk between the funds’ borrowers and 

investors. Banks and other financial institutions 

are large borrowers from money market funds; 

a sudden withdrawal from money market funds 

by investors, particularly large institutional 

investors, thus reduces short-term funding to 

the financial sector. The municipal bond market 

also relies heavily on money market funds and 

is thus vulnerable to a liquidity shock hitting 

the funds.

A simple but important measure of liquidity in 

money market funds is the weighted average 

maturity of their assets. Chart 4.2.15 shows that 

this increased between July 2006 and October 

2009, dropped sharply in 2010, and is currently 

around 45 days. However, a more complete 

picture of liquidity risk requires information on 

investor concentration: a fund dominated by a 

small number of large institutional investors will 

generally need greater liquidity than one with a 

broad base of small retail investors.

The OFR monitors money market fund holdings 

through monthly SEC filings. The filings are 

required of all funds covered by Rule 2a-7 of the 

Investment Company Act of 1940 (2a-7 funds). 

Form N-MFP provides valuable disclosure on 

money market fund investments. Remaining 

data gaps include the lack of information 

on a fund’s investor characteristics, better 

information on repo collateral and collateral 

pricing, separate reporting of coupons on 

investments, and more consistent reporting 

of issuers—a task that will be facilitated by the 

adoption of an LEI.

Services similar to those of money market funds 

are provided by funds and separate accounts just 

outside the 2a-7 umbrella. With stricter liquidity 

requirements added to 2a-7 rules in 2010, 

liquidity risk may have migrated to non-2a-7 

Chart 4.2.15 Taxable Money Market Funds: Average 

 Maturity of Portfolio

JAN

2012

JAN

2010

JAN

2008

JAN

2006

JAN

2004

JAN

2002

JAN

2000

0

10

20

30

40

50

60

70

Number of Days

Source: Investment Company Institute, Haver Analytics

4.2.15 Taxable Money Market Funds:

 Average Maturity of Portfolio

funds that invest at slightly longer maturities 

and take on greater credit risk.1 The absence 

of data on these near substitutes for regulated 

money market funds could represent a data 

gap in monitoring liquidity and run risk in this 

important channel for short-term funding. The 

OFR will continue to work with the SEC and 

other FSOC members to consider this migration 

and its implications for risk monitoring.

Liquidity Guarantees

Liquidity guarantees—typically in the form 

of guarantees from third parties to provide 

emergency short-term funding—can protect 

investors and can serve as buffers against 

temporary mismatches in cash flows between 

assets and liabilities. However, guarantees 

are also a potential transmission channel for 

liquidity risk if not carefully managed and 

monitored. A widespread call on guarantees 

is likely to coincide with other stresses on the 

market, creating the potential for amplified 

risk. Moreover, skittishness regarding whether 

implicit guarantees will be honored can create 

market stress. 

SIVs have lost favor with investors, but legacy 

asset-backed commercial paper (ABCP) 
102 2012 OFR // Annual Report

Chart 4.2.16 VRDN Dealer Inventories and SIFMA 

 Municipal Swap Index

JUN

2012

APR

2012

FEB

2012

DEC

2011

OCT

2011

AUG

2011

JUN

2011

APR

2011

Billions of US$ Basis Points

0

5

10

15

20

25

30

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Weekly VRDN Dealer Inventories (Billions of US$)

SIFMA Municipal Swap Index (Basis Points)

Source: Bloomberg

4.2.16 VRDN Dealer Inventories and SIFMA

 Municipal Swap Index

conduits continue to operate on a large scale. 

Approximately 7 percent of prime money 

market fund assets are invested in ABCP and 4 

percent of money market fund assets overall. 

A key difference between the two structures is 

that ABCP conduits carry a committed liquidity 

guarantee from a sponsor, usually a bank. An 

SIV typically has a much more limited liquidity 

backstop or just an implicit guarantee based on 

the sponsor’s reputation. Citigroup bailed out its 

SIVs even though it had not provided liquidity 

facilities or guarantees to these vehicles. In 

defining the scope of new liquidity requirements, 

the Basel III liquidity coverage ratio specifically 

identifies non-contractual contingent funding 

obligations that a bank may incur to avoid 

reputational damage.

Most money market fund investments in 

municipal securities take the form of variable 

rate demand notes (VRDNs). The demand 

feature allows the holder to put the security 

back to a liquidity provider at par, typically 

on a weekly basis; this feature transforms a 

medium- or long-term municipal security into 

an investment eligible for a 2a-7 fund to buy. 

VRDNs are usually backed by a letter of credit 

or a standby bond purchase agreement from 

a third party, typically a bank. The potential 

systemic concern is the possibility of a sudden 

and widespread exercise of the demand option, 

straining the resources of liquidity providers at 

a time of other stresses in the financial markets. 

In the summer of 2011, with concerns rising 

over events in Europe and uncertainty about 

the debt ceiling in the U.S., dealer inventories 

of VRDNs climbed as investors seeking liquidity 

exercised their demand option (see Chart 

4.2.16, which also shows the SIFMA Municipal 

Swap index, reflecting the rate demanded for 

new issues).

Monitoring liquidity guarantees is an essential 

element of monitoring liquidity risk. There 

is currently no mechanism for aggregating 

financial guarantees across markets. Guarantees 

are often difficult to observe before they are 

invoked, and this is particularly true of implicit 

or perceived guarantees. Identifying where a 

breakdown in an implicit guarantee has the 

potential to create significant disruptions to 

the financial system presents a difficult but 

important research challenge.

The freezing of the auction rate securities market 

in 2008 illustrates the potential risk. The major 

dealers in these securities had traditionally 

supported the market by participating in the 

auction when necessary. But in February 2008, 

weakened by a worsening financial crisis, the 

investment banks declined to bid, allowing 

a string of auction failures. Investors sued, 

alleging that the auction rate securities had 

been sold as risk-free—sold with an implicit 

guarantee—leading dealers to buy back many of 

the securities they had sold. The freezing of this 

market aggravated the deterioration of funding 

liquidity as investors moved to safer assets. With 

greater clarity in advance, both investors and 

dealers would have been better prepared for the 

disruption in the market.

Hedge Funds and Liquidity

Hedge funds can affect and be affected by both 

market and funding liquidity in numerous ways. 

Many hedge funds hold illiquid assets; many 
Addressing Data Gaps 103

also constrain their investors’ liquidity through 

limits on redemptions, but the potential for a 

liquidity mismatch remains. With high leverage, 

the risk of such a mismatch is magnified, as 

a rapid deleveraging and sell-off of illiquid 

assets may trigger a financial crisis. Crowded 

trades in the form of similar positions held by 

many funds increase the risk of a simultaneous 

sell-off and point to the need for increased 

reporting through Form PF. Hedge funds often 

rely on prime brokers for short-term funding, 

making the funds—and their highly leveraged 

investments in illiquid assets—vulnerable to 

liquidity shocks affecting dealers that might 

otherwise have no direct impact on the funds. 

These types of interconnections in the financial 

system, and their implications, are the focus of 

the next subsection.

4.2.3 Interconnectedness

Monitoring threats to financial stability requires 

understanding the network of connections 

through which financial distress can propagate 

through the financial system. In fact, the 

same financial institutions are linked through 

multiple networks defined by multiple types of 

connections. As a result, firm failure can present 

a shock in the market.

One-way Credit Extension. Institutions are linked 

through borrowing and lending. The failure of a 

borrower causes a loss to the lender. Since most 

lenders are themselves also borrowers, large 

undiversified losses can cascade. 

Swaps. Swap transactions create networks of 

counterparties. Swaps are a form of two-way 

credit extension because a swap initially valued 

at zero may become an asset for one party and 

a liability for the other, depending on changes 

in market variables. Failures can propagate 

through this network as well.

Ownership. A parent and a subsidiary can be a 

source of strength or a source of vulnerability to 

each other. Understanding these relationships 

requires the ability to track ownership 

networks—networks with thousands of nodes 

for even a single large, complex financial 

institution. An important data gap preventing 

the understanding of financial networks is 

the lack of comprehensive and standardized 

information on ownership and various types 

of affiliations. The LEI helps to fill this gap, as 

discussed further in Chapter 5.

Service Provision. Financial institutions are 

interlinked through the many services they 

provide to each other, including mortgage 

servicing, custody, clearing and transfer services, 

brokerage, and investment management. The 

failure of a large, complex service provider 

could disrupt the functioning of many other 

financial institutions. 

Contingent Exposures. The links in a network 

of contingent exposures are latent until 

activated by a contingency. An undrawn 

and uncommitted line of credit provides an 

example. Other examples include options 

embedded in other transactions and events such 

as downgrades that trigger collateral calls.

Guarantees. As noted in several places in 

this chapter, both contractual and implicit 

guarantees create important and sometimes 

vulnerable links between financial institutions. 

Reliable guarantees can enhance financial 

stability, but poorly managed guarantees can 

provoke a loss of confidence, and implicit 

guarantees can contribute to moral hazard.

Correlation and Concentration. Financial 

institutions operate in a shared market and 

are thus subject to common shocks. Investors 

of various types are interconnected, even if 

they do not transact with each other, through 

correlations in their asset returns. These 

correlations may lead them to unwind their 

positions simultaneously, creating a cascading 

decline in prices. This risk is exacerbated 

by crowded trades, that is, through the 

concentration of similar positions by many 

market participants. Such concentration creates 

fragility in the financial system and, with existing 

data, is difficult to detect. The development of 

standard product identifiers will help support 

the detection of risky concentrations. 
104 2012 OFR // Annual Report

These types of interconnections are illustrated 

through credit default swaps, short-term 

funding markets, and hedge funds.

Credit Default Swaps. A current focus of 

the OFR is the CDS market, which exhibits 

interconnectedness between protection 

buyers and protection sellers, and between 

CDS counterparties and reference entities. 

Interconnections in the CDS market have 

traditionally been opaque. They represent 

a potential threat to financial stability. 

Government intervention to prevent an AIG 

bankruptcy was prompted at least in part by 

fear of the potential impact on AIG’s CDS 

counterparties. The OFR can help avert the 

need to make crisis decisions with limited 

information through ongoing analysis and 

monitoring of financial networks.

Short-term Funding Markets. These largely 

unregulated markets connect banks with 

funding sources that can quickly pull back 

in times of stress. Money market funds are a 

potential source of vulnerability for banks and 

others that rely on them for short-term funding 

(Chart 3.1.1). 

Hedge Funds. Hedge funds were among the 

first financial institutions hit by the collapse 

of subprime mortgage-backed securities in 

2007, and the multiple roles of hedge funds in 

this episode illustrate the need for better data 

to monitor interconnections in the financial 

system. Many hedge funds took leveraged 

positions in subprime mortgage-backed 

collateralized debt obligations (CDOs) and 

faced a liquidity squeeze as falling prices led 

to margin calls from investment banks. This 

liquidity squeeze also affected long-short equity 

funds with no direct subprime exposure. As 

market conditions worsened and investors 

pulled out of hedge funds, the funds withdrew 

assets from their prime brokers, sharply 

reducing liquidity at the largest broker-dealers.

Hedge funds were also active managers of CDOs 

and among the largest investors in first-loss 

positions in these pools. Their participation was 

thus essential to the growth of the subprime 

mortgage market. Many hedge funds engaged 

in correlation trading, which involved taking 

long and short positions in different tranches of 

CDOs. The lack of transparency on the funds’ 

multiple and potentially conflicting roles has 

led to accusations of impropriety. The many 

interconnections between hedge funds and the 

investment products at the heart of the financial 

crisis reinforce the need for better data on these 

linkages to improve the monitoring of networks 

in the financial system.

Across all of these instruments and markets, the 

ability to assess the degree and potential impact 

of interconnectedness depends upon the ability 

of financial stability analysts to uniquely define 

counterparties, financial products, and terms 

and conditions. An established LEI and similar 

common unique global identifiers for financial 

instruments would enable the aggregation 

of risks to identify exposures regardless of 

the source of the transaction data. These 

data standards, combined with standardized 

definitions and disclosures of terms such as 

collateral and haircuts where appropriate, are 

the building blocks of transactions, positions, 

and ownership interests. They describe the form 

of the connection between financial market 

participants and they provide insights into the 

mechanism by which financial instability can be 

transmitted and amplified. 

Endnote

1. In this regard, the OCC issued a proposed rulemaking 

in April 2012 that would partially align the requirements 

for short-term bank common and collective investment 

funds with the SEC’s revisions to Rule 2a-7 (OCC, 2012).

References for Chapter 4

Abbe, Emmanuel A., Amir E. Khandani, and Andrew W. 

Lo. “Privacy-Preserving Methods for Sharing Financial Risk 

Exposures.” Working Paper, November 19, 2011. 

Financial Stability Board and International Monetary Fund. 

The Financial Crisis and Information Gaps. Report to the 

G-20 Finance Ministers and Central Bank Governors, FSB 

and IMF, October 29, 2009.

Financial Stability Board and International Monetary Fund. 

The Financial Crisis and Information Gaps: Progress Report, 

Action Plans and Timetables. FSB and IMF, May 31, 2010.
Addressing Data Gaps 105

Financial Stability Board and International Monetary Fund. 

The Financial Crisis and Information Gaps: Implementation 

Progress Report. FSB and IMF, June 30, 2011.

Financial Stability Oversight Council. Annual Report. 

Washington, DC: FSOC, 2012. 

Geanakoplos, John and Lasse Heje Pedersen. “Monitoring 

Leverage.” Working Paper no. 1838, New Haven, CT: Cowles 

Foundation, Yale University, November 1, 2011. 

Lucas, Deborah. “Evaluating the Government as a Source 

of Systemic Risk.” Paper prepared for the Conference on 

Systemic Risk and Data Issues, The Center for Financial 

Policy at the University of Maryland, Washington, DC, 

October 5, 2011. 

Martin, Antoine, David Skeie, and Ernst-Ludwig Von Thadden. 

“Repo Runs.” Staff Report no. 444, New York: Federal 

Reserve Bank of New York, January 2012. 

National Institute of Standards and Technology. Standards 

for Security Categorization of Federal Information and 

Information Systems. Federal Information Processing 

Standards Publication 199. Gaithersburg, MD: NIST, 

February 2004. 

Office of the Comptroller of the Currency. Short-Term 

Investment Funds. Notice of Proposed Rulemaking, Federal 

Register 77, no. 68, April 9. Washington, DC: OCC, 2012, 

21057–21065. 

Vause, Nicholas. “Enhanced BIS Statistics on Credit Risk 

Transfer.” BIS Quarterly Review (December 2011): 85–89. 
106 2012 OFR // Annual Report
Promoting Data Standards 107

5 Promoting Data Standards

Stronger and more consistent financial data standards will enhance 

financial stability by addressing a major deficiency that impairs decisionmaking. Data standards, when implemented appropriately, will promote 

data transparency, comparability, and quality, enabling aggregation of risks, 

financial stability monitoring, and better firm risk management. Congress 

mandated the OFR to standardize the types and formats of data reported 

and collected. We have focused initially on one of the most fundamental 

standards: the need for a global Legal Entity Identifier (LEI).

5.1 What Are Data Standards?

Consumers and businesses are familiar with the value of data standards to promote understanding 

and comparability by providing common, clear definitions—examples include nutrition information published on packaged foods, the bar codes that, through the use of scanners, identify products at 

supermarket checkout lanes, and destination and other information on shipping containers. Standards 

also promote efficiencies and economies of scale—for example, note the growth of the Internet following 

common acceptance of the Internet Protocol.

The financial services industry and financial 

regulators have long employed data standards. 

There are common identifiers for registered 

securities, regulated legal entities, and certain 

financial transactions. Financial data vendors 

also use identifiers for their products, some of 

which have become de facto standards. 

But these standards, while widespread in some 

cases, are not global or universal and are 

often plagued by gaps and overlap. They are 

sometimes subject to limits on use when they 

are proprietary, there is no common vocabulary 

or mapping to navigate among them, and they 

were not conceived or operated with a systemic 

perspective. There is also no consistent standard 

used to measure data quality.

The lack of high-quality, consistent, and 

accessible data was a key source of risk in the 

financial crisis. Risk systems designed to assess 

counterparty risk, interconnections, and shortterm funding were strained because, in part, 

the data they required and even the reports 

they generated lacked standards for basic data 

identifiers, elements, and terms. Regulators 

and policymakers were caught trying to 

aggregate information from disparate systems, 

each with proprietary naming conventions for 

counterparties and instruments. Differences 

in the amount and consistency of information 

on terms and conditions of the data meant 

that even when common transactions were 

identified, there was limited assurance that they 

could be compared with certainty. The lack 
108 2012 OFR // Annual Report

of consistent and high-quality data not only 

exacerbated the buildup of risks, it also limited 

the ability to act decisively in the crisis. In sum, 

the market lacks standards on how to exchange, 

transport, and aggregate financial data. 

Better data standards can solve many of 

these problems. Standard entity identifiers 

can uniquely identify parties to financial 

transactions; standard product identifiers can 

allow for comparability across financial products; 

and semantic standards about terms of contracts 

can provide precise definitions to describe 

the meaning of data. In addition, data can be 

assessed based on the quality of the information. 

Each of these standards promotes comparability, 

which means that information can be reliably 

combined from different sources and systems 

and that terms and definitions mean the same 

thing—with no ambiguity—regardless of where 

the data came from.

Data standards sometimes evolve organically, 

as markets coalesce around a dominant 

convention, and they sometimes are set by 

industry organizations either through informal 

cooperation—such as the development of 

the Financial products Markup Language 

(FpML)—or formal consensus bodies like the 

International Organization for Standardization 

(ISO). Financial companies should welcome 

data standards: they reduce operating costs; they 

promote automation, so that information does 

not have to be manually checked and cleaned; 

they allow companies to aggregate their activities 

for analysis; and they promote straight-through 

processing, meaning that a transaction can be 

tracked consistently from the front office to 

the back office. For all these reasons, standards 

promote sound risk management. 

In the short run, however, implementing 

formal data standards presents a collective 

action problem. Like national defense, data 

standards represent a public good—something 

available to everyone, whether or not he or she 

has borne any of the costs of providing them. 

Many in industry recognize the benefits of 

data standards and are eager to benefit from 

them. However, those benefits—for internal 

economic and operational risk management, 

for prudential supervision, for measuring and 

protecting financial stability—are long-term and 

diffuse, while the costs—incurred as a result of 

changing processes and systems to adapt to the 

standards—are immediate and concentrated 

for market participants, companies, and 

regulators themselves. Consequently, as with 

some other public goods, data standards require 

appropriate governance and oversight from 

public authorities. 

That is why Congress assigned to the OFR the 

responsibility to promote and facilitate the 

development of financial data standards. The 

OFR has begun this important work, focusing 

initially on the need for a global LEI. We are 

now working to implement the global LEI and 

also are developing our priorities for further 

standards work. 

This responsibility will require constant vigilance 

as financial markets and products evolve and 

standards grow obsolete (Box I: The Growth of 

Financial Market Data). The OFR and financial 

supervisors will not be able to monitor financial 

stability and financial companies will not be able 

to manage their risks if data standards don’t 

keep up with these developments. At the same 

time, the requirement of any new standard 

should only be introduced after a thoughtful, 

careful analysis. The OFR is committed to 

engaging with the private sector and industry 

standard-setting bodies and working with 

regulators to align standards, as appropriate in 

well-defined cases.

There are many financial data standards in use 

today. One way to view financial data needs and 

standards can be by what they describe: entities, 

financial instruments, financial and business 

reporting, and transactions.

5.1.1 Entities

The financial crisis brought attention to 

the lack of comparability and consistency 

among entity identification standards. 
Promoting Data Standards 109

Market participants—and their regulators—

were unable to aggregate and then analyze 

their credit exposures to troubled financial 

institutions with many legal subsidiaries 

because there were no unique global entity 

identification standards in place that would 

facilitate the construction of definitive 

ownership or legal entity hierarchies. A large 

financial institution might have thousands 

of legal entities, each with their own names. 

Moreover, while there were identification 

methods in use in the market, they were 

diverse, incomplete, overlapping, and not 

directly comparable; this presented additional 

problems because of differing maintenance 

protocols, such as in describing the surviving 

entity of a corporate merger, the distinctions 

between different kinds of entities, or the reuse 

of identification numbers. As described further 

below, the OFR has made the establishment 

of a global and unique LEI one of its top 

early priorities and is active in the global LEI 

development effort overseen by the Financial 

Stability Board and endorsed by the G20.

A wide variety of entity identification schemes 

are used today to identify business, financial, 

and other entities that are involved in financial 

transactions. A number of regulatory standards 

have been established by agencies, including 

the Research Statistics Supervision Discount 

Identification (RSSD ID), which is a primary 

identifier used by the bank regulators, and 

the Central Index Key, which is used by the 

SEC to identify issuers, funds, and certain 

shareholders who have filed disclosures. The 

Financial Industry Regulatory Authority also 

has standards that it uses to identify brokerdealers and investment advisers who register 

with them. Various private sector organizations, 

vendors, and market participants also maintain 

proprietary entity identification standards that 

have met with varying levels of acceptance 

and adoption in particular markets, sectors, or 

applications. Some of these private standards 

have been accepted by the ISO.

Broad use of an LEI and the further spread 

of entity standards across the financial system 

could help market participants and regulators 

understand the linkages and relationships 

among legal entities. The incorporation of LEIs 

into hierarchies of related entities—based upon 

underlying factors such as ownership, control, 

or different types of exposure—would further 

facilitate aggregation. Ultimately, it would be a 

powerful tool to facilitate the analysis of such 

phenomena as network effects and spillovers in 

a crisis. 

5.1.2 Financial Instruments

As with entity identifiers, there are multiple 

schemes both for the identification and 

for the description of the several million 

financial instruments that are currently in 

the marketplace. The impetus for financial 

instrument identifiers in the United States 

came from the “paper crunch” of the late 

1960s, when trading volumes overwhelmed the 

ability of market participants to process and 

clear trades that were paper-based. This event 

led to the creation in 1968 of the CUSIP Service 

Bureau, a for-profit joint venture of Standard & 

Poor’s and the American Bankers Association. 

CUSIP provides a common language for 

identifying financial instruments such as stocks 

and bonds. There is also an ISO standard for 

individual securities known as the International 

Securities Identification Number (ISIN). The 

CUSIP Service Bureau is the American member 

of the international association that maintains 

the ISIN standard. 

But the CUSIP standard is primarily used for 

stocks, bonds, and some other instruments. 

Not all products have definitive standards. In 

the past decade, the evolution of specialized 

over-the-counter (OTC) markets such as 

credit default swaps has led to a proliferation 

of proprietary standards in this space. As with 

entity identifiers, extensive investment in 

software, data, and expertise has been required 

to maintain relatively “clean” and usable 

identifiers; nonetheless, errors and mismatches 

occur regularly. Because financial instruments, 

like entities, are not static, the data describing 

these instruments must be updated and 

maintained as corporate actions occur. 
110 2012 OFR // Annual Report

BOX I. THE GROWTH OF FINANCIAL MARKET DATA

The size and complexity of financial data has grown exponentially, reflecting the 

growth and complexity of financial activity. These changes pose increasing challenges 

to data managers.

The growth of the financial sector in the United 

States has been extraordinary. From 1952 to 

2011, nominal GDP grew by 4,100 percent and 

financial sector assets grew by 16,000 percent, 

according to Flow of Funds data. The sector 

has also become extraordinarily complex with 

the growing ubiquity of derivatives and other 

risk-shifting products. 

These developments create data management 

challenges because they increase the amount 

of data that has to be taken in, organized, and 

assessed. For example, every purchase of a 

share of stock is associated with a trade order; 

electronic quotes indicating the willingness of 

market participants to buy or sell a given amount 

at a given price; confirmation messages that the 

trade has been completed and settled; entries into 

the accounts at broker-dealers, mutual funds, or 

other agents; and data that gets wrapped into risk 

reporting of financial intermediaries. 

Where all this data goes—and how effectively it 

is used, compared, and aggregated—determines 

how well the financial system manages risk. Any 

risks that are introduced in a balance sheet, 

trading book, or personal portfolio because of 

data gaps and a lack of data standards only 

grow more significant and potentially costly as 

the amount of data increases. 

The charts illustrate the growth in financial 

sector data in the U.S. in the 2000s. Chart I.1

shows the growth in electronic messages for

the equities and options markets. Electronic 

messages include quotes and orders reported 

to 15 different markets. They are reported as 

the number of electronic messages per second 

for the busiest minute of the day; in essence, 

this reflects the peak strain on the electronic 

systems that are the backbone of these 

trading platforms. Since 2000, this measure 

has increased from about 7,000 to 2,400,000 

messages per second.

The growth in message traffic that creates these 

new data demands is not driven primarily by 

increases in the number of trades; between 

2000 and 2011, annual trade volume in equities 

rose just 10 percent. Rather, it reflects increases 

in the number of quotes—indications that a 

market participant is willing to buy or sell—

made possible by technological developments. 

Technology has increased the capacity of 

market participants to evaluate financial 

instruments and generate quotes, enabled 

algorithmic and high frequency strategies that 

play out across multiple trading venues and 

financial instruments, and increased the speed 

with which information can be processed. In 

2000, options markets transmitted about 350 
Promoting Data Standards 111

quotes for every trade. By 2011, that number 

had increased to about 7,750 (Chart I.2). 

Meanwhile, options and equities are just a part of 

the financial markets. Similar trends are playing 

out in markets for fixed-income securities, 

futures, swaps, and other derivatives. And these 

financial instruments are often fundamentally 

related to one another—for example, options 

on the same underlying asset, or exchangetraded funds and the underlying stocks in their 

portfolios. Large and increasing volumes of data 

are generated by dozens of trading venues and 

hundreds of financial institutions across these 

markets and internationally. 

What is the implication of this data explosion 

for financial markets? Increased complexity 

and risk. The financial system needs to be able 

to compare, aggregate, and rely on millions 

of records created daily by different computer 

systems with different identifiers, different 

naming conventions, and different definitions. 

Such a complex and interconnected system is 

designed to fail without strong data standards 

and a common dictionary and definitions.

Chart I.1 Peak Messaging for Options and Equities Markets

2001 2003 2005 2007 2009 2011

Messages per Second at the Peak Minute 

(Millions)

0

0.5

1.0

1.5

2.0

2.5

Options Equities

I.1 Peak Messaging for Options and Equities Markets

Source: Statistics compiled by the Financial Information 

Forum with data from BATS, Direct Edge, SIAC, NASDAQ 

and NYSE Arca 

Chart I.2 OPRA Maximum Quote to Trade Ratio

2001 2003 2005 2007 2009 2011

0

Quotes per Trade (Thousands)

1

2

3

4

5

6

7

8

9

Source: Statistics compiled by the Financial Information 

Forum with data from SIAC

I.2 OPRA Maximum Quote to Trade Ratio
112 2012 OFR // Annual Report

Derivatives pose special problems for 

standardization because they derive their value 

from underlying instruments. Asset-backed 

securities similarly represent collections of 

instruments, such as credit card receivables or 

mortgages. Tracing the contents of a product 

like a mortgage-backed security is especially 

difficult because there is no standard way 

to identify and link the specific underlying 

mortgages to the security. Other complex 

securities, such as collateralized debt obligations 

based on managed pools of assets, pose 

problems because of their complexity. Bilateral 

swaps can also pose difficulty because of their 

customized nature. 

5.1.3 Financial and Business Reporting

Companies report information in public financial 

disclosures (such as 10Ks for the SEC), regulatory 

performance reporting (such as Call Reports for 

banks and Focus Reports for broker-dealers), 

and reporting of fund portfolios and positions. 

These reports have been dominated by standards 

imposed by regulators or other standard setters 

and implemented by vendors of accounting 

and portfolio software systems. This has been 

valuable for internal purposes for companies and 

institutions, but even post-merger integration 

of companies with systems provided by different 

vendors have been remarkably difficult to 

combine, even after years of effort. This will 

remain an issue until standards are adopted and 

used on a widespread basis.

The implementation of XBRL (eXtensible 

Business Reporting Language) is an 

important example of cooperation between 

the regulatory and business community to 

improve data standards with broad benefits. 

Developed in 1998 by a team of accountants 

and technologists, XBRL is maintained by 

XBRL International, Inc. and its units at the 

national level, and it is used extensively in the 

financial markets. In practice, standardized 

lists (“taxonomies”) of definitions (“tags”) are 

developed, maintained, and used for reporting. 

The taxonomy for U.S. GAAP (Generally 

Accepted Accounting Principles) reporting 

to the SEC, which required all publicly listed 

companies to report using XBRL starting in 

2009, is maintained by the Financial Accounting 

Standards Board, while the taxonomy for the 

Call Reports is maintained by the Federal 

Financial Institutions Examination Council 

(FFIEC)(Box J: Case Studies in the Adoption of 

Data Standards by U.S. Regulators). 

5.1.4 Transactions

Financial transactions represent an area in 

which the industry has developed standards 

without government intervention, although 

there are gaps and weaknesses in those 

standards. The Financial Information eXchange 

(FIX) protocol was developed in 1992 out 

of the trading relationship between Fidelity 

Investments and Salomon Brothers; it was 

incorporated as an independent standards 

organization, FIX Protocol, Ltd., which now 

oversees a standard for the communication 

of electronic messaging and transactions. 

This is an example of how a de facto standard 

has become a global standard used widely in 

financial market transactions. The FIX standard 

has become ubiquitous in the electronic trading 

of listed securities, including equities, bonds, 

commodities, currencies, options, and other 

listed derivatives. 

Similarly, FpML was developed from eXtensible 

Markup Language in 1997 at JPMorgan to 

provide a methodology to consistently transact 

derivatives instruments and has become a de 

facto standard for transactions in derivatives. 

An FpML organization was created in 1999 to 

maintain the standard. Again, derivatives and 

complex securities pose particular problems 

in standardizing transactions data, but these 

problems are tractable. 

Financial market infrastructure systems 

used for payment, clearing, settlement, and 

collateral management are typically based on 

a combination of FIX, FpML, and the SWIFT 

(Society for Worldwide Interbank Financial 

Telecommunication) messaging protocol, 

depending on the types of instruments and 

the trading venues. The use of ontologies and 
Promoting Data Standards 113

semantics is a promising new avenue to resolve 

discrepancies in this area.

5.1.5 Definitions of Common Concepts

Behind the preceding discussion of entities, 

products, reporting, and transactions is 

the notion that consistent definitions and 

representations of common concepts across 

the financial system reduce risk and enhance 

financial stability. In general, every reader has a 

shared idea of what the words “bond” or “stock” 

mean when used in the context of a financial 

instrument. The commonly shared concepts 

of these terms ensure the speaker and listener 

agree to their meanings in conversation without 

further elaboration or definition. But other 

terms in the financial lexicon are often murky 

and undefined. The definitions and terms of 

a structured product or an OTC derivative 

are examples where the lack of consistency in 

terminology can prove costly.

Within the context of electronic systems that 

capture the data associated with financial 

transactions, instruments, and positions, the 

ability for multiple systems to rely on the 

common definitions of terms and the meanings 

of names would provide important benefits for 

aggregation and operational consistency. Users 

of the combined data could rely on the fact that 

differences are not attributable to differences 

in data design or definition. Data could be 

more easily aggregated to evaluate firmwide, 

marketwide, or systemwide exposures.

Common definitions often reside within a single 

type of transaction, financial product, report, 

or entity. For example, the term “net income” 

can be defined in a common calculation 

for all annual 10-K accounting reports, or 

a “call feature” can be designed to include 

similar characteristics or types of terms for all 

callable bonds. They also can also reside across 

transactions, products, reports, or entities. The 

same description could be assigned to the term 

“net income” in any and all reports where net 

income is reported. 

The value of common concepts exists at a very 

granular level for data collections. It is exactly 

at this building block level where differences 

in definitions limit comparability as data are 

combined and aggregated. Financial innovation 

often occurs by changing, often slightly, the 

terms and conditions of an existing instrument 

to create something new. But when the way the 

data are collected and stored does not capture 

differences in the definition of commonly 

shared terms, risks can be aggravated. 

5.1.6 Libraries and Repositories: 

Keeping Track of Concepts 

and Structures

As standards are developed for entities, 

transactions, reporting positions, definitions, 

and other dimensions of the financial system, 

it is important to establish a strong and welldesigned system for tracking definitions, 

ensuring that where possible there are 

consistent definitions and mapping across 

standards, and that there is a pool of existing 

concepts, definitions, and tags to draw from 

when new data models are being characterized 

or built. OFR has the mandate, in coordination 

with other agencies and institutions, to develop 

a library or repository for data representations 

and metadata.
114 2012 OFR // Annual Report

BOX J. CASE STUDIES IN THE ADOPTION OF DATA STANDARDS 

BY U.S. REGULATORS

The OFR will increasingly work with the regulatory agencies to help promulgate 

and encourage the adoption of data standards that facilitate the sharing of data 

among agencies and promote the transparency of financial activity. The concept of 

interagency collaboration is not new; the U.S. financial regulatory agencies have been 

sharing data with one another for decades and have moved in recent years to better 

standardize the collection and distribution of data.

The Central Data Repository (CDR) provides one 

example of an interagency collaboration project 

aimed to improve the intake, management, 

and distribution of data through the adoption of 

common data standards. The banking agencies 

on the Federal Financial Institutions Examination 

Council (FFIEC)—the Federal Reserve, FDIC, 

and OCC—collaborated to build the CDR, 

a data collection and distribution system for 

Call Report data built on eXtensible Business 

Reporting Language, or XBRL. 

The business case for the project was clear 

and compelling. Prior to the CDR and the 

use of XBRL, the process of collecting, 

managing, sharing, and publishing Call 

Report data was inefficient, had multiple 

handoffs among participants in the data 

collection process, required significant time to 

achieve high data quality, and lacked process 

transparency. Validation of adherence to 

reporting requirements and the quality of data 

submissions were not uniformly applied to all 

submissions because of the multiple systems 

involved in the process. Thus, accurate data 

could only be achieved at the expense of the 

timeliness of the data.

By adopting the use of XBRL within the 

CDR, the agencies were able to achieve 

a transformation in the data management 

process for Call Report data. Reporting 

requirements and data quality requirements 

are now expressed electronically and in 

machine readable format using XBRL and 

can be shared among all participants of the 

data supply chain. This revolutionary way to 

share reporting metadata among the agencies, 

reporting institutions and report preparation 

vendors enabled the agencies to realize tangible 

business benefits, including cleaner data, 

faster data inflow, increased productivity, and 

seamless throughput.

Soon after the implementation of the CDR, the 

agencies released a Public Data Distribution 

(PDD) system built on the CDR to modernize 

and standardize the way Call Report data are 

distributed to the public. The PDD system 

leverages the CDR’s internal metadata and 

XBRL taxonomies to automate the flow of 

information directly from the data intake point, 

through validation, to direct publication to the 

public after the data pass certain validation 

criteria. This automated throughput increases 

the timeliness of the data for public consumption 

as well as the transparency of the standards 

used to validate the quality of the data.

Another example is provided by the effort to 

improve information about money market 

funds (MMFs). 

The SEC in 2009 mandated that MMFs file 

monthly a new Form N-MFP with detailed 

information about their holdings, among 
Promoting Data Standards 115

other information, in a file using XML and a 

standardized set of tags and definitions. This 

made such information available for the first 

time—and in a format that the OFR’s Data 

Center can take in from the SEC for use by 

researchers and for reporting to the FSOC. The 

OFR can share the transformed MMF data back 

to the SEC and other FSOC agencies, saving 

them effort and preserving comparability. 

However, lack of a standard, universal set 

of identifiers makes it difficult to ensure that 

securities issued by multiple entities that are 

related, but which have different names, are 

in fact the same entities; because of this, 

aggregating exposures is slower than it should 

be and prone to errors that would disappear 

with a proper entity identifier. The lack of 

consistent identification standards for the 

entities associated with portfolio securities was 

and will continue to be a barrier to effective 

analysis and monitoring of the MMF sector.

5.2 Benefits of Data Standards

Improved data standards would create important benefits for market participants, regulators, and the 

OFR in its mission to analyze threats to financial stability.

5.2.1 Benefits to Industry

Standardization significantly reduces costs 

and risks for private companies. Institutions 

spend billions of dollars simply to cleanse, 

correlate, link, and maintain vital but complex 

information on entities, securities, transactions, 

and other financial information, most of 

which are dependent on one or more existing 

standards. But since each financial company 

creates its own master list of entity and product 

identifiers, each set depends on different 

standards, and multiple standards schemes are 

required to gain full coverage of a domain, 

for example, across instruments or entities. 

Businesses spend a great deal of money on 

technology, tools, and staff to index and map 

records across different standards—including 

the often-necessary internal standards across 

their organization—and to ensure that data 

integrity is maintained.

Even after a company makes such investments, 

there remain gaps and inaccuracies. For 

example, a major cause of failed trades is the 

use of inaccurate standardized data such as 

company or product identifiers. And, if the 

quality of institutional data is poor—say, about 

its own counterparties and their ultimate parent 

companies—then a financial institution’s own 

risk management will be inaccurate, which 

could contribute to erroneous decisions and 

inappropriate risk-taking. 

In short, improved standards are important 

tools for institutions to advance their own 

resilience to risk, via better and stronger 

reporting tools, reporting data quality, and more 

timely information. The expense of improved 

industry standards and adoption of global 

standards could be outweighed by benefits to 

the gathering, maintaining, cleansing, and use 
116 2012 OFR // Annual Report

of critical data for the industry’s own operations, 

risk management, and regulatory compliance.

Industry practitioners have long been aware of 

these issues but are hampered by both collective 

action problems and internal incentive issues.

5.2.2 Benefits to Regulators

Data standards are a vital and powerful tool that 

the Council and its member agencies can use 

to reduce the complexity of sharing and using 

data and to improve the information provided 

to policymakers. 

Among the U.S. banking regulators on the 

FFIEC, there is some standardization of 

collections in the bank Call Reports and other 

regulatory data, using the XBRL data standard 

and a set of common definitions for financial 

terms. However, agencies vary widely in the 

complexity of their data analysis requirements 

and corresponding data management systems. 

There are many opportunities to converge on 

existing standards or to develop new ones where 

none exist. This is essential to understand a 

financial system dominated by large, complex 

financial institutions that cross agency lines. 

Common standards will save money for 

regulators, increase regulatory productivity and 

improve outcomes at both the microprudential 

and macroprudential levels.

The opportunities for sharing information, 

savings, improved productivity, and better 

analysis and analytical tools are as great for the 

Council as the benefits can be for the OFR and 

for industry. There has been enthusiasm among 

the Council agencies about the opportunity 

to develop common standards and to share 

existing data. The OFR has been working with 

the agencies, directly and through the Council 

and its Data Committee, in order to facilitate 

greater interagency data sharing and to promote 

data standards. 

5.2.3 Benefits to 

Financial Stability Monitoring

The OFR’s ability to research and monitor the 

financial system is hindered by the fact that 

financial data are fragmented into innumerable 

sets, each with its own technical, definitional, 

classification, identification, and other 

standards. Financial stability monitoring and 

analysis are hindered when data from multiple 

institutions, sectors, or markets cannot be 

linked, integrated, and analyzed on a timely or 

accurate basis.

For example, the analysis of interconnectedness 

among large, complex financial institutions 

requires aggregation of data about counterparty 

risk exposures for large numbers of complex 

and ever-changing positions. While the data 

describing some securities—say, exchangetraded equities or basic plain vanilla swaps—are 

relatively standardized, data on some of the 

most important securities are non-standard or 

have multiple non-integrated standards. This 

can happen because of convention or because 

there are multiple service providers, each with 

its own proprietary standard. Even where there 

is a data standard, the lack of other, related 

standards prevents reliable analysis of the data. 

Data scrubbing and other traditional 

approaches can be adequate for some analytical 

purposes that require limited information, for 

example in studies of single sectors of securities 

or markets. However, given OFR’s financial 

stability mission and its forward-looking 

posture, traditional matching and maintenance 

techniques are not likely to produce actionable 

data fast enough. In order to use data for 

research or for monitoring on behalf of the 

Council, OFR would have to carry out activities 

that might be prohibitively expensive, extremely 

time-consuming, and even then likely to yield 

incomplete and imprecise data. Where strong 

data standards exist, the OFR has been able to 

gather and analyze data swiftly, as in the case of 

the data about money market funds contained 

in the SEC’s new Form N-MFP. 

Data standards become even more crucial 

when it comes to complex markets or products, 

such as OTC derivatives, or for combining data 

for securities that are traded across multiple 

markets. For example, despite the fact that repos 
Promoting Data Standards 117

are major funding vehicles for large, complex 

financial institutions, data standards do not 

exist. As a result, it remains difficult to gather 

and compare these important securities with 

respect to portfolio holdings, trading, and their 

other characteristics. To the extent that the repo 

market is vital to the stability of the financial 

markets, analysts must be able to routinely and 

precisely access data about this market.

Fueled by globalization and advances in 

computer power, derivative markets have 

exploded in size and complexity over the past 

20 years. Techniques for structuring securities 

composed of multiple underlying instruments 

have led to an almost infinite possible 

number of connections among firms, their 

counterparties, and the underlying assets at 

risk. This complexity has tended to overwhelm 

the more mundane accounting and back-office 

processing systems in the supply chain, creating 

what has been described as a “data fog,” where 

specific, discreet answers to questions about who 

owes who and how much are incredibly difficult 

to resolve (Tett, 2012). Data standards are not a 

panacea, but they are a necessary and required 

part of any solution.

With better standards, the Council and the OFR 

will be able to conduct more and more reliable 

research into the sources of threats to the 

financial system. Researchers will also be better 

situated to evaluate the efficacy of the guardrails 

and shock absorbers put in place to limit the 

buildup of risks. Policies for consideration by 

the Council can be developed more confidently, 

practically, and on a timely basis.

5.3 The LEI Initiative

A Legal Entity Identifier (LEI) is a unique code to identify legally distinct entities that engage in 

financial market activities. Longstanding issues with incompatible systems have contributed to delays 

and errors in risk assessments for both supervisors and industry participants. Building on earlier work 

by Treasury staff and the industry in 2010, OFR staff initiated discussions with the FSB in July 2011, 

leading to a global initiative to address the issue. This initiative, endorsed by the G20 and led by an 

FSB Expert Group of foreign and U.S. authorities, including the OFR, made substantial progress in the 

past 12 months and is an impressive example of cooperation among diverse regulators, standard-setters, 

and private market participants in pursuit of a standard with deep and clear benefits. In May 2012, 

the ISO published an LEI standard, consisting of a 20-character alphanumeric code and a minimal set 

of reference data. In June, the G20 endorsed the FSB’s recommendations calling for the implementation 

of a global LEI by mid-2013, consistent with the ISO standard. OFR staff will continue to play a leading role as this process moves forward.

Historically, the financial industry has lacked a 

globally accepted standard to identify the parties 

to financial transactions or the legal entities 

that create financial instruments. Over time, 

market participants and the supervisory 

community created a variety of public and 

proprietary entity identifiers that address 

specific needs but none that provides a single 

industry-wide or global solution.

This gap in standards has made it difficult for 

risk managers and the supervisory community 

to analyze counterparty risks, credit exposures, 

and the relationships between large financial 

companies and their subsidiaries. Market 

participants have borne the expense of 

building and maintaining custom applications 

that translate and map among systems of 

identifiers—but these applications require 
118 2012 OFR // Annual Report

significant effort, particularly in updating 

identifiers following corporate mergers and 

acquisitions, and often relate to only narrow 

segments of the market. The lack of a standard 

entity identifier has made it difficult for 

supervisors to monitor and analyze threats 

to financial stability, and risk managers have 

been unable to manage firmwide risks on a 

timely basis.

While the lack of a universal global entity 

identification system has been a problem for 

decades, the financial crisis exposed the depth 

of the problem. When Lehman collapsed in 

2008, neither financial regulators nor private 

sector risk managers were able to view the total 

extent to which important market participants 

were exposed to Lehman and its many legal 

entities, nor how market participants were 

connected to each other in global markets. 

Industry proposals to address the lack of a 

standard for entity identification go back at 

least 20 years. Collective action problems and 

structural incentive issues have prevented 

private industry from solving this problem on 

its own. It is generally agreed that regulators 

can and should play a role in assisting in the 

creation and global adoption of a standard 

when this occurs by requiring standards for 

regulatory reporting. 

In 2010, a task group of U.S. regulatory agencies 

published a discussion paper on the need for a 

standardized global LEI (Bottega and Powell, 

2011). Several of the contributors to that 

report have since joined the OFR. Based on its 

authority under Dodd-Frank and leveraging 

the strong interagency and private industry 

consensus on the need for a global LEI, the 

OFR’s first policy statement in November 2010 

called for the establishment of a universal 

standard for identifying entities through a 

consensus process including the financial 

industry and international standard-setting 

bodies (OFR, 2010). In the same month, the 

SEC and CFTC proposed rules, also under 

Dodd-Frank, for the reporting of swaps and 

security-based swaps that would require a 

unique identifier for counterparties, should one 

be available. 

The response to the OFR, SEC, and CFTC 

statements among the industry, international 

bodies, and foreign regulators has been very 

positive. In 2011, a global coalition of financial 

services firms and trade associations published a 

proposal for industry requirements for a global 

LEI solution (SIFMA, 2011). Beginning in July 

2011, the OFR helped lead the U.S. engagement 

with national and global authorities, standardsetters, and industry organizations to advance 

the LEI initiative on the international level. 

With the endorsement of the G20, the FSB 

coordinated efforts of the global regulatory 

community to begin to develop governance and 

standards recommendations for a global LEI. 

The FSB held a multi-day workshop in Basel, 

Switzerland in September 2011 inviting all 

stakeholders, public and private, to engage 

in a range of discussions on the development 

and implementation of a global LEI. Following 

the workshop, in December 2011 the G20 

endorsed the creation of the FSB LEI Expert 

Group to study the issue in an expedited 

fashion and to create recommendations for 

delivery to the G20 Leaders Summit in June 

2012. OFR’s engagement through the FSB LEI 

Expert Group was deep and extensive, chairing 

and co-chairing working groups and actively 

working with public and private stakeholders 

to develop consensus. The culmination of 

these efforts was the delivery of the LEI Expert 

Group recommendations to the FSB Plenary 

meeting on May 29–30, 2012, addressing critical 

issues of governance, operating principles, and 

structure for an LEI and recommending its 

implementation (FSB, 2012). 

In May 2012, ISO published its LEI standard, 

ISO 17442-2012. ISO 17442-2012 describes a 

20-character alphanumeric code and a limited 

set of reference data that enable unique 

identification of global entities and defines 

robust open governance of the issuance and 

maintenance of the LEI data. The LEI will be 

available worldwide and is scalable. LEI codes 
Promoting Data Standards 119

will be unique and will persist with their entities 

over time (ISO, 2012). OFR staff participated 

in the ISO LEI standard-setting process and 

initiated ISO Secretariat interactions with the 

FSB LEI Expert Group.

On June 20, 2012, the G20 leaders endorsed 

the FSB recommendations on next steps 

for implementing the global LEI proposal, 

consistent with the ISO standard. Following 

that endorsement, the FSB established the LEI 

Implementation Group to follow through on 

the timetable set forth in the recommendation 

for the global LEI system to be operational 

by March 2013. OFR is the primary lead for 

the U.S. regulatory community, working with 

U.S. regulators and private industry, and is the 

co-chair of the FSB Implementation Group for 

the Americas. 

The OFR is also working closely with the 

Council agencies to prepare for the availability 

of a global LEI. For example, it is working 

closely with the CFTC to facilitate development 

and implementation of the CFTC Interim 

Compliant Identifier (CICI) for swaps. The 

CICI is compliant with the ISO standard and 

represents an early implementation of the 

global LEI system. 

References for Chapter 5

Bottega, John A., and Linda F. Powell. “Creating a 

Linchpin for Financial Data: Toward a Universal Legal Entity 

Identifier.” Finance and Economics Discussion Series 

2011-07, Washington, DC: Board of Governors of the 

Federal Reserve System, January 21, 2011. 

Financial Stability Board. A Global Legal Entity Identifier for 

Financial Markets. Report to the G-20 Finance Ministers 

and Central Bank Governors. FSB, June 8, 2012.

International Organization for Standardization. “ISO 

17442:2012. Financial services—Legal Entity Identifier 

(LEI).” ISO, 2012. http://www.iso.org/iso/catalogue_

detail?csnumber=59771 (accessed July 3, 2012).

Office of Financial Research. Statement on Legal Entity 

Identification for Financial Contracts. Federal Register 

75, no. 229. Washington, DC: OFR, November 30, 2010, 

74146-74148. 

Securities Industry and Financial Markets Association. 

Requirements for a Global Legal Entity Identifier (LEI) 

Solution. SIFMA, May 3, 2011.

Tett, Gillian. “Guiding light needed to cut through the data 

fog.” Financial Times, March 8, 2012.
120 2012 OFR // Annual Report
The Agenda Ahead 121

6 The Agenda Ahead

For the coming year, the OFR will continue to assess and 

monitor threats to financial stability, to evaluate mitigants 

to those threats, and to improve the scope and quality of 

financial data required for that work, focusing on repo and 

other short-term funding markets. We will conduct further 

research on financial stability measurement, stress testing, 

and risk management, and we will begin new research 

applying network analysis to the study of the financial system. 

We will also work toward implementation of the Legal Entity 

Identifier (LEI) in 2013 and initiate further work on financial 

data standards.1

This first Annual Report describes the foundational work to fulfill the OFR’s 

statutory mandates. To date, the OFR has developed frameworks to understand 

the financial system and conduct research on financial stability; to monitor 

developments and sources of vulnerability in the financial system; to identify 

data needs for research and monitoring and to address data gaps; to build an 

infrastructure to support the Council and its own researchers; to promote and 

help implement data standards; and to build the institution to facilitate that work. 

Getting the foundations right represents only the first of many steps toward 

achieving these goals. The architecture of the financial system will continue to 

change and evolve rapidly in response to market and regulatory forces. The more 

we learn about the financial system and its relationship to the economy, the more 

new metrics and questions will dominate the debate. Thus, the agenda ahead 

involves continuing to build on those foundations while expanding our ability 

to take on new projects that respond to the expectations of the statute and the 

evolving needs of the Council. 

This report describes in separate chapters our agendas for assessing financial 

stability, conducting research, addressing data gaps, and promoting data 

standards, but it is important to note that these activities are closely interwoven. 

Good research and monitoring depends on good data, but research questions 

determine data needs and help identify the gaps where data may need to be 

organized, standardized, linked, or collected. Data gaps may also be identified by 

Council agencies or through interactions with other stakeholders. 
122 2012 OFR // Annual Report

As discussed in Chapter 2, three broad themes currently drive our data and 

research agendas: To understand how the financial system is evolving, to assess 

emerging threats to financial stability, and to evaluate mitigants to potential 

threats. While measuring, monitoring, and analyzing the stability of the financial 

system will always require agility due to the constant innovation and evolution in 

institutions and markets, we expect that these themes will continue to serve as a 

compass for our work in the future. 

To pursue a complex and evolving research agenda, the Office will continue to 

build our capabilities for monitoring, investigating, and reporting on potential 

threats to financial stability. The research agenda will focus on how each of the six 

basic functions of the financial system—credit allocation and leverage, maturity 

transformation, risk transfer, price discovery, liquidity provision, and facilitation of 

payments—are conducive to risk buildups and how they behave under stress. 

We are particularly interested in the forces that promote the migration of activity 

into unregulated or lightly regulated markets, the so-called shadow banking 

system. Experts disagree on the precise boundaries of the term “shadow banking.” 

In our view, it would always encompass credit intermediation by unregulated 

financial institutions—even in conjunction with or on behalf of regulated financial 

institutions—in combination with the creation of money-like liabilities, involving 

leverage and maturity transformation, in opaque markets. 

Short-term funding markets, especially for secured repo transactions, play a pivotal 

role in this chain of intermediation and can propagate distress across the financial 

system during market disruptions. We plan to focus extensively on understanding 

and collecting better data on repo markets in the coming year (Box K: Improving 

Research and Data on Repo Markets).

The three research topics that are highlighted in Chapter 3 of this annual 

report—financial stability indicators, stress tests, and risk management—will always 

be top priorities for the Office, as mandated in the statute. 

Two dimensions of stress-testing technology will be immediate concerns. First, the 

Office will investigate practical steps to help practitioners and supervisors validate 

their models against one another. The Comprehensive Capital Analysis and Review 

(CCAR) exercise generated two sets of stressed results for each institution, one 

using the institution’s internal models and another using supervisory models. 

Comparing an institution’s estimated losses with those implied by CCAR models 

should permit both parties to improve the quality and consistency of their 

modeling—contributing to the state of the art in risk measurement. Second, the 

CCAR process generates a significant amount of institution-specific data which 

has not yet been aggregated for macroprudential analysis. We intend to evaluate 

how individual institutions’ CCAR results can be combined to identify potentially 

disruptive shared risk exposures.

Other important themes for future research include characteristics of credit 

cycles and connections and feedback loops between the financial system and the 

economy. Housing finance has been and will continue to be a key source of such 
The Agenda Ahead 123

connections and feedback loops. In light of the international nature of financial 

activities, we will also be very interested in improving our understanding of crossborder transactions and positions. 

Another theme is the nature of financial networks, which can be described either 

by instruments and obligations (what is being traded) or by counterparties (who 

is trading with whom). The OFR needs to understand how networks work in 

order to analyze behaviors such as contagion during financial crises. Network 

analysis can help uncover latent connections among market participants—who 

typically know their own exposures to counterparties but not the exposures of 

their counterparties to other counterparties—and foster understanding of how 

those interconnections can break down during a crisis. Of course, the application 

of network analysis to financial systems presents novel analytical challenges. There 

are many participants of different sizes and different roles and an ever-increasing 

number of instruments that allow risk to be transferred. Stress testing such a 

system is very complex but could help us identify specific structural elements that 

are most vulnerable to shocks and most likely to transmit shocks through the 

network. That knowledge, in turn, could allow for construction of early warning 

models to provide policymakers with information about potential threats to 

financial stability. 

Each of these research themes will contribute to our understanding of risks 

and vulnerabilities in the financial system and the interconnections among 

companies that can contribute to contagion in a crisis. Each will also help us 

identify gaps where supervisors need data to be better organized, standardized, 

or collected. In the coming year, the Office will continue our working paper 

series; subsequent working papers are forthcoming on the use of agent-based 

modeling as a tool for stress tests, contingent capital as a countercyclical tool, 

and macroprudential policy. We will also continue to strengthen our ties with 

the community of academic and other financial experts, building on the 

conference on the macroprudential toolkit that we hosted in December 2011 

(Gudmundson, 2011). The OFR’s Financial Research Advisory Committee will 

promote a broad perspective on financial stability issues and provide further 

opportunities for collaboration.

The OFR’s data agenda is closely tied to our research agenda. We have a very 

important mandate to address gaps in the data needed to monitor threats to 

financial stability. FSOC member agencies’ data collections provide an important 

first source of information that has yet to be fully utilized for the purposes of 

macroprudential policy. In the coming year, we will focus on ways to catalogue, 

connect, and share among agencies what is newly or already collected, creating 

additional benefits to financial stability analysis while minimizing duplication and 

the burden or cost to the private sector.

As noted in Section 4.2, we will focus our near-term data efforts on understanding 

metrics for the sources of leverage, liquidity risk, and interconnections among 

financial firms—particularly as they emerge in the derivatives and shadow banking 

markets. The experience of the past 10 years has proven the systemic dangers 

that can emerge when supervisors do not follow risks that are building in these 
124 2012 OFR // Annual Report

BOX K. IMPROVING RESEARCH AND DATA ON REPO MARKETS

The OFR believes that understanding the repo market is crucial to assessing 

vulnerabilities in the financial system and designing policy tools to mitigate them.

For years before the financial crisis, repos 

provided a stable and liquid market in which 

companies could fund long-term assets 

with short-term debt. The presence of highquality, liquid collateral seemed to assure that 

borrowers would be able to continue to roll over 

their funding. Repo lenders could rely on both 

the counterparty’s creditworthiness and the 

collateral underlying the transaction. 

Yet, in 2007 and 2008, repo financing dried up 

for companies like Countrywide, a mortgage 

lender, and Bear Stearns, an investment bank. 

The conventional wisdom is that money market 

funds and other repo lenders withheld funding 

from these companies and others because of 

the real or perceived costs related to seizing 

and selling collateral, or because of concerns 

about runs by their own investors. This behavior 

surprised market participants and supervisors 

and had contagion effects in financial markets.

Why did the apparent security of repo liabilities 

disappear? Were money market funds more 

likely than other repo lenders to pull their funds? 

As counterparties’ creditworthiness became 

more questionable, why didn’t repo lenders 

simply increase their required haircuts, to make 

the collateral provide more protection? Was 

this funding withdrawal preceded by other 

signs of tightening, such as a change in rates 

or haircuts? In general, should regulators view 

repo finance as equivalent to unsecured credit? 

Following on the experience during the 

crisis, supervisors would like to understand 

the extent to which repo lenders rely on the 

borrower’s creditworthiness rather than on the 

value of collateral posted in the transaction. 

Understanding the repo market requires 

collection of transactions-level data about the 

repo market—information that is presently 

unavailable to regulators.

Although the repo data collected by the Federal 

Reserve Bank of New York have improved 

considerably since the crisis, that data remain 

insufficient to understand when and how repo 

financing conditions are changing in ways that 

might affect financial stability (Adrian and others, 

2012). Research needs to encompass all three 

repo markets: tri-party, Delivery versus Payment, 

and General Collateral Finance. The behavior of 

these markets differed starkly in the crisis. 

The benefits of repo market analysis will accrue 

to both on-site supervisors and to those 

monitoring threats to the financial system more 

broadly. A more granular view of the repo 

markets would help improve macroprudential 

policy in three ways. First, it would provide 

information about the degree of stress in financial 

markets as a whole. Second, it would help inform 

policy—for example, there have been proposals 

to address the procyclicality in repo markets 

by introducing policies on haircuts, akin to the 

margin requirements that the Federal Reserve 

imposes under Regulation T in the stock market. 

Third, it would allow financial stability analysts 

to learn more about the mechanics of funding 

markets, such as the transfer of risk or trade 

segregation within prime brokerage transactions, 

and in the relationship between repo financing 

and securities lending (FSB and IMF, 2012).
The Agenda Ahead 125

markets (Tarullo, 2012). While significant progress has been made, particularly 

in the emergence of swaps trade repositories, the level of transparency in these 

markets must improve. Failure to address data gaps in these areas is and will 

remain a threat to financial stability and a significant focus for the OFR and 

financial supervisors. To that end, we have also begun to receive data about credit 

derivatives, money markets, and repo markets, and we expect that these data will 

be the source for valuable OFR research, in collaboration with Council member 

experts, in the coming year.

To promote data standards, the OFR will continue to lead the initiative among 

U.S. regulatory agencies toward the establishment of a global Legal Entity 

Identifier (LEI), working with the international regulatory community and private 

industry. The International Organization for Standardization’s publication of an 

LEI standard and the G20 leaders’ March 2013 deadline are historic developments 

in an international public-private collaborative effort to promote transparency and 

efficiency for financial institutions and their regulators. 

While the LEI initiative is the top OFR standards priority at this time, future 

agenda items are being identified and considered. Standards gaps that may be 

considered include product classifications and identifiers, semantic standards, and 

quality standards. OFR will maintain and continually update its understanding of 

the standards priorities of its researchers, the Council agencies, and the industry. 

The OFR is also obligated by statute to standardize and publish lists of entities, 

products, and their associated reference data, as a standards activity that will have 

immediate benefit for the public. We plan to get these efforts under way in the 

coming fiscal year. 

The OFR will continue to develop and refine its technology architecture, policies, 

and procedures, and to add analytic tools to allow for the further acquisition, 

cataloging, validation, aggregation, and distribution of data. This will provide 

the ability to quickly respond to new requests for data while ensuring the 

confidentiality, security, and proper use and distribution of the data it collects. 

In each of our activities, the OFR is collaborating closely with the Council member 

agencies that have a role in financial stability supervision and regulation. The 

OFR has begun to provide data and analytical services to the Council and its 

member agencies, such as the analysis to support the Council’s work in identifying 

criteria for designating asset management companies for enhanced prudential 

supervision by the Federal Reserve as required by Dodd-Frank. We have supported 

the establishment of the Council Data Committee as a forum to discuss datarelated issues among member agencies. There is also an important international 

component to all of our work; for example, we are working to promote improved 

data collection and data standards through international bodies such as the 

Financial Stability Board and the G20. 
126 2012 OFR // Annual Report

Success in all of these endeavors will depend on building a strong professional 

organization and a secure and robust technological infrastructure. In all cases, 

we will carefully consider costs and make sure that the technological and data 

infrastructure is secure before receiving sensitive data already available to 

regulators or undertaking any new data collections.

Endnote

1. The OFR’s Strategic Framework, issued in March 2012, described our near-term objectives: (1) 

Ramp up services to FSOC, its member agencies, and their staffs, (2) Begin to publish OFR data 

and analysis, (3) Accelerate work to establish the secure information technology platforms and 

business systems critical to achieving our mission, and (4) Continue to build and reinforce the 

OFR’s institutional infrastructure (OFR, 2012).

References for Chapter 6

Adrian, Tobias, Brian Begalle, Adam Copeland, and Antoine Martin. “Repo and Securities Lending.” 

Staff Report no. 529, New York: Federal Reserve Bank of New York, January 2012. 

Financial Stability Board and International Monetary Fund. Securities Lending and Repos: Market 

Overview and Financial Stability Issues. Interim Report of the FSB Workstream on Securities Lending 

and Repos. FSB and IMF, April 27, 2012. 

Gudmundson, Erika. “Reflections from Attendees of ‘The Macroprudential Toolkit’ Conference.” 

U.S. Department of the Treasury, December 7, 2011. http://www.treasury.gov/connect/blog/Pages/

Reflections-from-Attendees-at-The-Macroprudential-Toolkit-Conference.aspx (accessed 

July 3, 2012).

Office of Financial Research. “Strategic Framework: FY 2012-2014.” Treasury Notes, U.S. 

Department of the Treasury, March 15, 2012. 

Tarullo, Daniel K. “Shadow Banking After the Financial Crisis.” Speech delivered at the Conference 

on Challenges in Global Finance: The Role of Asia, Federal Reserve Bank of San Francisco, San 

Francisco, June 12, 2012. 
Appendix A: Progress in Institution Building 127

Appendix A: Progress in Institution Building 

Overview

The OFR has made substantial progress in 

building the institutional architecture to 

support its core research and data-related 

mandates. Key upfront objectives have included 

defining the OFR’s strategic framework—

starting from the foundation established in 

the legislation and building through outreach 

and broad-based consultations—and building 

the organizational structures to support sound 

human resource and financial management.

Establishing a Sound 

Strategic Framework

The OFR Strategic Framework: FY2012–2014,

published in March 2012, outlines the mission, 

goals, objectives, and implementation priorities 

that will help to ensure that the OFR’s efforts 

and investments are well targeted. The 

framework reflects substantial consultations in 

the period since enactment of the Dodd-Frank 

Act with a full range of stakeholders, including 

Council members, their agencies and staff, 

members of Congress, industry representatives, 

as well as academics, financial authorities, and 

other researchers. Under this framework, the 

OFR has identified a core set of priorities linked 

to five strategic goals:

1. Support the Council through the secure 

provision of high-quality financial data 

and analysis needed to monitor threats to 

financial stability.

2. Develop and promote data-related standards 

and best practices.

3. Establish a center of excellence for research 

on financial stability and promote best 

practices for financial risk management.

4. Provide the public with key data 

and analysis, while protecting 

sensitive information.

5. Establish the OFR as an efficient 

organization and world-class workplace.

Operational Framework

The OFR is establishing a secure data and 

technology infrastructure, ramping up its hiring, 

and making progress in building its institutional 

framework—all essential to delivering on its 

core research and data-related mandates.

Human Resources

The OFR plans to build to steady state staffing 

levels of 275–300 within the next two to 

three years. The organization recognizes the 

broad range of core competencies required 

by the Office, including macroeconomics, 

finance, statistics and risk management, 

and applied sciences; data management, 

analytic and support services, information 

standards, information technology, program 

management, and relationship management; 

and law, communications, strategy, policy, and 

resource management. 

The Dodd-Frank Act emphasized use of efficient 

and innovative structures to attract and retain 

needed talent, including fellowships and 

partnerships with outside experts. Foundational 

work on the OFR’s human resources strategy 

was articulated in the OFR’s first report to 

Congress on these issues in September 2011; 

the second annual report on human resources 

later this year will provide an update on the 

OFR’s plans for recruitment and retention, 

training and workforce development, and 

workforce flexibility. 

Policies and Procedures

The OFR’s financial management follows 

Treasury protocols, with the organization’s 

financial activities and controls reviewed as part 

of the Department’s Consolidated Audit and the 

budget developed in line with Circular A-11 
128 2012 OFR // Annual Report

guidance as part of the President’s Budget. 

The Office has also developed additional 

controls, project review mechanisms, and 

decision-making protocols to ensure spending is 

well targeted and effectively monitored. 

Through July 20, 2012, the OFR and the 

Council have been funded through transfers 

from the Board of Governors of the Federal 

Reserve System. Thereafter, OFR and Council 

funding comes from assessments on bank 

holding companies with total consolidated assets 

of $50 billion or more and nonbank financial 

companies supervised by the Board 

of Governors. A Treasury rule to establish 

this assessment schedule was published on 

May 21, 2012, and a notice of fees was published 

on June 29, 2012, with the first assessment to 

be collected on July 20, 2012.

Building on Treasury and Treasury 

Departmental Offices policies where 

appropriate, the OFR is also expanding initial 

policies and procedures to support sound 

and efficient operations and rigorous internal 

controls. The Office of the Inspector General 

and the Government Accountability Office 

periodically audits the OFR’s activities, and the 

Director of the OFR will testify annually on 

these issues. 

Informational Infrastructure

The OFR’s data and analytical mandates require 

the establishment of secure analytical and 

database platforms, implementation of sound 

business systems, and the steady expansion 

of data acquisition—through both shared 

procurement and efficient collection from 

financial institutions, consistent with the need 

to avoid duplicative and unnecessary requests. 

The OFR has sought to make rapid progress 

in systems implementation but also recognizes 

the need to ensure that investments in 

information technology and business processes, 

infrastructure, and security structures are well 

designed and cost-effective. To meet these twin 

needs, the OFR has leveraged existing Treasury 

systems and established a secure short-term 

data and analytical IT environment to meet 

immediate needs, while initiating the design 

and planning of a long-term information and 

security architecture. As part of these efforts, the 

OFR is establishing robust controls and bestpractice procedures, including strong data and 

information security protocols.
Appendix B: OFR Activities and Collaboration 129

Appendix B: OFR Activities and Collaboration 

Overview

Successful achievement of the OFR’s research 

and data-related mandates requires strong 

collaboration with a wide range of stakeholders, 

including Financial Stability Oversight Council 

members, their agencies and staff, industry 

representatives, academics, other researchers, 

and the public. 

Collaboration with the 

Council and its Member Agencies

A key goal for the OFR is to support the Council 

through research and data-related services. 

In this context, the OFR is collaborating with 

researchers at other Council agencies on both 

data and analytical activities, supported by 

ongoing work to reinforce secure information 

sharing technology and protocols. The OFR 

is also actively supporting the Council Data 

Committee as a forum for collaboration and 

input on the OFR’s data-related activities, 

including, for example, informal working 

groups on information technology and on 

establishing an inventory of data held by 

Council member agencies as a first step to 

strengthen data-sharing and to identify 

data gaps.

Financial Research 

Advisory Committee

In April 2012, the OFR announced that it 

will establish the Financial Research Advisory 

Committee. Chosen from a highly distinguished 

pool of more than 150 applicants, the Advisory 

Committee will include eminent researchers 

and risk experts, as well as those with noted 

expertise in the fields of data and technology. 

Committee members are being selected based 

on their expertise in economics, financial 

institutions, and markets, statistical analysis, 

financial markets analysis, econometrics, 

applied sciences, risk management, data 

management and information standards, 

technology, and other areas related to OFR’s 

duties and authorities. The committee will serve 

as an invaluable resource that will broaden 

the OFR’s analytical perspective and provide a 

critical link to diverse knowledge, experience, 

and perspectives. 

Network for Collaboration

The OFR has emphasized high-level interactions 

with academics and practitioners in its efforts 

to build a virtual research community. This 

collaboration has already been informed by 

OFR publications and a high-level conference 

in late 2011. As part of this initiative, the 

OFR Fellowship Program will also be used to 

attract expertise to supplement the permanent 

workforce, keeping OFR on the cutting edge of 

research and data management. 

Working Paper Series 

and Research Seminar Series

The OFR has developed the Working Paper 

Series and Research Seminar Series as 

communication tools for sharing its work 

with external constituents. Both of the 

communication outlets have been implemented 

as a platform for presenting OFR staff research, 

as well as collaborative research conducted by 

OFR staff and outside experts. 

The OFR launched the Working Paper Series 

in January of 2012. The Working Paper 

Series offers staff and outside co-authors an 

opportunity to disseminate their preliminary 

research findings in a format intended to 

generate discussion and critical comments. The 

goal is to inform and improve the quality of the 
130 2012 OFR // Annual Report

analysis conducted by the OFR. To date, two 

studies have been published:

•	 A Survey of Systemic Risk Analytics, 

coauthored by Dimitrios Bisias (MIT 

Operations Research Center), Mark Flood 

(OFR), Andrew W. Lo (MIT Sloan School of 

Management), and Stravros Valavanis (MIT 

Laboratory for Financial Engineering) 

•	 Forging Best Practices in Risk Management,

coauthored by Mark J. Flannery (University 

of Florida and OFR), Paul Glasserman 

(Columbia University and OFR), David K. 

A. Mordecai (Risk Economics, Inc., and 

NYU Courant Institute of Mathematical 

Sciences), and Cliff Rossi (University of 

Maryland and OFR)

The OFR Research Seminar Series provides a 

forum for staff of Council member agencies, 

other financial authorities, academics and other 

researchers to discuss in depth analysis on topics 

related to threats to financial stability, and for 

the OFR to solicit feedback on current and 

future research initiatives. The OFR has held 

dozens of widely attended seminars since the fall 

of 2011.

Conference 

On December 1 and 2, 2011, the OFR and 

the Council hosted a conference, entitled 

The Macroprudential Toolkit: Measurement and 

Analysis, in Washington, DC, which brought 

together thought leaders from the financial 

regulatory community, academia, public interest 

groups, and the financial services industry 

to discuss data and technology issues and 

analytical approaches for assessing, monitoring 

and mitigating threats to financial stability. 

The conference also provided an invaluable 

opportunity to receive broad-based input on 

strategic priorities for the OFR.
Glossary 131

Glossary 

2a-7 Funds

Rule 2a-7 of the Investment Company Act of 

1940 lays out requirements specific to money 

market funds. It defines accounting practices 

that permit a fund to report a stable net asset 

value of $1 per share and sets investment rules 

(see Money Market Fund).

Absorption Ratio (AR)

A summary measure of the degree of comovement in asset prices, from Kritzman and 

others (2011) (see References for Chapter 3).

Agent-Based Models (ABM)

A simulation model that tracks the actions of 

agents with specified rules of behavior as they 

interact over time. 

Arbitrage

A combination of purchases and sales of 

the same asset that yields a profit from the 

differences in prices across markets with little 

or no risk.

Asset-Backed Commercial 

Paper (ABCP)

Short-term debt that has a fixed maturity of 

up to 270 days and is backed by some financial 

asset, such as trade receivables, consumer debt 

receivables, auto and equipment loans or leases, 

or asset-backed securities.

Asset-Backed Security (ABS)

A debt instrument that is backed by specific 

financial assets that generate the cash flow used 

to service the debt instrument.

Bank for International Settlements (BIS)

An international financial organization that 

serves central banks in their pursuit of monetary 

and financial stability, helping to foster 

international cooperation in those areas and 

acting 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 in accordance 

with 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 committee of bank supervisors 

that develops and issues international standards 

on bank capital adequacy. In 1988 the BCBS 

introduced a capital measurement system 

commonly known as the Basel Capital Accord 

or Basel I. In 2004 the BCBS issued a revised 

framework that is commonly referred to as 

Basel II. Following the financial crisis, the BCBS 

developed new global standards for the banking 

system that are collectively referred to as Basel III.

Broker-Dealer

An entity that is engaged in the business of 

buying and selling securities for itself and others.

Call Report

A report of a bank’s condition and income that 

all federally insured depository institutions in 

the U.S. must file on a quarterly basis.

Central Clearing 

A settlement system in which securities or 

derivatives of a specific type are cleared by one 

entity, a clearing house or central counterparty, 

which guarantees the trades. It is an alternative 

to bilateral or over-the-counter trading (see 

Over-the-Counter).

Central Counterparty (CCP)

An entity that is interposed between the initial 

participants to a bilateral transaction and 

becomes the buyer to every seller and the seller 
132 2012 OFR // Annual Report

to every buyer of a specified set of contracts or 

financial instruments.

Clearing Bank

A commercial bank that facilitates payment and 

settlement of financial transactions, such as 

check clearing or facilitating trades between the 

sellers and buyers of securities or other financial 

instruments or contracts.

Collateral

Any asset pledged by a borrower to guarantee 

payment of a debt. 

Collateralized Debt Obligation (CDO)

A type of structured asset-backed security that 

has tranches with distinct interest rates, payment 

flows, and risk levels.

Commercial Paper (CP)

Short-term (maturity of up to 270 days), 

unsecured corporate debt.

Comprehensive Capital 

Analysis and Review (CCAR)

A regular cross-institution study, most recently 

completed in March 2012, conducted by the 

Federal Reserve and other supervisors of the 

capital plans and capital planning processes of 

the 19 largest U.S. bank holding companies.

Conditional Value at Risk (CoVaR)

A measure of the value at risk to the financial 

system conditional on distress at a single 

financial institution, from Adrian and 

Brunnermeier (2010) (see References for 

Chapter 3).

Contingent Liability

A liability that is only incurred depending on 

the outcome of a future event.

Countercyclical 

Movement of a financial or macroeconomic 

variable in the opposite direction of the 

business cycle. A variable is procyclical if it tends 

to increase when the economy is growing and 

decrease when it is shrinking.

Credit Cycle

The rise in credit creation during the 

expansion phase of the business cycle, often 

accompanied by a relaxation of underwriting 

and other credit standards, and the subsequent 

decline in the availability of credit during 

economic contractions. 

Credit Default Swap (CDS)

A derivative contract in which one party (the 

protection seller) agrees to make a payment 

to another party (the protection buyer) in the 

event of default of a third party (the reference 

entity), in exchange for fixed payments from 

the protection buyer to the protection seller 

(see Swap).

Credit Risk Transfer

A financial transaction, typically through credit 

default swaps, that changes who bears the risk of 

default or changes in the creditworthiness of a 

counterparty or debtor. 

Credit Value Adjustment (CVA)

An adjustment made to the value of an OTC 

derivative transaction to reflect the risk that the 

counterparty to the transaction could default.

Crowded Trade

A trade in which the market participants have 

large and similar positions, creating the risk 

that there will be insufficient liquidity should 

market participants seek to unwind their 

positions simultaneously. 

Cyclical Risk

Any financial or economic risk that is closely 

tied to the business cycle.

Debt Value Adjustment (DVA)

An adjustment made to the value of a firm’s 

liabilities as a result of a change in the firm’s 

own creditworthiness. 

Derivative

A financial contract, such as a swap, option, or 

futures contract, that derives its value from the 

price of some other security, commodity, or 

other asset.
Glossary 133

Discount Window

The Federal Reserve facility for extending credit 

directly to eligible financial institutions.

Distressed Asset Sale

Refer to Fire Sale.

Duration Risk

The sensitivity of the prices of bonds and other 

fixed-income securities to changes in the level of 

interest rates.

European Union

An economic and political confederation 

of 27 nations.

Euro Area

An economic region that is comprised of all of 

the European Union countries that use the euro 

as their national currency.

Exchange-Traded Fund (ETF)

An investment fund whose shares are traded on 

an exchange. ETFs offer continuous pricing, 

unlike mutual funds which offer only end-of-day 

pricing. ETFs are often designed to track an 

index or a portfolio of assets.

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 the supervision of 

financial institutions. Members include the 

Federal Reserve, the FDIC, the NCUA, the 

OCC, the CFPB, and a representative of state 

financial supervisors.

Federal Funds Rate 

The interest rate at which depository institutions 

trade balances held in their reserve accounts 

at the Federal Reserve, usually overnight. The 

Federal Open Market Committee establishes 

the target rate for trading in the federal funds 

market and the Federal Reserve Bank of New 

York implements it.

Financial Accounting 

Standards Board (FASB)

A not-for-profit organization responsible for 

developing and updating the GAAP that governs 

American accounting.

Financial Contagion

The process by which losses at one institution 

spread to other institutions through the 

financial system. 

Financial Intermediation

Any financial service in which a third party (the 

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. 

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 developments in the international 

financial system on behalf of the G20 nations. 

The FSB was established by the G20 in 2009 at 

the London summit. The FSB 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 sell-off drives down prices, 

potentially below their intrinsic value, when the 

quantities to be sold are large relative to the 

typical volume of transactions.

Fixed Net Asset Value (Fixed NAV)

Net asset value is the value of a mutual fund’s 

or ETF’s assets divided by the number of its 

shares. The NAV is set daily, usually at the 

close of the market. Under Rule 2a-7, money 

market funds are allowed to round their NAV, 
134 2012 OFR // Annual Report

maintaining an effective fixed NAV of $1 per 

share, unless the value of the fund’s assets fall 

below $0.995 per share. 

Form N-MFP

A monthly disclosure of holdings by money 

market funds. SEC Rule 30b1-7 establishes the 

technical and legal details of N-MFP filings. 

Form PF (Form Private Funds)

A reporting form to the CFTC and SEC that 

provides information on the activities and 

holdings of hedge fund managers, private equity 

fund managers, and related entities. Depending 

on fund size, reporting is annual or quarterly.

Funding Liquidity

The availability of credit to finance the purchase 

of financial assets. 

Futures

A standardized, exchange-traded contract to buy 

or sell an underlying asset at a specified date 

and price. 

General Collateral Finance (GCF) 

An interdealer 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 tri-party 

clearing banks (see Tri-Party Repo).

Generally Accepted 

Accounting Principles (GAAP) 

The accounting and financial reporting 

standards set by FASB for publicly-traded 

companies in the United States.

Government-Sponsored 

Enterprise (GSE) 

A privately owned financial institution that has a 

federal charter authorized by law and a mission 

to promote the flow of credit to the housing 

market, student loan, or other specific sectors.

Gross Domestic Product (GDP)

The broadest measure of aggregate economic 

activity, measuring the total value of all final 

goods and services produced within a country’s 

borders during a specific period.

Haircut

The discount at which an asset can be pledged 

as collateral. For example, a million dollar bond 

with a 5 percent haircut would collateralize a 

$950,000 loan.

Interest Rate Swap

A swap in which the parties swap interest rate 

cash flows, typically between a fixed rate and a 

floating rate (see Swap).

International Monetary Fund (IMF)

An international organization created at the end 

of World War II to stabilize 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 Standardization (ISO)

The world’s largest developer of voluntary 

international standards in products, services, 

and good practices. 

International Swaps and 

Derivatives Association (ISDA)

A trade association of over-the-counter 

derivatives participants. The ISDA Master 

Agreements standardized derivative terms to 

simplify netting and reduce legal risks.

Legal Entity Identifier (LEI)

A uniform system that identifies parties to 

financial transactions.

Leverage

The use of borrowed money to finance 

investments or conduct financial activities. 

Liquidity

See Market Liquidity and Funding Liquidity.

Liquidity Coverage Ratio (LCR)

A Basel III standard to ensure that a bank 

maintains adequate unencumbered, high-quality 
Glossary 135

liquid assets to meet its anticipated liquidity 

needs for a 30-day horizon under a liquidity 

stress scenario specified by supervisors. 

Loan Origination

The point in the loan process in which the 

borrower applies for a loan and the lender 

approves or declines the loan.

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 when considering the 

financing of a mortgage.

London Interbank Offered Rate (LIBOR)

The interest rate at which banks can borrow 

unsecured funds from other banks in London 

wholesale money markets, as measured by daily 

surveys of the British Bankers’ Association. 

The published rate is a trimmed average of the 

rates obtained in the survey. Many contracts 

are tied to the level of LIBOR, making it an 

important benchmark.

Long Term Capital Management

A highly leveraged fixed-income arbitrage 

hedge fund that failed in 1998 during the Asian 

and Russian financial crises.

Macroprudential Supervision

Supervision aimed at promoting the stability 

of the financial system as a whole (see 

Microprudential Supervision).

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. A market 

with a lot of depth will have low price impact 

from trading.

Market Liquidity

The ability of a market to absorb large and 

frequent transactions with limited price impact 

and low transaction costs.

Maturity Transformation

The funding of long-term assets with short-term 

liabilities. This creates a balance-sheet 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 firm to 

ensure soundness and honest dealings (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 

Rule 2a-7.

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. The most prominent examples are an 

equity call option, which provides the right but 

not the obligation to buy a block of shares at a 

fixed price for a fixed period, and an equity put 

option, which similarly grants the right to sell a 

block of shares. 

Overnight Indexed Swap (OIS)

An interest rate swap that serves as a measure 

of investor expectations of an average effective 

overnight rate over the term of the swap (see 

Interest Rate Swap). 

Over-the-Counter (OTC)

A method of trading that does not involve 

an organized exchange. In over-the-counter 

markets, participants trade directly with 
136 2012 OFR // Annual Report

each other, typically through voice or 

computer communication.

Prime Broker 

Brokerage that provides a range of services 

to hedge funds, including securities 

lending, financing, trade execution, and 

cash management. 

Procyclical

See Countercyclical.

Regulatory Arbitrage

The practice of taking advantage of differences 

between regulatory regimes to avoid their costs 

or constraints. 

Rehypothecation

The reuse by a broker of collateral posted by a 

client, typically a hedge fund, for the broker’s 

use for securities lending or as collateral for its 

own borrowing.

Repo Run

A situation in which 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 (Repo)

A transaction in which one party sells a security 

to another party, while agreeing to repurchase 

it from the counterparty at some date in the 

future at an agreed price, often done on an 

overnight basis as a form of liquidity for banks. 

The net effect is essentially a collateralized loan.

Risk Management

The business and regulatory process of 

identifying and measuring risks and then 

responding to them. Categories of risk include 

operations, credit, market, control, liquidity, 

model, and regulatory.

Risk Premium

The difference between the expected return 

of an asset and that of a risk-free asset. It is the 

investor’s premium for bearing the risks of 

holding that asset. 

Risk Transformation

A form of financial intermediation in which 

an intermediary raises funds from risk-averse 

investors and then uses those funds to provide 

capital to borrowers for risky ventures. Risk is 

retained by the intermediary and its capital 

serves as a buffer against losses for its investors.

Securities Industry and Financial 

Markets Association (SIFMA)

A securities industry trade group 

representing securities firms, banks, and 

asset management companies. 

Securities Lending/Borrowing

The temporary transfer of securities from one 

party to another for a specified fee and term, 

in exchange for collateral in the form of 

cash or securities.

Securitization

A financial transaction in which assets such 

as mortgage loans are pooled and securities 

representing interests in the pool are issued.

Shadow Banking System 

Credit intermediation by unregulated or lightly 

regulated financial institutions in combination 

with the creation of money-like liabilities, 

involving leverage and maturity transformation, 

in opaque markets. 

Short-Term Wholesale Funding 

Funding instruments that are typically issued 

to institutional investors to raise large amounts 

of funding for short periods. Examples include 

large checkable and time deposits, commercial 

paper, and repurchase agreements.

Sovereign Debt Crisis

A financial crisis created by the potential or 

actual default of government debt. 

Stress Test

A modeling exercise where asset prices are 

shocked a pre-specified amount, sometimes 
Glossary 137

along with other financial and economic 

variables, in order to observe the effect on 

financial institutions or markets.

Structural Risk

Aspects of the design of the financial system that 

make it vulnerable to a shock. Structural risks 

could include excessive leverage or liquidity, 

crowded trades, large credit concentrations, 

poor governance, overreliance on one or a small 

number of essential service providers, or data 

and analytical gaps.

Structured Investment Vehicle (SIV)

A specific type of off-balance-sheet entity, 

popular before the financial crisis of 2007–2009, 

that issued short- and medium-term securities 

and invested the funds in a mix of assets, 

including asset-backed securities. SIVs sought to 

profit from the credit spread between long-term 

and short-term financial products.

Supervisory Capital 

Assessment Program (SCAP)

A stress test, conducted in 2009, designed to 

estimate the capital needs of U.S. bank holding 

companies with assets exceeding $100 billion 

under an adverse macroeconomic scenario; it 

was administered by the Federal Reserve, OCC, 

and FDIC.

Swap

An exchange of cash flows agreed by two parties 

with defined terms and over a fixed period.

Swap Execution Facility 

A term defined in the Dodd-Frank Act as a 

trading platform which market participants use 

to execute and trade swaps by accepting bids 

and offers made by other participants.

SWIFT Messages

Payment and account reconciliation 

messages transmitted over the SWIFT 

(Society for Worldwide Interbank Financial 

Telecommunication) network, a secure platform 

designed to promote standards for international 

communication and fund transfers. 

Systemic Expected Shortfall (SES)

The propensity for a financial firm to be 

undercapitalized when the system as a whole 

is undercapitalized, from Acharya and others 

(2010) (see References for Chapter 3).

Tail Risk

The risk of an extreme drop in the value of 

an asset. These risks are particularly difficult 

to model because they require estimating rare 

events with limited precedence. 

Term Premium

The excess yield an investor must receive in 

order to purchase a longer maturity bond over a 

shorter maturity bond of the same issuer.

Tier 1 Capital Ratio and 

Tier 1 Common Capital Ratios

Two measures of banking capital adequacy 

defined in the Basel accords in which capital 

is compared to total risk-weighted assets. 

Tier 1 capital includes common stock, preferred 

stock, and retained earnings. Tier 1 common 

capital is more narrowly defined and excludes 

preferred stock. 

Too Big To Fail

The assumption among market participants 

that some financial institutions are so large 

and interconnected that the government 

would rescue them in a crisis due to the 

perceived threats that their failure could pose 

to financial stability.

Tri-Party Repo

A repurchase agreement in which a third 

party agent, such as a clearing bank, acts as an 

intermediary to facilitate the exchange of cash 

and collateral between the two counterparties. 

In addition to providing operational services 

to participants, the tri-party agents in the U.S. 

tri-party repo market extend large amounts of 

intraday credit to facilitate the daily settlement 

of tri-party repos.
138 2012 OFR // Annual Report

Underwriting Standards

Terms, conditions, and criteria used to 

determine the extension of credit in the form of 

a loan or bond.

Value at Risk (VaR)

An important tool for market risk management 

that measures the risk of loss of a portfolio. 

The VaR projects the maximum expected loss 

for a given time horizon and probability. For 

example, the VaR over 10 days and with 99 

percent certainty measures the most one would 

expect to lose over a 10-day period, 99 percent 

of the time.

Variable Rate Demand Note (VRDN)

A type of security that allows a municipality to 

borrow over the long run while paying shortterm interest rates.

XBRL (eXtensible Business 

Reporting Language)

A global reporting standard that enables 

the free and open exchange of business and 

financial information. The language is XMLbased and uses XML syntax. 

XML (eXtensible Markup Language)

A markup language that defines a set of rules for 

encoding documents or data structures that are 

both human-readable and machine-readable.
Abbreviations 139

Abbreviations 

ABCP Asset-Backed Commercial Paper

ABM Agent-Based Model

ABS Asset-Backed Securities

AIG American International Group

AR Absorption Ratio

BCBS Basel Committee 

on Banking Supervision

BHC Bank Holding Company

BIS Bank for International Settlements

CBO Congressional Budget Office

CCAR Comprehensive Capital 

Analysis and Review

CCP Central Counterparty

CDO Collateralized Debt Obligation

CDR Central Data Repository

CDS Credit Default Swap

CFPB Consumer Financial 

Protection Bureau

CFSI Cleveland Financial Stress Index

CFTC Commodity Futures 

Trading Commission

CGFS Committee on the 

Global Financial System

CICI CFTC Interim 

Compliant Identifier 

CMO Collateralized 

Mortgage Obligation

CoVaR Conditional Value at Risk

CRO Chief Risk Officer

CRSP Center for Research 

in Security Prices

CUSIP Committee on Uniform Security 

Identification Procedures

CVA Credit Value Adjustment

DJIA Dow Jones Industrial Average

DOJ Department of Justice

DVA Debt Value Adjustment

DVP Delivery versus Payment

DWCF Dow Jones U.S. Total 

Stock Market Index

EBA European Banking Authority 

ECB European Central Bank

EU European Union

EURIBOR Euro Interbank Offered Rate

FASB Financial Accounting 

Standards Board
140 2012 OFR // Annual Report

FCIC Financial Crisis 

Inquiry Commission

FDIC Federal Deposit 

Insurance Corporation

FFIEC Federal Financial Institutions 

Examination Council

FHA Federal Housing Administration

FHFA Federal Housing Finance Agency

FICC Fixed Income 

Clearing Corporation

FICO Fair Isaac Corporation

FIO Federal Insurance Office

FISMA Federal Information Security 

Management Act of 2002

FIX Financial Information eXchange

FpML Financial products 

Markup Language

FRASER Federal Reserve Archival System 

for Economic Research

FRB Federal Reserve Board 

(Board of Governors of 

the Federal Reserve System)

FRED Federal Reserve 

Economic Data

FSAP Financial Sector 

Assessment Program

FSB Financial Stability Board

FSI Financial Stress Index

FSOC Financial Stability 

Oversight Council

G20 Group of Twenty Finance Ministers 

and Central Bank Governors

GAAP Generally Accepted 

Accounting Principles

GCF General Collateral Finance

GDP Gross Domestic Product

GSE Government-Sponsored Enterprise

HAMP Home Affordable 

Modification Program

HPI House (or Home) Price Index

ICERC Interagency Country 

Exposure Review Committee

IMF International Monetary Fund

ISDA International Swaps and 

Derivatives Association

ISIN International Securities 

Identification Number

ISO International Organization 

for Standardization

KCFSI Kansas City Financial Stress Index

LEI Legal Entity Identifier

LIBOR London Interbank Offered Rate

LTCM Long Term Capital Management

MBS Mortgage-Backed Securities

MMF Money Market Fund

NCUA National Credit Union 

Administration
Abbreviations 141

NFCI National Financial 

Conditions Index

NIST National Institute of 

Standards and Technology

NYSE New York Stock Exchange

OCC Office of the Comptroller 

of the Currency

OFR Office of Financial Research

OIS Overnight Indexed Swap

OPRA Options Price Reporting Authority

OTC Over-the-Counter

PDD Public Data Distribution

RSSD ID Research Statistics Supervision 

Discount Identification 

RWA Risk-Weighted Assets

SCAP Supervisory Capital 

Assessment Program

SEC Securities and 

Exchange Commission

SEF Swap Execution Facility

SES Systemic Expected Shortfall

SIAC Securities Industry 

Automation Corporation

SIFMA Securities Industry and 

Financial Markets Association

SIV Structured Investment Vehicle

STLFSI St. Louis Financial Stress Index

SWIFT Society for Worldwide Interbank 

Financial Telecommunication

TBTF Too Big to Fail

VaR Value at Risk

VIX Chicago Board Options 

Exchange Volatility Index

VRDN Variable Rate Demand Note

XBRL eXtensible Business 

Reporting Language

XML eXtensible Markup Language
142 2012 OFR // Annual Report
Notes on the Data 143

Notes on the Data 

Glossary of Certain 

Government Data Sources

Flow of Funds: Data release compiled and 

published by the Federal Reserve.

FR Y-9C: Consolidated financial statement filed 

with the Federal Reserve by domestic bank 

holding companies.

FR 2004: Financial report filed with the Federal 

Reserve by primary dealers in U.S. government 

securities about their market activity.

FRED: The Federal Reserve Bank of St. Louis 

maintains the Federal Reserve Economic Data 

(FRED) database, which contains economic 

time series data from a variety of data sources.

FRASER: The Federal Reserve Bank of St. Louis 

maintains the Federal Reserve Archival System 

for Economic Research (FRASER), which 

contains historical data on the U.S. economy 

and financial system.

Other

Certain data were obtained through 

Haver Analytics.

Certain data were obtained through the 

Center for Research in Security Prices (CRSP).

Bloomberg data: © 2012 Bloomberg Financial 

LLP. All rights reserved. Used with permission.

Tri-Party Repo Infrastructure Reform Task 

Force: Industry working group sponsored by the 

Federal Reserve Bank of New York to address 

vulnerabilities in the tri-party repo market.
Office of Financial Research

U.S. Department of the Treasury

www.treasury.gov/ofr