OFR

OFFICE OF FINANCIAL RESEARCH

2013 ANNUAL REPORT

i

Table of Contents

Letter from the Director

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

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

2.1 Financial Stability Monitoring Framework..............................................................7

Monitoring Tools....................................................................................................9

2.2 Current Threats to Financial Stability...................................................................12

Asset Management and Financial Stability ......................................................... 20

Financial Intermediation in China.........................................................................27

3. Evaluating Macroprudential Policy.........................................................33

3.1 A Framework for Evaluating Policy Tools ............................................................ 33

3.2 Emerging U.S. Macroprudential Tools ................................................................. 44

4. Research on Financial Stability .............................................................47

4.1 Research Agenda ................................................................................................47

Developing a Funding Map to Help Understand Financial Connectedness ........... 48

4.2 The Supply and Demand for Short-Term Funding................................................ 50

4.3 Measuring Liquidity in the Financial System........................................................57

4.4 Contagion in Financial Networks ........................................................................ 63

5. Addressing Data Gaps ..........................................................................71

5.1 Data Gaps Agenda ..............................................................................................71

5.2 Regulatory Data Availability.................................................................................72

5.3 Progress in Identifying and Addressing Data Gaps...............................................74

Techniques to Protect Confidential Data ..............................................................76

5.4 Data Gaps Priorities ............................................................................................78

Uses and Gaps in Historical Financial Data ......................................................... 84

5.5 Preliminary Research Based on Recent Data Collections .................................... 86

6. Promoting Data Standards....................................................................97

6.1 Data Standards Agenda ......................................................................................97

What are Financial Data Standards?....................................................................97

Principles for Data Collection.............................................................................100

Why Identifiers? ................................................................................................101

6.2 Trends in Information Standards........................................................................102

6.3 Legal Entity Identifier ........................................................................................103

6.4 Identifying Financial Instruments.......................................................................107

Universal Mortgage Identifer .............................................................................108

6.5 Improving the Integrity of Reported Data ...........................................................109

7. Agenda Ahead.................................................................................... 111
2013 OFR Annual Report ii

Table of Contents continued

Appendix A – Progress in Institution Building ....................................................... 115

Appendix B – OFR Outreach and Collaboration..................................................... 119

Glossary ............................................................................................................ 123

Bibliography ........................................................................................................ 133

Endnotes ............................................................................................................ 143
iii Letter from the Director

Letter from the Director

December 2013

Since the Office of Financial Research published its first annual report in July 2012, the United States 

financial system and its institutions, markets, and infrastructure have continued to stabilize and 

strengthen. Likewise, the Office has continued to grow over that period, and is beginning to deliver on 

our core mission: to fill critical gaps in financial data and analysis for the benefit of the Financial Stability 

Oversight Council and, ultimately, the public. I am pleased to present this 2013 Annual Report to 

Congress — our second — to document our progress and to lay out the agenda for the work ahead.

Although the U.S. financial system is stronger and functioning more smoothly than it was 17 months 

ago, threats to financial stability remain. This report analyzes those threats. Among them are 

vulnerabilities in short-term, wholesale funding markets. The current financial environment, marked 

by low interest rates and low volatility, has spurred risk-taking, making markets and institutions more 

vulnerable to a sharp increase in interest rates, volatility, or both. Operational risks could also destabilize 

the “plumbing” of the financial system — the infrastructure for payments, clearing, and settlement. In 

addition, uncertainty about the U.S. fiscal outlook could threaten financial markets. 

For our part, the past year and a half has marked a transition in organizational priorities. Our primary 

focus has shifted from standing up the OFR to building on the progress we are making to deliver on our 

mandate. Nonetheless, continuing to build the OFR remains critical to our success. For example, we are 

filling key management positions to provide leadership, and our staff has nearly doubled as we move 

toward our steady-state workforce. We have completed construction of the first phase of the information 

technology needed to manage and analyze large financial datasets. We continue to refine our strategic 

planning and goals to meet our mandates, and we have put in place performance goals and measures 

to track our progress and to promote transparency and accountability in fulfilling our mission. 

As we have done over the past two years, the OFR is developing new analytical tools and refining 

existing ones to assess and monitor threats to financial stability. This report includes two examples. The 

first is a Financial Stability Monitor that provides a snapshot or “heatmap” of several financial stability 

indicators. The second is a tool recommended by the OFR’s Financial Research Advisory Committee: 

a detailed funding-and-liquidity map to help pinpoint vulnerabilities in markets for securities financing 

transactions. 

The Office is also establishing a framework and criteria for assessing policy tools needed to mitigate 

threats to financial stability. This effort includes fulfilling our statutory mandate to evaluate stress tests, 

as well as studying the impact of policies related to financial stability and providing related advice.

To be clear — the Office does not make policy; the Council does. However, the OFR is in an objective 

position to appraise today’s financial stability tools and the new tools that are being developed for what 

has become known as the “macroprudential toolkit.” This toolkit has not yet been tested and is far from 

complete. For example, supervision of the financial system is evolving from a bank-centric view to a 

holistic design that balances appropriate prudential oversight with a broader approach spanning the 

entire financial system. Our job is to inform the debates about these tools by evaluating alternatives amid 

evolving needs, and to assess how they may complement or conflict.

We can’t analyze what we can’t measure, and gaps in the scope and quality of financial data still 

significantly limit the ability of policymakers to assess threats and evaluate ways to mitigate them. The 

OFR’s mandate includes identifying and filling such gaps, and we are starting to deliver. For example, 

we are collaborating with Federal Reserve staff to fill gaps in data about U.S. repo markets. Our report 
2013 OFR Annual Report iv

in September 2013 on Asset Management and Financial Stability also threw important data gaps into 

sharp relief. As we work to fill them, no priority is higher than preserving and strengthening the security 

of sensitive data, and we will take all necessary and appropriate precautions to ensure that any data we 

collect will be stored and used safely and securely.

Progress in developing and implementing critical standards for financial data has accelerated over 

the past year and a half. The need for such standards is compelling. They are essential to be able 

to aggregate, analyze, compare, and link financial datasets, so that these data will accurately reflect 

financial activity and positions, as well as the crucial linkages between institutions and markets. 

Because the Legal Entity Identifier initiative is a basic building block in the standards architecture, we 

have invested substantial effort and resources to assure its successful adoption, and the progress cited 

in this report is encouraging.

In our 2012 Annual Report, we stated that the Office may promulgate regulations to standardize the 

types and formats of data reported and collected on behalf of regulatory agencies. This year, we are 

engaging with relevant regulators to help standardize the formats used to collect derivatives data from 

swap data repositories, which will facilitate aggregating them on a global basis.

We also noted last year that the Office will produce and maintain catalogues of reference entities and 

instruments for financial data. With help from our advisory committee, we now have a plan to develop 

those catalogues. However, much more work is needed to identify financial instruments and products, 

and to map the relationships among them. 

This report, like the one in 2012, reflects the views of the OFR and our staff, but with needed 

collaboration and engagement from Council member organizations and their staffs. Our report is now 

published about six months after the Council’s Annual Report and attempts to build on that document. 

In that sense, the two reports are complementary. The Council report is comprehensive and proposes 

ways to mitigate vulnerabilities in the financial system, while the OFR report dives more deeply into 

specific issues and evaluates policy tools.

Strong collaboration is a hallmark of the way that the Council and the Office function. Vigorous 

discussions among the Council member organizations and their staffs have elevated the rigor and 

quality of our work and the Council’s understanding of that work for its benefit and the benefit of the 

public. Although fundamental uncertainty will always exist about threats and risks across the financial 

system, we believe that by working together, we can achieve better analytical and empirical outcomes 

to promote financial stability. I hope that this report reflects the wisdom and the power of that process. 

Richard Berner

Director, Office of Financial Research

Letter from the Director (continued)
1 Executive Summary

1 Executive Summary

This second Annual Report to Congress

documents the meaningful progress the Office of 

Financial Research (OFR) has made in meeting 

our statutory mandates and mission since we 

published our first annual report in July 2012. 

The Dodd-Frank Wall Street Reform and Consumer 

Protection Act of 2010 (Dodd-Frank Act) requires the 

OFR to report annually to Congress on: 

1. the state of the U.S. financial system, including 

an analysis of any potential threats to financial 

stability; 

2. the status of efforts by the Office in meeting our 

mission; and 

3. key findings from OFR research and analysis of 

the financial system.

Chapter 2 in this report outlines ways we expanded our 

data and tools for assessing and monitoring threats to 

financial stability on behalf of the Financial Stability 

Oversight Council (Council). 

Chapter 3 presents a framework for analyzing the effectiveness of tools and policies that make up a macroprudential toolkit for promoting financial stability. 

Chapter 4 discusses OFR research on the sources and 

uses of short-term funding, liquidity monitoring, and 

financial network analysis. We have made our basic 

research results extensively available in several published working papers and articles. 

Chapter 5 summarizes our framework for prioritizing 

gaps in the data needed for financial stability analysis, 

discusses the progress of the Office and other regulators in addressing those gaps, and lists our priorities 

for the future.

Chapter 6 describes our strategy and projects for 

promoting financial data standards. It documents our 

leadership on data standards initiatives, especially 

to the implementation of the global Legal Entity 

Identifier system.

Our agenda for the coming year builds on this foundation. The final chapter sets out our plans to continue 

expanding our monitoring, research, and analytical 

capabilities and publications; improving the scope and 

quality of data related to markets, particularly shortterm funding markets; and promoting essential data 

standards. Over a longer time period, we will extend 

that work and respond to the Council’s needs through 

our financial stability data and research programs.

Analyzing Threats to Financial Stability

The Office has a mandate to assess risks to the financial stability of the United States and to monitor, 

investigate, and report to Congress and the Council on 

changes in those risks. 

To fulfill that mandate, we report regularly to the 

Council on developments in the financial system and 

on tools and metrics for detecting and measuring 

potential threats to financial stability. Financial stability occurs when the financial system, even under stress, 

operates sufficiently to provide its six basic functions 

for the economy — credit allocation and leverage, 

maturity transformation, risk transfer, price discovery, 

liquidity provision, and facilitation of payments. 

Building on the framework outlined in our 2012 

Annual Report, we analyze threats to financial stability as vulnerabilities that result in market failure and 

disruptions to financial activity with adverse consequences for the economy. Threats to financial stability 

can emerge from within or outside the financial system 

and can be cyclical or structural. 

In this report, we present a prototype Financial 

Stability Monitor, a comprehensive new tool to track 

threats and the interplay among them. The monitor 

addresses five components of financial stability risk: 

macroeconomic, market, credit, funding and liquidity, 
2013 OFR Annual Report 2

and contagion. This is Version 1.0; the monitor will 

continue to evolve as we develop and test its performance, evaluate new indicators, and respond to the 

ways financial innovation may change intermediation, 

asset allocation, and risk management.

Our analysis suggests that threats to financial stability have generally abated since the publication of the 

OFR’s 2012 Annual Report and the Council’s more 

recent 2013 Annual Report. However, threats remain; 

chief among them are: vulnerabilities in markets for 

securities financing transactions and credit, vulnerabilities to an increase in interest rates and volatility, 

operational risks, and uncertainty about the U.S. fiscal 

policy outlook. 

The indicators in our Financial Stability Monitor suggest 

that market participants are taking credit and duration 

risk as they reach for yield amid persistently low interest 

rates and are likely vulnerable to interest-rate and volatility shocks. Our pricing models suggest there may be 

mispriced credit risk amid weakening standards for loan 

underwriting, as reflected in looser covenants and bank 

lending conditions.

The Office and the Council continue to highlight 

financial stability risks related to securities financing 

transactions in repos (repurchase agreements) and 

other short-term funding markets. These markets 

are still exposed to the risk of runs and fire sales. 

Vulnerabilities remain in money market funds and 

similar sources of cash in these markets. In our report, 

Asset Management and Financial Stability, we drew attention to a related risk — a rapid unwinding in response 

to market shocks of the reinvestment of cash collateral 

in securities lending transactions. Financial entities 

that engage in leveraged carry trades (borrowing 

short-term to invest in long-term assets) are particularly vulnerable. Examples include the rapid growth 

in mortgage real estate investment trusts, which hold 

mortgage-backed securities and finance them in the 

repo market.

Notwithstanding near-term fiscal improvement, concerns about the longer-term U.S. fiscal policy outlook 

persist. These concerns reflect lack of a clear resolution 

of the nation’s long-term budgetary challenges and 

the uncertainty about the process for implementing 

sustainable fiscal adjustments. Future episodes of 

fiscal brinksmanship could in the short run result in 

abrupt, destabilizing changes in government bond 

prices, potential deleveraging of financial obligations 

(because of the extensive use of Treasury securities as 

collateral in financial markets), and contagion to other 

markets. Longer-term risks include the potential erosion of Treasury debt as a global benchmark and the 

U.S. dollar’s reserve currency status. 

In this report, we also highlight the work we did at the 

request of the Council to inform the Council’s consideration of potential threats to financial stability in 

asset management activities and firms. In keeping with 

our mandates for transparency and accountability, we 

published a report requested by the Council on the 

asset management industry in September (see Chapter 

2 and OFR, 2013). 

Evaluating Macroprudential Policy

Macroprudential policies are meant to reduce the 

likelihood and severity of financial crises, strengthen 

the financial system by addressing specific vulnerabilities, and foster market discipline. The Dodd-Frank 

Act requires the Office to analyze such policies as part 

of its research function, which includes conducting, 

coordinating, and sponsoring research to support and 

improve regulation of financial entities and markets; 

evaluating and reporting on stress tests or other stability-related evaluations of financial entities supervised 

by Council member agencies; conducting studies and 

providing advice on the impact of policies related to 

systemic risk; and promoting best practices for financial risk management. 

Chapter 3 describes a framework for evaluating the 

impact and effectiveness of macroprudential policy 

tools. Implementing such tools requires an ongoing 

assessment of potential threats to financial stability, 

which in turn depends on high-quality and detailed 

data and a comprehensive inventory of the policy 

toolkit available to mitigate those threats. Evaluation 

involves choosing a framework for assessing the effectiveness of such tools and specifying criteria for picking 

the right tool for the job, while achieving balance 

between macroprudential tools and other policies.

U.S. regulators have expanded the macroprudential 

toolkit since the recent financial crisis. Supervision 

of the largest bank holding companies has expanded 

to include annual assessments of capital adequacy, 
3 Executive Summary

including supervisory stress tests, and mandatory resolution plans through living wills. Banking regulators 

have issued rules that strengthen the level and quality 

of capital, including supplementary leverage ratios for 

large firms, specific capital conservation buffers, and 

mechanisms to invoke countercyclical capital buffers. 

The banking regulators have also issued proposed 

rules for the largest banking organizations that would 

further increase their required supplemental capital 

ratios and establish liquidity coverage ratio requirements. Under the Dodd-Frank Act, the Council has 

designated three nonbank financial companies and 

eight financial market utilities for heightened prudential supervision by the Federal Reserve Board.

Gaps still remain in the U.S. and global macroprudential toolkits. For example, although regulators have 

begun to address vulnerabilities leading to the risk of 

runs on repo and money market funds, we believe that 

better tools are needed. Also, as noted in a recent OFR 

working paper, stress testing of large bank holding 

companies in the United States — a valuable exercise 

used to determine regulatory capital and liquidity 

planning at these institutions — could be improved by 

incorporating funding risks, potential spillovers, and 

feedback effects (see Bookstaber and others, 2013). 

Progress on that front is coming. For the first time in 

2014, eight bank holding companies with substantial 

trading or custodial operations will be required to test 

a counterparty default scenario, and, as in prior years, 

six bank holding companies with large trading operations will test a global market shock. More information 

about the supervisory stress testing approaches for 

U.S. financial companies would enhance transparency, 

investor confidence, and dissemination of best practices within the supervisory community.

Conducting Research on Financial Stability

The Office is required by statute to perform research 

on risks to financial stability and to evaluate attempts 

to mitigate those risks. 

Chapter 4 summarizes three OFR research projects. 

In the first, we analyze the sources and uses of shortterm funding. In the second, we outline tools for 

measuring and monitoring market liquidity, examining the measurement of liquidity shocks across asset 

classes. In the third, we show how network analysis 

can improve our understanding of contagion among 

financial firms who are exposed to each other. 

We distribute the results of such research in the OFR 

Working Paper Series. To date, we have published 

12 working papers. The research is diverse, with the 

strong common theme of improving our collective 

understanding of the causes and consequences of 

financial instability. Examples include examining the 

interconnected nature of financial institutions and 

markets, the roles that market participants play in 

creating and amplifying financial distortions, and the 

effects of regulations and risk management policies. 

Recent policy-related topics include analyses of proposed contingent capital rules, the historical use of 

macroprudential policy in the United States to moderate the credit cycle, and scenario design in regulatory 

stress testing. 

We also conduct and publish research to support our 

mandate to improve the quality of financial data. For 

example, an OFR working paper explained how modern cryptography techniques may allow financial regulators and supervisors to mask identifying information 

and share data without compromising confidentiality.

Addressing Data Gaps

We have a statutory responsibility to improve the scope 

of data available for financial stability monitoring. 

Chapter 5 describes how we identify and fill data gaps 

on behalf of the Council. 

The OFR identifies data needs through our research 

and monitoring and close collaboration with the 

Council and its member agencies. To take stock of 

available data to meet those needs, the Office compiled and maintains an inventory describing the data 

that Council member agencies purchase or collect. 

Comparing data needs with this inventory can help 

identify gaps, avoid duplication, reduce costs, minimize regulatory burden, and take advantage of 

existing data sources to the extent possible. To fill the 

gaps, we collaborate across the Council to develop 

priorities and then look to alternative ways to expand 

the scope of data, such as by better organizing existing data, promoting data standards, and collecting 

new data. 
2013 OFR Annual Report 4

Chapter 5 cites specific data gaps the Office has begun 

to address. Our focus in 2014 will be to improve data 

measuring the sources and uses of short-term funding; 

collect data gauging the activities and terms of securities 

financing transactions, such as in the three segments 

of U.S. repo markets; and, if requested by the Council, 

address data gaps in asset management activities. 

Protecting the security of sensitive data is our highest priority. At the same time, we are promoting ways 

to share such data consistent with that goal. To that 

end, we collaborate with Council member agencies 

and through the Council’s Data Committee. Council 

member agencies have signed data-sharing agreements 

to protect, secure, and treat shared data consistently. 

With others in the Council, and responding to the 

suggestion of inspectors general, we are working to 

craft an interagency agreement that cross references 

different data security classification schemes to ensure 

that similar data will have the same security controls 

wherever they may be used. 

The chapter also profiles two other data issues. First, 

it outlines our strategy for managing data, a process 

that ensures data quality is maintained through the 

data life cycle. In addition, it presents early results and 

lessons learned from our analysis of data measuring 

activity in money market funds, credit derivatives, and 

hedge funds. 

Promoting Data Standards 

Chapter 6 makes the case for data standards and 

explains the Office’s data standards strategy. Standards 

set common, clear definitions for financial entities, 

instruments, positions, and transactions. Common 

definitions promote comparability so that terms and 

definitions mean the same thing regardless of the data 

source and information can be reliably combined from 

different sources and systems. Better financial data 

standards can improve reporting and risk management 

for firms, support microprudential supervision and 

market oversight, and promote macroprudential monitoring by the Council and the Office to assess risks to 

the financial system as a whole.

The Office supports the Council and its member agencies by standardizing the types and formats of data 

reported and collected. The Office’s process for promoting data standards includes: (1) evaluating whether 

standards are needed for a particular set of data, and 

whether developing them fits the Office’s mission; (2) 

determining whether the data in question are good 

candidates for standardization, and (3) determining 

the Office’s strategy. 

The Office continues to be a leader in the initiative 

to establish a Legal Entity Identifier (LEI), a unique, 

global standard for identifying parties to financial 

transactions. The LEI will help market participants 

and government regulators aggregate, compare, and 

analyze data for greater insights. The Office also 

recently published a working paper, with collaboration 

from certain Council member agencies, describing the 

need for and strategy to develop a unique mortgage 

loan-level identifier.

In addition, the Office assists Council member agencies with data standards initiatives. For example, we 

are working with the Commodity Futures Trading 

Commission and global regulators to align and standardize derivatives reporting.

The Agenda Ahead 

In 2014, we will extend and expand our work in several 

dimensions. We will continue to pursue the initiatives 

described in this Annual Report and to build our institutional, human capital, and technological resources. We 

will focus on filling the most critical gaps in financial 

stability data and analysis for the benefit of the Council 

and the public. 

Our top analytical priorities for 2014 include:

• developing the prototype Financial Stability 

Monitor described in Chapter 2 as a tool regularly 

used by policymakers; 

• further developing our capacity and tools to 

identify, assess, and monitor threats to financial 

stability; 

• improving and implementing the framework 

described in Chapter 3 to evaluate stress tests and 

the macroprudential toolkit; 

• extending the work in Chapter 4 on markets for 

securities financing transactions; 

• following up on our work on asset management 

to analyze new data collected from private fund 

advisers by the SEC; and 

• continuing to expand our research on financial 
5 Executive Summary

stability, risk management, and related topics, and 

to make results available through our Working 

Paper Series and other publications. 

Our top data priorities for 2014 include: • creating reference databases for financial entities 

and financial instruments;

• improving the scope and quality of data related 

to repo and other markets for securities financing 

transactions; 

• filling the data gaps identified in our asset 

management report, including for separate 

accounts and securities lending; 

• identifying and obtaining the data we need to 

understand better the liquidity and funding flows 

discussed in Chapter 4; 

• promoting the incorporation of the LEI in market 

practice and regulations; 

• assisting and advising market regulators and our 

global counterparts in improving the standards 

needed to collect and share data measuring 

derivatives transactions and positions collected in 

trade and swap data repositories; and 

• further developing the in-house capacity for 

efficient and effective data collection and 

management. 

Our 2014 priorities for institution-building are aimed 

at strengthening our technological infrastructure 

and human capital resources and building the virtual 

research and data communities that we can use to 

meet our mission. We are now implementing the tech-

nology infrastructure to securely collect and analyze 

very large datasets. We have a strong and capable staff 

and continue to recruit dedicated professionals who 

can help us achieve our mission. 

We awarded our first research grant in September 2013 

through our collaboration with the National Science 

Foundation and expect to expand that program next 

year. We will continue to sponsor conferences and 

research on financial stability and related topics. 

We have received and begun to respond to valuable 

advice from our external Financial Research Advisory 

Committee about data and research. In addition, we 

continue to work closely with the Council and its mem-

ber agencies, whose support has been essential from 

the start.

7 Analyzing Threats to Financial Stability

2 Analyzing Threats to Financial Stability

The first section of this chapter describes the 

Office’s framework for evaluating threats to 

financial stability. The second section assesses 

and reports on specific threats and potential 

transmission channels. Chief among them are 

risks in short-term funding markets and credit 

markets, vulnerabilities to an increase in interest 

rates and volatility, and uncertainty about U.S. 

fiscal policy.

2.1 Financial Stability Monitoring 

Framework

The Office’s framework for assessing threats to financial 

stability will serve as a monitoring device to identify vulnerabilities by focusing on selected economic indicators, financial 

market indexes, and other data. This prototype Financial 

Stability Monitor builds on a framework outlined in the 

OFR’s 2012 Annual Report and draws from other academic 

contributions to risk monitoring. It will continue to evolve as 

new, forward-looking indicators emerge.

Threats to financial stability are vulnerabilities, typically exposed by shocks, that disrupt the functioning of 

the financial system and spread through it with adverse 

consequences for the economy. The purpose of a financial stability monitor is to give policymakers a periodic 

assessment of potential fragilities to help ensure a 

smoothly operating financial system. 

A monitoring framework should assess whether the 

financial system would be able to provide its basic 

services in the face of shocks. The Office’s 2012 Annual 

Report outlined six services of a well-functioning financial system: credit allocation and leverage, maturity 

transformation, risk transfer, price discovery, liquidity provision, and facilitation of payments. Smooth 

functioning of these services leads to financial stability, 

but each may be vulnerable to shocks when market 

participants take them to extremes through excessive 

risk-taking, leverage, maturity transformation, and 

liquidity transformation. By focusing on those functions, we can monitor risks building within and across 

markets and institutions. Other monitoring approaches 

are typically organized along institutional or market 

lines, or by sector.1

In this chapter, we present a prototype Financial 

Stability Monitor for tracking vulnerabilities and 

assessing how shocks may propagate, drawing on work 

by other researchers and existing tools for assessing 

financial stability (see insert on Monitoring Tools).2

This framework assesses vulnerabilities that may stem 

from or amplify risks, focusing on credit, liquidity, 

funding, market, and macroeconomic risks, and interconnections that may transmit shocks across institutions and markets. 

Vulnerabilities, Shocks, and Threats

Financial stability monitoring has evolved partly in 

response to lessons learned from the financial crisis. 

As the crisis showed, analysis of regulated entities may 

overlook weaknesses or a buildup of risk elsewhere 

in the financial system and may miss the ripple effect 

from failures in markets or institutions to risk repricing 

and asset fire sales. The crisis demonstrated how shortterm funding runs can lead to broader problems with 

liquidity and solvency. It also showed how seemingly 

benign market conditions can lead to a buildup of risk. 

The Office’s framework for monitoring focuses on 

vulnerabilities in the financial system rather than 

shocks to it. Vulnerabilities are determinants of 

instability; shocks expose them. Examples of vulnerabilities include the potential for runs in money market 

funds that promise a fixed net asset value; lax loan 

underwriting standards; a rapid buildup of credit; 

insufficient bank capital or liquidity buffers to absorb 

losses or withdrawals; and excessive maturity transformation promoted by a period of low interest rates. 

Vulnerabilities may arise through market failures, 

incentives that lead to excessive risk-taking and the 
2013 OFR Annual Report 8

Threats can be transmitted and magnified through 

various channels. Shocks originating in the financial sector are transmitted to the real economy, for 

instance, when banks and other financial institutions 

respond to liquidity or capital shortfalls by reducing 

their credit extension to nonfinancial companies (see 

BCBS, 2011). The recent global financial crisis underscored important linkages between the broad economy 

and the financial system. For example, shocks in trade 

finance credit led to declines in global trade, and asset 

fire sales and impaired funding channels caused a 

sharp decline in credit availability and associated contractions in economic activity in many countries.

Components of the OFR Financial Stability 

Monitor

Our prototype Financial Stability Monitor is displayed 

in Figure 2. It tracks five categories of financial system 

distress, using a mix of economic indicators, market 

indexes, and measurements calculated by the Office:

• Macroeconomic risk, including risks to economic 

growth, external balances, confidence channels, 

or price channels. Examples of indicators 

include financial conditions, sovereign financing 

needs, current account balances, or inflation 

expectations.

• Market risk, including the risk of losses across 

key asset classes and investment strategies due to 

adverse movements in interest rates, exchange 

rates, and other asset prices. Indicators include 

duration of investor positions, positioning, 

valuations of risk premiums, uncovered foreign 

exchange positions, asset-liability mismatches, or 

volatility. 

• Credit risk, defined as the potential for a 

counterparty to fail to meet its financial 

obligations. Indicators include both marketimplied and balance sheet measures, including 

credit spreads, balance sheet leverage, bank 

lending conditions, or asset quality.

• Funding and liquidity risk, defined as the risk 

that market participants cannot borrow funds 

or sell securities over a relatively short time 

without negatively impacting prices. Indicators 

include measures of market depth and breadth, 

dependence on short-term wholesale funding, or 

changes in the tenor and types of assets under 

management of short-term investors.

Figure 1. Vulnerability to Shocks and Price of Risk

Vulnerability 

of Financial System

Size of Shock

Price of Risk

Vulnerability 

of Financial System

with Tighter 

Regulation

Source: Adrian, Covitz, and Liang (2013)

loss of market discipline, or new financial products or 

trading strategies that lead to a transformation of risk 

in a way that is not well understood. The wide range 

of possible conditions requires a dynamic approach. 

Financial system vulnerabilities can be structural or 

cyclical.3 Determining the nature of the vulnerability 

is critical to designing the appropriate policy response, 

as described further in Chapter 3. 

Shocks that expose these vulnerabilities can originate 

from inside or outside the financial system. Financial 

shocks may take several forms — the default of a major 

market participant, a sudden loss of market confidence 

in a particular asset or firm, or a disruption or failure 

in market infrastructure. Attempting to minimize 

the incidence of shocks is unlikely to be a successful 

strategy to promote financial stability because they are 

inherently difficult to predict (see Liang, 2013) and, 

furthermore, can also mask structural problems.

A more practical approach is to strengthen the financial system at its points of vulnerability to be more 

resilient when shocks inevitably hit. Distortions to 

incentives that lower the price of risk (typically accompanied by low volatility) tend to spur risk-taking, which 

can increase vulnerability of the financial system to 

shocks in the future (as illustrated by the dark blue 

line in Figure 1). Policies can make the financial system 

more resilient to shocks, for instance, by increasing 

the cost of securities financing transactions or raising 

capital standards.
9 Analyzing Threats to Financial Stability

MONITORING TOOLS 

A number of tools have been developed to assess risks to 

financial stability since the financial crisis, as described in 

the OFR’s first working paper (see Bisias and others, 2012). 

Many build on the experience and lessons learned from 

past crises and some involve more than one approach (see 

Blancher and others, 2013). These tools fall into several 

categories: 

Financial soundness of individual institutions.

Analyzing individual banks’ balance sheets enables comparisons across industry sectors and countries. Detailed 

data about assets and liabilities are necessary to anticipate 

how shocks may originate within financial companies. 

These measures have weaknesses: many are backward 

looking and not timely, and, since they typically focus 

on individual institutions, they do not take into account 

spillovers. There are also large gaps in data about some 

nonbank financial companies’ activities.

Linkages between economic stress and financial 

stress. This macroeconomic approach uses stress tests, 

scenario analysis, and statistical models to identify the 

vulnerability of the financial system to shocks. Such 

models can be valuable, although because they are based 

on historical experience, they do not always handle rare, 

out-of-sample extreme events well.

Financial market-based indicators. These measures 

provide a high-frequency readout of vulnerabilities and 

can be combined with other models. For example, a 

tool identifying market conditions in which investors are 

under-compensated for risk can be paired with balance 

sheet data indicating excessive leverage, which together 

could signal a potential vulnerability. A weakness of 

market-based financial stability indicators is that they tend 

to be coincident rather than predictive, and they may be 

unreliable during periods of stress. 

Interdependencies. This group of tools uses network 

analysis to map linkages among institutions and markets 

to identify potential contagion risks. A financial network 

model can simulate how a shock may be transmitted 

through the flow of funds, or show contributions to risk 

(see Section 4.4, for example).

Assessing threats using each of these tools in isolation may 

be misleading, because threats to financial stability may 

arise from many sources. A diverse and complementary set 

of tools is required. Bottom-up analysis can complement 

top-down approaches.

The monitoring framework should be flexible to allow for 

assessing risks at different levels, whether at the institution and market level or at a more aggregated macroeconomic and systemwide level. This recognizes that fragilities may appear at the micro level, but not at the macro 

level, or vice versa (see Hellwig, 1994). Such monitoring 

can inform the development of theoretical and empirical 

macro-financial models that seek to capture the complexity 

that stems from the interconnectedness of markets and 

institutions, feedback loops, and other accelerants that 

may amplify an initial shock.

• Contagion risk, defined as the vulnerability of 

the financial system to sudden shocks that may 

spread through seemingly unrelated parts of the 

financial system. We analyze indirect measures of 

interconnectedness, such as the systemic expected 

shortfall model, the conditional value at risk 

model, and the distressed insurance premium 

model (see Acharya and others, 2010; Adrian and 

Brunnermeier, 2011; and Huang, Zhou, and Zhu, 

2011). Data on claims that banks have on each 

other, and their exposures to other sectors, are 

also included in this category.

We acknowledge three limitations to our framework. 

First, the metrics we employ in the monitor are largely 

contemporaneous. We will incorporate new, forward-looking indicators into our framework as they are 

developed. For example, the connection between volatility and leverage, discussed below, may offer insights 

to construct forward-looking indicators. Second, the 
2013 OFR Annual Report 10

monitor should be treated as a starting point. It should 

be complemented by rigorous and robust quantitative 

assessments, such as stress tests, and qualitative market 

surveillance of supervisory and regulatory frameworks 

that are informed by knowledge about specific institutions. Finally, some risks are currently not readily 

quantifiable; in those cases, we apply a measure of 

judgment, and we will work to develop ways to quantify 

these risks.

In Figure 2, each of the five risk categories is evaluated 

using a series of underlying indicators based on maximum and minimum (mostly) daily levels prevailing 

from January 1, 1990 (if available) to the present. Some 

of the metrics — for example, volatility, lending conditions, positioning — treat both the maximum and 

minimum levels as states of high risk. For instance, we 

treat episodes of very low volatility and very high volatility as both representing stability risks, consistent with 

the “volatility paradox” hypothesis (see discussion in 

Section 2.2). The current positioning is determined by 

the cumulative z-scores (the sample mean subtracted 

from the latest reading and divided by the standard 

deviation) of the underlying indicators. This allows 

the indicators to be normalized. Each risk category is 

constructed as an equal-weighted average across the 

prevailing risk levels for each of the underlying indicators. The results are presented on a heat map spectrum 

in the Financial Stability Monitor; green means risks to 

stability are low and red indicates elevated risks.4

Figure 2 summarizes our current assessment of risks to 

financial stability (as of October 2013) relative to the 

Office’s last annual report (July 2012). Across most of 

the elements we monitor, risks have generally abated 

over that period. But prior vulnerabilities remain, and 

new ones have surfaced.

U.S. macroeconomic conditions have generally 

improved amid easy monetary and financial conditions, gains in the housing market, and improving 

business and consumer sentiment. However, uncertainty about the U.S. fiscal policy outlook remains an 

ongoing risk. Market-implied sovereign and fiscal risk 

signals remain relatively elevated, reflecting in part 

concern about sovereign credit quality after the federal 

government shutdown and debt ceiling extension in 

October 2013. 

Market risk measures remain near long-term average 

levels, but a few pockets of vulnerability are notable. 

Duration risk, or the sensitivity of bond investments to 

a change in interest rates, is elevated. That signals an 

increasing risk of sizeable portfolio losses in the event 

of an unanticipated rise in interest rates. The low cost 

of volatility can lead to an expansion of trading strategies that depend on volatility remaining low, which 

could lead to destabilizing losses in the event of a sharp 

rise in volatility. 

Credit risk measures are mixed. Since we published 

our last annual report, the ability of households 

to service debt, measured by payments of interest 

and principal relative to income, has improved. 

Delinquencies and default rates continue to decline 

or remain low. Although measures of credit risk based 

on the credit default swaps market suggest limited 

demand for insurance against default risk, pockets 

of risk continue to grow. Credit risk for nonfinancial 

corporations has increased slightly. The quality of 

bonds and loans issued has declined, with evidence of 

increased risk-taking in the leveraged loan sector and 

reduced compensation for risk. U.S. bank loan officers 

also report less stringent loan underwriting standards. 

Although leverage for households and banks has been 

declining since the financial crisis, corporate and 

other balance sheets show a rise in leverage over the 

same period, leaving systemwide balance sheet leverage 

still close to record levels. 

Liquidity and funding risk measures show no obvious 

signs of imminent stress. However, the current environment of low rates and low volatility may suggest too sanguine a view of overall funding and liquidity dynamics. 

Broker-dealer inventories have diminished, owing to 

efforts to reduce market leverage and to a shift in funding and trading models. Eventually, this shift could 

lead to more sustained, structural reductions in market 

liquidity that amplify other vulnerabilities, for instance 

in a rising interest rate environment.

Contagion risk and interconnectedness have moderated since the Office’s last annual report, according 

to several measurements based on an average of data 

from the six biggest U.S. bank holding companies. The 

systemic expected shortfall indicator, which predicts 

the expected percentage loss in equity value for large 

banks in a distressed market, is now at a post-crisis low. 

The CoVaR measure, which calculates the value at risk 
11 Analyzing Threats to Financial Stability

2012 2013

Low Risk High Risk

2012 2013

Note: Green signies low nancial stability risks, while red signies elevated risks. The gure represents a 

series of underlying indicators based on maximum and minimum (mostly) daily levels prevailing from 

January 1, 1990 (if available) to the present. Each risk category is constructed as an equal-weighted 

average across the prevailing risk levels for each of the indicators in the underlying categories. 

Positioning is determined by the average z-scores of the underlying indicators. 2013 represents data as 

of the end of October 2013 and 2012 as of the end of July 2012.

Funding Stress

Market Liquidity

Balance Sheet Liquidity

Intermediation

Corporate Sector

Households

Financial Institutions

Joint Distress

Asset Market Interdependence

Interconnectedness

Volatility

Interest Rate Risk

Positioning

Economic Activity 

Sovereign Risk

Ination Uncertainty

UNDERLYING INDICATORS

MACRO

CONTAGION

MARKET

FUNDING/

LIQUIDITY

CREDIT

Figure 2. OFR Financial Stability Monitor

Sources: Bloomberg L.P., Haver Analytics, Congressional Budget Office, Federal Reserve, European Central Bank, Bank of England, Bank of Japan, OFR analysis
2013 OFR Annual Report 12

for the financial system based on distress at a single 

large bank, shows an improvement as large banks have 

increased capital and liquidity buffers. The distressed 

insurance premium, which represents a hypothetical 

insurance premium against financial distress and can 

be interpreted as each institution’s marginal contribution to systemic vulnerability, has moderated and 

remains in a low-risk state. Improvements in balance 

sheet management in the U.S. banking sector have 

reinforced the declines experienced in these measures 

over the past year. In addition, the absorption ratio 

indicator, an aggregate measure of co-movement in 

market prices for a range of assets, has declined during 

the past year, and reflects moderate risk. 

2.2 Current Threats to Financial 

Stability

This section identifies emerging risks and vulnerabilities in 

the financial system, and revisits threats cited in the Council’s 

2013 Annual Report and the OFR’s 2012 Annual 

Report. The OFR financial stability framework prioritizes 

these risks.

Since the last OFR annual report in July 2012, uncertainty about U.S. interest rates led to a broad sell-off 

across global markets, unveiling important fragilities. 

Figure 3 shows the performance of global asset markets 

relative to a three-year average and the relative shift 

in risk appetite. Green represents a lower appetite for 

risk, for example, as represented by lower equity prices, 

wider credit spreads, or lower Treasury yields, while red 

represents a higher appetite for risk. The dotted line — 

an average of five asset classes — suggests that despite 

the decline in prices of certain risky assets, overall risk 

appetite remains above levels prevailing at the time of 

the last annual report. In particular, risks in the euro 

area have abated as its banking and sovereign debt 

crisis has morphed into a more manageable economic 

recession risk. Liquidity has improved, equity values 

have recovered, and credit spreads have tightened 

on sovereign debt issued by Greece, Ireland, Italy, 

Portugal, and Spain. 

There was a sizeable correction in asset markets in May 

2013 and June 2013 on expectations that improvements 

in the U.S. economy could prompt the Federal Reserve 

to taper the asset purchase program sooner than 

expected. Underperformance was most pronounced 

in emerging markets, with sovereign external and 

local currency debt spreads widening, implied default 

risk rising, and currencies coming under pressure. 

Poor performance was also pronounced in high-risk 

sectors and sectors that had previously benefited most 

from excess liquidity. Those concerns have since partly 

abated and equity and credit markets have recovered, 

but the episode was an important mini-stress test for 

markets that could presage the potential reaction to 

monetary policy tightening when it does occur.

In early October 2013, increased sovereign risk concerns related to the U.S. debt ceiling impasse and 

government shutdown led to a sharp rise in interest 

rate volatility, a widening in near-term sovereign credit 

default swap spreads, and a rise in measures of risk in 

short-term secured and unsecured funding markets. 

The episode was short-lived, and most sovereign risk 

measures returned to earlier prevailing benign levels 

after the debt ceiling was temporarily extended.

Although market conditions have since calmed, challenges 

remain. Threats to stability can be generally categorized 

in two ways: (1) cyclical or structural, and (2) inside or 

outside the financial system (Figure 4). Grouping threats 

in this way helps focus on the causes behind each threat, 

rather than just symptoms, although some threats contain 

both cyclical and structural causes. Many of the threats 

previously flagged by the Office and the Council in their 

respective annual reports remain relevant. 

This section highlights the following potential threats:

• the risk of runs and asset fire sales in repurchase 

(repo) markets;

• excessive credit risk-taking and weaker 

underwriting standards;

• exposure to duration risk in the event of a sudden, 

unanticipated rise in interest rates; 

• exposure to shocks from greater risk-taking when 

volatility is low;

• the risk of impaired trading liquidity;

• spillovers to and from emerging markets;

• operational risk from automated trading systems, 

including high-frequency trading; and

• unresolved risks associated with uncertainty about 

the U.S. fiscal outlook.

These risks in isolation do not necessarily lead to 

systemic weakness. But, in combination, they may 
Analyzing Threats to Financial Stability 13

Figure 3. Financial Market Heat Map

S&P 500

Euro Stoxx 50

MSCI EM Equities

U.S. Treasuries (10y yield)

EMBI global (yield)

U.S. CDS (1y)

CDX IG (5y)

CDX HY (5y)

iTraxx IG (5y)

CDX EM (5y)

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

Fwd LIBOR-fwd OIS (3m)

EUR/USD basis (2y)

USD/JPY basis (2y)

U.S. (VIX)

Europe (V2X)

U.S. 1y10y swaption vol

EU 1y10y swaption vol

Japan 1y10y swaption vol

EUR/USD ATM vol (6m)

USD/JPY ATM vol (6m)

EM-VXY (3m)

2013

average*

2012

average*

EQUITIES BONDS CREDI

T LIQUIDITY VOL

FUNDING/

ATILITY

RATES EQUITY FX

Lower Higher

2012 OFR Annual Report Percentile Rank (Aug. 1, 2012)

2013 OFR Annual Report Percentile Rank (Oct. 25, 2013)

RISK APPETITE

* Average across all ve asset classes on Aug. 1, 2012, and on Oct. 25, 2013. Not all underlying series in the averages are shown above. 

Note: The gure illustrates asset performance over the last three years to represent the post-crisis period. Each indicator is scaled to a percentile range rank 

between 0 and 100 and compared to the levels prevailing over the past three years. The middle of the risk appetite range corresponds to the median during 

the period, zero corresponds to the level consistent with the highest risk aversion during the period, and 100 corresponds to the level consistent with the 

lowest risk aversion during the period. The light blue marker represents the percentile rank as of the 2012 OFR Annual Report in relation to its three-year 

history. The direction of the arrow displays the change in the risk appetite between the two OFR annual reports.

Lower Risk Appetite = Lower equity prices, lower U.S. Treasury yields, wider credit spreads, higher volatility

Higher Risk Appetite = Higher equity prices, higher U.S. Treasury yields, tighter credit spreads, lower volatility

Sources: Bloomberg L.P., Markit Group, Ltd, MorganMarkets, OFR analysis
2013 OFR Annual Report 14

leave the financial system more susceptible to adverse 

shocks. 

Wholesale Funding Market Run Risk and 

Fire Sales

In past reports, the Office and the Council have each 

highlighted financial stability risks related to repo 

markets. Regulators and market participants have 

made progress in reducing vulnerabilities in the repo 

markets to runs and asset fire sales.5 Concentration 

has declined, collateral quality has improved, the volume of intraday credit has decreased, and some repo 

maturities have extended.6 Potential ways to mitigate 

remaining risks in repo markets include the creation of 

an orderly liquidation facility, limitations on collateral 

types, an extension of repo maturities, and minimum 

collateral haircuts. These changes would enhance the 

macroprudential toolkit, although their extension to 

transactions between unregulated entities and harmonization with non-U.S. markets would need to be 

addressed (see Chapter 3).

Secured funding markets are still exposed to potential repo runs, which could amplify and transmit risk 

systemwide. Run risk stems from three current weaknesses. First, broker-dealers and others who obtain 

financing in the repo market are vulnerable to runs 

by counterparties. The Securities and Exchange 

Commission has proposed requiring prime money 

market funds to adopt floating share prices or impose 

Figure 5. Prime Money Market Funds’ Exposure to 

Counterparty Failure (net assets in $ billions)

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

Sep

2012

Dec

2012

Mar

2013

Jun

2013

Sep

2013

30+ Counterparties

20-29 Counterparties

10-19 Counterparties

Note: Excludes exposure through repurchase agreements, asset-backed 

commercial paper, and guarantees of variable rate demand notes. Prime 

funds only, assuming a 40 percent recovery rate, based on the actual net 

asset value of each fund's stated portfolio.

0-9 Counterparties

Number of different counterparties that could each 

cause a fund to break the buck if it failed.

Figure 4. Threats to Stability

Internal External

Cyclical • Excessive risk-taking in credit 

• Leveraged finance intermediation

• Vulnerability to interest rate shocks

• Vulnerability to volatility shocks

• Asymmetric market liquidity risks. Diminished liquidity intermediation

• Eurozone sovereign risk, 

bank resolution delays, 

and corporate debt 

overhangs

• Capital flow reversals and 

severe asset price volatility 

in emerging markets

Structural • Vulnerability to runs in funding markets and destabilizing fire sales

• Gaps in risk management and operational risk

• Diminished liquidity intermediation

• Data and analytical gaps and lack of data standards, contributing to low 

transparency or insight into financial exposures (see chapters 5 and 6)

• Domestic fiscal policy

• Cyber attacks

Source: OFR analysis

Sources: SEC Form N-MFP, OFR analysis
Analyzing Threats to Financial Stability 15

Figure 6. The Money Fund Network: Top Prime Fund Holdings ($ billions)

$10

billion

FUNDS OBLIGATIONS*

Firm A

Firm B

Firm C

Firm D

Firm E

Firm F

Firm G

Firm H

Firm I

Firm J

Fund 1

Fund 2

Fund 3

Fund 4

Fund 5

Fund 6

Fund 7

Fund 8

Fund 9

Fund 10

*Short-term obligations of nancial institutions.

Note: The analysis involved private issuers only, excluding government, municipal, agency, and sovereign issuers. 

Assets were measured by aggregate principal value of holdings as reported on Sept. 30, 2013. 

Sources: SEC Form N-MFP, OFR analysis
2013 OFR Annual Report 16

Figure 7. Shadow Banking versus Traditional Banking 

Liabilities ($ trillions)

0

5

10

15

20

25

1952 1962 1972 1982 1992 2002 2013

Traditional Banking Liabilities

Shadow Banking Liabilities

Note: Traditional banking liabilities refer to total liabilities of U.S.-chartered 

depository institutions, foreign banking ofces in the United States, banks 

in U.S.-afliated areas, credit unions, and holding companies, less 

corporate bonds they have issued and other long-term liabilities. Shadow 

banking liabilities (netted from overlaps with the Federal Reserve Flow of 

Funds Table L.110) refer to the sum of total outstanding open market

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

pooled securities (prior to Q4 2008), total liabilities of asset-backed 

securities issuers, and total shares outstanding of money market funds.

Q1 2008 

$20.1

Sources: Pozsar and others (2012), Federal Reserve, Haver Analytics, OFR analysis

liquidity fees or restrictions on withdrawals that could 

help reduce the likelihood of runs, but this system has 

yet to be tested (see FSOC, 2012b, and SEC, 2013a). 

Figure 5 illustrates the vulnerability of prime money 

market funds to counterparty failure. The most vulnerable funds would break the buck — fall below the 

$1 per share 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. Figure 6 

illustrates the connections of the largest money market 

funds to the institutional issuers whose securities they 

hold. In addition, forced asset (fire) sales are a risk if 

cash providers (such as money market funds) withdraw 

cash and collateral providers (typically broker-dealers) 

are unable to finance their positions. Finally, contagion risk could aggravate and extend such asset sales if 

the inability to unwind illiquid assets adds pressure on 

other securities and market participants.

Progress in addressing these risks for financial institutions has been mixed. The total and repo liabilities 

of shadow banking entities have declined significantly 

since the financial crisis (see Figure 7). However, 

mortgage real estate investment trusts (REITs) — leveraged investment vehicles that borrow shorter-term 

funds in the repo market and invest in longer-term 

agency mortgage-backed securities (MBS) — have not 

followed this trend.7

Mortgage REITs have grown nearly fourfold since 2008 

and now own about $350 billion of MBS, or 5 percent, 

of the agency MBS market.8 Two firms dominate the 

sector, collectively holding two-thirds of assets. By 

leveraging investor funds about eight times, mortgage 

REITs returned annual dividend yields of about 15 

percent to their investors over the past four years, 

when most fixed-income investments earned far less. 

Mortgage REITs obtain nearly all of their leverage in 

the repo market, secured by MBS collateral.

Lenders typically require that borrowers pledge 5 

percent more collateral than the value of the loan, 

which implies that a mortgage REIT that is leveraged 

eight times must pledge more than 90 percent of its 

MBS portfolio to secure repo financing, leaving few 

unencumbered assets on its balance sheet. If repo 

lenders demand significantly more collateral or refuse 

to extend credit in adverse circumstances, mortgage 

REITs may be forced to sell MBS holdings. Timely 

asset liquidation and settlement may not be feasible 

in some cases, since a large portion of agency MBS 

trades occurs in a market that settles only once a 

month (see Figure 8).

Although their MBS holdings account for a relatively 

small share of the market, distress among mortgage 

REITs could have impacts on the broader repo market 

because agency MBS accounts for roughly one-third 

of the collateral in the triparty repo market. Mortgage 

REITs also embody interest rate and convexity risks, 

concentration risk, and leverage. For these reasons, 

forced-asset sales by mortgage REITs could amplify 

price declines and volatility in the MBS market and 

broader funding markets, particularly in an already 

stressed market. 9

Figure 9 illustrates how the combination of these risks 

could lead to spiraling losses at mortgage REITs. These 

are not merely hypothetical concerns. During May 

2013 and June 2013, when interest rates rose sharply 

and MBS spreads widened, mortgage REITs sold about 
Analyzing Threats to Financial Stability 17

Figure 8. Example of a Mortgage REIT Fire Sale

Agency MBS

Market

Repo

REITs Other

Investors

Asset Sales

Broker-Dealers

Asset sales by leveraged investors 

including REITs contribute to a fire 

sale, causing further losses among 

REITs and other MBS investors 

REITs sell 

assets to meet 

margin calls

A rate shock requires 

REITs to post 

additional collateral

Sources: Bloomberg L.P., REIT financial statements, OFR analysis

Figure 9. Example of Stress on a Mortgage REIT Balance Sheet

MBS 115.0 Repo 100.0 MBS 110.0 Repo 100.0 MBS 80.0 Repo 70.0

:hcihw fo :hcihw fo :hcihw fo 

0.501derebmucne 0.501derebmucne 5.37derebmucne 

0.01derebmucnenu 0.5derebmucnenu 5.6derebmucnenu 

ytiuqE 0.51 qE ytiu 10.0 Equity 10.0

egareveL 7.7 egareveL 11.0 Leverage 8.0

tucriaH %5 tucriaH %5 tucriaH %5

Asset Sales 30.0

MBS 75.0 Repo 70.0 MBS 58.0 Repo 53.0 MBS 40.0 Repo 35.0

:hcihw fo :hcihw fo :hcihw fo 

e 5.37derebmucn 0.85derebmucne 2.83derebmucne 

5.1derebmucnenu 0.0derebmucnenu 8.1derebmucnenu 

Equity 5.0 ytiuqE 0.5 ytiuqE 0.5

0.51egareveL 6.11egareveL gareveL e .8 0

tucriaH %5 tucriaH 9% Haircut 9%

Asset Sales 17.0 Asset Sales 18.0

Cumulative Asset Sales 65.0

(4) Selling by REITs and other leveraged 

investors leads to further MBS price declines. 

(5) Lenders raise haircuts to account for 

increased volatility, requiring further sales. 

(6) Investors demand a return to eight-times 

leverage, leading to more sales. 

(3) REITs are unable to raise capital and sell 

some assets to reduce duration and leverage. 

(2) MBS price declines reduce book value, 

equity, and repoable assets, and increase 

leverage. 

(1) Starting sample REIT balance sheet.

Assets Liabilities Assets Liabilities Assets Liabilities

Assets Liabilities Assets Liabilities Assets Liabilities

Source: Bloomberg L.P., nancial statements, OFR analysis Sources: Bloomberg L.P., REIT financial statements, OFR analysis
2013 OFR Annual Report 18

$45 billion of MBS, or roughly 12 percent of their 

holdings. Most all those sales were to prevent leverage 

ratios from rising sharply. Although many mortgage 

REITs sold assets and decreased the duration of their 

portfolios, some allowed their leverage to rise, which 

exposed them to greater potential losses from subsequent increases in interest rates. More data would be 

needed to assess the likelihood and potential market 

impacts of distressed selling by mortgage REITs (see 

Chapter 5).

U.S.-based foreign banking offices also rely heavily 

on wholesale funding (see Figure 10).10 Since the start 

of 2009, the combined share of repo funding channeled to foreign banking offices and mortgage REITs 

has increased about 20 percent to about 55 percent, 

while the share of repo borrowing by U.S. banks and 

broker-dealers has fallen to 34 percent. The dependence on wholesale funding by U.S. branches of 

foreign banks, which includes both repo and large 

time deposits, stems from their limited access to retail 

deposits. U.S. foreign banking offices have increasingly loaned borrowed funds to broker-dealers, which 

could imply vulnerabilities for the broker-dealer 

community if U.S.-based foreign banks’ access to 

wholesale funding was to be unexpectedly curtailed. 

Foreign banks obtain large amounts of secured 

financing from money market funds and other shortterm, risk-averse investors. The large share of repo 

funding channeled to foreign banks means that 

shocks abroad can impact U.S. funding markets. For 

example, the European crisis of 2011 contributed to 

funding strains for U.S. branches of European banks 

as money market funds reduced exposures to jumbo 

certificates of deposit. 

Credit Risk 

Among nonfinancial corporations, leverage has been 

increasing since 2012, and there are reasons to be 

concerned about a potential deterioration in corporate 

balance sheets once interest rates begin rising. 

After the crisis, nonfinancial corporations managed 

their balance sheets conservatively to reduce debt and 

build liquidity, while profits grew at an accelerating 

rate. Since 2010, however, leverage on investment-grade 

and high-yield corporate balance sheets has been 

rising (see Figure 11). Early in the cycle, most of that 

Figure 10. Fed Funds and Repo Liabilities by Entity Type 

(percent)

0

25

50

75

100

2009 2010 2011 2012 2013

Others

Brokers-Dealers (net)

U.S.-Chartered Depository Institutions

U.S.-Based Foreign Banking Ofces

Mortgage Real Estate Investment Trusts

Sources: Federal Reserve, Haver Analytics, OFR analysis
Analyzing Threats to Financial Stability 19

Figure 11. Share of Corporates Exhibiting a Year-onYear Increase in Leverage (percent)

0

10

20

30

40

50

60

70

1997 1999 2001 2003 2005 2007 2009 2011 2013

Note: Leverage is dened as the ratio of net debt to EBITDA (earnings 

before interest, taxes, depreciation, and amortization).

increase was at corporations with strong credit ratings and low debt. More recently, weaker companies 

have followed suit. Corporate cash buffers have been 

steadily diminishing, reversing the hoarding that took 

place earlier. 

Underwriting standards continue to weaken by some 

measures. Companies with low credit ratings have been 

among the biggest issuers of new debt, with recent 

transactions turning more aggressive. There has been 

a spate of payment-in-kind bonds, which pay interest 

or dividends to investors with additional debt. Also, 

less-strict terms are being used in legal covenants 

attached to leveraged loans. Another sign of weaker 

underwriting standards are dividend-refinancing 

loans, which increase leverage through the financing 

of shareholder dividends by reducing the capital stock 

that buffers a firm from insolvency. 

Relatively easy financial conditions often are accompanied by, or lead to, a compression in risk premiums and 

higher asset prices. Loans with weaker covenants (covlite loans) carry less stringent borrower obligations and 

represent one example of mispriced credit risk. With 

fewer investor protections for cov-lite loans, expectations of recovery on default are lower. Consequently, 

a cov-lite risk premium should exist to account for 

lesser creditor protection (fewer covenants) and lower 

expected recoveries. Based on historical recovery and 

default rates, these loans should command a risk premium of 30 to 35 basis points, but are currently priced 

below or only on par with other comparable loans 

requiring stronger protections for lenders.11 By the 

same token, despite the deterioration in fundamentals, 

corporate borrowing costs and the spread investors are 

willing to pay per unit of balance sheet risk are at historically low levels, implying a lower price of credit risk 

and greater risk of a sharper adjustment in reaction to 

an adverse shock.

Duration and Interest Rate Risk

Investment portfolios now face growing duration risk 

— the risk that investors will incur outsized losses in 

the event of an unexpected rise in interest rates as a 

result of exposure to long-dated, fixed-rate bonds. 

Courtesy of a long period of low yields, low volatility, 

and investors’ search for yield, duration risk is at recent 

historical highs.12 Portfolio allocations to fixed income 

instruments also remain above the recent historical 

Sources: Morgan Stanley, Bloomberg L.P.

Figure 12. Cumulative Mutual Fund Flows ($ billions)

-300

-100

100

300

500

700

2003 2005 2007 2009 2011 2013

Equities Bonds

Trendline Trendline

$200

$200

Sources: EPFR Global, Haver Analytics, OFR analysis
2013 OFR Annual Report 20

ASSET MANAGEMENT AND FINANCIAL STABILITY

The OFR issued a report in September 2013 analyzing the asset 

management industry and its activities to understand the threats 

they might pose to financial stability and to assess potential channels through which they may transmit or amplify such threats. 

The Financial Stability Oversight Council requested the report to 

better inform its analysis of whether — and how — to consider 

such firms for enhanced prudential standards and supervision 

under Section 113 of the Dodd-Frank Act.

Figure 13. Asset Management Industry Overview

PRIVATE INVESTABLE 

ASSETSa

(assets under management in $ billions) Registered 

Investment Advisers

Separate Accountsd

$10,076 

Insurance Companies

Insurance

Separate Accountsf

$2,070 

Off-Balance-Sheet

Separate Accounts

$6,030 

Source: P&I/OFR, NAIC

Bank Holding 

Companies & Banks

Private Fund Firms 

Regulatory AUMh

Hedge Funds

$4,767 

Private Equity Funds

$2,717 

Other Private Funds

$2,293 

Source: SEC Form ADV

ASSET MANAGERSa

(assets under management in $ billions)

a Figures include double-counting due to cross-investing among managers and multisourcing of data in construction of table.

b

 Includes all nonexempt registered investment advisers as reported on SEC Form ADV.

c

 Some insurance companies reporting data to Pensions & Investments (P&I) classify insurance separate accounts and other on-balance-sheet assets as assets under management.

d

 Separate accounts estimated by deducting registered funds from total worldwide assets under management using P&I data

e

 Mutual funds registered in the United States.

f

 Separate accounts managed by an insurance company, the assets are on the insurance company’s balance sheet. g

 Does not include limited purpose trust companies with state charters.

h

 Regulatory AUM refers to gross assets under management, without adjusting for leverage.

Registered 

Investment 

Advisers (RIAs) 

$45,400b

Broker-Dealer Firms

(with RIA affilliates included above)

$1,642b 

Source: SIFMA 

Insurance

Companies

$8,670c

Source: P&I/OFR

Bank Holding 

Companies

$16,008 

Source: P&I/OFR

Money Market Funds: $2,651

Fixed-Income Funds: $3,313

Bonds: $3,061

Exchange-traded (ETFs): $252

Taxable: $2,358

Tax-free: $293

Other Strategies: $1,173

Equity Funds: $6,044

Foundations & 

Endowments

Foundations: $646

Endowments: $406

Sources: Foundation Center, 2012 NACUBO

Commonfund Study of Endowments

Defined Benefit 

Plan Sponsors

Private Pensions: $6,636

State and Local: $3,193

Federal Government: $1,582 

Source: Haver Analytics

Total: $11,412

Total: $1,052

U.S. & International: $5,104

ETFs: $940

Short-Term Investment/

Cash Funds: $239

Fixed-Income Funds: $439

Taxable: $433

Tax-free: $6

Other Strategies: $305

Equity Funds: $1,354

U.S.: $748

International: $606

Stock/Bond Blend: $196

Specialty/Other: $109

Common & Collective 

Trust Fundsg 

$2,337

Separate Accounts 

$10,377 

Mutual Fundse

$13,181 

Source: P&I/OFR, Morningstar

Source: P&I/OFR, call reports

Allocation: $873 | ETFs: $3

 Alternative: $92 | ETFs: $36

Commodities: $51 | ETFs: $118

Source: OFR (2013)
21 Analyzing Threats to Financial Stability [Insert text]

In the report, Asset Management and Financial Stability, we estimate that, at the end of 2012, the U.S. asset management industry 

oversaw the allocation of approximately $53 trillion in financial 

assets (see Figure 13). The exact figure is unobtainable because of 

double-counting due to cross-investing and other issues. However, 

industry sources have similarly estimated total assets under management at $54.8 trillion (see IAA and SIFMA, 2013). 

The report notes that the asset management industry is central 

to the allocation of financial assets on behalf of investors. By 

facilitating investment for a broad cross-section of individuals and 

institutions, discretionary asset management plays a key role in 

capital formation and credit intermediation, while spreading any 

gains or losses across a diverse population of market participants. 

A cornerstone of the report is that the asset management industry 

and its activities differ in important ways from commercial banking 

and insurance. Asset managers act primarily as agents, managing 

assets on behalf of clients. Losses are borne by — and gains 

accrue to — clients rather than asset management firms. In contrast, commercial banks and insurance companies typically act as 

principals, directly bearing losses and accruing gains.

However, the risks in some types of asset management activities are similar to those in activities at banks and other nonbank 

financial companies. These activities increasingly cut across the 

financial system in a variety of ways. For example, asset managers 

may create funds that can be close substitutes for the money-like 

liabilities created by banks. They engage in various forms of 

liquidity transformation, primarily, but not 

exclusively, through collective investment 

vehicles, and they provide liquidity to 

clients and to financial markets. 

We chose asset management activities, 

rather than firms, to be the basic building 

blocks for analyzing the industry, because 

firms have a diverse mix of business 

models, offer a broad variety of funds, and 

engage in many activities. This approach 

permitted the flexibility to analyze risks 

posed by firms (firm divisions, or firms 

as consolidated entities) or by industry 

market sectors by aggregating activities and assessing the interplay among 

them. Analyzing activities individually 

or in combination permitted analysis of 

transmission channels for risks, as well as 

assessments of how the practices of the industry or its firms could 

amplify risks to financial markets, institutions, or funds. 

The scope of the report excludes hedge funds and other private 

funds because the SEC Form PF and the OFR are evaluating data 

being collected on these funds. It also excludes money market 

funds because the Council earlier released a detailed analysis 

of these funds and their risks, and the SEC recently proposed additional reforms. 

The asset management report focuses on four key factors that 

make the industry vulnerable to financial shocks (see Figure 14):

• “reaching for yield” and herding behaviors; 

• redemption risk in collective investment vehicles; 

• leverage, which can amplify asset price movements and 

increase the potential for fire sales; and 

• firms as sources of risk. 

The report also discusses two potential transmission channels: 

disruptions in markets caused by fire sales, and exposures of 

creditors, counterparties, and investors. 

Registered funds, publicly listed companies, and banks and 

other regulated entities provide information about their activities. However, the report highlights data gaps related to asset 

management, particularly to separate accounts managed by U.S. 

asset managers and activities such as repo transactions and the 

reinvestment of cash collateral from securities lending. We discuss 

these further in Section 5.4.

Figure 14. Asset Management Activities, Vulnerabilities, and Transmission Channels

TRANSMISSION

CHANNELS

Disruptions in 

markets caused by 

fire sales

Exposure of 

creditors, 

counterparties, 

and investors

Example 1: Losses in highly leveraged funds, combined with reaching for yield behavior, could amplify re 

sales and adversely affect fund counterparties. 

VULNERABILITIES

Reaching for yield 

& herding

Redemption risk

Leverage

Firms as sources 

of risk

Example 2: During a crisis, the rapid unwinding of investments of cash collateral from securities lending 

could pose risks that amplify re sales and trigger runs. 

ACTIVITIES*

Asset allocation

Portfolio selection

Management of fund liquidity

Management of fund leverage

Risk management & central functions

Possibility of sponsor support

Securities lending cash reinvestment

Management of firm capital & liquidity

Firm-level

Fund-level

Source: OFR (2013)

* The activities and examples are illustrative and not exhaustive.
2013 OFR Annual Report 22

trend, despite the rise in yields in May 2013 and June 

2013 (see Figure 12). Thus, losses from a given change 

in interest rates would be larger than in the past. 

These positions increase the vulnerability for some 

market participants to outsized losses that could be 

difficult to absorb in the event of an unanticipated 

increase in long-term rates. To assess the degree of vulnerability, we simulated an adverse interest rate shock 

to estimate losses by bond funds from an instantaneous 

parallel shift in the yield curve of 100 basis points from 

current levels. We then compared the impact of such 

losses in today’s context to loss rates from a similar 

hypothetical scenario during the three previous periods of U.S. monetary policy tightening. Losses during 

each tightening cycle are calculated by averaging 

monthly estimated losses, where the Barclays Capital 

U.S. Aggregate Bond Index is used as a proxy for 

duration and mutual fund bond holdings are based on 

data from the Investment Company Institute. Figure 15 

shows that losses could rise to nearly $200 billion (or 

5.5 percent of GDP), underscoring that current bond 

portfolios are vulnerable to a sudden, unanticipated 

rise in long-term rates. 

Interest rate risk extends beyond nonfinancial bond 

portfolios. On the asset side, banks have increased 

their holdings of longer-term assets, leaving them more 

exposed to interest rate risk. On the liability side, U.S. 

banks have seen dramatic growth in their non-interestbearing deposits relative to total banking system 

liabilities. The ratio now stands at a 30-year high. It 

is unclear how much of the growth is attributable to 

structural factors or cyclical factors. Challenges exist 

for banks and regulators in modeling the behavior of 

these deposits as interest rates rise. There is a nonnegligible risk that deposits would shift to alternative, 

higher-yielding investments as rates rise. 

In the event of an adverse interest rate shock, policymakers would likely adopt actions aimed at tempering the 

rise, for instance through communication and fine-tuning policies. However, determining the underlying 

drivers of the rise could be challenging. For instance, 

the roughly 100 basis point rise in long-term rates that 

took place during the May–June period mostly reflected 

an increase in term premiums (the extra yield needed 

for investors to hold a long-term bond instead of a series 

of short-term bonds) rather than short-rate expectations 

(see Adrian and Fleming, 2013). 

Figure 15. Estimated Impact of an Immediate 

100-Basis-Point Shock to Interest Rates 

($ billions and percent)

4.0

5.0

6.0%

0

50

100

150

$200

Feb 1994-

Feb 1995

Jul 1999- 

May 2000

Jul 2004-

Jul 2006

Jul 2013

Estimated Loss to U.S. Bond Funds ($ billions)

Estimated Percent Loss Rate to U.S. Bonds

Note: Hypothetical rate tightening beginning November 2013. For 

1994-95 cycle, bond convexity set to zero as convexity data for 

that period are unavailable. 

Periods of U.S. Monetary Policy Tightening

Sources: Investment Company Institute, Haver Analytics, Barclays Capital, OFR analysis
Analyzing Threats to Financial Stability 23

To understand this rise in the term premium, we 

evaluate the statistical relationship between the term 

premium and its drivers. Decomposing the term premium is a challenging task, in part because the term 

premium itself is unobservable. Following Gagnon 

and others (2010), we constructed a model in which 

the term premium (the difference between long-term 

and short-term bond yields) on 10-year U.S. Treasury 

securities is a function of macroeconomic fundamentals and uncertainty, volatility in financial markets, 

and supply factors.13 We estimated the model over the 

past 22 years, and assessed drivers of increases and 

decreases in the term premium during the pre- and 

post-crisis periods.

Figure 16 summarizes our main findings. During most 

of the 1990s, the term premium steadily declined, 

driven predominantly by an improvement in macroeconomic factors, as unemployment and inflation 

decreased steadily. By contrast, interest rate volatility, 

reflecting interest rate uncertainty, was a key driver 

of the rise in the term premium from late 1998 to 

2000. Beginning in 2008, the Federal Reserve’s asset 

purchase program became an important driver of 

the decline in the term premium, while macroeconomic factors became less important.14 During the 

most recent period, our model suggests that increased 

interest rate volatility has more than accounted for the 

rapid rise in long-term rates, reflecting increased difficulty evaluating the future direction of interest rates. 

Although our model is imperfect, the preliminary 

findings suggest that changes in the term premium will 

be strongly tied to investor perceptions of the future 

path of nontraditional monetary policy as the Federal 

Reserve pares back its asset purchases.

Volatility: Low for Long Risks

Excessive credit risk-taking, ample liquidity, and low 

volatility increase the threat of a volatility-induced 

shock, when a sudden spike in volatility creates a 

plunge in asset prices and surge in risk aversion. Low 

volatility does not necessarily imply low risks to financial stability. Future risks to financial stability may 

actually rise precisely during periods of low volatility, 

a pattern that has been referred to as the “volatility 

paradox” (see Bookstaber, 2011). 

The risk-taking behavior of financial firms typically 

moves inversely to financial market volatility. A 

Figure 16. Drivers of Change in 10-Year U.S. Treasury 

Term Premium (percentage points)

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Jun 1992-

Jun 1998

Jul 1998-

Jan 2000

Oct 2008-

Jul 2012

Apr-Aug

2013

Macro (ination, 

ination uncertainty, 

unemployment gap)

Interest rate volatility

Equity volatility

Federal Reserve asset

purchases

Residual

Term 

Premium

Figure 17. Assets Under Management in Low Volatility 

Trading Strategies ($ billions)

0.0

0.1

0.2

0.3

2007 2008 2009 2010 2011 2012 2013

% of Total AUM

$58.5

$40.7

$19.3

$8.5 $5.5 $3.5 $5.9

Sources: Haver Analytics, Federal Reserve, University of Michigan, OFR analysis

Sources: eVestment, Morningstar Inc., Bloomberg L.P., OFR analysis
2013 OFR Annual Report 24

financial institution that has set internal limits on daily 

trading losses has more opportunities to take risk when 

market volatility declines. If such a firm does nothing 

to change its assets, liabilities, or trading strategies, its 

value at risk will also decline. The firm’s management 

may react by taking on additional risk to reap higher 

profits. Low volatility reduces the price of risktaking, which creates incentives to take on more risk. 

Declining market volatility will reduce risk premiums, 

increase the attractiveness of future cash flows from 

debt or earnings, and boost asset prices. 

For these reasons, the current environment of low 

interest rates and suppressed volatility has increased 

the attractiveness of trading strategies that depend 

on interest rates remaining low as the price of risk has 

declined (see Figure 17). Such trading positions are 

highly exposed to a reversal in volatility. The figure 

likely understates the true magnitude of low volatility trading strategies, because it does not include 

option-embedded low volatility structured products.

There are other ways for financial firms to adjust 

their risk profiles. For instance, firms borrow funds to 

acquire risky assets, pushing up prices of these assets 

and driving down risk-adjusted performance until supply and demand come closer to balance. By the same 

token, trading strategies can themselves be leveraged. 

Increasing leverage during times of low volatility can 

set the stage for future vulnerability because leverage is 

an essential ingredient for funding runs and fire sales, 

two contributors to financial instability.15 

Figure 18 shows that, on average, firms increase their 

debt when market volatility is low.16 Even though debt 

increases, risk-weighted assets do not increase, presumably because low volatility of returns lowers the risk 

weights. This means as firms increasingly use debt to 

finance the purchase of assets, they may not have to 

proportionately adjust their capital ratios because riskweighted assets do not change.

A key reason why low volatility encourages leverage is 

that, at least indirectly, it reduces the cost of wholesale 

funding. When those costs are low, incentives to add 

leverage increase. Firms may use the cheap funds they 

raise during low-volatility periods to purchase illiquid 

assets, the prices of which are rising. 

By comparison, in periods of rising volatility, the cost 

of funding is rising and the prices of risky assets are 

Figure 19. Corporate Bond Inventories, Repos, and 

Market Size

0

1

2

3

4

5

6

7

8

9

10

0

50

100

150

200

250

300

350

2002 2004 2006 2008 2010 2012

Net Broker-Dealer Inventories (left axis)

Outstanding Corporate Bond Repo (left axis)

Corporate Debt Outstanding (right axis)

$ billions $ trillions

Note: 2013 values include latest available data.

Figure 18. Change in Debt and Risk-Weighted Assets to 

Average Market Volatility (Q2 2001-Q2 2013)

-$1,000

-$500

$0

$500

$1,000

$1,500

$2,000

10 20 30 40 50

Total Liabilities Risk-Weighted Assets (RWA)

Trendline Trendline

Change in Liabilities or RWA

($ billions)

Annual Realized

Volatility (%)

Note: Data include Bank of America, Bank of New York Mellon, Bear

Stearns, Citigroup, Goldman Sachs, JPMorgan Chase, Lehman Brothers, 

Merrill Lynch, Morgan Stanley, U.S. Bancorp, and Wells Fargo. Liabilities 

and RWA values were adjusted for ination using the GDP deator.

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

Sources: Haver Analytics, Securities Industry and Financial Markets Association
Analyzing Threats to Financial Stability 25

falling. Unwinding those positions quickly in such 

circumstances may be difficult or impossible. Nor is 

it easy to quickly raise additional equity to lower debt 

levels when volatility is rising. Overleveraged investors 

whose portfolios are losing in value may be subject to 

margin calls and have to sell assets in a declining market to raise cash. That means fluctuations in market 

volatility can sow the seeds of instability by producing 

waves of aggregate increases in leverage, followed by 

troughs of aggregate deleveraging, with the inherent 

danger of fire sales.

Impaired Trading Liquidity 

Impaired trading liquidity — the inability to execute 

large trades without having a significant impact on 

market prices — could aggravate some of the threats 

already discussed. Market liquidity measures show a 

mixed picture. The current high levels of central bank 

liquidity may be masking some weakness in trading 

liquidity. Within the corporate bond market, some 

evidence indicates that liquidity is more bifurcated 

than before the crisis.17 Liquidity has become increasingly concentrated, with large, investment-grade bonds 

showing the strongest liquidity, while some smaller, 

high-yield issues have become less liquid. The gap 

has widened as broker-dealers’ securities holdings 

have shifted toward larger, more frequently traded 

corporate bonds. The growth in exchange-traded 

funds within the corporate bond market increases the 

potential to weaken market liquidity during periods of 

market stress (see Figure 20 and OFR, 2013).18

The sources of diminished trading liquidity are not 

fully understood. A commonly cited source is reduced 

broker-dealer capacity and a higher premium for the 

risk of holding inventory. Broker-dealer inventories of 

fixed-income instruments have declined since 2007, 

particularly for corporate bonds. The shift in inventories has occurred against the backdrop of an expanding corporate bond market, reducing the ability of 

broker-dealers to act as shock absorbers during market 

stress (see Figure 19). Other changes since the crisis 

may have also affected structural market liquidity, 

including shifts in the investor base, risk appetite, and 

trading behavior. 

Figure 20. ETF Price Premium or Discount to Net Asset 

Value (percent)

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Jan

2013

Feb

2013

Mar

2013

Apr

2013

May

2013

Jun

2013

iShares iBoxx High Yield Corporate Bond ETF

SPDR Barclays High Yield Bond ETF

SPDR S&P 500 ETF Trust

Note: 3-day moving average.

Figure 21. Rolling 2-Month Correlation Between 

Changes in 10-Year U.S. Treasury Yields and 

Emerging Market Bond Yields

-1.0

-0.6

-0.2

0.2

0.6

1.0

2011 2012 2013

Correlation coefcient

Sources: Bloomberg L.P., MorganMarkets, OFR analysis

Source: Bloomberg L.P.
2013 OFR Annual Report 26

Foreign Risks: Spillovers to and from 

Emerging Markets 

Accommodative monetary policies in advanced economies, strong domestic fundamentals in select emerging 

markets, and a structural increase in investor allocations have led to strong cross-border portfolio flows to 

emerging markets over the last few years. Foreign flows 

have predominantly targeted emerging market bonds, 

with cross-over and nondedicated emerging market 

investors increasing their footprint. In some emerging 

markets, domestic policies have encouraged local companies to expand debt to high levels and boost leverage 

(see IMF, 2013e). 

Increased sensitivity between the U.S. risk-free rate 

and emerging market capital inflows has increased the 

vulnerability of capital flows to a sudden increase in 

U.S. rates. A reversal in capital flows could highlight 

vulnerabilities that have built up, particularly where 

sovereigns and corporates have become dependent 

on capital inflows to meet near-term borrowing and 

refinancing needs. An abrupt reversal in inflows would 

be damaging for countries with external imbalances or 

near-term refinancing needs.

Yield-seeking capital flows across borders, driven 

by both external and domestic factors, have driven 

a decline in local bond yields.19 Markets for emerging-market bonds have grown increasingly more 

sensitive to changes in U.S. interest rates (Figure 21). 

Rises in yields for 10-year Treasury bonds have been 

accompanied by a depreciation in emerging-market 

currencies, higher bond yields, and weakness in equity 

valuations. 

The sell-off in emerging markets that began in late 

May illustrates what could happen once U.S. monetary 

conditions tighten. Higher U.S. interest rates coincided 

with a pullback in capital flows to emerging markets 

and increased instability in emerging market assets. 

The first phase of the sell-off was concentrated in 

highly liquid proxy trades (trades that use one asset 

class to take positions in another asset class — for 

example, positions in commodity producers’ assets 

to proxy for China). The second phase saw a more 

pronounced sell-off in assets that had been the primary beneficiaries from excess liquidity since the start 

of the Federal Reserve’s quantitative easing program 

(see Figure 22). The third phase reflected further 

Figure 22. Change in 10-Year Local Currency Emerging 

Market Sovereign Bond Yields

(percentage points)

-6

-5

-4

-3

-2

-1

1

-0.4 0.0 0.4 0.8 1.2 1.6

Thailand

Taiwan China

India

Turkey

Indonesia

Colombia

Poland

Philippines

Czech

Republic

Malaysia

Russia

Israel

Hungary Mexico

South

Africa Peru

Korea

Brazil

Jun 2010-May 2013 

Jun-Oct 2013 

Figure 23. Domestic Fundamentals in Emerging Markets 

(percent as a share of GDP) 

Argentina

Brazil

Chile

China

Colombia

Czech 

Republic

Hungary

India

Indonesia

Israel

Korea Malaysia

Mexico

Peru

Philippines

Poland

Russia

South 

Africa

Taiwan

Thailand

Turkey

-8

-4

0

4

8

12

2 4 6 8

Note: Data are comprised of trailing four quarter averages ending 

June 2013.

Current account balance

FX reserves 

Sources: Bloomberg L.P., OFR analysis

Sources: Haver Analytics, OFR analysis 
27 Analyzing Threats to Financial Stability

New channels for credit intermediation have reshaped 

China’s financial system and driven growth in the ratio 

of private nonfinancial sector credit to GDP from 117 to 

170 percent between 2008 and 2012. Most notable has 

been the growth of wealth management products (WMPs), 

off-balance-sheet investment products offered by banks, 

trusts, and securities companies.

Wealth management products provide investors a higher 

return than deposits and can be invested in any combination 

of government securities, corporate bonds, trust loans, interbank loans, securitized loans from banks, and other assets. 

The growth of the WMP market has been rapid and 

extensive. Restricted by a 75 percent loan-to-deposit 

cap and other constraints, banks sought to transition into 

WMP issuance and broader portfolio lending. Outstanding 

bank-issued WMPs, including those issued in cooperation 

with trust and securities companies, were 7.6 trillion renminbi (Rmb) at the end of 2012 ($1.3 trillion), up from Rmb 

800 billion at the end of 2008. Including WMPs issued by 

trust and securities companies without bank participation, 

total WMP issuance is estimated to be between Rmb 8.5 

billion and Rmb 9.8 billion (see Rothman, 2013). 

Risks in WMPs vary. Bank-issued WMPs generally have 

terms of six months or less, invest in liquid assets with 

shorter maturities such as government securities, and 

offer returns 50 to 200 basis points higher than deposit 

rates. Bank-issued WMPs may be on or off a bank’s 

balance sheet, depending on product guarantees. WMPs 

issued by trust and securities companies may offer 

returns of 10 percent to 15 percent, and invest in riskier 

assets, such as weaker quality corporate loans, loans to 

local government financing vehicles, and similar products. 

Chinese regulators have tried to control excesses in 

the WMP sector. In March, China’s banking regulator 

announced new rules to limit WMPs invested in riskier nonstandard debt instruments. In June, China’s central bank 

refrained from alleviating tight interbank liquidity conditions 

for several trading sessions, driving interbank lending rates 

to 30 percent, compared to a typical rate of 5 percent. The 

initial reluctance of the central bank to address the liquidity 

squeeze was interpreted by the market as a policy decision 

to restrain the rapid growth of off-balance-sheet lending. 

China’s economic growth has slowed despite rapid 

increases in credit. Banks are facing significant headwinds. 

A number of bank loans issued during the 2008-09 stimulus have been restructured or rolled over in recent years to 

avoid nonpayment. In past years, banks had ample liquidity 

because of limited competition to deposits from alternative investments. However, with the growth of WMPs and 

nonlending credit channels, competition for deposits has 

intensified, leading to increasing strains on bank liquidity. 

Despite these risks, there are reasons to believe that the 

threats to financial stability in China may be manageable. 

High reserve requirements of 20 percent permit room for 

easing if nonperforming loans rise, and the central government has low debt, permitting room for policy support. 

Though lending to small and medium-sized enterprises has 

increased, the majority of borrowers continues to be larger 

state-owned enterprises, and most capital is provided 

through state-owned financial institutions. 

Also, despite the growth in off-balance-sheet funding 

through WMPs and other products, China’s financial sector 

is still funded primarily by domestic deposits and options 

for international investment are limited, reducing the 

likelihood of capital flight. On top of this, China’s foreign 

exchange reserves, at more than $3.5 trillion, are the largest in the world, providing additional financial support. 

A closed capital account and financial strength to manage 

a crisis make a material threat to U.S. financial stability 

unlikely from current conditions in China. However, U.S. 

financial markets are not impervious to economic and 

financial activity in China. Inflows of Chinese capital contributed to low U.S. interest rates and the housing bubble 

in the mid-2000s. A collapse in global commodity prices, 

reduced prices for manufactured goods, or lower returns 

for foreign companies invested in Chinese markets could 

create headwinds for global economies. The current shifts 

in China’s financial markets are significant and in time 

could have a large impact on global financial markets.

FINANCIAL INTERMEDIATION IN CHINA
2013 OFR Annual Report 28

Figure 24. Policy Buffers in Emerging Markets

Argentina

Brazil

Chile

China

Colombia

Czech Republic

Hungary

India

Indonesia

Korea

Malaysia

Mexico

Peru

Philippines

Poland

Russia

South Africa

Thailand

Turkey

Monetaryb Externalc Fiscala

Note: Data as of July 2012 and November 2013.

a Green if gross nancing needs are less than 5% of GDP and 

overall balance is less negative than -2% of GDP; red if gross 

nancing needs are greater than 10% of GDP or overall balance 

is more negative than -3% of GDP; yellow otherwise.

b For ination targeters, green if the policy rate is at least 4% and 

projected CPI ination is at least 1 percentage point lower than 

the (upper bound of the) ination target (range); red if the policy 

rate is lower than 2% or projected ination is above the (upper

end of the) ination target (range); and yellow otherwise. For 

non-ination targeters, green if the policy rate is at least 4% and 

projected ination is 3% or lower; red if the policy rate is lower 

than 2% or projected ination is above 6%; and yellow

otherwise. Countries operating under currency board regimes are 

coded as red.

c Green if current account balance is greater than 6% of GDP and 

FX Reserves are greater than 6% of GDP; or if current account 

balance is less negative than -5% of GDP and FX reserves are 

greater than 40% of GDP; red if current account balance is more 

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

GDP; yellow otherwise.

Capacity to Absorb Shock

Low Moderate High 

2012 2013 2012 2013 2012 2013

differentiation, with heightened volatility in emerging 

market assets with the weakest domestic fundamentals 

(for example, weaker relative growth prospects, low 

or eroding foreign exchange reserves, large external 

financing needs, high levels of leverage, or limited 

policy buffers). On these measures, markets in several 

countries look vulnerable, including Turkey, South 

Africa, India, Indonesia, and Brazil (see Figure 23). 

Sustained volatility can feed on itself, spilling over to 

other risk assets as losses trigger fund redemptions 

and asset sales. Emerging markets are generally more 

resilient as an asset class than in the past, thanks to liberalized exchange rates, more prudent macroeconomic 

policies, and issuance of debt in local currencies rather 

than in dollars. But vulnerabilities remain, including 

the buildup of corporate debt and leverage (see IMF, 

2013e), rapid nonbank credit growth (see insert on 

Financial Intermediation in China), and diminished 

policy buffers. Emerging markets are now larger and 

more connected to developed markets, which means 

stress can be more readily transmitted directly or 

indirectly to the U.S. through various conduits, including funding, foreign exchange, credit, and growth 

channels.20

An increase in U.S. policy rates could create challenges 

for overseas central banks seeking to maintain a looser 

monetary policy stance. An already challenging policy 

environment for certain emerging markets with less 

capacity to absorb external shocks increases the risk of 

a policy error. Figure 24 shows significant differentiation in monetary, external, and fiscal buffers. Since our 

last annual report, buffers have eroded in Indonesia, 

Malaysia, Peru, India, and South Africa. The thinner 

cushion means less room for stimulus, more difficulty 

in managing external shocks, and a greater risk of a 

policy error.

Operational Risk 

Developing more secure internal risk controls and 

risk management systems remains an ongoing structural risk theme. The Council’s latest annual report 

cited technological and operational failures, natural 

disasters, and cyber-attacks as potential sources of 

significant financial system stress (see Figure 25). 

One key source of operational risk across all markets 

is the growing role of automated trading systems, 

including high-frequency trading systems, which use 

Sources: Haver Analytics, International Monetary Fund, various central banks, OFR 

analysis
Analyzing Threats to Financial Stability 29

sophisticated algorithms to place rapid-fire orders after 

analyzing large volumes of market data. 

Automated trading represents a significant portion 

of daily equity and foreign exchange volumes and a 

sizable portion of Treasury market volumes. Given 

these volumes, high-frequency trading poses several 

potential financial stability risks, suggesting that closer 

monitoring may be warranted. 

Liquidity is the most commonly cited concern. Some 

studies suggest high-frequency trading improves market liquidity in equity and foreign exchange markets 

by narrowing bid-ask spreads, suppressing volatility, 

and improving price discovery (see Hendershott, 

Jones, and Menkveld, 2011, and Chaboud and others, 

2009). But research suggests that such activity may 

disappear during periods of high volatility. Liquidity 

could decline if large losses accumulated quickly and 

unexpectedly, and trading controls were inadequate. 

Also, liquidity provided by high-frequency trades is 

not the same as the liquidity provided by traditional 

market-makers, as it lacks depth due to the small size 

of quotes and the fact that high-frequency-trading 

firms have no market-making obligation. If there are 

fewer traditional market-makers because of narrower 

bid-offer spreads, the exit of high-frequency traders 

during times of market stress could reduce liquidity 

(see Barker and Pomeranets, 2011). 

Price discovery is another concern. The proliferation 

of trading in private market venues such as single-dealer trading platforms and dark pools — off-exchange venues that let large institutions trade anonymously — may be tied to the rise of high-frequency 

trades. This type of activity potentially leads to market 

Technology Failures Natural Disasters Cyber Attacks

Risk Highly automated trading systems 

are vulnerable to technology 

failures. Automated and highfrequency trading execution may 

compound impact of trading or 

operational errors.

May cause severe damage to 

energy, telecommunications, and 

transportation infrastructures, 

critical post-trade financial 

utilities, including core settlement 

and clearing functions.

Cyber-attacks and hacking cause 

disruptions, which affect the 

financial system’s resilience and 

soundness

Recent episodes Flash Crash (2010) – 9 percent 

daily plunge in Dow Jones.

Facebook IPO (2012) – 

dysfunction in Nasdaq systems 

delayed trading.

Knight Capital (2012) – $440 

million trading loss triggered from 

unintended orders due to software 

malfunction.

Goldman Sachs (2013) – 

erroneous orders in equity options.

Nasdaq (2013) – trading halt due 

to processor outage.

Superstorm Sandy (October 

2012) – caused a two-day closure 

of the NYSE and NASDAQ. Fixedincome markets were also closed 

for a day. Money markets had 

minor disruptions.

A number of financial institutions 

have experienced distributed 

denial of service (DDoS) attacks, 

which overload a targeted 

computer server with e-mail 

traffic, and hacking attacks.

JPMorgan Chase and Regions 

Financial Corp. (July 2013) – 

experienced intermittent outages 

that were DDoS-related.

OpUSA (May and September 

2013) – hackers coordinated 

online attacks against banking and 

government websites in operation 

they called “OpUSA.”

Mitigants Strengthen internal, external, and 

systemic controls

Strengthen contingency planning 

and testing, incident management, 

personnel, and dependencies

Ensure that safeguards keep 

up with rapid technological 

advancements

Figure 25. Examples of Operational Risks
2013 OFR Annual Report 30

fragmentation, affects transparency, and impairs price 

discovery. Studies have shown that high-frequency traders tend to follow a price reversal strategy (rapidly buying after price declines and selling after increases), and 

are quick to detect price anomalies and act to stabilize 

prices. However, high-frequency traders also employ 

a narrower range of investment strategies, which may 

distort asset prices if similar trades are executed by 

several firms simultaneously. 

The potential for increased market volatility is also a 

risk, although the evidence is mixed on precisely how 

high-frequency trading may affect volatility. Zhang 

(2010) shows that high-frequency trading increases 

stock market volatility, particularly among stocks with 

a high proportion held by buy-and-hold institutional 

investors. The supply of such stocks for trading each 

day may be small, and high-frequency trading could 

significantly impact price volatility. Other studies 

suggest that high-frequency trading distorts market 

conditions through prices, but not necessarily through 

the volume of shares traded (see Brogaard, 2010).

Risk management measures used by high-frequency 

trading firms are not well understood and require 

further investigation, particularly regarding controls, 

capital, and the framework for assessing intraday 

positions that change rapidly. Weak risk management 

of high-frequency trading increases the potential for 

counterparty risk because prime brokerages are the 

primary channel to public trading venues for highfrequency trading clients. 

From a financial stability perspective, the OFR is 

focused on the potential interaction of various risks, 

such as a volatility shock amid extended portfolio duration and an inability or unwillingness of marketmakers 

to provide liquidity. In that scenario, high-frequency 

trading systems may obscure price discovery, exaggerate 

illiquidity, increase volatility, and contribute to extreme 

price changes. The initial trigger may be a loss by a large 

institution that leads to a market disruption, with a cascading effect on markets and market participants. 

U.S. Fiscal Policy Outlook 

Despite the recent sharp narrowing in the U.S. federal 

budget deficit, the U.S. fiscal policy outlook carries 

financial stability risks, driven by three factors. First, a 

rapid pace of deficit reduction carries economic costs. 

Second, a clear resolution of the nation’s long-term 

fiscal challenges is still lacking. Finally, the political 

process for implementing sustainable fiscal adjustments has become more uncertain.

Caps on discretionary spending and other measures 

have led to near-term fiscal improvement. The federal 

deficit has declined sharply over the past two years 

from an estimated 8.7 percent of GDP in fiscal year 

2011 to 4.1 percent in 2013. Under current law, and 

due in part to additional cuts to take effect in January 

2014 as part of the budget sequester, the Congressional 

Budget Office projects the deficit will decline to 2.1 

percent of GDP by fiscal year 2015 (see CBO, 2013a). 

But this substantial fiscal adjustment carries two risks. 

First, it has created a fiscal drag on an economy that 

remains weak. Second, it creates an extra burden on 

other policy levers to support the economy. A policy 

mix that keeps short-term interest rates and unconventional monetary policy tools in place for an extended 

period potentially increases future risks to financial 

stability, whether through excessive risk-taking in 

credit markets or through volatility and interest rate 

shocks.

In the longer term, two other adverse trends cause 

greater concern. The absence of bipartisan agreement 

has raised questions about whether long-term fiscal 

problems may be resolved smoothly, as noted in the 

Council’s 2013 Annual Report and in the Office’s 2012 

Annual Report. The budget sequester did not address 

the longer-term sources of U.S. budget challenges. 

Without further action, federal deficits and debt are 

likely to rise again as a share of GDP after 2018, as 

growth in entitlement outlays and debt service outstrips economic growth (see CBO, 2013b). 

Equally concerning, the political process to address 

fiscal challenges has become increasingly uncertain, 

as shown by the debt-ceiling crisis in the summer of 

2011, the “fiscal cliff” crisis in December 2012, and the 

government shutdown and debate over extending the 

debt ceiling in October 2013.

Delays in addressing long-term challenges could have 

longer-term, and potentially permanent, adverse financial stability consequences, including:

Increased credit and liquidity risk premiums. What 

sets Treasury securities apart from other highly rated 
Analyzing Threats to Financial Stability 31

sovereign credits is their creditworthiness and high 

liquidity. The strong creditworthiness of Treasury 

debt is reflected in the safe-haven role it plays during 

periods of broad market volatility. The liquidity of 

Treasuries is evidenced by their tight bid-ask spreads, 

high turnover rates, and prevalence as benchmarks in 

financial transactions. Doubts about the ability of the 

U.S. government to meet fiscal challenges could contribute to increased credit-risk premiums and an erosion of the liquidity advantage, translating into structurally higher yields, a steeper Treasury curve, and 

increased volatility. Spillovers into risk assets would 

likely follow, including wider credit spreads, lower asset 

prices, and reduced liquidity.

Erosion of safe haven and reserve currency status.

Concerns about U.S. sovereign risk could lead central 

banks to reduce their dollar reserves and diversify 

into other currencies. That would boost United States 

borrowing costs and weaken the dollar, with spillover 

effects on other U.S. and global assets, in the absence 

of offsetting policy actions. 

In addition, near-term, future episodes of fiscal uncertainty could result in shorter-term, destabilizing effects 

on financial markets, with adverse consequences for 

the U.S. and global economies, such as: 

Forced deleveraging pressure. Concerns about potential payment delays likely would have pronounced 

market impacts. Yields on short-term Treasury bills 

could rise and the markets for derivatives and term 

repos could also be affected, as Treasuries are the 

most frequently used collateral in these markets, 

other than cash. 

During the latest episode, government-only U.S. money 

market funds — which hold about $950 billion of assets 

— registered a $55 billion decline in assets under management during the first two weeks of October. Once 

the political impasse ended, flows swiftly reversed. But 

if such actions persisted, they could lead more counterparties to sell, potentially resulting in asset fire sales in 

a worst case scenario. Even without delayed payments, 

Treasury repo market lenders could permanently 

increase discounts on borrowers’ collateral, triggering 

more margin calls and a wave of deleveraging because 

of the mere risk of such action.

Cascade of downgrades. Major credit rating agencies 

have warned that episodes of uncertainty over the 

fiscal outlook could contribute to pressure to downgrade U.S. government debt. Further U.S. sovereign 

downgrades would put at risk the ratings of other entities, particularly financial institutions, clearinghouses, 

the government-sponsored enterprises, municipalities, 

and any institution (including foreign entities) with 

large exposure to Treasuries. A single-notch downgrade would not be expected to have much impact on 

markets. Some types of Treasury investors are insensitive to price and ratings, and would likely retain large 

holdings as long as liquidity remained sufficient. Other 

investors with specific mandates might simply adjust 

their mandates.21 But a multinotch downgrade could 

be more damaging.

Effects on other dollar-denominated assets. The combination of reduced U.S. sovereign creditworthiness, 

systemwide downgrades, higher haircuts, and reduced 

access to financing would likely lead to a broader correction in fixed-income securities that are priced off of 

the Treasury curve. 

Operational risk. Segmentation in the Treasury 

market between securities that are at greater risk 

of nonpayment and other Treasury securities could 

affect collateral used for repo transactions, margins 

for futures exchanges, or over-the-counter derivative 

transactions.22 Impaired collateral that is ineligible 

for the Federal Reserve’s funds transfer system would 

have to be settled through other means, raising the 

risk of payment-and-settlement failures. This kind of 

cash payment delay or default could result in a “credit 

event,” triggering certain conditions attached to credit 

default swaps.23 Although net and gross amounts 

outstanding are small relative to the size of the market 

and the credit default swap payout on protection is low, 

potential operational challenges could arise if the full 

amount of outstanding credit default swap contracts 

were settled simultaneously. 

Economic and institutional implications. Aside from 

the direct market impact, other broader macroeconomic effects would be likely if there were a protracted 

impasse over the federal debt limit or an outright 

government default on its debt. Job creation, consumer 

spending, consumer confidence, and economic growth 

would be hurt, potentially leading to pressure on 

overall financial conditions and asset markets (see U.S. 

Treasury, 2013).

33 Evaluating Macroprudential Policy

3 Evaluating Macroprudential Policy

The OFR has a statutory mandate to study and 

advise on the impact of policies related to financial 

stability, and to evaluate and report on stress 

tests. This chapter describes a framework for 

evaluating macroprudential policies and other 

tools designed to promote financial stability, and 

examines several tools proposed for use in the 

United States. 

3.1 A Framework for Evaluating 

Policy Tools

Macroprudential policies seek to strengthen the financial 

system by addressing specific vulnerabilities and fostering 

market discipline. This section (1) provides a brief recap 

of the threat assessment framework discussed in Chapter 

2, which is needed to inform macroprudential policy; (2) 

discusses the objectives of macroprudential policy and the 

policy toolkit; (3) explains the criteria for evaluating the 

effectiveness of macroprudential policy, including potential 

unintended consequences; and (4) discusses the criteria for 

picking the right policy tools to respond to specific threats.

Financial stability is a statutory policy objective for the 

Council and its member agencies. Policy analysis is 

focused on assessing threats to financial stability and 

policymakers are creating more tools to combat those 

threats. Federal Reserve Chairman Ben Bernanke 

recently noted that “a central bank must take into 

account risks to financial stability if it is to help achieve 

good macroeconomic performance” (see Bernanke, 

2013). As described in Chapter 2, the OFR and the 

Council are developing tools for assessing and monitoring threats to financial stability, but significant gaps 

remain in the availability of data and in the indicators 

to measure and monitor threats. 

Implementing macroprudential policies to address 

those threats is inherently complicated. That is because 

macroprudential policies and tools are specifically 

designed to address vulnerabilities across the financial 

system, not just on one part of the system. Moreover, 

threats often arise from several vulnerabilities. As discussed below, several tools may be needed in combination — a toolkit — to mitigate them. 

Macroprudential policy tools should work alongside 

microprudential regulation, which focuses on safety 

and soundness of individual financial institutions 

and the functioning of financial markets. These tools 

should also work alongside monetary policy, which promotes price stability and maximum employment and 

output. Indeed, the three may be complementary and 

give policymakers more options to achieve multiple 

policy goals. However, they may also conflict with each 

other. For example, the recent financial crisis illustrated that monetary policy may achieve stable growth 

and low inflation even as imbalances build within the 

financial system. 

The U.S. toolkit has grown significantly to include new 

rules for capital and liquidity for banks and bank holding companies, including the countercyclical capital 

buffer and the proposed liquidity coverage ratio. Stress 

testing, capital planning, orderly resolution authority, 

and incentives for centrally clearing derivatives also 

are part of the toolkit. Understandably, many additions 

to the U.S. toolkit tend to concentrate on banks and 

bank holding companies. However, it is also important 

to look at tools that have been proposed to address 

threats that may arise outside the banking system, such 

as tools to reduce market vulnerabilities. In addition, it 

is worth examining tools adopted or proposed in other 

countries, for example, targeted countercyclical tools 

to address excesses in specific credit markets. 

Macroprudential tools are as inherently complicated 

to evaluate as they are to implement. First, metrics for 

gauging financial stability are elusive and multifaceted, 

reflecting the numerous services the financial system 

provides. Second, we often rely on forensic analysis 

to judge policy effectiveness, lacking evidence that a 

particular policy resulted in a specific financial stability 
2013 OFR Annual Report 34 2013 OFR Annual Report

outcome. That’s obviously true when analyzing any 

economic or financial policy tool, but unlike microprudential and monetary policy tools that have been tested 

and evaluated, macroprudential tools are newer and 

in many cases have yet to be used, let alone tested and 

evaluated. Consequently, the framework for evaluating 

such tools must be developed from the ground up. 

Finally, reflecting the nature of the toolkit, financial 

stability tools must be examined both individually and 

in combination with others. 

Four elements are critical to implementing a macroprudential policy framework: 

1. an ongoing assessment of potential threats 

to financial stability, which depends on highquality, comprehensive, and detailed data; 

2. a comprehensive policy toolkit to mitigate those 

threats, because with multiple threats and a 

complex and diverse financial system, the policy 

tools need to be diverse and targeted; 

3. a framework to assess the effectiveness of such 

tools, drawing on the experience in the United 

States and other countries; and 

4. criteria to pick the right macroprudential policy 

tool for the job, including getting the balance 

right with monetary policy and microprudential 

regulation. 

Ongoing Assessment of Threats

A macroprudential threat assessment must first 

evaluate the vulnerabilities in the system and then 

identify potential shocks that would expose those 

vulnerabilities. 

As discussed in Chapter 2, the threat assessment 

should target the causes of vulnerabilities to identify 

possible mitigants. For example, these vulnerabilities 

could emerge through market failures, a loss of market 

discipline, or misaligned incentives. 

Macroprudential policy design also depends on understanding financial innovation. Financial activities and 

risks are constantly evolving. Innovation can make the 

financial system run more smoothly, and well-managed 

financial innovation is critical to an efficiently functioning financial system that can adapt to changing 

circumstances. 

But innovation can also shift risks in unexpected 

ways. In recent decades, many innovations in financial 

products had been widely seen as enhancing the ability 

of market participants to divert risks to those best 

equipped to manage them. But during the financial 

crisis, innovations in many cases allowed increased 

risk-taking by buyers and sellers and made the financial system vulnerable to shocks. In addition, some 

innovations result from efforts to evade prudential 

regulations and market oversight. Other innovations 

could represent attempts to gain advantage by institutions that have dominant positions or even monopolies 

in particular markets. 

Macroprudential policy analysts must track the risks 

in financial intermediation activities that are evolving through the development of new products, new 

activities, and new types of financial firms. For that 

reason, a key complement to our quantitative monitoring framework is a qualitative assessment of structural 

vulnerabilities, including an assessment of the potential unintended impacts of regulatory and accounting 

policies on different types of financial institutions. 

Policymakers recognize that fundamental uncertainty 

exists in anticipating shocks that trigger crises. The 

potential for future crises necessitates developing policy 

tools that help identify vulnerabilities before the shocks 

hit, tools to use during a crisis to reduce its severity, and 

tools to repair and promote recovery in the aftermath.

Defining the Macroprudential Policy Toolkit

The best-developed U.S. macroprudential policies 

address vulnerabilities that arise in the provision of 

credit or from interconnectedness, and that bolster resolution planning before and during a crisis. However, 

macroprudential policies in the United States have 

largely remained bank-centric since the crisis. Judging 

by the growing share of credit intermediation in nonbank institutions and in capital markets in Figure 26, 

financial activity appears to have continued to migrate 

toward securities markets, as these new bank-oriented 

tools have reduced the relative profitability of such 

activities in banks. 

Macroprudential policy addresses vulnerabilities in 

the financial system and promotes incentives that 

restore market discipline to reduce the likelihood of 

financial crises and lessen the severity of any crises that 
35 Evaluating Macroprudential Policy

occur. Macroprudential policy tools can act either as 

shock absorbers to prevent shocks from disrupting the 

performance of the financial system, or guardrails to 

set incentives or controls for the activities of financial 

institutions and help to restore market discipline. 

In defining the toolkit, the key questions are: 

• Who does the tool affect? 

• What risks does the tool address? 

• Under what circumstances is the tool used? 

• How is the tool used? 

Who Does the Tool Affect?

Does the tool apply narrowly to one type of financial 

firm or broadly to a type of activity, regardless of the 

type of firm? Although banks have been subject to 

more microprudential regulation given their leading 

role in financial activities and the presence of federal 

deposit insurance, regulatory differences can prompt 

risky activity to migrate from banks to nonbank financial firms. 

What Risks Does the Tool Address?

Policymakers need multiple types of tools to address 

the five types of financial system distress described in 

Chapter 2 — macroeconomic risk, market risk, credit 

risk, funding and liquidity risk, and contagion risk. 

Under What Circumstances Is the Tool Used?

Pre-crisis macroprudential policies are designed to 

reduce the likelihood of future crises or limit the severity of a disruption, and would be deployed to respond 

to issues identified by financial stability monitoring. 

Crisis-response macroprudential policies reflect the 

recognition that crises can occur and that policymakers need to have tools to limit their impact — to 

respond and prevent distress in credit and funding 

from creating feedback loops between the financial 

sector and the real economy. 

Pre-crisis policies can be countercyclical or structural 

(through-the-cycle). Structural policies address aspects 

of the financial sector that could create vulnerabilities 

throughout the cycle; these include policies that promote simpler, easier-to-resolve financial institutions. 

Countercyclical policies come in two types: discretionary (policymakers have to activate the policy) and automatic (criteria for activating the policy are determined 

in advance). Automatic stabilizers, like the anti-lock 

25

35

45

55

65

1980 1988 1996 2004 2012

Share Intermediated Through Depository Institutions

Share Intermediated Through Securities Markets

Figure 3.1 Origin of Private Nonnancial Debt

Outstanding (percent)

Source: Morgan Stanley calculations based on Federal 

Reserve Flow of Funds reports. 

Figure 26. Origin of Private Nonfinancial Debt 

Outstanding (percent)

Source: Morgan Stanley calculations based on Federal Reserve Flow of Funds data

brakes in a car, deploy automatically when vulnerabilities arise. An advantage of automatic stabilizers is that 

they do not depend on consensus or the judgment of 

policymakers. 

How Is the Tool Used?

Is the tool broad-based or sector-specific? Does the tool 

address flows or amounts outstanding? Does the tool 

address prices or quantities? Capital standards, which 

address the level of capital, tend to be broad-based. 

Tools used outside the United States, such as limits on 

loan-to-value (LTV) or debt-to-income (DTI) ratios, 

tend to focus on risks in specific sectors and on flows. 

They address loan originations rather than existing 

loans. Capital standards affect the supply of credit by 

affecting the cost of lending from the perspective of 

the bank or other financial institution. LTV and DTI 

limits affect the demand for credit by increasing the 

cost to the borrower and reducing the pool of eligible 

borrowers for certain types of loans. 

Figure 27 summarizes macroprudential policies being 

used or discussed internationally, not all of which 

would be easily implemented in the United States. 

Bold-faced examples reflect the current U.S. macroprudential policy toolkit. Tools are organized by when 

(for example, pre-crisis or post-crisis) and how they are 

used (for example, cyclical or structural).
2013 OFR Annual Report 36 2013 OFR Annual Report

Pre-Crisis Cyclical Policies. Most of the policy discussion and focus internationally is on pre-crisis macroprudential tools that are countercyclical, particularly 

to moderate excessive credit growth or mitigate the 

effects in the event of a downturn. Such tools include 

countercyclical capital buffers for large internationally 

active banks, which would alter capital requirements 

when federal regulators determine that excessive credit 

growth is contributing to an increase in systemic risk; 

time-varying capital risk weights for banks; and LTV 

and DTI limits, which could apply just to bank lending 

or to all lending of a given type. 

These tools require identification of metrics for triggering activation, as well as decisions on how much 

to ramp up or taper down to affect credit provision. 

Other potential pre-crisis tools relate to funding risks, 

such as targeted reserve requirements or capital surcharges on short-term wholesale funding. 

Pre-Crisis Structural Policies. There are two basic 

types of macroprudential tools for use through-the-cycle: policies to affect how the system performs its basic 

tasks and policies to promote resilience. Examples of 

structural policy initiatives affecting the functioning of 

the U.S. financial system include strengthened capital 

and liquidity requirements, and heightened supervision of large firms; reforms to money market fund regulation, which would strengthen the industry’s liquidity 

and increase its resilience to funding runs; and margin 

requirements for over-the-counter derivatives and securities financing transactions (SFTs). 

Since the financial crisis, substantial international 

dialogue has focused on new structural policies to 

promote greater resilience, particularly in banking. 

The Basel Committee on Banking Supervision has 

produced a new framework for heightened microprudential regulation to strengthen large banks’ resilience, while including a macroprudential overlay. For 

example, the new framework includes an extra capital 

conservation buffer of 2.5 percent of risk-weighted 

assets above the regulatory minimum capital requirement. Banks that fall below the buffer would face limits 

on dividends and executive compensation. The goal of 

this standard is to build a buffer against unexpected 

losses, thus reducing the likelihood of banks falling 

below regulatory minimums.

The Basel Committee also proposed a liquidity coverage 

ratio, which is an example of a pre-crisis, structural policy directed at the funding channel (see BCBS, 2010b). 

This regulation would require banks and bank holding 

companies to have sufficient liquidity to cover a 30-day 

liquidity stress event. In October, the federal bank 

regulators released a proposed rule that would implement the liquidity coverage ratio in the United States. 

Internationally, some bank supervisors have established 

caps on net open foreign exchange positions to reduce 

vulnerabilities to external funding shocks. 

In the United States, supervisory stress testing, especially focused on large, complex financial institutions, 

is an important new structural tool for promoting 

resilience. Supervisory stress testing has become a 

central component of the supervision of large bank 

holding companies and promotes market discipline by 

providing the public with information about individual 

banks’ resilience under stress. 

Under Dodd-Frank Act stress testing mandates, large 

bank holding companies and the Federal Reserve are 

required to produce annual stress tests based on three 

scenarios provided by the Federal Reserve — a baseline 

scenario, an adverse scenario, and a severely adverse 

scenario. The Dodd-Frank Act also requires the largest 

bank holding companies to produce stress tests based 

on their own scenarios six months after the annual 

stress tests. 

For the Federal Reserve’s Comprehensive Capital 

Analysis and Review (CCAR), bank holding companies 

must generate two additional sets of stress tests based 

on their own, company-developed baseline and stress 

scenarios. CCAR also requires institutions to alter their 

capital distribution plans if they are found to have 

deficient capital under the severely adverse scenario or 

weaknesses in their capital planning processes.

To address potential threats arising in nonbank 

financial companies, the Basel Committee and the 

International Association of Insurance Supervisors 

(IAIS) have been working to strengthen oversight 

of global systemically important nonbank financial 

institutions. The Financial Stability Board is coordinating global efforts to apply higher capital requirements 

to these companies. In July 2013, the IAIS released 

its assessment methodology and policy measures for 
37 Evaluating Macroprudential Policy

Figure 27. Types of Macroprudential Tools

Who What (risks) When and How Examples*

Banks and 

Nonbanks

Credit (general or 

sectoral specific)

Pre-crisis Cyclical Banks: Countercyclical capital buffer (general), timevarying risk weights (sectoral), loan-to-value (LTV) or debtto-income (DTI) requirements (sectoral), ceilings on credit 

growth (sectoral), variations in stress test scenarios

Nonbanks: Could also have LTV, DTI, or credit growth limits

Structural, 

through the cycle

Banks: Capital conservation buffer, require higher quality 

capital

Crisis 

Management

Banks: Government sales of troubled institutions

Post-crisis Banks: Countercyclical capital buffer (general)

Automatic 

stabilizer

Banks: Dynamic provisioning

Funding Pre-crisis Cyclical Banks: Reserve requirements

Structural, 

through the cycle

Banks: Liquidity coverage ratio (LCR) (general), minimum 

haircuts on repo with nonbanks (sectoral), limits on net open 

FX positions (sectoral), short-term wholesale funding capital 

surcharges

Nonbanks: for money market funds, liquidity requirements

or floating net asset values

Crisis 

Management

Banks: Restoring liquidity requirements

Nonbanks: Central bank lending under emergency 

circumstances (limited under Dodd-Frank)

Post-crisis Banks: Relaxing liquidity requirements

Automatic 

stabilizer

Interconnectedness/ 

resolvability

Pre-crisis Structural, 

through the cycle

Banks & nonbanks: Nonbank designation and heightened 

prudential standards for large bank and nonbank 

institutions, higher capital requirements for large, 

complex financial institutions, over-the-counter derivative 

clearing and margin requirements, bank stress testing

Crisis 

Management 

and Postcrisis

Banks and nonbanks: Living wills, new orderly liquidation 

authority of Federal Deposit Insurance Corporation

Automatic 

stabilizer

Source: OFR analysis

*Examples in boldface have been adopted by U.S. policymakers
2013 OFR Annual Report 38 2013 OFR Annual Report

global systemically important insurers, and the FSB 

endorsed the measures (see IAIS, 2013). 

In the United States, the Financial Stability Oversight 

Council has designated three nonbank financial 

companies for enhanced prudential standards and 

Federal Reserve supervision under the Dodd-Frank 

Act. Separately, the Council has designated eight financial market utilities for enhanced risk-management 

standards appropriate for their functions of clearing 

and settlement transactions of cash, securities, and 

derivatives and netting exposures among financial 

companies.

Crisis Stabilization and Post-Crisis Policies. Crisis 

stabilization policies depend on the nature of the risk 

amplification channel. To prevent amplification of 

risks through funding shocks, the Federal Reserve has 

the authority as lender of last resort to lend to solvent 

banks and designated financial market utilities against 

eligible collateral. To address amplification of risks 

through other firms that have extensive interconnections in financial markets, the longstanding powers of 

the Federal Deposit Insurance Corporation (FDIC) 

to resolve banks have now been expanded to include 

authority in certain circumstances to resolve nonbank 

financial companies including bank holding companies. 

Internationally, policymakers continue to discuss issues 

and plan for the resolution of a cross-border firm. 

Automatic Stabilizers. Automatic stabilizers are 

designed to mitigate financial shocks as a matter of 

course, rather than depend on policymakers to make 

judgments about cyclical risks. For example, Spain and 

Portugal use dynamic provisioning, which requires 

banks to build reserves for loan losses that would be 

sufficient throughout the business cycle (see Alberola, 

Trucharte, and Vega, 2011).1

 This policy would address 

the tendency of banks to neglect to set aside sufficient 

reserves in good times and then provision aggressively when borrowers begin having trouble repaying 

their loans, behavior that could have implications for 

financial stability if it reinforces the cost of downturns 

(see Bikker and Metzemakers, 2005). Dynamic provisioning would represent a change in longstanding 

accounting practices that allow banks to set aside 

reserves for a loan only when the loan is shown to be 

impaired. Because of its automatic, through-the-cycle 

component, dynamic provisioning is different from 

discretionary cyclical tools like the countercyclical capital buffer (see Clerc, Drumetz, and Jaudoin, 2001). 

Contingent capital notes, or CoCos, also could act as 

automatic stabilizers. CoCos are bond-like instruments 

that convert into equity capital and automatically 

replenish the bank’s capital under certain situations 

— for example, if the bank’s regulatory equity capital 

ratio falls below the required limit. Some regulators 

are willing to treat these notes as equity capital before 

their conversion, because investors will bear losses in 

certain adverse situations. Regulators in the eurozone 

have embraced these instruments and, to date, eurozone banks have been the main issuers. One important 

concern about CoCos is that triggering conversion of 

debt into equity capital when a firm is already under 

stress might further weaken the firm. 

Evaluating the Effectiveness of 

Macroprudential Policy Tools

Evaluators of macroprudential tools must answer three 

questions: Does the tool have the intended effect? 

What are the drawbacks? Are there any unintended 

consequences?

Does the Tool Have the Intended Effect?

The goals of a macroprudential policy tool need to 

be clear. Policymakers could have various goals in 

strengthening lending standards, for example, trying 

to prevent households from taking on more debt than 

they can afford or trying indirectly to limit the growth 

in housing credit to prevent an asset price bubble. 

A macroprudential policy should target the ultimate 

cause of the problem rather than its symptoms. For 

example, if the expectation of redemption at par 

in money market funds makes them vulnerable 

to redemption runs, then the use of gates to limit 

redemptions when shocks occur may perversely make 

money funds more subject to run risk as investors 

attempt to be the first to exit. 

An increase in experimentation with macroprudential 

tools across the world provides some evidence about 

their effectiveness. Most international policymakers 

who have used macroprudential tools believe they are 

effective and usage has been extensive in recent years, 

according to a recent study of 49 countries with a macroprudential policy framework (see Lim and others, 2011). 
39 Evaluating Macroprudential Policy

But every country’s financial system is unique. A policy 

that works well in a country where financial activity 

occurs predominantly in banks may work very differently in a country where financial activity takes place in 

diverse markets and financial institutions. Several studies have found that higher amounts of equity capital 

can be costly to banks and lead to a decline in lending, 

although estimates vary among studies involving multiple countries (see Figure 28). 

Analysts have conducted three types of empirical analysis to determine whether tools work.

First, case studies examine historical episodes in depth 

to understand why a tool was used, whether it worked 

as intended, and what the costs or unintended consequences were. For example, a recent OFR working 

paper described U.S. efforts to combat excesses in the 

credit cycle over the past 100 years, including tools that 

operated on the demand for credit through lending 

terms (margin requirements, LTV ratio caps, DTI 

ratio caps, maturity limits) and on the supply of credit 

through banks and other intermediaries (supervisory 

pressure, reserve requirements, interest rate ceilings). 

For many years, U.S. policymakers believed these tools 

were effective (see Elliott, Feldberg, and Lehnert, 

2013). Comparisons to past episodes, of course, must 

be made carefully, because structural changes in 

institutions and markets over time could alter the 

effectiveness of a given policy tool and its ability to 

achieve a desired policy outcome. Nevertheless, this 

type of research is necessary to evaluate these policies.

Second, “event studies” examine the performance of 

a particular risk variable after a policy tool was introduced or revised. A staff report by the International 

Monetary Fund (IMF) contained event studies across 

several countries (see IMF, 2013b). For example, South 

Korea imposed a tax in 2011 on banks’ noncore foreign currency liabilities. According to the IMF paper, 

this tax took effect at the same time as a substantial 

reduction in short-term foreign exchange liabilities. 

Similarly, New Zealand required in 2010 that 65 percent of banks’ activities be funded by core funding, 

defined as local deposits and long-term wholesale funding. The authors concluded that this policy was successful because banks maintained core funding well above 

the required ratio after the law’s implementation.

Third, analysis can estimate the direct effects of macroprudential policy more precisely by accounting for the 

accompanying monetary and fiscal policies, and broadening the scope of the analysis. Lim and others (2011) 

demonstrated that several tools, including LTV caps, 

DTI ratio restrictions, liquidity mismatch restrictions, 

and countercyclical capital requirements, could be 

effective across a range of institutional environments 

and across countries at different levels of financial 

Figure 28. Estimated Impact on Credit Conditions of a 100-Basis-Point Permanent Increase in U.S. Banks’ Capital 

Requirements

Study Description Change in Loan 

Rates (bps)

Change in Loan 

Volumes (%)

Macroeconomic Assessment Group 

(2010)2

Range of models using multicountry dataset. 15.5 -1.38

Elliott (2009)3 Loan pricing equation calibrated for U.S. 

banks.

4.8 n/a

Cosimano and Hakura (2011)4 Structural model of capital channel of 

monetary policy using multicountry dataset.

12.3 -0.98

Slovik and Cournede (2011)5 Accounting-based model applied to aggregate 

banking sector balance sheets. Data from 

United States, eurozone, and Japan.

14.4 n/a

King (2010) Accounting-based model for representative 

bank, uses multicountry dataset.

15.0 n/a

Source: OFR analysis
2013 OFR Annual Report 40 2013 OFR Annual Report

development. An additional advantage of this type of 

analysis is that the authors are able to demonstrate that 

these tools often work even better in combination with 

monetary policy.

Research on macroprudential policy is still quite new. 

Although the current consensus is that these tools can 

hit their targets, the magnitude of the effect appears to 

depend on the country’s financial system. 

What Are the Drawbacks?

Some might argue that policymakers should take any 

actions necessary to prevent future crises, given the 

material decline in economic output associated with 

the financial crisis. But costs and other drawbacks also 

matter. 

Cyclical policies that achieve the goal of reducing 

credit in a particular sector will have at least a shortterm effect on borrowers and the economy. Throughthe-cycle policies that promote the resilience of the 

financial sector may have greater permanent effects 

on economic output than cyclical policies or automatic 

stabilizers. 

For example, activating a countercyclical capital buffer 

might require banks to raise equity capital. An analysis 

of that policy’s impact would begin with an estimate of 

banks’ equity cost of capital and then predict a range 

of possible responses by the banks, such as reducing 

lending and passing on their higher funding costs to 

borrowers. Such an analysis could reveal the potential effect on the economy, as well as the possibility of 

uneven impacts across different types of companies — 

in particular, smaller corporate borrowers tend to have 

fewer choices than larger borrowers. 

Analysts use a range of macroeconomic models to estimate the effects of macroprudential tools on output. 

Analysts need to predict whether monetary authorities 

will adjust their tools to support the goals of the macroprudential tool or counteract the effects of the tool by 

easing credit overall when policymakers decide to limit 

credit in a particular sector. 

Are There Any Unintended Consequences?

Financial activity tends to migrate to markets or 

institutions that face less strict regulation, a phenomenon often described as regulatory arbitrage. 

Countercyclical measures designed to stem a certain 

type of lending may simply lead to the development 

of new markets and products that perform a similar 

function outside the scope of regulation. In that way, 

creating a policy to address one risk may lead to a 

shift from banks to nonbanks or market-based credit 

intermediation. Similarly, without international policy 

coordination, new regulations in one country may 

lead global financial firms to move risky activity to less 

regulated countries.

Because of arbitrage risk, having the ability to broaden 

the scope or boundaries of regulation can be critical 

for authorities in some cases. In the U.S., supply-side 

tools such as reserve requirements and interest-rate 

ceilings have been more susceptible to arbitrage in the 

past because increasing the cost of lending for banks 

creates an opportunity for other types of companies to 

step in. In earlier eras, the United States had success 

with demand-side tools like LTV limits and margin 

requirements, but only when the Federal Reserve had 

authority to apply those tools to all or most lenders and 

borrowers. 

Other countries have learned similar lessons in 

more recent macroprudential policy experiments. 

Multinational studies show that the effectiveness of 

macroprudential policies is both general and robust, 

although there is some evidence of unintended consequences. For example, Arregui and others (2013) 

found that LTV, DTI, risk weights, and reserve requirements dampen credit growth, but reserve requirements 

may be more subject to regulatory arbitrage as companies not affected by the requirements respond by 

increasing their lending. 

An IMF study noted that when Croatia capped credit 

growth by banks, banks sold securities and cut unused 

credit lines to continue extending new loans. When 

South Korea imposed tighter LTV limits on banks in 

2009, nonbank institutions expanded their lending 

(see IMF, 2013b). 

Regulatory arbitrage can also have a cross-border 

dimension. For example, when Britain’s Financial 

Services Authority set bank-specific minimum capital 

ratios for banks under its jurisdiction during the 1990s 

and early 2000s, domestic banks’ lending fell (see 

Aiyar, Calomiris, and Wieladek, 2012). But the policy’s overall effectiveness was lessened because foreign 

banks operating in Britain were not subject to the 

requirements and increased lending. 
41 Evaluating Macroprudential Policy

Conversely, international coordination can increase a 

macroprudential tool’s effectiveness. When the Basel 

Committee agreed in 2011 to add a countercyclical 

capital buffer to the new global capital standard, the 

committee included a reciprocity principle: If U.S. 

regulators require U.S. banks to hold capital buffers, 

foreign regulators will require their regulated banks to 

hold similar buffers on U.S. exposures. But the liquidity coverage ratio rule included no such coordination 

mechanism — a U.S. decision to ease liquidity requirements in a crisis would not provide relief to a foreign 

bank operating here, unless that bank’s home regulator were to also ease.

Policy changes can also create risks. For example, the 

narrower the range of assets included in banks’ liquidity buffers under the liquidity coverage ratio, the more 

prone the banking system may be to fire sales because 

large institutions hold similar assets. Additionally, a 

mispricing of risk in capital risk weights can incentivize 

institutions to become over-exposed to a particular 

asset class. An example of mispricing risk can be seen 

in the case of European banks that were allowed to 

carry sovereign debt from a eurozone member country with a zero risk weight. This also happened with 

U.S. banks’ ownership of preferred shares of Fannie 

Mae and Freddie Mac, whose prices fell to near zero 

in September 2008. Although equity securities are 

generally not permissible investments for depository 

institutions, regulators allowed banks to hold shares of 

Fannie Mae and Freddie Mac because of the perceived 

special status of the government-sponsored enterprises 

and the assumption of a government guarantee. Losses 

on those securities during the crisis played a role in 

an estimated 15 U.S. bank failures and two distressed 

mergers (see Rice and Rose, 2012). 

Finally, policies can be complementary or they can 

conflict, and predicting how a new tool might interact 

with existing or future policies can be difficult. Some 

policies can be mutually reinforcing. For example, 

after the recent financial crisis, a number of regulatory 

changes were aimed at realigning the definition of 

bank regulatory capital more closely with market-based 

measures that focused on tangible common equity, in 

which investors had greater confidence. 

Policies can also push in different directions. The 

liquidity coverage ratio requirement will obligate 

large U.S. banks and bank holding companies to 

build up substantial buffers of liquid securities in 

their available-for-sale (AFS) portfolios to reduce the 

likelihood of funding difficulties and asset fire sales. 

The new rule also requires the same companies to 

immediately credit unrealized AFS gains and charge 

AFS losses to regulatory capital. Together, the changes 

could increase the volatility of large banks’ regulatory 

capital ratios. Under previous capital rules, banks only 

recorded gains or losses when they sold securities. 

Picking the Right Tool 

Monetary, microprudential, macroprudential, and 

other economic policies can influence financial stability. Policymakers should assign the right tool to each 

vulnerability, following the “assignment principle” 

described by Mundell (1962). In the example Mundell 

presented of a country with a fixed exchange rate, 

failure to follow this principle with respect to the use 

of monetary and fiscal policies to address external 

and internal threats to its macroeconomic stability 

could result in a situation worse than before the policy 

changes were introduced. 

Policymakers should select policies based on the objectives that they can most influence — the ones with the 

biggest bang for the buck. Targeted policies with clear, 

direct effects on a financial stability threat (such as 

minimum universal haircuts for securities financing 

transactions) are preferable to general policies with 

diffuse effects (such as the impact of monetary policy 

or activating a countercyclical capital buffer). 

Policymakers cannot achieve financial stability objectives without specific tools to address potential sources 

of distress — macroeconomic, market, credit, funding, 

and contagion risks — across various types of institutions and points in the credit cycle. Developing a complete set of macroprudential policies before a financial 

crisis is critical. 

What About Monetary, Microprudential, and Other 

Financial Policies?

Once threats or vulnerabilities to financial stability 

have been identified, policymakers should consider 

whether using monetary policy, a microprudential response, or a macroprudential tool is the best 

approach. Monetary and microprudential policies 

can contribute to financial stability, but the assignment principle suggests that they are — and should 
2013 OFR Annual Report 42 2013 OFR Annual Report

incorporating macroprudential considerations into 

the supervision of large bank holding companies (see 

Board of Governors, 2012a).

Bank supervisors have also strengthened supervisory 

stress testing and developed other structural tools 

affecting both large banks and nonbank financial 

institutions, such as enhanced prudential standards 

and resolution regimes. But because microprudential 

policies are aimed at making specific institutions safe 

and sound, they do not address the buildup of risk 

across the financial system. Additionally, microprudential policies may not be able to address vulnerabilities 

to the three types of shock described in Chapter 2: 

default of a major market participant, sudden loss of 

confidence in a particular asset or firm, or a disruption 

or failure in market infrastructure. 

Following these principles rigidly may also be problematic. Macroprudential tools are untested and microprudential tools alone are unlikely to restrain credit 

booms. The setting of monetary policy may also need 

to take financial stability considerations into account. 

Indeed, Federal Reserve Governor Jeremy Stein has 

noted that, “while monetary policy may not be quite 

the right tool for the job, it has one important advantage relative to supervision and regulation — namely 

that it gets in all of the cracks” (see Stein, 2013a).

An OFR working paper on the history of U.S. macroprudential policy pointed out that, as macroprudential 

policies fell into disuse in the U.S. in the mid-1980s, 

supervisors continued to issue supervisory guidance on 

risks (subprime lending in 1999, home-equity lending 

and commercial property in 2005, and nontraditional 

mortgages in 2006). But these warnings did not have 

much impact on credit growth, partly because so many 

lenders were not regulated as banks. But, the paper 

noted, the period was also the first major credit expansion in recent history when supervisory guidance was 

not backed up by direct policy actions such as tighter 

monetary policy, interest-rate ceilings, or credit controls 

(see Elliott, Feldberg, and Lehnert, 2012). 

What Gaps Exist in U.S. Macroprudential Policy?

The development of macroprudential policies in the 

U.S. is ongoing. International experts have argued that 

policymakers need to develop macroprudential tools 

during non-crisis periods. A broader macroprudential 

policy toolkit can offer higher net economic benefits 

 Figure 29. Macroprudential Toolkit: How Does It Relate 

to Others?

Price 

Stability

Financial 

Stability

Institution 

Soundness/

Market Conduct

Monetary

Policy

Macroprudential

Policy

Microprudential

Policy

Source: OFR analysis

be — focused primarily on their assigned goals of 

price stability and safety and soundness, respectively 

(see Figure 29). These policies can be in sync with 

macroprudential policy or they can create challenges 

(see Stein, 2013a). At the very least, macroprudential 

policymakers need to take monetary and microprudential policies into account. 

As a corollary, authorities generally should not rely on 

monetary policy alone to address potential threats to 

financial stability for several reasons (see IMF, 2013a, 

and CGFS, 2010): 

• Monetary policy targets price stability and full 

employment and cannot be fully dedicated to 

addressing financial stability concerns. 

• Monetary policy tools are blunt; they act on 

general credit and liquidity conditions and cannot 

necessarily address excesses in specific sectors of 

the financial system. 

• There are questions about the effectiveness of 

monetary policy in responding to systemwide 

financial risk in an environment of stable inflation.

Monetary policy may also have unintended effects on 

financial stability, suggesting the need for a targeted 

macroprudential response. 

Microprudential supervision is an important complementary tool for promoting financial stability. Over 

the past 10 years, U.S. bank supervisors have taken the 

lead in creating and strengthening microprudential 

tools, for example, through capital and liquidity regulations. The Federal Reserve recently issued guidance 
43 Evaluating Macroprudential Policy

and reduce the cost of mitigating future crises. It also 

can improve policymakers’ ability to tailor solutions 

to emerging risks and changing market practices. An 

incomplete toolkit can be ineffective and inefficient. 

The U.S. macroprudential policy toolkit is growing, 

but it still has gaps. First, there is an unresolved debate 

about the relative benefits and costs of cyclical and 

structural tools. Certain cyclical policies and automatic 

stabilizers may address cyclical vulnerabilities in a 

more targeted way than structural policies, but they 

may be harder to implement and involve long-term 

costs. Second, there are gaps in the toolkit for 

post-crisis stabilization and recovery. Better monitoring 

tools can improve policymakers’ ability to limit the 

frequency and severity of crises. 

Important measures to address vulnerabilities in the 

activities of nonbank financial companies have been 

implemented since the crisis. The Council has designated several nonbank financial companies and financial market utilities for heightened oversight by the 

Federal Reserve. The SEC has revised its rules regarding money market funds, and proposals for further 

reform are being considered. 

Supervisory stress testing of large bank holding companies in the United States has become a valuable and 

important supervisory tool that contributes to regulatory capital and liquidity planning at these institutions. A recent OFR working paper described current 

stress-testing practices as Version 2.0, representing 

a generational improvement over pre-crisis practices, and recommended a research agenda to better 

align stress testing with macroprudential goals. The 

paper argued that supervisory stress testing could be 

enhanced by increasing the number of stress scenarios 

or running “reverse” stress tests that seek to determine 

what type of shock would lead to the failure of a firm. 

The paper also argued that the next generation of 

stress tests could incorporate funding risks and potential spillover and feedback effects (see Bookstaber and 

others, 2013). 

Another OFR working paper described how agentbased models could contribute to stress testing by modeling how a crisis would develop based on the reactions 

of individual market participants (see Bookstaber, 

2012). Two OFR papers in 2013 also addressed 

the problem of designing stress test scenarios and 

developing reverse stress tests (see Glasserman, Kang, 

and Kang, 2013, and Flood and Korenko, 2013). 

Supervisors have made important progress in achieving these goals. As in previous years, six bank holding 

companies with large trading operations will include 

a global market shock in their stress scenarios in 2014. 

And for the first time, supervisors will require eight 

bank holding companies with substantial trading or 

custodial operations to include a counterparty default 

scenario in their stress test results.

Tools are lacking to address vulnerabilities in SFTs, such 

as in the triparty repo agreement market, which can 

give rise to runs or fire sales. Federal Reserve Governor 

Jeremy Stein recently noted that, although many of 

these tools, such as capital and liquidity requirements, 

“are likely to be helpful in fortifying individual regulated institutions — in reducing the probability that, say, 

a given bank or broker-dealer will run into solvency or 

liquidity problems — they fall short as a comprehensive, 

marketwide approach to the fire-sales problem associated with SFTs” (see Stein, 2013b). 

From an institutional perspective, broker-dealers and 

other nonbanks lack two funding backstops that banks 

enjoy: deposit insurance and access to the lender of 

last resort. As a result, their funding is vulnerable to 

runs and fire sales. Federal Reserve Bank of New York 

President William Dudley in February raised the question of whether, to make markets for SFTs more stable, 

such entities should be given access to a permanent 

funding facility, or even the discount window, with 

the quid pro quo clearly being appropriate prudential 

oversight (see Dudley, 2013). The Bank of England 

recently took a step in that direction with broader lending arrangements, or liquidity insurance, for banks, 

and a commitment to investigate extending the facility 

to nonbanks (see Bank of England, 2013). 

Other tools are needed to address threats that arise 

outside the banking system. To address threats arising 

in short-term, wholesale funding markets, one option 

is to require higher levels of capital at institutions 

reliant on short-term wholesale funding. Another 

option would be to increase capital or liquidity requirements for assets related to such securities financing 

transactions. 

Beyond the perimeter of prudential bank regulation, 

the Financial Stability Board (FSB) has proposed 
2013 OFR Annual Report 44 2013 OFR Annual Report

haircut floors for certain SFTs to limit the extent to 

which banks and nonbanks can obtain leverage through 

these transactions. (A haircut is the percentage amount 

subtracted from the market value of a security to determine its value as collateral.) In August, an FSB proposal 

took the first step toward such a framework. The board 

argued that “these measures, if appropriately implemented, would help counteract pro-cyclical fluctuations 

in securities financing” (FSB, 2013c).6 Federal Reserve 

Governor Daniel Tarullo recently noted, “Like minimum margin requirements for derivatives, numerical 

floors for SFT haircuts would be intended to serve as a 

mechanism for limiting the buildup of leverage at the 

security level, and could mitigate the risk of procyclical 

margin calls” (see Tarullo, 2013).

More analysis of how to implement such tools is 

needed, however. As a practical matter, the low proposed levels for such floors make it unclear how much 

they would deter leverage. Moreover, the scope of 

proposed application in the FSB proposal is limited to 

a subset of institutions and asset classes, so application 

could promote migration of activity elsewhere in the 

financial system — in other words, regulatory arbitrage. Such minimum haircuts likely should be universal to be effective.

We will work in 2014 to evaluate such proposals.

3.2 Emerging U.S. Macroprudential 

Tools 

Two key policy tools have been created since the financial 

crisis, the countercyclical capital buffer and the liquidity 

coverage ratio. The Federal Reserve and the Office of the 

Comptroller of the Currency (OCC) announced the final U.S. 

rule on countercyclical capital buffers in October 2013; the 

FDIC issued an interim final rule in July (FDIC, 2013). As 

noted earlier, the Federal Reserve, FDIC, and OCC released 

a proposed liquidity coverage ratio rule for public comment 

in October 2013; comments will be taken through January 

2014 (Board of Governors, 2013d). 

Countercyclical Capital Buffer

The final U.S. rule implementing the Basel III accord 

created a countercyclical capital buffer for large banks 

and bank holding companies, subject to a phase-in 

beginning January 2016 (OCC and FRB, 2013). The 

new U.S. rule gives regulators the ability to require the 

largest banks to hold between zero and 2.5 percentage points in extra common equity tier one capital 

as a countercyclical capital buffer during a period of 

heightened concerns about credit growth. The countercyclical buffer has been designed to supplement 

the capital conservation buffer, which is fixed at 2.5 

percentage points. 

U.S. regulators have not announced the metrics they 

will use to decide whether to trigger activation and 

release of the countercyclical capital buffer. In the 

final rule implementing the buffer, they say they 

may activate it based on “a range of macroeconomic, 

financial, and supervisory information indicating an 

increase in systemic risk including, but not limited to, 

the ratio of credit to gross domestic product, a variety of asset prices, other factors indicative of relative 

credit and liquidity expansion or contraction, funding 

spreads, credit condition surveys, indices based on 

credit default swap spreads, options implied volatility, 

and measures of systemic risk.” 

The Basel Committee on Banking Supervision noted 

that regulators need to exercise judgment when 

activating the countercyclical capital buffer within 

their jurisdictions, but suggested using deviations of 

the ratio of nonfinancial private-sector credit-to-GDP 

from long-term average trends in determining whether 

or not to activate it (see BCBS, 2010a). An analysis by 

Drehmann, Borio, and Tsatsaronis (2011) across 36 

countries showed the divergence in credit-to-GDP to be 

a more effective leading indicator than several other 

possibilities (including credit growth, GDP growth, 

real estate prices, measures of banking system performance, and funding costs). 

Other analysts have noted weaknesses in the indicator. 

First, it ignores the potential for excess growth in lending to the financial sector itself, or to the public sector, 

including government-sponsored enterprises, municipalities, and foreign governments. Second, it misses the 

potential for excessive lending by U.S. banking organizations overseas, which was an important element in 

the debt crisis in less-developed countries during the 

1970s and 1980s. 

In January 2013, the Bank of England’s Financial Policy 

Committee published 17 core indicators that it will 

use to assess bank and nonbank balance sheets, and 
45 Evaluating Macroprudential Policy

financial market conditions, in considering whether to 

activate its countercyclical capital buffer. 

Another challenge in developing alternative metrics 

is that they need to be sufficiently forward-looking to 

allow supervisors to announce an increase in the countercyclical capital buffer requirement about one year 

ahead of its implementation (see BCBS, 2010a, and 

OCC and FRB, 2013). 

The definition of country exposure also raises some 

concerns about effectiveness. If U.S. supervisors activate the buffer requirement, only U.S. exposures are 

directly affected. The rule establishes reciprocity for 

foreign supervisors’ activation because U.S. regulators 

will require banks to hold a countercyclical capital buffer on exposures in that foreign jurisdiction, consistent 

with the buffer level set by the foreign supervisor. U.S. 

supervisors cannot independently use the countercyclical capital buffer as a mechanism to slow excess foreign 

credit growth. 

U.S. banks and bank holding companies are required 

to determine the contribution weight assigned to a 

jurisdiction’s countercyclical capital buffer requirement by dividing the total risk-weighted assets for the 

banking organization’s private-sector credit exposures 

located in the jurisdiction by the total risk-weighted 

assets for all of the banking organization’s private-sector credit exposures. Exposures to government-sponsored enterprise securities, sovereigns, and other 

public sector entities are not included in the measure 

of private-sector credit exposures.

Figure 30 shows how the countercyclical capital buffer 

would be calculated for a covered bank’s exposures 

across multiple jurisdictions. A capital requirement of 

140 basis points (1.4 percent) would be applied to all of 

the bank’s risk-weighted assets. 

Is the Countercyclical Capital Buffer an Effective 

Tool?

The countercyclical capital buffer may bring important 

benefits to financial stability by promoting capital-raising when the cost of issuing equity is relatively low. By 

requiring banks to maintain higher levels of capital, 

the buffer can make the financial sector more resilient 

by increasing banks’ ability to absorb losses. 

Still, there are a number of reasons to question the 

effectiveness of the buffer at curtailing lending during 

a credit boom. It applies only to certain banks and may 

be less able or unable to slow credit growth given the 

capacity and incentives for nonbanks not subject to the 

buffer to offer credit. 

The benefits of the countercyclical capital buffer as a 

macroprudential tool may be limited to pre-crisis prevention. It is unclear whether the countercyclical capital buffer requirement will be able to act as a post-crisis 

tool and encourage banks to continue lending during 

an economic downturn. Once firms are forced to hold 

a capital buffer, banks may choose not to release this 

capital even when allowed to do so because raising 

capital later could be difficult and costly. This behavior 

could dampen any expansionary effect. 

Countercyclical 

Capital Buffer Set by 

National Supervisor

Risk-Weighted Asset 

for Private-Sector 

Exposures 

Contributing Weight Countercyclical 

Capital Buffer 

Requirement

Non-U.S. Jurisdiction 1 2.0 250 0.29 0.6

Non-U.S. Jurisdiction 2 1.5 100 0.12 0.2

United States 1.0 500 0.59 0.6

TOTAL 850 1.0 1.4

Figure 30. Example of a Large U.S. Bank’s Countercyclical Capital Buffer Calculation (percent)

Sources: Office of the Comptroller of the Currency, Federal Reserve (2013)
2013 OFR Annual Report 46 2013 OFR Annual Report

The effectiveness of capital-related tools also depends 

on the design of regulations that weight capital according to risk. The countercyclical capital buffer may 

be less effective when risk weights are not correct or 

when opportunities exist for banks to optimize their 

risk-weighted assets.7

 For example, when faced with 

higher capital requirements, banks may over-invest in 

assets with relatively low regulatory risk weights, such 

as credit to the public sector or corporate bonds on the 

cusp of investment grade.

Finally, the countercyclical capital buffer is a broad 

tool to manage the supply of credit. If policymakers 

are concerned about excess credit growth to a specific 

sector, banks could still increase lending to that sector 

while reducing lending to other sectors. Although 

the overall supply of credit could decrease when the 

buffer requirement is activated, credit growth in the 

areas that most concern policymakers may not slow. In 

this regard, the Swiss National Bank’s activation of a 

sector-specific countercyclical capital buffer requirement of 100 basis points targeted at mortgage loans 

financing residential real estate in Switzerland is worth 

further study (see SNB, 2013).

Liquidity Coverage Ratio 

The proposed liquidity coverage ratio could be used as 

a macroprudential tool. A Basel Committee paper noted 

that during periods of stress, supervisors could take 

appropriate action to allow banks to draw down their 

liquidity buffers, falling below the minimum liquidity 

coverage ratio (see BCBS, 2013a). The paper, however, 

did not elaborate on possible triggers for supervisors 

to consider, or guidance on whether such drawdowns 

below the minimum liquidity coverage ratio should be 

sanctioned only for systemic stress events. Effective communication of a policy decision to waive the required 

liquidity coverage ratio for one or more large banks 

could be essential to market confidence. 

Unlike the reciprocity provisions for the countercyclical capital buffer, the liquidity coverage ratio requirement lacks an international coordination mechanism. 

If U.S. supervisors decided to waive liquidity coverage 

ratio requirements, other jurisdictions supervising the 

foreign operations of U.S. banks would have no obligation to follow suit. 

The liquidity coverage ratio may also have unintended 

consequences on financial stability. The unwinding of 

collateral swap transactions as cash inflows included 

in the ratio could allow institutions to circumvent the 

cap on less liquid buffer assets. In addition, banks and 

bank holding companies could have an incentive to 

undertake repo or reverse repo transactions to alter 

their liquidity coverage ratios. This provision could 

lead firms to use secured funding markets more, 

thereby increasing interconnectedness.

The countercyclical capital buffer and liquidity coverage ratio requirement affect only large U.S. banks and 

bank holding companies. The limited scope of these 

tools could affect risk-taking within the financial sector 

in three ways. First, it could encourage nonbank credit 

intermediation, which limits the effectiveness of these 

policies on financial sector risk-taking. Second, it could 

lead to greater risk-taking by mid-size and smaller 

banks, which are exempt from the requirements. 

Third, it could encourage banking holding companies to continue to engage in excess credit creation 

or liquidity risk-taking through their nonbank legal 

entities. Such risk-taking would pose financial stability 

risks, given other legal and regulatory limits on the 

ability of depository institutions to support nonbank 

affiliates in the United States.
47 Research on Financial Stability

4 Research on Financial Stability

Unlike most members of the Financial Stability 

Oversight Council, the Office of Financial Research 

has no policymaking or supervisory responsibilities. 

That allows the Office to focus on providing 

independent basic research, necessary and usable 

data, and policy and market analysis for the public 

and to support the work of the Council. 

4.1 Research Agenda

Our research agenda supports our statutory mission as the 

Office (1) monitors and develop metrics for reporting on 

potential threats to financial stability, while continuing 

to improve our understanding of how the financial system 

evolves in providing basic services; (2) assesses the causes 

and consequences of financial instability; (3) evaluates 

regulatory policies and risk management practices, 

particularly stress tests; and (4) contributes to improvements 

in the scope and quality of data used for financial stability 

monitoring. 

Although our mandate complements those of the other 

member organizations in the Council, our goal is not 

to duplicate their efforts. Rather, our goal is to fill gaps 

in their analytical and empirical work, because none of 

them individually has the responsibility to look across 

the entire financial system. The Office’s unique perspective helps assure the comprehensive nature of work 

across the Council to identify and monitor threats to 

financial stability and to evaluate stress tests and other 

macroprudential policy tools. 

Our research agenda has four components: 

1. Financial stability monitoring and metrics.

Financial stability metrics have proliferated 

in the academic and regulatory communities 

since the financial crisis. Some attempt to 

provide forward-looking indicators of financial 

stress. Others try to explain current financial 

conditions and vulnerabilities. 

2. Causes and consequences of financial 

instability. The crisis highlighted the fact that 

neither regulators nor market participants 

sufficiently understood the causes and potential 

consequences of financial instability. 

3. Regulatory policy and risk management 

practices. As part of our research function, we 

are required to analyze the impact of policies 

related to financial stability. We conduct, 

coordinate, and sponsor research to help 

improve regulation of financial entities and 

markets. We also conduct studies and advise on 

policies related to systemic risk. In addition, the 

Office is required to promote best practices for 

financial risk management and evaluate and 

report on stress tests and other stability-related 

evaluations of financial entities supervised by 

Council member agencies. 

4. Scope and quality of financial data. Our 

research supports the Office’s statutory 

responsibility to improve the scope and quality of 

financial data (see Chapter 5 and Chapter 6). 

Recent Research Projects

We are improving our understanding of the financial 

system through original analysis and novel use of data. 

We are also developing and fine-tuning metrics that 

can provide insights on emerging threats to financial 

stability. 

Our first working paper gave an overview of such metrics and our 2012 Annual Report evaluated the performance of 11 metrics in four financial crises — 1929, 

1987, 1998, and 2008 (see Bisias and others, 2012; OFR, 

2012).1

 In December 2013, as a service to the academic 

and regulatory communities, we published the source 

code for the metrics described in that paper on the 

OFR’s website.2

The Office also has several research tracks that focus 

on understanding how financial crises emerge and 
2013 OFR Annual Report 48

DEVELOPING A FUNDING MAP TO HELP UNDERSTAND FINANCIAL 

CONNECTEDNESS

Researchers often represent the dynamics of the financial 

system, including its vulnerabilities, as a network, with financial 

entities as the nodes and funding linkages at the edges. 

As a first step in developing a funding map for the broader 

financial system, the OFR has initially focused on a typical bank or 

dealer (see Figure 31). For the largest and most complex financial 

institutions, these activities can be conducted through a variety 

of legal entities, including a broker-dealer to house the prime brokerage and a bank to take deposits and enter derivative contracts. 

The Office began working on the map after the Financial Research 

Advisory Committee presented a recommendation along with a 

prototype funding map committee members had developed at its 

second meeting on Aug. 1, 2013 (see FRAC, 2013). There are a 

wide range of uses for the funding map, as follows:

1. Determining Data Needs. Because the map depicts detailed 

funding flows, it can highlight key data needs, gauge the size of 

flows, and — most important for spotting emerging vulnerabilities in the financial system — identify flows that are dominating 

or growing. 

2. Assessing Financial Vulnerabilities. The funding map may 

be a valuable resource for understanding the routes to a financial 

crisis because a firm’s interactions with its customers and 

funding sources are pathways to broader risk. For example, the 

path between hedge funds and a prime broker is critical to fire 

sales; the paths connecting the cash providers to the financing 

desk and the trading operations of banks or dealers are critical to 

understanding funding runs. A central component of the funding 

map is the durability of funding, that is, the degree to which 

funding can be relied on in the face of stress. Flow analysis, 

coupled with an awareness of the durability of those flows, helps 

show ways to reduce vulnerabilities by highlighting alternative 

routes, illustrating how one pathway for critical flows might 

substitute for another. 

3. Modeling the Financial System and Its Vulnerabilities. The 

funding map can provide a framework for building network models 

that rely on the flows of securities and funding, as well as agentbased models that focus on the agents on each end. Once populated with the necessary data, the models can show the possible 

paths of contagion. For example, in the face of a drop in liquidity, 

the financing desk may reduce financing to the prime broker, 

leading the prime broker to pull back its funding operations. The 

funding map is essentially a visual representation of the financial 

system. Visual approaches can augment modeling and be useful as 

a higher-level tool for policymaking and crisis management.

4. Expanding the Funding Map. The funding map can be developed along three lines: (1) entities such as hedge funds and money 

market funds can be mapped; (2) entities and actions can be 

added, including clearing and settlement, markets, exchanges and 

other trading venues, insurance companies, mortgage originators, 

credit insurance, the central bank, and international linkages; and 

(3) more detail can be added. For example, detail on durability of 

funding, its liquidity, and credit quality could be shown.

What the Funding Map Shows

Figure 31 shows the flows of funding and securities from entities 

that represent the bank’s primary business activities and funding 

sources into the bank or dealer, as well as the flows between 

functional units within the bank or dealer. The bank or dealer 

is at the center. Flows come into the bank or dealer from other 

banks or dealers and from clients and depositors. 

This funding map shows, in detail, specific functional areas 

within the bank or dealer where the connections occur, internal workings of the firm, and how funding and securities move 

between key elements of the firm:

• prime brokerage - sells financial services to hedge funds, including 

leverage through margin loans and securities for short-selling 

activity; 

• trading desk - supplies liquidity through its market-making activity 

and gives clients leverage for fixed income products; 

• financing desk - borrows and lends cash with high-quality securities used as collateral (secured funding) and also takes unsecured 

debt, such as deposits, from retail and institutional investors; 

• derivatives transactions; and 

• treasury function - provides the longer-term financing of the bank.

The financing operation is key. Here, securities received in the 

form of prime broker receivables become collateral to obtain 

funding through repurchase market transactions, and securities 

are obtained through the reverse repurchase market. 

The funding map also shows the connections of the bank or dealer 

to various types of financial institutions, including institutional 

customers such as hedge funds, cash providers such as money 

market funds, security lenders, and other banks or dealers. 

The map properly shows the bank operating within the interbank 

market as one part of the broader financial landscape. The bank 

also is connected to entities in its role as provider of funding and 

securities, often the same types of entities that provide the bank 

with its funding and securities.
49 Research on Financial Stability [Insert text]

Asset Managers

Sovereign 

Wealth Funds

Insurance 

Companies

Pension Funds

Money Market 

Funds 

(SEC Rule 2a-7)

Hedge 

Funds

Repo Central 

Counterparty*

Triparty 

Agents

Retail and 

Institutional 

Depositors

Institutional 

Clients and 

Banks/Dealers

Banks/Dealers

Repo Central 

Counterparty*

Hedge 

Funds

Securities 

Lenders

Longer-term 

Borrowers

Asset Markets

Institutional 

Clients and 

Banks/Dealers

Banks/Dealers

Flow of Cash (right to left) Flow of Securities (left to right) More durable funding 

Less durable funding

Securities 

Borrowed

Client and Firm 

Short Covering

Reverse Repo 

Client Financing 

Fixed Income 

Short Covering

Inventory Owned

Market Making

Hedging

Receivables 

Collateralized 

Uncollateralized

Customer 

Payables

Client Short

Customer 

Receivables

Client Leverage

Secured 

Funding

Term

Short-Term

Deposits

Insured

Uninsured

Inventory Short

Market making

Hedging

Payables

Collateralized

Uncollateralized

Equity

 Unsecured Debt 

Long-Term

 Short-Term (CP)

Cash 

Providers

Loans

Borrower Bank and Dealer Activity Asset Holder

ASSETS LIABILITIES

Clearing 

Exchanges

PRIME BROKERAGE

FINANCING DESK

TRADING DESK

DERIVATIVES

TREASURY

NOTE: Lighter valued boxes indicate intermediaries such as markets, exchanges, or clearing centers.

* The funding hierarchy addresses the general risks involved with different funding sources. These risks are not associated with specic counterparties, because 

different counterparties may have varying degrees of risk as a source of funds.

Figure 31. Funding Map
2013 OFR Annual Report 50 2013 OFR Annual Report

spread. The funding and liquidity map described on 

the preceding pages charts the flows of cash and securities through a large financial institution with banking 

and dealer operations and could be useful for that 

purpose.

To better understand the effects of contagion during 

financial crises, we also use new datasets and employ 

network analysis. Section 4.4, based in part on an OFR 

working paper (see Glasserman and Young, 2013), 

shows how interconnectedness among financial institutions can amplify the effect of negative shocks to their 

balance sheets. Another research track highlighted in 

a working paper (see Bookstaber, 2012) explores how 

agent-based models can investigate the behaviors of 

individual firms (agents) to explain outcomes in complex systems.

In the area of policy, recent working papers analyzed 

proposed contingent capital rules (see Chen and others, 2013) and described the historical use of macroprudential policy in the U.S. to moderate the credit 

cycle (see Elliott, Feldberg, and Lehnert, 2013). Two 

recent working papers discussed scenario design in 

regulatory stress testing (see Glasserman, Kang, and 

Kang, 2013, and Flood and Korenko, 2013). Another 

paper discussed how stress testing could be adapted to 

support financial stability monitoring and to incorporate feedback effects during crises (see Bookstaber and 

others, 2013). 

In addition, the Office recently produced a working 

paper about the need for data standards for the mortgage market. Standards might encompass identifiers 

for mortgage products, loans, and specific properties 

(see McCormick and Calahan, 2013). This initiative, 

which emerged from a group of Council member agencies that supervise mortgage originators, relates to the 

Legal Entity Identifier initiative (see Chapter 6). 

Agenda Ahead

We continue to evaluate proposed metrics and develop 

new metrics for possible inclusion in our monitoring 

efforts. One project, highlighted in Section 4.3 of this 

chapter, entails developing metrics for monitoring 

market liquidity in different types of financial markets. 

The Office is working to incorporate nonpublic data in 

its analytical tools, while protecting the confidentiality 

of those data so that the results can be shared. 

Our current agent-based modeling work is exploring two prominent dynamics in financial crises: fire 

sales and funding runs. The first project models the 

interaction among leveraged hedge funds and other 

market participants to understand how fire sales 

develop and what determines their severity. The second 

project models the interactions among banks and 

market-based funding sources, such as money market 

funds and repurchase (repo) markets, to better understand runs and their dynamics. 

An important focus of our research agenda is on the 

forces that promote the migration of financial activities 

into unregulated or lightly regulated institutions and 

markets involving the creation of money-like liabilities. 

Section 4.2 analyzes the supply and demand for shortterm funding. Concerns about risks in these markets 

have driven the Office’s early data collections and 

analyses (see Chapter 5). We have devoted resources 

to analyze and develop monitoring capabilities to keep 

track of developments in hedge funds and money 

market funds, using data that market regulators now 

collect. To understand vulnerabilities in short-term 

funding and derivatives markets, we are also analyzing 

and monitoring repo and credit derivatives markets. 

4.2 The Supply and Demand for 

Short-Term Funding

Investor runs on short-term funding instruments played a 

critical role in the recent financial crisis. Private sources 

provided liquidity and credit backstops in short-term 

funding markets. When investors doubted the safety of those 

guarantees, they quickly withdrew their funding, and this 

stress was transmitted and amplified through the financial 

system. Although those markets have stabilized, they remain 

vulnerable to runs and fire sales. Analysis is important to 

ascertain the nature of such vulnerabilities.

This section describes a preliminary analysis of the 

supply and demand for short-term funding instruments and markets. Following tradition, we analyze the 

uses or demand for short-term funds — the ways that 

issuers use money-like liabilities to fund longer-term 

assets. We also analyze the sources or supply of such 

funds — the characteristics of the purchasers and 

the nature of their preference for such money market instruments. Focusing on both sources and uses 

highlights gaps both in the available data and in the 
51 Research on Financial Stability

analysis of economic forces that drive the market for 

money market instruments. 

Financial intermediaries often finance long-term, 

illiquid investments with short-term, liquid liabilities, 

such as commercial paper and repos. Nonbank financial companies using such short-term funding vehicles 

may be exposed to runs and other vulnerabilities as 

the activities of liquidity and maturity transformation create potential mismatches, and their funding 

instruments aren’t protected by deposit insurance or 

access to liquidity from the central bank. Creditors 

worried about the creditworthiness of their claims may 

withdraw funding, potentially resulting in significant 

losses that can lead to fire sales and failure. The recent 

financial crisis highlighted these vulnerabilities. 

To reduce the apparent risk and cost of credit in such 

liquid funding vehicles, issuers made use of private 

credit and liquidity guarantees — such as bank-provided, standby lines of credit for commercial paper. 

During the crisis, investors realized that those guarantees could be withdrawn or had doubts about the 

capacity of private firms to provide such guarantees. 

Liquidation of opaque, risky collateral by nonbank 

intermediaries promoted fire sales, which were intensified by runs on their funding sources, resulting in a 

dramatic, rapid deleveraging, and contraction in both 

sides of balance sheets. 

Despite that shrinkage, securitization and short-term, 

wholesale funding vehicles remain important features 

of a market-based financial system. More than 60 percent of credit transactions are intermediated outside 

insured depository institutions. New requirements for 

higher capital and liquidity by traditional banks and 

their parent holding companies may create incentives 

to move activities to more lightly-regulated institutions. 

Accordingly, analysis of short-term funding markets 

remains an important aspect of financial stability 

monitoring. 

In this preliminary analysis, we examine both the borrowers who use short-term funding and the investors 

who supply it. However, the data available to describe 

and quantify the short-term funding marketplace are 

not sufficiently detailed to create a complete picture 

of both supply and demand. We identify the nature of 

these gaps and suggest ways to fill them.

Market-Based Financing and Money Market 

Instruments 

Market-based financing, or credit intermediation outside the commercial banking system, gave rise to financial stability concerns during the crisis.3

 In this analysis, we focus specifically on the demand for, and supply 

of, money-market instruments that were considered 

“cash-equivalents” before the crisis. Investors held such 

assets in their portfolios because of their apparent low 

risk and high liquidity. Nonbank financial intermediaries conducted transformation of maturity, credit, and 

liquidity by creating or issuing such money-like liabilities. As noted earlier, however, these entities do not 

have explicit access to central bank liquidity or public 

sector guarantees (see Pozsar and others, 2012). So 

when these nonbank firms came under stress, investors 

ran. Analyzing both the demand for, and supply of, 

such instruments helps in understanding the risk of 

runs in short-term, wholesale funding markets.

Such shadow banking activities before the crisis can be 

illustrated by the creation and financing of a mortgage-backed security without a government guarantee. Credit transformation occurred when mortgages 

were pooled and long-term, AAA-rated bonds were 

issued and used as collateral. Maturity transformation 

occurred when those long-term bonds were financed 

through the issuance of short-term liabilities, such 

as asset-backed commercial paper. Liquidity transformation occurred when the commercial paper was 

purchased by a money market fund promising a stable 

net-asset value to its investors. Liquidity transformation 

creates an instrument akin to a bank deposit; investors 

expect to be able to withdraw their funds at any time 

without a loss of principal.

Money market instruments are large-denomination 

debt instruments with low credit risk and short maturity (less than one year, and often less than one week). 

Demand for these instruments reflects a natural 

preference by many investors for liquidity, the ability to 

exchange their investments for cash on short notice at 

a particular price. Because of their short maturity and 

low credit risk, money market instruments are treated 

by many investors as though they guarantee payout at 

full or par value. 

Investors in money market instruments include retail 

investors. They also include institutional cash pools 
2013 OFR Annual Report 52 2013 OFR Annual Report

— large, short-term cash balances of nonfinancial corporations and institutional investors (see Pozsar, 2011). 

Institutional cash pools allow a wide variety of financial 

firms, nonfinancial companies, foreign official sector 

investors such as central banks, and institutional investors such as mutual funds and pension funds to invest 

their surplus cash. Money market instruments include 

short-term obligations of the U.S. Treasury (Treasury 

bills) and commercial banking short-term obligations, 

such as uninsured certificates of deposits (large CDs), 

as well as short-term liabilities of shadow banks.

From the perspective of investors in money market 

instruments, Treasury bills and short-term obligations 

of commercial banks are close substitutes for the shortterm liabilities of shadow banking. However, there is 

a crucial difference: Treasury bills are backed by the 

full faith and credit of the U.S. government, and even 

uninsured large CDs of commercial banks are backstopped by access to the Federal Reserve’s discount 

window. In contrast, shadow banking liabilities, which 

typically offer higher yields, are usually backed only 

by private — not public — guarantees. In some cases, 

these private guarantees may be provided by commercial banks, but even in such cases, nonbank liabilities 

are more distant from public sector backstops. 

Run Risk

Investors in money market instruments run when 

shocks trigger fears of default and prices of these 

instruments decline sharply, undermining the belief 

that the instruments can be redeemed at par. To avoid 

the adverse effects on the real economy from historical 

bank runs and subsequent failures of banks, governments have long chosen to provide banks with access 

to the lender of last resort and deposit insurance, while 

subjecting them to prudential regulation to reduce the 

moral hazard associated with this access. 

Absent such backstops, run risk is present in any shortterm liability that finances a (even slightly) longer term 

asset. But different types of short-term instruments — 

commercial paper, repos, money fund shares — have 

different degrees of run risk. For example:

• If even a small money market fund breaks the 

buck, it can trigger a run on similar funds, which 

occurred with the Reserve Primary Fund on 

September 16, 2008. 

• Runs can also occur in repo markets (see Gorton 

and Metrick, 2012). Run risk is more prevalent 

in the triparty than in the bilateral repo market 

(see Begalle and others, 2013). A key driver of 

contagion early in the financial crisis was the 

unwillingness of money market investors to finance 

mortgage-related securities in the repo market.

• In contrast, the short-term debt issued by Fannie 

Mae and Freddie Mac, the two largest housingrelated government-sponsored enterprises (GSEs), 

was less subject to run risk partly because of a 

widespread belief that these entities had an implicit 

government guarantee. 

• Some run risk still exists in depository institutions 

for liabilities that aren’t federally insured. For 

example, there was a “silent run” by uninsured 

deposits and other wholesale creditors on 

Wachovia Bank in September 2008. After the 

failure of Washington Mutual, Wachovia lost $5.7 

billion of uninsured deposits and $1.1 billion 

of commercial paper and repos in a single day, 

according to government officials (see FCIC, 2011). 

Concern about the possibility of further runs led 

the Federal Deposit Insurance Corporation (FDIC) 

to extend temporary unlimited insurance coverage 

to all noninterest bearing transaction accounts at 

insured depository institutions and temporarily 

guarantee senior unsecured debt newly issued by 

insured depository institutions and bank holding 

companies. The same concern prompted Congress 

to increase the FDIC’s insurance limit from 

$100,000 to $250,000.

Understanding such differences in run risk — based 

on properties of the instrument (such as collateral), 

properties of the issuer, and the risk preferences of 

investors — may lead to better mitigants to runs.

Sources and Uses of Short-Term Funds

We use an accounting framework to help identify the 

sources and uses of short-term funds. A key source of 

funds in the money market (from the buyers of the 

debt or investors) are institutional cash pools. The 

primary uses of funds (issuers of debt or the borrowers) are the U.S. Treasury, commercial banks, nonbank 

financial companies, and highly rated nonfinancial 

companies. 

We track the sources and uses of short-term funds 

to understand developments in short-term funding 

in general and shadow banking specifically. A key 
53 Research on Financial Stability

objective is to evaluate how different investors may 

respond to changes in relative prices of these instruments. We eventually would like to test the hypothesis 

that investors who provide cash in short-term funding 

markets vary in their sensitivity to prices and other factors. The research summarized here begins this effort 

by identifying and tracking the components over time. 

The Demand for Short-Term Investments (Sources of 

Funds)

The sources of short-term funds are investors (nonfinancial and financial firms, households, and institutional investors) that want to hold money market 

instruments. Investors hold these instruments for three 

reasons: 

1. to help achieve their preferred trade-off between 

portfolio risk and return; 

2. as a liquidity buffer that reduces the costs of 

financial transactions; and 

3. for regulatory or tax purposes. 

The Federal Reserve’s Flow of Funds accounts, which 

map the flow of money within sectors of the economy, reveal information about borrowers and lenders 

in short-term funding markets and the forms of the 

lending. We aggregated Flow of Funds data related 

to investors and lenders into broad sector categories. 

To gauge their relative sizes and growth over time, we 

reported their total short-term investments for the 

end of the second quarter of 2007 and the end of the 

second quarter of 2013. Data limitations prevented us 

from drawing a complete picture of sources of shortterm funds (see Chapter 5).

Total institutional cash pools stood at $7 trillion at the 

end of the second quarter of 2013, according to our 

measurements, essentially unchanged from $6.9 trillion at the end of the second quarter of 2007. In sharp 

contrast, bank deposits held by households soared by 

35 percent to $7.3 trillion at the end of the second 

quarter of 2013 from $5.4 trillion at the end of the 

second quarter of 2007.

The following is a breakdown by investor type:

• Nonbank financial intermediaries, including 

securities lenders, held $1.2 trillion in shortterm investments in the second quarter of 2013, 

down substantially from $1.9 trillion in 2007. 

Intermediaries hold short-term instruments to 

satisfy their liquidity buffer requirements. Return 

considerations are secondary. Two important 

changes have affected this sector’s liquidity needs. 

First, the liquidity coverage ratio requirement 

introduced under Basel III (see Chapter 3) 

has prompted broker-dealers to increase their 

liquidity buffers, even prior to implementation. 

Second, securities lending activity has declined 

substantially since 2007, reducing the short-term 

investments held by securities lenders. 

• Equity and bond mutual funds held $0.6 trillion 

in short-term instruments in the second quarter 

of 2013, or 5 percent of total assets, up from $0.3 

trillion in 2007. Note that these are short-term 

holdings of funds with primarily long-term assets 

such as stocks. Short-term holdings provide a 

liquidity buffer to accommodate redemptions and 

new investments. Here, too, return considerations 

are secondary. 

• Foreign central banks held $1.3 trillion in the 

second quarter of 2013, up from $1 trillion in 2007. 

These holdings are calculated using data from the 

Bank for International Settlements and the Flow of 

Funds (see McCauley and Rigaudy, 2011). 

• U.S. nonfinancial corporations held $1.6 trillion 

in the second quarter of 2013, up from $1.3 

trillion in 2007. Many multinational firms hold 

cash in foreign subsidiaries to defer or avoid taxes 

associated with paying the funds to shareholders. 

The investments also serve as a liquidity buffer for 

future business investments. 

• Other institutional investors, such as state and 

local governments and pension funds, held $1.3 

trillion in short-term instruments in the second 

quarter of 2013, up slightly from $1.2 trillion in 

2007. These investments are motivated by cash 

needs of these investors, as well as their portfolio 

preferences. 

• Households held $1 trillion in prime funds in 

2013, down slightly from $1.1 trillion in 2007. 

We treat household prime money market fund 

holdings as institutional cash pools because 

investment allocations are made by fund managers, 

just as they are with other institutional cash 

pools. This is a portion of the $8.3 trillion that 

households held in money market investments (up 

substantially from $6.5 trillion in 2007), mostly in 

bank deposits.
2013 OFR Annual Report 54 2013 OFR Annual Report

The Supply of Short-Term Investments (Uses of Funds)

The short-term funds supplied by households and institutions are used to make long-term investments. These 

investments are made either directly by the issuers of 

the short-term debt — primarily Treasury bills issued 

by the U.S. Treasury — or indirectly, through financial 

intermediaries that issue the short-term debt. 

Figure 32 shows the uses of short-term funds in the 

second quarters of 2007 and 2013. For 2007: 

• The first column represents more than $5 trillion 

of retail commercial bank deposits held by 

households. 

• The second column shows the $8 trillion stock of 

wholesale money market instruments issued by 

financial intermediaries. 

• The third column shows direct issuance of shortterm debt by nonfinancial entities. This includes $1 

trillion of U.S. Treasury bills, as well as commercial 

paper issued by nonfinancial corporations. 

Wholesale money market instruments are created 

largely by commercial banks and the shadow banking 

Figure 32. Short-term Funding by Source in the Second Quarters of 2007 and 2013 ($ billions)

$5,374

$1,256

$8,074

2007 2013 2007 2013 2007 2013

Shadow

Banking

Dealers

GSEs

Others Comm Paper 

REITs

Commercial 

Banks

Bank

Deposits

CP

Treasury 

Bills

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

$8,000

Direct

Borrowing

Retail 

Intermediation

Wholesale

Intermediation

Dealers

Others

GSEs

$3,151

$7,447

Commercial 

Banks

Shadow 

Banking

$7,332

CP

Treasury 

Bills

Bank

Deposits

Figure 4.2 Short-Term Funding by Source in Q2 2007-Q2 2013 ($ billions) 

Source: Federal Reserve, U.S. Treasury, Investment Company Institute, Haver Analytics, SIFMA, OFR analysis 

system. The second column shows that in 2007, commercial banks issued approximately 45 percent of this 

short-term debt in the form of institutional checking accounts, institutional time deposits, repos, and 

commercial paper. By our definition, shadow banking 

accounted for the remaining $4.4 trillion of the $8.1 

trillion.4

Repos issued by broker-dealers, including those that 

are parts of bank holding companies, accounted for 

about half of shadow banking activity. Asset-backed 

commercial paper and discount notes issued by Fannie 

Mae and Freddie Mac are major components of the 

remainder. 

This $4.4 trillion represents a measure of the core of 

the shadow banking system in 2007. From a financial 

stability perspective, this is a critical metric because 

it represents runnable funds (although run risk in 

shadow banking varies by component). And for investors in money market instruments, it also contains the 

primary alternative to insured commercial bank deposits and Treasury bills. 

Sources: Federal Reserve, U.S. Treasury, Haver Analytics, Securities Industry and Financial Markets Association, OFR analysis
55 Research on Financial Stability

Figure 32 also shows a snapshot of a much different 

market for short-term funding in the second quarter of 

2013, more than five years after the crisis began. Four 

major changes are apparent since 2007: 

1. Short-term liabilities of commercial banks 

became a much larger share of total shortterm debt. The increase in total bank shortterm liabilities likely reflects several factors, 

including the need to fund an exceptionally 

large expansion in bank reserves (bank assets) 

because of expansionary monetary policy. 

In addition, retail and institutional checking 

deposits increased by more than $1.5 trillion, 

influenced in part by the increased deposit 

insurance provided by the FDIC during the 

crisis. 

2. Wholesale intermediated short-term debt 

excluding commercial bank debt dropped $2 

trillion, from $4.4 trillion to $2.4 trillion.

3. GSE short-term debt is not only smaller by $350 

billion, but also has less run risk since Fannie 

Mae and Freddie Mac entered conservatorship in 

September 2008. 

4. The quantity of Treasury bills in private hands 

increased by $2 trillion. The Treasury increased 

issuance of bills to support large deficits during 

and after the crisis. In addition, the Federal 

Reserve has reduced its holdings of Treasury 

bills by more than $250 billion since mid-2007.

The Evolution of Short-Term Funding

As shadow banking activity contracted sharply during 

the crisis, investors fled from unsecured money market 

instruments to the safety of Treasury bills, short-term 

debt of the housing GSEs, and insured bank deposits. 

As financial and economic conditions continue gradually to improve, the allure of safe Treasury bills and 

bank deposits may fade, and improving creditworthiness may lead to a rebound in shadow banking. 

A key point of our analysis is that the magnitude of 

the rebound will depend on both supply and demand: 

the ability to issue money-market instruments on 

economically-attractive terms, and the appetite of 

investors (mainly the institutional cash pools) for these 

instruments. 

One significant limitation of this analysis is that it 

does not look closely at what determines the size of 

institutional cash pools. We have taken the first step of 

measuring these pools and their evolution over time. 

The next step is to analyze the factors that drive the 

size of cash pools and different types of investors in 

such pools to learn how sensitive they are to relative 

risk and return.

We plan to investigate these questions to form a more 

complete picture of supply and demand for shortterm debt and a better understanding of how and why 

shadow banking activity varies over time. By monitoring sources and uses of short-term funds, we can establish a better early warning system for shadow banking 

activity and associated run risk. 

An Important Caveat: Data Gaps

The sources of short-term funds should equal the uses 

of short-term funds. And to clear markets, yields will 

adjust so that supply equals demand. However, according to the calculations in this chapter, the sum of the 

uses of short-term funding substantially exceeds the 

sum of the sources of short-term funding. Excluding 

household retail deposits, uses add up to just under $11 

trillion, while sources equal only $7 trillion. 

This mismatch stems from the methodology of the 

Flow of Funds accounts. This is not a construction 

flaw in these accounts, and the money is not missing. 

The problem is that reported data are insufficiently 

detailed to determine who holds the debt. The level 

of detail about debt issuance exceeds the detail about 

asset holdings. 

Detail is lacking across two dimensions. First, for some 

types of entities, short-term financial assets are not 

broken out separately from long-term financial assets 

in the Federal Reserve’s data collection. Second, certain types of funds, notably hedge funds and separate 

accounts (see Chapter 5), are lumped together with 

households. The difference between reported sources 

and uses is created by the inability to allocate the 

ownership of some money market instruments among 

banks, hedge funds, and separate accounts. About $4 

trillion in issuance cannot be matched with holders of 

the debt. 
2013 OFR Annual Report 56 2013 OFR Annual Report

Although we do not know who owns this $4 trillion, we 

can determine the types of debt instruments that make 

up the data gap. For each type of debt instrument in 

the uses of short-term funds reported in Figure 32, we 

subtract the amount for which ownership is known. 

The remainder is the data gap for that instrument (see 

Figure 33). 

The Flow of Funds report has detailed information 

on asset holdings of money market funds. The first 

column in Figure 33 shows the breakdown. Missing are 

the asset holdings of funds similar to money market 

funds but not regulated under the SEC’s rule 2a-7, 

which sets requirements on money market funds, 

including for liquidity and disclosure. The Flow of 

Funds does not collect that information. Institutional 

cash pool investments in these funds are lumped 

together with direct holdings of short-term assets. 

The second column in Figure 33 breaks down the measured direct holdings of money market instruments by 

the uses of short-term funds. For most types of institutions, Flow of Funds data do not break down Treasury 

holdings into Treasury bills and longer-term securities. 

Our calculations assume that all Treasury securities 

held by nonfinancial corporations are Treasury bills, 

which is consistent with the idea that corporate treasurers prefer to hold their cash in short-term investments. 

In practice, some large firms also invest their cash in 

longer-term instruments. This adds a layer of noise to 

the calculations we perform with Flow of Funds data. 

We also assume that institutional holdings of large 

time deposits and bank CDs equal banks’ issuance 

of these instruments, an assumption that introduces 

additional noise. 

The third column of Figure 33 represents the $4 

trillion data gap in Flow of Funds. It displays the shortterm instruments without a home that can be identified with the data. The largest component is Treasury 

bills. A substantial amount of the bills are likely held 

by commercial banks. We know banks hold approximately $600 billion in Treasury bills, notes, and bonds. 

The bulk of the remaining short-term instruments are 

probably held by cash pools of hedge funds and separate accounts. Data from the Securities and Exchange 

Commission’s Form PF, which the Office is currently 

analyzing, ultimately may help in quantifying hedge 

fund holdings.

Figure 33. Short-Term Funding Data Gaps ($ billions)

Sources: Federal Reserve, U.S. Treasury, Investment Company Institute, Haver 

Analytics, Securities Industry and Financial Markets Association, OFR analysis

Bank

Deposits

Repos 

and CP

Banking

Deposits

Repos 

and CP

Treasury 

Bills

0

2,000

4,000

6,000

Bank 

Deposits

Repos 

and CP

Treasury 

Bills

Treasury 

Bills

Other

Owned by 

Money Market 

Funds

Owned by

Cash Pools

Data Gaps:

Unknown

Owners

Other
57 Research on Financial Stability

4.3 Measuring Liquidity in the 

Financial System

Liquidity represents the ability of market participants to 

execute transactions and meet their obligations in a timely 

fashion and with minimal transaction costs. Historically, 

sudden shifts in liquidity have characterized financial crises. 

This section describes our research to identify changes in 

liquidity patterns across a wide range of markets using priceimpact measures, which assess the ability of a market to absorb 

large buy-and-sell orders without significantly affecting the 

price quoted for subsequent trades. This research contributes to

the Office’s financial stability monitoring program.

Liquidity represents the ability of market participants 

to buy and sell assets for cash and to deliver cash to 

meet their obligations. Because the obligations of most 

financial contracts are stated in terms of cash, this 

has important implications for the functioning of the 

financial system. The alternative typically is a costly 

legal default. Without liquidity, markets cannot carry 

out their basic function of price discovery by determining fair-market prices for financial assets. 

The recent financial crisis demonstrated the costs 

of a widespread breakdown in liquidity. Short-term 

funding markets — particularly the repo market and 

asset-backed commercial paper — experienced runs 

by nervous investors. Markets for certain assets with 

uncertain valuations experienced fire sales, exacerbated by margin calls on leveraged investors. 

Runs and fire sales reflect shocks to funding liquidity 

and market liquidity. Funding liquidity describes the 

ability of core participants in the financial markets to 

borrow on reasonable terms. For banks that have large 

broker-dealer subsidiaries, much of that borrowing is 

in the repo market and other wholesale funding markets. Market liquidity describes the ability of market 

participants to buy and sell securities and other financial assets over a relatively short time without having a 

significant price impact. 

This section focuses on a price-impact measure that 

combines daily price, trading volume, and volatility 

data to measure market liquidity, based on the work of 

Kyle and Obizhaeva (2011a, 2011b). Our analysis makes 

two contributions to that research. 

First, it covers a broad range of data from 33 financial 

markets, including equities, volatility index futures, 

oil futures, and corporate bonds. Second, it applies a 

statistical method to analyze how markets shift from 

one state of liquidity to another. The analysis shows a 

surprising consistency across all of these markets. 

Three liquidity regimes — low, intermediate, and high 

liquidity — were adequate to describe each market 

from 2004 to 2012. Our analysis also showed significant 

co-movement across markets. For example, the low 

liquidity regime described all markets in 2008 during 

the financial crisis. 

Why Liquidity Matters 

Liquidity matters because it facilitates exchanges that 

would otherwise be more expensive or even infeasible. 

Firms should be able to borrow easily if they are funding genuine opportunities for investment or trading. 

But when funding liquidity is limited, financial decisions may be based on arbitrary limits on participants’ 

ability to borrow rather than expectations of economic 

gains, losses, or risks. These borrowing constraints can 

arise if the immediate need for funding exceeds the 

supply of funds available.

Demand can shift suddenly in response to new uncertainties. As a result, episodes of illiquidity can emerge 

abruptly, often with sharp price adjustments. For example, in August 2007, a broad range of funding markets 

experienced a run as lenders suddenly questioned 

borrower creditworthiness and the quality of collateral. 

The episode was triggered when an absence of trading 

activity prevented BNP Paribas, a large France-based 

bank, from marking to market several of its investment 

funds backed by subprime mortgages. 

A key element in liquidity is the market structure, 

including the number, type, and size of market participants, as well as institutional rules for transactions. 

In a robust and transparent market, multiple market 

participants are available and willing to buy and sell, 

and institutional arrangements are fair, reliable, and 

well understood. Shortcomings in market structure can 

damage liquidity. For example, uncertainty about the 

solvency of particular firms may scare away potential 

investors or counterparties, even when that uncertainty 

is not grounded in fact. 
2013 OFR Annual Report 58 2013 OFR Annual Report

Funding liquidity and market liquidity are closely 

connected. Figure 34 uses bank balance sheets to show 

how funding shocks can lead a bank or other financial intermediary to sell assets, potentially affecting 

market liquidity (see Brunnermeier and Pedersen, 

2009). Central banks can buffer sudden shifts in 

aggregate liquidity by providing liquidity in their role 

as lenders of last resort, as they did after Lehman 

Brothers failed in 2008. 

Measuring Liquidity 

Price Impact: Combining Prices and Quantities

No single measure of liquidity exists. To assess funding 

liquidity, market participants typically look at price 

measures, such as the spread between the three-month 

U.S. Treasury rate and the three-month London 

interbank offered rate (Libor); quantity measures such 

as the aggregate money supply and banking sector 

liabilities; and institution-level information about 

the ability of banks to cover expected cash outflows 

(see Domanski, Fender, and McGuire, 2011). Kyle and 

Obizhaeva have developed a theory of price impact 

based on the pace of trading activity, and an empirical 

implementation that depends only on reported daily 

prices, returns, and trading volumes. Unlike earlier 

price-impact measures, such as Kyle (1985), the KyleObizhaeva measure can be calculated at daily frequencies and applies to a wide range of markets.

Market liquidity is typically measured by the discount 

required to buy or sell a given security in a relatively 

short time. A number of metrics exist for market 

liquidity, such as bid-ask spreads (the difference 

between quoted prices on offers to buy versus offers to 

sell) and trading volumes, but these tools are typically 

restricted to a single market (see Goyenko, Holden, 

and Trzcinka, 2009).

Access to data is a key consideration. Because timely 

data related to prices are widely available, simple 

measures based solely on price information have been 

useful in recent years. For example, the TreasuryEurodollar (TED) and Libor-overnight index swap 

spreads capture deviations in the interbank markets 

for borrowing that is free of credit risk. However, prices 

can change for a number of reasons. Information on 

quantities can help distinguish liquidity demand factors from supply factors. 

Our price-impact measure of liquidity builds on the 

Kyle-Obizhaeva research to view liquidity as the impact 

on subsequent prices of a standard, large transaction 

Figure 34. Liquidity Transformation via Financial Intermediation

Source: OFR analysis

* This is an arbitrary sampling of markets

Equities

Assets

Wholesale 

Funding

Markets

Liabilities

Market Liquidity Funding Liquidity

Corporate 

bonds

Additional

markets...

Derivatives

ABS & MBS

Mortgage

loans

CONTINGENT OFFICIAL LIQUIDITY 

BACKSTOPS

Ofcial Liquidity

Intermediary Firms 

Bank A

Asset Markets*

Net worth

Assets

Liabilities

Bank B

Net worth

Assets

Liabilities

Bank Z

Net worth

* This is an arbitrary sampling of markets.
59 Research on Financial Stability

in a given market. The approach uses historical 

returns, the current price, and daily volume to calculate liquidity. 

Price impact refers to the change in market prices 

caused by a one-directional flow of orders (buy or sell) 

of a given size. It is always a challenge for an investor or 

dealer to sell a large block of stock, especially during 

stressful conditions when crowded trades can generate 

fire-sale price drops. A key to price-impact analysis of 

Kyle and Obizhaeva is an “invariance hypothesis” — 

that there are certain broad behavioral patterns that 

are repeated across all markets, but with different 

intensity. The measure carefully adjusts the observed 

data related to price changes to account for differences 

in trading volume and volatility. 

Understanding and identifying these liquidity events 

is an important contribution to monitoring financial 

stability. By design, price-impact measures reveal the 

nonlinearity of market depth in the face of transaction 

pressure. Kyle and Obizhaeva proposed a technique 

that scales the calculations according to the intensity 

of trading activity, so the price-impact measures are 

unchanged by institutional arrangements (rules for 

trading) or by the pace of the trading activity. This 

approach supports a systemwide analysis by using the 

same method to calculate price impacts across a range 

of markets. 

Analysis of Price-Impact Measures 

This analysis adds to the Kyle-Obizhaeva price-impact 

measure by calculating the measure on a daily basis 

across 33 markets, covering thousands of individual 

securities in four distinct asset classes. 

The Office’s analysis has a greater scope than earlier 

studies. By casting a wide net across diverse instruments, this approach has a better chance of detecting emerging anomalies in liquidity, identifying key 

liquidity indicators, and discerning important patterns 

among the markets being monitored.

Our analysis also adds to the Kyle-Obizhaeva measure 

by using a statistical method known as the “hidden 

Markov chain” to determine whether different markets 

follow similar patterns as they switch from one state of 

liquidity to another. The analysis showed a surprising 

consistency across all of these markets. 

We used the hidden Markov chain method because it 

enables the estimation of latent or unobserved processes that govern how the price impact behaves under 

changing market conditions. These methods allow for 

the identification of the number of liquidity states and 

the probability that the markets are in any particular 

state at a given time. 

Latency means that liquidity problems are often not 

observable until the availability of liquidity is actually 

tested. Latency is a well-known challenge in measuring 

liquidity to capture the prices of recent trades, or the 

current best bid and offer, to gauge how deep or resilient the markets may be. A number of trading mechanisms restrict information availability with closed 

limit-order books, anonymous brokerage, and special 

trading venues for large block trades. To hedge against 

these and other liquidity surprises, financial firms 

frequently arrange for contingent liquidity in the form 

of lines of credit or derivative contracts. 

The Markov analysis also addresses nonlinearity — 

the fact that not all changes in transaction size have 

Figure 35. Change in Total Leverage and Volatility for 

Selected Banks for Q2 2000-Q1 2013 

($ billions)

Sources: Bloomberg L.P., OFR analysis

800

400

400

800

1,000 500 500 1,000

Equity

Liabilities, Low Volatility Quarter

Liabilities, Medium-Low Volatility Quarter

Liabilities, Medium-High Volatility Quarter

Liabilities, High Volatility Quarter

Figure 4.5 Quarterly Change in Total Leverage and Volatility 

for Selected Banks - Q2 2000-Q1 2013 ($ billions)

Note: Population includes Bank of America, Bank of New York 

Mellon, Bear Stearns, Citigroup, Goldman Sachs, JPMorgan 

Chase, Lehman Brothers, Merrill Lynch, Morgan Stanley, U.S. 

Bancorp, and Wells Fargo.

Source: Bloomberg L.P., OFR analysis

Change 

in Equity or Liabilities

Change 

in Assets
2013 OFR Annual Report 60 2013 OFR Annual Report

Figure 36. Daily Price-Impact Range and Probabilities of 

Liquidity States (Part A and B)

Sources: Center for Research in Security Prices, Bloomberg L.P., Mergent, Inc., Wharton 

Research Data Services, Financial Industry Regulatory Authority, OFR analysis

4

6

8

10

12

25th Percentile

50th Percentile

75th Percentile

price-impact 

(log scale)

cumulative

probability 

(percent)

Funding

market

distress

Lehman 

Brothers 

failure

0

20

40

60

80

100

May

2004

May

2006

May

2008

Aug

2007

Sep

2008

May

2010

May

2012

Low Price Impact

Medium Price Impact

High Price Impact

B.

A.

hampers the ability to extrapolate from measurement 

of often observed, small-scale impacts to the rarer, 

large impacts of greatest concern. 

At the systemic level, where interactions among participants can produce self-amplifying feedback loops, the 

stakes for nonlinearity are even higher. Because banks 

and other financial intermediaries provide the service of 

maturity transformation, they are especially vulnerable 

to liquidity surprises, such as runs (see Tirole, 2011). 

Figure 35 shows that vulnerability fluctuates as banks 

leverage and deleverage their balance sheets through 

the business cycle (see Adrian, Colla, and Shin, 

2012). Increases in aggregate leverage correspond to 

increases in measures of global liquidity, because bank 

deposits and other liabilities are key components of 

liquid assets for other participants in the system. In 

the recent cycle, bank leverage also correlated with 

market volatility. In Figure 35, periods of low volatility 

— shown in green and yellow markers — tend to 

match increases in bank leverage. Episodes of high 

volatility — shown in orange and red — correspond 

to decreases in leverage. Unfortunately, the leverageliquidity spiral works in reverse as well, leading to debt 

overhangs as the system contracts, with increases in 

institutional risk aversion and liquidity hoarding.

Results

Although our research is in early stages, it already 

shows promising results. The initial dataset comprises: 

• all U.S. equities from the Center for Research in 

Security Prices 

• all U.S. corporate bonds from TRACE (the 

Trading Reporting and Compliance Engine 

database), which began in 2002

• light sweet crude oil (West Texas Intermediate) 

futures from the Chicago Mercantile Exchange; and 

• market volatility index (VIX®) futures from the Chicago 

Board Options Exchange, which began in 2004 

The analysis starts with 2004, when all series are available. We grouped the equities and corporate bonds data 

into nine industry-based market indexes, using one-digit 

Standard Industrial Classification (SIC) codes.5

VIX® futures data are available for nine maturities, and 

crude oil futures data are available for six, with neardated contracts more active than long-maturity futures. 

The combined data cover 33 markets over nearly 10 

a proportionate impact on prices — by identifying 

the number of liquidity states necessary to describe 

the relationship. The more complex the relationship 

between transaction size and price impact, the more 

liquidity states. 

Several studies have shown that large new buy or sell 

orders can move prices significantly before additional 

liquidity providers arrive to dampen the effect (see 

Kyle and Obizhaeva, 2011a and 2011b). Nonlinearity 
61 Research on Financial Stability

years, including the recent financial crisis. For each 

market, daily price and volume information were used 

to calculate daily price-impact measures.

Part A of Figure 36 summarizes the behavior of the 

daily price impact measures over time. It shows the 

daily median value of the price impact measure across 

all 33 series, which typically represents a different 

market each day, along with the daily cross-sectional 

distribution between the 25th and 75th percentiles. For 

comparison with more familiar liquidity measures, 

Figure 37 shows the TED spread. 

As predicted, the price impact measures responded 

together with the TED spread to the large illiquidity 

events of the crisis, such as the August 2007 funding 

market distress, and the 2008 demise of Bear Stearns 

and Lehman Brothers. But the price impact measures 

offer two advantages over the TED spread. First, we 

were able to measure price impact individually across 

a wide range of markets. When aggregate illiquidity 

spiked, this collection of metrics helped in identifying 

the contributing subsectors of the financial system. 

Second, the framework provided daily liquidity data to 

support a systematic investigation of reliable patterns 

in illiquidity across markets. Because these data can be 

measured in a regular and consistent way, they can be 

used with well-understood statistical tools.

A preliminary Markov chain analysis indicated that a 

simple set of three latent liquidity states is adequate to 

capture the key behavior of each series, including nonlinear responses. This insight held across all 33 markets. 

For each series, the calibration produced an estimate of 

the probability that the underlying system was in each of 

these three unobserved states for each day. 

Figure 37. Daily Observations of Price-Impact Probabilities and the TED Spread (Part A and B)

Sources: Center for Research in Security Prices, Bloomberg L.P., Mergent, Inc., Wharton Research Data Services, Financial Industry Regulatory Authority, OFR analysis

0

1

2

3

4

5%

Note: TED Spread is the price difference between three-month futures contracts for U.S. Treasuries and three-month contracts for Eurodollars.

A.

B.

Low Price Impact

Medium Price Impact

High Price Impact

Dominant State

Jan

2007

Jan

2008

Jan

2009

Jan

2010

Financial 

Sector 

Stocks

Financial 

Sector 

Bonds

Volatility 

Index 

Futures

Crude Oil 

Futures

TED 

Spread

Funding market

distress

Lehman Brothers 

failure

Aug

2007

Sep

2008
2013 OFR Annual Report 62 2013 OFR Annual Report

Figure 36, Part B, presents the cross-sectional averages 

across the 33 series of these three probabilities, which 

must add up to one. Red indicates the likelihood of 

high price impact, blue indicates low price impact, and 

yellow indicates the intermediate state. Although market 

liquidity displayed diversity for these 33 series, there 

were periods of common behavior. For example, the 

funding market distress in August 2007 corresponded 

to a sharp but short-lived spike in the probability of the 

low-liquidity (high price-impact) state. Similarly, the 

liquidity crisis after the failure of Lehman Brothers is 

plainly visible as the steep and more persistent spike in 

September 2008, preceded by a series of pronounced 

foreshocks over the course of the year.

Figure 37, Part A, shows a small set of ribbons of daily 

data related to the liquidity regimes for 4 of the 33 

markets, using color to indicate relative levels. It shows 

the combined probabilities of the three estimated states 

over time, with blue indicating a high probability of the 

high-liquidity state, red for low liquidity, and yellow for 

the intermediate state. The TED spread is included for 

comparison to a more traditional measure.

A monitoring framework for measuring liquidity on 

comparable terms across a broad range of markets 

appears to be feasible. Measurement at relatively 

high-frequency — daily — is possible.

Figure 37 also shows the following: 

• Markets display diversity in their liquidity 

responses. Equity markets responded strongly and 

immediately to the run on repo funding market 

distress, but all markets did not share this reaction. 

A single aggregate measure like the TED spread 

cannot capture this cross-sectional variation. 

• Liquidity has largely recovered for this sample of 

market sectors since the financial crisis. 

• Liquidity in financial stocks remained depressed 

throughout late 2007 and 2008, consistent with 

increased uncertainty about the sector. 

• Liquidity was distressed for an extended period 

following the Lehman failure.

• Illiquidity foreshocks were detectable in financial 

stocks before the Lehman Brothers failure. This 

pattern, if reliable, may ultimately help in making 

liquidity forecasts.

These preliminary results are also notable for what 

they leave out. For example, the focus here was on 

price-impact measures, but other common metrics of 

market liquidity, such as implied bid-ask spreads, can 

be calculated at daily frequencies. In particular, this 

analysis focused on market liquidity — the markets 

for financial firms’ assets, shown in Figure 37— as 

opposed to the wholesale funding markets that play a 

central role in global liquidity. This omission reflects 

a data gap. For example, information on daily trading volumes in the wholesale funding markets can be 

inaccessible. In cases such as bilateral and triparty 

repo markets, the Office is working with the Federal 

Reserve Bank of New York to obtain data. In other 

cases, such as the foreign exchange swap market, no 

one has assembled the data in one place. If such data 

were readily available, we could extend our analysis to 

include markets that provide funding liquidity.

Summary and Future Research

Liquidity is an elusive, yet essential component of the 

modern financial system. It is elusive because, conceptually, it is hard to define. Empirically, it is hard to measure and predict, especially in times of crisis. Liquidity 

is essential to convert claims into cash, and almost all 

claims in the financial markets must be settled in cash. 

A breakdown in liquidity leads to a breakdown of the 

financial system.

Our research used daily market data from a wide range 

of financial markets to create a collection of 33 daily 

price-impact measures. We modeled each of these daily 

price-impact measures using a hidden Markov chain 

model that identifies sudden changes in price impact. 

Although we modeled the price impact in each market 

independently, there was surprising consistency among 

all these markets. Low liquidity occurred across all 

markets in 2008 during the financial crisis. Our analysis found interesting differences in markets before and 

after the crisis, offering potential bellwether markets 

that can predict the spread of stress in market liquidity 

and financial crisis. 

This framework for tracking liquidity states can be 

extended in several ways. In the future, we can consider additional metrics for market liquidity, such as 

bid-ask spreads or summaries of the order book, perhaps combining them with the price-impact measure 

to create a variable measure of liquidity. 
63 Research on Financial Stability

Our future research will broaden the cross section of 

financial instruments or look at the markets at a more 

detailed level by disaggregating the equities data to 

examine liquidity for individual stocks. We can also 

extend the modeling framework so the changes in the 

latent or unobserved liquidity regimes are at times 

driven by common factors, which would give a modelbased summary of what causes systemwide constriction 

in liquidity.

4.4 Contagion in Financial Networks

An important objective of our research is to understand how 

the structure of the financial system affects its vulnerability to 

contagion, the spread of severe shocks across institutions and 

markets. Network analysis provides a framework for studying 

the effects of interconnections and their role in contagion 

during financial crises. This section reviews financial 

stability questions addressed through network analysis and 

describes insights from a recent OFR working paper. 

Network research allows the quantification of contagion and loss amplification generated by interconnections without detailed knowledge of network structure, 

which is seldom available in practice. Instead, this 

approach relies on institution-specific information: 

size, leverage, and the Office’s new index of financial 

connectivity. The work highlights the importance of 

interconnectedness in the transmission of shocks to 

the financial system.

Introduction to Network Models

Assessing the interconnectedness of the financial 

system is essential for understanding how a negative 

shock to the balance sheet of one financial institution 

can spread to others, potentially leading to a cascade 

of write-downs and defaults. A negative shock to 

asset prices more generally can be amplified through 

interconnections. Recent examples include the effects 

of the Lehman Brothers bankruptcy, losses across the 

system that could have resulted from the failure of 

insurer American International Group, Inc., and the 

threat to some European banks from potential sovereign defaults by Greece, Italy, and Portugal. 

Rapidly growing research literature shows how 

contagion can occur and how the financial network 

can amplify or dampen such cascades. A network 

model contains a set of institutions, or the network 

nodes, and their payment obligations, which define 

the links in the network. For example, a network could 

be a set of banks, broker-dealers, hedge funds, and 

nonfinancial actors that have outstanding loans to 

one another. Networks of payment obligations help 

financial institutions redistribute risks and provide 

services to nonfinancial sectors of the economy. 

However, an adverse shock to one institution may 

prevent it from fulfilling its obligations to other 

institutions, and when this occurs, the default spreads 

through the network to other institutions. 

The broad agenda of financial network analysis is to 

address the following questions: How do financial networks form, and how do the formation processes affect 

financial stability? What is the vulnerability of an existing network to contagion and which institutions and 

markets contribute most to this vulnerability? What are 

the best measures of contagion and loss amplification? 

Through what mechanisms beyond direct payment 

obligations can shocks spread through a network? 

What types of network configurations enhance financial stability? 

These questions are listed roughly in order of increasing difficulty, and more progress has been made on 

the first few questions. The literature is too extensive to 

permit a comprehensive review here, so this discussion 

will simply mention a few key references. Eisenberg 

and Noe (2001) provide a theoretical framework for 

evaluating default cascades. Furfine (2003) and Upper 

and Worms (2004) apply network analysis to data from 

specific interbank networks. Gai and Kapadia (2010) 

and Haldane and May (2011) explore complex largescale networks through computer simulations. More 

references are discussed in Allen and Babus (2009). 

Much of this literature assumes detailed knowledge of 

the interconnections that make up a financial network. 

But in practice, complete information about obligations between financial institutions is unavailable. 

A recent OFR working paper offers a framework for 

quantifying the potential impact of financial networks 

on default contagion and loss amplification using 

only information about individual institutions (see 

Glasserman and Young, 2013). 

In the working paper, each institution’s size, leverage, 

and reliance on other financial institutions for funding 

were combined to measure the systemic importance 
2013 OFR Annual Report 64 2013 OFR Annual Report

of an institution and its connectedness to the rest of 

the financial system. This research also highlights 

how mechanisms beyond simple payment shortfalls or 

bankruptcy costs can increase vulnerability to shocks. 

Our research identifies credit quality deterioration 

and loss of confidence as important mechanisms in 

understanding how financial networks magnify shocks. 

Alternatives to network measures of connectivity often 

draw on correlations in market prices (see Acharya 

and others, 2010, Adrian and Brunnermeier, 2011, and 

Huang, Zhou, and Zhu, 2011).

Financial Connectivity and Implications for 

Contagion

This section gives an example of financial connectivity and its implications. The example is first used 

to introduce network terminology and the Office’s 

contagion index and measure of financial connectivity. 

The same example is then used to show how networks 

can amplify losses and how the extent of loss amplification can be gauged through our measure of financial 

connectivity.

A network model consists of a set of nodes, or financial 

institutions, and a set of payment obligations linking 

them. The nodes can represent banks, broker-dealers, 

hedge funds, pension funds, financial arms of nonfinancial corporations, and others. Payment obligations can arise from financial instruments, including 

loans, repurchase agreements, swaps, and derivatives. 

Figure 38 illustrates how such arrangements can 

create chains of obligations between institutions. In 

this example, household customers deposit funds 

in a Money Center Bank and a Regional Bank. The 

Regional Bank earns interest on loans to nonfinancial firms and other banks. The Money Center Bank 

lends to firms and to a Broker-Dealer, which funds a 

Mortgage Company that gives mortgages to households and bundles the loans into mortgage-backed 

securities. The mortgage securities are used as collateral for the loans by the Broker-Dealer, and they 

are reused as collateral against loans by the bank. Of 

course, there are many other possible links and other 

types of institutions not included in this example, such 

as money market funds, hedge funds, pension funds, 

and insurance companies.

Traditionally, a network model focuses on the amounts 

a given institution owes to others at the end of the relevant accounting period, rather than on the initial loan 

amounts. That means in a network model the arrows in 

Figure 38 would be reversed, and the dollar amounts 

owed at the end of the accounting period would be 

indicated beside the arrows. 

Figure 38 shows a hypothetical example based on the 

funding chains in Figure 38. Four financial firms are 

the nodes in this financial network, and depositors, 

households, and nonfinancial companies make up 

a generalized external sector. The dollar figures are 

chosen to keep the example simple.

Each node or financial institution in the network has 

a set of balance sheet information that can combine to 

measure the node’s significance as a source of contagion. A node’s net worth equals its assets minus its 

liabilities, or the difference between the numbers on 

the incoming edges and the numbers on the outgoing 

edges at that node. The outside assets of a node are 

the total amount owed to that node by households, 

Figure 38. Funding Chains 

Money Center 

Bank

Mortgage

Company

Households

Intermediary

Firms

Intermediary

Firms

Depositors

Regional

Bank Depositors

Broker-Dealer
65 Research on Financial Stability

nonfinancial firms, governments, and other nonfinancial depositors. A node’s outside leverage is the ratio of 

its outside assets to its net worth. The financial connectivity of a node is the proportion of its liabilities held 

by other financial institutions. 

The Office has developed a contagion index to measure 

the systemic importance of a node or financial institution: 

 Financial Connectivity x Net Worth x (Outside Leverage – 1) 

= Contagion Index

In this formula, the larger a node’s outside leverage, 

the more vulnerable it is to a default due to a shortfall 

in payments from its outside assets. If all outside assets 

were to default, the node would default as well, creating a shortfall in its payments to its creditors equal to: 

Outside Assets – Net Worth 

= Net Worth x (Outside Leverage – 1)

The shortfall amount transmitted to others in the 

financial system is calculated by multiplying this 

expression by the node’s financial connectivity parameter, which yields the contagion index. The contagion 

index is designed to measure the extent to which 

shocks that originate outside the financial system — 

for example, a drop in real estate prices — get amplified through the financial system.

Figure 39 shows these ideas. A Mortgage Company 

has assets of $100 in mortgage payments and liabilities 

of $98 in the form of short-term loans from BrokerDealer. That gives the Mortgage Company a net worth 

of $2 and outside leverage of 50:1. Suppose that 5 

percent of the Mortgage Company’s mortgages default. 

Then the Mortgage Company defaults because it can 

pay only $95 of the $98 it owes to the Broker-Dealer. 

Next, the Broker-Dealer defaults because it owes $96 

to the Money Center Bank. The cascade ends there 

Figure 39. Example of a Financial Network with Specific Obligations

because the Money Center Bank can absorb the shortfall without defaulting. The total write-down in asset 

values is as follows:

$5 (shortfall in payments to the Mortgage Company) 

$3 (shortfall in payments to the Broker-Dealer) 

$1 (shortfall in payments to the Money Center Bank)

$9 (systemwide loss in value)

Suppose instead that 50 percent of the Mortgage 

Company’s mortgages default, as shown in Figure 40. 

This figure shows the actual payments made after the 

defaults, in contrast to the promised payments shown 

in Figure 39. As a result of the mortgage defaults, the 

Mortgage Company defaults, causing a write-down of 

its obligation to the Broker-Dealer from $98 to $50. 

This causes the Broker-Dealer to default on its obligations to the Money Center Bank, which defaults in its 

obligations to the Regional Bank, which defaults in its 

obligations to depositors. The total write-down in asset 

values is:

$50 (shortfall payments to Mortgage Company)

$48 (shortfall payments to Broker-Dealer) 

$46 (shortfall in payments to the Money Center Bank)

$24 (shortfall in the Money Center Bank’s payments

 to depositors)

$6 (shortfall in the Money Center Bank’s payments

 to the Regional Bank)

$4 (shortfall in the Regional Bank’s payments to

 depositors) 

$178 (system-wide loss in value) 

This example shows that even a modest write-down in 

the assets of one node can trigger a cascade of defaults 

throughout the network and the total loss in value can 

be substantially greater than the amount of the initial 

default. 

In this example, the Mortgage Company node is particularly sensitive for several reasons. First, it is highly 

Money Center 

Bank

Mortgage

Company

Households

Intermediary

Firms

Intermediary

Firms

Depositors

Regional

Bank Depositors

Broker-Dealer

$96 $200

$100 $148

$50

$100 $98

$170
2013 OFR Annual Report 66 2013 OFR Annual Report

leveraged at 50:1 which makes it more vulnerable to 

default. Second, it is highly connected to the rest of the 

financial system with a financial connectivity value of 

1, which means a decline in payments to the Mortgage 

Company has a large impact on other financial 

companies. 

Contrast this with the Money Center Bank node. It has 

more assets and greater net worth but is less highly 

leveraged than the Mortgage Company. Another 

crucial difference is that the Money Center Bank has 

relatively few obligations within the financial sector, so 

a decline in its asset value will have limited effect on 

other financial companies. The contagion indexes of 

the Money Center Bank and the Mortgage Company 

are as follows:

Money Center 

Bank

Mortgage 

Company 

Financial Connectivity 0.2 1

Net Worth $16 $2

Outside Leverage 10.625 50

OFR Contagion Index 30.8 98

In this example, the Mortgage Company can potentially channel more than three times the losses into 

the financial system than the Money Center Bank. 

The degree to which these losses would actually topple 

other nodes depends on the size and leverage of the 

downstream nodes to which the Mortgage Company or 

the Money Center Bank has obligations. However, the 

analysis highlights how a large degree of interconnections amplify losses by channeling them back into the 

financial system. 

Figure 39 assumes complete knowledge of the financial network for purposes of the example. However, 

the contagion index for each financial institution or 

node can be computed without a complete picture of 

the network, using limited information available from 

each node. This feature distinguishes our approach 

from other researchers’ network analysis of financial 

systems.

Loss Amplification and Node Depth

Financial network losses can be amplified through 

cycles of obligations among financial institutions. For 

example, if the Mortgage Company places some of its 

cash in the Regional Bank, then the Regional Bank 

owes the Mortgage Company, and there is a cycle of 

obligations shown in Figure 41. (Other amounts in 

the figure are adjusted to keep the total assets and 

liabilities at each node equal to their values in Figure 

39). If the Mortgage Company’s mortgages lose most 

of their value and are written down to $14, then all 

nodes in the system default, including the Regional 

Bank. This means that the Regional Bank cannot 

repay the Mortgage Company in full, so the Mortgage 

Company’s total assets must be written down further. 

This shortfall courses through the system, causing 

deeper write-downs and a further loss of value at 

all the financial institutions or nodes. As shown by 

Eisenberg and Noe (2001), a unique set of payments 

clears the system, with the shortfall at each node allocated proportionately among the node’s obligations. 

The clearing payments are shown in Figure 41. This 

figure shows the actual payments made following the 

default of the Households; Figure 41 shows the promised payments. 

Figure 40. Impact on Network if Half of the Mortgage Company’s Mortgages Default

$96 $200

$100 $148

$100 $98

$170

$50

$50 $176

$144

$50 $50

$44

Money Center 

Bank

Mortgage

Company

Households

Intermediary

Firms

Intermediary

Firms

Depositors

Regional

Bank Depositors

Broker-Dealer
67 Research on Financial Stability

There is a useful method for estimating how much a 

financial institution contributes to the amplification 

of losses within the financial system, whether or not 

there are cycles of obligations (see Glasserman and 

Young, 2013). Using the example in Figure 42, we can

compute the proportion of each financial institution’s 

obligations to every other node and to the external sector. The result is shown in Figure 43. We would choose 

a node and place one dollar there. Suppose that, in 

each period, the dollar is passed to another node with 

the estimated probabilities shown in Figure 43. These 

probabilities are simply the proportions of payment 

obligations due from one node to another.

For example, if a dollar starts at the Money Center 

Bank, it flows to the external sector with a probability 

of 80 percent and to the Regional Bank with a probability of 20 percent. From the Regional Bank, it flows 

to the external sector with a probability of 75 percent 

and to the Mortgage Company with a probability of 25 

percent. 

The depth of a financial institution node is the 

expected number of periods needed for a dollar to exit 

the financial system starting from that node. A node 

will be deep if it has a large proportion of its obligations to the financial system and if its obligations are to 

financial institutions that also have many obligations 

Figure 41. Financial Network with a Cycle of Payment Obligations

Money Center 

Bank

Mortgage

Company

Households

Intermediary

Firms

Depositors

Regional

Bank Depositors

Broker-Dealer

$96 $200

$111

$50

$63 $98

$170

Intermediary

Firms

$100

$37

Figure 42. Impact When Households Pay Only $14, Instead of $63 Owed

Money Center 

Bank

Mortgage

Company

Households

Intermediary

Firms

Intermediary

Firms

Depositors

Regional

Bank Depositors

Broker-Dealer $96 $200

$111

$100

$63 $98

$170

$50

$50 $176

$37

$36 $108

$14 $50

$44
2013 OFR Annual Report 68 2013 OFR Annual Report

A third factor is the spread of mark-to-market losses 

through the financial network because of credit quality 

deterioration or loss of confidence. When a financial 

institution’s credit quality is impaired, its liabilities 

decline in value, transmitting a loss to the holders of 

those liabilities. Some of those holders may include 

the providers of backstops and hedging and insurance 

strategies to the issuers of liabilities. This channel 

spreads losses through the financial network, even if 

no financial institution fails initially.

Measuring Financial Connectivity 

A financial network’s structure affects the probability 

of contagion and the size of the losses generated by 

contagion. In practice, details of a network’s structure 

will not be known precisely and are constantly in flux. 

However, an assessment of the overall vulnerability of 

the financial system is still possible by examining its 

interconnectedness, or the degree to which financial 

institutions have obligations to each other as compared 

to their obligations to the nonfinancial sector. 

Because of the numerous types of transactions financial institutions have with each other, no single measure corresponds exactly to the measure of financial 

connectivity in the network model above. Nevertheless, 

several indicators of financial connectivity give insight 

into trends in network vulnerabilities, particularly 

when compared across time or across institutions.

Figure 44 offers one approach to estimating trends 

in the connectivity of the U.S. financial system at an 

aggregate level. The chart is based on Federal Reserve 

data related to the composition of assets and liabilities of the 50 largest U.S. bank holding companies, 

as measured by their asset base in March 2013 (see 

FRS, 2013). The lower curve shows the ratio of noncore liabilities to total liabilities. Noncore liabilities 

are calculated following the definition in the Federal 

to the financial system. In a financial crisis, when all 

nodes default, the node depth gauges the extent to 

which initial losses at a given node are amplified as 

they course through the system. In the example of 

Figure 40, the node depths are as follows:

Mortgage Company 3.37

Broker-Dealer 2.37

Regional Bank 1.84

Money Center Bank 1.37

This example shows that the Mortgage Company is 

a critical node. It is the most vulnerable to default 

because it is highly leveraged, and the large size of 

its outside asset base means the magnitude of losses 

is likely to be large when default occurs. Also, the 

Mortgage Company’s initial losses are highly amplified 

because the node is deeply interconnected within the 

financial system. 

This example of a network model leaves out several 

factors that may contribute to contagion. One is bankruptcy costs. When an institution fails, the amount 

available to pay its creditors is reduced by legal and 

administrative costs, and delays would add additional 

cost if payments were not made promptly. These costs 

have a knock-on effect by increasing the probability 

that a given default will trigger further defaults and by 

magnifying the total systemic losses that result from 

the default. 

Fire sales are a second factor that can increase systemic losses. When one institution gets into trouble, it 

may have to liquidate assets, which may depress prices 

and impair the balance sheets of other institutions 

holding the same assets. The results are more selling, 

a downward spiral in asset values, and a greater risk 

that defaults will cascade through the network. (This 

dynamic is incorporated into a network model in 

Cifuentes, Ferrucci, and Shin 2005).

External sector

Money Center 

Bank

Mortgage

Company

Regional

Bank Broker-Dealer

80%

100%

20% 25% 100%

75%

Figure 43. Transition Probabilities Among Network Participants
69 Research on Financial Stability

Reserve’s Y-9 peer reports and consist of large time 

deposits, federal funds purchased, brokered deposits, 

repurchase agreements, and foreign bank deposits. 

To the extent that the majority of these liabilities are 

to other financial institutions, we can view this ratio 

as a conservative estimate of the average financial 

connectivity of the large bank holding companies 

taken as a group. 

The top curve adds off-balance-sheet liabilities and 

noncore liabilities as a percentage of total liabilities. 

The off-balance-sheet items are the gross negative fair 

values of credit derivatives and derivatives on interest 

rates, foreign exchange, equities, and commodities. 

Assuming that most of these liabilities are to other 

financial institutions, this curve estimates an upper 

bound on the average financial connectivity of the 

large bank holding companies.

Taken together, the overall picture that emerges is of 

increasing financial connectivity in the lead-up to the 

crisis, followed by a decline. Over the entire period, 

however, the off-balance-sheet derivative liabilities 

have increased as a percentage of total liabilities, as 

is reflected in the increased gap between the higher 

and lower curves. This gap represents the percentage 

of total liabilities in derivatives. As seen in the figure, 

it has grown from slightly over 10 percent of total 

liabilities in 2002 to more than 20 percent in the first 

quarter of 2013. 

The pattern of increasing connectivity leading up to 

the crisis underscores the importance of monitoring 

connectivity as a potential source of vulnerability. 

Although the trends are clear, the lack of precision in 

these estimates points to a data gap in financial stability 

monitoring. Financial regulators do not have, even at an 

aggregate level, comprehensive measures of what fraction of liabilities of financial institutions are obligations 

to other financial institutions. The post-2008 declines 

in the indicators presented here may well reverse with 

time, and new channels of connectivity are likely to 

emerge as the financial system continues to evolve.

Next Step: Financial Connectivity Index

The Office is developing a new index of financial 

connectivity which, together with size and leverage, 

measures a financial institution’s potential contribution to financial contagion without detailed knowledge 

0.0

0.2

0.4

0.6

0.8

2002 2005 2008 2011 2013

Upper Bound Lower Bound

Note: Top 50 U.S. bank holding companies by assets as of March 2013.

Upper bound calculated as (Noncore Funding + Off-Balance-Sheet

Derivative Liabilities)/(Total Liabilities + Off-Balance-Sheet Derivative 

Liabilities). Lower bound calculated as (Noncore Funding)/(Total Liabilities 

+ Off-Balance-Sheet Derivative Liabilities). 

Figure 44. Trends in Financial Connectivity of 50 Largest 

U.S. Bank Holding Companies (ratio)

Sources: FR Y-9C data from Federal Reserve System (2013).

of network structure. Our research also introduces 

node depth to quantify loss amplification in a network, 

shows how financial connectivity determines node 

depth, and highlights the importance of mechanisms 

such as bankruptcy costs and credit quality deterioration in spreading losses throughout financial networks. 

The financial connectivity index, which measures 

the how much a financial institution relies on other 

financial institutions for funding, is a promising tool 

for monitoring financial stability. The calculation of 

this index from available data is still at an early stage, 

in part because of the diversity in institutions in the 

financial system and because of the diversity of transactions with each other. We plan to continue to develop 

our theoretical understanding of financial networks 

and the practical application of network analysis for 

financial stability monitoring.

71 Addressing Data Gaps

5 Addressing Data Gaps 

This chapter highlights the Office’s progress in 

executing our data gaps agenda, as described 

in the OFR 2012 Annual Report. It describes our 

approach and framework for assessing data gaps, 

the inventory the Office has compiled of the data 

collections of Financial Stability Oversight Council 

member agencies, the regulatory community’s 

recent efforts to improve financial data, and our 

ongoing data priorities. The final section presents 

the Office’s preliminary research results based on 

data related to credit derivatives, money market 

funds, and private funds.

5.1 Data Gaps Agenda

Comprehensive, timely, and granular data are essential 

to the Office’s ability to conduct the financial stability 

monitoring, analysis, and research described in this report. 

To ensure sufficient data are available, the Office pursues its 

agenda through the following process: (1) identify the data 

needed for financial stability analysis; (2) analyze existing 

and available data, and determine gaps; and (3) identify 

the cause of the gaps, prioritize the needs and feasibility of 

collecting the data, and fill the gaps. 

Regulators have significantly improved and expanded 

their data collections about the financial system since 

the recent financial crisis. 

As financial markets evolve and change, so will the 

types of data needed to assess risks. As a result, we 

must continuously review our data gaps priorities. We 

work with domestic and international regulators to 

address gaps in data needed to monitor the stability of 

the financial system by connecting existing data from 

an array of sources, promoting financial data standards, and collecting new data. 

The Council’s Data Committee created a working 

group in July 2013 to explore data gaps and evaluate 

the feasibility of sharing or collecting data according to 

the Council’s research and policy priorities. To assist in 

identifying the data currently available and the potential gaps or overlaps, we have compiled a preliminary 

inventory of the financial data collected or purchased 

by Council member agencies. 

The Office has put a high priority on learning more 

about the causes and consequences of liquidity risk, 

runs, and fire sales. To that end, we will work in 2014 

to improve the scope and availability of data related to 

the sources of funds for short-term wholesale funding 

instruments. We will specifically seek to address data 

gaps in securities financing transactions, including 

repurchase agreements (repos) and securities lending. 

We will explore data gaps about activities that may 

involve significant financial risks outside the scope of 

regulatory reporting requirements. This chapter highlights captive insurers, nonbank mortgage servicers, 

and mortgage real estate investment trusts.

Keeping data safe and secure continues to be the 

Office’s highest priority. The Office is constructing an 

analytic environment and implementing a robust data 

and technology infrastructure to further secure, manage, and analyze large amounts of data (see Appendix 

A). The Office is also exploring ways that nonpublic 

information can be used for analysis and monitoring 

by a broad set of experts while protecting confidentiality (see following insert on Techniques to Protect 

Confidential Data).

Identify Financial Stability Data Needs

The Office identifies data needs through its analytic and 

monitoring activities. For example, the Office seeks data 

to measure possible vulnerabilities in the markets caused 

by linkages and counterparty exposures in new or existing products and activities that develop outside of regulatory supervision. We discuss and refine our data needs 

agenda in the Council’s Data Committee (coordinated 

by the Office) and Systemic Risk Committee, as well as in 

our Financial Research Advisory Committee. 
2013 OFR Annual Report 72 2013 OFR Annual Report

Analyze Existing Data and Determine Gaps

The Dodd-Frank Act requires the OFR to rely on data 

already collected by Council member agencies, when 

possible, before requesting additional data for financial 

stability analysis and monitoring. To avoid duplication 

and take stock of existing data, we have compiled a 

preliminary inventory of data that Council member 

agencies purchase or collect. We are sharing the inventory, which will serve as a reference catalogue, with all 

Council member agencies and will make portions of the 

inventory public when appropriate (see Section 5.2).

To further assess available data, we review others’ 

monitoring and analysis to determine if data they used 

would suffice for our needs. We also conduct market 

research to determine whether commercially available 

data could fill our needs and whether analytical tools 

and software could help us use existing data more 

effectively.

Prioritize and Fill Gaps

Once we identify data gaps, we set priorities and determine a strategy for filling them. Priorities are based 

on our research and monitoring agendas, and on an 

assessment of how critical the gap is to understanding 

threats and vulnerabilities in the financial system.

In determining our strategy, it is important to understand what causes gaps. The data simply may not exist 

in the form needed for monitoring purposes. In that 

case, the Office would have to define data requirements, evaluate the feasibility and difficulty of obtaining the data, identify the best way to fill the gap, and 

develop a collection strategy. 

If the data do exist, they may not be accessible due to 

confidentiality, privacy, or data-sharing limitations. 

The data may be inadequate because they are not 

detailed enough for analysis, focused on the wrong 

items, too limited in scope, or of poor quality. In addition, the data may be impossible to compare or aggregate because of a lack of data standards. 

The Office has several ways to fill data gaps. First, we 

can acquire existing datasets gathered by Council 

member agencies and integrate them. Second, we can 

coordinate with Council member agencies to improve 

the scope, quality, or frequency of data they collect. 

Third, we can collect new data directly from the industry. Fourth, we can create and promote data standards 

to improve transparency and comparability among 

datasets (see Chapter 6). 

5.2 Regulatory Data Availability

Using the inventory the Office is compiling, we are developing a 

framework for understanding regulatory data. 

There are more than 500 datasets collected by Council 

member agencies (see Figure 45).1 These can be broken into six categories: 

1. Financial data include financial statements such as 

Securities and Exchange Commission (SEC) Forms 

10-Q and 10-K, asset pricing and valuation data, 

position or transaction data such as credit default 

swaps or repos, market and institutional flows, and 

information on the portfolio holdings of financial 

institutions.

2. Supervisory data result from bank examinations, 

surveillance, or other supervisory interaction with 

supervised companies.

3. Application data are from applications submitted 

by regulated banks and other firms to engage in 

mergers, acquisitions, branch openings, or other 

activities.

Figure 45. Regulators’ Datasets by Type 

Other Complaints

Structure

Supervisory

Application

Financial

Source: OFR analysis
73 Addressing Data Gaps

4. Complaints data reflect complaints from the public 

about regulated firms’ activities.

5. Structure data describe the organizational structure or hierarchy of financial institutions.

6. Other data are miscellaneous datasets that do not 

fit neatly into the other categories. 

The most common dataset types in the inventory (by 

number of datasets, rather than data volume) are 

financial, application, and supervisory datasets, as 

shown in Figure 45. 

Financial Datasets. Information reported to banking agencies includes general balance sheets and 

income statements, capital adequacy reports, and data 

required under the Home Mortgage Disclosure Act. 

Some banking agencies also collect information about 

specific holdings, transactions, or exposures from 

certain types of institutions. There are also specialized datasets related to each agency’s mission, such 

as the detailed deposit data that the Federal Deposit 

Insurance Corporation collects to support its deposit 

insurance function.

The Commodity Futures Trading Commission and 

the SEC collect transaction data for specific markets 

and asset classes, trade repository data, and information on specific portfolio holdings, as well as information about balance sheets and income statements. 

Reporting requirements vary significantly by institution type and asset class. 

Supervisory Datasets. Supervisory datasets focus 

largely on three areas: (1) safety and soundness and 

other information resulting from examinations of 

financial institutions, such as supervisory ratings of 

individual banks; (2) required supervisory filings, for 

example, on specified financial holdings or events; 

and (3) records of citations, violations, or enforcement 

actions. These datasets are mostly confidential. 

Application Datasets and Others. Banking regulators’ application datasets are largely related to structural changes, such as requests for mergers or charter 

Figure 46. Examples of Data Collected by Council Member Agencies

Data Category Regulator Form Number Name Description

Financial Federal Financial 

Institutions 

Examination Council 

FFIEC 031/041 Consolidated Reports 

of Condition and 

Income (call reports)

Quarterly balance sheet, income 

statement, and supplemental 

schedules

Application Securities 

and Exchange 

Commission

Form TA-1 Registered Transfer 

Agents 

Application to register as transfer 

agent to keep track of the people 

and organizations that own its stocks 

and bonds. The form is submitted 

to the SEC or to a banking regulator, 

depending on the applicant.

Examination / 

Supervisory

National Credit 

Union Administration

Form 4501 Credit Union Online Executives’ contact information and 

certification of compliance with 

security procedures, suspicious 

activity reporting, and Bank Secrecy 

Act requirements.

Structure Federal Deposit 

Insurance 

Corporation

Form 5 (6800/05) Annual Statement of 

Beneficial Ownership 

of Securities

Officers and directors disclose 

transactions and holdings.

Complaints Office of the 

Comptroller of the 

Currency

Online form

at www.

helpwithmybank.gov

Customer Complaint 

Form

Consumer complaints about a specific 

national bank or federally insured 

thrift.
2013 OFR Annual Report 74

changes. Market regulators’ application datasets 

often relate to specific registrations and certifications. 

Complaints datasets reported by Council member 

agencies involve consumer complaints and investor complaints. Structure datasets, which are mostly reported by 

banking supervisors, contain hierarchy and ownership 

data, merger notifications, and company name changes. 

Datasets categorized as “other” vary in topic, and are 

often unique to the reporting agency.

The scope of regulatory data varies. Banking agencies 

collect standardized, publicly available data about 

balance sheets and income statements, such as the call 

reports filed by banks and the FR Y-9C reports filed by 

bank holding companies. Banking agencies also collect 

supervisory data that cover specific activities and risks, 

such as off-balance-sheet information and counterparty 

information, although that information is not always 

comparable among market participants and is often 

confidential. Data collections by market regulators vary 

depending on the financial sector. For example, money 

market funds report monthly portfolio holdings on 

Form N-MFP. Broker-dealers submit financial condition 

information quarterly on Form X-17A-5, also known as 

the FOCUS reports.

Common Security Classifications to 

Promote Data Sharing

Council member agencies are forging bilateral 

data-sharing agreements to assure all participants that 

shared data will be protected, secured, and treated 

consistently. Under these agreements, an agency 

requesting data from another Council member agency 

must keep the data at least as secure as the agency 

providing the data. These agreements are consistent 

with the analysis of data sharing in the Council by the 

Council of Inspectors General for Financial Oversight 

(see CIGFO, 2012). 

For those data-sharing agreements to work, agencies 

must first agree on definitions of information security 

classifications and how to apply them. Historically, for 

example, different agencies may have had different 

policies around handling data that they defined as 

“restricted” or “high security.” 

The Office led and partnered with the Council’s Data 

Committee to align security classification categories. 

An interagency working group established a common 

framework for information security practices, processes, and compliance requirements so that data can 

be shared with an assurance of equivalent protections 

among the members. 

The working group built upon existing standards and 

agreements in the protection of the data, but with 

a focus on communicating and documenting the 

mutual understanding of the security responsibilities 

when sharing data. The working group has established principles for data sharing and responsibilities 

between agencies, procedures that emphasize joint 

communication and control, and documentation of 

the data request process, controls, and responsibilities. 

The National Institute of Standards and Technology 

assisted the working group in aligning the framework 

with the Federal Information Security Management 

Act of 2002 and the Federal Information Protection 

Standards. These federal standards represent the common base to which all federal agency classifications are 

mapped. 

5.3 Progress in Identifying and 

Addressing Data Gaps

Improvements in U.S. Regulatory Data 

Collections

Regulators have improved their data collections in 

several important ways. Depository institutions are 

submitting more detailed reports, large hedge funds 

are filing quarterly information about their portfolios, 

and money market funds are submitting monthly data. 

Also, the Financial Industry Regulatory Authority 

(FINRA), the largest private self-regulatory organization that oversees broker-dealers, has increased its 

monitoring of alternative trading systems that are 

commonly known as “dark pools.” 

The Federal Reserve recently introduced two new 

forms, the FR Y-14 and FR Y-15, requiring more information from the largest bank holding companies. 

The FR Y-14 supports the Federal Reserve’s annual 

Comprehensive Capital Analysis and Review, which 

includes a supervisory stress test to gauge institutions’ 

resilience to shocks. On this form, companies provide 

more detail than is available on the FR Y-9C reports 

about their exposures to specific asset classes and 
75 Addressing Data Gaps

about their income statements. The Federal Reserve 

began in 2013 to require a stress test summary and 

scenario information in the FR Y-14 on a semiannual 

basis, rather than annually (see Board of Governors, 

2013c). The Federal Reserve also revised the reporting 

frequency from three to four times per year for the 

schedules in the FR Y-14 on which companies report 

details about their regulatory capital instruments. FRY14 data are confidential, although the Federal Reserve 

releases a public summary of the annual stress test 

results for each institution (see Board of Governors, 

2013a and 2013b). 

The Federal Reserve uses the FR Y-15 to monitor the 

financial stability risks posed by bank holding companies that are subject to enhanced prudential standards 

under the Dodd-Frank Act, and to determine capital 

requirements for global systemically important banks 

(G-SIBs). Annual collections began with December 

31, 2012 data (see Board of Governors, 2012b). Filers 

include all bank holding companies with $50 billion 

or more in total consolidated assets, although the first 

collection was limited to the eight bank holding companies the FSB designated as G-SIBs in 2012 (see FSB, 

2013d). This information is available on the Federal 

Reserve System’s National Information Center website 

(see FRS, 2013). 

Bank regulators made a significant change in 2013 

to reports filed by depository institutions. The commercial bank call reports and the comparable FR 

Y-9C Reports filed by bank holding companies were 

expanded to include a new schedule — RI-C for banks 

and HI-C for bank holding companies — to give 

detailed information about allowances for loan-andlease losses, effective with the March 31, 2013 filing. 

Filers must record the allowances they have set aside 

for losses in six categories: (1) commercial real estate, 

(2) residential real estate, (3) real estate construction, 

(4) commercial, (5) credit cards and other consumer, 

and (6) an unallocated category for unspecified losses. 

Regulators consider the detailed allowance information more consistent with the methodologies that institutions use to comply with accounting rules. Analysts 

may also be able to gain a better understanding of 

institutions’ allowance practices and how they change 

over time.

The SEC now collects information from hedge funds 

and money market funds. 

Advisors for hedge funds and other private funds with 

more than $150 million in assets under management 

have been electronically filing annual portfolio information through the SEC’s Form PF since July 2012. 

Hedge fund advisers with at least $1.5 billion in hedge 

fund assets under management submit quarterly data 

with respect to each qualifying hedge fund that has 

more than $500 million in net assets. The form also 

collects more detailed data from large private equity 

fund advisers and large liquidity fund advisers.

In July 2013, the SEC issued its first required annual 

report to Congress describing its uses of Form PF data 

and providing aggregate information. Detailed information about individual funds and advisers is confidential (SEC, 2013b). In June 2013, the SEC proposed 

revisions to Form PF that would require large liquidity 

fund advisers to submit information similar to that 

collected on form N-MFP from registered money market funds (SEC, 2013a). The Office has a data-sharing 

agreement with the SEC that allows access to the data 

on a rolling monthly basis.

Money market funds have been filing monthly financial information with the SEC on Form N-MFP since 

2010. The SEC posts this information on its public 

website 60 days after the end of each month. The SEC 

has proposed money market fund reforms that would 

increase the amount of detail in Form N-MFP. The 

proposed changes would also require money market 

funds to report legal entity identifiers and at least one 

other security identifier for each portfolio holding; 

disclose each investment’s fair value, as defined by 

U.S. accounting rules; and disclose the purchase date, 

price, and yield of each investment. 

FINRA, which collects data under the delegated 

authority of the SEC, proposed a rule change in 

September 2013 to require more information to be 

reported by private trading venues, sometimes referred 

to as “dark pools” (see SEC, 2013c). These private 

trading venues, or alternative trading systems, are now 

required to submit quarterly volume information to 

the SEC on their activities.

FINRA separately began taking a closer look in July 

2013 at controls over high-frequency trading. Highfrequency trading uses sophisticated computers and 

algorithms to analyze several markets simultaneously 

and execute orders at extremely fast speeds. FINRA is 
2013 OFR Annual Report 76 2013 OFR Annual Report

TECHNIQUES TO PROTECT CONFIDENTIAL DATA

Our research investigates new techniques to enable the 

analysis of sensitive data while protecting confidentiality.

Regulators must balance transparency and confidentiality 

of data gathered from financial institutions. Their traditional 

choice has been either full publication or full confidentiality. 

New cryptographic tools create the possibility of a middle 

ground by reliably filtering information that should not 

be disclosed, as we described in a recent working paper 

(Flood and others, 2013). These techniques are still at 

an early stage of research. The OFR is exploring whether 

they are sufficiently mature to justify more extensive 

prototyping.

Confidentiality and Statistical Data Privacy

Two tools stand out as potentially useful for balancing 

confidentiality and transparency of financial data. The first 

tool of secure multiparty computation is complementary to 

the second, statistical data privacy. 

Secure multiparty computation emulates an incorruptible trusted party that receives secret information from 

several companies, securely and accurately performs 

computations, and reveals only the final results. This 

technique could allow regulatory agencies to compute 

aggregate statistics jointly without physically pooling 

data. This would help reduce concerns about security 

breaches or legal restrictions on explicitly sharing data 

with other organizations. 

The other technique — statistical data privacy — determines whether seemingly innocuous data releases, such as 

specific summaries, are actually safe to reveal. Statistical 

data privacy aims to understand and limit what confidential information is released. Even a final product, such as 

aggregate statistics, can sometimes reveal confidential 

information regarding individual investors or individual 

institutions through inferences drawn in combination with 

other available information. Statistical data privacy tests 

how different the situation might appear to a thoughtful 

observer who sees a particular data release.

Three scenarios described in the working paper illustrate 

how new privacy techniques might be applied to specific 

challenges for financial regulation and disclosure: 

1. domestic supervisory agencies to the public (Case 1)

2. domestic agencies to the public and the research 

community (Case 2)

3. domestic to international agencies (Case 3)

Case 1: Publication of Aggregated Sensitive Data 

Since the financial crisis, a number of financial regulators 

have begun publicizing financial conditions indices. The 

indices aim to capture the state of an extremely complex 

financial system by collecting market and other data, 

and distilling the information in a single number that 

can be tracked through time. The Federal Reserve Bank 

of Cleveland’s Systemic Assessment of the Financial 

Environment (SAFE) and Cleveland Financial Stress Index 

(CFSI) are two examples.

CFSI uses publicly available data to compute an index 

that, because it relies on public data, can be released but 

has relatively low statistical accuracy. By contrast, SAFE 

blends some confidential data with publicly available data to 

compute an index. SAFE appears to be more accurate, but 

may not be published because of worries about disclosing 

confidential information. 

Using techniques for statistical data privacy, it may be 

possible to confirm that the SAFE index (or some “sanitized” variant of it) has rigorous privacy guarantees for the 

confidential data and the individual institutions involved. 

Case 2: Retail Loan Information to Support Research

Regulators collect datasets from banks about home 

mortgages, credit cards, and other retail loans. Supervisory 

analysis would benefit if this data could be shared with 

the broader research community. But information in these 

datasets is extremely sensitive, carrying detailed information 

about accounts and borrowers. Anonymizing the data would 

be insufficient to protect confidentiality, because so much 

information about housing loans is available through public 

records and could be reverse-engineered to reveal private 
77 Addressing Data Gaps

focusing on collecting information about how high-frequency trading algorithms are developed and used; 

who creates, tests, and modifies the algorithms; and 

what contingency plans exist for computer malfunctions (see FINRA, 2013). The collection of this information will help market regulators better understand 

the functioning of high-frequency trading and potential threats it may pose to financial stability.

International Data Efforts

Since the financial crisis, international financial 

forums have identified critical data gaps, issued policies and guidance for global cooperation to close data 

gaps, and collected data through international collaboration. The Office participates in these international 

efforts, recognizing that closing data gaps for monitoring and analyzing threats to financial stability requires 

global cooperation. 

One major project is the Data Gaps Initiative, launched 

in 2009 by the G-20, a forum of finance ministers 

and heads of central banks, and coordinated by the 

Financial Stability Board (FSB). In 2010, the FSB made 

20 recommendations to fill critical information gaps 

identified through broad international consultation. 

The G-20 endorsed the recommendations, which 

include improving the communication of official 

statistics to help identify and monitor potential threats 

to financial stability, connections across countries, 

and interconnections in domestic economies. In 2013, 

the Office began participating in FSB work on banks’ 

interactions with money market funds, securitization, 

securities lending and repos, and other shadow banking entities. 

Other international regulatory efforts would make 

regulatory data more uniform. The Basel III reforms 

regarding capital and liquidity would standardize 

reporting among international institutions in key risk 

areas. The Basel III accord is a voluntary standard for 

banking soundness devised in 2010 by the banking 

supervisors and central bank governors of 25 countries who serve on the Basel Committee on Banking 

Supervision. Although originally scheduled to be 

phased in from 2013 through 2015, full implementation has been delayed until 2019.

The Basel Committee’s January 2013 revisions to the 

liquidity coverage ratio standard amended definitions 

to provide clarity and improve consistency regarding 

information. Tools developed for statistical data privacy 

might be used to certify what sorts of data releases (for 

example, aggregated or randomized data) might expose the 

broad patterns useful to supervisors and researchers, while 

protecting individual borrowers’ privacy. The challenges in 

this area are great, however, and the potential risks and 

rewards are complex.

Case 3: International Sharing

In April 2009, during the financial crisis, the G-20 (Group 

of 20) nations launched the Data Gaps Initiative to identify 

and close supervisory information gaps throughout the 

financial system. The G-20 is a forum of finance ministers 

and central bank governors from 19 countries and the 

European Union begun in 1999 to encourage international 

cooperation on global economic issues.

An initial report of 20 recommendations included two on 

sharing data about individual institutions with international groups of supervisors. The Bank for International 

Settlements has begun centralizing and sharing data, 

relying on highly restrictive access controls and physical 

security protocols to maintain confidentiality. 

Confidentiality concerns are heightened with international 

data sharing, because of limited cross-border authority 

to enforce agreements, resolve disputes, and remedy 

breaches. Secure computation technologies can help promote international sharing by emulating a trusted party to 

avoid the need for physical sharing of all the raw inputs to 

a particular computation. Instead, only narrow outputs — 

carefully calculated by local agencies — are shared, which 

are then assembled into the final aggregate. Protocols for 

statistical data privacy can also address concerns about 

whether data summaries intended to help decisionmaking 

by international regulators will reveal private information 

about individual firms. 

These cases show how limited information sharing would 

improve transparency for the public, promote coordination 

among supervisors, and enhance market discipline while 

still protecting confidential and personal information.
2013 OFR Annual Report 78 2013 OFR Annual Report

high-quality liquid assets and net cash outflows, 

improving data quality and comparability (see BCBS, 

2013a). In July 2013, the Basel Committee proposed a 

common set of disclosure standards for banks to assess 

their liquidity coverage ratios (see BCBS, 2013b). 

5.4 Data Gaps Priorities

The research and monitoring of the Office have identified 

key gaps in our understanding of repo markets, shadow 

banking, and the asset management industry. This section 

describes what we know and do not know about these 

markets because of gaps in data. Other data gaps continue to 

become apparent as the financial sector evolves; we highlight 

developments in insurance, mortgage servicing, and real 

estate investment trusts. Gaps are also created because of the 

inability to link data from different sources.

Repo Markets

Since the financial crisis, policymakers and academics have recognized the importance of repo markets 

to financial stability. Short-term funding obtained 

through repos 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 

contracts. If a financial company begins to have difficulty in accessing repo markets, either through higher 

discounting of its collateral pledged for a loan or outright exclusion from markets, the firm may have to sell 

assets. If these sales are substantial, they can depress 

the assets’ market values, creating fire sales that can 

transmit the firm’s difficulties to others.

Efforts to collect repo market data are complicated by the 

fact that there are three separate repo markets. 

Bilateral repos are the simplest arrangement, in 

which the lender takes control of the borrower’s 

collateral and promises to return it when the loan 

is repaid. Data related to this market segment are 

limited to survey information collected by the Federal 

Reserve Bank of New York.

Triparty repos involve one of two clearing banks — 

Bank of New York Mellon Corporation or JPMorgan 

Chase & Co. — that hold collateral on behalf of the 

lender and provide collateral valuation and netting 

services. Borrowers and lenders negotiate loan terms 

directly with one another and then report them to one 

or both of the clearing banks. Data about these markets are held in these banks.

The General Collateral Finance (GCF) market is 

an anonymous wholesale repo market that is almost 

exclusively dealer-to-dealer. The GCF market is markedly different from the other markets in that the Fixed 

Income Clearing Corporation (FICC) guarantees each 

transaction and requires that participating firms meet 

a high solvency standard, effectively limiting who can 

participate. Also, unlike the other repo markets, the 

borrower’s creditworthiness does not affect the terms 

of GCF transactions. FICC collects and maintains data 

about this market segment.

Anecdotal evidence suggests that the bilateral 

market suffered the greatest disruptions during the 

recent financial crisis. Unfortunately, little reliable data 

are available for this market because of its decentralized structure. 

More information is currently available about the 

triparty repo market than the other two markets. The 

Federal Reserve Bank of New York publishes summary 

triparty repo statistics every month, using information 

provided by the clearing banks.

Figure 47 provides details on different types of collateral used in the triparty market. It shows the ten 

generic asset classes that are used to secure repo loans 

in the triparty market, although most transactions 

involve U.S. Treasuries or agency mortgage-backed 

securities issued by a government-sponsored enterprise. Included are several types of allowable collateral, such as asset-backed securities, that suffered a 

breakdown in market confidence and lost value during 

the crisis. The right half of the figure indicates that 

collateral margin levels, or haircuts, vary widely, but we 

know nothing about the correlation between required 

haircuts and a borrower’s perceived credit condition, 

or about the extent to which repo interest rates and 

margins complement one another in determining an 

institution’s available repo funding. Information about 

changes in margins over time would help monitor 

whether the market is again starting to lose confidence 

in a particular type of collateral.

The aggregated data in Figure 47 can identify the 

extent to which repo borrowers are posting less liquid 

collateral — the sort of collateral that might cause a 

fire sale if it had to be liquidated in a hurry. But these 
79 Addressing Data Gaps

aggregate data do not indicate whether the weaker 

credit risks are responsible for a change in collateral 

offerings, and so they cannot identify the likelihood 

that a fire sale might occur. Understanding developments in repo markets and other short-term financing 

arrangements requires data about the rates, haircuts, 

length of loan, and collateral type for each large 

institution. 

As an initial step toward better understanding the 

terms of borrowing in these markets, the Office has 

done a preliminary analysis of GCF transactions 

data obtained from the FICC for 42 business days in 

February and March, 2012. Below are a few observations based on this limited, but still complex, dataset. 

Figure 48 shows the daily value of all new trades in 

this dataset. Over the sample period, the mean gross 

amount of GCF repo funding was about $193 billion. 

These data could also provide information about 

the widely known “window-dressing” effect, in which 

financial institutions make their quarterly financial 

statements appear stronger. The extent of this window 

dressing may be indicated by the repo volume decline 

Figure 47. Triparty Repo Statistics as of October 9, 2013 

Cash Investor Margin Levels

Asset Group Collateral 

Value (billions)

Share of 

Total

Concentration of 

Top 3 Dealers

10th 

Percentile

Median 90th 

Percentile

ABS Investment Grade $16.70 1.0% 49.7% 5.0% 6.0% 13.0%

ABS Noninvestment Grade $21.38 1.3% 57.2% 5.0% 8.0% 22.8%

Agency CMOs $77.92 4.8% 37.0% 2.0% 3.0% 7.0%

Agency Debentures & Strips $98.06 6.0% 30.5% 2.0% 2.0% 3.0%

Agency MBSa $530.66 32.5% 32.4% 2.0% 2.0% 3.0%

CMO Private-Label (Investment 

& Noninvestment Grade)

$39.19 2.4% 48.6% 3.0% 8.0% 20.0%

Corporates Investment Grade $46.27 2.8% 29.7% 3.7% 5.0% 10.0%

Corporates Noninvestment 

Grade

$19.49 1.2% 36.8% 3.0% 8.0% 15.9%

Equities $119.54 7.3% 47.2% 5.0% 8.0% 15.0%

Money Market $23.70 1.5% 52.8% 2.0% 5.0% 5.0%

U.S. Treasuries/Strips $40.83 2.5% 41.7% 2.0% 2.0% 3.0%

U.S. Treasuries excluding Strips $574.56 35.2% 27.3% 2.0% 2.0% 2.0%

Otherb $22.18 1.4%

Total $1,630.49

a

 Mortgage-backed securities

b

 Other includes credit default obligations, international, municipal debt, and whole loans

Source: Federal Reserve Bank of New York; available at http://www.newyorkfed.org/banking/pdf/oct13_tpr_stats.pdf (accessed November 22, 2013).
2013 OFR Annual Report 80 2013 OFR Annual Report

10 lend only cash, 15 to 20 only borrow, and about half 

act as both borrowers and lenders. 

About 300 transactions occur each day, and the mean 

transaction size is about $650 million. The market is 

largely over by 8:30 a.m. EST, by which time nearly 

half of the transactions have occurred, accounting for 

nearly two-thirds of the day’s dollars traded. Finally, 

quite a few repo transactions occur at rates below the 

federal funds rate, or in other words, below the cost of 

unsecured funding for similar institutions. Although 

this appears to reflect recent limitations in the number of firms offering to lend federal funds, it would be 

valuable to understand any patterns in these apparent 

abnormalities.

Although available data provide some insight into the 

repo markets, there is not sufficient data to explore 

flows between the three components in the repo market. In addition, more comprehensive data related to 

bilateral repos are needed to explore market disruptions. Without detailed, institution-specific information about all three components of the repo market, 

supervisors are handicapped in identifying potential 

sources of instability originating in this market, or 

originating with institutions that regularly fund themselves in this market.

Asset Management

Asset Management and Financial Stability, the report that 

the Office delivered in September 2013 to the Council 

at the Council’s request about the asset management 

industry and its activities, cited significant gaps in 

data about the industry that limit the ability to evaluate potential threats to financial stability. The report 

focused on data gaps associated with securities lending 

activities and the management of funds in separate 

accounts (see OFR, 2013).

Securities Lending. Securities lenders include mutual 

funds, exchange-traded funds, insurance companies, 

pension funds, and other institutional investors. They 

lend portfolio securities to earn additional income. The 

direct borrowers include broker-dealers that most often 

re-lend the borrowed securities to hedge funds and others for short selling and other permitted purposes. 

In a securities lending transaction, a security is temporarily transferred from a lender to a borrower in 

Figure 48. Daily Transactions in the General Collateral 

Finance Repo Market ($ billions)

0

50

100

150

200

250

Feb 1

2012

Mar 1

2012

Mar 30

2012

Source: Fixed Income Clearing Corporation (FICC)

from $213 billion on March 29 to $151 billion on 

March 30, the last business day of the quarter. 

The Federal Reserve Bank of New York also publishes 

summary information about outstanding repo transactions in the GCF repo market for one day each month. 

An abbreviated version of this report for February 9, 

2012, is in Figure 49. As this chart reveals, more than 

two-thirds of repo transactions are on a term basis.

However, this table understates the short-term nature 

of the market. Virtually all term repo trades are rolled 

over every day, as shown in Figure 50. Many of the 

trades with indicated maturities of three days were 

initiated on a Friday and matured three calendar days 

— but only one business day — later, on a Monday. 

Similarly, most of the four-day maturity trades were initiated on the Friday before a Monday federal holiday. 

Categorizing these Friday transactions as “overnight” 

implies that 95.1 percent of GCF repo dollars traded 

(and 91.7 percent of the trades) matured in a single 

day. Longer-term repos in Figure 50 include only newly 

negotiated term deals.

The available GCF data have other interesting features. 

As in the triparty repo market, shown in Figure 47, the 

largest collateral classes by far are the U.S. Treasuries 

and agency fixed-rate mortgage-backed securities. The 

data also reveal that only 45 to 50 institutions participate in the GCF market on a typical day. Of these, 5 to 
81 Addressing Data Gaps

exchange for cash or other collateral. In the United 

States, securities lending cash collateral is typically 

invested in commingled funds (registered money market funds and unregistered short-term funds) and separate accounts. The cash collateral must be returned to 

the borrower upon loan termination. Securities lenders often retain agents, typically banks or trust companies, to manage the cash collateral. In addition, many 

broker-dealers act as intermediaries for hedge funds or 

other clients that want to borrow the security. 

Securities lenders generally consider these transactions 

low-risk sources of income. In the United States, no single, comprehensive regulatory framework is applicable 

to all securities lending and borrowing, but numerous 

regulatory requirements apply to the various types of 

securities lenders and borrowers, imposed by the relevant supervisory regulators. If a securities lender fears its 

reinvested cash collateral will lose value due to market 

stress or that the borrower will be unable to return the 

securities, the lender may recall the loaned securities. 

Alternatively, a borrower may seek to return securities if 

it believes that its posted collateral is at risk.

The unwinding of securities lending transactions 

contributed to market stress during the financial crisis. 

In some cases, collateral was invested in illiquid assets, 

such as structured investment vehicles and Lehman 

Brothers notes, resulting in losses and forced asset sales 

as firms sought to raise cash. These losses amplified 

fire sales and runs, contributing to distress in money 

markets and other short-term funding markets. 

Lack of data related to securities lending transactions and the reinvestment of cash collateral limit the 

effective monitoring of securities lending activities. 

Market data provide information on the agent brokers 

involved in transactions and lending prices, but do not 

reveal the beneficial lenders and borrowers behind the 

transactions. It is consequently difficult to know the 

depth of securities lending in a particular issue at any 

given time, the extent of counterparty exposures, or 

the number of times that an issue has been re-lent. 

Collecting transaction-level data and position data 

about securities lending between large international 

financial institutions, including the composition of the 

underlying cash collateral reinvestment assets, would 

give regulators a clearer view of market activities. 

The Dodd-Frank Act requires the SEC to adopt rules 

Figure 49. Value of General Collateral Finance Repos 

Netted and Traded on or Before Feb. 9, 2012, 

for Clearing ($ billions)

Collateral Type Overnight Term

U.S. Treasuries $151.2 $292.8 

Agency (other than MBS*) $21.9 $40.2 

Agency MBS* $109.9 $300.7 

Total: $283.0 $633.7 

* Mortgage-backed securities

Source: Federal Reserve Bank of New York

Figure 50. Maturity Distribution of New Transactions in 

the General Collateral Finance Repo Market 

($ billions)

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

1 2-4 5-8 9-30 31-60 60-90 90+

Note: Data over 42-day sample period from February 2012 to March 2012.

Maturity (days)

Sources: FICC, OFR analysis

increasing the transparency of information about securities lending available to broker-dealers and investors. 

Such rules could also help fill some of these data gaps. 

Separate Accounts. The Office’s asset management 

report also highlighted data gaps about separate 

accounts managed by registered investment advisers, 

banks, and insurance companies. Together, those 

accounts total more than $25 trillion and represent 

about half of assets under management in U.S. markets. For some firms, separate accounts represent a 
2013 OFR Annual Report 82 2013 OFR Annual Report

Other Emerging Data Gaps

This section describes data gaps with respect to data 

mapping, captive insurers, nonbank mortgage servicers, and mortgage real estate investment trusts. It 

also describes how historical financial data can help 

researchers develop financial stability measures, investigate financial crises, analyze policy, and compare 

alternate institutional and regulatory structures (see 

insert on Uses and Gaps in Historical Financial Data).

Data Mapping. Regulatory analysis is often hampered 

by the difficulty of combining datasets obtained from 

different sources. For example, to study the impact 

of credit ratings on asset prices, a researcher would 

need financial statement data, asset price data, rating 

information from all applicable rating agencies, and 

organizational information. Before beginning this 

research, the researcher would need to join these unrelated datasets efficiently over a long period. 

The Legal Entity Identifier (LEI) initiative (see 

Chapter 6) will help facilitate this type of analysis once 

the LEI system is adopted throughout the financial 

industry. However, financial stability analysis cannot 

wait until full adoption of data standards. There is an 

interim need for mapping entities and financial instruments with different identifiers on different datasets. 

To support financial stability analysis, the Office is 

mapping the LEI to regulatory entity identifiers and 

other proprietary industry identifiers. Mapping entity 

identifiers requires manual matching of names and 

addresses and verification of abbreviations and spelling 

differences, and requires constant updating. 

Captive Insurers. Traditional insurance companies 

submit large amounts of data to state regulators. 

This is not the case with certain captive insurers. 

Deficiencies in transparency and concerns regarding 

the lack of uniform regulatory requirements relating 

to certain captive insurers are being discussed by state 

insurance regulators.2

 

Captive insurers were originally created to underwrite 

the risks of parent companies. However, the use of 

captives has expanded in different states and jurisdictions over the years. One example is the growth 

of captives assuming third-party risk from affiliated 

traditional commercial insurers. In addition, a growing number of special purpose vehicles have also been 

significantly larger percentage of assets under management than registered funds.

In a separate account, an asset manager selects assets 

on behalf of large institutional investors or high networth individuals under terms defined in an investment management agreement. As separate accounts 

are privately offered, the investment management 

agreement establishes investment management restrictions. Clients retain direct and sole ownership of assets 

under management. Separate accounts are excluded 

from regulation and reporting requirements under the 

Investment Company Act of 1940 (1940 Act), and are 

not subject to the same restrictions as registered funds 

on investment concentration, leverage, derivative use, 

or liquidity. Adviser management of these accounts 

is regulated under the Investment Advisers Act of 

1940, state securities regulations, or bank fiduciary 

regulations.

Some separate accounts have a similar investment 

strategy to registered funds. For example, a corporate 

retirement plan may establish a separate account based 

on an existing strategy that excludes the employer’s 

own stock, or an investor with a social responsibility 

mandate may establish a separate account excluding 

stocks from specific sectors. In other cases, separate 

accounts may have significant investments in illiquid 

securities or derivatives, securities lending arrangements, or rely on additional leverage. 

As we noted in our asset management report, data are 

limited for analyzing aggregate exposures and asset 

holdings in separate accounts. Some private data providers gather data about separate accounts, but asset 

managers provide the data only on a voluntary basis and 

these data are inconsistent. Some investment management agreements, particularly those for sovereign 

wealth funds and foreign central banks, prohibit asset 

managers from disclosing details about strategies and 

holdings. 

Due to these data limitations, regulators cannot evaluate potential ways that separate account exposures or 

asset sales could affect markets. For example, separate 

accounts may be significant suppliers of securities in 

the securities lending market and may include large 

holdings of illiquid assets.
83 Addressing Data Gaps

licensed under state laws, sometimes called captive 

laws, to take on risk from affiliates through reinsurance, securitization or reserve financing. 

National Association of Insurance Commissioners data 

illustrate the rapid rise in reinsurance credits taken 

by traditional U.S. insurers (including property and 

casualty, life, and health insurance companies) from 

transactions with affiliated U.S. captives. Figure 51 

shows traditional U.S. insurers in 2012 reported nearly 

$90 billion in reinsurance credits from those transactions, compared to just $1.6 billion in 2003. All state 

laws allow U.S. insurers to either recognize an asset 

or reduce the reserve liability on regulatory financial 

statements for so-called ceded reinsurance transactions, in which a portion of a risk in insurance policies 

is transferred from a primary insurer to a reinsurer.

The data in Figure 51 do not include offshore affiliate transactions due to the difficulty in determining 

whether an offshore affiliate was a captive or traditional 

insurer. Additionally, due to a lack of data, the graph 

does not include “two-step transactions” that occur 

when an insurer transfers insurance to another insurer, 

which then transfers that risk to a captive subsidiary 

affiliated with the original insurer (see NYDFS, 2013).

Figure 51 shows that more transparency is needed 

about the financial condition of affiliated captive insurers assuming third-party risk. The need for transparency is supported by other factors, including a growing 

number of states that allow captive insurers, special 

purpose vehicles, or both within their borders and the 

accreditation program3

 of the National Association 

of Insurance Commissioners (NAIC) that does not 

address uniform reporting and financial solvency 

standards for many categories of captives and special 

purpose vehicles (see NAIC, 2013).4

 

State insurance regulators, through the NAIC, have 

been exploring options for increasing transparency in 

the area. These were discussed in a recent white paper 

by the NAIC’s Captives and Special Purpose Vehicle 

Subgroup. Although there are several ways to achieve 

this, one option could be to require captive insurers to 

submit public financial filings. By filling this data gap, 

policyholders, nondomestic regulators, ratings agencies, investors, and other players in the financial market could benefit in different ways, including greater 

awareness of potential regulatory arbitrage situations 

among states where looser regulatory requirements are 

perceived to exist. 

Nonbank Mortgage Servicers. Significant data gaps 

persist in the mortgage finance industry. Although 

the Office of the Comptroller of the Currency (OCC) 

requires information on mortgage loan servicing from 

large national banks, the data do not include a representative sample of riskier subprime loans or loans 

serviced by smaller banks (see Dugan, 2008). 

The OCC data also exclude the growing number of 

nonbank companies that service mortgage loans. 

No federal, state, or industry entity is responsible for 

ensuring the safety and soundness of nonbank mortgage servicers. For banks with significant servicing 

operations, banking regulators analyze those risks as a 

part of the overall examination process. However, the 

expansion of nonbank servicers means an increasing 

share of the market is not subject to this scrutiny.

SEC-registered nonbank servicers file quarterly and 

annual financial statements that disclose some information about their operations, but the information is 

aimed at investors and does not contain detailed risk 

data about their mortgage servicing. These financial 

statements and other disclosures provide a snapshot of 

a servicer’s health, but lack the data needed to analyze 

Figure 51. Reinsurance Credits Taken by U.S. Insurers

($ billions)

Source: National Association of Insurance Commissioners, OFR analysis

0

20

40

60

80

100

2004 2006 2008 2010 2012

Note: Credits are a result of transactions with afliated U.S. captives
2013 OFR Annual Report 84 2013 OFR Annual Report

USES AND GAPS IN HISTORICAL FINANCIAL DATA

Filling gaps in historical financial data could help the 

Office design financial stability measures, investigate the 

causes and nature of financial crises, analyze the effects 

of government policy on financial stability, and compare 

the strengths and weaknesses of alternate institutional and 

regulatory structures.

Historical analysis plays a special role in our understanding 

of financial stability. Panics and crises, the most notable 

episodes of instability, fortunately occur rarely. But over 

long intervals, episodes of instability reveal a range of 

vulnerabilities. The cause and appropriate policy response 

can differ markedly from one episode to the next. 

Investigations of historical financial crises are forensic 

exercises. After a shock has occurred, questions focus on 

why and how events unfolded. Past crises and responses 

to them can provide useful lessons for assessing vulnerabilities in the financial system in the context of evolving 

market and regulatory forces. Such analyses depend on 

good data.

Our 2012 Annual Report analyzed four historical financial 

crises — in 1929, 1987, 1998, and 2007-09 — using 11 

measures that researchers have proposed to help authorities monitor threats to financial stability. Two measures of 

the interconnectedness of large bank holding companies 

reacted much more strongly in 2007-09 than in 1998, 

reflecting that banks and their leverage were more central 

to the recent episode (see OFR, 2012). A key lesson from 

that analysis was that more complete and higher-quality 

data would help in developing better measures to improve 

monitoring capabilities in the future. 

Historical analysis of financial data can support our 

understanding of how financial crises emerge and spread. 

Although past crises followed familiar patterns, each 

episode had unique features. The 20 minor and 8 major 

banking panics in the United States between 1830 and 

1935 varied significantly in their origins, as well as in the 

relative role of credit and the nature of the official response 

(see Jalil, 2013). 

The Panic of 1907 contains interesting parallels and contrasts to our own crisis a century later. Both involved runs 

on wholesale collateral markets, which many banks relied 

on for their short-term funding. In 1907, equities were the 

collateral. In 2007, the collateral consisted of a broad array 

of securities including complex securitizations backed 

by subprime mortgages (see Gorton, 2008). In 1907, in 

the absence of national deposit insurance, runs by retail 

depositors also played a role.

Long-horizon studies demonstrate the power of comparing a wide range of financial arrangements, stresses, 

and policy responses. The classic work of Friedman and 

Schwartz (1963) used annual data about banks and the 

money supply, aggregated at the state and national levels, 

to track broad patterns in monetary developments and policies over the course of decades. More recently, Schularick 

and Taylor (2012) compared annual aggregates for money, 

credit, and output over more than a century. They identified 

two broad eras divided by World War II and distinguished 

by dramatically different crisis dynamics that apply to 14 

developed economies from 1870 to 2008. 

Although these studies show the strength of historical 

analysis, they also suggest the possible benefits of more 

detailed data. The abrupt crisis phase of a stress episode 

typically stabilizes within days or weeks. Annual observations cannot depict these events in any detail. Similarly, 

national or statewide aggregates miss the dynamics of 

a crisis as it spreads through certain institutions but not 

others.

Comparisons among firms could help answer the question 

of how much leverage is too much. Augmenting bank-bybank data with information on correspondent relationships 

connecting regional banks and country banks to the large 

clearing banks in reserve cities might help policymakers 

understand the implications of interbank funding networks 

in a crisis. For example, such analysis could illuminate to 

what extent depositors are able to withdraw from specific 

banks targeted for their risk exposures or whether they 

tend to run indiscriminately during signs of stress.
85 Addressing Data Gaps

Better historical data are also needed for analysis of proposed macroprudential policies. One of our recent working 

papers analyzed the impacts of macroprudential policy 

tools that the Federal Reserve and other agencies used to 

curb credit market excesses over 100 years (see Elliott, 

Feldberg, and Lehnert, 2013). The analysis was limited by 

aggregate data. More detailed data about banks and other 

companies would enable analysis of the effects of policy 

changes on individual institutions and the extent to which 

nonbank lenders step in when policy constrains banks.

Analysis of historical data can also allow us to consider the 

effect of policies over time. For example, comparing banking 

today with banking before deposit insurance and lender-oflast resort facilities may shed light on the impacts of reforms 

on short-term funding markets, augmenting research that 

shows how various tools may reduce the probability of runs 

(see Begalle and others, 2013). 

Addressing Historical Data Gaps

Under the Office’s data gaps framework, the first step 

in filling historical data gaps is to assess the prominent 

features of past financial crises that may hold lessons for 

today and decide what data are needed to describe and 

analyze them. The second step is to take an inventory of 

existing collectible data. 

For many years, regulators, news organizations, information 

vendors, and others have captured and published financial 

data. This record includes traditional accounting reports 

and market data related to prices and volumes, survey data, 

credit ratings, traditional news, and documentary materials, 

such as meeting minutes.

Some historical information is already publicly available.5

The Federal Reserve’s All Bank Statistics, for example, 

is available online; portions have been digitized and are 

readable by computer software. These data have been 

instrumental in furthering our understanding of the role 

of banks in the crash of 1929 and the subsequent Great 

Depression. 

Other significant data sources exist but have not been 

digitized. For example, most state banking departments 

have published bank accounting reports for years, typically 

annually, but many still exist only in print.

The recently released internal archives of the Federal 

Reserve banks can also provide valuable insights. For 

example, Jaremski (2013) used these data to show that, 

prior to the creation of the Federal Reserve in 1914, banks 

that were members of private clearinghouses were less 

likely to fail during panics, but more likely to fail during 

other periods. 

In some cases, historical data may not exist. Data are 

limited by what was collected. For example, until recently 

bank regulatory reports focused on the details of funding 

sources rather than lending portfolios, hampering any 

analysis of banks’ exposures to credit risk and how they 

adjusted their lending in response to policy changes or 

financial crises. 

Gaps generally exist because researchers have been unable 

to locate the aging hard-copy reports that could be sources 

for digital collections. Closing these information gaps is 

challenging because of the high cost of digitizing and validating the data. Although many economists have assembled 

historical data related to financial markets and institutions, 

these datasets are costly and time-consuming to compile, 

and, as a result, they tend to remain closely held to support 

individual researchers’ ongoing work. 

In recent years, constructing datasets has become cheaper 

and easier. Institutions, such as the Federal Reserve 

Bank of St. Louis and the HathiTrust Digital Library, have 

digitized historical documents and released them to the 

public. New technologies, such as better optical-character 

recognition software and new outsourcing services for data 

entry, have also made the process easier.
2013 OFR Annual Report 86 2013 OFR Annual Report

rates, MBS yields, haircuts, and funding runs could help 

evaluate mortgage REIT performance through a variety 

of adverse scenarios. The Office is exploring avenues for 

gaining a better understanding of this market.

5.5 Preliminary Research Based on 

Recent Data Collections

Ongoing data collection and sharing initiatives broaden the 

Office’s access to valuable information concerning financial 

stability. However, there is no perfect real-world dataset. 

Part of the research process is learning what can be done 

with available data. How reliably do the data reflect the 

quantities that they are supposed to measure? How noisy are 

the data? What questions can be readily answered? What 

questions can be addressed only indirectly? 

This section summarizes the beginnings of research 

programs that use three newly available datasets. Two 

of the datasets contain information about money-fund 

asset holdings and credit default swap transactions 

that is high-quality, comprehensive, and separated 

into components, or disaggregated. The other dataset 

contains information about hedge funds and is more 

limited. In our analysis, we discuss what we can learn 

and cannot learn from these data. Each of the following sections begins by posing the main question our 

research attempts to answer. 

Active Management of Money Market Funds

How do managers of money market funds adjust the composition of fund assets at times of financial market stress? 

Money market funds play a significant role in the financial system. The soundness of the funds and investor 

confidence in their soundness are important to financial stability. The strategies funds use to manage their 

risks affect other asset markets. For example, a sudden 

shift by money market funds out of a particular asset 

class can disrupt other markets. 

In 2010, the SEC required money market funds to file 

a monthly report on portfolio holdings as of the last 

business day of the previous month on its new Form 

N-MFP (see Rule 30b1-7 under the 1940 Act). Form 

N-MFP reports help regulators understand risks faced 

by money market funds and the Office actively monitors the filings. But gaps remain. 

adverse scenarios and evaluate how well a nonbank 

servicer is prepared to weather an economic downturn.

These data gaps could have implications for policy 

proposals. A mortgage finance reform bill proposed in 

the U.S. House of Representatives in July 2013 includes 

language to address conflicts of interest by preventing 

loan servicers that hold a junior lien on a property from 

servicing other loans on the same property (see H.R. 

2767, 2013). But because regulators do not currently 

collect comprehensive mortgage servicing information, 

evaluating the effects of such a policy on financial stability is not possible because we cannot accurately determine how often this conflict occurs across the United 

States. An expanded collection of mortgage servicing 

data from banks and nonbank servicers identified consistently over time as mortgages were transferred and 

sold could address this data gap.

Mortgage Real Estate Investment Trusts. As noted 

in Chapter 2, mortgage real estate investment trusts 

(REITs) are leveraged investment vehicles with large 

holdings of agency mortgage-backed securities (MBS). 

The sector depends heavily on the repo market and 

is highly concentrated, with two firms accounting for 

about 60 percent of sector assets. In May and June 2013, 

mortgage REITs shed roughly $45 billion of MBS as 

interest rates rose, due to their exposure to duration risk 

and basis risk. These sales likely contributed to rapid 

increases in yields and volatility in the MBS market. 

Sparse data are available to evaluate the risks posed by 

mortgage REITs. Although most mortgage REITs are 

publicly listed companies, the depth of data presented 

in public filings varies widely across firms, and on 

the whole the data are inadequate to assess the risks 

they pose to financial stability. Mortgage REITs are 

generally excluded from reporting requirements of 

the Investment Company Act of 1940. Although the 

Dodd-Frank Act increased the reporting requirements 

of hedge funds and private equity funds, the requirements on mortgage REITs remain unchanged.

More data are needed to understand the risks posed by 

mortgage REITs. Information describing the distribution of their portfolio holdings, borrowings, and derivative positions across tenors, rates, haircuts, and other 

instrument-specific parameters would contribute to a 

greater understanding of the vulnerabilities of mortgage REITs. Stress tests against shocks to borrowing 
87 Addressing Data Gaps

If there were no gaps in available data, regulators 

could monitor the price volatilities of all assets held in 

fund portfolios on a real-time basis. In practice, regulators face a time lag when monitoring asset holdings. 

Although asset holdings are available from the SEC 

through Form N-MFP, the Office has limited information on the secondary-market trading of money market 

fund assets because some market data are available 

only to registered traders.

We use information from other sources to draw 

inferences about the risks embedded in money fund 

assets and the asset-allocation strategies funds used 

to control these risks. In particular, we closely watch 

developments in the eurozone and the potential 

effects on money fund risks and possible spillovers to 

U.S. financial markets.6

 One barometer of risk in the 

eurozone is the market for credit default swaps (CDS 

or swaps), financial instruments that insure against a 

government defaulting on its debt. Investors can buy 

sovereign default CDS to hedge against risk, or to bet 

on a country’s future. 

The Office used money fund data from Form N-MFP 

to examine how the eurozone sovereign debt crisis 

spilled into prime money funds. Prime money funds 

are required to invest in high-quality, short-term 

debt securities, and typically do not hold eurozone 

sovereign debt. However, the funds have an indirect 

exposure to eurozone sovereign debt because they 

may invest in some short-term corporate debt issued by 

European banks and financial firms. That corporate 

debt is sensitive to the risk of sovereign debt default 

because the issuing firms are based in the region or 

invest in the foreign debt.

Figure 52 displays the costs of insuring against the 

failure of sovereign debt, as measured by CDS spreads 

for eurozone countries. Spreads for Ireland, Italy, and 

Spain were high and rising throughout 2010. Prime 

money funds were largely unaffected by this trend 

because their eurozone exposures were concentrated 

primarily among entities based in Germany and 

France. Spreads for these countries were low and stable 

through the end of October 2010, averaging about 38 

basis points for Germany and 65 for France. These 

spreads rose slightly in early 2011, but then returned 

to spreads of about 40 and 75 basis points respectively, 

by mid-2011. However, spreads increased through 

the remainder of 2011. The figure shows that several 

eurozone CDS spreads, including those of Germany 

and France, began to rise in mid-2011. 

Data collected on Form N-MFP indicate that prime 

money market funds reduced their holdings of debt 

issued by eurozone financial institutions while eurozone CDS spreads were rising. Figure 53 shows that 

from late 2010 through the middle of 2011, prime 

money funds held about $500 billion in debt issued 

by banks and financial institutions in eurozone countries, or 30 percent of total prime fund assets. By the 

end of 2011, prime fund exposure to these institutions 

dropped by half.

Was this decline the result of investors running from 

particular funds? In other words, did investors sell 

their holdings of prime money market funds with 

heavy eurozone investments and increase their holdings of prime funds with low eurozone investments? Or 

was the change the result of individual fund managers 

shifting the composition of their portfolios away from 

eurozone investments? The financial stability implications of events such as the sovereign debt crisis heavily 

depend on the reason for the decline in the overall 

eurozone exposure of prime fund assets. 

Figure 52. Five-Year Credit Default Swaps Spreads for 

Select Eurozone Countries (basis points)

0

200

400

600

800

1,000

1,200

1,400

Nov

2010

May

2011

Nov

2011

May

2012

Nov

2012

May

2013

Austria Belgium

France Germany

Ireland Italy

Netherlands Spain

Source: Markit Group, Ltd
2013 OFR Annual Report 88 2013 OFR Annual Report

Figure 54. Change in the Eurozone Holdings of Prime 

Money Market Funds (percent)

-80

-60

-40

-20

0

Jul

2011

Oct

2011

Jan

2012

Apr

2012

Jul

2012

Oct

2012

Jan

2013

Apr

2013

Investor Response

Fund Response

Interaction Term

Note: Percent change in eurozone assets is calculated by the percentage 

change in principal value since June 2011. 

Source: OFR analysis of Form N-MFP data

The Office concluded that this decline was almost 

entirely attributable to prime funds pulling assets out 

of the eurozone, rather than investors fleeing those 

prime funds with relatively high eurozone exposures. 

Figure 54 shows the total change from June 2011 in 

money funds’ eurozone-based assets. The change 

is separated into two components. The first component is the investor response of pulling money out 

of some funds and putting money into other funds. 

The figure shows this component is negligible. The 

second component is the response by fund managers of changing portfolio compositions of individual 

funds, which accounts for almost all of the change in 

eurozone-based assets. (There is also a third component, which is a tiny interaction term representing the 

change in net asset values multiplied by the change in 

portfolio composition). 

Our research also shows that prime funds altered their 

portfolios in two other ways in response to rising risks 

in the eurozone. First, funds shifted their holdings into 

eurozone assets with significantly shorter maturities. 

The average maturity of eurozone-based assets held by 

prime funds dropped from about 45 days in mid-2011 

to only 20 days by the end of 2011 (see Figure 55). 

Second, prime funds shifted the composition of their 

eurozone-based assets away from countries with higher 

perceived risks of sovereign debt default, such as 

Belgium and Spain, and bought more assets in lessrisky Germany. 

One way to visualize this shift is to compare two 

monthly measures of CDS spreads. The first measure 

acts as a baseline, weighting each country’s monthly 

CDS spread by the fraction of fund assets associated 

with that country in October 2010, when spreads were 

relatively low and stable. The light blue line in Figure 

56 shows this weighted average rises from about 70 

basis points in mid-2011 to about 200 basis points at 

the end of 2011. 

The second measure in Figure 56 weights each country’s monthly CDS spread by the fraction of primefund assets associated with that country as of that 

same month. That means if fund assets gradually 

shift toward a particular country, the weight on that 

country’s CDS spread gradually increases over time. 

The actual weighted average line, in dark blue, shows 

an increase from about 70 basis points in mid-2011 to 

Figure 53. Eurozone-Based Assets of Prime Money 

Market Funds

0

5

10

15

20

25

30

35

40

0

150

300

450

600

Nov

2010

May

2011

Nov

2011

May

2012

Nov

2012

May

2013

Assets ($ billions) Ratio %

Note: Assets equal aggregate principal value. Ratio equals 

eurozone/Total Prime.

Sources: OFR analysis of Form N-MFP data; filings for individual funds are available on 

the SEC’s EDGAR website (www.sec.gov/edgar.shtml).
89 Addressing Data Gaps

only 120 basis points at the end of 2011. The difference 

between the two weighted averages in Figure 56 shows 

that funds shifted assets toward issuers in countries 

with less-risky CDS spreads.

One interpretation of the shifts is that they show fund 

managers actively reacting to default risks. The portfolio adjustments that took place through the end of 

2011 support this interpretation. However, the figures 

show that fund managers did not return to eurozone 

assets after 2011, when sovereign default risks fell. As 

of mid-2013, eurozone CDS spreads had declined to 

the lower levels of late 2010, but fund investments in 

eurozone assets remained less than 20 percent of total 

assets. In addition, the average maturity of these assets 

was noticeably shorter (less than 40 days) than the 

average maturity of non-eurozone assets held by prime 

funds (about 50 days). An alternative interpretation 

consistent with this evidence is that fund managers 

have pulled back from eurozone exposure because of 

the events of 2010 and 2011.

Activity in the Sovereign CDS Market

If external events caused one or more major players 

to withdraw from the sovereign debt CDS market, 

how vulnerable would the market be? With newly 

acquired data in hand, we set out in 2013 to answer 

that question.

In the OFR 2012 Annual Report, we highlighted the 

work of the Office to fill data gaps in derivatives, such 

as CDS. The Office now has position, transaction, and 

pricing information about CDS contracts from the 

Depository Trust & Clearing Corporation (DTCC), a firm 

that provides clearing and settlement services for financial transactions. A close look at the market for sovereign 

CDS shows the value and limitations of these data. 

The sovereign debt CDS market is an example of an 

over-the-counter dealer market. Trades in this type of 

market, unlike an exchange, are individually negotiated between participants and subsequently reported 

to DTCC. A dealer market consists of market makers 

and end users. End users are firms or other entities, 

such as governments, that want to take a particular 

position — in this case, either to buy or sell protection 

against default. End users contact dealers, who act as 

intermediaries, buying protection from some end users 

and selling protection to others. If a dealer’s trading 

Figure 55. Weighted-Average Maturity of Assets in 

Prime Money Market Funds (days)

0

20

40

60

80

Nov

2010

May

2011

Nov

2011

May

2012

Nov

2012

May

2013

eurozone Non-eurozone

Note: Assets equals aggregate principal value

Source: OFR analysis of Form N-MFP data

Figure 56. Weighted-Average Sovereign Five-Year 

CDS Spread for Prime Money Market Fund 

Eurozone-Based Assets (basis points)

0

50

100

150

200

250

Nov

2010

May

2011

Nov

2011

May

2012

Nov

2012

May

2013

Actual Weighted Average

Weighted Average Using Holdings 

of November 2010

Note: Weighted by aggregate principal value. CDS spreads are monthly 

averages by country. Analysis includes Austria, Belgium, Italy, France, 

Germany, Netherlands, Spain, and Ireland. 

Sources: Form N-MFP, Markit Group, Ltd, OFR analysis
2013 OFR Annual Report 90 2013 OFR Annual Report

market participants in 2010 traded CDS contracts with 

all of the other 1,417 market participants, there would be 

5,668 (4 multiplied by 1,417) trading pairs in 2010. 

An immediate conclusion to draw from these statistics 

is that trading activity was fairly light. The average 

market participant traded about two-and-a-half contracts per month. But in a dealer market, there is no 

“average” participant. Dealers are much more active 

participants than end users. Because the dataset does 

not explicitly identify dealers, we could not distinguish 

dealers from end users. Instead, we broke down market 

participants by their overall line of business. We first 

grouped commercial and investment banks together, 

and checked whether characterizing them as market 

makers would be reasonable.

Banks Are Market Makers 

The exposure of U.S. banks across all sovereign CDS 

contracts is shown in Figure 58. The green lines at 

the top and bottom of the graph show end-of-week 

total gross exposures and the blue lines show the 

same information after netting, or offsetting, trading 

positions involving the same bank and sovereign. Buyprotection positions are separated from sell-protection 

positions. For example, if a bank bought protection of 

$10 million on Spain and sold protection of $10 million 

on Spain, the two contracts would net to zero. If the 

buy-protection positions and sell-protection positions 

were for different sovereigns, this figure would show no 

netting. There would also be no netting if the buy-protection positions and sell-protection positions were for 

different commercial banks.

Figure 57. Trading in Sovereign CDS for Germany, Greece, France, Ireland, Italy, Portugal, and Spain

2010 2011 2012

Number of Market Participants 1,421 1,674 1,423

Number of Trading Pairs 7,542 10,120 7,911

Average Number of Transactions per Month 3,841 4,721 3,491

Average Notional Principal Traded per Month ($ billion equivalent) 88 93 73

Gross Notional Principal Outstanding at the End of April ($ billion equivalent) 803 1,054 1,033

Sources: Depository Trust & Clearing Corporation (DTCC), OFR analysis

volume from end users is predominantly on one side of 

the market, the dealer will often enter into offsetting 

trades with other dealers to hedge its market exposure.

We analyzed millions of records, beginning with highlevel snapshots of market activity. We summarize trading 

activity in seven sovereign CDS contracts (Germany, 

France, Italy, Portugal, Spain, Ireland, and Greece) 

from January 1, 2010, through April 26, 2012. Figure 57 

reports the number of firms that traded in the sovereign 

CDS market, how frequently they traded, the size of 

their trades, and a measure of the aggregate positions 

traders have taken through CDS contracts. 

Trade size was measured by notional principal, which is 

the face amount of a hypothetical bond. A firm can buy 

protection against the default of a hypothetical $10 million bond. Similarly, aggregate positions are measured 

by aggregate notional principal. Gross notional principal adds up across all existing contracts. 

For example, if a firm purchased default protection 

with a notional principal of $10 million and sold 

default protection with a notional principal of $5 

million, the firm’s gross notional exposure would be 

$15 million. The other side of these contracts would 

also have an aggregate gross notional exposure of $15 

million. The aggregate notional principal calculations 

reported here are one-sided. For this example, aggregate notional exposure is $15 million, not $30 million.

Statistics in the table are summed across the seven contracts and calculated separately for each calendar year. 

In the table, a “trading pair” is made up of two firms that 

trade with one another. For example, if four of the 1,421 
91 Addressing Data Gaps

The figure shows that banks have large gross positions on the buying and selling sides of sovereign CDS 

contracts. Aggregate net positions are about one-fifth 

the size of aggregate gross positions — strong evidence 

that the banking sector is engaged in market-making 

activity in the sovereign CDS market. In April 2012, 

aggregate positions of banks are larger for sold protection (under $300 billion) than for bought protection 

(over $200 billion). In other words, banks are providing default protection to other market participants.

Hedge Funds Are Buyers

Who is buying the default protection from banks? Our 

analysis indicates that hedge funds are often on the 

buying side of these contracts. Figure 59 reports gross 

exposures for the hedge fund sector, calculated the 

same way as bank gross exposures in Figure 58. Net 

exposures are not displayed because they are almost 

identical to gross exposures. The near equivalence 

shows that hedge funds are end users in this market, 

not market makers. For this sample, the hedge fund 

sector is on average net $30 billion short (buying protection) and almost all hedge funds are on the same 

side of these contracts.

Hedge funds are the most active participants in the 

sovereign CDS market, followed by mutual funds. 

Figure 59 also displays gross aggregate exposures for 

mutual funds. As with hedge funds, there is no reason 

to display net aggregate exposures, because they are 

indistinguishable from gross exposures. Unlike hedge 

funds, mutual funds largely sell protection in the 

sovereign CDS market. More importantly, exposures 

are substantially smaller than those of the hedge fund 

sector. The peak gross exposure is about $10 billion in 

sold protection. Exposures for business sectors other 

than hedge funds and mutual funds are even smaller. 

These data do not allow us to conclude that hedge 

funds are betting that sovereign credit will deteriorate 

or that mutual funds are betting that sovereign credit 

will improve. If hedge funds have substantial direct 

holdings of sovereign debt, they may be hedging some 

of their exposure through the CDS market. We also 

cannot infer that the mutual fund sector is exposed to 

the risk of a decline in sovereign credit quality. 

These exposures for hedge funds and mutual funds 

point to the possibility of destabilization due to correlated trading activity. Correlated trading occurs 

Figure 58. Aggregated Gross and Net Notional 

Exposures for Banks ($ billions)

100

0

200

$300

$300

200

100

Apr

2010

Jan

2010

Apr

2012

Jan

2012

Jul

2010

Oct

2010

Apr

2011

Jan

2011

Jul

2011

Oct

2011

Gross bought Net bought

Gross sold Net sold

Sources: Depository Trust & Clearing Corporation (DTCC), OFR analysis

when firms in the same sector attempt to trade on the 

same side of a market. If the same adverse shock hit 

several large hedge funds and substantially decreased 

their capital, the funds would likely pull back from 

risk-taking activities. The funds might sell assets and 

reduce their positions in derivative markets. Even if the 

original shock were unrelated to sovereign credit risk, 

these large hedge funds might simultaneously attempt 

to unwind their buy-protection sovereign CDS contracts. A shock to hedge funds could spill over into the 

sovereign debt market. 

Correlated trading is of particular concern because 

market makers and end users trade infrequently. 

Figure 60 displays trading activity in three-month 

increments. Each column represents the aggregate 

notional volume of trading for the banking sector. 

There are no trades in this market that do not have a 

bank on at least one side of the transaction. A trade 

between two banks shows up twice in this figure.
2013 OFR Annual Report 92 2013 OFR Annual Report

During the three-month period from December 2011 

through February 2012, banks were on one side of 

about $280 billion of notional principal in sovereign 

CDS trades. Although $280 billion may seem large, 

most trading was between banks as they reallocated 

risks among themselves. These trades did not shift risk 

to, or from, end users. Transactions between banks and 

all end users totaled only $30 billion during this threemonth period. The sum equals the net position of 

hedge funds as of April 2012 (see Figure 59). If hedge 

funds collectively attempted to liquidate their positions 

within the span of a couple of weeks, the sovereign 

CDS market could suffer considerable strain. 

Another concern is that market-making activity is 

highly concentrated in sovereign CDS. Figure 61 

displays the dollar-weighted fraction of trading in this 

market that is done through the top two market makers, broken down by type of counterparty. For example, 

during December 2011 to February 2012, 40 percent 

of hedge-fund trading in sovereign CDS was done 

through two market-making banks. During the threemonth periods between January 2010 and February 

2012, between 30 percent and 60 percent of all mutual-fund trading in sovereign CDS was done through 

two banks. The identities of the top two market makers 

may change over time and across types of counterparties (see Chen and others, 2011). 

Highly concentrated trading has implications for financial stability. If an important market maker pulls back 

from trading, will the market continue to function 

smoothly? One way to shed light on this question is to 

examine whether market makers can be substituted. 

Does the identity of the most active market makers 

change over time? Do market makers that dominate 

transactions with one type of counterparty also dominate transactions with other types? 

Figure 62 helps answer these questions by showing the 

share of transactions over time and across counterparty type involving the two market makers with the 

largest trading activity over the entire sample. 

In Figure 62, the two market makers are the same at 

all dates and for all counterparties, which differs from 

Figure 61. A comparison reveals that market making 

dominance with one type of counterparty did not correspond to dominance with another. For example, hedge 

funds traded relatively little with these two market 

Figure 60. Three-Month Trading Activity as Measured by 

Notional Amount of CDS Contracts ($ billions)

0

50

100

150

200

250

300

350

Others

Hedge Funds

Banks 

Jan 

2012

Oct 

2011

Jul 

2011

Apr 

2011

Jan 

2011

Oct 

2010

July 

2010

Apr 

2010

Jan 

2010

Sources: Depository Trust & Clearing Corporation (DTCC), OFR analysis

Figure 59. Aggregated Gross Notional Exposure for 

Hedge Funds and Mutual Funds ($ billions)

25

0

50

Hedge Fund Bought

Mutual Fund Bought

Hedge Fund Sold

Mutual Fund Sold

25

Jan 

2012

Apr 

2012

Oct 

2011

Jul 

2011

Apr 

2011

Jan 

2011

Oct 

2010

July 

2010

Apr 

2010

Jan 

2010

Sources: Depository Trust & Clearing Corporation (DTCC), OFR analysis
93 Addressing Data Gaps

makers. Even for counterparties that traded actively with 

these market makers, the combined share of the two 

market makers varied widely over time. The evidence 

of flexibility in market making activity suggests that the 

sovereign CDS market may be resilient to the loss of one 

or two market makers.

As these figures show, transaction-level data help identify aspects of the CDS market that can affect financial 

stability. However, our research did not explore why 

end users chose to buy or sell credit protection in the 

sovereign CDS market. We cannot address that question unless we know more about the portfolios of the 

end users. 

For example, are hedge funds heavy purchasers of 

protection because they are hedging other positions 

in their portfolios? Or are they collectively betting on 

deterioration in a specific country’s credit quality? 

What magnitude of collateral calls could market makers face in adverse conditions?

The Office’s future research will integrate these data with 

other datasets and attempt to answer these questions.

Hedge Fund Leverage

Do highly leveraged hedge funds measure and manage risks differently than hedge funds that are less 

highly leveraged?

As required by the Dodd-Frank Act, the SEC and 

CFTC created Forms PF, and CPO-PQR, respectively, 

to gather confidential risk information from advisers of hedge funds and other private funds. Certain 

large advisers began reporting information about 

fund assets, leverage, and risk exposures in 2012. 

Researchers at the OFR and the SEC are evaluating the 

quality and coverage of the data from early returns of 

Form PF. Form PF is a potentially rich data source, one 

that we plan to use to better understand the role hedge 

funds play in the financial system, monitor risks in the 

private fund industry, and research threats to financial 

stability.

Every new data collection initiative has growing 

pains, and Form PF is no exception. Filling data gaps 

begins with data collection, but ensuring complete 

and accurate data takes time and requires an ongoing 

assessment of data quality. Because Form PF collection 

is so new, caution is important in interpreting research 

Figure 62. Share of the Top Two Overall Market Makers 

in Trading (percent)

0

10

20

30

40

50

60

70

Figure 5.5.11 Share of Overall Top Market Makers in 

Trading (percent)

Mutual Funds

Hedge Funds

Banks

Source: Depository Trust Clearing Corporation, OFR analysis

Jan 

2012

Oct 

2011

Jul 

2011

Apr 

2011

Jan 

2011

Oct 

2010

July 

2010

Apr 

2010

Jan 

2010

Sources: Depository Trust & Clearing Corporation (DTCC), OFR analysis

Figure 61. Concentration in Market Making Activity for 

Trades (percent)

0

10

20

30

40

50

60

70

Figure 5.5.10 Concentration in Market Making Activity 

for Trades (percent)

Mutual Funds

Hedge Funds

Banks

Source: Depository Trust Clearing Corporation, OFR analysis

Jan 

2012

Oct 

2011

Jul 

2011

Apr 

2011

Jan 

2011

Oct 

2010

July 

2010

Apr 

2010

Jan 

2010

Sources: Depository Trust & Clearing Corporation (DTCC), OFR analysis
2013 OFR Annual Report 94 2013 OFR Annual Report

results at this stage. The following summary discusses 

the Office’s first experience analyzing Form PF data. 

The Office received data that approximately 4,000 funds 

submitted to the SEC during the past year. The filings 

contain portfolio-level information for about 8,000 

hedge funds and 10,000 other private funds. Funds 

reported gross and net asset values. About 1 percent of 

the filings reported a gross asset value less than the net 

asset value. Another 1 percent of the filings reported 

a negative net asset value. The funds are required to 

submit annual breakdowns of their assets by the methodology used to estimate fair values. Approximately 5 

percent of the filings that should include this breakdown contained zeros for all categories. These findings 

could indicate data problems.

Out of the 8,000 hedge funds that filed a Form PF, 

about 1,400 are defined by the SEC as “qualifying 

hedge funds,” each with a net asset value of at least 

$500 million.7

 Qualifying hedge funds are required 

to submit quarterly reports that include additional 

data about how much of their borrowing is secured 

and unsecured. More than 10 percent of qualifying 

hedge funds reported total borrowing amounts that 

differed significantly from the sum of the secured and 

unsecured borrowing amounts listed.8

 A few filings — 

approximately 1 percent — reported that secured and 

unsecured borrowing were zero while also reporting 

that total borrowings were positive. 

We focused our analysis on hedge fund leverage. A 

fund with higher leverage or debt is typically more vulnerable to adverse events, if all other factors are equal. 

We wanted to find out if funds with higher leverage 

hold more transparent assets that are easier to trade, 

based on Form PF data. 

The first step explored the relationship between a 

hedge fund’s leverage and the fraction of its assets 

that are hard to value. Leverage is measured by debt 

divided by net asset value and does not include leverage associated with the use of derivatives. Debt is 

measured by the greater of: (1) the reported total 

borrowed funds and (2) the reported unsecured plus 

secured borrowing. The fraction of assets that are 

hard to value is measured by the ratio of assets that are 

valued using unobservable inputs, such as modeling 

assumptions, to the fund’s total assets.9 The liquidity of 

hard-to-value assets may mean funds heavily invested 

in such assets are exposed to greater funding risk due 

to potential fire sales. 

We sorted hedge funds into five categories by their 

reported leverage.10 The first category contains funds 

that report zero leverage, which includes about half 

of the funds. The other four categories contain the 

remaining funds, broken into quartiles by leverage.11

Figure 63 shows that on average, funds with higher 

leverage have a lower proportion of hard-to-value 

assets. Hard-to-value assets represent a little more than 

20 percent of the assets of funds with no leverage.12

For the category of funds with the highest leverage 

(mean ratio of debt to net asset value of about 2.8), 

the corresponding fraction was less than 5 percent. 

That suggests funds with larger leverage ratios may be 

choosing assets that are relatively easier to dispose of 

during a crisis. 

Another important research question is whether highly 

levered funds are carefully monitoring their risk 

exposure. A rough way to judge this monitoring is by 

whether the fund calculates the Value-at-Risk (VaR) 

of its portfolio. Qualifying hedge funds are required 

to report whether they regularly calculate VaR as a 

risk management tool. Figure 64 shows how many of 

the qualifying hedge funds included VaR calculations, 

based on leverage in their quarterly filings.

Figure 63. The Proportion of Hard-to-Value Assets 

Based on Leverage (percent)

0

5

10

15

20

25

1 2 3 4 5

Average Leverage by Groups

Note: Group 1 has zero average leverage and is made up of 2,628 hedge 

funds. Groups 2, 3, 4, and 5 each have 665 hedge funds.

Source: OFR analysis of SEC Form PF data from the 5,288 funds that report relevant 

information on Form PF.
95 Addressing Data Gaps

Funds with higher leverage were somewhat more likely 

to calculate VaR, as shown in Figure 64. For example, 

although only 33 percent of the hedge funds with no 

leverage Group 1 regularly estimated VaR, the corresponding ratio was 51 percent for the funds with the 

highest leverage. This does not necessarily imply that 

funds chose to calculate VaR owing to their leverage. 

A more likely link is that funds with more leverage also 

tended to be larger, as measured by net asset value. 

Larger funds were also more likely to calculate VaR. 

Finally, we examined the relationship between leverage 

and the level of VaR. We analyzed 510 qualifying hedge 

funds that reported a VaR measure after dividing them 

into five categories based on reported VaR. Perhaps 

the most interesting result is that 87 of these funds 

reported a VaR of zero. This is another data problem. 

We do not know what the fund advisers meant by 

reporting values of zero. These funds are placed in 

their own category and the remaining funds are placed 

in quartiles according to reported VaR.

The data in Figure 65 show that on average, funds that 

reported higher values of VaR also tended to report 

lower leverage. For example, the group with the highest 

VaR had an average leverage ratio of around 0.8, about 

half that of the group with the next-highest VaR. This 

pattern suggests that funds with more leverage take on 

less risk. But recall that for most funds, this risk-taking 

measure is unavailable. We cannot put much weight 

on this result without alternative measures of portfolio 

risk to confirm it.

Figure 64. Likelihood of Value at Risk (VaR) Calculation 

Based on Leverage

0

100

200

300

400

500

1 2 3 4 5

Average Leverage by Groups

Note: The OFR divided the qualifying funds into ve groups based on 

average leverage. Group 1 has zero average leverage; Group 2 has 0.02; 

Group 3 has 0.33; Group 4 has 0.63; Group 5 has 9.16.

Number of Funds that do not Calculate VaR

Number of Funds that Calculate VaR

Source: OFR analysis using SEC Form PF data from the 1,338 qualifying funds that 

responded to the question.

Figure 65. Average Hedge Fund Leverage Based on 

Value at Risk (VaR)

0.0

0.5

1.0

1.5

2.0

2.5

1 2 3 4 5

Percentage of Average VaR by Groups

Average leverage

Note: Group 1 has 87 funds, Group 2 has 105 funds, and Groups 3, 4, and 

5 each have 106 funds.

Source: OFR analysis of SEC Form PF data from the 510 funds that reported they 

calculate VaR.

97 Promoting Data Standards

Data standards benefit market participants and 

regulators by reducing the cost of data collection 

and by facilitating the sharing, exchange, 

comparison, and aggregation of data for analysis 

and risk management. Congress assigned 

the OFR responsibility for promoting financial 

data standards and helping to develop them. 

Accordingly, for example, the Office has played 

a central role in the international initiative to 

establish a global Legal Entity Identifier (LEI) to 

precisely identify parties to financial transactions. 

This chapter outlines our broader data standards 

agenda, summarizes recent trends, and describes 

recent progress in implementing the LEI and other 

standards initiatives.

6.1 Data Standards Agenda

To fulfill its statutory mission, the OFR provides advice, 

support, and leadership in the development, use, and 

integration of financial data standards. Once the need for a 

new standard has been identified, we: (1) evaluate whether 

addressing the need lies within the scope and mission of the 

Office, (2) determine whether the proposal represents a good 

candidate for standardization, and (3) determine the Office’s 

strategy. 

The financial crisis revealed fundamental deficiencies 

in the ability of market participants to manage and 

analyze the terabytes of data generated daily. The 

lack of consistent standards for precisely identifying 

financial entities and defining financial instruments 

prevented institutions from assessing and measuring building risks within and across business lines. 

Similarly, regulators and supervisors were unable to 

assess risks comprehensively from firm to firm, much 

less across firms or segments in the financial system.

6 Promoting Data Standards

Data standards define the format, content, and 

syntax of data, providing a common language that 

enables precise identification of entities and instruments, the relationships among them, and the data to 

describe them (see insert on What Are Financial Data 

Standards?). Data aggregation is possible only when 

data standards exist.

WHAT ARE FINANCIAL DATA 

STANDARDS?

Financial markets rely on data standards to function 

smoothly. These standards depend on commonly 

accepted definitions of concepts like “equities” and 

“swaps.”

Entity identifiers identify specific legal entities, which 

could include parent companies and their subsidiaries 

as well as off-balance-sheet vehicles.

Instrument identifiers identify financial instruments 

like stocks, bonds, and loans. For example, there is 

the International Organization for Standardization 

(ISO) standard for individual securities known as the 

International Securities Identification Number.

Standards for financial and business reporting

align information reported by companies to the public, 

for example on financial disclosures and regulatory 

reports. An important initiative is XBRL, or eXtensible 

Business Reporting Language, which enables free and 

open exchange of business and financial information.

Transaction standards align information used in 

financial transactions. For example, the Mortgage 

Industry Standards Maintenance Organization developed a language that enables consistency in describing 

mortgage transactions.

Source: OFR (2012)
2013 OFR Annual Report 98 2013 OFR Annual Report

Government or industry organizations may support 

standardization to overcome such obstacles, commonly 

known as collective action problems. Statutory or regulatory requirements can help develop and implement 

standards; for example, regulators may need to collect 

data for policymaking or to investigate an emerging 

concern. Regulatory oversight can help ensure that 

standards are adopted and implemented consistently 

for the benefit of the business and policy communities. 

Relevance to Our Mission 

By improving data quality, data standards can also 

improve the Council’s and our financial stability 

monitoring. We can assist Council member agencies 

in data standardization when a lack of data sources or 

inconsistent practices among agencies for data classification and collection hampers regulatory sharing. For 

example, by using the Legal Entity Identifier (LEI), 

regulators can more easily aggregate and analyze data 

to evaluate where risks may be building across the 

financial system. 

The Office promotes data standards that can add 

clarity, insight, and common understanding about 

financial markets. For example, making more information available or establishing new standards could help 

integrate transactional data within specific markets. 

Once the need for a new standard is identified, we analyze whether a standards project is relevant to our mission and scope (see Figure 66). Under the Dodd-Frank 

Act, the Office is required to standardize the types and 

formats of data reported and collected on behalf of 

the Council. We work closely with the Council’s Data 

Committee, with international authorities, and with 

financial institutions and standard-setting bodies. The 

Office also benefits from the advice of industry experts 

on our Financial Research Advisory Committee and 

the committee’s data subcommittee. 

We use the following criteria to evaluate potential data 

standards projects. Such projects must:

1. improve Council member data collection,

2. support financial stability analysis,

3. promote regulatory data sharing and comparing, or 

4. promote financial stability.

Data standards are widely used in numerous industries, 

such as healthcare and consumer goods. Without bar 

codes, for example, supply-chain management would 

be impossible. Financial data standards are less prevalent, but a strong consensus has developed that wider 

use of data standards is critical for financial-market 

participants and policymakers.

Why Data Standards?

Standards are needed to: (1) improve reporting and 

risk management for firms, (2) support prudential 

supervision and market oversight, and (3) promote and 

improve the efficiency of macroprudential monitoring 

by the Council and the Office. In fact, standardization 

can enable the same data to serve all three purposes. 

Data standards allow the exchange of data between 

systems; aggregation of data from multiple sources; 

comparison of data among unrelated systems; and 

automation of processes for storing, reporting, and 

processing data. 

Data standards also enhance data quality and support consistent rules for what metadata are required. 

Ideally, data standards are adopted when new types of 

data are introduced, or new requirements are initiated 

for reporting or transacting data. In practice, the legacy of insufficient financial data standards means new 

standards must be created for existing and emerging 

data needs. 

Data standards can develop in several ways. Businesses 

often develop standards without government involvement. For example, the mortgage industry, working 

through a standard-setting body, voluntarily adopted 

an automated process more than a decade ago for a 

manual mortgage application process that was slow, 

resource-intensive, and often produced inconsistent 

results (see Hutto, 2003; Pafenburg, 2004). But private-market participants do not always work together 

to adopt or implement common standards. A standard 

that is not implemented has no value.

Standards are a public good that benefit users and the 

community. As with many public goods, the up-front 

work needed to define and use a standard can be costly 

for the first user. In addition, some market participants 

may benefit from the lack of a commonly accepted 

standard. 
99 Promoting Data Standards

In 2014, we will continue to provide significant support 

to the international initiative establishing an LEI for 

each legal entity participating in global financial markets. This identification will help regulators aggregate, 

compare, and analyze data better than currently possible (see Section 6.3). 

Our agenda also includes moving forward to meet the 

Dodd-Frank Act mandate to publish databases of financial companies and financial instruments, providing 

continuing assistance to Council member agencies with 

data standards initiatives, and beginning to identify 

opportunities to optimize the collecting and reporting 

of regulatory data among Council member agencies. 

Regulators need to collect data for prudential supervision and market oversight, regulatory compliance, and 

financial stability monitoring. Improving existing data 

collections and assisting Council member agencies in 

their initiatives can help align data standardization 

among Council member agencies, and thus enhance 

coordination and data sharing. Also, providing data to 

regulators is easier and less costly for financial firms 

if they can draw on the definitions and reporting 

structures they use internally (see Principles for Data 

Collection).

The Dodd-Frank Act requires additional financial 

data collection and regulatory reporting and promotes better financial standards. For example, the 

Consumer Financial Protection Bureau (CFPB) is 

required to assess identifiers for mortgages, properties, 

and legal entities in the mortgage market. Our work 

on a universal mortgage identifier, in collaboration 

with the CFPB and other Council member agencies, is 

described in more detail in Section 6.4 and in a recent 

OFR working paper. 

Another Dodd-Frank provision requires companies to 

report certain over-the-counter (OTC) derivatives trading to the Securities and Exchange Commission (SEC) 

or to the Commodity Futures Trading Commission 

(CFTC). We are working with the CFTC and other 

regulators around the world to align and standardize 

derivatives reporting (see Section 6.4). 

Is It a Good Candidate for Standardization?

We analyze a range of factors to determine whether a 

type of data is a good candidate for standardization. 

Good candidates share uniform data characteristics, 

involve a large volume of data, have sufficient information available, often involve multilateral exchanges, and 

are relatively easy to execute. 

From initial market inception through maturity, a financial product generates a growing volume of data that 

must be processed or assessed. A new financial product 

may have to wait until it wins market acceptance before 

it becomes a good candidate for data standardization. 

Large volumes of data with a standard form, or syntax 

and meaning — such as in the case of corporate bonds 

— are more easily parsed by computers for business 

analysis, data distribution, and record keeping. Often, 

market participants wait to see significant trading 

volumes from a new financial product — such as a 

Figure 66. Data Standardization Considerations

Supports nancial 

stability analysis 

Promotes regulatory data

sharing and exchange 

Promotes nancial 

stability

Is it within the OFR 

mission and scope? 

Is it a “good candidate”

 for standardization?

Uniform characteristics 

Sufcient information 

about the data

Large volume of 

data to exchange

Determine OFR’s 

strategy/execution

Leader

Advisor

Improves Council 

member data collection 

Ease of execution

Multilateral exchange

Partner

Source: OFR
2013 OFR Annual Report 100 2013 OFR Annual Report

derivative or swap — before investing in the technology 

to fully integrate the product’s data into their automated systems. 

Successful standardization depends on having enough 

information about the characteristics and business 

usage of data to standardize them properly. A data standard must be defined in detail to support the intended 

business purpose. 

Data exchanged among multiple parties in the financial 

marketplace also represent good candidates for standardization. Integrating data with varied definitions and 

syntax can be expensive and difficult, and costs could 

increase as more companies participate. Standards can 

facilitate that integration and help reduce these costs and 

identifiers are a good example of this (see insert, Why 

Identifiers?). On the other hand, for projects that are ad 

hoc in nature, with limited data integration complexity 

and low volume or frequency, customized work may be 

appropriate. 

PRINCIPLES FOR DATA COLLECTION

A successful data collection effort obtains data at an 

acceptable level of quality for the intended purpose. The 

data should be electronically parsed, easily stored, and easily used for its purpose. Although each situation is different 

for data collection, regulatory reporting, or data sharing, the 

following guiding principles can help as Council member 

agencies pursue new data collections:

Identify the business purpose for collecting the data.

Whether the data are collected by government mandate or 

incentive, there should be a compelling reason for participants 

to submit data and a generally valid purpose for its collection. 

Understand what specific data are needed and how 

the data would be produced. If the data are not physically available or do not exist in the detail needed, analyze 

how the data could be generated. A good understanding 

of the data definition is needed to ensure that the data are 

collected at the right point in the business process for the 

intended use.

Understand that modern data collection is electronic. 

Today’s financial markets use electronic systems to conduct 

business. Paper forms are yesterday’s tool. Data collected 

will undergo statistical analysis and exploration with computer software. To reduce potential sources of error, transmitting data system-to-system eliminates manual steps on 

both ends of the data collection process. 

Determine if the data can be submitted using the prevailing standard in the market. Open, consensus-based 

industry standards include feedback from a representative 

sample of the industry and reflect the systems, data, and 

business processes already in use. Standards based on 

market participants’ existing practices have a better chance 

of success. Feedback from an appropriate industry standards group is valuable.

Precisely define what data must be submitted. Publish 

specifications that include data names and definitions, data 

typing, acceptable enumerations, and integrity restrictions 

that market participants need to know when preparing data 

submissions. Furnish specific examples and user guidance. 

Set up mechanisms for participants to ask questions and get 

answers. 

Use validation controls at the point of data collection in 

the form of submittal failures or messaging. Validation 

controls create some upfront work but ensure that data 

received are valid, consistent, and usable. Include validation 

controls when data collection measures are being designed 

so participants can build this step into their procedures.

Give participants time to test their data formation 

process and submittal system. An integration period 

lets both sides test processes without the pressure of a 

real-time submittal. A testing period helps ensure a data 

collection project does not stymie a financial company’s 

usual flow of business. 

Seek ongoing feedback to refine controls, specifications, and data that are collected. Be prepared to evolve 

as business needs and processes change, and use feedback 

to improve.
101 Promoting Data Standards

Any project to help regulators collect standardized 

data from financial institutions should make the process as easy as possible for all users. Standards must 

be clearly defined, documented, communicated, and 

supported. Reporting companies need information 

about the standard itself and written guidance to 

prevent misunderstandings. Industry participants and 

regulators must also have the infrastructure to support 

the technologies, formats, and processes required to 

submit and accept the data. 

A standardization project should consider whether 

companies in the industry already have the needed data 

within their business processes, what format the data are 

in, and how detailed the data are. Aligning data reporting standards to data definitions and structures already 

in use by the industry is simpler, more efficient, and less 

likely to increase support costs. This approach also helps 

prevent errors that can be introduced if existing data 

must be mapped to different formats, is subject to “prework,” or otherwise must be manually manipulated to 

make the data usable.

Determine the Role of the Office

After a data standardization project is identified, the 

Office must decide how to participate. Participation 

can take the form of leadership or collaboration with a 

regulatory agency or participation in consensus-based 

organizations. In some situations, we may decide to exercise our rulemaking authority, granted by the DoddFrank Act.

Given its statutory underpinnings, the Office is ideally 

suited to coordinate standardization requiring the 

involvement of more than one regulator, domestically or 

internationally. Our role in laying the foundation for a 

global LEI is a good example. 

The word “identifier” figures prominently in discussions 

related to the financial services industry, in part because the 

industry has been slow to adopt data-sharing and the efficiencies it can bring. Manufacturing and distribution industries already invested heavily in systems to connect and 

communicate, and in the process became adept at sharing 

information to relay business messages. This typically 

involves some form of shared identification — participants 

in a given system maintain shared datasets that enable 

them to be precise in ascribing the results of transactions to 

parties in the transaction. 

Even when data are collected in an organized manner, dataset information comes from a variety of sources and integrating it is a costly and time-consuming process. Moving 

large blocks of data across a network is almost always 

slower and more expensive than moving smaller blocks. 

This leads to the creation of filing and shorthand systems 

that allow small groups of information to “point” to larger 

amounts of recorded information. For example, when one 

firm sends a message to another that refers to GM shares, 

the assumption is that both parties will look up and identify 

the issuer as General Motors Co. 

An identifier is a form of shorthand, or “smaller representation” in the world of data standards. In the case of a 

computer representation of an entity, the full description 

may involve hundreds of individual facts. However, a small 

subset of those facts may be sufficient to establish the 

entity’s uniqueness. 

A shared identification system of any type requires agreement among participants on how to maintain lists of many 

things (organizations, products, infrastructure, geographies, 

time zones, and others) necessary to get the job done. This 

is where the financial services industry has unique challenges. Some companies may have a disincentive to share 

information. 

The financial services industry needs shared identification 

systems that require standardized definitions, assignment, 

and maintenance methods. When an identification platform 

already exists, it typically is specific to a given jurisdiction or 

asset class and does not meet the industry’s current needs. 

The creation and enhancement of shared identification 

systems are critical to the OFR’s mission to analyze risks to 

financial stability. 

WHY IDENTIFIERS?
2013 OFR Annual Report 102 2013 OFR Annual Report

We may collaborate with industry organizations to 

develop open standards. Open standards fit with the 

industry’s underlying business processes, technologies, 

and business knowledge. Standards developed with 

industry organizations are more likely to be easily integrated into existing systems and processes, and prove 

less burdensome for companies to adopt. 

We can reduce some of the data-reporting burden on 

market participants and regulators by collaborating with 

Council member agencies on their individual data standardization or data collection projects. By commenting 

on draft plans and proposed rules, we can also contribute to the data reporting priorities of Council member 

agencies and reduce the cumulative burden of regulatory reporting on market participants. We have expertise in data standardization, collection, and processing. 

By advising Council member agencies, we can promote 

the adoption of open, consensus-based information 

standards and help agencies align technologies, data 

uses, and implementation practices. 

Under section 153 (c)(2) of the Dodd-Frank Act, the 

Office has certain authority to propose and finalize 

regulations requiring Council member agencies to standardize, align, or collect data. As we work to integrate 

data across sources and across Council member agencies, we are gaining practical experience in determining where standardization is needed for more efficient 

aggregation and analysis.

6.2 Trends in Information Standards

Information standards are evolving rapidly on several fronts. 

The G-20 countries are moving ahead to implement a 2009 

plan requiring regulatory reporting of OTC derivatives (see 

FSB, 2013a). Establishment of the LEI infrastructure and 

operational processes to support it is well underway. The SEC 

has adopted a rule requiring self-regulatory organizations to 

submit plans to implement the consolidated audit trail and 

corporations are adopting the eXtensible Business Reporting 

Language, known as XBRL, for financial reporting. 

Internationally, governments are talking about how to align 

regulatory practices across markets and national borders. 

Figure 67. Local Operating Units

Dutch Chamber 

of Commerce

Krajowy 

Depozyt 

Papierów 

Wartościowych 

S.A. (Poland)

Irish Stock 

Exchange

Palestine

Securities Exchange

National Settlement 

Depository (Russia)

China Financial 

Standardization 

Technical Committee

Takasbank 

(Turkey)

London Stock

Exchange

Central Bank 

of Argentina

Unione Italiana per le

Cameredi Commercio,

Industria, Artigianato

e Agricoltura

Institut National de la

Statistique et des Etudes

Economiques (France)

DTCC/SWIFT

CICI (USA)

WM 

Datenservice

(Germany)

National Board for Patents 

and Registration (PRH) - Finland

Source: LEI Regulatory Oversight Committee
103 Promoting Data Standards

The need to quickly process and analyze large sets of 

data has become increasingly the norm and requires 

a digital data stream, which a user can customize to 

display raw data, a report, a form, or even a Web page. 

The role of information standards to exchange digital 

data streams is becoming increasingly important with 

automated data use. Because regulators need data that 

are easier to access quickly and integrate with existing 

data, they have adopted methods the financial industry uses to conduct transactions. Amid the changes 

in data exchange-and-transmittal methods, industry 

standards bodies, financial firms, software providers, 

and others are closely following the development of 

regulatory reporting initiatives in order to comply with 

new requirements. 

Industry standards organizations are seeing a trend of 

consolidation, integration, and alignment among standards to simplify reporting and transacting business. 

Greater integration of standards creates synergies in 

data definition and structure that can speed up data 

analysis using several different sources.

However, the amount of time and effort required to 

launch a new digital data collection initiative should 

not be underestimated. Adopting new or modified 

data collection processes can take years, especially in 

domains with lengthy and complicated supply chains. 

Success requires preparation and detailed analysis, 

methodical planning and risk management, and 

perseverance. 

6.3 Legal Entity Identifier 

The Office’s top data standards priority is supporting the 

global initiative to assign a unique Legal Entity Identifier 

(LEI) to every financial market participant. Each LEI is a 

20-digit alphanumeric code that precisely identifies parties to 

financial transactions. When linked to transactional data or 

reporting, the identifiers help regulators understand market 

activities and see where risks may be building. The Office 

and other domestic regulators have used a combination 

of regulatory compulsion, international regulatory 

coordination, and consensus-based standard setting to 

develop the global LEI system.

LEI Update

International efforts to develop the global LEI system 

made significant progress in 2013. A three-tiered governance structure was finalized and interim identifiers were issued to thousands of financial companies 

around the world. The Office and other regulators are 

beginning to work with the resulting data.

Overseeing the global system is the LEI Regulatory 

Oversight Committee (ROC), made up of 57 central 

banks, regulators, and other international authorities. 

In January 2013, the ROC selected the Office’s Chief 

Counsel as its chairman and officials from the Bank 

of France and the Japan Financial Services Agency as 

vice chairmen. During the year, the ROC developed 

procedural guidelines, launched a new website at 

www.leiroc.org, and continued work on the governance of the global LEI system. 

The next level of governance is the Global Legal 

Entity Identifier Foundation, a Swiss-based 

LEI Development Timeline

2010 2011

July November December May June July September 

Dodd-Frank 

Act creates 

OFR; requires 

CFTC, SEC 

to issue rules 

for reporting 

swaps 

transactions

CFTC, SEC propose 

rules to report swaps 

transactions, express 

preference for a universal 

identifier 

OFR issues policy 

statement advocating LEI

U.S. regulators 

issue discussion 

paper about LEI 

as a linchpin for 

financial data 

Global 

Financial 

Markets 

Association 

proposes 

industry 

requirements 

for LEI

Canadian Securities 

Administrators call 

for swaps traders 

to have unique 

identifier based 

on international 

standards 

Hong Kong 

Monetary Authority 

says it will work 

with Hong Kong 

Trade Repository 

to consider 

incorporating global 

LEI into reporting 

G-20’s 

Financial 

Stability Board 

holds publicprivate sector 

workshop 

to begin 

developing LEI 
2013 OFR Annual Report 104 2013 OFR Annual Report

not-for-profit organization, which will serve as the 

central operating unit responsible for ensuring that 

companies and countries that adopt the LEI adhere 

to its standards for reliability, quality, and uniqueness. 

In 2013, the ROC began organizing the foundation 

and is in the process of selecting an international 

board of directors to run it. 

At the grassroots level, a series of local operating units 

will serve as the registrars to assign LEIs to financial 

companies and each of their legal entities. Local 

operating units, which will also validate and maintain 

reference data, can be established by private firms or 

by governments, and can be country-specific, regional, 

or international. 

Several interim local operating units already have been 

created. As of mid-November 2013, an initial group 

of nine had issued more than 100,000 interim identifiers, or pre-LEIs, to financial companies and their 

subsidiaries. Notably, five of these local operating units 

had been “endorsed” by the ROC, meaning that the 

codes they issue can be used for reporting purposes in 

regulations issued by ROC member countries. These 

entities are:

• CICI Utility, created by Depository Trust 

& Clearing Corporation and the Society 

for Worldwide Interbank Financial 

Telecommunication;

• WM Datenservice, a German company; 

• INSEE, France’s National Institute of Statistics and 

Economic Studies; 

• London Stock Exchange; and

• Takasbank in Turkey.

Because it is critical that these pre-LEIs seamlessly 

transition into permanent LEIs once the global system 

is up and running, the ROC has worked to ensure that 

they meet regulatory needs, are consistent with one 

another, and are fully compliant with LEI data quality 

standards. Other pre-local operating units are operational and are expected to be reviewed for endorsement as they apply to the ROC.

Other organizations have announced plans to launch 

local operating units and are in varying stages of development (see Figure 67).

LEI History 

Regulators and market participants use a wide variety 

of identification methods to keep track of individual 

financial institutions, but these identifiers are diverse, 

incomplete, overlapping, and not directly comparable. 

Some market participants have long identified the risks 

created by the absence of a universal standard. The 

financial crisis served as an impetus by demonstrating 

the urgency of the problem and the potential impacts 

on financial stability. For example, following the 

collapse of Lehman Brothers, neither regulators nor 

private-sector risk managers were able to view the total 

extent to which important market participants were 

exposed to Lehman and its legal entities. 

Following the crisis, U.S. and global regulators collaborated to study data standards that could have assisted 

with analysis during the financial crisis and agreed 

that an LEI system was a foundational need (see LEI 

Development Timeline). In November 2010, the Office 

issued a policy statement calling for LEIs and noted 

the potential for rulemaking requiring the use of an 

LEI Development Timeline (continued)

2011 2012 

October December January May June July September 

SEC, CFTC 

finalize rule 

requiring hedge 

funds and 

commodity pool 

operators to 

report leverage 

on Form PF with 

optional use of 

LEI

CFTC approves 

final rule requiring 

identifier in 

regulatory 

reporting of 

swaps trading, 

mandates “CFTC 

Interim Compliant 

Identifier” until 

LEI system ready

Bank for International 

Settlements and 

International 

Organization 

of Securities 

Commissions urge 

use of LEI by trade 

repositories as part of 

swaps market reforms

International 

Organization for 

Standardization 

issues ISO 17442 

specifications for 

LEI data standard 

for financial 

services industry

G-20 leaders 

endorse plan 

for global LEI 

system 

Financial Stability 

Board convenes 

LEI private sector 

preparatory group

EU adopts Reg. 

648/2012 requiring 

LEI in swaps 

reporting

National 

Association 

of Insurance 

Commissioners 

says it will require 

LEI in regulatory 

reporting
105 Promoting Data Standards

identifier (see OFR, 2010). The CFTC and SEC each 

proposed rules that month requiring the use of LEIs 

if a system was developed through a consensus process 

and globally adopted. A Federal Reserve white paper 

followed, calling for a public-private partnership to 

develop an LEI system that took into consideration the 

industry standards for data identification (see Bottega 

and Powell, 2011). Officials took these steps because 

they considered the LEI necessary for effective regulation, and because private markets had failed to develop 

one independently. 

Public and private efforts helped to push the project along. The International Organization for 

Standardization (ISO), an independent, nongovernmental organization made up of national standards 

bodies from 163 countries, served as an important 

forum to build consensus. In June 2012, that organization adopted ISO 17442 as a LEI standard for financial 

services companies. 

Around the same time, a private-sector preparatory 

group began meeting to discuss ways to build a global 

system. The work of the ISO and the private-sector 

group was critical because private-sector business 

activities cross jurisdictional lines. A well-designed 

international standard benefits regulators who want a 

standard for effective supervision, as well as private-sector organizations that need a standard for risk management and operational efficiencies. As a result, the 

private sector has played an important role in assuring 

that the LEI standard and system are fit for industry 

purposes as well as regulatory purposes. 

U.S. regulators also finalized key rules in 2011 that 

would rely on the LEI. The SEC and CFTC ordered 

hedge funds and other private funds to begin reporting certain information on Form PF (SEC) and Form 

CPO-PQR (CFTC) and included optional use of the 

LEI. The SEC also adopted rules updating investment 

advisor reports and added the LEI as a reporting 

element. In early 2012, the CFTC finalized a swaps 

reporting rule that requires the LEI. 

Meanwhile, regulators around the globe began working together on an international identification system. 

The Office assisted in that work because a global standard would bring greater transparency than a standard 

used only in the United States. 

By the end of 2012, the G-20 had endorsed a final charter for the LEI Regulatory Oversight Committee and 

designated ISO 17442 as the standard that regulators 

would use. That allowed the CFTC, which had adopted 

swaps reporting rules in early 2012, to designate a utility as the LEI provider for its swaps rule, as long as the 

utility complied with ISO 17442. As a result, the CFTC 

Interim Compliant Identifiers (CICIs) are being used 

until the global LEI system is fully operational. At that 

time, CICIs will convert to permanent LEIs. In Europe, 

similar rules were adopted, making more urgent the 

need for a global system. The interim system was established, allowing market participants to receive codes 

from endorsed pre-local operating units for regulatory 

reporting in Europe or the United States regardless of 

where the code was obtained. 

Putting the LEI System to Work

For regulators and market participants to realize its 

benefits fully, the global LEI must be integrated into 

the economic system. This means extending the use of 

2013

October January March July August October and November

Financial Stability 

Board implementation 

group drafts charter 

creating LEI Regulatory 

Oversight Committee 

to govern global LEI 

system

Regulatory 

Oversight 

Committee 

created to 

govern LEI 

system 

Regulatory Oversight 

Committee begins 

work to form a 

nonprofit foundation 

in Switzerland to 

act as the central 

operating unit of the 

global LEI system

LEI Regulatory 

Oversight 

Committee 

issues interim 

requirements 

for local 

operating units 

in the global LEI 

system

The OFR’s Financial 

Research Advisory 

Committee recommends 

the OFR adopt the global 

LEI system to meet 

Dodd-Frank Act mandate 

for a financial instrument 

database

Regulatory Oversight 

Committee formally 

approves five pre-local 

operating units, and codes 

issued by them will be 

globally recognized for 

regulatory reporting
2013 OFR Annual Report 106 2013 OFR Annual Report

LEIs to follow financial transactions, normalize data 

reporting, and identify corporate hierarchies. 

We are working with the financial industry and standard-setting organizations to incorporate LEI data 

records into the standards they manage. Each LEI data 

record includes the legal name, location, and other 

information supplied by an applicant company or 

subsidiary when it registered for an identifier. We are 

also collecting feedback to improve LEI practices and to 

make the data available for transactional flows for financial products, interactions, and regulatory reporting. 

Integrating LEI data structures with industry standards 

will encourage the routine use of the LEI in business. 

Routine use will require educating market professionals 

about the LEI system and how it operates, and collaborating with international regulators to align data 

practices. Working with industry and market participants may also yield information about how to connect 

markets and data in new ways. As LEIs are integrated 

into existing or new financial information standards, 

the Office is encouraging Council member agencies to 

adopt and use them. We expect new initiatives and proposed rules to require LEI use by legal entities that are 

parties to, or associated with, financial transactions and 

regulatory reporting. 

One example of LEI integration is in the mortgage 

industry. The Office worked with the Mortgage Industry 

Standards Maintenance Organization (MISMO), a 

data standards body for the mortgage industry, to add 

the LEI data record to the industry’s standard for data 

exchange. That means a mortgage transaction can carry 

the LEI of each financial institution involved from the 

initial loan to securitization to servicing. 

Data points and definitions for LEI data were discussed by industry participants and resulted in MISMO 

equivalents for LEI data to be added in Version 3.3 of 

the MISMO standard. The Federal Housing Finance 

Agency (FHFA) and OFR are also working together 

to support the reporting of the standard’s LEI data in 

regulatory reporting and transaction flows. Adding 

LEI to the MISMO standard for mortgage transactions 

will promote transparency and help regulators see risks 

to financial stability. This partnership is a model for 

future OFR efforts to support and supplement regulators’ initiatives.

Financial Entity Hierarchies

Eventually, the LEI system will include basic hierarchical information. Until that point, as the operational 

framework for the global LEI system is put into place, 

regulators are examining how to track the often complex web of legal entities within large financial companies and the relationships among them.

Large banking organizations in the United States are 

generally organized within bank holding companies, 

each of which may have thousands of subsidiaries, 

business units, and legal entities around the world. 

The complexity of bank holding companies has been 

increasing for years; some bank holding companies 

have more than 4,000 subsidiaries globally. 

Every bank holding company has multiple, complex 

interconnections among its subsidiaries through 

financial guarantees and legal ties, and to other bank 

holding companies through transactions and commitments. The complexity of the financial network makes 

understanding potential channels of contagion during 

a crisis. 

U.S. financial regulators collect information on the 

corporate structures and control of domestic banks 

and foreign banks with a presence in the United States. 

For example, the Federal Reserve, which supervises 

bank holding companies, collects corporate structure 

and ownership data through its Forms FR Y-6, Y-7, and 

Y-10 and publishes some of the data on the National 

Information Center website. 

There are differences and gaps among the financial 

regulators that collect corporate hierarchy information, 

and data are not always shared easily. For instance, certain entities are not automatically or routinely reported 

to the National Information Center. Hierarchy relationship data is not collected on broker-dealers, pension 

funds, and other nonbank financial firms that are not 

part of a bank holding company.

We are studying the possibility of consolidating and 

standardizing corporate hierarchy information in 

collaboration with financial regulators. This approach 

is similar to the one that regulators of depository 

institutions have taken in their successful collaboration 

in aligning regulatory reporting and examination procedures through participation in the Federal Financial 

Institutions Examination Council. Better information 
107 Promoting Data Standards

about corporate hierarchies, coupled with the LEI, will 

give U.S. financial regulators deeper insight into how 

large financial institutions are structured internally 

and how they are connected to each other.

6.4 Identifying Financial 

Instruments

The Office has also placed a high priority on promoting 

financial instrument identifiers. We are helping Council 

member agencies evaluate identifiers for swaps and 

mortgages and develop strategies for standardizing the 

collection and aggregation of data in swap data repositories. 

We are also analyzing the general framework for how 

financial products could be more precisely identified and 

developing a strategy to introduce a financial instruments 

database as required by the Dodd-Frank Act.

A financial instrument can be viewed as an agreement 

between counterparties to exchange cash flows of 

specific terms. Mature financial products such as 

stocks and bonds often have standardized agreements. 

But innovative financial products have terms 

and conditions that vary widely. Inside financial 

institutions, an instrument is usually classified 

according to characteristics such as ownership rights 

and terms and conditions. This type of internal 

classification is used by companies for back-office 

processing of transactions, front-end analysis, or other 

roles within the product lifecycle.

However, the financial industry’s existing classification 

system has not kept up with the evolution of complicated 

new financial products. For example, the ISO standard 

for Classification of Financial Instruments (CFI) organizes products as equities, debt instruments, commodities, entitlements or “rights,” options, or futures. The 

category “other” is assigned to some derivatives and 

structured products that have emerged since the most 

recent update of the CFI standard. 

The Office is working with the data standards community to help the ISO update its financial product 

classifications. As part of that work, we are assessing 

new methodologies that may help integrate alternative classification approaches recommended by 

the European Securities and Markets Authority, the 

International Swaps and Derivatives Association, and 

other industry groups that are trying to redefine the 

way products are classified.

Financial instrument identifiers have existed for 

decades, but still do not uniformly cover all instruments traded in the global financial markets. For 

example, the CFTC has issued requirements for the 

construction and use of a Unique Swap Identifier, 

as called for in the Dodd-Frank Act to fill the gap in 

clearly identifying swap transactions. 

Not surprisingly, product identifiers have generally 

lagged behind the evolution of innovative new financial instruments into standardized products. As a new 

financial instrument gains traction in the market, it 

may evolve from highly customized transactions to 

more standardized terms that allow the use of electronic trading platforms. As long as the transactions 

are profitable and the business remains small, private 

firms will not have an incentive to standardize their 

language or create costly information collection systems. But as financial instruments become successful 

in the market, they may move from voice and paper 

transactions to electronic trading platforms using 

more standardized definitions and formats. 

Derivatives pose an additional challenge with respect 

to product identifiers. With derivatives or swaps, the 

distinction between transactions and instruments is 

less clear — in some cases, because each transaction is 

unique, the transaction is the instrument. As a derivative instrument becomes popular and widespread, 

the need for an instrument identifier becomes clearer. 

When all financial instruments are assigned an identifier, a richer variety of analysis, comparison, and 

aggregation becomes possible.

Designing a Universal Mortgage Identifier

The recent housing crisis exposed data gaps, risk-management failures, and shortcomings in operational controls throughout the mortgage finance system. Lenders 

also had problems connecting mortgage origination 

data to performance data, tracking loan modifications, 

and verifying loan underwriting practices (see insert 

on Universal Mortgage Identifier). 

Given the size, complexity, and fragmented nature of 

the mortgage system, regulators need a clear and consistent identifier of each mortgage. Although several 
2013 OFR Annual Report 108 2013 OFR Annual Report Promoting Data Standards

UNIVERSAL MORTGAGE IDENTIFIER

A recent OFR working paper prepared with an interagency 

working group described how a universal mortgage identifier could improve aggregation, comparability, and analysis 

in the U.S. mortgage industry. The group agreed that a 

universal mortgage identifier should have the following 

characteristics (see McCormick and Calahan, 2013; see also 

Bottega and Powell, 2011).

Uniqueness and singularity. The identifier should be 

unique and assigned to one mortgage. No mortgage should 

be assigned more than one identifier.

Persistence. The identifier should remain with its mortgage 

until the loan is terminated, regardless of the holder of the 

loan or any modifications made to it.

Extensibility. The identification system must allow for 

growth in the number of unique identifiers issued, without 

having to reuse identifiers.

Neutrality. Identifiers should be neutral and have no information encoded in the identifier itself. This is important for 

persistence, extensibility, and privacy. For example, embedding a ZIP code in an identifier would limit the number of 

bits of information practically available. An identifier with an 

embedded a ZIP code would only be useful so long as the 

ZIP code with the greatest number of mortgages over time 

did not run out of identifiers. 

Reliability. A universal mortgage identifier must be reliable 

to be adopted by market participants. The mechanism 

to assign unique identifiers must be robust, the identifier 

should not conflict with other systems, and reference databases must be independent of any entity that could go out of 

existence in the future.

Open Standard. The identifier should be based on an open, 

voluntary, and consensus standard.

Public Availability. The identifier must be free of any contractual restrictions and available for use by the mortgage 

industry, regulators, and researchers. 

Privacy Protection. A mortgage holder’s personal information must be kept private and confidential throughout the life 

of a mortgage. Beyond simply not embedding information in 

the identifier itself, the identifier system must be designed 

to prevent re-identification.

Incentive for Compatibility. As an incentive to invest in 

the system market, participants should benefit from using 

the identifier. Regulators should adopt a single identifier for 

reporting purposes to help encourage coordination between 

participants in the market.

Registration Process. Identifiers must be promptly 

assigned during the mortgage application process to prevent 

disruption to the mortgage market. 

Quality Assurance. Errors are often introduced in data in 

the normal course of doing business. To protect the integrity 

of a universal mortgage identifier, quality control practices 

must be adopted, including best practices, such as using 

checksums and good governance practices, and assigning 

clear responsibility for acquiring each new identifier.
109 Promoting Data Standards

identification systems exist, no single universal identifier is shared across all government agencies or the 

industry. As a result, cross-referencing existing identifiers is difficult and inaccurate because of ambiguities 

and inconsistencies in industry relationships. Ideally, a 

standard developed by a voluntary consensus standards 

body would also meet the needs of government, including privacy requirements.

The Office has been working with housing-related 

federal regulatory agencies to assess the need for a 

universal mortgage identifier that would promote 

transparency in the mortgage industry and connect 

fragmented data from the mortgage lifecycle, while 

safeguarding personal information. An interagency 

working group has defined the need and requirements 

for an identifier and the characteristics it should have, 

applying lessons learned from our experience with the 

global LEI project. 

Privacy is a top concern, specifically in deciding who 

has access to the identifier, which mortgage documents 

must carry the identifier, and how to connect simultaneous or sequential liens taken out by a borrower on a 

particular property. Another important consideration 

is how to structure and govern a system of entities that 

would issue mortgage identifiers and to ensure that 

each identifier is unique. The Office released a working paper on this subject in December 2013.

6.5 Improving the Integrity of 

Reported Data

Derivatives Market Reforms

Promoting transparency in the OTC derivatives or 

swaps markets — including equity, credit, interest rate, 

foreign exchange, and commodity derivatives — is a 

priority for international regulators, given the market’s 

decentralized nature and still-developing infrastructure. The lack of information about a buildup in large 

counterparty exposures to OTC derivatives contributed to the market contagion and the complexity of 

the workouts associated with the 2008 collapse of 

Lehman Brothers and the near-defaults of American 

International Group, Inc. and Bear Stearns (see CPSS 

and IOSCO, 2012).

Interconnections of participants in the OTC derivatives market and limited transparency of counterparty 

credit risk exposures contributed to contagion in the 

recent financial crisis. No central clearing or reporting 

existed to promote a free flow of market information. 

The derivatives market has grown since the financial crisis and now totals more than $690 trillion in 

notional amounts outstanding (see BIS, 2013).

The Dodd-Frank Act amended the Commodity 

Exchange Act to establish a new regulatory framework 

for swaps. Derivatives dealers and major swaps participants are now required to register with regulators, 

meet clearing and trade execution requirements on 

standardized derivatives products, and comply with 

recordkeeping and real-time reporting of swaps.

All swaps, cleared or not, must be reported to new 

entities known as swap data repositories, beginning 

when a swap is created and ending when it expires. 

The repositories collect and maintain confidential data 

about transactions and make the data electronically 

available to regulators (see CFTC, 2013).

The CFTC issued rules identifying specific data fields 

that must be reported for every swap and for classes 

of swaps. The repositories, however, have significant discretion in refining and interpreting the data 

fields because the fields vary among product groups, 

such as interest rate swaps and credit default swaps, 

and between cleared and uncleared transactions. A 

required format was not defined in detail, and the 

resulting variations in reporting have hampered 

regulators’ efforts to aggregate and analyze the data. 

We are working with the CFTC to create additional 

standards.

The Office is also participating with the OTC 

Derivatives Regulators Forum and the Financial 

Stability Board to improve derivatives data reporting, in collaboration with Council member agencies. 

Global coordination is important in creating standards 

that will help regulators aggregate and analyze OTC 

derivative transactions. The Office supports the four 

principles of data reporting described by the Basel 

Committee for effective risk aggregation and risk 

reporting: (1) robust governance and infrastructure, 

(2) reliable data that reconciles with accounting books 

and strong controls, (3) accuracy of risk reporting 

treated analogously to accounting materiality, and 
2013 OFR Annual Report 110 2013 OFR Annual Report

(4) active and engaged role for supervisors in the 

entire risk reporting process (see BCBS, 2013b).

Financial Instrument Database and Financial 

Company Database

The Dodd-Frank Act requires the Office to create a 

financial company reference database and a financial 

instrument reference database, both accessible to the 

public. We have initiated a set of projects to fulfill the 

requirement and to meet internal research and analysis 

needs for each type of database.

A reference database identifies and describes common 

formats used to prepare the underlying descriptive 

data, and contains a broad range of data that must be 

closely maintained. Some data, such as identifiers and 

core descriptive material, rarely change while other 

data, such as market prices, change daily. In between 

are data that change periodically, such as credit ratings, transfer agents, and variable rate resets.

The global LEI system’s assignment of unique identifiers will ultimately make up the financial company 

reference database mandated by the Dodd-Frank Act. 

The Office is working with the industry to define a 

common methodology to consolidate reference files 

from all local operating units. When the LEI system 

is fully operational, the Global Legal Entity Identifier 

Foundation will oversee, coordinate, and maintain a 

reference database. 

Creating a financial instrument reference database 

poses a different challenge. The Office is assessing 

approaches to creating the reference tool to standardize 

descriptive information about the structure and characteristics of financial instruments. There is no common 

language to define and describe financial instruments 

because companies, bond issuers, vendors, and other 

financial market participants use their own terms for 

data attributes, often based on their business needs. 

The Office also received a recommendation from its 

Financial Research Advisory Committee in August 

2013 that we create a financial instrument reference 

database by developing a way to precisely capture the 

contract terms of all financial instrument types and to 

build categories of financial products based on how 

the underlying transaction data are used. The committee emphasized that the Office should work with 

financial market participants on a shared industry 

understanding of terms and concepts. 

The advisory committee established a working group 

to help the Office generate a shared industry understanding of terms and concepts for financial product classification. The group is focusing on how the 

industry would use a reference database for cross-reference purposes. The group is also examining how the 

database would relate to standards for financial product transactions already in use, including the ISO’s 

financial industry messaging standards, the mortgage 

industry’s technology standards, and the electronic 

coding of corporate financial statements. In addition, 

the working group will assess the processes for registering instrument types and granting public access to the 

database. 

We look forward to the working group’s findings and 

the committee’s recommendations based on those 

findings, as we conduct our own analysis of methodologies, tools, and processes available and suitable for a 

financial instrument database. In defining and creating the reference database utilities, the Office will take 

a collaborative approach to ensure the two databases 

support the use envisioned by the Dodd-Frank Act. We 

also will continue to work with our advisory committee 

and the Council to determine the best course forward. 
111 Agenda Ahead

7 Agenda Ahead

In 2013, the Office continued to develop a 

toolkit for assessing and monitoring threats to 

financial stability, to evaluate policies for mitigating 

those threats, to perform fundamental research 

promoting financial stability, and to improve the 

scope and quality of key financial data. In 2014, 

we will extend and expand that work in several 

dimensions. We will:

• analyze policy issues related to financial stability 

and provide that analysis to the Financial 

Stability Oversight Council and the public; 

• devote considerable resources to building 

the infrastructure for managing, cleaning, and 

protecting financial data; 

• augment our secure data warehouse and 

analytic environment to provide analysts the 

data and tools to perform deep analysis of the 

financial system and threats to its stability; and 

• enhance the Office’s institutional infrastructure 

to support those activities.

Research Priorities 

The OFR has three core research activities: (1) to identify, assess, and monitor potential threats to financial 

stability, and develop tools for measuring and monitoring those threats; (2) to conduct studies and provide 

advice on the impact of policies designed to address 

those threats; and (3) to conduct basic research that 

contributes to the understanding of financial stability 

(see the Dodd-Frank Act, Sections 153 and 154).

Monitoring activities in 2014 will focus on improving 

the prototype Financial Stability Monitor to become 

a product regularly used by policymakers, as well as 

developing and testing new tools to help identify and 

monitor key vulnerabilities and threats. Tools will 

include quantitative metrics and monitors, as well as 

qualitative surveillance because not all threats are 

measurable. We will continue to support the Financial 

Stabiity Oversight Council in assessing and monitoring 

the threats to financial stability identified in its 2013 

Annual Report, and on the related threats we identified 

in Chapter 2 of this report. Key among them are risks 

in markets for short-term funding and credit, vulnerabilities to an increase in interest rates and volatility, 

and uncertainty about U.S. fiscal policy. 

The Office does not make policy. Rather, our job is to 

inform the debates about macroprudential policy tools 

by evaluating their effectiveness and assessing how they 

may complement or conflict with other types of policy. 

We are continuing to build our policy analysis team. As 

noted in Chapter 3, our policy agenda for 2014 centers 

on fulfilling our mandates to study the impact of policies related to financial stability and to promote best 

practices in risk management. Projects in the coming 

year will focus on policies that address vulnerabilities 

in markets for securities financing transactions. 

We will also continue our work on stress testing practices, following up on several OFR working papers in 

2013. One paper will further employ agent-based modeling to understand how private decisions affect market 

outcomes, as described in our 2012 Annual Report and 

our third working paper, with specific projects on fire 

sales and funding runs. At the same time, our ability to 

evaluate the effectiveness of supervisory stress testing 

will be greatly enhanced through access to data on 

the outcomes of those tests and we are in the preliminary stages of working with Council member agencies 

to determine how best to use those data. The OFR’s 

external Financial Research Advisory Committee has 

recommended that the Office request access to supervisory stress test data.

Our basic research program for 2014 will complement 

our first two core activities in several ways. We will 

pursue further the three research projects highlighted 

in Chapter 4 of this report: (1) the supply and demand 

for short-term funding instruments, (2) the development of metrics for monitoring market liquidity, and 
2013 OFR Annual Report 112 2013 OFR Annual Report

(3) the analysis of financial networks. We will publish 

a working paper describing the methodology used in 

the Financial Stability Monitor. We will continue to 

analyze repos, credit derivatives, and money market 

funds. We will continue to analyze the asset management industry, focusing on risks and vulnerabilities of 

hedge funds and other private funds using data from 

Form PF. We will also find ways to incorporate nonpublic data in analytical tools, while protecting the confidentiality of those data so the results can be shared, as 

described in OFR Working Paper no. 11. 

Data Priorities 

The Office’s data priorities, discussed in chapters 5 

and 6, support our research agenda. They include 

fostering the adoption of the Legal Entity Identifier 

(LEI) through regulation and market acceptance. We 

are also evaluating instrument identification. This 

effort includes beginning work on reference databases 

for financial entities and financial instruments, and 

pursuing comprehensive organizational hierarchy 

data. Another priority is to address data gaps related to 

repos, the asset management industry, and mortgage 

real estate investment trusts. In addition, we will begin 

to identify opportunities to optimize data collection 

and reporting, while reducing reporting burden when 

possible. This initiative includes working with other 

regulators on standards for swap data repositories. 

We will continue to provide leadership to the Council’s 

Data Committee, a group of researchers, data scientists, and data policy representatives from each Council 

member agency. The goal of the committee is to collaborate to improve our collective ability to fill data gaps; 

promote the use of data standards; share information, 

while keeping it secure; and promote best practices in 

data collection, management, and cataloguing. The 

2014 agenda for the committee, which meets monthly, 

will focus on recommendations for filling data gaps 

and improving data standards. The agenda will also 

include helping standardize the formats to collect 

derivatives data from swap data repositories and promoting development of a universal mortgage identifier.

Promoting LEI implementation will remain our top 

data standards priority for 2014. The global LEI initiative has reached several milestones, including the 

launching of so-called “pre-LEIs.” In 2014, we will help 

make the Global LEI Foundation operational and 

continue to encourage adoption of the LEI in reporting 

requirements.

Institutional Priorities

In 2013, the Office designed and purchased the major 

components of the secure analytic environment and 

we are now implementing the system to collect, process, store, manage, administer, and analyze data in 

a secure fashion. By mid-2014, we expect to deploy all 

of the functionality for the processing and analysis of 

large and complex datasets. Other significant activities in 2014 will include developing a suite of data 

management applications to complement off-the-shelf 

products to more efficiently and effectively organize, 

manage, search, and access information in a wide 

range of formats. 

The infrastructure is being built to include extensive 

tracking of data access and data lineage. Tracking 

data access increases security over confidential data. 

Tracking data lineage helps in managing the use of 

data efficiently, recreating and verifying analysis, and 

using historical data to conduct policy analysis. The 

analytic environment will also include robust metadata 

(descriptive data) to cross-reference and define the 

data, as well as semantic tagging to enhance the usability of data. 

As the Office continues to mature, we will adjust our 

organizational structure in 2014 to streamline functions, consolidate organizational elements, and better 

meet the needs of stakeholders. Our staffing efforts 

will transition from stand-up mode to a steady-state 

effort as we approach our goal of 275 to 300 full-time 

employees in 2015. Building a talented staff dedicated 

to analyzing risks to financial stability means not only 

hiring and retaining, but also training and developing 

the staff to enhance skill sets. 

A mature organization demands a culture of measurement, performance, and accountability in all aspects 

of achieving its mission. The Office has established 

policies and procedures to support sound and efficient 

operations and rigorous internal controls. In 2014 the 

Office will undertake a top-to-bottom review of current 

policies and procedures, improving and adding where 

necessary. 
113 Agenda Ahead

In addition, the Office will develop a 2015-19 strategic 

plan that will more fully articulate the vision and mission of the Office, as well as its goals and strategies for 

the next four years. In collaboration with stakeholders, 

the new strategic plan will identify key capabilities that 

the organization will need for responding to changes 

in the financial system, stakeholder needs, and technology. The plan will help inform our long-term investment in people, processes, and technologies.

We continue our outreach and collaborative efforts 

through our network of outside researchers, academics, industry experts, and others. We have begun to 

respond to the initial set of recommendations from our 

external Financial Research Advisory Committee and 

we look forward to further work with that group. We 

will expand the grants program launched in mid-2013 

in collaboration with the National Science Foundation 

and will continue to sponsor conferences and research 

on financial stability and related topics.

In all our activities, we will continue to work closely 

with the Council and its member agencies. The Office’s 

efforts to date have been significantly enhanced by the 

broad and deep expertise of their staffs and by a spirit 

of collaboration that results from the close alignment of our mission with theirs. Success in all these 

endeavors will also depend on continued progress in 

developing strong leadership, attracting and retaining 

dedicated professionals who strive to create an organization that is a thought leader in its field, and building 

a support team that provides unparalleled service to 

enable the organization to accomplish its mission.
2013 OFR Annual Report 114 2013 OFR Annual Report
115 Appendixes

Appendixes

A. Progress in Institution Building

At just over three years old, the Office of Financial 

Research has made substantial progress in building 

core operational systems and procedures, guided by 

the need to have a cost-effective, transparent, and 

accountable infrastructure that reflects best practices. 

We recognize we have to balance speed with prudence 

as the organization continues to mature and expand 

basic functions with ongoing review and improvements 

based on early experience.

Establishing a Sound Strategic and 

Operational Framework

The Office outlined its mission, goals, objectives, 

and implementation priorities in the OFR Strategic 

Framework: FY 2012-2014. Under that framework, the 

Office established five strategic goals to help achieve 

its mission:

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.

Our day-to-day operations are linked to strategic priorities by policies and procedures, as well as mechanisms 

for planning and performance management. The 

Office’s financial management follows Department 

of the Treasury rules; financial activities and controls 

are reviewed as part of the department’s consolidated 

audit and the budget is developed in line with Office 

of Management and Budget Circular A-11 guidance as 

part of the President’s Budget. We have also developed 

additional controls, project review mechanisms, and 

decision-making protocols to ensure that spending is 

well targeted and effectively monitored. 

The Office of the Inspector General and the 

Government Accountability Office periodically audit 

the Office’s activities. Our Director testifies before 

Congressional committees with oversight responsibility, most recently in March 2013 before the House 

Financial Services Subcommittee on Oversight and 

Investigations.

Measuring Our Performance

In fiscal year (FY) 2013, the Office established performance measures tied to its strategic goals. The 

measures are designed to track our performance 

in achieving goals, such as publishing research and 

acquiring datasets. These measures are shared annually with the public through the President’s Budget. 

Figure 68. OFR Hiring and Targets

Projected 

FY2015

Projected 

FY2014

FY2011 FY2012 FY2013

282

OFR Staff*

250

180

120

30

* Permanant, reimbursable, and detailed staff

Source: OFR
2013 OFR Annual Report 116 2013 OFR Annual Report

The Director also sets quarterly priorities for each 

group within the organization.

We are now developing a new strategic plan for FY 

2015-19. In this long-term strategic planning process, 

we are assessing the organization internally and externally to devise a plan that will more fully articulate the 

vision and mission of the Office, as well as its goals and 

strategies for the next four years. We anticipate releasing the new plan in early calendar year 2014.

Organization and Workforce Building

The Dodd-Frank Wall Street Reform and Consumer 

Protection Act, which established the Office, emphasized the use of efficient and innovative tools to attract 

and retain a talented workforce, including fellowships 

and internships. Using multiple recruitment methods, 

we have built a talented staff dedicated to strengthening the understanding of risks to financial stability. 

A critical priority for the Office as we work to achieve 

full staffing levels is recruiting the specialized and highly 

trained employees necessary to fulfill our mission.

We increased our staff from 30 in FY 2011 to 120 at 

the end of FY 2012 and more than 180 by the end of 

FY 2013. 

Our goal for FY 2014 is 250 employees. We are planning 

to reach a total of 282 employees in FY 2015, including 

permanent, reimbursable, and detailed staff members. 

The Office’s senior management team currently comprises the Director, a Deputy Director for each of the 

two centers (Data Center and Research and Analysis 

Center), and six Chiefs (Chief Data Officer, Chief 

Technology Officer, Chief of Analytical Strategy, Chief 

of External Affairs, Chief Operating Officer, and Chief 

Counsel). OFR Deputy Directors and Chiefs oversee 18 

sections led by 17 Associate Directors.

About half of our staff will ultimately be in the Data 

Center, split between its data and technology groups. 

The Research and Analysis Center will constitute 

approximately 22 percent of the staff. The Office of 

the Director and support functions (Counsel, External 

Affairs, and Operations) will make up the remaining 

27 percent. 

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 longterm 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.

Figure 69. OFR Planned Workforce Distribution

Ofce of the 

Director and 

Support Ofces

27%

Research 

and Analysis

22%

Data Center

Technology

23%

Data Center

Data

28%

Source: OFR

ENABLING LEGISLATION FOR THE 

OFFICE OF FINANCIAL RESEARCH
117 Appendixes

Figure 70. OFR Organizational Structure

Director

Deputy Director

Data 

Center

Chief 

Technology 

Ofcer 

Deputy Director

Research and 

Analysis Center 

AD

Data

Collection

AD

Data Strategy

and Standards

AD

Data

Services

AD

Data and

Enterprise

Analysis

AD

Application 

Development/

Decision Support

AD

Information

Security

AD

Data 

Management/

Administration

AD

Technology 

Strategy

AD

Systems

 Engineering2

AD

Financial

Institutions 

and Markets

AD

Macroeconomics

and Finance

AD

Current 

Analysis

AD

Policy 

Studies

AD

Statistics 

and Risk 

Management

AD

Program 

Management

Management

Support2

AD

Strategy

Governance and

Performance

Chief 

Data 

Ofcer 

Chief 

 of Analytical 

Strategy

Chief 

Operating 

Ofcer 

Chief of Staff and 

Office of the Director

Chief 

Counsel1

Note: AD = Associate Director

1

 Reports to the Department of Treasury Office of General Counsel. 

2

 Includes services through the Department of Treasury Departmental Offices.

Office of Financial Research Organization Chart

AD

Industry and 

Academic 

Relations

AD

Communications

AD

Government

Affairs 

Chief 

 of External 

Affairs

Source: OFR
2013 OFR Annual Report 118 2013 OFR Annual Report

Facilities

Most OFR employees are based in our headquarters in 

Washington, D.C. We also have a small office in New 

York City to support research and data-related interactions with the New York-based financial community, 

including regulators, data providers, academics, and 

financial market participants.

We will continue to have a small number of work 

arrangements with contributors outside Washington, 

D.C., and New York to support our research collaboration with academics. 

Information Technology Infrastructure

The Office needs systems for data acquisition, management, and dissemination under strict rules for security 

and data sharing. In addition, we are building a robust 

research and analytical environment for handling 

large amounts of data to support complex financial 

models, computations, and analysis. We are also building a secure platform for collaboration and communication with Financial Stability Oversight Council 

member agencies and outside researchers. 

During FY 2013, we expanded the short-term analytical environment (initially established in FY 2012) to 

meet data growth requirements and improve performance. We also put in place several basic components 

of the longer-term environment and completed the 

architectural activities, planning, and design for the 

remaining components. 

The Office has access to more than 50 distinct sources 

of data related to the economy and the financial system. In addition, for the data stored within the Office, 

we are building a long-term analytic environment that 

allows incremental capacity increases when needed. 

Over the next year, we expect to host several hundred 

terabytes of data. 

As an office in the Treasury Department, the Office 

benefits from the Treasury’s secure information technology infrastructure and follows Treasury’s security 

policies. We are expanding security controls as necessary for OFR-specific systems and data, and for sharing 

information among Council member agencies. 

ORGANIZATIONAL STRUCTURE AND 

RESPONSIBILITIES OF THE OFFICE OF 

FINANCIAL RESEARCH

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;

(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.
119 Appendixes

Finance

On May 21, 2012, Treasury issued a final rule for 

collecting assessments from bank holding companies 

with total consolidated assets of $50 billion or greater 

and an interim final rule for collecting assessments 

from nonbank financial companies supervised by the 

Federal Reserve Board. The initial assessment on July 

20, 2012, was based on a fee rate of about $7,700 per $1 

billion of assets held by the assessed companies. 

Treasury did not replenish the Financial Research 

Fund until September 2013 because FY 2012 expenditures were lower than projected. Treasury collected $35 

million on September 16, 2013. This assessment was 

based on a fee rate of about $2,021 per $1 billion of 

assets held by the assessed companies.

Our FY 2014 estimated budget is $86 million. Details 

are available in the FY 2014 budget and are provided 

annually in the President’s Budget.

B. OFR Outreach and Collaboration

The Dodd-Frank Wall Street Reform and Consumer 

Protection Act requires the Office to work closely with 

the Financial Stability Oversight Council, its member 

agencies, and a wide range of stakeholders, including 

industry representatives, academics, other researchers, 

and the public.

In addition, the Office is tasked with making data and 

information available to the public. To achieve these 

objectives, we expanded relationships and increased 

our outreach efforts in the past year. 

OFR Outreach 

The Director and employees from across the organization have participated in extending our outreach 

and improving communications with the public and 

other stakeholders. 

For example, the Office realigned the Division of 

External Affairs to coordinate engagement with 

external stakeholders and partners in government. 

The Office named a Chief of External Affairs, added 

staff members, defined roles for communications and 

Congressional affairs, and incorporated the outreach 

function for industry and academia. In addition, the 

Office improved its website, making it more userfriendly and expanding available content. About 4,000 

subscribers now receive notifications when the Office 

posts new Web content. 

Congressional Testimony

OFR Director Richard Berner testified in March 2013 

before the House Financial Services Subcommittee 

on Oversight and Investigations. Director Berner 

described the Office’s major accomplishments and key 

priorities, as well as the steps under way to enhance the 

transparency and accountability of the Office.

OFR Collaboration 

We are leveraging the expertise of our staff members 

through partnerships and collaborations in an array of 

initiatives to meet the mandate of fostering a network 

of outside researchers, academics, industry experts, and 

others. Our “virtual research-and-data community” also 

encompasses visiting scholars, fellows, and interns.

The OFR Research Seminar Series is an ongoing 

initiative to keep Office researchers and analysts 

engaged and up-to-date on the latest work in the field 

of financial research. 

Seminars are held frequently, usually one or more times 

per week. 

These events bring some of the brightest minds in 

the country to the OFR’s offices to discuss innovative 

research related to financial stability. OFR researchers and other staff members engage with the visiting 

experts in active discussions about the research and 

related topics.

OFR researchers and analysts also have the opportunity to ask for feedback on research initiatives they are 

working on or planning.

During 2013, the Office held more than 70 seminars 

through September. During 2012, the Office held more 

than 60 seminars.

RESEARCH SEMINAR SERIES BRINGS 

TOGETHER EXPERTISE
2013 OFR Annual Report 120 2013 OFR Annual Report

Following are the working papers the OFR published in 

2013, through December:

“Common Ground: The Need for a Universal Mortgage 

Loan Identifier,” OFR Working Paper no. 12 (December 

5, 2013), coauthored by Matthew McCormick and Lynn 

Calahan.

“Cryptography and the Economics of Supervisory 

Information: Balancing Transparency and 

Confidentiality,” OFR Working Paper no. 11 (September 

4, 2013), coauthored by Mark Flood, Jonathan Katz, 

Stephen Ong, and Adam Smith.

“Stress Tests to Promote Financial Stability: Assessing 

Progress and Looking to the Future,” OFR Working 

Paper no. 10 (July 18, 2013), coauthored by Rick 

Bookstaber, Jill Cetina, Greg Feldberg, Mark Flood, and 

Paul Glasserman.

“How Likely Is Contagion in Financial Networks?” OFR 

Working Paper no. 9 (June 21, 2013), coauthored by 

Paul Glasserman and H. Peyton Young.

“The History of Cyclical Macroprudential Policy in the 

United States,” OFR Working Paper no. 8 (May 15, 

2013), coauthored by Douglas J. Elliott, Greg Feldberg, 

and Andreas Lehnert.

“Stress Scenario Selection by Empirical Likelihood,” 

OFR Working Paper no. 7 (April 9, 2013), coauthored by 

Paul Glasserman, Chulmin Kang, and Wanmo Kang.

“Hedge Fund Contagion and Risk adjusted Returns: 

A Markov switching Dynamic Factor Approach,” OFR 

Working Paper no. 6 (March 13, 2013), coauthored by 

Ozgur (Ozzy) Akay, Zeynep Senyuz, and Emre Yoldas.

“Systematic Scenario Selection,” OFR Working Paper 

no. 5 (February 7, 2013), coauthored by Mark D. Flood 

and George G. Korenko.

“CoCos, Bail-In, and Tail Risk,” OFR Working Paper no. 

4 (January 23, 2013), coauthored by Nan Chen, Paul 

Glasserman, Behzad Nouri, and Markus Pelger.

OFFICE OF FINANCIAL RESEARCH 

WORKING PAPERS IN 2013

Financial Research Advisory Committee

In 2012, the Office established the Financial Research 

Advisory Committee, made up of 30 distinguished 

professionals in economics, data management, risk 

management, information technology, and other 

fields. The members include two Nobel laureates in 

economics, leaders in business and nonprofit fields, 

and prominent researchers at major universities and 

think tanks.

The committee first met in December 2012 in 

Washington, D.C. At the second committee meeting 

in New York City in August 2013, OFR Director Berner 

cited the Office’s five strategic priorities for the coming 

year and noted that the advisory committee’s three 

subcommittees had been meeting since December 

to develop substantive recommendations to help the 

Office fulfill its mandate. The full committee formally 

adopted the recommendations during the meeting and 

presented them to us.

The recommendations are on our website at: 

www.treasury.gov/initiatives/ofr/about/Pages/

Financial-Research-Advisory-Committee.aspx

Fellowship Program

The Dodd-Frank Act authorized the Office to establish a fellowship program, in consultation with the 

Chairperson of the Council, for outside experts from 

academia, industry, and elsewhere in government to 

spend up to two years at the OFR performing research 

and training employees.

The Office created the Fellowship Program in 2012 

to support our staff through research and training 

related to financial stability, risk management, financial data management, information technology, and 

information security. 

We began recruiting fellows in the fall of 2012 and will 

continue recruitment aligned with the academic calendar. 

In addition to our Fellowship Program, the Office uses the 

Treasury Departmental Offices’ Fellowship program.

We have emphasized high-level interactions with 

academics and practitioners in our efforts to build a 

virtual research community. As part of this initiative, 

the OFR Fellowship Program is attracting expertise to 
121 Appendixes

supplement the permanent workforce and keep us on 

the leading edge of research and data management.

Grants Program

In May 2013, the Office announced a partnership with 

the National Science Foundation (NSF) to promote 

and support novel research related to financial stability. We are collaborating with the NSF’s Directorate for 

Computer and Information Science and Engineering 

to fund grants for innovative research on approaches 

to computing and information processing to identify 

and analyze risks to the financial system. 

The joint OFR-NSF program is based on the NSF’s 

Early-concept Grant for Exploratory Research 

(EAGER) program. The program is one way we are 

fulfilling our mandate to conduct, coordinate, and 

sponsor research to support and improve regulation of 

financial entities and markets.

Topics include analysis of financial networks, methods 

for measuring threats to financial stability, standardization of financial data, interconnections in the financial 

system, financial risk management techniques, data 

security, secure data sharing, and other subjects.

The NSF awarded the first grant in the program in 

September 2013 for a multidisciplinary project to 

examine the impact of high-speed trading activity 

on the financial system. This research will also yield 

insights into working with extremely large financial 

datasets in a supercomputing environment. The 

researchers conducting the work are based at the 

University of Illinois at Urbana-Champaign and the 

San Diego Supercomputing Center.

For more about the program, see www.treasury.gov/

initiatives/ofr/research/Pages/GrantsProgram.aspx

Working Paper Series

The Office launched its Working Paper Series in 

January 2012 for staff researchers to collaborate 

with outside research experts, expanding our virtual 

research community and leveraging the expertise of 

our staff. These papers are intended to trigger a collaborative cycle of lively discussion among researchers and 

subsequent feedback to help fine tune our research to 

promote financial stability. 

Through December 2013, the Office published 12 

working papers — 9 papers in 2013 alone.

Participation in External Events

Office leaders and staff members participate in a wide 

range of events related to financial stability research, 

data, and analysis. During 2013, they spoke at industry 

and acedemic conferences, and participated in panels 

discussing specific aspects of data standards, financial 

analysis, systemic risk, and other topics. 

Conferences 

Each year, the Office and the Council jointly hold a 

conference in Washington, D.C., to bring 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. These conferences are also invaluable opportunities to receive broadbased input on the Office’s strategic priorities.

In December 2012, the Office and the Council 

co-hosted the second annual conference, “Assessing 

Financial Intermediation: Measurement and Analysis.” 

The event examined recent changes in financial institutions, assessed markets and activities, and explored 

future implications for financial stability, risk management, and policy.

The third annual conference is scheduled to be held 

in January 2014 in Washington, D.C. The agenda for 

the event and conference materials will be posted on 

our website.

In May 2013, the Federal Reserve Bank of Cleveland 

and the Office jointly sponsored “Financial Stability 

Analysis: Using the Tools, Finding the Data,” a conference focused on the data and tools needed for measuring potential threats to financial stability. 

In September 2013, the Federal Reserve Bank of New 

York and the OFR jointly sponsored the “Conference 

on Stable Funding,” a set of discussions assessing vulnerabilities in short-term funding markets and exploring ways to mitigate those vulnerabilities. 

All OFR speeches, Congressional testimony, press 

releases, and information about conferences and 

events are posted online at www.treasury.gov/ofr.

123 Glossary

Glossary

2a-7 Funds Rule 2a-7 of the Investment Company Act of 1940 sets requirements and investment rules 

for money market funds. It defines accounting practices that permit a fund to report a 

stable net asset value of $1 per share (see Money Market Fund).

Agency Mortgage-Backed 

Securities

A mortgage-backed security issued or guaranteed by federal agencies or governmentsponsored enterprises.

Agent-Based Models 

(ABM)

A simulation model that tracks the actions of agents with specified rules of behavior as 

they interact over time. 

Algorithmic Trading A computer trading strategy that uses mathematical models to determine the best time 

to buy blocks of shares without significantly affecting a stock’s price, or to capture gains 

based on calculations of share prices changes.

Alternative Trading 

Systems 

Private trading networks, including some characterized by low transparency and 

restricted market participant access. “Dark Pools” in equity trading are examples.

Arbitrage The simultaneous buying and selling of the same asset in different markets to make a 

profit from price differences 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 financial 

assets such as trade receivables, consumer debt receivables, auto and equipment loans 

or leases, or asset-backed securities.

Authorized Participant An entity, typically a broker-dealer, through which an exchange-traded fund acquires 

desired assets in exchange for equally valued shares of the fund. 

Automatic Stabilizer An economic instrument or policy tool that seeks to moderate economic fluctuations as a 

matter of course without direct intervention by policymakers. 

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 forum for the cooperation of bank supervisors that aims to improve 

banking supervision worldwide. The BCBS develops guidelines and supervisory standards, 

such as standards on capital adequacy, the core principles for effective banking 

supervision, and the Concordat on cross-border banking supervision. Following the 

financial crisis, the BCBS developed new global capital and liquidity standards for the 

banking system that are collectively referred to as Basel III.

Basis Point A financial instrument unit of change equal to 1/100th of 1 percent.

Broker-Dealer An entity that buys and sells securities for itself and others.

Call Report A report of a bank’s condition and income that all federally insured U.S. depository 

institutions must file on a quarterly basis.

Capital Requirement A requirement that a specified fraction of a financial institution’s funding come from 

equity rather than debt.
2013 OFR Annual Report 124

Captive Insurer An entity that provides insurance for its parent company.

Carry Trade An investment strategy involving borrowing at low interest rates to purchase assets that 

yield higher returns.

Central Clearing A settlement system in which securities or derivatives of a specific type are cleared by 

one entity, a clearing house or central counterparty, which guarantees the trades. It is an 

alternative to bilateral or over-the-counter trading (see Over-the-Counter).

Clearing Bank A commercial bank that facilitates payment and settlement of financial transactions, such 

as check clearing or matching trades between the sellers and buyers of securities and 

other financial instruments or contracts.

Closed Limit-Order Book The record of unexecuted limit orders for a security placed by traders. Only the keeper 

of the book has access to the details of all the orders; depending on local rules, certain 

privileged participants, such as market makers or specialists, may have access to the 

highest-priced bid and lowest-priced offer. 

Collateral Any asset pledged by a borrower to guarantee payment of a debt. 

Commercial Paper (CP) Short-term (maturity of up to 270 days), unsecured corporate debt.

Comprehensive Capital 

Analysis and Review 

(CCAR)

An annual exercise by the Federal Reserve to ensure that institutions have robust, 

forward-looking capital planning processes that account for their unique risks and 

sufficient capital to continue operations throughout times of economic and financial 

stress.

Conditional Value at Risk 

(CoVaR)

A measure of the value at risk of the financial system conditional on distress at a single 

financial institution, from Adrian and Brunnermeier (2010).

Contagion The process by which losses at one institution spread to other institutions through the 

financial system. 

Contingent Capital Notes 

(CoCos)

A type of security that converts into a specified number of shares upon occurrence of 

a specified event. For a contingent capital note issued by a bank, conversion may be 

contingent on its level of Tier 1 capital falling below a given threshold. 

Contingent Liability A liability that is only incurred depending on the outcome of a future event.

Convexity Event Risk Risk that an initial increase in long-term interest rates can be significantly amplified 

by many MBS investors actively hedging the duration of their MBS. Convexity events 

can result in rapid changes in long-term interest rates, sharp increases in interest rate 

volatility, and reduced liquidity in fixed income markets. 

Correlation Risk The risk that the value of two or more assets will move in tandem, increasing the volatility 

of a portfolio and potentially leading to large, simultaneous losses. Correlation risk is 

typically mitigated through hedging.

Countercyclical The movement of a financial or macroeconomic variable in the opposite direction of 

the business cycle. A variable is procyclical if it tends to increase when the economy is 

growing and decrease when it is shrinking.

Countercyclical Capital 

Buffer 

A policy requiring banks to build up capital buffers during favorable economic periods that 

can be used to absorb losses in unfavorable periods. 

Counterparty Exposure The vulnerability that a counterparty in a derivatives contract may default on its 

obligations.

Covenant-lite Loans Loans that do not include typical protections for lenders, such as requirements that the 

borrower deliver annual reports or restrictions on loan-to-value ratios.

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. 
Glossary 125

Credit Default Swap (CDS) A bilateral contract protecting against the risk of default by a borrower. The buyer of CDS 

protection makes periodic payments to the seller and in return will receive a payoff if the 

borrower defaults, similar to an insurance contract. The buyer does not need to own the 

loan covered by the swap. 

Credit Risk The risk that a borrower may default on its obligations.

Credit 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 Spread The difference in yield between a security and an otherwise similar security of higher 

quality.

Cyclical Risk Any financial or economic risk that is closely tied to the business cycle.

Cyclical Macroprudential 

Policy

A policy that seeks to moderate cyclical vulnerabilities, curbing excess leverage or funding 

risk in an upswing and easing constraints on leverage or funding in a crisis. 

Data Committee A committee within the Financial Stability Oversight Council whose mission is to “support 

FSOC coordination of, and consultation on, agency rulemakings on data collection, and 

seek to minimize duplication of data gathering operations.”

Data Gap A term which describes instances where purchased, collected, or derived data currently 

collected by financial regulatory agencies is insufficient to perform necessary research or 

mandated regulatory monitoring.

Debt-to-Income Ratio For a borrower, the ratio of debt payments to income. 

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.

Discount Window The Federal Reserve facility for extending credit directly to eligible depository institutions.

Distressed Insurance 

Premium (DIP)

An indicator of a firm’s vulnerability to systemic instability. DIP uses information from 

firms’ credit default swap spreads and equity prices to measure the implied cost of 

insuring a given firm against broader financial distress. 

Duration The sensitivity of the prices of bonds and other fixed-income securities to changes in the 

level of interest rates.

Eurozone An economic region comprised of all 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.

Fair Value Models Models for determining the value of an asset based on the price at which the asset could 

be bought or sold between two willing parties.

Federal Financial 

Institutions Examination 

Council (FFIEC)

An interagency body that prescribes uniform principles, standards, and report forms 

for the federal examination of financial institutions. The FFIEC makes recommendations 

to promote uniformity in 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.

Feedback A pattern of behavior in which an outcome in one sector or area of the economy is 

potentially magnified or reduced beyond its original scope due to the impact of the 

outcome on another area of the economy. 
2013 OFR Annual Report 126

Financial Intermediation Any financial service in which a third party or intermediary matches lenders and investors 

with entrepreneurs and other borrowers in need of capital. Often investors and borrowers 

do not have precisely matching needs, and the intermediary’s capital is put at risk to 

transform the credit risk and maturity of the liabilities to meet the needs of investors. 

Financial Research 

Advisory Committee

A committee providing advice and recommendations to the OFR as it carries out its duties 

and authorities under the Dodd-Frank Act.

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 G-20 nations. The FSB was established by the G-20 in 

2009 and is the successor to the earlier Financial Stability Forum.

Financial Stability 

Oversight Council

Council established by the Dodd-Frank Act with the responsibility to identify potential 

risks to the financial stability of the United States, promote market discipline, and 

respond to emerging threats to financial stability. The Council has ten voting members: 

the Secretary of the Treasury, who chairs the Council, Chairman of the Federal Reserve, 

Comptroller of the Currency, Director of the Consumer Financial Protection Bureau, 

Chairperson of the U.S. Securities and Exchange Commission, Chairperson of the 

Federal Deposit Insurance Corporation, Chairperson of the Commodity Futures Trading 

Commission, Director of the Federal Housing Finance Agency, Chairman of the National 

Credit Union Administration Board, and an independent member with insurance expertise 

appointed by the President. There are also five non-voting members: the Director of the 

Office of Financial Research (part of the Treasury Department and established by the 

Dodd-Frank Act), the Director of the Federal Insurance Office (also part of the Treasury 

Department), a state insurance commissioner, a state banking supervisor, and a state 

securities commissioner.

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.

Fiscal Risk Risk stemming from deviations in fiscal policy from expectations. 

Form FR Y-14A A set of semiannual balance sheet projections based on a range of potential 

macroeconomic scenarios that are submitted by bank holding companies with $50 

billion or more in consolidated assets. The Federal Reserve uses the data to assess 

capital adequacy as part of its Comprehensive Capital Analysis and Review program, and 

discloses summary results of stress testing.

Form FR Y-14Q A form that collects detailed data on bank holding companies’ various asset classes, 

capital components, and income on a quarterly basis, which the Federal Reserve uses to 

support supervisory stress testing models and monitoring.

Form FR Y-15 An annual report of systemic risk data submitted by bank holding companies with $50 

billion or more in consolidated assets to the Federal Reserve. Banks may request that 

their information be kept confidential. 

Form FR Y-9C A quarterly financial statement submitted by bank holding companies with $500 million 

or more in consolidated assets. The form is the Federal Reserve’s primary tool to monitor 

financial conditions between on-site inspections. 

Form N-MFP A monthly disclosure of portfolio holdings submitted by money market funds to the SEC, 

which makes the information publicly available. SEC Rule 30b1-7 establishes the technical 

and legal details of N-MFP filings. 

Form PF (Private Funds) A periodic report of portfolio holdings, leverage, and risk management submitted by 

hedge funds, private equity funds, and related entities. The reports are filed with the SEC 

and CFTC, which keep the information confidential. The Dodd-Frank Act mandated the 

reporting to help the Financial Stability Oversight Council monitor risks. Depending on 

fund size, reporting is annual or quarterly.
Glossary 127

Form X-17A-5 (“FOCUS 

reports”)

A quarterly or monthly financial condition report submitted by broker-dealers to the SEC. 

Funding Liquidity The availability of credit to finance the purchase of financial assets. 

Funding Run A situation in which a financial institution faces heavy redemptions and is forced to 

liquidate positions. A funding run can involve feedback effects, such as asset fire sales. 

Futures Standardized, exchange-traded contracts to buy or sell an underlying asset at a specified 

date and price. 

General Collateral Finance 

(GCF) 

An interdealer repo market in which the Fixed Income Clearing Corporation plays the role 

of intraday central counterparty. Trades are netted at the end of each day and settled at 

the triparty clearing banks (see Triparty Repo).

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 loans, 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.

Hedge Fund A pooled investment vehicle that is available to accredited investors and can charge a 

performance fee on unrealized gains, borrow more than one half of its net asset value, 

and sell assets short and trade complex derivative instruments that cannot be traded by 

mutual funds.

Hidden Markov Chain A mathematical model for the behavior of a system that is not fully observable. For 

financial markets, this defines a form of latent structure with a fixed set of possible 

economic “states” at any given time. The probability of switching states at a particular 

time depends only on the current state and not, for example, on historical trends.

High-Frequency Trading A trading strategy that uses technology to execute market orders in fractions of seconds.

Home Mortgage Disclosure 

Act (HMDA)

A law requiring lending institutions to report mortgage loan data.

Impaired Trading Liquidity The inability to trade in large size without having a significant impact on market price.

Implied Volatility When pricing an option, the volatility required as an input to the option pricing model to 

yield the current market price.

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).

Institutional Cash Pools Large, short-term cash balances of nonfinancial corporations and institutional investors, 

such as asset managers, securities lenders, and pension funds.

International Association 

of Insurance Supervisors 

(IAIS)

An international organization of insurance regulators and supervisors that aims to promote 

effective and globally consistent supervision of the insurance industry and contribute to 

financial stability. 

International Monetary 

Fund (IMF)

An 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 practices. 
2013 OFR Annual Report 128

International Swaps and 

Derivatives Association 

(ISDA)

A trade association of over-the-counter derivatives participants. The ISDA Master 

Agreement, used by many derivatives dealers and counterparties, standardized derivative 

terms to simplify netting and reduce legal risks.

Legal Entity Identifier (LEI) A unique 20-digit alphanumeric code assigned to each legal entity within a company that 

participates in global financial markets. 

Leverage The use of borrowed money to finance investments or conduct financial activities. 

Leveraged Loans Loans extended to a borrower who already has significant amounts of debt or whose debt 

is not rated investment-grade by credit rating agencies.

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 

liquid assets to meet its anticipated liquidity needs for a 30-day horizon under a liquidity 

stress scenario specified by supervisors. 

Liquidity Intermediation The use of short-term, liquid deposits to make long-term, illiquid loans.

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 global benchmark.

Macroeconomic Risk Risk from changes in the economy or macroeconomic policy.

Macroprudential 

Supervision

Supervision to promote the stability of the financial system as a whole (see 

Microprudential Supervision).

Margin Call A requirement by a broker that a borrower increase the collateral pledged against a loan 

in response to changes in the collateral’s value.

Margin Requirement Rules governing the necessary collateral for a derivative, loan, or related security required 

to cover, in whole or in part, the credit risk one party poses to another. 

Market Depth The ability of a market to absorb excess demand to buy or sell a security without affecting 

the price quoted for subsequent trades. 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.

Market Participant Symbol A unique character-combination that represents an entity listed on an exchange, such as a 

stock’s ticker symbol.

Market Risk The risk that an asset’s value will change due to unanticipated movements in market 

prices.

Maturity Risk The risk that a bond’s value will decrease before it matures. The probability of price 

fluctuation increases over longer time horizons, so interest rates are typically increasing 

in maturity.

Mark to Market The process of revaluing a portfolio or security to reflect current or recent market prices.

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.
Glossary 129

Microprudential 

Supervision

Supervision of the activities of a bank, financial firm, or other components of a financial 

system (see Macroprudential Supervision).

Money Market Fund (MMF) A fund that typically invests in government securities, certificates of deposit, commercial 

paper, or other highly liquid and low-risk securities. Some MMFs are governed by the 

SEC’s Rule 2a-7.

Mortgage Real Estate 

Investment Trust (mREIT)

Investment vehicle that borrows short-term funds in repo markets and invests in real 

estate and real-estate securities.

Nonlinearity The characteristic of an economic relationship among two or more variables that is not a 

simple (linear) proportion. For example, a compound growth rate is a nonlinear function of 

time.

Net Asset Value (NAV) The price per share of a mutual fund or exchange-traded fund based on the value of the 

fund’s shares at the close of the previous day.

Operational Risk Risks occurring during the normal operation of a business, including, for example, failed 

internal processes.

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. Examples include 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 used by banks to hedge against fluctuations in official interest rates 

set by central banks. In an OIS, an agreed index of an overnight floating rate, such as the 

Federal Funds Rate, is exchanged for a fixed rate for a set period (see Interest Rate Swap). 

Over-the-Counter (OTC) Trading that does not involve an organized exchange. In over-the-counter markets, 

participants trade directly with each other, typically through voice or computer 

communication.

Payment-In-Kind (PIK) 

Bonds

A bond that compensates the holder with other bonds rather than cash.

Price Impact Measure The change in the market price of a security caused by a one-directional order flow (buy 

or sell) of a given size (see Market Depth).

Prime Broker Brokerage offering a range of services to hedge funds, including securities lending, 

financing, trade execution, and cash management. 

Procyclical See Countercyclical.

Provisioning The recording of a liability by a bank or other financial institution to cover expected losses 

on loans. 

Refinancing Risk The risk that a borrower will face liquidity problems if it is unable to rollover its existing 

debt.

Regulatory Arbitrage The practice of taking advantage of differences between regulatory regimes to avoid their 

costs or constraints.

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 and agrees to 

repurchase it at a certain date in the future at an agreed price. Banks often do this on an 

overnight basis as a form of liquidity that is essentially a collateralized loan.

Reverse Repurchase 

Agreement

A term for a repo from the perspective of the counterparty that is lending the cash (see 

Repurchase Agreement).
2013 OFR Annual Report 130

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, legal, 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.

Risk-Weighted Assets Banks are required to assign a risk weighting, or a degree of credit risk, to residential 

mortgages, commercial real estate, derivative contracts, cash, and other assets to help 

supervisors calculate a minimum capital requirement. For example, a Treasury bond 

unconditionally guaranteed by the U.S. government has a zero risk weighting. 

Run Risk The risk that investors lose confidence in an institution — due to concerns about 

counterparties, collateral, solvency, or related issues — and respond by pulling back their 

funding.

Scenario Analysis An analysis of performance under a range of possible market situations and different 

interest rates. An example might include analyzing a bank’s capital ratio by simulating a 

range of adverse shocks to the value of its assets.

Secure Multiparty 

Computation

A body of computational tools to support information security by performing calculations 

jointly on data from multiple sources without ever sharing the various source datasets.

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 Credit intermediation done outside of the insured depository system, involving leverage, 

maturity transformation, and the creation of money-like liabilities. 

Shared National Credit 

Program

An interagency review of large syndicated bank loans in the United States conducted by 

the bank regulators.

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. 

Statistical Data Privacy A body of computational tools for assessing with known precision whether releases of 

statistical summaries are likely to reveal private data about individuals comprised by the 

summary.

Stress Test A modeling exercise where asset prices are shocked a pre-specified amount, sometimes 

along with other financial and economic variables, to observe the effect on financial 

institutions or markets.

Structural Macroprudential 

Policy (or Through-theCycle)

A policy that seeks to address potential structural vulnerabilities of the financial sector 

(see Cyclical Macroprudential Policy). 

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.

Swap An exchange of cash flows agreed by two parties with defined terms and over a fixed period.
Glossary 131

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.

Syndicated Loan A loan, usually provided to a single borrower, by a group of lenders.

Systemic Expected 

Shortfall (SES)

A systemic risk indicator that estimates the extent to which the market value equity of a 

financial firm would be depleted by a decline in equity prices. 

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 

Ratio

Two measures of banking capital adequacy defined in the Basel international banking 

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. 

Treasury-Eurodollar (TED) 

Spread

The spread between the three-month U.S. Treasury rate and the three-month LIBOR rate.

Triparty Repo A repurchase agreement in which a third party agent, such as a clearing bank, acts as 

an intermediary for the exchange of cash and collateral between the two counterparties. 

In addition to providing operational services to participants, the triparty agents in the 

U.S. triparty repo market extend large amounts of intraday credit to facilitate the daily 

settlement of triparty repos.

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) A tool measuring the risk of portfolio losses. The VaR projects the probability and 

maximum expected loss for a specific time period. 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.

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141 Endnotes

Endnotes

CHAPTER 2

1 For instance, in their monitoring framework, Adrian, Covitz, and Liang (2013) evaluate vulnerabilities affecting 

large complex financial institutions, shadow banks, asset markets, and nonfinancial institutions. The International 

Monetary Fund’s vulnerability exercise is based on an assessment of risks to fiscal, financial, external, and real 

sectors (in the case of emerging markets), although it also includes elements of contagion and asset valuations in 

assessing risks to advanced economies.

2 See, for instance, Acharya and others (2010), Adrian, Covitz, and Liang (2013), Aspachs and others (2006), 

Blancher and others (2013), Dattels and others (2010), FSOC (2011, 2012a, 2013), Haldane, Hall, and Pezzini 

(2007), OFR (2012), and Sarlin and Peltonen (2011).

3 Granted, some vulnerabilities may be both cyclical and structural. For instance, diminished trading liquidity may be 

driven by structural shifts in the market microstructure as well as by cyclical supply/demand forces.

4 A forthcoming working paper will provide further details on the selection criteria, methodology, and performance.

5 Adrian and Shin (2010), Begalle and others (2013), Brunnermeier and Pedersen (2009), Copeland, Martin, and

Walker (2011), and Gorton and Metrick (2012).

6 Since the financial crisis, a larger share of collateral backing triparty repos has shifted from riskier private sector 

assets to government-related securities. Securities dealers have also diversified funding sources and lengthened 

the maturity of their repo books, and as a result in aggregate now finance only 9 percent of their liabilities with 

repo, down from more than 40 percent prior to the crisis. The market has also become less concentrated, with 

the top three dealers accounting for about 30 percent of average daily volume, down from 50 percent before the 

crisis, which reduces the difficulty for the financial system of absorbing a potential counterparty default. Although a 

substantial amount of repos is still financed overnight, the maturity of repos has lengthened.

7 Much of the mortgage REIT repo funding is concentrated in 30-day tenors, which is relatively short-term compared 

to the duration of their assets. 

8 Based on data from Bloomberg L.P. and the Federal Reserve.

9 Mortgage REITs hedge some of their interest rate risk with derivatives, such as swaps and options, but these 

hedges may be insufficient to protect their portfolios from large losses if interest rates rise. In addition, they may 

lose access to funding if their creditors suspect they are insufficiently hedged and facing large losses.

10 The Federal Reserve has proposed a draft rule that would require foreign banks with a significant U.S. presence to 

consolidate most of their U.S. operations into an intermediate holding company in an effort to reduce potential arbitrage of enhanced U.S. prudential capital and liquidity requirements (see Board of Governors, 2012c). However, 

the draft rule would not require consolidation of foreign banking offices’ state supervised branches and agencies, 

which could result in shifting risks across subsidiaries of bank holding companies. 

11 This assumes a recovery rate of 60 percent of cov-lite loans and 70 percent on other leveraged loans and the 

same default rate to cover the additional expected loss associated with a cov-lite loan in a normal credit cycle. See 

Richmond (2013). 

12 Duration estimates are derived from the Barclays Capital Aggregate Bond Index, which is a market capitalization-weighted index of Treasury securities, agency bonds, mortgage-backed bonds, corporate bonds, and a small 

amount of foreign bonds traded in the United States. Asset managers, index mangers, and others often seek to 

manage their portfolios against the Barclays index. 

13 Our dependent variable is the term premium estimated by Kim and Wright (2005). Macroeconomic factors include 

the unemployment gap (the difference between the seasonally-adjusted unemployment rate and the natural rate 

of unemployment), core consumer price inflation, and inflation uncertainty. Financial variables include a polynomial 

function of the MOVE index of option-implied interest rate volatility, the VIX index of option-implied equity volatility, 

the supply of Treasury securities held by the Federal Reserve’s System Open Market Account portfolio as a share 

of GDP, and the supply of publicly held marketable Treasury securities, excluding Federal Reserve holdings, as a 

share of GDP.

14 Asset purchases affect the term premium in several ways. Large-scale asset purchases decrease the supply of 

publicly traded Treasury securities, suppressing yields (Gagnon and others, 2010). In a low-rate environment, 
2013 OFR Annual Report 142

investors that reach for yield need less compensation for taking duration risk, and an increase in private buying of 

longer-term assets further suppresses the term premium. Other factors complicate this relationship. For instance, 

the central bank’s decision to increase or decrease asset purchases is endogenous to other factors known to affect the term premium, such as macroeconomic conditions and market volatility. As a result, the ultimate drivers of 

the term premium are more difficult to understand and to predict as monetary policy accommodation is removed.

15 Funding runs, described in Brunnermeier and Pedersen (2009), occur when asset managers of leveraged funds are 

forced to sell assets, leading to sharp and sudden drops in asset prices, and in turn, further rounds of forced sales. 

Because assets are used as collateral for increased leverage, the risk-bearing capacity of the financial system can be 

severely diminished. Geanakoplos (2009) and Fostel and Geanakoplos (2008) show how leverage cycles can cause 

contagion. Adrian and Shin (2010) provide a theoretical analysis of the link between leverage and volatility. 

16 Equity volatility is used as a proxy for broad-scale risk. 

17 Market commentary and various trading platform data sources suggest that lean inventories and tight non-government repo financing have at least been partly responsible for the migration of broker-dealers toward larger, more 

frequently-traded issues. 

18 The May-June 2013 sell-off in bond markets provided an informative test of how ETF market participants react to 

market stress. During the episode, fixed-income ETFs were particularly popular for investors, as reflected in higher 

volumes in these ETFs relative to the underlying assets. Many ETFs, particularly high-yield corporate bond ETFs, 

began trading at significant and volatile premiums and discounts to their net asset value (NAV) and in some cases 

authorized participants temporarily ceased transmitting redemption orders to various ETFs or opted to redeem 

shares only in-kind (rather than make available a cash redemption option). While most market participants were not 

concerned by these events, questions remain going forward as to how market participants in the ETF ecosystem 

will react in future periods of market stress, especially as dealer inventories of fixed-income securities decline and 

ETFs continue to expand.

19 IMF (2013d) shows that external factors (such as lower global risk and stimulative U.S. monetary policy) explain 

nearly all of the compression in emerging market bond spreads.

20 Global asset prices are increasingly being driven by a single factor reflecting overall risks rather than multiple, idiosyncractic factors.

21 After the downgrade of the U.S. sovereign credit rating by S&P in 2011, many institutional investors adjusted their 

mandates to avoid automatic forced sales of Treasuries in the event of an additional downgrade. Moreover, based 

on historical downgrades of highly-rated sovereign credits, there generally has not been much sustained impact 

on yields in the run-up to, or following, a credit rating downgrade. The impact has been more pronounced and 

sustained when a downgrade is accompanied by a broader economic or financial crisis and following multinotch 

downgrades (IMF, 2011).

22 There is no cross-default provision for Treasury securities on Fedwire, the settlement funds transfer system run by 

the Federal Reserve, so only Treasury securities with a missed or delayed payment would be considered ineligible 

collateral. Other Treasury securities would remain unimpaired and accepted as collateral. However, repo rates, 

haircuts, and counterparty limits could still be affected on unimpaired Treasury securities. 

23 The International Swaps and Derivatives Association permits a three-day grace period on missing a payment before 

being considered in default. In theory, a short-lived technical default might not necessarily trigger CDS contracts.

CHAPTER 3

1 A study by Fillat and Montoriol-Garriga (2010) finds that had U.S. banks set aside larger general provisions when 

the economy was strong, akin to Spanish banks, they could have better absorbed loan losses during the U.S. 

financial crisis and reduced by half the amount of Troubled Asset Relief Program funds required. However, these 

higher provisions would have been depleted by 2009 for the U.S. banking system in aggregate, highlighting the 

idea that a fixed capital buffer requirement is still needed to mitigate unexpected credit shocks.

2 Change in capital ratio implemented over eight years. Reported numbers are unweighted median values across models from Table 1 in Macroeconomic Assessment Group (2010) and are changes relative to baseline after 35 quarters.

3 From Table 2, average of changes in loan rate per 100 basis points change in capital ratio.

4 From Table 10a.

5 Average over the United States, the euro area, and Japan, using data from 2004-06. The United States number is 

20.5 basis points.

6 The recent proposal by the Financial Stability Board to institute haircut floors for repos is a step in the direction of enhancing the nonbank macroprudential toolkit (FSB, 2013b and 2013c). However, these restrictions would apply only 

to repo transactions not centrally cleared, in which nonbanks (entities not subject to capital and liquidity regulation) 

receive financing from banks or broker-dealers (entities subject to such regulation) against collateral other than gov-
143 Endnotes

ernment securities. This situation leaves open the possibility of a buildup of risk in nonbank-to-nonbank repo transactions and also does not address the fact that, in some non-U.S. markets, no haircut is applied to repo transactions 

backed by government securities, even though these securities experience price volatility and may also have credit 

risk. It is relevant to monitor the quantity of nonbank-to-nonbank repo as well as whether the lack of a haircut floor 

for non-U.S. government debt increases reliance of financial institutions on this collateral. As discussed in Chapter 5, 

better data related to the U.S. repo market are needed to assess these and other potential risks.

7 Opportunities for risk weight optimization in the United States are somewhat limited by the Collins amendment in 

the Dodd-Frank Act, which does not allow large banks to use risk weights generated by internal models below 

those set by regulators for smaller institutions. However, because the Collins amendment does not apply overseas, 

foreign banks’ risk weight optimization could still undercut U.S. efforts to activate the countercyclical capital buffer 

requirement by failing to slow foreign credit provision to the United States. 

CHAPTER 4

 1 The working papers are available on the Working Paper Series page under Research on the OFR website at www.

treasury.gov/OFR.

2 The code can be found on the Working Paper Series page under Research on the OFR website at www.treasury.

gov/OFR.

3 The Financial Stability Board defines shadow banking as any “credit intermediation involving entities and activities 

(fully or partially) outside the regular banking system” (see FSB, 2013b).

4 This represents an upper estimate, since broker-dealers in bank holding companies have the ability to move some 

assets into their banks and obtain discount window financing.

5 The analysis covers categories SIC 0 through SIC 8. The category for SIC 9 is too small and miscellaneous to produce meaningful results. Liquidity measurement for the corporate bond markets is often complicated by the fact 

that individual bonds typically trade infrequently. This measurement challenge has become more significant since 

the crisis (see IMF, 2012). In this context, it helps to aggregate the individual bonds into broader indexes.

CHAPTER 5

1 These datasets are generally collected following Paperwork Reduction Act requirements, and the fact that they 

exist is publicly known.

2 More than 4,700 individual insurance legal entities file quarterly and annual public financial statements with the 

National Association of Insurance Commissioners. Each electronic filing may contain over 235,000 data elements.

3 The NAIC Accreditation Program seeks to establish and maintain standards to promote sound insurance company 

financial solvency regulation.

4 With the exception of risk retention groups formed under captive laws, which are addressed in NAIC accreditation 

standards.

5 See FRASER, a website maintained by the Federal Reserve Bank of St. Louis, at http://fraser.stlouisfed.org/publication/?pid=39

6 The eurozone currently comprises Austria, Belgium, Cyprus, Estonia, Finland, France, Germany, Greece, Ireland, 

Italy, Luxembourg, Malta, the Netherlands, Portugal, Slovakia, Slovenia, and Spain. However, money market funds 

have exposure only to issuers in Austria, Belgium, France, Germany, Ireland, Italy, Luxembourg, the Netherlands, 

and Spain. Luxembourg is excluded from the analysis due to the lack of credit default swap data on its sovereign 

debt.

7 The assets of feeder funds, parallel funds, and dependent parallel managed accounts are included in determining 

whether a reporting fund exceeds the $500 million threshold.

8 The threshold is that the two numbers differ by more than 25 percent.

9 The ratio of Level 3 assets to the sum of Level 1, Level 2, Level 3, and cost-based assets. These values are reported as all zero for many funds and those funds are discarded from the analysis.

10 We deleted about 60 funds with reported net asset value less than $5 million.

11 Only four funds report leverage greater than 50. These funds were discarded from the analysis.

12 The leverage reported for each category is a weighted average of leverage across the funds in a given category, in 

which the weights are gross assets. Similarly, the proportion of hard-to-value assets is a weighted average using 

the same weights. The results were similar when net assets were used as weights.


OFFICE OF FINANCIAL RESEARCH

U.S. Department of the Treasury

www.treasury.gov/OFR