Deriving the
Economic Impactof Derivatives
Growth Through Risk Management

March 2014

Deriving the
Economic Impactof Derivatives
Growth Through Risk Management

Apanard (Penny) Prabha, Keith Savard,and Heather Wickramarachi
Research Support
Stephen Lin, Donald Markwardt, and Nan Zhang
Project Directors
Ross DeVol and Perry Wong

March 2014

ACKNOWLEDGMENTS
This project evolved from discussions with various stakeholders of the Milken Institute who encouraged us to examine
derivatives markets in the aftermath of the financial crisis and Great Recession. It was made possible through thesupport of the CME Group. The views expressed in the report, however, are solely those of the Milken Institute.
The Milken Institute would like to give special thanks to the research department of the CME Group for theirvaluable suggestions, feedback, and overall leadership throughout the research process. The authors appreciate theindispensable efforts of Milken Institute Managing Director Mindy Silverstein in securing the resources required toconvert a methodological outline into a final product. Additionally, we appreciate the encouragement provided byMilken Institute CEO Michael Klowden in this undertaking.
The Milken Institute Research team is grateful for the advice, counsel, and expertise provided by reviewers Richard
Sandor, chairman and CEO of Environmental Financial Products LLC; Ross Levine, Willis H. Booth Chair in Banking
and Finance at the Haas School of Business, University of California at Berkeley; and James Barth, the Lowder
Eminent Scholar in Finance at Auburn University. All are Senior Fellows of the Milken Institute. Richard Sandor isa true financial innovator known as the “father of financial futures.” Ross Levine is among the most highly regarded
researchers in the operations of financial systems and functioning of the economy. Jim Barth is an expert in financial
institutions and capital markets, both domestic and global, with special emphasis on regulatory issues. Our colleagues’
insights significantly improved the quality and clarity of this report.

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Contents
Executive Summary....................................................................................... 1
Key Findings....................................................................................................................... 1
An Empirical Foundation................................................................................................... 2
Derivatives: A Market Mainstay Amid Globalization........................................................ 4
Why Use Derivatives? ........................................................................................................ 5
Regulatory Reform and the Future................................................................................... 6

Introduction.................................................................................................... 9
1. History....................................................................................................... 13
1.1 Integration and Oversight in the Modern Era............................................................ 14

2. Global Growth and Recent Trends........................................................ 19
2.1 Volatility and Technology........................................................................................... 22
2.2 The Exchange-Traded Market................................................................................... 25
2.3 The OTC Market.......................................................................................................... 27
2.4 Different Environments, Different Strengths........................................................... 30

3. Risk, Price, and Cost................................................................................ 33
3.1 Managing Risk............................................................................................................ 33
3.2 Price Discovery.......................................................................................................... 40
3.3 Liquidity and Lower Costs......................................................................................... 42

4. Measuring Derivatives’ Impact Empirically........................................ 45
4.1 Bolstering Bank Lending .......................................................................................... 48
4.2 Augmenting the Value of Non-Financial Firms........................................................ 55
4.3 Overall Impact on the U.S. Economy......................................................................... 59
4.4 What Futures Do for the Economy............................................................................ 63

5. A New Framework................................................................................... 69
5.1 Clearing and Capital................................................................................................... 69
5.2 Costs Will Influence Use............................................................................................ 71
5.3 Extraterritoriality and Regulatory Arbitrage........................................................... 71

References and Appendixes........................................................................ 73
About the Authors...................................................................................... 104

Executive
SummaryThis study is being released at a time when the global economy is still struggling to heal itself, with thehelp of central banks, from a crisis that ravaged the financial system. Prior to the calamity that began
six years ago, derivatives use grew at an astounding rate as a broad array of businesses and investorssought to reap their benefits. During the period of the crisis and its aftermath, over-the-counter (OTC)
derivatives—credit derivatives in particular—were severely criticized as a factor in raising counterparty
risk and contributing to a near-shutdown of the financial system. Consequently, significant regulatorychange has been instituted with the aim of increasing transparency and reducing systemic risk.
It is time for a fact-based assessment of the role derivative products play in commerce. If these financial
instruments are so harmful, as some parties believe, why do so many banks and non-financial firms use
them in the course of everyday business? What has been lost in the drama surrounding derivatives is anunderstanding of the positive impact, primarily through risk mitigation, that most of these instruments
have had on U.S. economic growth over the past decade.

KEY FINDINGS»» Banks’ use of derivatives, by permitting greater extension of credit to the private sector, increased U.S.
quarterly real GDP by about $2.7 billion each quarter from Q1 2003 to Q3 2012.

»» Derivatives use by non-financial firms increased U.S. quarterly real GDP by about $1 billion during
the same period by improving their ability to undertake capital investments.

»» Combined, derivatives expanded U.S. real GDP by about $3.7 billion each quarter. The total increasein economic activity was 1.1 percent ($149.5 billion) between 2003 and 2012.

»» By the end of 2012, employment had been boosted by 530,400 (0.6 percent) and industrial production
2.1 percent.
Derivatives’ role during the financial crisis and subsequent anemic recovery is also examined. Derivatives areshown to have an even larger positive impact relative to the pre-crisis period. This is not surprising giventhat firms use derivatives to minimize cash-flow volatility associated with underlying risk.
This first-of-its-kind study of derivatives’ quantitative impact on economic growth is built on a solid androbust methodological foundation, an extensive literature survey, and careful attention to data gatheringand empirical analysis. Several methodological challenges were overcome in attempting to discern theeffects of derivatives use on overall U.S. economic performance.

1Case studies of the airline, energy, and food-processing industries provide detail and context for themacroeconomic analysis. As reported in 10-K filings, many firms in these industries saved hundreds ofmillions of dollars in costs or enjoyed other net gains through derivatives use during the course of a fiscal
year. Their choice of instrument varies from forwards to futures and options as well as swaps. Companies that
use these derivatives report their impact on pricing, output, supply chains, and other factors.
Considering the scale of the derivatives market, while the notional value (the value of the contracts’
underlying assets) of OTC derivatives amounted to $633 trillion (nine times global GDP) at year-end 2012,
the gross market value (fair value or amount at risk) and gross credit exposure after netting (sum of positive
and negative fair values across counterparties) were orders of magnitude less. This is relevant in assessing
potential systemic risk under the new regulatory requirements and in making comparisons with traditional
exchange-traded derivatives, which are often mistakenly viewed as dwarfed by OTC instruments.

Lending Support€
Foreign Exchange%Interest Rates

Banks use derivatives contracts to hedge risk stemming primarily from the movements of interest rates and currency values. A stronger
financial position promotes a higher volume of lending, which spurs the growth of industries across the economy.

AN EMPIRICAL FOUNDATION
In order to fully appreciate the study’s findings, it is important to understand how they were derived.
The use of derivatives by banks and non-financial firms has an indirect impact on economic growth via
various channels. To encompass that overall impact, the analysis is divided into two steps.
The first is estimating the influence of bank use on lending and the effects of non-financial firm use on
firm value. The second step links those results to the macroeconomy. Although derivatives influence
the U.S. economy in other ways, our quantitative assessment focuses especially on credit extension andfirm value. Small and medium-sized enterprises rely on bank credit to expand capacity, and the nation’s
economic health depends on the health of that sector.

2

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESOur statistical analysis demonstrates that banks’ derivatives use allows for a larger volume of commercial
and industrial loans, holding other factors constant, increasing business investment. Additionally, it confirmsthat investors assign higher valuations to non-financial firms using derivative products, and those valuations
boost their willingness and ability to expand operations.
In estimating the broad macroeconomic effect, we used two alternate approaches. One is based on a puremeasure of statistical association which uses current and past values of variables in a system to determine
their relationships. A key advantage is that a limited number of variables are necessary to perform theestimation. The second approach uses a structural model of the economy. This provides a separate estimateof the resulting changes in real GDP growth and includes further detail on investment, industrial production,
employment, wages and incomes, and consumption, in addition to many other variables. Nevertheless,the approaches yield consistent results, warranting a high level of confidence.
This study examines the benefits of exchange-traded derivatives. The use of futures contracts has a positive
association in all statistical formulations, suggesting that they help banks and non-financial firms manage
risk, enabling banks to extend more loans and firms to invest more capital. Indeed, while the estimatedrelationships of the futures use variable do not pass conventional tests of significance, they make futures’
economic benefits clear to users, policymakers, and other stakeholders.

A Value PlayOur research shows that companies that use derivatives tend to have an edge in firm value over those that don’t. Further, this increasein firm value has a significant positive impact on overall economic growth.

EXECUTIVE SUMMARY

3

DERIVATIVES: A MARKET MAINSTAY AMID GLOBALIZATION
Derivatives are financial instruments in the form of contracts, the value of which is derived from the value of anunderlying asset. The trading of derivatives is done in two types of markets: organized exchanges and over thecounter. An important feature of derivatives exchanges is the interposition of a clearinghouse that serves asa counterparty to reduce the default risk of parties engaged in the contract. Derivatives traded through the
OTC market are privately negotiated and customized to the specifications of the counterparties involved.
Rudimentary exchange-based derivatives trading was recorded as early as the 1500s, but only recently have
we seen a rapid increase in the creation of such exchanges around the world. While the major derivatives
exchanges are located in mature economies (e.g., CME Group, Deutsche Borse AG, NYSE Euronext),
the development of the global marketplace indicates expanding opportunities and demand in emerging
economies. Based on turnover data (gross value of new deals during a given period), about two-thirds ofderivatives are traded on exchanges in advanced countries, while they are traded almost equally on OTC
and exchange markets in emerging economies.
Derivatives markets are populated by four main types of contracts: forwards, futures, options, and swaps.
The general concepts are similar, with their value derived from the price of an underlying asset. Differences include
some of the functions and features of the contracts as well as the markets where the various types of derivatives
are traded. The largest derivatives market by notional amounts is swaps and the second-largest is forwards.
In recent decades, volatility in stocks and interest rates, along with the globalization of capital markets,
has spurred demand for financial instruments to unbundle risks. From that perspective, interest rate
derivatives are the most widely traded among global OTC derivatives, accounting for 77 percent of notionalamounts outstanding in 2012. They first became popular in the late 1970s and early 1980s, when corporations
were grappling with wide fluctuations in rates. Foreign exchange derivatives are the second-largest category.
Rapid globalization has added exchange rate risk to the concerns of many businesses and investors.
One newer type of derivative used in risk mitigation is the credit default swap (CDS). This is a contractthat allows the buyer to hedge the credit risk of debt securities contingent upon a credit event that befalls
the issuer, such as a bankruptcy or payment default. Over the past 10 years, the CDS market grew at atremendous pace, reflecting in part strong housing sales and the expansion of mortgage-backed securities
prior to 2008. Although recognized as a useful hedging tool, credit default swaps have also come under adark cloud for their misuse.
The U.S. Financial Crisis Inquiry Commission (FCIC) identified OTC derivatives—in particular, CDSs—
as one of eight major factors that contributed to the financial crisis and subsequent Great Recession.
One principal factor identified by the FCIC was the leverage—use of borrowed funds for making
investments—that permeated the financial system. Many mistakenly assign disruptions caused byoverleverage to the “D-word,” derivatives.
The absence of regulatory oversight played a considerable role in the buildup of the one-way protection
writing position that AIG established in the CDS market, which was concentrated in insuring against default
of mortgage-backed securities. If a clearing regime (posting of collateral based on daily marking to market)

4

DERIVING THE ECONOMIC IMPACT OF DERIVATIVEShad been implemented, AIG’s losses would not have been permitted to accumulate, and what became afinancial crisis might have been much less damaging. In short, history might have been quite different.

A Lift for Airlines

1$

INCREASE
PER BARREL$MILLION

INCREASE IN OPERATING COSTSJET FUEL

Fuel is often a carrier’s largest operating expense, and a particularly volatile one as well. In 2011, United calculated that every $1 increase
per barrel added $95 million in costs. Airlines use derivatives on similar fuels to hedge against adverse price shifts, which may allow
them to increase their investment in physical capital, add flights, and prevent out-of-control costs from impacting consumers.

WHY USE DERIVATIVES?
Investors generally use derivatives for three purposes: risk management, price discovery, and reductionof transaction costs. In a traditional banking model, a maturity mismatch between assets and liabilities
subjects banks to interest rate risk. Derivatives mitigate this risk, which often contributes to capital
adequacy, profitability, and lowering the probability of bank failure. In addition, banks make markets
in derivatives to meet the risk management needs of financial and non-financial firm customers. In theprocess, they generate fees and other revenue from this trading as well as lower their cost of funding.
For non-financial firms, derivatives can assist in risk management associated with cash flow volatility
arising from adverse changes in interest rates, exchange rates and commodity and equity prices. The tax
code also offers incentives for hedging cash-flow volatility and income. A hedging strategy involving
derivatives might alleviate underinvestment caused by insufficient cash flow and risk aversion.
The information that can be extracted from derivatives, such as price discovery, is another important benefit.
In “complete” markets (when agents can buy insurance contracts to protect against any future state of theworld), trading on derivative exchanges should reveal no new information to market participants. However,the lack of completeness means that informed traders could prefer to own futures or options in lieu of the

EXECUTIVE SUMMARY

5

underlying assets, and this might reveal new information about price. Most studies done on developed nations
show that the futures market leads the spot market and therefore serves as the focal point for price discovery.
Another positive identified with derivatives is reduced transaction costs through narrower bid-ask
spreads. Spot markets with derivatives often have more liquidity and lower trading fees than markets
without. If investors want exposure to the S&P 500, for instance, but wish to avoid the expense ofpurchasing all the underlying securities, they can trade index options and futures for the same resultwith less cost.

REGULATORY REFORM AND THE FUTURE
Derivatives’ outlook is veiled by regulatory changes taking place in the United States and other major
markets. The Dodd-Frank Wall Street Reform and Consumer Protection Act (commonly known as DoddFrank) was signed into law in July 2010, five months before the release of the Financial Crisis Inquiry Report.
It focused on addressing the lack of transparency as well as the absence of capital and collateral requirements
in some derivatives markets, primarily OTC-related. Title VII of Dodd-Frank, also called the Wall Street
Transparency and Accountability Act, granted the Commodity Futures Trading Commission (CFTC)and the Securities and Exchange Commission (SEC) authority to regulate swap derivatives. The SEC wasdesignated to have specific authority over “security-based swaps.” Other titles under Dodd-Frank addressedbroader issues of interconnectedness among firms and concentrations of risk in derivatives markets.
Whether Dodd-Frank’s remedies will succeed remains for history to judge. Many of the measures linked
with changes to OTC derivatives and the requirement to use “swap execution facilities” have been put
in place only recently. Even if the adjustment is less than smooth, the past suggests that derivatives willcontinue to play a pivotal role in economic and financial activities. Their continued contribution toeconomic growth will depend on the markets becoming more transparent and liquid, enabling end users
to generate competitive returns while effectively hedging risk.

6

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESThe authors hope that the results of this study will
provide a more balanced view of these instruments
and further their use in ways that promote economic
expansion and contribute to overall financial stability.

Introduction
In undertaking this research, the authors were aware that financial derivatives have been analyzed anddiscussed ad nauseam in recent years. Ironically, derivatives have been frequently maligned by those who
confuse them with other financial instruments. This has not stopped derivatives from being erroneously
portrayed as the creations of Ph.D.-trained financial engineer types, used by Wall Street bankers to entice
investors seeking leverage as well as by businesses looking to enhance profits and/or reduce risk.
Derivatives, particularly over-the-counter (OTC) instruments, are blamed for any number of financial maladies,including the recent global financial crisis. They have been tagged with catchy phrases like “financial
weapons of mass destruction” by none other than Warren Buffett. Nevertheless, Buffett’s Berkshire
Hathaway has benefited substantially from the use of derivatives, by the company’s own admission.
This is not to say that there have been no problems with some types of derivatives. Most recently,
credit default swaps (CDS) have come under scrutiny, as the history chapter in this study shows. However,the fact that derivatives have been with us for many centuries is a testament to the contribution they havemade in promoting commerce and benefiting businesses of all forms and sizes. Direct benefits are manifest
in risk mitigation, price discovery, and liquidity enhancement. Derivatives assist in completing markets,1
providing firms and individuals with financial flexibility as well as investment opportunity. By reducing
risk they also reduce costs, which generally means that borrowing and lending (as illustrated in the homemortgage industry) can occur at lower expense than would otherwise be the case.
While the qualitative benefits of derivatives are well-known and taught in university finance programs,
a tremendous amount of deep-seated skepticism remains. This study breaks new ground by rigorously
quantifying, for the first time in a publicly available study, derivatives’ positive impact on economic growth.
Primarily, we focus on their role in facilitating credit availability through banks. We also look at the effects
derivatives have had on non-financial firms. That portion of the analysis offers broader insight into the useof various types of derivatives and their ability to enhance firm value.
Regarding the future, the transformed regulatory environment in the United States and other global
financial centers poses the question of whether the evolving product mix and costs of derivatives willallow them to step up their contribution to economic growth. The emphasis on increased transparency
and reduced counterparty risk, among other aims, should dampen fears of systemic risk that had been
associated with unsupervised OTC derivatives activity. The authors hope that, in some small way, the resultsof this study will provide a more balanced view of these instruments and further their use in ways that
promote economic expansion and contribute to overall financial stability.
1.

In economics, a complete market (or complete system of markets) is one in which the complete set of possible gambles on future states-of-the-world can beconstructed with existing assets without friction.

9

The paper proceeds as follows: Section 1 details the historical development and importance of derivatives
and derivatives markets. Here, the authors demonstrate how these products have promoted commerceand highlight their evolution as the global financial system has expanded in scale and complexity.
Section 2 assesses the growth of the global derivatives market and all types of derivatives contracts. We discuss
the varied features and distinctions among derivatives traded over the counter and on exchanges while
highlighting emerging trends in the expansion of exchanges worldwide. Since the topic has received somuch media attention, the analysis sheds light on public misunderstandings that stem from the varied
ways of measuring derivatives’ value, the roles of derivatives and structured products during the global
financial crisis, and the growth of these instruments in relation to other financial markets.
In Section 3, the authors look at the channels through which derivatives impact the real economy,
focusing on risk management, price discovery, and transaction costs. Risk management is of criticalimportance, with financial exposure stemming from adverse changes in interest rates, foreign exchange
rates, and commodity and equity prices. We address the way banks and non-financial firms mitigate risk
through exchange-traded and OTC derivatives and how this remedy theoretically influences firm value
and credit supply.
The main focus of this study lies in Section 4, where the authors empirically assess the impact of derivatives
use by banks and non-financial firms on credit supply and firm value,2 respectively, and how these effects
influence the real economy. Although other studies have assessed the impact on bank lending and firm
value, none has extended the analysis to the effect on economic growth.
This assessment is divided into four parts. Parts 1 and 2 quantitatively estimate the relationships between,on the one hand, banks’ derivatives use and credit extension, and, on the other, non-financial firms’
derivatives use and firm value. Part 3 investigates the impact of firm value and bank lending on economic
growth. Coupled with the estimates derived from Parts 1 and 2, it enables us to assess derivatives’ overall
impact on the U.S. economy. Part 4 extends the analysis to the use of futures contracts and economic growth.
Section 5 concludes our study with an evaluation of the future of exchange-traded and OTC derivatives.
Regulatory changes, along with accompanying administrative rules born of the financial crisis, will likely
have a significant impact on the market structure of derivatives in the coming years. The focus on
reducing systemic, and by extension counterparty, risk could result in a material migration from OTC
markets to exchanges. However, the speed and degree of this adjustment is still to be determined andwill depend inter alia on how rules concerning end users are interpreted and how they influence thecosts of the instruments. This section highlights these and other major trends likely to emerge and offers
perspective on how they might affect economic growth.

2.

10Firm value is defined in terms of the market-to-book value ratio.

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESBesides the rapid development of digitaltechnology and its use in finance, the growth in
OTC derivatives in the 1990s and early 2000s was
spurred by a lax, perhaps at times controversial,
regulatory environment.

1. History
The financial instruments we know as derivatives have played a vital role in economic activities dating backas far as Antiquity. From helping to secure the supply of commodities to facilitating trade and providing
hedges against a variety of risks, derivatives use has evolved along with changes in commerce, the forms offinancial assets, and their regulation. Trading activities and the platforms on which they take place also
have undergone significant change, but with new regulation, instead of market preferences, sometimes leading
to the transformation. In addition, technology has had a prominent role in the modern history of derivatives
trading, particularly in facilitating access to these markets for non-financial businesses.
The first known use of derivatives dates to 2000 B.C., when merchants, in what is now called the Kingdomof Bahrain, engaged in consignment transactions for goods destined for India. A few hundred years later,
the 48th law in the Code of Hammurabi established a contractual relation in the form of a put option.
Ancient Greece and Rome also played roles in the history of derivatives. According to the writings ofAristotle, around 580 B.C. a philosopher and mathematician named Thales purchased options on olivepresses and made a fortune from an unusually large harvest by leasing out the presses at a substantial
premium. In the Roman Empire for many years, laws did not recognize the transferability of contract rights
and obligations, impeding derivatives use. However, commercial realities forced change, resulting in theuse of contracts for future deliveries. Roman law would go so far as to enforce the intentions of contracting
parties, even if they were speculative (Swan, 2000).
Derivatives continued to facilitate merchandise and commodities trade during the Middle Ages. Much of
the activity at the height of the period took place in Italy, where merchants engaged in commerce spanning
the Mediterranean Sea and beyond. A form of commodity forward contracts—known as Commandas—
developed along with the bill of exchange. As commercial trade expanded, the exchange business grew.
Professional money changers emerged, along with the trading of these bills.
As this activity multiplied, centralized markets for goods sprang up. An early version was the periodic
fair, which was supervised by church institutions. Markets became specialized to respond to the tradingneeds of varied merchant groups. For their part, derivatives largely remained, in today’s terminology,“over the counter” but with the counters closely aligned with the individual markets. Periodic medieval
markets lost their importance in trade over time, leading to permanent trading places located at thejunction of port sites and land routes (Swan, 2000).
The exodus from the Middle Ages coincided with the spread of derivatives to other parts of Europe and as
far as Japan. Around 1600, forward and options contracts on commodities, shipments, and securities were13

traded in Amsterdam. This was followed a few decades later by forward contracting on tulip bulbs
during the infamous Tulip Mania. A standardized futures contract for rice could be found in Osaka, Japan,
around 1650, although it is not known whether the contracts were marked to market on a daily basis and/or had credit guarantees.
The early part of the 17th century saw the first recorded short-selling attack stemming from the abuse of
options. The attack was directed at the Dutch East India Co., which was created as a joint stock company
in the Netherlands in 1602. Participants in this early attack were successful, but subsequent similar efforts
were less so, resulting in a number of bankruptcies. One of the outcomes of this episode was a ban on
short selling in 1610, which apparently was not effectively enforced (Gelderblom and Jonker, 2009).
Although derivatives trading in a regulated exchange can be traced back to the 12th century, the first formal
exchange for trading derivatives was the Royal Exchange in London, founded in 1565. The rise of England
as a maritime power is one factor cited as supporting the development of derivatives trade in the country.
In addition, English law recognized the transferability and negotiability of bills of exchange. Settlement wasalso facilitated through contracts for difference, in which a losing party could compensate the winning
party for the difference between the delivery price and the spot price at the termination of the agreement.
The trading of derivatives in 18th century England also brought us the term “bubble.” When the South Sea
joint stock company was established in 1711, its exclusive trade with Spain’s South American colonies was
widely expected to generate enormous profits. This led to the formation of ancillary companies called bubbles.
In 1720, the English Parliament passed the Bubble Act, prohibiting all joint stock companies not authorized
by royal charter. The law triggered a wave of turmoil in financial markets, resulting in a crash (Swan, 2000).
According to a subsequent investigation, the breakdown was attributed to those who dealt in options—mainly call options known as “refusals.” The legal consequences were that, after long debate, Parliament passed
the Sir John Barnard’s Act, which banned options in shares as well as short selling (Weber, 2008).

1.1 INTEGRATION AND OVERSIGHT IN THE MODERN ERA
An ocean away and more than a century later, the first formal commodities exchange was established in
the United States in 1848. In addition to tackling the problem of credit risk, the Chicago Board of Trade(CBOT) provided a centralized location to negotiate forward contracts. Under its aegis, the first exchangetraded derivatives contracts were listed in 1865, and in 1925 the first futures clearinghouse formed. In 2007,
the CBOT merged with the Chicago Mercantile Exchange to become the CME Group.
The recent history of derivatives is characterized by their broad integration into commerce and finance,
as evidenced by the creation of new products along with the platforms and tools for delivering them.
Besides the rapid development of digital technology and its use in finance, the growth in OTC derivatives
in the 1990s and early 2000s was spurred by a lax, perhaps at times controversial, regulatory environment.
Derivatives also benefited from advances in finance at the academic level. In 1973, for instance, Fischer Blackand Myron Scholes put forth their Nobel Prize-winning option pricing formula.

14

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESThe final decades of the 20th century witnessed new types of derivatives trading on currencies, bond andinterest rate futures, as well as options on share indexes. The Chicago Mercantile Exchange created one of
the first futures contracts that were not on physical commodities. The first currency futures were launched
in 1970 at the International Commercial Exchange in New York while the Bretton Woods system was stillin effect. Five years later, the interest rate futures contract based on Ginnie Mae mortgages was traded forthe first time on the CBOT (Girish, 2010).
This was followed in 1977 by the U.S. Treasury bond futures contract, which became the highest-volume
contract at the time. The flurry of activity continued with the creation of the Chicago Mercantile Exchange’s
Eurodollar contract in 1982 and the first stock index futures contract by the Kansas City Board of Trade.
The Chicago Mercantile Exchange quickly followed with its highly successful contract on the S&P 500 index.
While most derivatives were traded on exchanges up until this period, the 1980s marked the beginningof the era of swaps and other OTC derivatives. With a new generation of corporate financial managers
well‑versed in the use of derivatives, these instruments became essential for hedging, and in some cases
speculating on, interest rate, exchange rate, and commodity risks. As corporations’ financial risks became
more complex, so did the derivatives to deal with them. By 1991, the notional amount of OTC derivatives
trading had surpassed that of exchange-traded derivatives (Whaley, 2006).
The rapid growth in OTC derivatives was fueled in part by the emergence of credit derivatives in
the mid-1990s. The first CDSs were created by the J.P. Morgan investment bank (now JPMorgan Chase),
which led the industry away from relationship banking toward credit trading. By engaging in suchactivities, bankers and others were striving for higher returns while shedding buy-and-hold risk.
Despite all the positives associated with derivatives in the 1990s, a number of high-profile incidents
raised concerns among some regulators and others. In 1994, firms with deep financial experience suchas Procter & Gamble and Metallgesellschaft suffered large losses on derivatives trading—primarily using
swaps. Orange County, Calif., one of the wealthiest counties in the United States, declared bankruptcy,
allegedly due to derivatives trading involving leveraged repurchase agreements. The following year, theUnited Kingdom’s Barings Bank declared bankruptcy because of speculation on futures by a rogue trader
in its Singapore office (Chance, 1995).
These incidents did lead to minor changes in the way derivatives were sold, but for the most part firms
were responsible for tightening controls internally. Following the 1998 collapse of Long-Term CapitalManagement (LTCM), a giant hedge fund, the Report of the President’s Working Group on Financial
Markets recommended that the Securities and Exchange Commission (SEC), Commodity Futures
Trading Commission (CFTC), and U.S. Treasury receive expanded authority. This would have required
counterparties in OTC derivatives transactions to provide credit risk information and keep records on
concentrations, trading strategies, and risk models. Federal Reserve Chairman Alan Greenspan declined
to endorse these proposals but deferred to those regulators with supervisory authority, who took no
discernible action.

HISTORY

15

On the heels of these events, 12 international banks formed the Counterparty Risk Management PolicyGroup (CRMPG) to examine the practices that brought about the LTCM crisis. Recommendations for
self-regulatory practices were put forth to prevent a recurrence. There was mention of detailing certain
large exposure information on a consolidated group basis. However, the major thrust of the CRMPG
report was to oppose new regulation.
In late 2000, Congress passed and President Bill Clinton signed the Commodity Futures Modernization
Act (CFMA), satisfying calls for deregulation amid market volatility. The law removed OTC derivatives
transactions, including those related to energy, from all requirements of exchange trading and clearing
under the Commodity Exchange Act (CEA) so long as counterparties to swaps were “eligible contract
participants.” Except for issues related to fraud, the SEC was barred from OTC derivatives oversight.
Moreover, the CFMA expressly preempted state gaming and anti-bucket shop laws, which would havebarred the otherwise unregulated speculative activity granted under the act. In effect, almost no law
applied to this market (Greenberger, 2010).
In the aftermath of the CFMA’s passage, derivatives growth skyrocketed. Although this boom was
generally viewed as positive in helping to mitigate risk and enhance commerce, regulators and swap
dealers themselves expressed reservations about operational shortcomings of OTC markets. In 2005,
Timothy Geithner, then president of the Federal Reserve Bank of New York, assembled representativesof the world’s 14 largest banks to discuss his concern about substantial backlogs in the documentationof credit derivatives. He requested that banks clear up 80 percent of the backlog within a year and asked
them to form a clearinghouse for complex derivatives contracts.
The CRMPG issued two reports, the first in July 2005, which aired clear concerns about the viability of thecredit derivatives market. In particular, that report highlighted problems of identifying CDS counterparties
because of poor documentation. The second report, released in July 2008, acknowledged difficulties with thecredit derivatives settlement process and urged “swift action to create a clearinghouse for OTC derivatives,
starting with CDS” (Greenberger, 2010).
For critics of OTC derivatives and credit derivatives in particular, the global financial crisis beginning in2008 was seen as validating their views while presenting an opportunity to fundamentally alter the operational
structure of derivatives markets through sweeping legislation. The facile labeling of these instruments asfinancial WMD added to the momentum for change. The final report of the National Commission on the
Causes of the Financial and Economic Crisis in the United States took a more balanced view of derivatives’role in the collapse. While acknowledging that OTC derivatives contributed “significantly to this crisis,”the report went on to point out that these instruments were but one of eight major factors involved.
The Dodd-Frank Wall Street Reform and Consumer Protection Act (commonly known as Dodd-Frank)—
which was signed into law in July 2010, five months before the release of the Financial Crisis Inquiry Report—
clearly reflected the overall negative political and public sentiment toward derivatives. Particular emphasis
was placed on dealing with a lack of transparency and a buildup of losses on trading positions, as well as theabsence of capital and collateral requirements in some derivatives markets, primarily OTC-related. Title VII
of Dodd-Frank, also called the Wall Street Transparency and Accountability Act, granted the CFTC and SEC

16

DERIVING THE ECONOMIC IMPACT OF DERIVATIVES

authority to regulate swap derivatives. The SEC was designated to have specific authority over “security-based
swaps.” Other titles under Dodd-Frank addressed broader issues of interconnectedness among firms andconcentrations of risk in derivatives markets.
Whether Dodd-Frank’s remedies, including a push toward cleared (listed) derivatives, will succeed remains
to be seen. Many of the measures linked with changes to OTC derivatives and the requirement to use“swap execution facilities” have been put in place only recently. Even if the adjustment is less than smooth,the past suggests that derivatives will continue to play a pivotal role in economic and financial activities.

HISTORY

17

Given the expansion of international trade andfinancial activities, participants are likely to face
increasing risks, and derivatives markets are
expected to contribute to economic developmentby making these risks manageable.

2. Global Growthand Recent Trends
Derivatives are financial instruments in the form of contracts between two parties to engage in a transaction
at a future time. The value of the contract is derived from the value of an underlying asset (e.g., equity, bond,or commodity) or market variable (e.g., interest rate, exchange rate, stock index, or credit risk). The notionalamounts of a derivative contract refer to the principal value of the underlying asset.
Derivatives are traded in two types of markets: organized exchanges and over-the-counter (OTC).
An important feature of derivatives exchanges is the interposition of a clearinghouse that serves as acounterparty to reduce the default risk of parties engaged in the contract. Exchange markets also tradestandardized financial derivatives (e.g., futures and options contracts). Derivatives traded through the
OTC market, on the other hand, are privately negotiated and customized to the specifications of thecounterparties involved. OTC derivatives are executed bilaterally, and in most cases with derivatives
dealers (such as global commercial and investment banks), who either find a counterparty or serve as acounterparty themselves. In contrast to past practice, Dodd-Frank requires with some exceptions thatOTC derivatives be cleared by a derivatives clearing organization (DCO), and that the transactions tradeon swap execution facilities (SEFs) or designated contract markets (DCMs) (see Part 5 for regulation of
OTC derivatives under Dodd-Frank). European regulators and some Asian nations are taking a similar
approach. However, it is unclear whether all of the G-20 will concur.
The four main types of derivatives contracts are forwards, futures, options, and swaps. The general concepts
are similar, with their value derived from the price of an underlying asset. Differences include some of thefunctions and features of the contracts as well as the markets where the various types of derivative are traded.
Forwards and futures contracts are agreements between two parties to engage in a financial transaction
at a specified price and quantity and at a future (forward) date. Forwards, however, are customized
through negotiation. Since such contracts are bilaterally agreed upon and settled, the participants are
exposed to counterparty risk.
A futures contract has several features designed to overcome this risk. Futures are traded on organizedexchanges and represent a standardized agreement to deliver or receive a specific amount of a financial
instrument at a specific price and date. Trading on organized exchanges enhances transparency as prices
and other trade-related information are publicly displayed, while a central clearinghouse reduces
counterparty credit risk. In part, this risk is mitigated because a clearinghouse requires contributions(in the form of collateral or margin) from their counterparties and collects mark-to-market collateralupon changes in the value of contracts. Most financial futures contracts in the United States are tradedthrough the CME Group.

19An option is a contract that grants owners the right but not the obligation to purchase (a call option) or sell(a put option) a financial instrument for a specific price within a defined time period. It functions by having
the purchaser/owner pay the seller/writer an option premium for the right to buy or sell. The purchaser’s
potential loss is limited to the price of the premium, curbing the downside. In contrast, the seller of an
option receives the premium in return for risk exposure. Options are traded on organized exchanges and
OTC derivatives markets, though standardized options are traded solely on organized exchanges.
A swap is a financial contract to exchange a set of payments one party owns for a set of payments owned byanother party. The type most commonly traded is the interest-rate swap, which has increased in importanceas financial institutions seek to manage interest-rate risk. Such transactions involve the exchange of one setof fixed-rate interest payments for a set of variable-rate interest payments. Similar to forwards, swaps aretraded on the OTC market and subject to default/counterparty risk. However, all swaps not subject toenumerated exceptions are now required under Dodd-Frank to be cleared by a DCO and executed on
an SEF. These reforms are aimed at reducing counterparty risk associated with bilaterally traded OTC
derivatives, which policymakers and many commentators identified as a factor that magnified the global
financial crisis in 2008 (see Box 1).

20

DERIVING THE ECONOMIC IMPACT OF DERIVATIVES

BOX

1

Credit derivatives, risk, and the 2007-2009 financial crisis

Financial derivatives are recognized as a useful toolfor hedging risk. However, the collapse of American
International Group Inc., followed by its $85-billion
bailout in September 2008, illustrates that the misuseof financial instruments, along with an absence ofregulatory oversight, can pose serious dangers for thefinancial system.

in the mainstream use every day to manage risk on the
one hand, and derivatives that enabled big players like
Lehman Brothers and American International Group Inc.
(AIG) to pursue excessive risk taking, on the other hand.”In the years leading up to the crisis, AIG had been activein the credit derivatives business, including trading(CDSs). The giant insurer’s CDS portfolio was written
on complex collateralized debt obligations (CDOs) with
underlying residential mortgage-backed securities(MBSs) whose asset pools included subprime loans. In
this regard, AIG offered insurance in the form of CDSs
to investors to protect against default or decline in MBS
values, in exchange for a fee or premium.

“The only company known to have failed because of
its CDS obligations was AIG (American International
Group), and that firm appears to have been an outlier.
Blaming CDS for the financial crisis because one
company did not manage its risks properly is likeblaming lending generally when a bank fails. Like
everything else, derivatives can be misused, but there
is no evidence that the ‘interconnections’ among financial
institutions alleged have caused the crisis were significantly
enhanced by CDS or derivatives generally.”

AIG’s financial problems emerged after the U.S.
residential market started to deteriorate in late 2006
and early 2007. Defaults by mortgage borrowers led tomassive write-downs in AIG’s portfolio. The downgradesof AIG’s long-term debt by the major credit rating
agencies, which triggered additional cash collateralobligations, led to a severe liquidity shortage at thecompany. Finally, the government stepped in andcommitted more than $180 billion to repair AIG’s liquidity
situation amid fears that an abrupt collapse would trigger
massive losses throughout the global financial system.
Accordingly, the U.S. Financial Crisis Inquiry Commission(FCIC) identified OTC derivatives as one of the eight
major factors that contributed to the crisis. Its Final
Report (2011, p. 50) notes that a “key OTC derivative inthe financial crisis was the credit default swap.”
Some scholars and critics presented dissenting viewson the role derivatives played as described by the
FCIC. For example, Peery (2012, p. 21) points out that“members of the Crisis Commission… could not tell thedifference between the derivatives that many companies

Regarding the distinction between the functions ofderivatives and their misuse, Peter Wallison of theAmerican Enterprise Institute (2011, pp. 5-6) argued:

Nonetheless, the fact that AIG was not required to put aside
adequate capital reserves and could take huge positionsin the OTC derivatives market proved highly dangerous.
According to René Stulz (2009, p. 64), “The AIG situationis unusual, however, because it was mostly a protection
writer. Financial institutions more typically are bothprotection writers and protection buyers.”
In addition, several observers noted that if these OTC
derivatives transactions had been cleared, the losses
would have been much smaller and the massive
bailout would have been less likely. This is because aclearinghouse requires the transacting parties to post
initial margin (collateral) and make margin payments in
response to changes in values of the contracts from daily
mark-to-market. Therefore, in the case of AIG, the margin
calls would have come sooner and been more frequent(see IMF, 2010, p.99. Also see Pirrong, 2011, for discussion
on the economic function of central counterparties).

GLOBAL GROWTH AND RECENT TRENDS

21

2.1 VOLATILITY AND TECHNOLOGY
The derivatives markets were small until the 1970s. Since then, their growth has been driven by bothdemand and supply factors. In the 1970s and ’80s, economic conditions such as rising volatility in stocks,
interest rates, and exchange rates, along with the globalization of the capital markets, spurred demand for
instruments to hedge risk. Supply factors, such as advancing technology and financial engineering, which
aided the design of sophisticated derivatives, played an important role (in particular, the Black-Scholes[1973] formula to price options was widely acknowledged as an important factor).1

FIGUREIn the past two decades, the global derivatives markets have grown dramatically, despite experiencing a
slowdown after the onset of the global financial crisis in 2008. The size of the market cited in the news
media often refers to the notional amounts outstanding of contracts. Based on statistics from the Bank forInternational Settlements (BIS), the OTC derivatives market grew by a factor of eight, from $80 trillion to$633 trillion annually, between 1998 and 2012. The exchange-traded market expanded considerably as wellover that period, from $14 trillion to $54 trillion, or about four times (Figure 1).

1

The global derivatives market (notional amounts outstanding)
OTC derivatives$ trillions
800
700
600
500
400
300
200
100

12
ne

20

11
Ju

ne

20

10
Ju

ne

20

09
Ju

ne

20

08
Ju

ne

20

07
Ju

ne

20

06
Ju

ne

20

05
Ju

ne

20

04
Ju

ne

20

03
Ju

ne

20

02
Ju

ne

20

01
Ju

ne

20

00
Ju

Ju

ne

20

99
19
ne

Ju

Ju

ne

19

98

0

12

Ju
n

e

20

11
20

Ju
n

e

10
20

Ju
n

e

09
20

Ju
n

e

08
20

Ju
n

e

07
20

Ju
n

e

06
20

Ju
n

e

05
20

Ju
n

e

04
20

Ju
n

e

03
20

Ju
n

e

02
20

Ju
n

e

01
20

Ju
n

e

00
20
e

Ju
n

19
Ju
n

e

98
19
e
Ju
n

99

Exchange-traded derivatives$ trillions
100
90
80
70
60
50
40
30
20
10
0

Source: Bank for International Settlements, June 2013.

1.

22

According to Merton (1998), “The most influential development in terms of impact on finance practice was the Black-Scholes model for option pricing...
This success in turn increased the speed of adoption for quantitative financial models to help value options and assess risk exposures” (pp. 323-324).

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESAmong the four types of derivatives, swaps are the largest market by notional amount. The second-largest is
forwards (see Figure 2). Both swaps and forwards are traded on OTC markets, while futures and standardized
options are traded on organized exchanges. As discussed in detail in Part 2.4, simply comparing notionalamounts outstanding between exchanges and OTC derivatives markets can be misleading. For instance,
an offsetting OTC trade actually adds to the notional amount outstanding, while offsetting exchange-tradedtransactions are netted out. In other words, OTC trading data captures gross positions, while exchange
data represents net positions. Therefore, the growth of derivative types as presented in Figure 2 should becompared within the markets where the instruments are traded.
FIGURE

2

Notional amounts, by instrument$ trillions
800
700

Future (exch tr)

600

Options (exch tr)
Options (OTC)

500

Swaps (OTC)

400

Forwards (OTC)*

300

Credit derivatives (OTC)

200

Unallocated

100
0
1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

* Includes forex swaps, equity-linked swaps, and commodity swaps. The amounts outstanding for these categories are small and BIS reports
their data with forwards. Equity-linked forwards and swaps combined is 0.3% and commodity forwards and swaps is 0.3% of total contracts.
Note: Unallocated excluded.
Source: Bank for International Settlements, June 2013.

The development of derivatives can also be presented in terms of the risk category of the underlying assets(i.e., interest rate, foreign exchange, equity-linked, commodity, and credit derivatives), in addition to typeof instrument (i.e., forwards, futures, options, and swaps) and market (i.e., exchange-traded and OTC).
Appendix 1 provides data by market, risk category, and instrument.2
In terms of risk category of the underlying assets, interest rate derivatives are the most widely traded,accounting for about 77 percent of notional amounts outstanding of global OTC derivatives in 2012(see Figure 3). They became popular in the late 1970s and early 1980s, when corporations were grapplingwith wide rate fluctuations and sought financial instruments to reduce the associated risk. Although somemanufacturers were affected by rate movements, financial institutions, whose profit comes primarily frominterest-sensitive liabilities, were even more exposed. They became the primary users of interest rate swaps,
now the most commonly traded type of interest rate derivative.
2.

Categorization based on Bank for International Settlements methodology.

GLOBAL GROWTH AND RECENT TRENDS

23

FIGURE3

OTC derivatives by risk category
OTC derivatives:$633 trillion (2012)

100%
90%
80%

Unallocated
CDS
Commodity <1%

70%

Equity-linked 1%
Foreign exchange

60%

4% 7%
11%

50%
40%
30%

77%

20%

Interest rate

10%

12

11

20

10

20

09

20

08

CDS
Foreign exchange

20

07

20

06

20

05

20

04

20

03

20

02

20

01

UnallocatedEquity-linked

20

00

20

99

20

19

19

98

0%

CommodityInterest rate
Source: BIS, Milken Institute.

Foreign exchange derivatives, at 11 percent of the notional amount of the global OTC total, are thesecond-largest category, much smaller than interest rate derivatives. In recent decades, foreign financial
markets have become more accessible and international trade more open as technology reduced costs
associated with cross-border transactions. The trend increases demand for foreign exchange derivatives
to hedge currency risk, as global banks, institutional investors, and multinational corporations increase
their exposure to exchange rate risk. The foreign exchange derivatives market expanded from the notionalamount of $18 trillion in 1998 to $67 trillion in 2012. Activity in equity-linked and commodity derivatives
is relatively small, with each accounting for about 1 percent of the broader market.
Credit derivatives are a new type, with the credit default swap its predominant form. This is a contract thatallows the buyer to hedge the credit risk of debt securities contingent upon a credit event that befalls theissuer, such as a bankruptcy or payment default. With a CDS, a protection buyer makes periodic payments
to a protection seller. Until the credit event occurs, the protection seller benefits from the premium
payments it receives over the life of the CDS contract. If a credit event does occur, the seller is obligated tocompensate the buyer equal to the price of debt securities specified in the contract.3 Most reference entities(debt issuers) are corporations, and the primary sellers and buyers of credit default swaps include globally
active financial institutions.

3.

24Compensation upon settlement is discussed, for example, in Rajan (2007). The amount the protection seller has to pay the buyer depends on the type of settlements,
including the treatment of recovery.

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESOver the past 10 years, the CDS market has grown at an astounding pace. The notional amountoutstanding reached its peak of $58 trillion at year-end 2007, twice the size of a year earlier and a multipleof nine since 2004, according to BIS statistics. However, when compared to some other types of derivatives,
the CDS market is small. The category has accounted for 3 percent to 5 percent of the total notionalamount of OTC derivatives, except for a few years leading up to the financial crisis, when they jumped to
7 percent to 10 percent (see Figure 3).
One reason for CDS’ rapid growth was the heated activity in the housing market and the expansion ofmortgage-backed securities. Many financial institutions that invested in mortgage-backed securities
purchased CDS contracts to protect against default (see Box 1 on credit derivatives and the financial
crisis). The CDS market declined amid the financial crisis and has not recovered to previous levels.

2.2 THE EXCHANGE-TRADED MARKET
Exchange-traded derivatives activity was recorded as early as the 1500s, but only recently have we seen arapid increase in the number of such exchanges established around the world (see Figure 4). Between the
16th and 19th centuries, derivatives exchanges were located only in three mature economies, yet in the past
decade these marketplaces have been established in about 50 countries, both mature and emerging markets.
(Appendix 2 lists derivatives exchanges, their home countries, and the year of their creation.) In a recent
trend, some exchanges have consolidated and expanded operations outside their home countries (Kohli, 2012).
FIGURE

4

Number of derivatives exchanges worldwide†

Total number
350
300
250
200
150
100
50
0
1970

1973

1976

1979

1982

1985

1991

1994

1997

2000

2003

2006

2009

2012

† The data includes exchanges established since 1500 but the chart starts in 1970 because few existed earlier and globalization

accelerated around then. The net number of exchanges is represented, with an increase indicating more being established thanclosed or merged. Exchanges closed before 1970 are excluded.
Sources: World Federation of Exchanges, Futures Industry Association, Numa Directory of Futures and Options Exchanges,
Association of Futures Markets, International Organization for Standardization, Commodity Futures Trading Commission(1997), Tsetsekos and Varangis (1999), Santana-Boado and Brading (2001), Gorham and Xueqin (2002), Gorham and Singh(2009), Jorgensen, Kavajecz, and Swisher (2011), Belozertsev, Rutten, and Hollinger (2011), and Milken Institute.

GLOBAL GROWTH AND RECENT TRENDS

25

Derivatives exchanges trade standardized contracts. Since the exchanges record transactions, activity
can be tracked in a straightforward manner. The Futures Industry Association (FIA) and World Federationof Exchanges (WFE) collect futures and options trading information on their member exchanges.4
Their published surveys report the activity of listed derivatives in terms of trading volume (the numberof contracts traded) and open interest (the number of contracts entered into but not fulfilled by delivery).
Last year, 21.2 billion derivatives contracts were traded on organized exchanges worldwide, close to triple
the volume a decade ago.5 The peak was reached in 2011, when more than 25 billion contracts were traded.
Trading volume, reflecting the quantity of demand, is usually used as a measure of growth for exchanges.
Those with higher volume are often recognized as being successful in offering properly designed contracts.

FIGURE

Regarding geographic distribution, Europe and North America dominate the exchange-traded derivatives
market as measured by notional turnover and open interest—together holding more than 90 percent.
Exchanges in the Asia-Pacific region account for a larger proportion of trading volume (see Figure 5)than notional amounts. These distributions, based on different units of measurement, reflect the smaller
contracts traded on Asia-Pacific exchanges.

5

Derivatives traded on organized exchanges (2012), by region

Number of contracts traded21.2 billion

Open interest
640 million contracts

Notional amounts outstanding$54.4 trillion

5%

8%
36%
42%

43%

29%
63%

52%

22%

Americas

EAME (Europe, Africa, Middle East)

Asia PacificSources: Data for the number of contracts traded and open interest is from the World
Federation of Exchanges. Data for notional amounts outstanding is from BIS.

4.
5.

26

Appendix 3 provides data sources for global derivatives activity.
In 2003, the volume was 8.1 billion, according to the FIA’s annual survey. BIS also publishes the number of contracts outstanding and turnover of
exchange-traded derivatives.

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESAlthough the major derivatives exchanges are located in mature economies (e.g., CME Group, Deutsche Börse
AG, ICE/NYSE), demand for such products is rising in emerging economies. Derivatives exchanges in Brazil,
China, India, South Korea, and Russia have shown remarkable growth and are now ranked among the top tier(see Appendix 4). Many exchanges also pursue aggressive strategies to attract foreign trading firms througha combination of infrastructure and product development. Given the expansion of international trade andfinancial activities, participants are likely to face increasing risks, and derivatives markets are expected tocontribute to economic development by making these risks manageable. (Parts 3 and 4 of this study examine
the relationship between derivatives and economic performance for U.S. banks and non-financial firms.)

2.3 THE OTC MARKETOTC derivatives are privately negotiated and traded between two parties without intermediation through an
exchange (although OTC transactions may be cleared by a clearinghouse). Information on the trading of these
individual contracts is held by dealers, who are either buyers or sellers of contracts. BIS and the International
Swaps and Derivatives Association (ISDA) regularly survey global derivatives dealers and publish the results.
Both surveys report notional amounts outstanding, reflecting the scale and growth of activity.
The notional amount outstanding, which stood at $633 trillion at the end of 2012, according to BIS, have drawn
a great deal of attention and concern regarding possible risks tied to their scale. By this measure, the derivatives
market is approximately 10 times larger than world GDP and several times larger than other global financial
sectors. However, notional amounts outstanding, though a rough measure of derivatives activity, do not
represent the actual amount exchanged in a transaction or, generally, the amount at risk (ISDA, 2008;
OCC, 2012). The sums more closely related to the risks are gross market value and gross credit exposure(see Box 2 for definitions). As of 2012, their values are $25 trillion and $3.6 trillion, respectively, according to
BIS statistics. In 1998, gross market value was $3 trillion (see Figure 6).
FIGURE

6

Growth of the global OTC derivatives market (by gross market value)$ trillions
35
30
25
20
15
10
5

Ju
n.
De 199c. 8
Ju 199n. 8
De 199c. 9
Ju 199n. 9
De 200c. 0
Ju 200n. 0
De 200c. 1
Ju 200n. 1
De 200c. 2
Ju 200n. 2
De 200c. 3
Ju 200n. 3
De 200c. 4
Ju 200n. 4
De 200c. 5
Ju 200n. 5
De 200c. 6
Ju 200n. 6
De 200c. 7
Ju 200n. 7
De 200c. 8
Ju 200n. 8
De 200c. 9
Ju 200n. 9
De 201c. 0
Ju 201
n. 0
De 201c. 1
Ju 201n. 1
De 201
c. 2
20
12

0

Source: BIS, June 2013.

GLOBAL GROWTH AND RECENT TRENDS

27

BOX

2

Understanding risk measures of OTC derivatives(Notional amounts, gross market value, netting impacts, and gross credit exposure)“Notional amount outstanding” of OTC derivatives
contracts is commonly used to measure the activity
and size of the market. The amount refers to the valueof underlying assets specified in a contract. Cash flow
obligations, however, are a small percentage of thenotional amounts.

TABLE

For example, suppose an investor buys a derivative
contract from a bank to hedge the credit risk ofholding $1 million in IBM bonds. Assume further thatthe investor pays an annual premium of $1,000 overthe length of the contract in exchange for a one-time
payment equivalent to the bond’s par value if IBM

were to default on its debt. In this case, the bond is
the underlying asset and the $1 million is the notionalamount. The $1,000 premium or cash flow obligationof the investor (counterparty) is the fair value of thecontract and the amount at risk for the bank. Moreover,a bank can mitigate the risk of not being paid thepremium by an investor by entering into a new contract
with that same investor (e.g., by buying a new contract on
IBM bonds). The sum of the fair values of the outstanding
contracts between the two parties is known as “grossmarket value.”

A Impact of gross credit exposure and netting (2012)

OTC DERIVATIVES MARKET$ TRILLIONS% OF NOTIONAL AMOUNTS% OF GROSS MARKET VALUE

Notional value

633.3

n/a

n/a

Gross market value

24.7

3.9%

n/a

Gross credit exposure (after netting)

3.6

0.57%

14.57%

Gross credit exposure (after netting andadjusted for collateral)

1.1

0.17%

4.45%
n/a = not applicable.
Sources: BIS, ISDA, and Milken Institute.

Note: Unallocated excluded.

28

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESBIS reports the gross market value of global OTC
derivatives contracts along with the notional amount.
In 2012, gross market value was $24.7 trillion, or 3.9
percent of the notional amount of $633 trillion.

FIGUREIn the United States, banks can benefit from netting andposting collateral from a master netting agreement inaccordance with U.S. generally accepted accounting
principles. Based on the IBM example, the bank has apositive fair value from the first contract and a negative
fair value in the second contract. These cash flows canbe netted out according to legally enforceable bilateral
netting agreements. The sum of positive and negative
fair values between the counterparties (i.e., the bank andthe investor) after bilateral netting is known as “grosscredit exposure.”

According to the ISDA, the gross credit exposure of
global derivatives was $3.6 trillion, or 0.57 percent of
the notional amount outstanding, in 2012 (see Figure
A). Collateralization further reduces counterparty risk
exposure. The benefits of netting and collateral posted
further reduce credit exposure to 0.17 percent of thenotional amount and 4.45 percent of the gross market
value (see Table A).
Gross market value and gross credit exposure areconsidered more closely related to risk than the notionalamount of derivatives (ISDA, 2008).

A Impact of netting and gross credit exposure, OTC market$ trillions
40

30

Gross market value

20

Benefits of netting
10

0
1998

Gross credit exposure
1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

Sources: BIS, ISDA, and Milken Institute.

GLOBAL GROWTH AND RECENT TRENDS

29

Studies also point out that the gross market value of derivatives is a more appropriate measure thannotional amounts in comparing the size of the market with that of other financial sectors (and worldGDP). As noted by Markose (2012, p. 6), “The gross market value gives an estimate of the economic risk
in derivatives arising from the volatility of underlying reference/asset prices, leverage and hedge ratios,
duration, liquidity, and counterparty risk.”

FIGURE

Figure 7 shows this comparison. Based on market value, the size of the OTC derivatives market appears
smaller than other markets, although OTC derivatives activity has burgeoned in recent decades (see Figure 1).
The size of the global bond market (both private and public) was $84 trillion, which made it the largest
market, more than three times the scale of OTC derivatives as of 2012. The global stock market is double
the size of the OTC derivatives market.

7

Size of global financial markets ($ trillions), by type
2012

1998

87

Bond

30
72

GDP

30
52

Stock
OTC derivatives(gross market value)

27
25
3

Note: The size of the global bond market is based on total debt outstanding, global stock market is based on capitalization, and global OTC derivatives
market on gross market value. All data is in the nominal term as of 2012. GDP is based on the IMF estimate.
Sources: BIS, Bloomberg, World Bank, and Milken Institute.

2.4 DIFFERENT ENVIRONMENTS, DIFFERENT STRENGTHS
Trading derivatives through organized exchanges is recognized as more transparent. Exchanges typicallyoffer pre-trade price discovery. Furthermore, exchanges also distribute transaction prices, bids/offers,
and other trading information in real time. With such information available to all market participants aswell as regulators, trading on exchanges provides a high level of transparency. Information on bilateral
OTC transactions, on the other hand, is opaque to outside parties.
Exchanges also use a central counterparty (CCP) to clear and settle trades. Clearinghouses also serveas counterparties and set and enforce margin requirements. They are obliged to honor the trade in
the event of a party’s default; therefore, they reduce counterparty risk. In a traditional bilateral OTC
transaction, the counterparties bear the risk of default by each. In the aftermath of the global financial
crisis, some G-20 countries called for certain OTC derivatives to be cleared via CCPs as a way to reducecounterparty credit risk. (The risk can become systemic because losses from defaults can spread to30

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESparties who entered into contracts with the counterparties of the defaulted contracts). Therefore,both exchange-traded and cleared OTC derivatives benefit from credit protections through the clearing
and settling services provided by clearinghouses.
Nevertheless, the swift growth of the OTC derivatives market in the past reflects some of its advantages.
For example, participants can trade customized, complex, or illiquid products. This ability to design deals
makes OTC contracts suitable for corporations that seek to completely hedge the risk tied to specificassets in their portfolios.
In regard to the size of the two markets, it should be noted that the notional amounts outstanding for
OTC derivatives are not comparable with those of exchange-traded derivatives in terms of exposure.
Consider a generic example from an ISDA study (Pirrong, 2011, p. 7):“A may sell a contract; B may buy an identical contract and then sell it; and C may buy this contract. In abilateral OTC market, B’s offsetting positions remain open, and one (or even in some circumstances both)of its counterparties on these contract could lose from its default. In contrast, if all of these contracts arecleared through a CCP, B’s contract would be netted out and B’s contractual obligations would be extinguished.
If B went bankrupt, neither A nor C could suffer a default loss (as long as the CCP remains solvent).”
In the OTC market, traders may enter into a new transaction to hedge out a previous one. In terms ofrecorded transactions, the notional amount of the new contract will be accumulated to past transactions,even though the net exposure is reduced. In this example, the total notional amounts of B’s two contracts
with identical notional amounts will be counted double although their net risk is zero.
For exchange-traded derivatives, the existing contract is netted out by a CCP who takes the opposite
position in the same contract for each trader. That leaves the notional amounts outstanding the same orsmaller, and counterparty risk is extinguished. The positive net positions are then summed across traders.
As noted by Kleist (2012, p. 48), “For exchange-traded contracts, it is perfectly reasonable to net in thisway because, unlike OTC contracts, exchange-traded contracts have standardized size and settlement
dates and the same counterparty, i.e., the [clearinghouse].”To compare derivatives activity between OTC and exchange markets, turnover data is considered a more
appropriate measure than notional amounts. Turnover is defined as the gross value of new deals during
a given period and measured in terms of the nominal or notional amount of contracts. BIS’ Triennial
Central Bank Survey of Foreign Exchange and Derivatives Market Activity includes this data. A BIS study
shows that when gauging derivatives activity based on turnover data, exchanges are two-thirds larger
than OTC markets for a sample of advanced countries, while derivatives are traded in similar proportions
in both markets for emerging countries (Mihaljek and Packer, 2010). However, using notional amounts
outstanding, OTC derivatives were several times larger than those traded on exchanges—$633 trillion
versus $54 trillion.

GLOBAL GROWTH AND RECENT TRENDS

31

Most previous studies done on developed nations
show that the futures market leads the spot
market and therefore serves as the focal point for
price discovery.

3. Risk, Price, and Cost
This section examines the functions of derivatives and the costs and benefits associated with them.
Our framework is meant to improve understanding of the reasons derivatives contracts are widely andincreasingly used in financial markets. We focus on three major functions of derivatives: risk management,
price discovery, and reducing transaction costs.
Their use impacts the overall economy, affecting firm value, investment opportunities, and credit
availability. These effects will be assessed empirically in Part 4 of this study.

3.1 MANAGING RISK
Corporations are important players in derivatives markets. Non-financial firms typically use them to hedge
market risk. Banks, while taking positions in derivatives to mitigate their own risk exposure, also makemarkets in these instruments to meet the risk management needs of their corporate customers. In return,they generate fee and other revenue from this trading and lower their cost of funding. We discuss how
banks and non-financial firms use derivatives for risk management purposes in turn.

For banks, a means to hedge adverse rate change
Banks act as intermediaries by allocating financial resources from savers (those with excess funds) toborrowers (those in need of funds, e.g., entrepreneurs and firms). In a traditional model, banks’ assets(such as mortgage and commercial loans) are long-term, while liabilities (such as demand deposits) havemuch shorter terms. A maturity mismatch between assets and liabilities subjects banks to interest raterisk. That is, a change in relative interest rates impacts banks’ earnings and returns because much of theirprofit comes from the difference between interest received on loans and interest paid on deposits.
Managing interest rate risk is essential for banks to be profitable and mitigate the danger of insolvency
in a volatile interest rate environment.1 To reduce (or increase) their exposure, banks use interest rate
derivatives.2 In a developed theoretical model, Diamond (1984) demonstrates that hedging interest raterisk through the derivatives markets lowers the probability of bank failure. Because bank loans are illiquidand monitoring loan contracts is costly, banks have incentives to use a variety of means to increase thenet cost advantage of intermediation. The use of derivatives to help offset the mismatch of maturities
1.
2.

The maturity mismatch between assets and liabilities is often pointed to as a cause of many institutions’ insolvency during the savings and loan crisis in the late1970s and early 1980s as U.S. interest rates spiked (see Barth, 1991). For a bank with more rate-sensitive (i.e., short-term) liabilities than assets, a rise in rates will
reduce profits.
As an alternative, banks can adjust the duration of assets and liabilities (known as maturity or duration match). However, the costs can be very high in the shortrun. Since the mid-1980s, derivatives have been an increasingly important instrument for managing interest rate exposure because they do not require banks toadjust the maturities of assets and liabilities on their balance sheets.

33

can reduce the monitoring cost, and therefore enable banks to lend more effectively. The prediction from
Diamond’s model is supported by later empirical studies. Brewer, Minton, and Moser (2000) and Brewer,
Jackson, and Moser (2001), among others, find that hedged banks provide more efficient intermediation
than unhedged banks. (Appendix 5 summarizes findings of related empirical studies.)
Table 1 shows that the majority of banks that participate in the derivatives market use interest rate derivative
contracts. In fact, these account for 80 percent of total derivative notional amounts. Banks also use derivatives
to hedge against unexpected movements in foreign-exchange rates and commodity prices. Credit risk islikewise relevant. Banks manage it by using derivatives to protect against the possibility that borrowers who
invest in very uncertain projects can’t repay their loans. In the past decade, the total notional amounts ofderivative contracts held by U.S. commercial banks have increased tremendously, rising from $71 trillion
in 2003 to $227 trillion in 2012. The number of banks that participate in the derivatives market has also
increased. In 2003, there were 650 banks in that group (7.8 percent of U.S. commercial banks), which had
grown beyond 1,300 banks a decade later (18.7 percent).3
TABLE

1

Types of derivatives used in the U.S. banking system

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

PROPORTION OF U.S. BANKS THAT USE DERIVATIVES*
Interest rate

6.6%

7.9%

9.9%

11.3%

12.1%

13.3%

14.5%

15.8%

16.7%

17.9%

Forex

1.1%

1.1%

1.1%

1.1%

1.1%

1.1%

1.1%

1.3%

1.2%

1.3%

Credit

0.4%

0.4%

0.4%

0.6%

0.6%

0.6%

0.7%

0.7%

0.6%

0.7%

Other

1.5%

1.7%

2.1%

2.0%

2.0%

1.7%

1.6%

1.5%

1.4%

1.4%

Number of banks

8,348

8,179

8,056

7,922

7,788

7,568

7,321

6,999

6,789

7,248

TOTAL NOTIONAL AMOUNT ($BILLIONS)Interest rate

61,87675,53384,530107,435

129,491

175,895

181,455

193,399

187,866

181,463

Forex

7,185

8,607

9,289

11,90016,614

16,224

16,55520,99025,43627,781Credit

1,052

1,396

1,807

3,164

3,590

3,268

2,664

2,559

2,928

3,757

Other

1,001

2,347

5,822

9,020

15,86316,02920,716

31,65814,75913,998

Total

71,11387,878

101,449

131,519

165,559

211,416

221,390248,606

230,990

226,999*Banks may use more than one type. Equity and commodity contracts are among the other categories. The 2012 data is as of the third quarter.
Sources: FDIC, Milken Institute.

3.

34

The earliest data from the OCC’s Quarterly Report on Bank Trading and Derivatives Activities, published in 1998, show there were 447 banks using derivatives(about 4% of U.S. banks) with $33 trillion as the notional amount of all contracts.

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESDerivatives activity in the U.S. banking system is concentrated in a few large entities—93 percent of total
notional amounts is held by four banks: JPMorgan Chase, Citibank, Bank of America, and Goldman Sachs.
According to quarterly reports from the Office of the Comptroller of the Currency, more than 90 percent
of total notional amounts are held for trading purposes. Research suggests that the main reasons for thisconcentration are the availability of economies of scale in hedging and participants’ strong preference
for trading with highly rated, large dealer banks, which presumably pose lower counterparty risk
(Purnanandam, 2007; see also Karfoul, 2008, for discussion). These rationales imply that large banks’ability to provide risk management services on a large scale benefits corporate customers.

For non-financial firms, a flexible tool for hedging
The literature on corporate risk-management theory is extensive and varied. The most widely cited
determinants of corporate derivative use consider the costs of financial distress and risk (Smith and Stulz,
1985; Tufano, 1996; Bartram, Brown, and Fehle, 2009); taxes (Graham and Rogers, 2002; Donohoe, 2012);
the underinvestment problem (Froot, et al. 1993; Chiorean, Donohoe, and Sougiannis, 2012); incomesmoothing (Barton, 2001); and principal-agent conflicts associated with managerial incentives (Mayers andSmith, 1987; Smith and Stulz, 1985). Mitigating the risk associated with each factor has implications notjust for the firm but the overall economy.
Cash flow volatility, which can arise from adverse changes in interest rates, foreign exchange rates,
and commodity and equity prices, can rob firms of the liquidity needed to meet fixed costs (Bartram,
Brown, and Fehle, 2009). Strategies such as hedging can reduce the risk of missed obligations, thereby
lowering the costs and likelihood of financial distress (Smith and Stulz, 1985). Similarly, Myers (1977)shows that hedging can reduce the probability of distress and the likelihood that equity holders willpass up potentially value-generating projects. These theories predict that firms with higher potential
financial distress costs (i.e., are susceptible to cash flow volatility) are more likely than others to hedge
with derivatives that mitigate financial risk. Furthermore, the tax code offers incentives for hedging cashflow volatility and income. As shown by Graham and Rogers (2002), these incentives include larger debt
capacity, increased interest tax deductions, and reduced tax liability.

RISK, PRICE AND COST

35

BOX

3

Industry case study: Airlines

BACKGROUND
In 1978, the Airline Deregulation Act passed the U.S.
Congress, effectively removing all controls over domestic
routes and fares for the first time and giving carriers
the opportunity to operate as unregulated businesses.
This also meant that the profitability and even survivalof each airline was dependent on its ability to control
costs. Considering that fuel is often an airline’s largest
operating expense, the ability to manage unpredictable
fuel costs aids the effort to diminish price fluctuations
and stabilize cash flow, which in turn enables airlines tomore accurately estimate budgets, forecast earnings,
increase investment in physical capital and additional
flights, and prevent out-of-control costs from being
passed on to customers.

TABLE

BENEFITS OF HEDGING
One effective way to control fuel cost volatility is byhedging with fuel derivatives.1 Indeed, Southwest
Airlines’ director of corporate finance once stated,“If we don’t hedge our jet fuel price risk, we are
speculating. It is our fiduciary duty to try and hedge thisrisk.” This risk is substantial: United Airlines calculates
that a $1 increase in the price per barrel of jet fuelincreased operating expenses by $95 million in 2011.
That year, six of the seven major airlines used fuelderivatives in various forms to manage risk. However,this practice rarely involves jet fuel itself due to theBilliquid nature of that market. Instead, airlines hedge
fuels with high price correlations, such as heating andcrude oil.
Table B shows the derivative positions of the major U.S.
airlines for 2012, as well as the amount that hedging
decreased the firm’s fuel expense in the preceding year.
For United, this totaled a substantial $503 million in 2011.
Empirically, Carter, et al. (2006) find that jet fuelhedging is positively associated with airline firm value,
noting that the so-called “hedging premium” is greaterthan 5 percent. They also find that this relationshipincreases capital investment, which is consistent withrisk management theory that firms undertake hedging
strategies to mitigate the underinvestment problem.
This makes sense, given that when airlines face reduced
cash flow due to higher fuel or other costs, their ability toinvest in growth opportunities, such as purchasing new
aircraft, is dramatically reduced. Hedging mitigates thisrisk, boosting investment and overall profitability.
The most common hedging instruments used by major
U.S. airlines include both exchange-traded—futures andoptions—and OTC-traded derivatives. More specifically,
airlines use forward contracts, futures contracts, and othertypes, including options, collars, and swaps. The latter
three are most commonly used by the major carriers.

Fuel use and hedging operations of major U.S. airlines
FUEL COST(IN US$ MILLIONS)IN 2011

FUEL COST AS A %OF OPERATING
EXPENSE IN 2011

PERCENTFUEL HEDGED
IN 2012FUEL EXPENSE SAVED BYHEDGING WITH DERIVATIVES(US$ MILLION)

United12,375

37%

31%

503

Delta

11,783

Not reported

Not reported

420

American

8,304

33.2%

21%

335

Southwest

5,644

37.7%

50%

Not reported

US Airways2

3,400

36%

Not reported

Not reported

Alaska

1,298

34%

50%

$21.4

AIRLINE

Source: Company 10-K filings hand collected by the Milken Institute

1.
2.

Airlines also use interest rate and foreign exchange derivatives to manage leverage and currency risk.
US Airways stopped its hedging program in 2008.

36

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESFirms with foreign operations, sales, inputs, and/or foreign-denominated debt are susceptible to adversechanges in currency values. Geczy, Minton, and Schrand (1997), in a sample of 372 firms with foreignexchange exposure, find they primarily use currency derivatives to limit cash-flow fluctuations thatmay prevent them from investing in growth. Similarly, Allayannis and Ofek (2001), looking at 378 nonfinancial firms in the U.S., find that derivatives use reduces firms’ exchange rate risk and imply that sucha strategy increases corporate value.
Theory suggests that hedging strategies can also alleviate underinvestment caused by insufficient cash
flow. Froot, Scharfstein, and Stein (1993) argue that firm value is generated through investments in growth
opportunities. However, a problem arises when firms reduce such investments rather than obtain costly
external financing (Myers, 1977). Since cash flow can be disrupted by erratic movements in exchange rates,interest rates, and commodity prices, hedging these risks can shield cash flow, which can be channeledinto growth opportunities. Thus, hedging can mitigate underinvestment, benefiting firm value (Myers and
Majluf, 1984).
Several studies have empirically tested the underinvestment hypothesis, with mixed results. Positive
correlations between derivative use and the mitigation of underinvestment are generally expected forfirms positioned for swift growth. For example, studies find that derivatives users have greater researchand development (R&D) expenditures and market-to-book ratios than firms that do not use derivatives(Nance, Smith, and Smithson, 1993; Geczy, Minton, and Schrand, 1997; Gay and Nam, 1998; Allayannis
and Ofek, 2001; Lin and Smith, 2007). However, others do not find this relationship when accounting forsample size and composition, duration, and other proxies for growth (Allayannis, Brown, and Klapper,
2003; Bartram, Brown, and Fehle, 2009; Aretz and Bartram, 2010). Chiorean, Donohoe, and Sougiannis(2012) argue that the mixed results are due to issues of endogeneity and that growth opportunities andthe wherewithal to invest effectively vary widely across firms.
Empirically, derivatives use by non-financial firms to manage risk is found to increase firm value4
via future expected cash flows (e.g., Gay and Nam, 1998; Allayannis and Weston, 2001; Kim, Mathur,and Nam, 2006; Mackay and Moeller, 2007). Such instruments benefit firms in other ways as well. For
example, Gay, Lin, and Smith (2011) find that derivative-user firms enjoy a lower cost of capital andfewer financial constraints. Beneda (2013) reports a strong association between low earnings volatility
and derivatives use. Jin and Jorion (2006) and Bartram, Brown, and Conrad (2011) find strong evidence
that derivatives reduce firms’ risk and stock price volatility. Companies with higher value, lower cost ofcapital, and well-managed risk have better opportunities to make productive investments as a result of
more efficient allocation of resources. This strengthens the overall economy.

4.

See Part 4.2 for a detailed discussion of the relationship between non-financial firm derivative use and firm value, and Appendix 6 for a detailed summary of
empirical studies on this issue.

RISK, PRICE AND COST

37

BOX

4

Firm hedging

The following examples illustrate how derivatives can help insulate cash flows from unexpected changes and alleviate
the underinvestment problem.
FOREIGN EXCHANGE EXPOSURE
This year, U.S.-based Company X completed a credit
sale of 2,800 euros to a German firm, with payment due
three months later. At the time of sale, the price of a euro
was $1.60. However, when the money was collected, theexchange rate had dropped to $1.40 per euro. That adversechange in the exchange rate would reduce expected cash
flow by $560, or 13 percent of the sale originally valued
at $4,480. Suppose that Company X decided to mitigate
this risk through the use of derivatives. If the company, atthe time of sale, invested in a derivative contract giving it
the right to sell 2,800 euros at $1.60 each, it would havereceived $4,480 minus the cost of the contract.

COMMODITY RISK EXPOSURE
The Widget Co. knows that in about six months it willneed to purchase 30,000 ounces of silver to fulfill anorder for its most popular line of widgets. If the spot
price for silver is $13 per ounce and the six-monthfutures price is $12 per ounce, then buying the futurescontract would allow Widget Co. to lock in the 30,000
ounces at $12 each. Thus, Widget Co. will be able toclose its futures position and buy 30,000 ounces of silver
for $12 per ounce in six months, thereby reducing thecompany’s risk.

INTEREST RATE EXPOSURE
Suppose that in July, Company ABC will replace its$20 million of 8.5% bank notes that mature in
September. The company is exposed to the possibility
that interest rates may rise by September, thereby
increasing borrowing costs. To mitigate this, thecompany buys Treasury note futures in September ata price established in July. For example, let’s assume
that the price of Treasury note futures is quoted
as 95.24 in July. Since the trading unit is a 10-year,$100,000 security, the company might sell 210 Treasury
note futures to hedge the September debt issue. This
would provide a hedge of 210 × $100,000 × 95.24% =$20,000,400. If interest rates rise, borrowing costs will
go up because the company will have to sell notes at ahigher interest rate. However, this loss will be offset by
the gain produced by hedging the Treasury bond futures.
It should be said, however, that if interest rates decline(causing bond prices to rise), the benefit of being able toissue debt at that lower rate will be offset by the loss fromthe futures contracts.

38

DERIVING THE ECONOMIC IMPACT OF DERIVATIVES

BOX

5

Industry case study: Energy and utilities

BACKGROUND
The prices of natural gas and oil can be volatile. In
2008, they reached historical high levels, then declined
swiftly. This volatility represents a vulnerability forsome energy companies, potentially affecting cash flow,which ultimately impacts production investment andemployment. To reduce exposure to the price fluctuationsof natural gas and oil, energy companies pursue
hedging strategies to minimize the risk associated with
their expected production, (in the case of oil and gas
companies) and purchases (in the case of utilities).
BENEFITS OF HEDGINGSouthwestern Energy, an S&P 500 firm based in Houston,
Texas, states in its SEC filings that it had commodity
price hedges for 47 percent of its 2012 natural gas
production (compared to 45 percent in 2009, 30 percent
in 2010, and 52 percent in 2011). These risk management
activities increased gas sales by $315.6 million in 2011,$290.3 million in 2010, and $587.8 million in 2009, theresult of settled commodity transactions reflected in end
product sales.
In written testimony provided to Congress in 2009
regarding derivatives market reform and transparency,
the American Exploration & Production Council,
America’s Natural Gas Alliance, and the Independent
Petroleum Association of America submitted an impact
assessment of Southwestern Energy’s derivatives use.
Southwestern estimates the total impact of its hedging

1.

strategy in terms of production investment, taxes, andemployment. If the company did not hedge 48 percent
of its estimated 2009 production, there would have been$700 million less available for investing, which would
have resulted in the elimination of 240 shale wells fromits Fayetteville program.1 This hedging activity adds value
downstream, specifically revenue for royalty owners,
operating expenses that generate economic activity,and state taxes, totaling $600 million in this example.
Southwestern estimated that 1,000 oil and gas-related
jobs would be directly created and 500 more indirectly.
Similarly, utility companies have testified to their useof derivatives and its effect on customers. Due to thevolatility of natural gas and electricity prices, utilities
hedge their risk with derivatives. Indeed, the CEOs of the
National Rural Electric Cooperative Association and theDelaware Municipal Electric Corp. argue that eliminating
derivatives would increase customers’ already highpower bills because the cost of volatility would be
passed along to them. For example, without derivatives,
Delaware public power utilities would be exposed toas much as $5 million in higher costs annually. Thus,
hedging with derivatives can help utilities manage theircosts, which means lighter burdens on consumers.
It must be noted that energy companies, whether utilities
or producers, use derivatives to varying extents—and
some, Exxon Mobil for example, don’t use them at all.

At the time the report was presented, these numbers were estimates based on anticipated hedging operations. For 2009, 45 percent of production was hedged,
generating $587 million.

RISK, PRICE AND COST

39

3.2 PRICE DISCOVERYThe information that can be extracted from derivatives, such as price discovery, is another important
benefit. In complete markets, trading on derivatives exchanges should reveal no new information to market
participants since derivatives are redundant (Black and Scholes, 1973; Merton, 1973). The spot and derivatives
markets should simultaneously reflect the same information. However, a lack of market completeness meansthat informed traders may prefer to own futures or options in lieu of the underlying assets, and this could
reveal new information about price. Thus, price discovery refers to the use of derivatives prices (options or
futures) to predict future cash market prices, information that can be applied by policymakers and central
banks in decision-making as well as companies and banks in managing their risks.
Most previous studies done on developed nations show that the futures market leads the spot market andtherefore serves as the focal point for price discovery. Chakravarty, Gulen, and Mayhew (2004) find that
option prices on individual equities reflect market conditions more quickly and accurately than do theunderlying assets. Similarly, Fleming, Ostdiek, and Whaley (1996), examining the S&P 500 stock index,
index options and futures contracts, find that derivatives markets lead the spot market. Mizrach and Neely(2007), focusing on Treasury spot and futures markets, find that the 30-year futures contract contributes toprice discovery in the Treasury market. And Reichsfeld and Roache (2011) find that commodity futures do
indeed forecast spot prices. Such forecasting is important, particularly for policymakers in countries wherethe value of their resources can affect the terms of trade and levels of poverty and wealth.

40

DERIVING THE ECONOMIC IMPACT OF DERIVATIVES

BOX

6

Industry case study: Food processors

BACKGROUND
Food manufacturers, such as ConAgra, Cargill, andArcher Daniels Midland (ADM), use derivatives tomanage price risk for some of their principal ingredientsand energy needs, as well as interest rate and foreign
currency volatility. The inputs hedged most frequently
are wheat, corn, oats, soybean oil, meat, and energy.
The gains and losses from these strategies are reported
as cost of sales.
BENEFITS OF HEDGING
ADM’s financial reports detail its derivatives use and the
motivation for its hedging strategy. Particular risk factors
include the vulnerability of certain finished products toenergy prices and the effects of rising commodity prices
on production costs and operating results. Further, thecompany has subsidiaries in 75 countries that generatesales in local currencies, introducing foreign exchange risk.
To mitigate these risks, the company uses forward
fixed-price purchase and sale contracts for agriculturalcommodities, forward foreign exchange rate contracts,

interest-rate swaps, and OTC options contracts.
ADM frequently uses exchange-traded futures andOTC options to reduce the variability of cash flows
associated with its corn purchasing needs. The company
grinds more than 76 million bushels of that grain amonth. Executives say these derivatives contracts havehistorically been “highly effective at offsetting changes inprice movements of the underlying.”
For 2012, ADM realized $57 million in pretax gains onderivatives designated as hedging instruments, which wasreported in earnings. This amount was $409 million in 2011,
and in 2010 the company realized a loss of $141 million.
Other food processors document their derivatives aswell. ConAgra, for example, realized net derivative gainsof $74.8 million for fiscal 2013 (compared to a loss of$66.8 million in 2012 and a $35.1-million gain in 2011).
All were reflected in overall corporate financial results,
offsetting losses or gains in the primary business unit.

RISK, PRICE AND COST

41

3.3 LIQUIDITY AND LOWER COSTS
An extra benefit of derivatives use is enhanced liquidity. As discussed by Acharya et al. (2009), theaddition of derivatives to an underlying market can have several constructive effects. First, it brings inother players who use derivatives as a leveraged substitute for trading the underlying. Second, derivatives
may cut transaction costs through narrower bid-ask spreads. Consequently, it is thought that spot markets
with derivatives have more liquidity and lower transaction costs than markets without.
An intermediary may want to use the least expensive and most liquid and transparent market to hedge.
At a given time, this may include customizable OTC derivatives, but at other times, standardized futuresand options may provide the best alternative. For example, if an investor wants exposure to the S&P 500
but hopes to avoid the expense of purchasing all the underlying securities in the basket, he or she can tradeindex options and futures for the same exposure at lower cost.

TABLE

In Table 2, we show the prevalence of derivatives by type for non-financial members of the S&P 500.
This information, hand‑gathered from each firm’s 10-K SEC filings for each year, details the types ofderivatives used, whether foreign exchange, interest rate, commodity, credit, or equity. From 2003 to
2012, the number of non-financial firms in the S&P 500 that used derivatives grew by 17.3 percent,with firms that use foreign exchange derivatives accounting for most of the gains. That group increased
from 50.2 percent to 63.5 percent of the index.

2

Types of derivatives used by non-financial firms in the S&P 500

PROPORTION THAT USE DERIVATIVES*
Type

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

Foreign exchange

50.2%

51.4%

54.1%

55.5%

58.4%

60.1%

62.3%

63.9%

64.2%

63.5%

Interest rate

51.2%

51.0%

50.7%

52.4%

52.9%

50.5%

52.2%

53.6%

52.6%

47.8%

Commodity

26.9%

27.9%

29.6%

30.0%

31.0%

31.0%

31.3%

32.7%

32.9%

32.0%

Other

6.3%

6.3%

6.5%

7.0%

7.7%

7.9%

7.7%

8.4%

8.4%

8.2%

Total firms that use derivatives

307

315

325

331

343

345

351

361

358

360

*Non-financial firms may use more than one type, including equity and credit derivatives contracts.

42

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESThrough the use of derivatives, real GDP is higher
by 1.1 percent ($149.5 billion) from 2003 to 2012
than it would have been otherwise. Employment
is boosted by 530,400 (0.6 percent) and industrial
production 2.1 percent in 2012 by derivatives use.

4. Measuring Derivatives’
Impact Empirically
The use of derivatives by banks and non-financial firms has an impact on economic growth, albeit indirectly
and through different channels (see Box 7). Banks benefit from obtaining additional protection againstinterest rate, credit, and other market risks, which strengthen their financial position. Stronger banks can,in turn, provide more credit to the private sector. This positive relationship between derivatives use andlending activity is found in previous empirical work (e.g., Brewer, Minton, and Moser, 2000; Brewer,
Jackson, and Moser, 2001; Cebenoyan and Strahan, 2004; Purnanandam, 2007; Altunbasa, Gambacortab,and Marques-Ibanezc, 2009; see detailed discussion below). By providing credit and channeling funds
to productive sectors, banks perform an intermediation role. In economic literature, bank development(usually measured by the level of domestic credit relative to GDP) is identified as a determinant of long-term
growth (see, for example, Levine and Zervos, 1998; Beck and Levine, 2004).
For non-financial firms, the use of derivatives for risk management is found to increase expected cash
flows, and hence firm value (e.g., Gay and Nam, 1998; Allayannis and Weston, 2001; Kim, Mathur,and Nam, 2006; Mackay and Moeller, 2007). Such instruments benefit firms through other channels as
well. For example, Gay, Lin, and Smith (2011) find that derivative-user firms have a lower cost of capitaland fewer financial constraints. Jin and Jorion (2006) and Bartram, Brown, and Conrad (2011) find strong
evidence that derivatives reduce firms’ risk and stock price volatility. Firms with higher value, lower cost ofcapital, and lower risk have greater opportunities to make more productive investments as a result of theirefficient allocation of resources, and therefore spur stronger economic growth.
To assess the full impact of derivatives on the overall economy through these channels, we divide our
empirical analysis into two steps. The first is estimating the impact of banks’ use of derivatives on lending(Part 4.1) and of non-financial firms’ use of derivatives on firm value (Part 4.2). In the second step,
the effects of bank lending and firm value on economic growth are assessed using U.S. data. This step also
combines impacts from previous estimations for a final assessment of derivatives’ impact on the
U.S. economy (Part 4.3). While there are other ways in which derivatives can impact the economy,
our quantitative assessment focuses specially on their impact on credit extension and firm value.
To our knowledge, a study of derivatives use on the overall economy through these channels has notbeen done previously.

45

BOX

7

Who uses derivatives?

Participants in the derivatives markets are banks, nonbank financial institutions (asset managers, pension
funds, hedge funds, insurance companies, and mortgage
servicers), trading firms, individual investors, andgovernments. In addition to using derivatives for hedging
purposes, these participants also use them to speculate
and engage in arbitrage.
Figure B summarizes the channels through which theuse of derivatives impacts overall economic activity
as laid out in our empirical strategy in this section.
For other indirect channels (that we do not assess
quantitatively), see the discussion in Parts 2 and 3.
Financial institutions (particularly banks) and companies
are the major players in the global derivatives market.
Banks, while taking positions in derivatives to manage
risk, also make markets in them although the natureof this activity is changing with the adoption of Basel
III and implementation of the Volcker Rule. In the OTC
derivatives markets, banks serve as intermediaries
by matching two parties in a contract and/or serve

FIGUREB

as dealers by taking the counterparty position. Nonfinancial firms are end users who for the most part use
derivatives to hedge risk. Such instruments provide
economic benefits through various channels (describedin Figure B).
According to BIS statistics, financial institutions
dominate global derivatives activity (92 percent of thenotional amounts outstanding of OTC contracts). Of thetotal notional amounts, 27 percent represents activity
in which financial institutions serve as active dealers,
and they participate as end users in 65 percent oftransactions. Non-financial customers worldwide holdabout 8 percent, or about $45 trillion of the notionalamounts outstanding (see Figure C).
Other data sources broaden the picture of participantsin the derivatives markets. For example, in a 2010
Fitch Ratings survey on the use of credit derivatives by
global banks, 43% said they hold derivatives for trading
activities, 38% for hedging/credit risk management, and
32% for intermediary/market-making activities.

Channels of impact

DERIVATIVES MARKET

Participants

Functions of derivatives*

Economic benefits

Banks and otherfinancial institutions

Hedge exposure to interest
rate and credit risk

Increase bank lending(allocate capital to the
productive sector)

Non-financial firms

Hedge risk from operations

Others(governments, investors, etc.)

Hedge risk

(to reduce cost of capital, financial
constraint, and earnings volatility)(to reduce borrowing costs andenhance portfolio performance)

Increase firm value(expand investment
and capital stock)

Increase overall productivity* Part 3 of this study discusses additional functions of derivatives and other channels through which they influence economic activity.

46

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESFIGUREC OTC derivatives by counterparty
Global OTC derivatives
Notional amounts outstanding: $582 trillion (2012)
Non-financial
customers
Reporting dealers

8%

Other financial
institutions

27%

65%

Note: According to BIS, reporting dealers are mainly commercial and investment banks and securities houses that are active dealers.
Other financial institutions refer to financial end users. Non-financial participants are mainly firms and governments.
Source: BIS.

The use of derivatives by banks and non-financial firms can also be grouped into other categories:
BANKS AND OTHER FINANCIAL INSTITUTIONS»» 18.7% (about 1,300 banks) of U.S. commercial
banks participate in the derivatives market as of2012.

»» 99.8% of the notional amounts of derivatives

NON-FINANCIAL FIRMS»» 87%-89% of 416 non-financial firms in the S&P
500 report using derivatives.

»» 92% of the world’s 500 largest companies manage
their price risk by using derivatives (ISDA, 2009).

contracts are held by the top 25 U.S. commercial
banks (Office of the Comptroller of the Currency[OCC], Q4 2012).

»» 29 of the 30 companies that make up the Dow
Jones Industrial Average use derivatives.

»» 80% of derivatives instruments (by type ofunderlying asset) held by U.S. banks are interest
rate derivatives (OCC, Q4 2012).

»» 60.5% of derivatives instruments (by type of
product) held by U.S. banks are swaps, 17.4% areforwards, and 14.2% are options.

MEASURING DERIVATIVES’ IMPACT EMPIRICALLY

47

4.1 BOLSTERING BANK LENDING
Conceptual framework
Risk management theories emphasize the benefit of hedging to reduce the expected costs of financial
distress (Diamond, 1984; Smith and Stulz, 1985). The banking literature offers several explanations of how
derivatives instruments enable banks to manage risk and financial distress while generating additional
revenue, allowing them to provide more credit to the private sector.
As discussed and summarized in Purnanandam (2007), derivatives increase banks’ ability to raise externalfunding. These instruments reduce the maturity mismatch between assets and liabilities, thereby strengthening
internal cash flow and capital position and improving creditworthiness. Derivatives use also makes it easierfor banks to obtain funds from borrowings, which are uninsured. In many cases, banks may find it difficult
to borrow when uninsured depositors and creditors are uncertain about the condition of the institution.
This problem incurs agency costs (the principal-agent problem). Because hedging can minimize thesecosts (Diamond, 1984), banks that use derivatives should be able to raise uninsured funds more easily thanthose that do not. With these additional sources, banks engaged in hedging can use the funds obtained toinvest or lend on a larger scale than non-hedged banks.
While many empirical studies focus on derivatives as a tool for firms’ risk management, relatively few consider
their impact on bank lending. Those studies that do are concerned mainly with interest rate derivatives, credit
derivatives, or other credit risk transfer products. Most of these studies, however, examine only the pre-crisis
period. Our empirical assessment extends the literature by considering the crisis period as well.
Studies focusing on interest rate derivatives find robust evidence of a positive impact on bank lending (i.e.,
Brewer, Minton, and Moser, 2000; Brewer, Jackson, and Moser, 2001; Zhao and Moser, 2006). Their results
suggest that interest rate derivatives (regardless of type, e.g., swaps, options, forwards, and futures) allowbanks to increase lending at a higher rate than they would have otherwise. Purnanandam (2007) further
analyzes whether this relationship holds under different economic conditions. He finds that lending volumesof derivatives-user banks are unaffected by changes in the macroeconomic environment (i.e., when theFederal Reserve tightens monetary policy). When such events occur, the impact is largely offset by the gainfrom having the hedge in place, requiring only minor operational adjustments by the bank. Purnanandam’s
result confirms hedging theories that point to the benefits of derivatives as a buffer against downside risk.
The conclusion that credit derivatives and other credit risk transfer products increase bank lending is
not as widespread as in the case of interest rate derivatives. While Goderis et al. (2007) and Altunbasa,
Gambacortab, and Marques-Ibanezc (2009) find a positive impact, Hirtle (2009) finds only limited
evidence that credit derivatives are associated with expanded bank lending. In particular, Hirtle finds a
benefit only for credit extension to large corporate borrowers. Cebenoyan and Strahan (2004) find thatcredit risk management allows banks to lend more of their assets, but those banks also hold more riskyloans. These studies, however, use different proxies for credit risk transfer instruments (see Appendix 5
for a detailed summary of these studies).11.

48

Studies of credit derivatives/other credit risk transfer products have increased in recent years, focusing mainly on their role during the financial crisis and theirimpact on stability. See, for example, Nijskens and Wagner (2011), Norden, Buston, and Wagner (2011), Calistru (2012), Cyree, Huang, and Lindley (2012), andRodriguez-Moreno, Mayordomo, and Peña (2013). See Appendix 5 for detailed findings of these studies.

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESA positive relationship between derivatives and bank lending is supported by the data in Figure 8,
which compares loan volume of U.S. banks that use derivatives to that of U.S. banks that do not. The great
majority of loans are provided by banks that participate in the derivatives market. An econometric exercise
below, which controls for characteristics such as size and equity-to-asset ratio, confirms this relationship.
FIGURE

8

Commercial and industrial (C&I) loan volume, by derivatives‑user and non-user U.S. banks
Aggregated C&I loans

Weighted average of C&I loans to total loans$ billions

Percent

1,400

25

1,200
1,000

20

Derivatives-userbanks

Derivatives-user
banks

15

800
600

10

Non derivatives-user banks

400
200

5
Non derivatives-user banks002003 2004 2005 2006 2007 2008 2009 2010 2011 20122003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Note: Weighted average = S C&I loans/S total loans
Sources: FDIC, Milken Institute.

Empirical model: Data and methodology
We estimate the impact of derivatives on the growth of bank loans using a sample of FDIC-insured U.S.
depository institutions. The data for bank-specific variables, which is available on a quarterly basis, are obtained
from the Call Reports of Condition and Income that banks file with the Federal Reserve System.
The Call Reports provide information on the notional principal of derivatives that are classified by the typeof instrument (e.g., swaps, forwards, futures, options, and credit derivatives) and hedged risk (e.g., interest
rate, foreign exchange, and equity and commodity derivatives). Appendix 7 includes the bank variablesfrom the Call Reports that we use in our analysis.
In the empirical work below, our sample includes banks with total assets greater than $500 million as of
the third quarter of 2012, comprising 1,286 banks for the period Q1 2003–Q3 2012. Smaller banks withassets of less than $500 million generally do not participate in the derivatives market or hold a relatively
small amount in derivatives notional value, and they account for an insignificant portion of the market.
Specifically, 650 of 7,248 banks have assets less than $500 million and hold derivatives positions as of thethird quarter of 2012. The combined notional amount of derivatives for these 650 banks is $12 billion,
or 0.01% of the $227 trillion total for all U.S. banks.

MEASURING DERIVATIVES’ IMPACT EMPIRICALLY49

We follow the literature by relating banks’ derivatives use to credit extension to commercial and industrial(C&I) borrowers. Brewer, Minton, and Moser (2000) use C&I loan growth to measure intermediary
activity. They note that C&I loans present an important channel for credit flows between the financial andproduction sectors of the economy. Other studies also find a stronger linkage between derivatives and C&I
loans, but less so for other loan types (e.g., Cebenoyan and Strahan 2004; Mahieu and Xu, 2007; Minton,
Stulz, and Williamson, 2009; Deng, Elyasiani, and Mao, 2010). Drawing on Diamond’s (1984) model,
which suggests that banks should hedge and invest in the areas where they can exploit their monitoringbased cost advantage, these studies point out that lending to C&I firms is more risky. Therefore, banks canbenefit from hedging this loan type. To estimate the impact of derivatives on the growth of bank lending,we use a two-stage Heckman selection bias model. The problem of selection bias may arise because
a bank’s C&I loan activity might affect its decisions about derivatives use. Also, the decision to issueloans and use derivatives may be made simultaneously.2 A two-stage Heckman procedure addresses thisproblem by controlling for self-selection of banks that enter into derivatives contracts.
The first stage of the model uses probit regressions to estimate the probability that banks will enter thederivatives market. From the first stage, an inverse Mills ratio (Lambda) is obtained and added to ourfull model (second stage) to control for this decision. Both stages of estimation control for bank-specificfactors that previous research has found to be related to derivatives use and lending by banks. We report
regression results from the first stage separately in Appendix 8. Summary statistics of all variables in themodel are provided in this appendix as well.
For the second stage of the effect of derivatives on bank lending, the model specification is as follows:
Equation (1):The dependent variable, Bank Lendingi,t, is the quarterly growth rate of C&I loans for bank i at periodt [constructed from: ln(C&I loans)i,t - ln(C&I loans)i,t-1]. To test the impact of the use of derivatives
instruments on lending, we regress the loan growth variable on various measures of bank participationin derivatives markets. Derivatives is derivatives usages, which is a binary variable with a value of 1 for abank that reports a position in any type of derivative, and 0 otherwise. We also break down derivatives
usages by type. Binary variables are created for interest rate, foreign exchange, credit, and equity andcommodity derivatives (the latter two are combined into “Other derivatives” due to the relatively small
notional amounts banks hold).
Additionally, we create a set of dummies to capture the use of different types of instruments, i.e., swaps,
forwards, futures, options, and credit derivatives. In an additional analysis and robustness check section,
we test the impact of the extent of banks’ derivatives use measured by the ratio of a bank’s notionalamounts to its total assets. According to hedging theories discussed previously, we expect b1 to have a2.

50

See, for example, Carter and Sinkey (2000), who show empirically that a bank’s derivatives position involves an endogenous decision.

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESpositive sign. That is, derivatives-user banks can lend more because when these instruments are effectively
employed to manage risk, banks reduce their probability of default, enabling them to expand lending.
Our regressions include a set of bank-specific factors, X i,t, which are total assets; equity-to-total assets
ratio (Equity/TA); liquid assets-to-total assets ratio (Liquid/TA); net interest margin (NIM); returnon average assets (ROA); and unused loan commitments-to-total assets ratio. By controlling for thesebank characteristics, the coefficient of the derivatives variable captures their impact on lending activity,
conditional on other factors affecting banks’ intermediation process.
Total assets are included to control for banks’ size. Since size has a highly skewed distribution in thesample—the total assets of the largest bank are $1.85 trillion, while the smallest bank has $500 million in
assets (out of 1,286 sample banks)—we enter the size variable in the natural logarithm form (see Appendix
Table 8.1). A bank’s capital, liquidity, and profitability positions reflect the conditions that determine its
ability to lend. Healthier banks—those better capitalized, more liquid, and more profitable—are expected
to lend more. Therefore, the coefficients of Equity/TA, Liquid/TA, NIM, and ROA are expected to have apositive sign. We also include banks’ unused loan commitments ratio. This variable relates to C&I lending
because the majority of such loans are made under the loan commitment arrangement. Similar to the total
assets variable, the unused commitments ratio enters regressions in the log form due to its highly skewed
distribution. These bank control variables are lagged one year to minimize a reverse causality bias.
Lastly, the regressions control for bank- and time-fixed effects. Bank-fixed effects control for bank-specificdifferences in the error terms. Time-fixed effects (quarter-year dummies) take into account the variationof lending in different periods. Standard errors are clustered at the bank level. ɛi,t is an error term.

Results
Table 3 reports the results of the second stage of the Heckman selection bias model. The result of Model 1
shows that derivatives use positively and significantly impacts growth of C&I loans at the 1 percent
significance level. In Model 2, we decompose the derivatives-use variable into different types of hedgedrisks: interest rate derivatives, foreign exchange derivatives, credit derivatives, and other derivatives.
Since interest rate derivatives are the primary type used in the banking system (80% of derivatives heldby banks are interest rate derivatives as of Q3 2012), Model 2’s result, not surprisingly, shows a positiveand significant coefficient only for interest rate derivatives.3 The results in this table suggest that interest
rate derivatives allow banks to limit their risk from exposure to changing rates, thereby increasing theirability to provide credit. Banks that use derivatives are associated with a 0.95 percent increase in lending
compared to banks that do not.
Derivatives’ positive impact on lending implies that banks receive a benefit from using derivatives to hedge.
As noted by Brewer, Minton, and Moser (2000), when banks use derivatives to generate fee revenue fromtrading or speculating but not to aid lending, we should see a negative sign for the derivatives variable.
In addition, in an unreported regression we find that both listed and unlisted derivatives increase banklending. In other words, the result shows no significant difference in the coefficients of forwards, futures,
3.

This result may not necessarily imply that banks benefit less from using derivatives to hedge other types of risks. In most cases, banks that hedge foreign exchange,commodity, equity and credit risks also hedge with interest rate derivatives.

MEASURING DERIVATIVES’ IMPACT EMPIRICALLY

51

swaps, and options, though the test for equality of coefficients between swaps and other instruments turns
out to be significant in one case. This finding of swaps’ positive impact on lending can be expected giventhat they account for about 60 percent of derivatives held by banks.
For the bank-specific control variables, we find that banks with higher profits (measured by return on
assets) and those with more unused loan commitments are more likely to expand their C&I loans. However,banks’ size and capital and liquidity ratios are not significant, while net interest margin has a negative
impact. The unused loan commitments ratio is associated with the increase in C&I lending. The majority
of C&I loans are made under the loan commitment arrangement, in which the bank is obliged to provide
a specified loan amount to a customer firm upon request (as of February 2013, the Federal Reserve’s Surveyof Terms of Business Lending shows 76.7 percent of C&I loans were made under commitment contracts).
Therefore, banks with unused loan commitments are more likely to have higher C&I loan volumes
reported on their balance sheets in the following period. As noted below, these bank-specific factors may
have varied impacts on the growth of other loan types.
We perform several additional analyses and robustness checks. First, we examine the impact of the extent
of derivatives activities, measured by the ratio of the notional values of derivatives contracts to total assets(Derivatives/total assets). We enter Derivatives/total assets in the log form, since the distribution of thisvariable is highly skewed; i.e., a relatively large portion of derivatives contracts are held by a small numberof large banks. The results, reported in Appendix Table 9.1, confirm a positive relationship between banks’derivatives holdings and their C&I lending.4
As discussed earlier, we focus on banks’ C&I lending growth because previous studies point to such loans
as having greater exposure to credit and interest rate risk than other loan types. Nonetheless, we test the
impact of derivatives use on the growth of total loans and other loan categories. For this second analysis,
three estimations—with the dependent variable being growth of total loans, growth of consumer loans,and growth of real estate loans, respectively—are reported in Appendix Table 9.2.
In all three regressions, the coefficients of the derivatives use variable are positive but not significant at aconventional level. These results are consistent with Cebenoyan and Strahan (2004), who show that lending
to commercial businesses is more risky than lending to consumer and residential real estate. Therefore,banks appear to benefit more from managing their credit risks on business loans. Nonetheless, real estate
lending was perceived as more risky during the financial crisis, and further research should examine
derivatives’ contribution to managing the risk associated with such lending, particularly in times of crisis.
From our regressions, interestingly, banks’ equity capital and liquidity ratios are found to be a significant
determinant of lending, suggesting that better-capitalized banks have more liquid assets and are able tolend more. Capital and liquidity ratios are not significant in the C&I loan growth regressions (Table 3).

4.

52

We refer to the results in Table 3 as the main finding because we will use the estimated coefficients from Model 1, Table 3, to calculate the marginal effect ofderivatives use by banks, which will later be combined with the marginal effect of derivatives use by non-financial firms. In addition, as discussed in Part 2,
we recognize that notional values do not reflect the market risk of the derivatives contracts.

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESTABLE

3

Impact of derivatives use on U.S. bank lending

In(total assets)
Equity/total assets

MODEL 1

MODEL 2-2.659-2.514(1.625)(1.663)

0.140

0.133

(0.125)(0.126)-1.138***-1.165***(0.307)(0.312)Return on assets

0.422***

0.423***(0.092)(0.092)Liquid/total assets-0.005-0.007(0.025)(0.024)

Net interest margin

In(unused commitments/total assets)
Inverse Mills ratio
Use of derivatives

2.295***

2.335***(0.536)(0.550)14.544***14.984***(3.909)(4.027)0.945***(0.310)Use of interest rate derivatives

1.229***

Use of foreign exchange derivatives-0.172(0.356)(0.870)Use of other derivatives-0.821

Use of credit derivatives-0.253(0.891)(1.250)Constant-18.110***-18.483***(4.352)(4.449)Time-fixed effects

Yes

Yes

Bank-fixed effects

Yes

Yes

Number of observations

36,72436,724

Number of banks

1,286

1,286

Note: The dependent variable is the growth of C&I loans. The main explanatory variable in Model 1 (Use of derivatives) is a dummy variable indicating
1 for derivatives use by a bank in a given quarter; 0 otherwise. The main explanatory variable in Model 2 is dummy variables indicating 1 for each
derivative type in a given quarter; 0 otherwise. All explanatory variables are lagged one quarter. The reported results are from the second stageof the two-stage Heckman selection bias model, with correction for both bank- and time-fixed effects. Inverse Mills ratio is obtained from the firststage of the model (see Appendix 8). Clustered robust standard errors of the coefficients are included in parentheses. ***, **, and * indicate that thecoefficients are statistically significant at 1%, 5%, and 10%, respectively.

MEASURING DERIVATIVES’ IMPACT EMPIRICALLY

53

Third, since our sample coverage includes the period of the U.S. subprime mortgage crisis, we test whether
the relationship between derivatives use and bank lending differs in this period. For that purpose, we interact
a crisis dummy with the derivatives variable. A coefficient of the interaction term captures the difference of
the impact on derivatives users’ lending. A crisis dummy is assigned a value of 1 for the period from Q4 2007,at the onset of the crisis, to Q3 2009, and 0 otherwise. The regressions reported in Appendix Table 9.3 also
look at impacts in the first year (Q4 2007 to Q3 2008) and the second year (Q4 2008 to Q3 2009) of the crisis.
The banking crisis that led to a decline in lending was among the main explanations for the prolonged
nature of the recovery from the recent recession. In this additional analysis, we examine whether bankswith (or without) derivatives contributed more to the drop. The difference in the effect of derivatives
during bad times and good times is noted by Cyree, Huang, and Lindley (2012, p. 123), who write that“to the extent that derivatives act as a precautionary hedge against downside risk, the use of derivatives
by depository institutions will be more valuable in bad times than in good times. Conversely, banks
can construct a well-diversified portfolio of derivative securities to hedge against financial risks while
concurrently providing dealing services to their customers.”5
The results shown in the Appendix Table 9.3 indicate that during the crisis period, C&I lending by derivativesuser banks was 0.11 to 0.68 percent higher than non-user banks (i.e., from Model 1: 1.142-1.032 = 0.11).
This magnitude is considered relatively large when compared to the 1.71 percent average growth of C&I
loans over the sample period. Banks that offered C&I loans faced increased demand for liquidity via credit
lines drawn by corporate borrowers during the crisis (Ivashina and Scharfstein, 2010). These results imply
that derivatives-user banks had less difficulty meeting these demands than non-users during the crisis.
The findings are also in line with Purnanandam (2007) and Deng, Elyasiani, and Mao (2010), who argue
that the lending policies of banks that participate in the derivatives market are less sensitive to unexpectedchanges in the economic environment. Therefore, they can continue their intermediation role during
difficult times.
In an unreported set of regressions, we perform another robustness check by excluding failed banksfrom the sample. Some commentators argue that banks that participate in the derivatives markets taketoo much risk by pursuing reckless lending. If this is the case, our previous findings may be driven by
the activities of banks that eventually failed and perhaps contributed to the financial system’s instability.
However, we do not find this to be the case, as the estimated coefficient in the sample that excludes bankfailures is not much different from the main results reported in Table 3.6

5.
6.

54

The authors use a sample of 335 commercial banks to test whether the market performance of those using derivatives instruments differed from those that did
not between the high-growth period of 2003-05 and the low-growth period of 2007-09. They do not find evidence that derivatives “increased banks’ speculating
behaviors and significantly contributed to the loss of value.”
The fact that the results changed little after we excluded failed banks may reflect that relatively few large and medium-sized banks did fail during the crisis(our sample includes banks with assets that exceed $500 million). Many, however, were bailed out. Our analysis does not separate out those banks.

DERIVING THE ECONOMIC IMPACT OF DERIVATIVES

4.2 AUGMENTING THE VALUE OF NON-FINANCIAL FIRMS
Conceptual framework
Although the literature on derivatives use by non-financial firms has grown in two decades, the effect onrisk levels and market value is still not conclusive.7 This is not due to a lack of available data, which has
increased greatly, but to mixed empirical results. For example, Guay (1999), sampling only firms that use
derivatives, finds a decline in their total risk from interest rate changes, but no change in their market
risk. Allayannis and Weston (2001) find that hedging exchange rate risk is associated with an increasein firm value (measured by Tobin’s Q) of about 4 percent, and Graham and Rodgers (2002) present
evidence that hedging can add 1.1 percent to firm value by allowing firms to increase their debt capacity.
Assessing this impact on the oil industry, MacKay and Moeller (2007) find that refiners who hedge increase
firm value between 2 percent and 3 percent while Jin and Jorion (2006) find that the effect of hedging is
not statistically significant. Looking at the airline industry, Carter, Rogers, and Simkins (2006) find thatderivatives use elevates firm value between 5 percent and 10 percent. However, Guay and Kothari (2003)
find that the magnitude of cash flows generated by derivatives use is small and not likely to account forlarge changes in firm value.
Bartram, Brown, and Conrad (2011) and others have suggested that the main problems in these earlier studies
arise from endogeneity. That is, significant differences in both risk measures and corporate value between
firms that hedge and those that do not could be due to omitted variable bias. They find, while controlling for
selection bias via propensity matching and Heckman specification, strong evidence that derivatives use
reduces total risk and increases firm value.
In the following empirical assessment, we replicate prior research on the effect of total derivatives use,
as well as foreign exchange derivatives use, on firm value. Further, we extend the research by assessing
the impact of derivatives use on firm value during periods of heightened risk, such as the recent financial
crisis and economic downturn.

Empirical model: Data and methodology
We estimate the impact of derivatives on firm value using a sample of all non-financial firms in the S&P
500 for the period 2003-2012.8 Of the 500 listed companies, 416 are non-financial. These firms come froma range of industries, including information technology, utilities, and consumer goods. The data for firmspecific derivatives use was hand collected from 10-K filings with the SEC, while all other firm-specificvariables were downloaded from Bloomberg.
Hedging information is typically disclosed in two sections of a firm’s 10-K report. The first is “Item 7A.
Quantitative and Qualitative Disclosures About Market Risk.” The second is the “Notes to Consolidated
Financial Statements.” The information provided includes data on the notional amount of derivatives thatis further disaggregated by hedged risk (including foreign exchange, interest rate, commodity, and equity),
as well as derivative type (forward, option, swap, or futures).

7.
8.

Refer to the literature review table in Appendix 6 for a complete summary.
We include only firms that were part of the index as of 2012.

MEASURING DERIVATIVES’ IMPACT EMPIRICALLY

55

We follow the literature by relating non-financial firms’ derivatives use to firm value, typically measured by
Tobin’s Q. This is defined as the ratio of total assets minus the book value of equity plus the market valueof equity to the book value of assets. Similar to Allayannis, Lel, and Miller (2007), we take the market-tobook ratio as a proxy for Tobin’s Q.
To estimate the impact of non-financial firms’ derivatives use on firm value, we use a two-stage Heckman
selection bias model. The problem of selection bias may arise because internal decisions that affect firm
value can be related to the decision to hedge with derivatives—decisions that can be made simultaneously.
The two-stage Heckman procedure addresses this problem by controlling for the self-selection of firmsthat enter into derivatives contracts.9
Similar to the bank derivatives use estimation model detailed in Part 4.1, the first stage uses probit regressions
to estimate the decision of a firm to enter the derivatives market. From the first stage, an inverse Mills ratio(Lambda) is obtained and added to our full model (second stage) to control for this decision. Both stagesof estimation control for firm-specific factors that previous research has found to be related to derivatives
use and firm value. We report regression results from the first stage separately in Appendix Table 10.2.
Summary statistics of all variables in the model are provided in Appendix Table 10.1.
For the second stage of the effect of non-financial firm derivatives use on firm value, the model
specification follows:

For the dependent variable, Firm Valuei,t , we take the market-to-book ratio (see Allayannis, Lel, andMiller, 2007, and Allayannis and Weston, 2001) as a proxy for Tobin’s Q. Due to the skewedness of its
distribution— the median value is 2.7 while the mean is 3.2—we take the natural log of Tobin’s Q.
Our main explanatory variable, Derivatives, is a binary variable with a value of 1 if a firm reports
derivatives use of any type in a given year, 0 otherwise. We also disaggregate derivatives by risk category.
Binary variables are created for foreign exchange rate, interest rate, equity, and commodity derivatives.
This variable is lagged one year.
Our regressions control for a set of firm-specific factors: Xi,t, which is total assets; ROA; the debt-to-equity
ratio; capital expenditures to sales; and R&D-to-sales.10 By controlling for these characteristics, the coefficientof the derivatives variable captures the impact of derivatives use on firm value, all other factors being equal.
All control variables enter the regression lagged one year to minimize reverse causality bias.

9.
10.

56

This approach to address selection bias is similar to others in the recent literature, including Allayannis, Lel, and Miller (2007), and Bartram, Brown, and Conrad(2011), who use propensity score matching and/or selection bias techniques akin to that of Heckman.
See Table Appendix 10.1 for summary statistics of all variables.

DERIVING THE ECONOMIC IMPACT OF DERIVATIVES»» Size: Firm size has been shown to affect firm value (Mueller, 1987, and Peltzman, 1977), and larger
firms are more likely to use derivatives than smaller firms. Thus, we include total assets to control forfirm size. Because the distribution for size is highly skewed—the median value is $9.4 billion and themean is $25.4 billion—we take the size control variable in its logarithmic form.

»» Leverage: We control for leverage because a firm’s capital structure may affect its value. Leverage isdefined as the ratio of total debt to shareholder equity (commonly referred to as the debt-to-equity
ratio). That ratio is highly skewed in our sample (the median ratio is 49.2 and the mean is 115.9),
so we take the variable in its logarithmic form.

»» Profit: More profitable firms are more likely to have higher firm value. We control for profitability byusing a firm’s ROA, measured as the ratio of net income to total assets.

»» Growth opportunities: Because firm value may depend on future investment opportunities (Myers,1977), and firms with better growth opportunities are more likely to be hedgers (Froot, Scharfenstein,
and Stein, 1993, and Geczy, Minton, and Schrand, 1997), we control for a firm’s investment opportunities.
Like Allayannis and Weston (2001) and Allayannis, Lel, and Miller (2007), to capture this variable we
include the ratios of capital expenditures to sales and research and development expenditures to sales.
Where values are missing, we assume the amounts to be zero.

»» Industry effects:11 Firms that use derivatives may be concentrated in high-value sectors (such as energy
or industrials). If this is the case, hedging firms would have higher values, not because of derivatives use
but because of their industry. We control for industry effects by including the two-digit SIC codes.

»» Time effects: We include time-fixed effects (year dummies) to account for the variation in firm value
for different periods.

Results
Table 4 presents the results of the second stage of the Heckman selection bias model. The result of Model
1 shows that derivatives use has a positive and significant impact on firm value for the non-financial
firms in the S&P 500 at the 10 percent level. The coefficient of 0.052 indicates that non-financial firms that
hedge with derivatives are valued 5.2 percent higher than firms that do not, all other things being equal.
Following other literature assessing firm value and derivatives, in Model 2 we look at the use of foreignexchange derivatives by non-financial firms with foreign sales (Kim, Mathur, and Nam, 2006; Allayannis, Lel,and Miller, 2007; Gay, Lin, and Smith, 2011; Bartram, Brown, and Conrad, 2011). Foreign exchange derivatives
are used by the majority of non-financial firms (64 percent in 2012) to manage exchange rate risk.12 Thus, it is
not surprising that Model 2’s results show that the use of derivatives by non-financial firms with foreign sales
is positively and significantly associated with higher firm value on the order of 5 percent. This benefit thatderivatives use has on firm value can be referred to as the hedging premium. Allayannis and Weston (2001)
note that investors are likely to reward firms for derivatives use with higher valuations in the marketplace.
11.
12.

We include industry effects as opposed to firm effects due to multicollinearity and to be consistent with the literature. Our standard errors are clustered on individual firms.
In an unreported regression we also looked at the impact of the use of foreign exchange derivatives on the value of firms without foreign sales. The results arenegative and not statistically significant.

MEASURING DERIVATIVES’ IMPACT EMPIRICALLY57

TABLE

4

Derivatives use and firm value

In(total assets)
In(debt-to-equity ratio)
Return on assets
Capital expenditures to sales
R&D-to-sales
Inverse Mills ratio
Use of derivatives

MODEL 1

MODEL 2
FOREIGN SALES > 0-0.137***-0.110***(0.010)(0.015)0.085***

0.104***(0.012)(0.014)0.033***

0.031***(0.003)(0.003)-0.003***-0.005***(0.001)(0.001)0.019***

0.020***(0.002)(0.002)0.110

0.856**(0.103)(0.348)0.052*(0.028)Use of foreign exchange derivatives

0.050**(0.026)

Constant
Time-fixed effects

1.735***

1.220***(0.134)(0.275)

Yes

Yes

Industry-fixed effects

Yes

Yes

Number of observations

3,273

2,552

Note: The dependent variable is firm value, measured as the natural log of the market-to-book ratio. The main explanatory variable in Model 1 (Use ofderivatives) is a dummy variable indicating 1 for derivatives use by a non-financial firm in a given year; 0 otherwise. The main explanatory variable inModel 2 is dummy variables indicating 1 for the use of foreign exchange derivatives by a non-financial firm in a given year; 0 otherwise. All explanatory
variables are lagged one year. The reported results are from the second stage of the two-stage Heckman selection bias model, with correction for bothindustry- and time-fixed effects. Inverse Mills ratio is obtained from the first stage of the model (see Appendix 10). Clustered robust standard errors of
the coefficients are included in parentheses. ***, **, and * indicate that the coefficients are statistically significant at 1%, 5%, and 10%, respectively.

For the non-financial firm-specific control variables in Models 1 and 2, we find that firms with higher
leverage (measured by the debt-to-equity ratio), profits (return on assets), and investment opportunities(R&D-to-sales) are likely to have higher firm values. Both size (measured by total assets) and investment
opportunities (capital expenditures to sales) were statistically significant yet negative. The negative signfor size is in line with previous studies (Lang and Stulz, 1994; Allayannis and Weston, 2001) and may
indicate that larger firms grow at a slower rate than smaller firms, and since investors pay a premium forhigh growth rates, this discrepancy may be reflected in firm value. While the negative result for the ratioof capital expenditures to sales is surprising, it could be partly explained if firms are controlled by large
shareholders and any negative impact of overinvestment (Schleifer and Vishny, 1997).
We conducted several additional analyses and robustness checks. First, we examined whether derivatives
use by non-financial firms has an impact on another proxy for growth opportunities: cash flow. As statedin Part 3, reduced cash flow can constrain future investment and the pursuit of growth. The results are

58

DERIVING THE ECONOMIC IMPACT OF DERIVATIVES

reported in Appendix Table 11.1, Model 1. As with firm value, the impact of derivatives on cash flow is
positive, but the result is statistically insignificant. However, unreported results show that when only
crisis years are included, the results are positive and statistically significant.
Second, in a model assessing firm value, we included only observations in which the firm value(measured as the market-to-book ratio) is greater than 0. Some firms in our baseline sample have negative
market-to-book ratios, especially during crisis years. This negative value, potentially indicating that acompany incurred a string of losses, essentially renders the ratio meaningless. The results in Appendix
Table 11.1, Model 2, show that results are similar to our baseline model, indicating that the removal of thenegative observations is unnecessary.
Third, our baseline sample includes the period of the financial crisis and economic downturn. Therefore, we test
whether the relationship between derivatives use and firm value differs during this time. Summary statistics showthat the mean firm value for non-crisis years (2003-07) was 4.344, while mean firm value during the financial
crisis and economic downturn (2008-12) was 2.022—less than half the mean of the preceding four years.
Considering that firms use derivatives to minimize cash flow volatility associated with underlying risk, it makes
sense to assess their effect on firm value during the economic downturn (an inherently risky time) compared to
non-crisis years.
We divide the sample into two periods: 2003-07 and 2008-12. The latter years incorporate the financial
crisis and subsequent anemic recovery. The results, shown in Appendix Table 11.2, Models 1 and 2,
indicate that during both periods the impact of derivatives use on non-financial firm value is positive,
but statistically and economically significant only during the crisis and subsequent downturn, clearly
demonstrating a risk management function.

4.3 OVERALL IMPACT ON THE U.S. ECONOMY
The next step is to investigate the impacts of bank lending and firm value on economic growth. The resulting
estimates will be combined with those obtained from the previous two subsections—the impacts ofderivatives use on bank lending and firm value. This final operation will allow us to assess their overall
impact on the U.S. economy via these channels.
An empirical analysis examining the relationships among bank lending, firm value, and economic growth isbuilt on the finance-growth literature. Previous studies have used both cross-country analysis (e.g., Beck andLevine, 2004) and the time-series approach (e.g., Colombage, 2009) to study the linkage between financial
development (banking sector and stock market development) and economic performance. Because ouranalysis in Parts 4.1 and 4.2 employed U.S. data, we use time-series econometrics (the vector autoregressive[VAR] model), which tests the causality pattern between economic growth and other variables for anindividual country over time.
In the VAR model, we include three variables using quarterly data from Q1 2003 to Q3 2012. The firstis real gross domestic product (GDP) as a measure of overall economic activity. U.S. real GDP data is

MEASURING DERIVATIVES’ IMPACT EMPIRICALLY59

obtained from the Bureau of Economic Analysis. The second is bank lending, which is captured by theweighted C&I loans of the sample banks.13 As noted earlier in this section, many empirical studies havefound a positive association between economic development and bank development (commonly proxiedby the extent to which banks provide credit to the private sector).14 Our study focuses particularly onbanks’ lending to business (C&I loans) because previous studies such as Driscoll (2004) point out that thisloan type is likely to be most relevant to productive investment and output growth and because we findthat derivatives use facilitates greater C&I lending by banks. The third variable is the average market-tobook (M/B) value of non-financial firms in the S&P 500, which is a proxy for firm value. Although not a
direct measure of investment and production, M/B value captures a firm’s growth prospects.
The VAR technique treats variables in the model as potentially endogenous. Therefore, it allows us toevaluate the relationships without the need for prior knowledge about the direction of the variables’
causality. Testing the existence of a statistical relationship among the three variables involves three steps:
testing for stationarity, testing for co-integration or a long-run equilibrium relationship, and performing
the VAR model (or the vector error correction model if the variables are co-integrated). Appendix 12
provides a detailed discussion and findings for each step.
In sum, the VAR estimation suggests that 1 percent growth in banks’ C&I loans is associated with anincrease in quarterly real GDP growth by 0.013 percent. With alternative model specifications, this impact
ranges from 0.008 to 0.20 percent. Regarding the impact of firm value (M/B), the VAR estimation suggeststhat a 1-unit increase in firms’ M/B is associated with an increase in quarterly real GDP growth of 0.043 percent.
With alternative model specifications, this impact ranges from 0.039 percent to 0.6 percent. (See Appendix
Tables 12.3 and 12.4.)
In the last step, we provide the final assessment of the overall impacts of derivatives on the U.S. economy
via their use by banks and non-financial firms. Table 5 summarizes these impacts based on the main
results. It shows the estimated marginal effect of derivatives use on bank lending and how that contributes
to economic growth. It also demonstrates the impact of derivatives use by non-financial firms on firm
value, and the extent that firm value is associated with economic growth. The aggregation of theseestimates is equivalent to the broader impact of derivatives use on the U.S. economy. Since we also
run several sensitivity analyses and each model yields a slightly different estimated coefficient, we also
summarize these effects and report the overall impacts in ranges, which are shown in Table 6.
Key findings: Banks’ use of derivatives expands U.S. quarterly real GDP by 0.008 percent to 0.025 percent,or about $1.1 billion to $3.6 billion, each quarter from Q1 2003 to Q3 2012. Over this period, U.S. real
GDP grows $66 billion per quarter on average.

13.
14.

60

C&I loans for each bank is weighted by the bank’s assets (weight = assets of bank i/total assets of all sample banks) before aggregated across all sample banks in eachquarter over the sample period.
The literature emphasizes banks’ important role in mobilizing financial resources to fund investment opportunities, thereby enhancing economic performance.
See Levine (2005) for discussion and literature review. Nonetheless, the direction of the causality between bank development and economic development is notconclusive. Some studies find that financial development promotes economic growth, while others find that economic growth stimulates financial development,or the relationship is bidirectional.

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESTABLEWe find that derivatives use by non-financial firms increases U.S. quarterly real GDP by 0.002 percent to0.03 percent, or about $300 million to $4.5 billion each quarter from Q1 2003 to Q3 2012. Taken together,
the overall impact of derivatives use by banks and non-financial firms expands U.S. quarterly real GDP
growth by $1.4 billion to $8.1 billion each quarter from Q1 2003 to Q3 2012.

5

Derivatives and growth

ESTIMATEDMARGINAL
EFFECT

SAMPLE PERIOD: Q1 2003 – Q3 2012

IMPACT PER QUARTER(IN DOLLARS)

Impact of banks’ derivatives use
Marginal impact of derivatives use on bank lending (Table 3)

0.945

Marginal impact of bank lending on economic growth (Appendix Table 12.3)

0.013

Overall impact of banks’ derivatives use on economic growth

0.012+$1.8 billion

Impact of non-financial firms’ derivatives use
Marginal impact of derivatives use on firm value (Table 4)

0.052

Marginal impact of firm value on economic growth (Appendix Table 12.3)

0.00043

Overall impact of non-financial firms’ use of derivatives on economic growth

0.00002

Overall impact of derivatives use on U.S. economic growth+$0.3 billion+$1.4 billion

TABLE(U.S. real GDP grows $66 billion per quarter on average)

6

Ranges of overall impact

SAMPLE PERIOD:
Q1 2003 – Q3 2012

ESTIMATEDMARGINAL
EFFECT

IMPACTPER QUARTER(IN DOLLARS)Overall impact of banks’ use of derivatives on economic growth †0.008-0.025+$1.1 billion to $3.6 billion

0.00002-0.0003+$0.3 billion to $4.5 billion

Overall impact of non-financial firms’ use of derivatives on economic growth††
Overall impact of derivatives use on U.S. economic growth

From +$1.4 billion to $8.1 billion(U.S. real GDP grows $66 billion per quarter on average)†

Marginal effects of derivatives use on bank lending are from Table 3 (0.945 from Model 1 and 1.229 from Model 2). Marginal effects ofbank lending on economic growth are from Appendix Table 12.4.

†† Marginal effects of derivatives use on firm value are from Table 4 (.052 from Model 1). Marginal effects of firm value on economic growth
are from Appendix Table 12.4.

MEASURING DERIVATIVES’ IMPACT EMPIRICALLY

61

Macroeconomic model counterfactual simulations
To test the validity and robustness of the VAR estimates of derivatives’ use on overall U.S. economic
performance, we used a dynamic macroeconometric equilibrium growth model. This allows us to provide
alternative estimates of the resulting changes in investment, industrial production, employment, wages
and incomes, consumption, and real GDP growth attributable to the use of derivatives products. We canquantify these changes by comparing actual performance to a counterfactual simulation in which banks
and non-financial firms don’t use derivatives products from 2003 to 2012.
In growth models such as the one used in this analysis,15 the expansion rate of technical progress,
the available human capital, and the physical capital stock determine the productive potential of an
economy. Both technical progress and the capital stock are governed by investment, which in turn must
be in balance with post-tax capital costs, available savings, and the capacity requirements of current
spending. The capacity to supply goods and services is tied to a production function combining the basic
inputs of labor hours, energy use, business equipment and structures, and government infrastructure.
The “total factor productivity” of this composite of tangible inputs is driven by expenditures on researchand development that produce technological progress.
The first channel involved in estimating the broad economic impact is bank lending (effect of derivatives
use on the provision of commercial and industrial loans) to the business sector. Small and mediumsized enterprises rely heavily on C&I loans to expand productive capacity. Based on the bank lending
results from Part 4.1, we removed the incremental C&I loans attributable to banks’ use of derivatives andresolved the macro-model. Real private investment in equipment and structures respond to changes incommercial and industrial loans with a mean lag of two to four quarters. The elasticity of investment withrespect to such loans is 0.4, meaning that a sustained 10 percent increase in bank lending translates intoa 4 percent increase in private investment, holding other factors constant. The resulting counterfactual
simulation shows real GDP being 0.8 percent higher ($108.7 billion) in 2012 through additional banklending enabled by derivatives use.
The second channel generating broad economic impact is the effect of derivatives use on the value of
non-financial firms. We removed the higher valuation attributable to derivatives use based on the resultsof Part 4.2 and resolved the macro-model. In that case, private investment is reduced as firms are lessconfident and hesitate to expand capacity. This ripples through the economy and causes a broader decline
in economic activity. The counterfactual simulation results depict real GDP being 0.3 percent higher($40.8 billion) in 2012 based on non-financial firms’ use of derivatives.
Combining these two simulations yields compelling results that are consistent with the VAR estimates.
Through the use of derivatives, real GDP is higher by 1.1 percent ($149.5 billion) from 2003 to 2012 than
it would have been otherwise. Employment is boosted by 530,400 (0.6 percent) and industrial production
2.1 percent in 2012 by derivatives use. The $3.7‑billion overall impact of derivatives use on quarterly U.S.
economic growth compares to the $1.4 billion from Table 5 and the ranges of $1.4 billion to $8.1 billion
15.

62

The Oxford Economics Global Macro Model was used to perform these counterfactual historical simulations. This model has evolved over 30 years in response tochanging structural relationships in the macroeconomic environment.

DERIVING THE ECONOMIC IMPACT OF DERIVATIVES

from Table 6. A higher estimate from a dynamic macroeconomic model seems reasonable because it
includes all the feedbacks on the U.S. economy. The macro-model results suggest that the $1.4-billion
VAR estimate could be interpreted as conservative.

4.4 WHAT FUTURES DO FOR THE ECONOMY
In the aftermath of the global financial crisis, there have been calls for reform of the OTC derivatives
market. The principal objectives behind these regulatory developments are to increase transparency andreduce counterparty risks, therefore preventing the misuse of OTC derivatives and averting anothercrisis. Clearing is an important component of the push for reform. As noted by Fung and Webb (2012):
The push for mandatory clearing of OTC traded derivatives is as much a result of the long historyof success of exchange traded derivative markets in minimizing counterparty risk and promoting
transparency as the presumed failure of certain OTC traded derivative markets to handle
counterparty risk during the recent financial crisis. Simply stated, exchange traded derivative
markets worked well during the crisis while some OTC derivatives markets either did not orappeared not to work well (p. 9).
They continued:
Mandatory clearing in futures markets, for instance, has made defaults relatively rare and market
prices more transparent. Not surprisingly, a common view of how centralized clearing would
operate in the OTC derivatives markets mirrors how existing futures clearinghouses operate andmanage risk (that is, through imposing margin requirements and daily marking to market ofoutstanding positions) (p. 9).
In addition to their constructive role during the crisis, exchange-traded derivatives products perform
two important functions: risk management and price discovery. Their use is beneficial to banks, nonfinancial firms, and other economic agents such as producers, consumers, exporters, and importers as
they anticipate the future prices of goods and seek to lock in favorable prices.
Taking these benefits into account, we additionally perform an in-depth analysis to examine theeconomic advantages of exchange-traded derivatives products following the same empirical strategy
discussed earlier. We also evaluate the impact of the use of futures contracts on U.S. GDP growth, using acounterfactual approach.
In our empirical analysis, we examine the role of futures contracts during the financial crisis. Many critics
and commentators point to the need to distinguish among different types of derivatives and the roles eachplayed during the crisis. They clearly identified specific types of swaps and structured financial products,
such as synthetic collateral debt obligations, as the culprits that caused or exacerbated the economic distress(e.g., Litan, Brookings Paper, February 17, 2010, and Surowiecki, the New Yorker, May 17, 2010). No critics
have suggested that futures markets played a role. Recent studies, on the other hand, document thebenefits of exchange-traded derivatives after the onset of the financial crisis. Specifically, Yang and Baek

MEASURING DERIVATIVES’ IMPACT EMPIRICALLY

63

(2013) find that the trading volume of exchange derivatives in their sample of major U.S. banks and trust
companies increased the institutions’ return on assets and enterprise value from 2007 to 2011.
In our effort to discern the relationship of futures to overall economic performance through the impacts ofbanks and non-financial firms that use such contracts, we encountered several methodological challenges.
In the case of bank lending, for example, just 3 percent of observations report a futures position between
Q1 2003 and Q3 2012. Because of this small number of observations, standard tests of significance may
be overly restrictive. Even if conventional tests are not met, there may indeed be some efficacy in theassociations. Additionally, fewer non-financial firms use futures than other types of derivatives products.
To estimate the impact of futures on U.S. economic growth, we first estimate equations (1) and (2) byreplacing the total derivatives use variable with futures use—a binary variable with a value of 1 for a bankor non-financial firm that reports a futures position, and 0 otherwise. Appendix 13 reports regression
results for the sample period and crisis period.
The use of futures contracts has a positive sign in all regressions, suggesting that futures provide risk
management benefits to banks and non-financial firms, which allows banks to extend more loans andfirms to pursue greater investment opportunities. Indeed, while the estimated coefficients of the futuresuse variable do not pass conventional tests of significance, they are nonetheless meaningful as a guideline
for users, policymakers, and other stakeholders to see futures’ benefits for the broader economy.
In an econometric setting, therefore, the impacts on bank lending and firm value may be largely driven
by the use of other types of derivatives, while some of that explanatory power might accrue to exchangetraded products. The coefficients may become significant with additional entities using more exchangetraded derivative products. In other words, since we use a dichotomous variable in our empirical models,
as opposed to notional amounts, we simply capture the impact of the use of futures contracts as a risk
management tool in general, and that impact may be more apparent with increasing use.
The use of a binary variable to capture users and non-users of futures contracts further permits us toanalyze the counterfactual impacts on bank lending and firm value. This approach gives predictedvalues of the growth of bank lending and firm value by simulating the case in which all sample banks
participated in futures markets and the case in which none did. It therefore indicates the extent to which
non-user banks may lend more if they did use futures, and the extent to which non-financial firms may
increase their value if they participated in that market.
The counterfactual effects are presented in Table 7. Interpreting these results, the predicted growth ofbank lending, for example, increased from 1.69 percent per quarter if no banks used futures contracts to
2.47 percent if they all participated in the futures market. Firm value is also slightly higher with all firms
using futures to manage risk exposure than when none were used.
The more striking result of this counterfactual analysis points to the benefit of futures during the financial
crisis. The predicted growth of bank lending and firm value are much higher for futures users than nonusers in this period. For bank lending, predicted growth increased from 1.27 percent to 5.04 percent
per quarter if all sample banks hedged risk with futures contracts. In the case of non-financial firms,

64

DERIVING THE ECONOMIC IMPACT OF DERIVATIVES

the predicted average market-to-book ratio increased from 2.382 to 2.488 if all those in the sample used
futures. (Note that the difference in the counterfactual effect for non-financial firms is not trivial, since thestandard deviation of market-to-book ratios in the sample is much smaller than that of loan growth.)16

TABLE

In sum, the analysis suggests that futures contracts are an effective risk management instrument. By engaging
in futures activities, banks and non-financial firms can expand their operations, generating more output
and contributing to economic growth and development.

7

Counterfactual effects

FULL SAMPLE PERIODCRISIS PERIODPREDICTED IMPACT IF

PREDICTED IMPACT IF

All firms use
futures

None of thefirms use
futures

Difference(percentage
point)All firms use
futures

None of thefirms use
futures

Difference(percentage
point)

Impact on bank lending(quarterly)

2.47%

1.69%

0.78

5.04%

1.27%

3.77

Impact on non-financial firms’
M/B ratios (annually)

2.953

2.950

0.003

2.488

2.382

0.106

Note: The counterfactual effects on bank lending and firm value are calculated based on regression results reported in Appendix tables 13.1 and 13.2,
respectively. For each table, Model 1 is used for the full sample period and Model 2 for the crisis period.

After obtaining the marginal effects of the futures use variable on both bank lending and firm value
regressions, we combine them with the estimated effects of bank lending and firm value on economic
growth, following the same process described in Part 4.3. These steps allow us to assess the total impactof futures contract use on the broader U.S. economy. The findings are summarized in Table 8.
In the full sample period, we find that futures use by banks and non-financial firms is associated with
a $1.5-billion quarterly increase in U.S. real GDP. As noted earlier, this impact is considered relatively
large, as U.S. real GDP increased $66 billion per quarter on average over the sample period of 2003 to
2012. However, it should be noted that this estimate does not refer to the benefit of futures over otherderivatives types, since banks and non-financial firms that hedge often use more than one type of risk
management tool at the same time. The construction of the futures use variable in our model does notseparate out banks or non-financial firms that exclusively use futures contracts. Therefore, the estimate
simply captures the impact of futures use overall, though the effect is created by firms that use futures intheir broader risk management profile.17
16.
17.

The standard deviation of the growth of banks’ C&I loans is 17.83 percent, while the standard deviation of non- financial firms’ market-to-book ratios is 2.08
(or 0.73 in the log form as reported in Appendix Table 10.1).
This also explains why the economic impact of futures (+$1.5 billion per quarter) is slightly different from the impact of total derivatives (+$1.4 billion per quarter,as shown in Table 5), given that futures activity is relatively small compared to other types of OTC derivatives products.

MEASURING DERIVATIVES’ IMPACT EMPIRICALLY

65

TABLE

When performing an analysis using the crisis sample period, we find that the estimated coefficients offutures contracts use are much larger and get closer to significance at a conventional level (for example,
Model 3, Appendix Table 13.1). This suggests that as more observations on futures use become available(and they are used more broadly), their efficacy can become increasingly apparent. The final assessmentof their overall impact on the larger economy during the crisis indicates that the use of futures contracts
by banks and non-financial firms is associated with an output increase of $6.2 billion per quarter relative
to non-users. The implication is that the use of futures alleviates the severity of economic contraction asbanks and other firms hedge downside risk; therefore, they are able to continue allocating resources andgenerating output during the downturn. This larger impact also supports the notion that exchange-tradedderivatives, which are more regulated, plays an important role in minimizing counterparty risk andpromoting transparency in times of financial turmoil, as argued by Fung and Webb (2012).

8

Futures’ past effects

ESTIMATED MARGINAL EFFECTFOR OVERALL IMPACT†IMPACT PER QUARTER

Effect on economic growth
Full sample period††

0.00004-0.010+$1.5 billion

Crisis period

0.0034-0.039+$6.2 billion

Note: U.S. real GDP grows $66 billion per quarter on average.
† Marginal effect of overall impact of futures contracts are the multiplicative terms between the estimated coefficients of the effects of futures
on bank lending and firm value (Appendix Tables 13.1 and 13.2) and the estimated effects of bank lending and firm value on U.S. economic growth(Appendix Table 13.3).
†† The full sample period is from 2003 to 2012. The crisis period is from 2008 to 2009 (the impact of futures on bank lending during the crisis isestimated starting from the third quarter of 2007). Since there are too few observations to estimate the impact of bank lending and firm value on
economic growth from 2008 to 2009 (i.e., eight observations based on the quarterly data) using the VAR technique, the 2006-2012 period is used forthese estimations.

Thus far, we focused and empirically demonstrated the economic benefit of futures contracts based
mainly on the importance of risk management. As noted earlier, exchange-traded derivatives also provide
economic value through other mechanisms. Derivatives traded on exchanges disseminate price informationthat aids both private and public entities because exchanges make contract volume and price publicly
available. Such transparency helps markets function more efficiently. Derivatives exchanges (and theoperations of their clearinghouses) also reduce transaction costs through the following means:

66

DERIVING THE ECONOMIC IMPACT OF DERIVATIVES»» Contract standardization: Reduces transaction costs by specifying the “terms of trade,” specificallythe grade, quantity, and delivery location of a commodity, for instance. Or by specifying the terms ofan interest rate or foreign exchange contract, including rate and closing date. In regard to commodities,
all specification options are given to the sellers, who hold the supply. Thus, standardization allows
sellers to choose the terms of sale, which are spelled out in the contract. Both sellers and buyers are
responsible for understanding how the delivery process works.

»» Central marketplace: Derivatives exchanges bring together market participants who use them as aleveraged substitute for trading the underlying assets, whether that is corn, silver, or equities. This pares
costs by creating a venue where buyers and sellers meet in lieu of searching for counterparties.

»» Clearing: In exchange-traded derivatives markets, the clearinghouse takes the other side or interposes
itself in each transaction. Clearing reduces counterparty risks, and therefore transaction costs, by taking
on the burden of ensuring the creditworthiness of each trading partner.
Other benefits that have been attributed to futures trading include stabilizing prices in times of intense
fluctuation, facilitating complex production operations, balancing supply and demand levels beyond theimmediate time horizon, and encouraging competition.

MEASURING DERIVATIVES’ IMPACT EMPIRICALLY

67

Dodd-Frank’s imposition of regulated margin
requirements on both cleared and unclearedswaps will have a noticeable impact on the
cost-benefit analysis of hedging.

5. A New Framework
In offering a perspective on the future of derivatives, one might not be cavalier in saying that their growth
could approach its former trajectory once the critical pillars of the market’s ongoing transformation
are fully understood by end users. There is a case to be made that activity would gravitate to exchanges
because of cost advantages as well as a simplified, automated selection process. Aided by increasedtransparency and a new regulatory and market operating structure in the United States and elsewhere,
systemic risk would likely be reduced. In such circumstances, the contribution derivatives have made toeconomic growth, as shown by our research, could certainly continue.
Critics of this view might argue that it is too early to declare victory for the new derivatives framework.
They could reasonably contend that cross-border, margin, and Basel III regulations are not yet complete,
which is creating uncertainty about their true regulatory impact. Moreover, the realization is growing thatnavigating new regulations is not merely a compliance exercise. For end users, such as corporate treasurers,
there is a need to reevaluate how regulations will impact their risk management strategies and operational
systems. Many users are concerned that the new derivatives marketplace will terminate the flexibility they had
enjoyed in terms of hedging their risks, not only from a business portfolio perspective, but from an accounting
perspective. Too much standardization or too high a cost for customized derivatives products could raisequestions about the prospects for market expansion and their ability to meet the specific needs of users.
Our assessment of these two views is that it is still unclear how much the overall derivatives market will
tilt toward standardization and “vanilla-ization,” despite the three-year interval since the passage of DoddFrank. More important, however, is whether the future product mix and costs of derivatives will enable
them to contribute to economic growth at a level commensurate with that shown in our research from2003 to 2012. Although this can’t yet be answered directly, we can highlight major features, issues,and trends emerging from the new environment and offer our thoughts and observations about theirimpact on the health of the derivatives markets going forward.

5.1 CLEARING AND CAPITAL
Although not all regulations associated with Dodd-Frank are yet in place, enough has been done to enable
us to identify the new market structure for the trading of many former OTC derivatives. The primary aimof this structure is to reduce counterparty and systemic risk. Without going into great detail, henceforth all“standardized” swaps must be executed on a swap execution facility (SEF) or a designated contract market(DCM). The instruments need to be cleared at a derivatives clearing organization (DCO) and reported tothe marketplace via a swap data repository (SDR).

69

Looking ahead, industry experts project that 60 percent or more of over-the-counter derivatives trading
volume will be centrally cleared (Sleightholme and Singh, 2011). Whether or not this is achieved will
depend in part on how banks, as liquidity providers in the OTC model, rethink product distribution toclients. Moreover, market participants need to consider where they execute their derivatives business andthe associated connectivity implications.
The structure of the derivatives market as described above could be subject to further change, according
to some of the world’s top bankers. They have warned that fast-expanding clearinghouses on both sides of
the Atlantic Ocean pose a rising risk to the stability of the financial system. As trading volume throughclearinghouses ramps up, it is feared, counterparty risk will increase because current capital levels may
not be sufficient. Clearinghouse executives counter that their institutions have enough capital, and their
tough collateral and margin requirements provide crucial safety buffers (Stafford, 2013).

The Clearing Process on a Derivatives Exchange
Give-up or novation of trade to clearinghouse

Clearinghou
se R
ule
s

se
ghou
arin
Cle

Clearing member

Rules

Clearing member

Cu m
a

en or
t

Clearinghouses
en tomde erd I agreSDe
A a ment or
gree
ment

t
en
m em
e
gre reer a A g
Custom d ISDAeamend

Agreement to trade

End user

Trade counterparty

Clearinghouses, which often serve as counterparties in derivatives trading and enforce margin requirements, make efficient pricediscovery possible and reduce systemic risk.

In addition to the issue of adequate capital, there is concern that the structure of the derivatives market
favors broader exchange trading over cleared swaps. In particular, because the CFTC delayed issuing
SEF regulations, DCMs, like CME and IntercontinentalExchange, had head starts in establishing theirbusinesses, which favor futures over swaps. The CFTC also issued other rules that favor futures exchanges
over SEFs, including those relating to block trades and margins. As for block trades, a DCM is able to set
tradable size limits, whereas an SEF must follow a formula established by the futures agency (Commodity
Futures Trading Commission, 2013). With respect to margins, a market participant is currently required topost significantly more money to clear a swap transaction than a similar future on a DCM (Madigan, 2013).

70

DERIVING THE ECONOMIC IMPACT OF DERIVATIVES

5.2 COSTS WILL INFLUENCE USE
Prior to Dodd-Frank, the lack of regulated margin (collateral) requirements was a major driver of the
dynamic growth of swaps markets. The imposition of such requirements on both cleared and unclearedswaps will have a noticeable impact on the cost-benefit analysis of hedging. The issue of margin
requirements is made all the more acute by the impact of deleveraging, which has been ongoing since thefinancial crisis. According to industry sources, the total margin shortfall under the new market structure
could range from $800 billion to more than $2.5 trillion (Rowady, 2012). However, although these estimatesappear large, certain factors dampen the effect of margin constraints. These include offsets, improved risk
modeling methods, the expansion of acceptable forms of collateral and collateral rehypothecation. All ofthese have issues as far as regulators are concerned, which will take some time to resolve.
Whether the cost of meeting margin requirements will spur the migration of OTC derivatives activity
to exchanges remains to be seen. Whatever the outcome, it is widely believed that a great deal of exotic
derivatives activity will cease amid the high opportunity cost of capital associated with initial margin
requirements from central clearing counterparties, and the additional charges likely to be imposed onthese less-liquid bilateral trades. According to a study by the Tabb Group, more than $130 trillion inderivatives’ notional value might not be clearable. What broader impact this might have could take time
to assess. We could see a dynamic in which the hedgers hedging the hedges of other hedgers no longer
need to be involved because the originator of that activity is no longer participating.

5.3 EXTRATERRITORIALITY AND REGULATORY ARBITRAGE
The Dodd-Frank act has not only created a seismic shift in non-exchange traded derivatives markets in
the U.S., it has sent tremors through overseas markets. The commotion stemmed from concerns that U.S.
regulators would require any non-U.S. entity (including foreign branches of American banks) engaged inswap trading with a U.S. entity to comply with U.S. rules. Foreign regulators, particularly those in Europe,
have strenuously objected to this approach, stating that the efficient functioning of global derivatives
markets would be undermined by the United States’ trying to act as a proxy global regulator. For their part,U.S. banks have objected to extraterritoriality, believing they would be placed at a competitive disadvantage.
In mid-July, the CFTC agreed to phase in rules—which affect overseas branches of U.S. banks—and layout a process that could ultimately allow foreign banks to comply with home-country rules rather thanthose of the CFTC. Hopefully, this will allow for the smooth operation of global derivatives markets going
forward. However, much will depend on achieving comparability of regulations across jurisdictions tolimit opportunities for regulatory arbitrage.
EU-related derivatives activity is also undergoing change—albeit at a slower pace—through the Reviewof the Markets in Financial Instruments Directive (MiFID) and the European Market Infrastructure
Regulation (EMIR). Once they come into force, Europe’s new regulations will have an important bearing
on the future of non-exchange-traded derivatives, since nearly two-thirds of such global transactions havetaken place there, according to the BIS. The success of all derivatives in contributing to economic growth
will depend greatly on the ability of regulators and policymakers to foster more transparent and liquidmarkets that are subject to robust and effective risk management. For end users, the litmus test will betheir ability to generate competitive returns while effectively hedging risks.
A NEW FRAMEWORK

71

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78

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESREFERENCES AND APPENDIXES

79

80.31

88.20

95.20

111.18141.67197.17258.63299.26418.13585.93598.15603.90601.05647.78632.58

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

OTC
DERIVATIVES

54.43

58.34

67.95

73.12

57.75

79.09

69.40

57.26

46.52

36.70

23.83

23.76

14.25

13.59

13.98

EXCHANGETRADEDDERIVATIVES

687.01

706.12668.99677.02655.89665.02487.53356.52305.15233.87165.50134.93109.45101.79

94.29

TOTAL

489.70504.12465.26449.87432.66393.14291.58211.97190.50141.99101.66

77.57

64.67

60.09

50.01

INTEREST
RATE

67.36

63.35

57.80

49.18

50.04

56.24

40.27

31.36

29.29

24.48

18.45

16.75

15.67

14.34

18.01

FOREIGNEXCHANGE

25.07

28.63

29.90

32.69

41.88

58.24

28.65

13.91

6.40

CDS

6.25

5.98

5.63

5.94

6.47

8.47

7.49

5.79

4.38

3.79

2.31

1.88

1.89

1.81

1.49

EQUITYLINKEDOTC DERIVATIVES

2.59

3.09

2.92

2.94

4.43

8.46

7.12

5.43

1.44

1.41

0.92

0.60

0.66

0.55

0.41

COMMODITY

41.61

42.61

39.54

63.27

62.67

61.39

43.03

30.79

26.61

25.51

18.33

14.38

12.31

11.41

10.39

UNALLOCATED

22.64

21.72

21.01

20.63

18.73

26.77

24.48

20.71

18.16

13.12

9.96

9.27

7.91

7.92

8.03

INTEREST
RATE

1.25

0.98

1.13

0.97

0.65

1.10

1.02

0.77

0.62

0.50

0.35

0.33

0.37

0.34

0.29

EQUITY
INDEX

25.91

31.58

40.93

46.43

33.98

44.28

38.12

31.59

24.60

20.79

11.76

12.49

4.73

3.76

4.62

INTEREST
RATE

0.11

0.09

0.14

0.15

0.13

0.13

0.08

0.07

0.06

0.04

0.03

0.03

0.02

0.02

0.05

CURRENCYOPTIONS

4.30

3.75

4.56

4.80

4.13

6.62

5.53

4.01

2.95

2.16

1.69

1.56

1.14

1.51

0.95

EQUITY
INDEX

Source: Bank for International Settlements, June 2013.

0.23

0.22

0.17

0.14

0.13

0.18

0.18

0.12

0.11

0.09

0.05

0.07

0.08

0.04

0.03

CURRENCY

FUTURESEXCHANGE-TRADED DERIVATIVESAPPENDIX 1. Growth of derivatives markets, by market and risk category (notional amounts, $ trillions)

APPENDIX 2. Derivatives exchanges worldwide, 1500s-January 2013
(Bold indicates major exchanges; italic indicates exchange that was abolished, merged, or acquired)
EXCHANGE

COUNTRY

2012

EXCHANGE

COUNTRY

2008 (cont'd)

EXCHANGE

COUNTRY

2005 (cont'd)Bourse Africa

South Africa

Powernext Gas Futures

FranceBelgian Power Exchange

Belgium

NASDAQ OMX NLX

U.K.

Bucharest Spot Market

Romania

Nordic Derivatives Market

SwedenChi-X FX

BrazilDerivatives Regulated Market - Bvb

Romania

Pridneprovsk Commodity Exchange

UkraineSt. Petersburg International Mercantile

RussiaEuropean Climate Exchange

U.K.

2011
Africa Futures and Derivatives Exchange

Kenya

TurquoiseU.K.

NYMEX Europe

U.K.

Bangladesh Commodity Exchange

Bangladesh

Match Now

CanadaPLUS Markets Group

U.K.

Commodity Futures Exchange Limited

Nepal

Montreal Climate Exchange

CanadaPlus Markets

U.K.

Vietnam Commodity Exchange

Vietnam

Chi-X Global

U.S.

BATS Exchange

U.S.

Sibiu Stock Exchange

Romania

Houston Mercantile Exchange

U.S.

Direct Edge

U.S.

MICEX-RTS

RussiaNASDAQ OMX NFXU.S.

NYSE Arca Options

U.S.

Caribbean Exchange Network

Caribbean

NASDAQ OMX NOM

U.S.

2004

NYSE Amex Options

U.S.

NASDAQ OMX PHLX

U.S.

Malaysia Derivatives Exchange

Malaysia

NYSE Liffe US

U.S.

Thailand Futures Exchange

Thailand2010
African Commodities Exchange

MalawiBM&F Bovespa

BrazilAgricultural Futures Exchange of Thailand

ThailandBangla Mercantile Exchange

Bangladesh

BM&F Bovespa Carbon

BrazilSecuritised Derivatives Market

Italy

Ace Derivatives & Commodity Exchange

India

BM&F Bovespa Commodities and Futures

BrazilOlive Oil Futures Market

Spain

United Stock Exchange

India

BM&F Bovespa Foreign Exchange

BrazilCBOE Futures Exchange

U.S.

Indonesia Comm. and Derivatives Exch.

Indonesia

2007

Chicago Climate Futures Exchange

U.S.

Asian Derivative Exchange Limited

Nepal

Eurasian Trading System

Kazakhstan

Hedgestreet Exchange

U.S.

Nepal Spot Exchange Limited

Nepal

Dubai Mercantile Exchange

UAE

2003

Singapore Mercantile Exchange

Singapore

Prague Energy Exchange

Czech Rep.

Multi Commodity Exchange of India

India

CEE Stock Exchange

Austria

Bluenext

FranceNational Commodity and Derivatives Exch.

India

NASDAQ OMX Commodities Nord Pool

NorwayNYSE Liffe Paris

FranceBelarusian Universal Commodity Exchange Belarus

Ukrainian Mercantile Futures Exchange

UkraineEuropean Warrant Exchange

Germany

Ukrainian Futures Exchange

UkraineCarbon Trade Exchange

U.K.

Deutsche Börse Commodities

Germany

EDX London

U.K.

NASDAQ OMX N2EX

U.K.

NYSE Liffe Amsterdam

Netherlands

Chicago Climate Exchange

U.S.

C2 Options Exchange

U.S.

NYSE Liffe Lisbon

Portugal

INET Futures Exchange

U.S.

Cantor Financial Futures Exchange

U.S.

FXMarketSpace

U.K.

2002

Eris Exchange

U.S.

MTS Swap Market

U.K.

Shanghai Gold Exchange

World DR Exchange

U.S.

NYSE Liffe London

U.K.

National Multi Commodity Exchange of India India

2009

China

ICE Futures Canada

CanadaTajik Universal Goods and Commodity Exch. Tajikistan

JSE Yield-X

South Africa

Bonddesk

U.S.

ENDEX Power BE

Belgium

Tianjin Bohai Commodity Exchange

China

ICE Futures U.S.

U.S.

Komoditní Burza Praha

Czech Rep.

Indian Commodity Exchange

India

NASDAQ OMX Commodities

U.S.

European Energy Exchange

Germany

TDEX

Japan

NASDAQ OMX BX

U.S.

Universal Commodity Exchange of Moldova Moldova

Mercantile Exchange Nepal Limited

Nepal

NYSE Euronext

U.S.

European Energy Derivatives Exchange

Netherlands

APX ENDEX

Belgium

North American Derivatives Exchange

U.S.

Nord Pool Spot

NorwayPower Exchange Central Europe

Czech Rep.

US Futures Exchange

U.S.

National Mercantile Exchange

RussiaEPEX Spot

France2006

Turkish Derivatives Exchange

TurkeyTradegate

Germany

JSE Alternative ExchangeSouth Africa

ENDEX Power UK

U.K.

Burgundy

SwedenUganda Commodity Exchange

UgandaBoston Options Exchange

U.S.

Equiduct

U.K.

China Financial Futures Exchange

China

Island Futures Exchange

U.S.

MF Global Energy MTF

U.K.

Iran Mercantile Exchange

Iran

ICE Futures OTC

U.S.

Electronic Liquidity Exchange

U.S.

Commodities and Metal Exchange Nepal

Nepal

Merchants' Exchange

U.S.

Nodal Exchange

U.S.

Joint Asian Derivatives Exchange

Singapore

OneChicago

U.S.

World Green Exchange

U.S.

NASDAQ OMX Nordic Derivatives Mkt.

Iceland

Brazilian Commodities Exchange

BrazilFish Pool

NorwayGoiás Commodities Exchange

Brazil2008
Ethiopia Commodity Exchange

Ethiopia

Fishex

NorwayMinas Gerais Commodities Exchange

BrazilZambia Agricultural Commodities Exch.

ZambiaNew York Stock Exchange Arca

U.S.

Mato Grosso do Sul Comm. Exch.

BrazilChina Beijing Environmental Exchange

China

Philadelphia Board of Trade

U.S.

Paraná Commodities Exchange

Brazil

Tianjin Climate Exchange

China

Australian Securities Exchange

Australia

Rio Grande do Sul Commodities Exch.

BrazilHong Kong Mercantile Exchange

Hong Kong

Financial and Energy Exchange

Australia

Uberlandia Commodities Exchange

BrazilNational Spot Exchange

India

2005

Nepal Derivative Exchange Limited

Nepal

Iranian Oil Bourse

Iran

Austrian Energy Exchange

Austria

Derivative and Commodity Exchange

Nepal

Pakistan Mercantile Exchange

Pakistan

OTE Market

Czech Rep.

Singapore Commodity Exchange

Singapore

Dubai International Financial Exchange

UAE

Powernext

FrancePowernext Gas Spot

FranceDubai Gold & Commodities Exchange

UAE

Euronext.Liffe

Netherlands

80

DERIVING THE ECONOMIC IMPACT OF DERIVATIVES

2001

EXCHANGE

COUNTRY

2001 (cont'd)

EXCHANGE

COUNTRY1997

EXCHANGE

COUNTRY

1994 (cont'd)International Martime Exchange

NorwayKenya Agricultural Commodities Exch.

Kenya

Khanty-Mansiysk Oil Exchange

RussiaSlovenia Power Exchange

Slovenia

South African Futures ExchangeSouth Africa

Nizhny Novgorod Stock And Currency

RussiaICE Futures Europe

U.K.

Kansai Commodities Exchange

Japan

Samara Currency Interbank Exchange

RussiaVirt-X

U.K.

Osaka Mercantile Exchange

Japan

Natural Gas Exchange

CanadaArchipelago Exchange

U.S.

Sibiu Monetary and Financial Comm. Exch.

Romania

Australian Wool Exchange

Australia

Exchange Place Futures Exchange

U.S.

Moscow Stock Exchange

Russia

1993

Fxall

U.S.

St. Petersburg Futures Exchange

RussiaBeijing Commodity Exchange

China

NQLX Futures Exchange

U.S.

1996

China Commodity Futures of Hainan

ChinaWorld Energy Exchange

U.S.

Bond Exchange of South Africa

South Africa

Dalian Commodity Exchange

China

Australian Derivatives Exchange

Australia

Chubu Commodity Exchange

Japan

Shanghai Cereals, Oils, and Foodstuffs

China

Kazakh International Commodity Exch.

Kazakhstan

National Stock Exchange of India

India

2000
Uzbekistan Republican Currency Exch.

Uzbekistan

Kyrgyzstan Commodity and Raw Materials Kyrgyzstan

Kansai Agricultural Commodities Exchange JapanNYSE Liffe Brussels

Belgium

Malaysian Monetary Exchange

Malaysia

Baku Interbank Currency Exchange

Azerbaijan

Euronext

Netherlands

Ringgit Bond Market

Malaysia

Orenburg Farmer Exchange

RussiaEuropean Asian Exchange

RussiaEASDAQ

Belgium

Yenisei Commodity Exchange

RussiaAPX Power UK

U.K.

Nord Pool

NorwayIstanbul Gold Exchange

TurkeyGlobalCOAL

U.K.

Porto Derivatives Exchange

Portugal

Ukrainian Interbank Currency Exchange

UkraineUK Power Exchange

U.K.

Commodity Exchange of the Agroindustrial Ukraine

1992

FutureCom

U.S.

Prikarpatya Regional Commodity Exchange Ukraine

Shanghai Metal Exchange

China

Hotspot FX

U.S.

Rivne Commodity Exchange

UkraineSingapore Commodity Exchange

Singapore

Intercontinental Exchange

U.S.

Volynsc Specialized Agrarian Exchange

UkraineKhorezm Interregion Commodity Exchange Uzbekistan

Merchants’ Exchange of St. LouisU.S.

Citrus Associates of the New York Cotton

U.S.

Uzbek Commodity Exchange

Uzbekistan

OnExchange Board of Trade

U.S.

TradewebU.S.

Italian Interest Rate Derivatives Market

Italy

Italian Futures Market

Italy

Romanian Commodities Exchange

Romania

1999

1995

Shanghai Futures Exchange

China

S. African Futures Exch.- Agricultural

Jakarta Futures ExchangeIndonesia

Kuala Lumpur Options and Futures FinExch. Malaysia

Moscow Interbank Currency Exchange

RussiaSingapore Exchange

Singapore

Rousse Commodity Exchange

Bulgaria

Russian Southern Commodity Exchange

RussiaKorea Options & Futures ExchangeSouth Korea

Plodinová Burza Brno

Czech Rep.

St. Petersburg Currency Exchange

RussiaAPX Gas ZTP

Belgium

Czech Moravian Commodity Exch. Kladno Czech Rep.

Siberian Commodity Exchange

RussiaAthens Derivatives Exchange

GreeceWarsaw Commodity Exchange

Siberian Interbank Currency Exchange

RussiaAmsterdam Power Exchange

Netherlands

Rostov Currency, Comm., and Stock Exch. Russia

Urals Regional Currency Exchange

RussiaAmsterdam Power Exchange Gas

Netherlands

Russian Trading System

RussiaBratislava Commodity Exchange

Slovakia

Norwegian OTC Market

NorwayCommodity Exchange of Ljubljana

Slovenia

Reuters Dealing 2000/3000

U.K.

Polish Power Exchange

PolandCitrus Fruit and Comm. Futures - Valencia Spain

1991

APX Gas UK

U.K.

SIX Swiss Exchange

Switzerland

Austrian Futures and Options Exchange

Austria

ICAP Hyde Derivatives Ltd

U.K.

Dniprovscaya Agrarian Exchange

UkraineBelgian Futures and Options Exchange

Belgium

ICAP Energy

U.K.

Donbas Agroindustrial Exchange

UkrainePlovdiv Commodity Exchange

Bulgaria

Brokertec Futures Exchange

U.S.

Kiev Agroindustrial Exchange

UkraineSofia Commodity Exchange

Bulgaria

Currenex

U.S.

Tradepoint Investment Exchange

U.K.

Zagreb Stock Exchange

Croatia

Foreign Exchange Capital Markets

U.S.

ChemConnect

U.S.

Moscow Commodity Exchange

RussiaMatchbook FX

U.S.

New Zealand Futures & Options Exchange New ZealandMoscow Non-Ferrous Metals Exchange

RussiaYieldbroker

Australia

1994

Murmansk Commodity and Raw Material

Russia1998

South Africa

PolandChengdu United Futures Commodity

China

Russian Exchange

RussiaAbuja Securities and Comm. Exch.

Nigeria

Changchun United Futures Commodity

ChinaSaint Petersburg Commodity and Stock

RussiaYokohama Commodities Exchange

Japan

Chongqing Commodity Exchange

China

Vladivostok Commodity Exchange

RussiaComm. and Monetary Exch.Malaysia

Malaysia

China Foreign Exchange Trade System

China

Black Sea Commodity Exch.

UkraineTaiwan Futures Exchange

TaiwanGuangdong United Futures Exchange

China

Crimea Universal Exchange

UkraineBelarus Currency And Stock Exchange

Belarus

Shanghai Commodity Exchange

China

Kharkov Mercantile Exchange

Ukraine

Risk Management Exchange

Germany

Shanghai Foodstuffs Commodity Exchange

China

Mariupol Universal Commodity Exchange Ukraine

Eurex

Germany

Shenzhen Metal Exchange

China

Ukrainian Universal Commodity Exchange Ukraine

Skopje Commodity Exchange

Macedonia

Shenyang Commodity Exchange

China

Veinnitsa Universal Commodity Exchange Ukraine

Internet Direct Access Exchange

RussiaSuzhou Commodity Exchange

China

Zaporozhye Commodity Exchange

UkraineIberian Electricity Market

Spain

Tianjin Property Rights Exchange

China

London Commodity Exchange

U.K.

Spanish Electricity Market

Spain

Tianjin United Futures Exchange

China

London Commodity Exchange

U.K.

Sumy Commodity Exchange

UkraineGre Tai Securities Market

TaiwanTwin Cities Board of Trade

U.S.

Mexican Derivatives Exchange

MexicoCommodity and Raw Materials Exchange

Turkmenistan

MERFOX

Argentina

Italian Derivatives Market

Italy

REFERENCES AND APPENDIXES81

APPENDIX 2. Derivatives exchanges worldwide, 1500s-January 2013 (Continued)
EXCHANGE

COUNTRY1990

EXCHANGE

COUNTRY1984

EXCHANGE

COUNTRY

1500s-1962 (cont'd)

S. African Futures Exch. - Fin Mkt Division South Africa

Bharat Diamond Bourse

India

RAS Commodity Exchange (1920)

Singapore

Shanghai Stock Exchange

China

Osaka Textile Exchange

Japan

Hong Kong Stock Exchange (1914)

Hong Kong

Zhengzhou Commodity Exchange

ChinaTokyo Commodity Exchange

Japan

Adana Commodity Exchange (1913)

TurkeyLabuan International Financial Exchange

Malaysia

Singapore International Monetary Exch.

Singapore

Konya Grain Exchange (1912)

TurkeyYerevan Commodity and Raw Material

Armenia

Polatli Grain Exchange

TurkeyChinese Gold & Silver Exchange Society (1910) Hong Kong

Zagreb Money and ST Securities Market

Croatia

Toronto Futures Exchange

CanadaBuenos Aires Term Market (1910)

German Derivatives Exchange

Germany

New Zealand Futures Exchange

New Zealand

Buenos Aires Futures and Options Exch. (1907) Argentina

Pridneprovya Commodity Exchange

Ukraine1970-1983

Electronic Broking Services

U.S.

Chicago Rice and Cotton Exchange (1983)

U.S.

Australian Stock Exchange (1900)

Australia

New York Futures Exchange

U.S.

Tokyo Gold Exchange (1982)

Japan

Toyohashi Dried Cocoon Exchange (1894)

Japan

Argentina

Baltic Mercantile and Shipping Exchange (1903) U.K.

1989

London Intl. Fin Futures and Options Exch.(1982) U.K.

Yokohama Raw Silk Exchange (1894)

Japan

Tokyo International Financial Futures Exch. Japan

Maringá Mercantile and Futures Exch. (1982) Brazil

Fukuoka Futures Exchange (1893)

Japan

Tokyo Financial Exchange

Japan

Kuala Lumpur Commodity Exchange (1980) Malaysia

Izmir Mercantile Exchange (1891)

TurkeyBudapest Commodity Exchange

Hungary

Gold Exchange of Singapore (1980)

Singapore

Johannesburg Stock Exchange (1887)

South Africa

Spanish Financial Futures Market

Spain

International Petroleum Exchange (1980)

U.K.

Winnipeg Commodity Exchange (1887)

CanadaSpanish Fin. Futures Market Fixed Income Spain

Brazilian Futures Exchange (1980)

BrazilMinneapolis Grain Exchange (1885)

U.S.

Spanish Financial Futures Market Equity

Spain

Bolsa Mercantil de Colombia (1979)

Colombia

Rosario Futures Exchange (1884)

Argentina

Spanish Financial Derivatives Exchange

Spain

European Options Exchange (1978)

Netherlands

Commodity Exchange (1883)

U.S.

London Securities and Derivatives Exch.

U.K.

American Commodity Exchange (1978)

U.S.

Coffee, Sugar and Cocoa Exchange (1882)

U.S.

GFI Energymatch

U.S.

Hong Kong Futures Exchange (1976)

Hong Kong

Maebashi Dried Cocoon Exchange (1880)

Japan

1988

Australian Options Market (1976)

Australia

Osaka Chemical Textile Exchange (1880)

Japan

NASDAQ OMX Nordic Deriv - Copenhagen Denmark

TELCOT (1975)

U.S.

Osaka Sugar Exchange (1880)

Japan

Irish Futures and Options Exchange

Ireland

New Zealand Exchange (1974)

New Zealand

Osaka Securities Exchange (1878)

Japan

Swiss Options and Financial Futures Exch.

Switzerland

Chicago Board Options Exchange (1973)

U.S.

Tokyo Stock Exchange (1878)

Japan

GFI ForexmatchU.S.

CME International Monetary Market (1972) U.S.

London Metal Exchange (1877)

U.K.

Mercado Abierto Electrónico

Argentina

CME Index And Options Market (1972)

U.S.

Hokkaido Grain Exchange (1876)

Japan

Pacific Commodity Exchange (1972)

U.S.

Nagoya Grain and Sugar Exchange (1876)

Japan

1987
FUTOP

Denmark

Tokyo Sugar Exchange (1971)

Japan

Nagoya Textile Exchange (1876)

Japan

Finnish Options Market

Finland

NASDAQ OMX (1971)

U.S.

Bombay Stock Exchange (1875)

India

NASDAQ OMX Nordic Deriv —Helsinki

FinlandSão Paulo Commodities Exchange (1971)

BrazilChicago Mercantile Exchange (1874)

U.S.

MONEP

FranceTomato Products Assoc. of the NY Cotton (1970)

U.S.

Salt Lake City Mining Stock Exchange (1873) U.S.

London Futures and Options Exchange

U.K.

1500s-1962

Bremen Cotton Exchange (1872)

Germany

London Bullion Market

U.K.

Merchants' Exchange (1962)U.S.

New York Mercantile Exchange (1872)

U.S.

London Platinum And Palladium Market

U.K.

Sydney Futures Exchange (1960)

Australia

Sydney Stock Exchange (1871)

Australia

GFI Creditmatch

U.S.

Korea Exchange (1956)

South Korea

New York Cotton Exchange (1870)

U.S.

Memphis Board of Trade (1954)

U.S.

Kansas City Board of Trade (1856)

U.S.

1986
Finnish Options Exchange

Finland

Kobe Grain Exchange (1952)

Japan

Toronto Stock Exchange (1854)

CanadaMATIFFranceKobe Raw Silk Exchange (1952)

Japan

Buenos Aires Grain Exchange (1854)

Argentina

Amsterdam Financial Futures Market

Netherlands

Kobe Rubber Exchange (1952)

Japan

Paris Stock Exchange (1850)

FranceLondon Traded Options Market

U.K.

Osaka Grain Exchange (1952)

Japan

Chicago Board of Trade (1848)

U.S.

Pacific Futures Exchange

U.S.

Tokyo Grain Exchange (1952)

Japan

Boston Stock Exchange (1834)

U.S.

Tokyo Rubber Exchange (1952)

Japan

Odessa Commodity Exchange (1796)

Ukraine

1985
Manila International Futures Exchange

Philippines

Tokyo Textile Exchange (1951)

Japan

New York Stock Exchange (1790)

U.S.

NASDAQ OMX Nordic Deriv -Stockholm

SwedenSan Francisco Grain Exchange (1938)

U.S.

Philadelphia Petroleum Exchange (1790)

U.S.

Baltic International Freight Futures Exch.

U.K.

London Rubber Exchange (1932)

U.K.

Philadelphia Stock Exchange (1790)

U.S.

Financial Instruments Exchange

U.S.

New York Hide Exchange (1929)

U.S.

Corn Exchange of London (1745)

U.K.

Brazilian Mercantile and Futures Exchange

BrazilManila Commodity Exchange (1927)

Philippines

London Stock Exchange (1697)

U.K.

Deutsche Börse (1585)

Germany

Note: The list includes derivatives exchanges and all stock exchange subsidiaries that trade derivatives. Stock exchanges that were established to trade
only stock shares (but later may have traded derivatives) are excluded. The list also excludes the 50-plus commodity exchanges in China in the 1970s
that were consolidated into three exchanges in 1990 (which were listed as founded in 1990). China also has many regional property rights exchanges
that were excluded due to insufficient information about products traded.
Sources: World Federation of Exchanges, Futures Industry Association, Numa Directory of Futures and Options Exchanges,
Association of Futures Markets, International Organization for Standardization, Commodity Futures Trading Commission (1997),
Tsetsekos and Varangis (1999), Santana-Boado and Brading (2001), Gorham and Xueqin (2002), Gorham and Singh (2009),
Jorgensen, Kavajecz, and Swisher (2011), Belozertsev, Rutten, and Hollinger (2011), and Milken Institute.

82

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESREFERENCES AND APPENDIXES

83

Exchange and OTC

Forwards, futures,
options, swaps

Quarterly

Exchange and OTC

Frequency

MarketType of instruments Forwards, futures,
options, swaps

GlobalISDA

OTC - notionalamounts and grossmarket values
Exchanges - numberof contracts andturnover

Data is available inaggregate (for all
sample countries andby region)

Exchanges - http://
www.bis.org/statistics/
extderiv.htm
OTC - http://www.bis.
org/statistics/derstats.
htm

Unit ofmeasurement

Note

Website

Swaps

OTC

Monthly

http://www.isda.org/
statistics/historical.
html

Data is available inaggregate

Notional amounts

http://www.sifma.org/
research/statistics.
aspx

Data is available inaggregate

Notional amounts andnumber of contracts

Interest rate, currency, Interest rate, CDS
equity, CDS

Forwards, options,
swaps

OTC

Semi-annual

2010-present

GlobalSIFMA

Commodity, currency,
equityFutures, options

Exchange

Annual2002-present

Global exchanges

FIA

http://www.worldexchanges.org/
statistics

Data is available
for each exchange
memberhttp://www.
futuresindustry.org/
volume-.asp

Data is available
for each exchange
memberNumber of contracts
Number of contracts
traded, notional
tradedturnover, open interest

Commodity, currency,
equity, ETF, interest
rates

Futures, options

Exchange

Annual1995-present

Global exchanges

WFE

http://www.cftc.gov/
marketreports/
commitmentsoftraders/index.htm

Trading transaction
data

Notional amounts,
open interest

Commodity

Futures, options,swaps

Exchange

Weekly1986-present

U.S.

CFTC

Futures, options

Exchange traded

Daily

1993-present

U.S.

CME

http://occ.gov/topics/
capital-markets/
financial-markets /trading/derivatives/
derivatives-quarterlyreport.html

Derivatives data is forbanks only

Notional amounts

http://www.cmegroup.
com/market-data/
datamine-historicaldata/equity-executionstatistics.html

Trading transaction
dataOpen interest

Interest rate, currency, Equity, foreign
equity, commodity,
exchange, interest
credit derivatives
rate, commodity

Futures, options,forwards, swaps,
credit derivatives

Exchange and OTC

Quarterly

1996-present

U.S.

OCC

Note: BIS = Bank for International Settlements; BIS (Triennial) = BIS Triennial Central Bank Survey of Foreign Exchange and Derivatives Market Activity; ISDA = International Swaps and Derivatives Association;
SIFMA = Securities Industry and Financial Markets Association; WFE = World Federation of Exchanges; FIA = Futures Industry Association; CFTC = U.S. Commodity Futures Trading Commission; OCC = U.S.
Office of the Comptroller of the Currency; CME = Chicago Mercantile Exchange.

http://www.bis.org/
statistics/triennialrep/
guidelines_cbanks.
htm

Data is available foreach sample country

Average daily turnover

Interest rate, currency, Foreign exchange andequity, commodity
interest rate

Risk category

Every 3 years

1998, 2001, 2004, 2007, 1987-present
2010

Late 1980s

Time coverage

Global(54 countries)

Global(13 countries)

BIS(TRIENNIAL)

SampleBIS

APPENDIX 3. Data sources and availability

APPENDIX 4. Largest derivatives exchanges worldwide
Ranked by number of contracts traded in 2012 (millions of contracts)

TRADING VOLUMEEXCHANGE

1 CME GroupEXCHANGE

U.S.

COUNTRY

2,890.0

11 NYSE Euronext

EuropeCOUNTRY

TRADING VOLUME
955.8

2 National Stock Exchange of India

India

2,010.5

12 International Securities Exchange

U.S.

740.0

3 Korea Exchange

S. Korea

1,835.6

13 Dalian Commodity Exchange

China

633.0

4 Eurex

Germany

1,660.2

14 Shanghai Futures Exchange

China

365.3

5 BM&FBOVESPA

Brazil

1,632.2

15 Australian Securities Exchange

Australia

355.4

6 Chicago Board Options Exchange

U.S.

1,134.3

16 Zhengzhou Commodity Exchange

China

347.1

7 Moscow Exchange

Russia1,058.9

17 ICE Futures Europe

Europe281.2

8 NASDAQ OMX

U.S.

1,010.0

18 BSE Limited

India

243.7

9 NYSE Euronext

U.S.

990.3

19 ICE Futures U.S.

U.S.

206.4

10 MCX-SX

India

959.5

20 Japan Exchange Group Inc. (Osaka)

Japan

205.1

Ranked by open interest in 2012 (millions of contracts)

EXCHANGE

COUNTRY

OPEN INTERESTEXCHANGE

COUNTRY

OPEN INTEREST

1 Chicago Board Options Exchange

U.S.

257.5

11 Moscow Exchange

Russia7.6

2 Eurex

Germany

79.4

12 MexDer

Mexico

7.4

3 CME Group

U.S.

69.9

13 NASDAQ OMX Nordic Exchanges

Nordics

6.6

4 BM&FBOVESPA

Brazil61.1

14 ICE Futures Europe

Europe

5.8

5 NYSE Euronext

Europe45.8

15 Hong Kong Exchanges and Clearing

Hong Kong

5.3

6 Australian Securities Exchange

Australia

19.8

16 Japan Exchange Group Inc. (Osaka)

Japan

4.4

7 Johannesburg Stock ExchangeSouth Africa

13.9

17 Montreal Exchange (TMX Group)

Canada4.2

8 MEFF

Spain

11.9

18 Singapore Exchange (OTC)Singapore

2.6

9 LSE Group

UK

7.8

19 Korea Exchange

Korea

2.6

10 National Stock Exchange of India

India

7.8

20 MCX-SX

India

2.4

Source: World Federation of Exchanges

84

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESREFERENCES AND APPENDIXES

85

Securitization ↑banks’ ability toincrease credit supply

Advanced credit risk
transfer techniques ↑banks’ loans

Goderis et al. (2007)

Derivatives ↑ C&I
loan growth

Brewer, Minton, andMoser (2000)

Altunbasa,
Gambacortab, andMarguez-Ibanezc(2009)

Derivatives ↑ C&I
loan growth

Brewer, Jackson, andMoser (2001)

Limited evidence
derivatives ↑ banks’credit supply

Derivatives ↑ C&I
loan growth

Zhao and Moser (2006)

Hirtle (2009)

Derivatives ↑ lending
volumePurnanandam (2007)

DERIVATIVES VS. BANK LENDING

IMPACT OF
DERIVATIVES

Total loans

Growth of bank loans(excluding interbank
positions)Credit supply:
1. New loans extended
to commercial
borrowers,
2. C&I loans,
3. Change in C&I loans,
4. Average maturity
spread(the first three variables
scaled by total C&I loans)

Quarterly change in C&I
loans

Quarterly change in C&I
loans

Quarterly change in C&I
loans

Quarterly change in totalloans and leases

MEASURE OF
DEPENDENT VARIABLE
METHODOLOGY

Two-stage cross-sectional time series
regression to address endogeneity; time
dummy variables included. The firststage employs probit estimation to create
instrumental variables for interest rate
derivatives.

Pooled cross-sectional time-series
regressions. They also split the sample
into large, mid-size, and small banking
organizations. Sample was divided byderivative users and into two time periods(1986-1990 and 1991-1994).

Two-stage estimation with Newey-Westheteroskedasticity and autocorrelationconsistent standard errors. To address
endogeneity, the first stage employs probit
estimation to create instrumental variables
for interest rate derivatives (calculated
from the probability that a firm participatesin derivatives markets).

Cross-sectional time-series regressions
are estimated separately for the samples ofderivatives users and non-users. Standard
errors are corrected for heteroskedasticity
and autocorrelation.

A dummy variable for abank that adopts advanced
credit risk management
techniques (i.e., issuingcollateralized loan
obligations)

The ratio of securitizedloans to total assets

Credit derivatives use: ameasure of the extent ofcredit protection obtained
through credit derivatives(i.e., net credit derivatives
protection, credit protection
bought, credit protection
sold)Panel data; region-year dummies; systemGMM estimator; Hansen test; ArellanoBond AR 1 and 2 tests.

First differences and system General
Method of Moments (GMM); Sargan test forserial correlation.

Basic empirical approach is fixed effectpanel regressions relating banks’ supplyof credit to credit derivatives use. Modelfurther estimated on all loans, small loans,and large loans and included interaction
effects between net credit protection andhedging.

Credit derivatives and other structured products

Instrumental variables forinterest rate derivativesuse (interest rate swaps,
options, forwards, andfutures)Dummy variable for interest
rate derivative use (interest
rate swaps and futurescontracts)

Instrumental variables forinterest rate derivativesuse (interest rate swaps,
options, forwards, andfutures)

Banks are classified as
derivatives users if theyreport such holdings for theperiod 1985-94

Interest rate derivatives

MEASURE OF DERIVATIVES(EXPLANATORYVARIABLES)

857 large banks
worldwide (but only 65
banks that have issued aCLO) from 1995 to 2004
(annually)

2,947 euro-area banksfrom 1999 to 2005
(annually)

57 large banks from Q2
1997 to Q4 2006

734 FDIC-insured
commercial banks withtotal assets greater than$300 million from 1985
to 1992 with C&I loan
portfolios

154 U.S. bank holding
companies from 1986
to 1994942 FDIC-insured
commercial banks thathave portfolios of C&I
loans and total assets
greater than $300 million
from 1996 to 2004

2,869 U.S. banks (1,079
identified as derivatives
users) from 1997 to 2003

SAMPLE

This study finds that banks that transfer risk via
credit derivatives increased lending 50 percent.
The authors compared banks that issued at least
one collateralized loan obligation to a control
group that had not.

Securitization shelters banks’ loan supply fromthe effect of monetary policy tightening andstrengthens capacity to supply new loans.

This study finds only limited evidence that banks
increase the supply of credit as they obtain
additional protection through credit derivatives,
and then only for certain types of loans andborrowers (mostly for term borrowers, and of
those, mostly larger borrowers).

This study finds that the derivative markets allowbanks to increase lending at a greater rate thanthey would otherwise. The results indicate thatinterest rate swaps and futures are associatedwith higher C&I loan growth.

This study finds that BHCs that use derivatives
increase business lending faster than banks that
do not. Additional analysis of the relationships
between derivatives use and profitability,
efficiency, and risk sensitivity are also discussed
based on descriptive statistics of the BHC sample.

This study finds a significant positive relationshipbetween derivative activity and bank lending.
The authors conclude that “the interest-rate
derivatives allow commercial banks to lessen
their exposure to changes in interest rates, whichenables banks to increase their lending activities
without increasing the total risk level faced by thebanks. This consequently increases the banks’abilities to provide more intermediation services.”

The main purpose of this study is to assessthe difference in the impact of monetary policy(change in the federal funds rate) on lending
volume between derivatives users and non-users.
The author finds that derivatives user banks’lending volume is not sensitive to rate shocks,
while lending volume declines for non-user banks.

SUMMARY OF MAIN FINDINGSAPPENDIX 5. Selected empirical studies on the impact of derivatives use (by type) on bank lending
and financial stability

86

DERIVING THE ECONOMIC IMPACT OF DERIVATIVES

Risk management(loan sales) ↑ loans
to businesses

Performance of banks
that are derivatives
users is not different
from non-users during
the financial crisis
period.

• Foreign exchange
and credit
derivatives ↑systemic risk
• Interest rate
derivatives ↓systemic risk

• Credit derivatives ↓bank’s loan spreads.
• During the crisis,banks active incredit derivatives cut
lending less.

Cyree, Huang, andLindley (2012)Rodriguez-Moreno,
Mayordomo, and Peña
(2012)

Norden, Buston, andWagner (2011)

DERIVATIVES VS. FINANCIAL STABILITY

Cebenoyan andStrahan (2004)

IMPACT OF
DERIVATIVES• Loan spreads proxiedby spread of syndicatedloans over LIBOR• Bank lending proxiedby net charge-offs
and commercial loans
scaled by total assets

Five measures areconstructed (basedon, e.g., value-at-riskand expected shortfall)
to assess banks’contributions to systemic
riskBank performanceproxied by threemeasures: buy-and-holdreturns; buy-and-holdabnormal returns; Sharpe
ratios.

C&I loans/assets andcommercial real estateloans/assets

MEASURE OF
DEPENDENT VARIABLE

Gross and net positions ofcredit derivatives (in form ofcredit default swaps) scaled
by total assets

Total fair values of five
types of derivatives: credit,
interest rate, foreignexchange, equity, andcommodity

Dummy variables
for different types ofderivatives use (interest
rate, foreign exchange, andcredit default swap)

Loan sales and loanpurchases

MEASURE OF DERIVATIVES(EXPLANATORYVARIABLES)OLS and fixed effect regressions

A panel regression analysis (Prais-Winsten
regression with correlated panelscorrected standard errors, or PCSEs)

Heckman’s two-stage procedure to control
for the selection of banks that enter into
derivatives contracts. (The first stage uses
probit regressions to estimate the decisionthat banks will use derivatives. The fitted
value of the probit regression index
function is then used to calculate inverse
Mills ratios. These ratios are included tocorrect for potential self-selection bias.
Followed by multivariate regression.)

Cross-sectional, reduced form regressions

METHODOLOGY

76 U.S. banks from thefirst quarter of 1997 to
the fourth quarter of 2009
(the subsample for thecrisis period is from 2007
to 2009)

91 large U.S. bank holding
companies (assets > $5
billion) from 2002 to 2011

335 U.S. commercial
banks from 2003 to 2005
and 2007 to 2009

All U.S. commercial
banks from the Call
Reports from June 1987
to June 1988 (quarterly)

SAMPLE

This study discusses several potential channels
through which credit derivatives affect banks’lending behavior and wider economic activity.
The authors find that (gross) credit derivatives
lower corporate bond spreads, supporting the risk
management channel, and this benefit is passed
on to borrowers. During the crisis of 2007 to 2009,
banks that use credit derivatives cut back lending
less than other banks.

This study examines the impact of derivatives
holdings on the banks’ individual contributions tosystemic risk. The findings suggest that credit andforeign exchange derivatives and derivatives held
for trading purposes contribute to systemic risk.
The authors stress that the proportion of nonperforming loans over total loans and the leverage
ratio have much stronger impact on systemic risk
than derivatives holdings.

This study assesses whether the market
performance of banks using these derivatives
differed from banks that did not use them. The
authors find no evidence indicating the widespreadassertion that derivatives use increased banks’
speculative behavior and significantly contributed
to the loss of value during the 2007-09 subprime
mortgage crisis.

The authors point out that while activity in theloan sales market is associated with more loans
to businesses, this type of loan is consideredrisky. The authors also look at the impacts ofbank activity in the loan sales market on capital
structure, profits, and risk, in addition to theimpact of lending.

SUMMARY OF MAIN FINDINGSAPPENDIX 5. Selected empirical studies on the impact of derivatives use (by type) on bank lending
and financial stability (Continued)

REFERENCES AND APPENDIXES

87

No impact on firm’s
growth opportunities

Derivatives ↑ firm valueand ↓ firm risk

Derivatives ↓ firm’s
cost of equity capital

Derivatives ↓ firm risk

­Derivatives ↑ firm value­Derivatives ↑ firm value

Derivatives ↑ firm value

Chiorean, Donohoe, andSougiannis (2012)

Bartram, Brown, andConrad (2011)

Gay, Lin, and Smith (2011)

Guay (1999)

Allayannis, Lel, and Miller(2007)

Kim, Mathur, and Nam(2006)

Allayannis and Weston(2001)

IMPACT OF
DERIVATIVES

Firm value proxied by
Tobin’s Q

Firm’s value proxied by
Tobin’s Q

Firm’s value proxied by
Tobin’s Q

Change in firms’ total risk
(defined as the annualized
standard deviation of dailystock returns)

The cost of equity is
estimated from an annual
risk premium equation.

Firm’s risk proxied by cash
flow volatility,
standard deviation of stock
returns, and market betas;
firm’s value proxied by
Tobin’s Q

Growth opportunities
proxied by R&D
expenditure, Tobin’s Q,
market-to-book ratio,
price-to-earnings ratio

MEASURE OF DEPENDENTVARIABLE

A dummy variable for foreign
currency derivatives use;gross value of foreign currency
derivatives

Financial hedge ratio
measured as the notionalamount of currency
derivatives divided by foreign
activities; operational hedge
is constructed using firm’s
information such as operating
segments and geographic
areas.

A dummy variable for foreign
currency derivatives usage

METHODOLOGYOLS regressions; logit regressions
to examine firms’ decision to
begin using derivatives.

A pooled regression model andsimultaneous equation (to accountfor endogeneity concerns related
to a firm’s derivatives use andcapital structure decisions).

Propensity score matching (allows
for a sensitivity analysis on theselection bias)

Two-stage estimation toaddress endogeneity. First
stage uses a logit model toestimate the probability thata firm uses derivatives. The
second stage estimates theimpact of derivatives on growth
opportunities.

Pooled and fixed effects
regressions

Logit regressions for the relation
between operational and financial
hedging; three-stage leastsquares for the relation between
hedging and foreign exchange
risk exposure; OLS regression for
the relation between hedging andfirm value.

Fixed effects regressions

Currency

The aggregate notional
principal of a firm’s derivatives
positions scaled by market
value of assets.

A dummy variable forderivatives use variable
and the notional amount ofderivatives holdings (also use
dummies for interest rate,
foreign currency, or commodity
derivatives)

Dummy variables for the useof exchange rate, interest
rate, and commodity pricederivatives

A dummy variable equals to 1
if the firm reports a position inderivatives; a dummy variable
for new derivatives users.

Various

MEASURE OF DERIVATIVES(EXPLANATORY VARIABLES)

720 large U.S. non-financial
firms (assets > $500 million)
from 1990 to 1995

424 U.S. firms from 1996
to 2000. Firms reporting
foreign sales are operationallyhedged, while firms reporting
only export sales are nonoperationally hedged.

1,605 foreign firms in 39
countries with exchangetraded American depositary
receipts (ADRs) (i.e., those thatare cross-listed in the U.S.)
from 1990 to 1999.

All non-financial firms in the
Compact Disclosure databasefrom June 1990 to December
1994 (335 new derivatives
users identified)A large sample of U.S. nonfinancial firms from 1992 to1996 and 2002 to 2004

6,888 non-financial firms
headquartered in 47 countries
from 1998 to 2003

3,858 U.S. non-financial, nonutility firms from 2000 to 2008
(487 new derivatives users
during this period identified)

SAMPLEThe study finds firms that face currency risk
and use currency derivatives have a 4.9%
higher value than firms that do not use
currency derivatives.

This study investigates the relationshipbetween operational and financial hedging
activities and their effects on foreignexchange risk exposure and firm value.
The results indicate that financial hedging
adds 5.4% to firm value on average andoperational hedging increases firm value by
4.8% to 17.9%.

This study finds that the use of currency
derivatives increases firm value by 9% to
20% for those with foreign risk exposure. It
also shows the extent to which this positive
association between firm value and foreign
currency derivatives depends on countries’internal and external corporate governancestructures.

This study finds that firm risk declines
following derivatives use. The author
concludes that his finding is consistent withthe use of derivatives for hedging.

This study finds firms that use derivatives
have a lower cost of equity than non-users
by 24 to 78 basis points, attributed toderivatives users having lower systemic(market) risk and lower SMB (small minus
big size) beta.

This study finds strong evidence that theuse of derivatives reduces total risk andsystemic risk. The result is weaker for the
relationship between derivatives and firm
value. The authors find little evidence thatderivative type matters.

This study finds no evidence that derivatives
use leads to more growth opportunities andreduces underinvestment.

SUMMARY OF MAIN FINDINGSAPPENDIX 6. Selected empirical studies on the impact of derivatives use (by type) on non-financial firms’ growth 		and investment opportunities

88

DERIVING THE ECONOMIC IMPACT OF DERIVATIVES

Derivatives ↓exchange-rate exposure­Derivatives ↑ firm value

No impact on firm value;
derivatives ↓ firm’s
stock price sensitivity tooil and gas prices.

­­Derivatives ↑ firm value

Allayannis and Ofek (2001)

Mackay and Moeller (2007)

Jin and Jorion (2006)

Carter, Rogers, andSimkins (2006)

IMPACT OF
DERIVATIVES

Firm value proxied by
Tobin’s Q

Firm value proxied by
Tobin’s Q

Firm value proxied by
Tobin’s Q

Exchange-rate exposure
proxied by the sensitivityof firm’s stock return to an
unanticipated change in an
exchange rate

MEASURE OF DEPENDENTVARIABLE

Hedging activity proxied by thepercentage of next year’s fuelrequirements hedged. Swaps,
collars, and options are among
airlines’ instruments

A dummy variable and theextent of hedging activities(the latter measured by theratios of total hedges to
production and reserves).
Hedging activities refers tofutures, options, or swap
contracts as well as fixed pricephysical delivery contracts
and volumetric production
payments

Dummy variables for the levelof derivatives use and typeof instrument, which include
financial (e.g., interest rates,
foreign exchange) and energyrelated derivatives

METHODOLOGYOLS regressions, weighted leastsquares, and probit estimations

Pooled OLS and Feasible
Generalized Least Squares(FGLS); year dummies included

OLS regression with time
dummies and a Huber-White
sandwich estimate of standard
errors that accounts forclustering across firms andheteroskedasticity

Simultaneous equation
regressions; GMM

Commodity

A firm’s ratio of foreign
currency derivatives to total
assetsMEASURE OF DERIVATIVES(EXPLANATORY VARIABLES)

28 airlines from 1992 to 2003
(list from Financial Managementin 2006)

119 U.S. oil and gas firms from1998 to 2001

34 oil refiners from the CRSPCompustat data set fromMarch 1985 to June 2004
(quarterly data)

A sample of S&P 500 nonfinancial firms for 1993,
monthly data (also 1992-1994,
1991-1995)

SAMPLE

This paper’s results suggest that jet fuelhedging increases the value of U.S. airline
firms by 5% to 10%.

This study finds no difference in firm value
between those that hedge and those that donot. However, the authors find that hedging
reduces a firm’s stock price sensitivity to oiland gas prices.

This study finds that in the case of oil
refiners, a risk management program
substantially enhances firm value.

This study finds that foreign currency
derivatives reduce a firm’s foreignexchange-rate exposure. The authors
conclude that firms use derivatives forhedging, not speculative purposes. Firm’s
decision to use derivatives and its relation
to exposure factors (i.e., foreign sales andtrade) are also examined.

SUMMARY OF MAIN FINDINGSAPPENDIX 6. Selected empirical studies on the impact of derivatives use (by type) on non-financial firms’ growth		and investment opportunities (continued)

APPENDIX 7. Bank-specific variables and item ID number of derivatives
from Call Reports
DEFINITION/NOTEITEM NUMBER AND NOTES1

Derivatives

Interest rate contracts +foreign exchange contracts + equity, commodity,& other contracts + credit derivatives(alternately, futures + forwards + options + swaps + credit derivatives)

UBPRE278 + UBPRA534 + UBPRA535

Interest rate contracts

Total notional amount (e.g., gross amount) of derivative interest rate
contracts

UBPRE279

Foreign exchange contracts

Total notional amount (e.g., gross amount) of derivative foreign exchange
contracts

UBPRE280

Equity, commodity, and other contracts

Total notional amount of derivative equity, commodity, and othercontracts

UBPRE281

Credit derivatives

Credit derivatives on which the bank is guarantor + credit derivatives on
which the bank is beneficiaryUBPRA534 + UBPRA535

Futures

Futures interest rate contracts + futures foreign exchange contracts+ futures equity exchange contracts + futures commodity and othercontracts

RCFD8693 + RCFD8694 +RCFD8695 + RCFD8696

Forwards

Forward interest rate contracts + forward foreign exchange contracts+ forward equity exchange contracts + forward commodity and othercontracts

RCFD8697 + RCFD8698 +RCFD8699 + RCFD8700

Options

Written options + purchased options, both exchange-traded and overthe-counter

UBPRE283 + UBPRE286

Derivatives variables

Options (exchange-traded only)

Exchange-traded written options + Exchange-traded purchased options

UBPRE284 + UBPRE287

Options (OTC only)

Over-the-counter written options + over-the-counter purchased options

UBPRE285 + UBPRE288

Swaps

Interest rate swaps + foreign exchange swaps + equity swaps +commodity and other swaps

UBPRE289

Total loans and leases held for sale + loans and leases not held for sale,
net of unearned income (gross)

UBPR5369 + UBPRB528

Bank-specific variables
Total loans
Commercial and industrial (C&I) loans

Total commercial and industrial loans

UBPR1766

Individual loans

Domestic-office loans to individuals for household, family, and otherpersonal expendituresUBPRD665
UBPR1410

Real estate loans

Total of domestic-office loans secured by real estate

Total assets (consolidated)

Amount of total assets

UBPR2170

Total equity capital

Total bank equity capital plus non-controlling (minority) interests inconsolidated subsidiariesUBPRG105

Liquid assets

Calculated from: cash + federal funds sold and securities purchased
under agreements to resell + liquid securities

Cash

Amount of cash.

RCFD0010

Federal funds sold and securities
purchased under agreements to resell

Federal funds sold in domestic offices + securities purchased underagreements to resell.

RCONB987 + RCFDB989

Liquid securities

U.S. Treasury securities + U.S. government agency obligations +securities issued by states and political subdivisions (securities held-tomaturity and available-for-sale)

RCFD0211 + RCFD1289 + RCFD1294 + RCFD8496 +RCFD1287 + RCFD1293 + RCFD1298 + RCFD8499

Unused commitments as a percent of total
assetsThe unused portion of all commercial & industrial loan commitments,
commitments for loans to financial institutions, and all othercommitments divided by total assets

UBPRE266

Net interest margin

Net interest income as a percentage of average earning assets. Total
interest income on a tax-equivalent basis, less total interest expense,
divided by the average of the respective asset accounts involved ingenerating interest income

UBPRE018

Return on average assets

Net Income as a percentage of average assets. Average assets = a yearto-date average of the average assets reported in the Report of Condition
Schedule RC-K. Thus for the first quarter of the year, the average assets
from Call Schedule RC-K quarter will appear, while at the end of year,
assets for all four quarters would be averaged.

UBPRE013

1.

Items prefixed with “UBPR” are taken from the Uniform Bank Performance Reports; items prefixed with “RCFD” or “RCON” are taken from the Call Reports(Consolidated Reports of Condition and Income). RCFD refers to consolidated data and RCON refers to domestic data. For RCFD variables with missing data(no foreign transactions), RCON variables are used.

REFERENCES AND APPENDIXES89

APPENDIX 8. Summary statistics and first-stage regression results for banks’ 		
derivatives use and lending
In the first stage of the Heckman two-stage model, we use the probit model to estimate the determinantsof the decision of banks to hedge with derivatives. The estimated parameters from this stage are used to
calculate the inverse Mills ratio, which is then included as an explanatory variable in the second-stage
regression, which tests the impact of derivatives use on bank lending. This estimation procedure takes into
account the selection bias that may arise because only certain banks participate in the derivatives market.
In the probit model, the dependent variable is a binary variable of derivative usage (yes = 1 for users of any
type of derivatives and no = 0 for non-users). Each model includes time (quarter-year) dummies to capture
the change in derivatives use over time. The standard errors from estimates are clustered at the bank level.
Hedging theories predict that banks facing a higher likelihood of financial distress are more likely to hedge(Diamond, 1984; Smith and Stulz, 1985). Therefore, we include bank condition variables that reflect thelikelihood of (non)distress: banks’ capital adequacy (equity-to-total asset ratio), profitability (net interest
margin and return on assets), and liquidity (liquid-to-total asset ratio). We also include various categories ofloans (i.e., C&I loans and consumer loans, both relative to total loans) and the unused loan commitmentsto-total assets ratio since the literature points out different effects of loan types on hedging decisions(e.g., Cebenoyan and Strahan, 2004; Minton, Stulz, and Williamson, 2009). Lastly, we control for banks’size because large banks are expected to make greater use of derivatives due to economies of scale in risk
management. Appendix Table 8.1 reports the descriptive statistics of all variables in the model (both firstand second-stage regressions).
APPENDIX TABLE 8.1 Descriptive statistics
VARIABLESMEAN

MEDIAN

STD. DEV.

Growth of C&I loans (%)

1.71

0.97

17.83

Growth of total loans (%)

1.92

1.26

7.88

Growth of consumer loans (%)

-0.55

-1.16

19.20

Growth of real estate loans (%)

2.08

1.28

10.74

C&I loans/total loans (%)

15.74

13.64

11.20

Consumer loans/total loans (%)

6.35

3.17

10.16

Total loans/total assets (%)

66.85

68.88

13.55
74.46

Total assets ($ billions)

8.71

0.83

In(total assets)

0.19

-0.19

1.21

Equity/total assets (%)

10.17

9.53

3.15

Net interest margin (%)

3.88

3.82

1.30

Return on assets (%)

0.88

0.96

1.68

Liquid/total assets (%)

16.67

14.52

10.42

Unused commitments/total assets (%)

8.95

6.35

34.02

In(Unused commitments/total assets)

1.72

1.85

1.04

Use of derivatives

0.46

0

0.50

Use of interest rate derivatives

0.43

0

0.50

Use of foreign exchange derivatives

0.07

0

0.25

Use of other derivatives

0.07

0

0.25

Use of credit derivatives

0.03

0

0.17

In(derivatives/total assets)

0.38

0

9.01

In(interest rate derivatives/total assets)

32.26

0

844.61
61.94

In(foreign exchange derivatives/total assets)

3.93

0

In(other derivatives/total assets)

0.40

0

5.52

In(credit derivatives/total assets)

1.41

0

23.40

90

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESAppendix Table 8.2 presents the marginal effects from the probit estimations. Model 1 includes the ratio of
total loans to total assets, while in Model 2 we replace the total loan variable with two categories: C&I loanand consumer loan-to-total loan ratios. We find that only the C&I loan-to-total loan ratio has a significant andpositive coefficient, suggesting that banks with more C&I loans are more likely to participate in the derivatives
market (Model 2). This is in line with Minton, Stulz, and Williamson (2009), who find that banks with C&I
loans are more likely to hedge with credit derivatives because the majority of firms participating in the credit
default swap market are either large, investment-grade U.S. firms or foreign multinational companies.
According to DeYoung and Yom (2008), consumer loans are usually granted through a transaction-based
approach in which banks apply credit scoring. Consumer loans also tend to be more homogeneous and are madein large numbers. DeYoung and Yom argue that these characteristics of the consumer lending process generateless interest rate risk, helping to explain why we find no significant effect of consumer loans on hedging decisions.
Regarding bank control variables, our findings are consistent with hedging theories that predict the likelihoodof hedging increases with the likelihood of financial distress. In particular, we find that banks with lower
capital-to-asset ratios, lower profitability (as measured by net interest margin), and less liquidity—suchinstitutions are more likely to face financial distress—are more likely to use derivatives.
We also find a positive and significant effect of bank size, supporting the notion that larger banks enjoy
better economies of scale in risk management than smaller banks. We also find that the increase inunused loan commitments significantly increases the likelihood that banks will hedge with derivatives.
Unused commitments are subject to risk because interest rates may change between the time a loan
commitment contract is granted and when the firm takes it down. Banks with larger unused loan
commitments, therefore, are more likely to use derivatives.
APPENDIX TABLE 8.2 Probit estimation of the determinants of derivatives use
MODEL 1

MODEL 2In(total assets)

0.226***(0.014)0.225***(0.015)Equity/total assets-0.009**(0.004)-0.010**(0.004)

Net interest margin-0.009(0.018)-0.028**(0.014)Return on assets-0.00003(0.0003)

0.002(0.007)

Liquid/total assets-0.002*(0.001)-0.003**(0.001)In(unused commitments/total assets)

0.059***(0.011)0.057***(0.012)

Total loans/total assets-0.00003(0.001)

C&I loans/total loans

0.003**(0.001)

Consumer loans/total loans-0.00004(0.001)Time-fixed effects

Yes

Yes

Number of observations

43,154

38,637

Number of banks

1,319

1,319

Note: The dependent variable is a binary variable for derivatives use; 0 otherwise. The regression results are from the first stage of the two-stage
Heckman selection bias model, with correction for time-fixed effects. Clustered robust standard errors of the coefficients are included in parentheses.
***, **, and * indicate that the coefficients are statistically significant at 1%, 5%, and 10%, respectively.

REFERENCES AND APPENDIXES91

APPENDIX 9. Additional analysis and sensitivity checks for derivatives’
impact on U.S. bank lending
APPENDIX TABLE 9.1 Derivatives variables measured by the ratio
of notional amounts to total assets
MODEL 1

MODEL 2In(total assets)-2.877*(1.657)-2.482(1.789)Equity/total assets

0.151(0.126)0.132(0.133)

Net interest margin-1.107***(0.306)-1.167***(0.321)Return on assets

0.413***(0.091)

0.422***(0.093)Liquid/total assets-0.001(0.025)-0.005(0.025)In(unused commitments/total assets)

2.229***(0.537)2.345***(0.566)Inverse Mills ratio

13.915***(3.920)14.897***(4.254)In(total derivatives/total assets)

0.514**(0.226)In(interest rate derivatives/total assets)

0.593**(0.237)In(foreign exchange derivatives/total assets)-0.589(0.715)In(other derivatives/total assets)-1.232(1.044)In(credit derivatives/total assets)-0.353(1.006)

Constant-17.540***(4.325)-18.348***(4.556)Time-fixed effects

Yes

Yes

Bank-fixed effects

Yes

Yes

Number of observations

36,72336,724

Number of banks

1,286

1,286

Note: The dependent variable is the growth of C&I loans. All explanatory variables are lagged one quarter. The regression results are from the second
stage of the two-stage Heckman selection bias model, with correction for both bank- and time-fixed effects. Inverse Mills ratio is obtained from thefirst stage of the Heckman model (Appendix Table 8.2). Clustered robust standard errors of the coefficients are included in parentheses. ***, **, and *indicate that the coefficients are statistically significant at 1%, 5%, and 10%, respectively.

92

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESAPPENDIX TABLE 9.2 Bank lending is captured by growth of loan categories
DEPENDENT VARIABLES
MODEL 1TOTAL LOANS

MODEL 2
CONSUMER LOANS

MODEL 3
REAL ESTATE

In(total assets)-6.088***(0.908)-8.100***(1.313)-7.681***(1.516)Equity/total assets

0.317***(0.122)0.269**(0.133)0.383*(0.226)

Net interest margin

0.151(0.201)-0.443(0.301)0.590*(0.338)Return on assets

0.172(0.107)0.498**(0.205)0.208(0.168)Liquid/total assets

0.110***(0.025)0.030(0.029)0.137***(0.032)In(unused commitments/total assets)

0.634**(0.266)0.244(0.431)-0.054(0.593)Inverse Mills ratio-1.130(2.169)-3.688(3.151)-5.011(3.712)

Total derivatives

0.073(0.168)0.081(0.339)0.083(0.175)Constant-3.704(2.553)0.543(3.800)-0.328(3.747)

Time-fixed effects

Yes

Yes

Yes

Bank-fixed effects

Yes

Yes

Yes

Number of observations

37,330

37040

37133

Number of banks

1,318

1,303

1,311

Note: The dependent variable is the growth of total loans-to-total assets ratio, the growth of consumer loans to total loans, and the growth of realestate loans to total loans. The main explanatory variable (total derivatives use) is a dummy variable indicating 1 for use by a bank in a given quarter; 0
otherwise. All explanatory variables are lagged one quarter. The regression results are from the second stage of the two-stage Heckman selection bias
model, with correction for both bank- and time-fixed effects. Inverse Mills ratio is obtained from the first stage of the Heckman model (Table Appendix
2.2). Clustered robust standard errors of the coefficients are included in parentheses. ***, **, and * indicate that the coefficients are statisticallysignificant at 1%, 5%, and 10%, respectively.

REFERENCES AND APPENDIXES93

APPENDIX TABLE 9.3 Includes interactions with crisis dummy variables
In(total assets)
Equity/total assets

MODEL 1

MODEL 2

-2.573-2.637

MODEL 3-2.601(1.628)(1.626)(1.626)

0.135

0.138

0.137

(0.125)(0.125)(0.125)-1.149***-1.141***-1.145***(0.306)(0.307)(0.306)

0.423***

0.422***

0.422***(0.092)(0.092)(0.092)-0.005-0.005-0.005(0.025)(0.025)(0.025)2.313***

2.300***

2.308***(0.537)(0.537)(0.536)Inverse Mills ratio

14.749***14.594***14.684***(3.915)(3.913)(3.909)Total derivatives

1.142***

1.020***

1.040***(0.317)(0.321)(0.306)

Crisis1 (Q4 2007-Q3 2009)

6.991***

Use of derivatives x Crisis1-1.032**

Net interest margin
Return on assets
Liquid/total assets
In(unused commitments/total assets)(1.655)(0.400)
Crisis2 (Q4 2007–Q3 2008)

8.702***(1.457)

Use of derivatives x Crisis2-0.733(0.528)Crisis3 (Q4 2008–Q3 2009)

2.829***

Use of derivatives x Crisis3-0.358*(0.443)(0.187)
Constant-20.576***-18.171***-20.498***(4.312)(4.357)(4.304)Time-fixed effects

Yes

Yes

Yes

Bank-fixed effects

Yes

Yes

Yes

Number of observations

36,72436,72436,724

Number of banks

1,286

1,286

1,286

Note: The dependent variable is the growth of C&I loans. All explanatory variables, except crisis dummies, are lagged one quarter. The regression
results are from the second stage of the two-stage Heckman selection bias model, with correction for both bank- and time- fixed effects. Inverse Mills
ratio is obtained from the first stage of the Heckman model (Appendix Table 8.2). Clustered robust standard errors of the coefficients are included inparentheses. ***, **, and * indicate that the coefficients are statistically significant at 1%, 5%, and 10%, respectively.

94

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESAPPENDIX 10. Summary statistics and first-stage regression results for
non-financial firms’ derivatives use and firm value
In the first stage of the Heckman two-stage model, we use a probit model to estimate the determinants of
non-financial firms’ decision to hedge with derivatives. The estimated parameters from this stage are used
to calculate the inverse Mills ratio, which is included as a control variable in the second-stage regression.
That tests the impact of derivatives use on firm value. This estimation procedure takes into account the
selection bias that may arise with firms that choose to hedge with derivatives.
In the probit model, the dependent variable is binary variable derivatives use (1 for users of any type ofderivative and 0 for non-users). Each model includes time (year) dummies to capture the change in use.
The standard errors from estimates are clustered at the bank level.1
Financial hedging theory, which originated from violations of perfect market assumptions, states that risk
management strategies, such as hedging with derivatives, can add value if they mitigate sources of financial
distress, such as cash-flow volatility, that could lead to the risks of underinvestment and bankruptcy(Froot, Scharfenstein, and Stein, 1993, and Myers, 1977). (See Part 3.1 for a discussion of this issue and theassociated literature.) Therefore we include firm variables that reflect the likelihood of financial distress aswell as those associated with the ability to initiate hedging with derivatives in general.
These variables include: firm size (total assets), as empirical studies have shown that larger firms are more
likely to use derivatives due to the economy of scale required to initiate a hedging program; leverage (debtto-equity ratio), as the costs associated with financial distress incurred by debt (as well as interest rate risk)
may increase hedging; the quick ratio (a measure of liquidity), since the greater availability of internal funds
may mitigate the need for hedging; investment opportunities (R&D-to-sales), since previous research (Gezcy,
Minton, and Schrand, 1997, and Nance, Smith, and Smithson 1993) has shown that current investment
in R&D projects predicts future investment, which may necessitate hedging activities; and foreign sales,
since foreign exchange exposure has been shown to spur derivatives use. Appendix Table 10.1 reports thedescriptive statistics of all variables in first- and second-stage regression models.
APPENDIX TABLE 10.1 Descriptive statistics
MEAN

MEDIAN

STD. DEV.

In(market-to-book ratio)

VARIABLES

1.07

1.02

0.73

In(cash flow)

6.42

6.38

1.36

In(total assets)

9.21

9.15

1.28

In(debt-to-equity ratio)

3.65

3.92

1.53

Return on assets

7.35

6.71

7.75

R&D-to-sales

3.10

0

6.04

Capital expenditure to sales

7.62

3.43

16.77

Quick ratio

1.21

0.90

1.11

Foreign sales

0.75

1

0.43

Use of derivatives

0.82

1

0.39

Use of foreign exchange derivatives

0.58

1

0.49

1.

We also include industry-specific fixed effects. Including both firm fixed effects and industry fixed effects is not possible due to multicollinearity.

REFERENCES AND APPENDIXES

95

Table Appendix 10.2 presents the marginal effect from the probit estimations. Our findings are consistent
with hedging theories that predict the likelihood non-financial firms’ derivatives use. Specifically, we findthat firms with higher total assets, higher leverage, and foreign sales are more likely to use derivatives.
Investment opportunities, as measured by R&D-to-sales ratios, are positive but not significant, and thequick ratio is positive, contrary to prediction, but not significant.		
APPENDIX TABLE 10.2 Probit estimation of the determinants of non-financial firms’
derivatives use
MODEL 1In(total assets)

0.025**(0.012)In(debt-to-equity Ratio)

0.015*(0.009)Quick ratio

0.006(0.011)

R&D/sales

0.0005(0.002)Foreign sales

0.188***(0.048)Time-fixed effects

Yes

Industry-fixed effects

Yes

Number of observations

3,749

Number of firms

416

Note: The dependent variable is a binary variable for derivatives use indicating 1 for use; 0 otherwise. The regression results are from the first stage ofthe two-stage Heckman selection bias model, with correction for time-fixed effects. Clustered robust standard errors are included in parentheses. ***,**, and * indicate that the coefficients are statistically significant at 1%, 5%, and 10%, respectively.

96

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESAPPENDIX 11. Additional analysis and sensitivity checks for derivatives’
impact on firm value
APPENDIX TABLE 11.1 Impact of derivatives—cash flow and adjusted market book values
MODEL 1
CASH FLOW

MODEL 2
MARKET BOOK > 0

In(total assets)

0.867***(0.013)-0.137***(0.010)In(debt-to-equity ratio)-0.073***(0.011)0.085***(0.012)Return on assets

0.043***(0.003)0.033***(0.003)

Capital expenditures/sales-0.021***(0.003)-0.003***(0.001)

R&D/sales

0.027***(0.003)0.119***(0.002)Inverse Mills ratio-0.089(0.140)0.110(0.103)Use of derivatives

0.050(0.040)0.053*(0.028)-1.365***(0.162)

1.735***(0.134)Time-fixed effects

Yes

Yes

Industry-fixed effects

Yes

Yes

Number of observations

2,889

3,165

390

389

Constant

Number of firms

Note: In Model 1, the dependent variable is the natural log of a firm’s cash flow. In Model 2, the dependent variable is firm value (measured as thenatural log of the market-to-book ratio). The main explanatory variable in both Model 1 and 2 (use of derivatives) is a dummy variable indicating 1 forderivatives use by a non-financial firm in a given year; 0 otherwise. All explanatory variables are lagged one year. The reported results are from thesecond stage of the two-stage Heckman selection bias model, with correction for both industry- and time-fixed effects. Inverse Mills ratio is obtained
from the first stage of the model (see Appendix 10). Clustered robust standard errors of the coefficients are included in parentheses. ***, **, and *indicate that the coefficients are statistically significant at 1%, 5%, and 10%, respectively.

APPENDIX TABLE 11.2 Impact of derivatives on firm value: Non-crisis and crisis years
MODEL 1NON-CRISISMODEL 2CRISIS

In(total assets)-0.134***(0.014)-0.137***(0.013)In(debt-to-equity ratio)

0.053***(0.019)0.113***(0.016)Return on assets

0.033***(0.004)0.032***(0.003)

Capital expenditures/sales-0.004***(0.001)-0.003***(0.001)0.018**(0.002)0.020***(0.003)Inverse Mills ratio-0.011***(0.132)0.240(0.156)

Use of derivatives

0.003(0.039)0.107***(0.041)

1.925***(0.171)

1.063***(0.189)Time-fixed effects

Yes

Yes

Industry-fixed effects

Yes

Yes

Number of observations

1,420

1,853

416

416

R&D/sales

Constant

Number of firms

Note: In Model 1, the dependent variable is firm value (measured as the natural log of the market-to-book ratio) in years 2003 to 2007. In Model 2, thedependent variable is firm value (measured as the natural log of the market-to-book ratio) in years 2008 to 2012. The main explanatory variable inboth models (use of derivatives) is a dummy variable indicating 1 for derivatives use by a non-financial firm in a given year; 0 otherwise. All explanatory
variables are lagged one year. The reported results are from the second stage of the two-stage Heckman selection bias model, with correction for bothindustry- and time-fixed effects. Inverse Mills ratio is obtained from the first stage of the model (see Appendix 10). Clustered robust standard errors of
the coefficients are included in parentheses. ***, **, and * indicate that the coefficients are statistically significant at 1%, 5%, and 10%, respectively.

REFERENCES AND APPENDIXES97

APPENDIX 12. Impacts of bank lending and firm value on economic growth:
vector autoregressive analysis
We apply the vector autoregressive model (VAR) to estimate the relationships between banks’ C&I
lending, firm value (market-to-book ratio), and real GDP. Real GDP and C&I loans enter the estimationsin the natural logarithm form, while M/B is in ratio. The estimations are performed in three steps.
Step 1. Testing for stationarity
For a regression analysis between two time series to show a meaningful relationship, each series needs
to be stationary. Non-stationary data is identified roughly when the data depends on time (specifically,
when their mean and variance are not constant over time). Therefore, any two non-stationary series may
establish a statistically significant relationship even when they are theoretically unrelated (a so-calledspurious relationship). The augmented Dickey-Fuller (ADF) procedure is used to test stationarity properties.
In many cases, the first difference or the change in the value of the variable from period t-1 to t is found to
be stationary, although its level value is identified as non-stationary.
Appendix Table 12.1 presents the ADF test statistics for both the level and first difference (denoted by Δ) of thethree variables in our model. The results indicate that the first difference of log of real GDP, log of C&I loans,and M/B is stationary (the non-stationary null hypothesis of the first difference is rejected at the 1 percent
significance level). Therefore, we proceed by using the first difference of these three variables in the VAR model.
APPENDIX TABLE 12.1 Test for stationarity
ADF TEST STAT

1% CRITICAL VALUE

5% CRITICAL VALUE

H0: the level of the variable is non-stationary
Log of real GDP-2.009-3.662-2.964Log of C&I loans-1.659-3.668-2.966M/B-3.478-3.662-2.964ΔLog of real GDP-2.899***-2.639-1.950ΔLog of C&I loans-3.641***-2.641-1.950ΔM/B-8.686***-2.639-1.950H0: the first difference of the variable is non-stationary

Note: *** indicates significance at the 1 % level. The reported ADF statistics were generated by a model with constant and one lag. Including a trend
term (not reported) results in a similar conclusion.

Step 2. Testing for co-integration relationshipThe next step is to test for a long-run theoretical or equilibrium relationship between bank lending, firm
value, and real GDP. Any two variables may have a fundamental relationship even though they are nonstationary if they share similar stochastic trends. In this case, the two variables are said to be cointegrated.
To elaborate, the existence of the cointegration relationship between banks’ C&I loan volume and real
GDP indicates that when banks provide more C&I loans, real GDP will also change. Cointegration,
however, does not tell the direction of causality. In this example, the existence of the long-run relationshipalso indicates that a change of conditions in the real economy affects banks’ C&I loan volume.

98

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESAppendix Table 12.2 reports the cointegration test result, which shows that the variables in the model
are cointegrated. The null hypothesis that the variables are not cointegrated (r = 0) is rejected, and thenull hypothesis that there is one cointegrating equation (r ≤ 1) is accepted. In other words, evidence of
cointegration implies that the relationships among the three variables are not spurious, that there is atheoretical relationship among bank lending, firm value, and real GDP, and that they are in equilibriumin the long run.
APPENDIX TABLE 12.2 Co-integration test statistics
TRACE TESTMAXIMUM EIGENVALUE TEST

H0

Test statistics

5% critical value

Test statistics

r=0

35.14

29.68

22.98

5% critical value
20.97

r≤1

12.16

15.41

9.07

14.07

Note: This table reports results from Johansen’s cointegration tests based on the three-variable model (C&I loans, M/B, and real GDP) from Q1 2003
to Q3 2012; r refers to the number of cointegrating vectors.

Step 3. Vector error correction model
Next, we estimate the causal relationships of these three variables. Having determined one cointegration
vector from Step 2, we apply the vector error correction (VEC) model. This is a special form of the vector
autoregressive used when the time-series variables in the model are cointegrated. The VEC model gives
estimates of the magnitude of a change in one variable in response to a change in other variables over the
short run, as well as the speed of the change (captured by the so-called correction error coefficient).2
Appendix Table 12.3 reports the estimation results. The first equation (ΔLog of real GDP as the dependent
variable) shows that 1 percent growth of C&I loans would increase quarterly GDP growth by 0.013 percent,and a one-unit rise in M/B increases economic growth by 0.0043 percent. Note that these marginal effects
are not significant at a traditional level. The insignificance of estimates in the VEC model only reflects thelack of short-run responses to real GDP as the result of changes in bank lending and firm value in each quarter.
It is possible that the estimates can become significant when they are captured for a longer period orseveral quarters. Long-run relationships of these variables were established in the previous step through thecointegration test. Since the cointegration test does not provide marginal effects, we use these estimates from
Appendix Table 12.3 as a rough measure of the impacts of bank lending and firm value on economic growth.

2.

The result in Appendix Table 12.3 indicates that C&I loan growth and M/B respond to changed economic conditions. This result is captured by the statisticalsignificance of the error correction terms (αCE) in the C&I loans and M/B equations. The error correction terms also show the short-run adjustment of thesevariables in the model to reach the long-term equilibrium.

REFERENCES AND APPENDIXES99

APPENDIX TABLE 12.3 Vector error correction estimates
DEPENDENT VARIABLE
(1)

(2)

(3)

ΔLOG OF REAL GDP

ΔLOG OF C&I LOANS

ΔM/B

ΔLog of real GDP (-1)

0.574 (0.170)***0.918 (1.275)

155.276 (63.546)**

ΔLog of C&I loans (-1)

0.013 (0.019)

0.253 (0.143)*

23.225 (7.125)***

ΔM/B (-1)

0.00043 (0.0004)-0.006 (0.003)**-0.060 (0.148)

αCE-0.035 (0.047)

0.905 (0.355)**-76.80 (17.699)***Note: Standard errors are in parentheses. ***, **, * indicate significance at the 1%, 5%, and 10% levels, respectively.

We perform additional analyses by rerunning all steps for bivariate regressions among the three variables. The
results are shown in models (1) – (3) in Appendix Table 12.4 (only the regression with ΔLog of real GDP being
the dependent variable is reported). In addition, we use the weighted average instead of a simple average tocalculate quarterly M/B values for sample non-financial firms in the S&P 500 (Models 2 and 4).3 Based on thesealternative specifications, we find that a 1 percent increase in banks’ C&I lending is associated with an increasein quarterly real GDP from 0.008 to 0.02 percent. For the impacts of M/B, we find that a one-unit increase infirm value is associated with an expansion of quarterly real GDP of 0.0039 to 0.006 percent.
APPENDIX TABLE 12.4 Vector error correction estimates based onalternative model specifications
BIVARIATE MODELBIVARIATE MODEL

(1)

(2)

ΔLog of real GDP (-1)

0.442
(0.198)**ΔLog of C&I loans (-1)

0.008
(0.019)0.532***(0.167)

BIVARIATE MODEL
(3)

(4)

0.532
(0.146)***0.541(0.215) **0.020(0.021)

ΔM/B (-1)

0.00039(0.0004)

Δweighted M/B (-1)

0.005
(0.007)

Note: Standard errors are in parentheses. ***, **, * indicate significance at the 1%, 5%, and 10% levels, respectively.

3.

100

TRIVARIATE MODEL

For each quarter, the weighted average of the market-to-book value of the sample firms is calculated from ∑ market value/∑ book value.

DERIVING THE ECONOMIC IMPACT OF DERIVATIVES

0.006(0.008)APPENDIX 13. Estimated regression results of the impact of futures contracts
on the U.S. economy via bank lending and non-financial firms’
growth opportunities
APPENDIX TABLE 13.1 Impact of futures use on bank lending
MODEL 1:
FULL SAMPLE PERIOD
In(total assets)
Equity/total assets
Net interest margin
Return on assets
Liquid/total assets
In(unused commitments/total assets)
Inverse Mills ratio
Use of futures contracts

MODEL 2:CRISIS PERIODMODEL 3:
AFTER ONSET OF CRISIS-2.654*-18.950-3.415(1.703)(13.642)(4.998)0.143-0.1970.154(0.125)(0.543)(0.280)-1.110***-1.675-2.150***(0.310)(1.460)(0.805)0.420***

0.665***

0.515***(0.093)(0.192)(0.132)-0.003-0.024-0.034(0.025)(0.135)(0.054)2.257***

3.540

4.492***(0.535)(3.184)(1.162)14.068***24.359*24.922*(3.890)(31.430)(13.279)

0.784

3.767

5.010 15%

(1.703)(4.094)(3.459)-17.383***-13.650***-20.434***(4.326)(22.220)(10.833)

Time-fixed effects

Yes

Yes

Yes

Bank-fixed effects

Yes

Yes

Yes

Number of observations

36,724

9,571

22,113

Number of banks

1,286

1,069

1,283

Constant

Note: The dependent variable is the growth of C&I loans. The main explanatory variable (use of futures contracts) is a dummy variable indicating 1 fora bank that reports a futures position in a given quarter; 0 otherwise. All explanatory variables are lagged one quarter. The full sample period is from
Q1 2003 to Q3 2012, the crisis period is from Q3 2007 to Q4 2009, and the period after the onset of the crisis is from Q3 2007 to Q3 2012. The reported
results are from the second stage of the two-stage Heckman selection bias model, with correction for both bank- and time-fixed effects. Inverse Mills
ratio is obtained from the first stage of the model (see Appendix 8). Clustered robust standard errors of the coefficients are included in parentheses.
***, **, and * indicate that the coefficients are statistically significant at the 1%, 5%, and 10% levels, respectively.

REFERENCES AND APPENDIXES101

APPENDIX TABLE 13.2 Impact of futures use on firm value
MODEL 1:
FULL SAMPLE PERIOD
In(total assets)
In(debt-to-equity ratio)
Return on assets
Capital expenditures/sales
R&D/sales
Inverse Mills ratio
Use of futures contracts
Constant

MODEL 2:CRISIS PERIOD-0.137***-0.124***(0.010)(0.020)0.085***

0.105***(0.012)(0.023)0.033***

0.026***(0.003)(0.004)-0.003***-0.003**(0.001)(0.001)0.019***

0.027***(0.002)(0.003)

0.071

0.176

(0.099)(0.208)

0.001

0.040

(0.036)(0.077)1.792***

1.399***(0.130)(0.245)Time-fixed effects

Yes

Yes

Industry-fixed effects

Yes

Yes

Number of observations

3,273

734

Note: The dependent variable is firm value (measured as the natural log of the market-to-book ratio). Model 1 uses the sample period from 2003 to
2012. In Model 2, the sample period is from 2008 to 2009. The main explanatory variable in both models (use of futures contracts) is a dummy variableindicating 1 for derivatives use by a non-financial firm in a given year; 0 otherwise. All explanatory variables are lagged one year. The reported results
are from the second stage of the two-stage Heckman selection bias model, with correction for both industry- and time-fixed effects. Inverse Mills ratio
is obtained from the first stage of the model (see Appendix 10). Clustered robust standard errors of the coefficients are included in parentheses. ***, **,and * indicate that the coefficients are statistically significant at the 1%, 5%, and 10% levels, respectively.

APPENDIX TABLE 13.3 Vector error correction estimates for the crisis period

DEPENDENT VARIABLE
(1)

(2)

ΔLOG OF REAL GDP

ΔLOG OF C&I LOANS

(3)
ΔM/B

ΔLog of real GDP (-1)

0.535 (0.238)**

1.871 (1.321)

240.317 (76.837)***

ΔLog of C&I loans (-1)

0.0104 (0.030)

0.312 (0.167)*

24.991 (9.698)***ΔM/B (-1)

0.0008 (0.0005)*-0.005 (0.003)*-0.066 (0.167)

αCE-0.034 (0.067)

0.542 (0.374)-93.928 (21.776)***

Note: Since there are too few observations to estimate the impact of bank lending and firm value on economic growth from 2008 to 2009
(i.e., eight observations based on the quarterly data) using the VAR technique, the 2006-12 period is used for these estimations. Standard errors
are in parentheses. ***, **, * indicate significance at the 1%, 5% and 10% levels, respectively.

102

DERIVING THE ECONOMIC IMPACT OF DERIVATIVESAbout the Authors
Apanard (Penny) Prabha is a senior economist at the Institute whose research expertise is in financial
institutions and international finance. Her recent Milken Institute publications tackle current banking
and monetary policy issues relating to the causes and consequences of the global financial crisis.
Prabha’s research work has also been published in many peer-reviewed scholarly journals and presented
at international economic and finance conferences. She has authored and co-authored various empiricalresearch studies on global banking and capital markets that have been published in the Journal of International
Money and Finance, Journal of Banking Regulations, International Review of Finance, Open Economies
Review, Journal of International Financial Markets, Institutions & Money, and Journal of Financial
Economic Policy, among others. Prior to joining the Institute, Prabha was an assistant professor of economics
at the University of Illinois at Springfield. During that time, she also traveled for collaborative research
projects at the Center for Law, Economics and Financial Institutions at the Copenhagen Business School in
Denmark and the University of Chicago-UTCC Research Center at the University of the Thai Chamber ofCommerce in Thailand. While completing her Ph.D., Prabha also was a visiting scholar at the Claremont
Institute for Economic Policy Studies and the Freeman Program in Asian Political Economy at theClaremont Colleges, as well as a lecturer of economics at Pitzer College and the University of Redlands.
Keith Savard is senior managing economist at the Milken Institute. He has extensive executive management
experience with expertise in evaluating the interrelationship between economic fundamentals and activity
in global financial and commodity markets. He also has a background in sovereign risk analysis and applying
a disciplined economic approach to investment portfolio decision-making. Prior to joining the Milken
Institute, Savard was director of economic research and chief economist at Samba Financial Group (formerly
Saudi American Bank) in London. He also held positions at Zurich Investments, the Institute of International
Finance, the U.S. Department of State, and the Board of Governors of the Federal Reserve System.
Heather Wickramarachi is an economist with the Milken Institute. Her areas of expertise include emerging
markets, international finance and foreign direct investment. Wickramarachi received an M.A. in social
science, with a focus on international political economy and quantitative methodology, from the Universityof California, Irvine, where she is currently a Ph.D. student. She also studied energy planning andsustainable development at the University of Oslo in Norway. She presented her paper, "Do South-South
Bilateral Investment Treaties Increase Foreign Direct Investment? Intra-Regional Evidence from MENACountries," at the International Studies Association's annual conference. Most recently she presented
a paper, "Institutions, FDI, and Financial Deepening," co-authored with Tong Li, at the Transparency
Research Conference at HEC Paris.

104

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