J. Account. Public Policy 34 (2015) 291–318
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J. Account. Public Policy
journal homepage: www.elsevier.com/locate/jaccpubpol
Growth in financial derivatives: The public policy
and accounting incentives
A. Rashad Abdel-khalik a,⇑, Po-Chang Chen b
a
b
University of Illinois, United States
Miami University, United States
a b s t r a c t
During the period 1995–2012, U.S. financial institutions had contributed significantly to the growth in financial derivatives. The
notional amount of total derivatives held by the 25 largest U.S. bank
holding companies grew eighteen times from $16.6 trillion in 1995
to $308 trillion in 2012, while the U.S. GDP merely doubled from
$7.7 trillion to $16.2 trillion over the same period. In this paper, we
examine three possible drivers of this growth: (a) the GrammLeach-Bliley Act of 1999, (b) the Commodity Futures
Modernization Act of 2000, and (c) FAS 133 (now ASC 815),
Accounting for Derivative Instruments and Hedging Activities, which
became effective in 2000. Using a sample of U.S. bank holding companies, we find a temporal association between the passage of the
two Congressional Acts and the abnormal growth in trading/overthe-counter derivatives. We also predict and find that the use of cash
flow hedge accounting treatment helps reduce earnings volatility/
equity risk, and that firms increase their use of non-trading derivatives when facing high level of earnings volatility/equity risk.
Ó 2015 Elsevier Inc. All rights reserved.
1. Introduction
During the fifteen-year period between 1995 and 2012, total amounts of financial derivatives
have increased by 1700%, a rate that significantly outpaces the growth of gross domestic product
⇑ Corresponding author at: 2037D BIF, 515 East Gregory Drive, University of Illinois, Champaign, IL 61820, United States. Tel.:
+1 (217) 265 0539.
E-mail address: [email protected] (A.R. Abdel-khalik).
http://dx.doi.org/10.1016/j.jaccpubpol.2015.01.002
0278-4254/Ó 2015 Elsevier Inc. All rights reserved.
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A.R. Abdel-khalik, P.-C. Chen / J. Account. Public Policy 34 (2015) 291–318
(GDP) both globally (240%) and in the USA (212%). In this paper, we study potential drivers of this
phenomenal growth. Specifically, we examine two contemporaneous, yet not mutually exclusive
factors, i.e., changes in public policy and issuance of a comprehensive and rather complex set of
accounting standards to account for derivatives and hedging. The public policy changes we consider
are the outcome of two federal laws: the Financial Services Modernization Act in 1999 and the
Commodity Futures Modernization Act of 2000. The accounting standard under consideration is
FAS 133 (now ASC 815), which prescribes the accounting treatments for hedging that became
effective in 2000. Following the documentation of growth in derivatives, we examine each of these
factors in a separate section. Part I discusses the Congressional legislations and the institutional
changes these Acts have introduced; we use time trend analysis to estimate the impact of the Acts
on the growth of derivatives. Part II focuses on the effects of FAS 133 on the use of derivatives by
U.S. bank holding companies; we also examine the role of accounting standards in the growth of
derivatives.
1.1. Growth in financial derivatives
Financial derivatives are bilateral contracts that establish the rights and obligations of each party to
the contract based on the movements of prices or indexes of the underlying items, which could be
assets or events. Derivative contracts are either standardized or customized (i.e., self-tailored by the
two parties to the contract). Standardized derivatives are usually traded on organized (and regulated)
exchanges, whereas customized contracts are traded over-the-counter (OTC)—i.e., behind closed
doors—for which no regulation of any type exists aside from common contract and tort laws. Customized derivatives are flexible and tailored specifically to fit the risk mitigation or risk-taking strategies by the two identifiable parties of each contract. The size of the financial derivatives market could
be measured in three ways: (a) total notional amounts outstanding at a point in time, (b) the total fair
market values at which these contracts could be traded or settled at a point in time, and (c) the turnover amounts during a period of time. While many types of derivative contracts are of very recent origins, the statistics published by the Bank for International Settlements show that the notional
amounts of global derivatives have increased from $57.5 trillion in 1995 to $696 trillion in 2012.1 This
rate of growth has not been observed for any other economic activity. For example, during the same time
span, global GDP increased from $30.7 trillion to $72.4 trillion, and U.S. GDP increased from $9.1 trillion
to $16.2 trillion. These comparative statistics are shown in Table 1, and the trends of growth are illustrated in Fig. 1.
A significant portion of these derivatives is used by U.S. bank holding companies (BHC).2 As
presented in Fig. 2, the notional amounts of BHC’s derivatives have increased from about 46% of global
derivative amounts in 1998 to 59% in 2002. Beginning 2004, however, the proportion of BHC’s derivatives
began to decline, reaching its lowest level of about 22% of global derivatives at the height of the financial
crisis in 2008. Since then, this percentage has increased again, until it reached about 40% of the known
total global amounts in 2012.
All banks operating on U.S. soil fall under the regulatory jurisdiction of one or more of the following
agencies: the Federal Deposit Insurance Corporation (FDIC),3 the Office of the Comptroller of the Currency (OCC), and the Federal Reserve Bank. Bank holding companies with total consolidated assets of
$500 million or more are required to file FR Y-9C reports with the Federal Reserve Bank on a quarterly
basis. In FR Y-9C reports, bank holding companies are required to report their derivatives holdings along
various dimensions (e.g. contract type, trading versus non-trading purposes, notional amounts, and fair
values). Fig. 3 depicts the growth path of BHC’s use of derivatives in terms of both the notional amounts
1
2
3
Statistical releases on derivatives a www.bis.org.
We use the terms ‘‘bank holding company’’ and ‘‘BHC’’ interchangeably throughout the paper.
Deposit-taking banks are required to make periodic filings of the Call Report with FDIC.
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Table 1
OTC derivatives vs. GDP. Source: Bank for International Settlements (BIS) and World Bank.
Year
1995 (trillion)
2012 (trillion)
% Growth
Annual growth rate (%)
OTC derivatives
World GDP
USA GDP
$57.5
$30.2
$7.7
$696
$72.4
$16.2
1210
240
212
15.8
5.3
4.5
800.0
700.0
600.0
500.0
400.0
300.0
200.0
100.0
OTC
World GDP
2012
2010
2011
2009
2008
2007
2006
2005
2004
2003
2001
2002
2000
1999
1998
0.0
US GDP
Fig. 1. Growth in OTC and exchange-traded derivatives compared to gross domestic product (in USD trillion). Source: Bank for
International Settlements (BIS) and The World Bank.
Derivave Use by U.S. BHC vs. the World
%
0.70
0.60
0.50
0.40
0.30
0.20
0.10
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
0.00
Fig. 2. Notional amount of outstanding derivatives held by U.S. bank holding companies relative to global derivatives, 1998–
2012. Source: Bank for International Settlements (BIS) and FR Y9-C reports.
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A.R. Abdel-khalik, P.-C. Chen / J. Account. Public Policy 34 (2015) 291–318
Growth of Derivaves 1995 -2012
Noonal Amount (bold lines) & Fair Value (dashed lines)
Billion
Noonal_TR
Noonal_NTR
Total
FV_TR
FV_NTR
FV_Total
Trillion
16,000.00
350
14,000.00
300
12,000.00
250
10,000.00
200
8,000.00
150
6,000.00
100
4,000.00
50
2,000.00
1995q1
1995q4
1996q3
1997q2
1998q1
1998q4
1999q3
2000q2
2001q1
2001q4
2002q3
2003q2
2004q1
2004q4
2005q3
2006q2
2007q1
2007q4
2008q3
2009q2
2010q1
2010q4
2011q3
2012q2
-
Fig. 3. The notional and fair value amounts of derivatives used by U.S. bank holding companies over 1995–2012 (in USD). Source:
FR Y-9C reports. The notional amounts of derivatives are measured in trillions, and the fair values are measured in billions.
and fair values.4 The total notional amounts of outstanding derivatives have grown from $16.6 trillion in
1995 to $308 trillion in 2012. The fair values of these derivatives are estimated to be 3–4% of notional
amounts, averaging about $445 billion in 1995 and $12.5 trillion in 2012. By contrast, the U.S. GDP only
doubled from $7.7 trillion to $16.2 trillion during the same period.
1.2. The role of public policy changes and accounting standards
In this study, we offer two factors as possible drivers of the accelerated growth in derivatives used
by U.S. bank holding companies. The first factor arises from the significant changes in public policy
that facilitated writing and trading in over-the-counter (OTC) derivatives while prohibiting any
transparency. For this factor, we consider two Congressional Acts that had profound effects on trading
of derivatives. The first act is the Financial Services Modernization Act of 1999 that restored old
features of the economic system by repealing related provisions of the 1933 Glass-Steagall Act and
permitting deposit-taking banks and investment institutions to merge their operations and
affiliations. The second act is the Commodity Futures Modernization Act of 2000, which resulted in
the abrogation of all laws that prohibited gaming and gambling by betting on the directions of changes
in prices of securities or commodities.
In addition to these laws that had significant impact, the second likely factor in motivating the
growth in derivatives is the issuance of FAS 133, ‘‘Accounting for Derivative Instruments and Hedging
Activities,’’ which became effective in 2000. FAS 133 provides special accounting treatments for
derivatives designated by management as hedging instruments and meeting specific requirements
on hedge effectiveness as well as documentation.
We examine these two factors as drivers of growth in financial derivatives in two different parts of
this study. In Part I, we examine the effect of the changes in public policy by comparing the trend of
4
Some people argue that ‘‘notional amounts’’ are hypothetical and therefore do not convey information. In this paper we argue
against this view, because the fair market value of a derivative is equal to the present value of the notional amount times the
change in the underlying index or price. Therefore, ignoring credit risk for the moment, three variables determine the fair market
value: notional amount, change in the index or price, and the discount rate. Accordingly, the notional amounts and fair values of
derivatives should be correlated. As shown in Fig. 3, there is a similar trend of increase in the use of derivatives in terms of notional
amounts and fair values, although the latter seems to have more fluctuations over the time. For our empirical tests in Part II, we use
fair values of trading and non-trading derivatives in the regression analysis. We also conduct the same analysis using notional
amounts and find similar results. Therefore, we argue that both notional amounts and fair values could be used as proxy for
derivative activities.
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growth in derivatives between two periods: (a) 1995–2000, and (b) 2001–2012. Using time trend
analysis, we find that BHCs’ use of derivatives, for both trading and non-trading purposes, has increased
significantly since 2001. We also find that OTC-traded derivatives have substantially outgrown derivatives traded on organized exchanges. These findings suggest that the noted changes in public policy are
a likely to have facilitated the increase of derivatives used by U.S. bank holding companies.
In Part II, we take a micro firm-level analysis concerning the provision of special accounting treatments (i.e., FAS 133) for the financial derivatives designated by management as hedging instruments
of effective hedging relationships. Under FAS 133, hedge accounting treatments allow companies to
use financial derivatives for hedging purposes with less concern for their impact on the volatility of earnings. By so doing, companies are able to avoid incurring the penalties that could be exacted by investors
should the volatility of derivative instruments flow through reported earnings. In this segment, we
examine the effect of derivative usage on earnings volatility and market return volatility of bank holding
companies, which provides evidence on how FAS 133 may have incentivized firms to use more derivatives in risk management. We conduct the analysis with a sample of 397 bank holding companies (a total
of 7,645 firm-quarter observations) using derivatives held for trading and/or non-trading purposes.
Our empirical results show that the use of derivatives designated as cash flow hedge is negatively
associated with earnings volatility, a finding that is consistent with hedge accounting treatments that
remove from reported earnings the volatility that results from changes in fair values of effective hedging instruments. In addition, we also document a negative relationship between market return volatility and the extent of using cash flow hedge accounting, suggesting that the market also views the use
of cash flow hedge as a risk-reducing device. Subsequently, we find that bank holding companies use
more derivatives for non-trading purposes when faced with high levels of earnings volatility and
equity risk. Collectively, these findings indicate that hedge accounting appears to provide incentives
for firms to use more derivatives to reduce their risk exposure.5
The remainder of this paper discusses and analyzes the impact of public policy changes (Part I) and
accounting standards (Part II) on the growth in financial derivatives.
Part I: Public policy changes as a driver of growth in financial derivatives
2. Did congressional legislations in the USA incentivize the accelerated growth in financial
derivatives?
2.1. The Financial Services Modernization Act (Gramm-Leach-Bliley Act) of 1999
In mid-1999, Senator Phil Gramm (Texas) led the 106th Congress to pass a financial services bill
(Pub. L. 106–102, 113 Stat. 1338) that was signed by President Clinton into law on November 12,
1999 (United States Congress, 1999). This Act achieved two goals:
1. Ex-post approval of the merger of Citi Bank and The Travelers’ Group (which included Primerica,
Smith Barney, and Salmon Brothers brokerage houses) to form Citigroup. The merger took place
in 1998 and was clearly against the law, but extensive lobbying gave the Federal Reserve Bank
and OCC an excuse to let the merger stand in anticipation of being grandfathered under the
expected repeal of the separation provisions in the 1933 Glass-Steagall Act.6
5
The only feasible result is either having a smoothing effect or no effect on the volatility of earnings for two main reasons: (a)
Adopting hedge accounting is completely at the discretion of the management, and this adoption would not happen if it could lead
to increased earnings volatility. (b) The management has complete freedom in canceling, changing, or adjusting the hedging
designations.
6
The initial 1933 policy that was undone by the GLB Act goes back to the Great Depression period of 1929–1933 when more
than 11,000 banks collapsed. To restore confidence in the banking community, Congress enacted the Banking Act known as the
1933 Glass-Steagall Act. In addition to establishing the Federal Deposit Insurance Corporation, the Act made two major changes:
(1) Disallowing commercial banks from underwriting or trading in securities, except for U.S. Treasury and municipal bonds. (2)
Prohibiting investment banks from receiving or investing customers’ deposits. The separation of commercial and investment
banking affiliation and activities was the law of the land since 1933. Although several laws were enacted starting 1963 to weaken
the restrictions of the separation between commercial and investment banking, the main feature of Glass-Steagall Act remained
standing and became the object of intensive lobbying.
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2. Opening the door for establishing other financial malls like Citigroup. This opening gave way to
negotiating the merger between Chase Manhattan and JP Morgan & Co. that was approved by
OCC and the Federal Reserve Board on December 12, 2000.7 In a short period, JP Morgan Chase rivaled Citigroup in affecting changes in the financial environment. For example, at the end of June 2013,
total notional amounts of outstanding derivatives at JP Morgan Chase stood at $73 trillion compared
to the $69 trillion balance held by Citigroup.8
In summary, the 1999 (GLB) Act has returned the banking financial environment to the pre-1933
conditions that encouraged banks to take high risks and invest in risky assets.
2.2. The Commodity Futures Modernization Act of 2000 (CFMA)
The second major legislation with direct impact on writing and trading financial derivatives is the
enactment of the Commodity Futures Modernization Act or CFMA (Bill H.R. 5660, 106th) that was
signed by President Clinton on December 21, 2000 (United States Congress, 2000). CFMA repealed
all existing laws that prohibited wagering or betting on the movement of prices of commodities or
securities; i.e., these were known as anti-bucket shop laws. The text of the Act (page 105, Section 118,
H. R. Bill 5660, November 14, 2000) reads as follows9:
[(2) This Act shall supersede and preempt the application of any State or local law that prohibits or
regulates gaming or the operation of bucket shops (other than antifraud provisions of general
applicability) in the case of ‘‘(A) an electronic trading facility excluded under section 2(e) of this
Act; and ‘‘(B) an agreement, contract, or transaction that is excluded from this Act . . .]
The scope of CFMA went further to restrict the Commodity Futures Exchange Commission (CFTC)
from exercising any oversight in connection with OTC trades, operations, or disclosures.10 ,11 We argue
that the passage of this Act had a significant effect on the trading of over-the-counter derivatives,
because functionally, OTC markets are large bucket shops.12,13 The anti-bucket shop laws have a long history dating back to the mid-1800s. On May 8, 1905, the U.S. Supreme Court issued its ‘‘Bucket-Shop Decision’’ in the case of the City of Chicago Board of Trade v. Christie Grain and Stock Company, one of the
largest bucket shops in the USA at that time. Christie Grain and Stock claimed that CBOT was a large
7
On September 13, 2000, ABC News announced the plans for the merger and noted ‘‘A potential merger between banking
powerhouses Chase and J.P. Morgan is seen by analysts as a good match because the two firms’ array of services complement each
other (ABC News, 2000). One year later, Riva Atlas wrote an article in the New York Times celebrating the JP Morgan Chase merger
(Atlas, 2001). The two articles are representative of the mood on Wall Street, and they never mentioned the connection between
the merger and the Glass-Steagall Act.
8
Based on the statistics provided by BIS, these derivatives would have an estimated fair value of $2.0 trillion, while other assets
of the bank also total $2.00 trillion. This huge amount of fair value of derivatives is not to be found on the face of the balance sheet,
because unlike IFRS, U.S. GAAP permits netting.
9
http://www.cftc.gov/umc/groups/public/@lrrulesandstatutoryauthority/documents/file/ogchr5660.pdf.
10
This prohibition came at the heels of a historic battle between Brooksly Born, the Chair of CFTC, one side and Congress and the
President’s Working Group on Financial Markets on the other side. In an interview with ABC This Week in April 2010, President
Clinton admitted: Clinton: I Was Wrong to Listen to Wrong Advice against Regulating Derivatives. See Harris, April 17, 2010.
11
The boundaries of CFTC authority were drawn earlier. Under the Chair Wendy Gramm, the debate between CFTC and other
regulators led to the enactment of the Futures Trading Practices Act of 1992 (FTPA 1992) which led the CFTC to exempt swaps and
other hybrid instruments from its own oversight. The actions induced by the 1992 law became engrained in the minds of all
regulators who fought the efforts of CFTC Chairwoman Brooksley Born to bring OTC under the purview of CFTC and require
transparency.
12
A bucket shop is defined by the U.S. Supreme Court, in the case of Gatewood v. North Carolina (27, S. Ct. 167168) in 1906, as
‘‘an establishment, nominally for the transaction of a stock exchange business, or business of similar character, but really for the
registration of bets, or wagers, usually for small amounts, on the rise or fall of the prices of stocks, grain, oil, etc., there being no
transfer or delivery of the stock or commodities nominally dealt in.’’ (U.S. Supreme Court, 1906).
13
In a more recent ruling, the Appeals Court of Massachusetts in the case of Commonwealth v. Odom ‘‘danny’’ DoVale (November
19, 2002 through March 26, 2003) noted the following: An explanation of why the forbidden practice is called ‘‘bucketing’’ is that a
securities trading business that indulged in it would make trades all day long, throw the ticket that memorialized the trade into a
bucket, and decide at the end of the day which accounts to credit or debit with winning and losing trades.
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297
bucket shop, and the quotes generated on the trading floor were a public property. The U.S. Supreme
Court rejected both claims and offered the intent of consummating physical delivery as the primary criterion distinguishing bucket shops and organized exchanges like CBOT14:
The fact that contracts are satisfied in this way by set-off and the payment of differences detracts in
no degree from the good faith of the parties, and if the parties know when they make such
contracts that they are very likely to have a chance to satisfy them in that way and intend to make
use of it, that fact is perfectly consistent with a serious business purpose and an intent that the
contract shall mean what it says.
This and related significant court decisions had preceded the 1907 Bank Panic and the collapse of
the NYSE in October of that year. Bucket shops were singled out as the reason of the NYSE loss of
nearly 50% of its capitalization in October, 1907. Fearing more damage to its economy, the State of
New York outlawed bucket shops on May 25, 1908; any involvement with bucket shops became a
felony as of September 1. Shortly thereafter, other states followed suit and by 1920, bucket shops were
eradicated. These anti-bucket shop state laws continued to be in effect until December 21, 2000, when
President Clinton signed CFMA into law.
2.3. Consequences of the 1999 and 2000 Congressional Acts on the derivatives market
The impact of enacting GLB Act in 1999 and CFMA in 2000 extended far beyond expectations in
several respects:
2.3.1. Legitimizing gambling in securities
By comparing the essential features of bucket shops and OTC, we are persuaded that most of the
OTC trades in financial derivatives are gambles similar to the activities of bucket shops. The Courts
had ruled (in the decisions of 1906 and 2002, among others)15 that bucket shops are gambling establishments because they (i) accept bets on the directions of price movements of commodities or securities,
(ii) do not require making any investment, (iii) do not have any expectation of physical delivery, and (iv)
do not disclose information to others who are not party to the contract.
– These features characterize all OTC derivatives, as we know them nowadays. Additional observations should help in sharpening this comparison.
a. In a direct analogy, bucket shop bets have payoff functions similar to the payoff function of any
one of the following trading strategies:
i. Futures and forward contracts.16
ii. Interest rate swaps.
iii. Risk reversal bullish strategy—buy an out-of-the-money call option and sell an out-of-themoney put option.
14
15
16
U.S. Supreme Court. May 8, 1905, The Bucket-Shop Decision, Cornell University Library.
Such as those noted in footnotes 12 and 13.
The payoff of a bucket shop deal takes one of the following (forward and future) payoff functions.
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iv. Risk reversal bearish strategy—buy an out-of-the-money put option and sell an out-of-themoney call option.
b. Interest rate derivatives constitute about 78% of all outstanding amounts. At the end of June
2013, global OTC interest rate contracts had total notional amounts of $561 trillion (BIS
semiannual report).17 But the financial assets/liabilities of the entire world do not exceed
$111.00 trillion.18 This amount consists of equity and liability instruments, and not all of them
are necessarily exposed to interest rate risk. Even if they were, the $111 trillion would effectively
be an upper bound for the assets (liabilities) that all enterprises could hedge. It is therefore
arguable that $450 trillion of interest rate derivative contracts are ‘‘naked’’ or unrelated to risk
management.’’
– Interest rate contracts that do not hedge risk exposure of assets or liabilities are side bets similar to
bucket shops.
c. The evidence publicized about banking scandals related to trading derivatives reinforces the
notion of its similarity to bucket shop gambling. These include cases like Barings Bank (loss of
$1.2 billion), Allied Irish Bank (loss of $690 million), Morgan Stanley (loss of $9.0 billion), Amaranth (loss of $7.2 billion), Société Générale (loss of $7.2 billion), UBS (loss of $2.6 billion), and
JPMorgan Chase (loss of $6.2 billion), among many others. These cases offer vivid examples of
how financial institutions are allowed to use derivatives simply for speculation and gambling.
Indeed, the report of the U.S. Congress about ‘‘The London Whale’’ trades of JPMorgan Chase
presents the entire problem as making the wrong bets on the direction of the prices of credit
derivatives, which the bank had traded multiple times within any day.
d. These trades by the Chief Investment Office (CIO) of JPMorgan Chase and Morgan Stanley, for
example, were in naked credit default swaps as almost 80% of all outstanding credit default
swaps (CDSs). Simply put, CDSs are insurance-like contracts that allow an insured (called
protection buyer) to acquire protection (insurance) from a protection seller (insurer) against
the decline in the credit worthiness of an identifiable third party (reference party). CDSs are
intended for use in hedging credit risk exposure when the reference party actually owes the
protection buyer money. In these instances, the protection buyer has a legitimate risk that it
is trying to protect by hedging. However, the protection buyer could acquire the same CDSs
on the credit worthiness of any third party with whom neither the protection buyer nor the protection seller has any contractual relationship. In this case, the protection buyer is not exposed
to the credit risk of the reference party, and the CDSs are referred to as ‘naked.’ It is estimated
that about 80% of CDSs globally and in the USA are naked.
– Naked credit default swaps are side bets in exactly the same way as bucket shops.
2.3.2. Facilitating the use of depositors’ funds to trade in derivatives
By passing CFMA in 2000 soon after the 1999 GLB Act, Congress had in effect allowed banks unrestricted access to depositors’ funds and unfettered ability to write and trade in derivatives. The case of
JPMorgan Chase known as ‘‘The London Whale’’ is a prime example of this abuse. According to the Senate Report,
‘‘[I]n early 2012, the bank’s Chief Investment Office (CIO), which is charged with managing
$350 billion in excess deposits [i.e., deposits that the management did not wish to loan out], placed
a massive bet on a complex set of synthetic credit derivatives that, in 2012, lost at least $6.2 billion’’19 [Emphasis added].
17
Bank for International Settlements, November 2013, Statistical release OTC derivatives statistics at end-June 2013. Monetary
and Economic Department. www.bis.org.
18
Allianz Global Wealth Report 2013. Public Policy & Economic Research. Munich, September 2013. https://www.allianz.com/v_
1380204319000/media/economic_research/research_data/english_documents/wealth_of_private_households_in_germany/FinassphW.pdf.
19
United States Senate. March 15, 2013. JPMorgan Chase Whale Trades: A Case History of Derivatives Risks and Abuses. Majority
and Minority Staff Report. Permanent Subcommittee on Investigations (The Senate Report).
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299
The Senate Report continues to stress that the bank has mischaracterized high-risk trading as
hedging and continued to take on more risk. As a result of these investigations, JPMorgan Chase paid
more than $1.10 billion in fines to different regulatory agencies in the USA and UK, which added to the
trading loss of $6.20 billion.
3. Growth of derivatives following the two national Acts
3.1. Time trend analysis of the growth in derivatives over 1995–2012
The above discussion suggests that the two Congressional Acts of 1999 and 2000 had created an
environment that encourages and stimulates the growth in trading derivatives, i.e., derivatives not
used for hedging. Therefore, we predict an increase in the rate of growth of trading derivatives post
the two Acts. Since trading derivatives of bank holding companies account for over 98% of total derivatives (according to the OCC Quarterly Report), we also expect to observe a similar increase in the
growth of total derivatives. For non-trading derivatives, we do not have any ex ante prediction with
respect to the impact of these two Acts on their growth.20
The provisions of these legislations (especially CFMA) are also expected to trigger greater use of
OTC derivatives as compared to derivatives traded on organized exchanges, as CFMA removed century-old legal constraints on gambling and aggressive speculative trading in over-the-counter (OTC)
derivatives (Stout 2011). If the legislations are at force for the growth of derivatives, we expect to
observe (1) an increase in the use of OTC derivatives and (2) a higher growth of OTC derivatives than
the growth of exchange-traded derivatives after the passage of the two Acts. The latter prediction
could also help to distinguish between the effects of the Acts and those of FAS 133 on the growth
of derivatives because there is no a priori reason to expect that FAS 133 causes different rates of
growth for derivatives traded on organized exchanges vs. OTC.
To empirically examine our predictions, we conduct time trend analysis by regressing the use of
derivatives on several time period variables. Specifically, we estimate the following regression:
DERIVATIVES ¼ a þ b TIME þ c ACT þ d FCR þ e
ð1Þ
where DERIVATIVES are the aggregated balances of notional amounts measured in one of six ways:
total derivatives, trading derivatives, non-trading derivatives, OTC derivatives, exchange-trade
derivatives, and the percentage of derivatives traded over the counter. On the right hand side of the
equation, TIME is the number of quarterly periods from 1995 through 2012. The variable ACT is an
indicator variable set to 1 for the period beginning 2001, and zero otherwise. We select 2001 because
the last significant factor in our analysis is the signing of CFMA on December 21, 2000. A positive
coefficient on ACT would indicate an increase in the use of derivatives post the enactment of these
two laws. Lastly, we also include the indicator variable FCR, equal to 1 for the period beginning
2008 and zero otherwise, to account for the potential effect of the financial crisis.
We estimated the trend regression in (1) using data on derivatives for U.S. bank holding companies.
The data are collected from FR Y9-C reports over the period 1995–2012. As discussed previously, we
use bank holding companies for two reasons. First, they have more than $308 trillion in notional
amount of derivatives as of the end of December 2012, which accounts for nearly 40% of global
derivatives and is estimated to be more than 90% of total derivatives in the United States. Second,
the information contained in FR Y-9C reports has a reasonable degree of reliability because they are
typically subject to oversight and scrutiny by the Federal Reserve Bank.21
Table 2, Panel A, reports the results of estimating Eq. (1) using the first three dependent variables:
(1) total derivatives (NOTIONAL_TOTAL), (2) trading derivatives (NOTIONAL_TR), and (3) non-trading
derivatives (NOTIONAL_NTR). These results show that:
20
Since the hedge accounting standard (i.e., FAS 133) became effective around the same time as the passage of the two
Congressional Acts, it is possible that the growth of non-trading derivatives, if any, could reflect the effect of the accounting
standard. We devote Part II to the detailed discussion and analysis of the effect of FAS 133 on the growth of financial derivatives.
21
The breakdown between trading and non-trading derivatives is also reported in OCC’s Quarterly Report.
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Table 2
Panel A: Time trend analysis of U.S. bank holding companies’ use of derivatives, 1995–2012 (trading versus non-trading)
ðDepv ar ¼ a þ b TIME þ c ACT þ d FCR þ eÞ
Panel B: An alternative test of time trend analysis (the Pseudo event)
ðDepv ar ¼ a þ b TIME þ c ACT pseudo þ d FCR þ eÞ
Panel C: Time trend analysis of U.S. bank holding companies’ use of derivatives, 1995–2012 (OTC versus exchange-traded)
ðDepv ar ¼ a þ b TIME þ c ACT þ d FCR þ eÞ
Dependent variables
a
b
Panel A
NOTIONAL_TOTAL
NOTIONAL_TR
NOTIONAL_NTR
23.666***
23.652***
19.245***
0.151***
0.153***
0.123***
Panel B
NOTIONAL_TOTAL
NOTIONAL_TR
NOTIONAL_NTR
23.656***
23.645***
19.109***
0.187***
0.184***
0.314***
Panel C
NOTIONAL_OTC
NOTIONAL_ET
OTC_%
23.540***
21.947***
0.834***
0.160***
0.114***
0.004***
c
0.414***
0.382***
1.674***
0.089
0.094
0.113
0.372***
0.216**
0.024***
d
Adjusted R2
0.099
0.088
0.683***
0.968
0.968
0.955
0.294***
0.264***
1.620***
0.960
0.961
0.869
0.042
0.379***
0.019**
0.973
0.919
0.878
For Panel A, NOTIONAL_TOTAL is the natural logarithm of notional amounts of total derivatives. NOTIONAL_TR is the natural
logarithm of notional amounts of trading derivatives. NOTIONAL_NTR is the natural logarithm of notional amounts of nontrading derivatives. TIME is a trend variable equal to the difference between the current year and 1995. ACT is an indicator
variable equal to 1 if the year is 2001 and after, and 0 otherwise. FCR is an indicator variable equal to 1 if the year is 2008 and
after, and 0 otherwise.
For Panel B, ACT_pseudo is an indicator variable equal to 1 if the year is 2003 and after, and 0 otherwise. Other variables are the
same as in Panel A.
Differences in the estimated coefficients on ACT (Panel A) and ACT_pseudo (Panel B) for each of the time trend regressions:
NOTIONAL_TOTAL: H0: ACT = ACT_pseudo; v2(1) = 10.43; p-value = 0.01.
NOTIONAL_TR: H0: ACT = ACT_pseudo; v2(1) = 9.11; p-value = 0.01.
NOTIONAL_NTR: H0: ACT = ACT_pseudo; v2(1) = 26.78; p-value = 0.01.
For Panel C, NOTIONAL_OTC is the natural logarithm of notional amounts of total derivatives traded over the counter. NOTIONAL_ET is the natural logarithm of notional amounts of total derivatives traded at exchanges. OTC_% is the percentage of total
derivatives traded over the counter, measured as NOTIONAL_OTC/(NOTIONAL_OTC + NOTIONAL_ET). Other variables are the same
as in Panel A.
⁄
p < 0.10.
**
p < 0.05.
***
p < 0.01.
a. The average growth rate in total (trading) derivatives is estimated to be 16.7% (16.5%) per
year,22 while the average growth rate in non-trading derivatives is 13.1%.
b. There is a significant shift in the use of derivatives after 2001 as compared to earlier years. The
estimated coefficient on the indicator variable ACT in the regression of total derivatives (trading
derivatives) is 0.414 (0.382) and is significant at the one percent level. Economically, this
coefficient represents an average of 51.3% (46.5%) increase in the use of total (trading)
derivatives after 2001. In addition, there is also a significant increase in the use of non-trading
derivatives after 2001, as the estimated coefficient on ACT when using non-trading derivatives
as a dependent variable is significant at the 1% level.
22
The estimated coefficient on TIME in the regression of total derivatives is 0.151, so the average annual growth rate is
e0.151 = 16.3%. The average growth rates for trading and non-trading derivatives are calculated in the same way.
A.R. Abdel-khalik, P.-C. Chen / J. Account. Public Policy 34 (2015) 291–318
301
To validate that the significant growth in the use of derivatives started with the passage of the two
national Acts, we conduct a pseudo test using another year as the cutoff to examine the time trend of
derivative use. If the growth in derivatives is mainly due to the regulatory changes, we should observe
weaker results when a different cutoff is used as the ‘‘pseudo’’ event year. To implement this validation test, we replace ACT in Eq. (1) with ACT_pseudo, which is an indicator variable set to 1 for
the period beginning 2003, and zero otherwise.23
Table 2, Panel B, reports results from the pseudo test, and the estimated coefficients on ACT_pseudo
are not different from zero in all three regressions, i.e., total derivatives, trading derivatives, and nontrading derivatives. To formally examine the differences between the estimated effects of ACT and
ACT_pseudo on the growth of derivatives, we statistically compare the coefficients between the two
regressions. As shown in the bottom of Table 2, the estimated coefficients on ACT are significantly
higher than those on ACT_pseudo in all three regressions. The Chi-squared statistics indicate that
the differences are significant at the 1% level. In sum, these results show that there are no differences
between the levels of derivative uses before and after the year of 2003. Therefore, the pseudo test
lends further evidence in support of the notion that the significant increase in the use of derivatives
is more likely driven by the passage of the two national Acts in 2001 than by other events.24
Next, we analyze the time trend of OTC and exchange-traded derivatives and report the results in
Table 2, Panel C. Specifically, we estimate Eq. (1) using the other three dependent variables: (1) OTC
derivatives (NOTIONAL_OTC), (2) exchanged-traded derivatives (NOTIONAL_ET), and (3) the percentage
of derivatives traded over the counter (OTC_%). The results show that the use of both OTC and
exchange-traded derivatives has increased since the passage of the legislations. The estimated coefficient on ACT is 0.372 (0.216), which translates to an average of 45.1% (24.1%) increase in the use of OTC
(exchanged-traded) derivatives after 2001. In addition, the results from regressing OTC% over the time
variables show that the proportion of derivatives traded over the counter has also significantly
increased since 2001. The estimated coefficient is 0.024 and is significant at the one percent level. This
finding suggests that while derivatives traded in both places had increased, OTC derivatives grew
more rapidly than exchange-traded derivatives after the passage of the two legislations.
3.2. Difference between the big five and other bank holding companies
The use of derivatives by bank holding companies is not evenly distributed across different entities,
since large dealer banks dominate the transactions of trading derivatives. By the end of 2012, trading
derivatives held by the largest five bank holding companies (Big5) consisting of JPMorgan Chase, Bank
of America, Citigroup, Goldman Sachs, and Morgan Stanley accounted for over 95% of all U.S. BHCs’
trading derivatives. Using the derivative data collected from FR Y-9C reports, we show in Fig. 4 that
the growth patterns of trading and non-trading derivatives for the Big5 BHCs are distinctly different
from the growth pattern in other, smaller bank holding companies. Fig. 4, Panel A, shows that trading
derivatives of the Big5 BHC continued on an increasing growth path with a significant jump in 2009,
whereas other bank holding companies reduced the rate of growth in their trading derivatives around
the period 2005/2006. The patterns of growth of non-trading derivatives are in Panel B of Fig. 4. Three
features of Panel B are surprising: (a) non-trading derivatives of the Big5 BHC continued to grow on a
steady path from 1995 to 2010, when a sharp increase is observed; (b) between 1995 and 2010, the
Big5 BHC had smaller amounts of non-trading derivatives as compared to the remaining bank holding
companies; and (c) non-trading derivatives for other BHCs increased significantly between 2001 and
2005, then went on a declining pattern in 2006 and 2007, but nevertheless remained above the level of
non-trading derivatives of the Big5 BHC until 2010.25
23
The choice of 2003 as the pseudo-event year is ad-hoc. In unreported analysis, we use 2002 as the cutoff and find qualitatively
similar results.
24
We thank the anonymous reviewer for suggesting this pseudo test to further strengthen our analysis.
25
We also re-estimate our time trend regression, i.e., Eq. (1), using these two subsamples to statistically examine whether the
trend of the growth of derivatives differs between Big5 and other bank holding companies. The results (unreported) show that the
use of trading and non-trading derivatives has significantly increased for both Big5 and other BHCs after the passage of two Acts.
Therefore, our findings suggest that the passage of two Congressional Acts is significantly associated with the growth of derivatives
held by U.S. bank holding companies after 2001 and that the effects are observed for all bank holding companies.
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Panel A: Trading Derivatives
Trading Derivaves Held by
Big5 BHCs versus Other BHCs
Trillion
300
250
200
150
100
50
BIG5
2011
2012
2009
2010
2007
2008
2006
2005
2003
2004
2001
2002
2000
1999
1997
1998
1996
1995
0
Others
Panel B: Non-Trading Derivatives
Non-Trading Derivaves Held by
Big5 BHCs versus Other BHCs
Trillion
BIG5
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
4
3
2
1
0
Others
Fig. 4. Notional amounts of derivatives held by Big 5 vs. other bank holding companies over 1995–2012 (in USD). Source: FR Y9C reports. The Big5 U.S. bank holding companies are J.P. Morgan Chase & Co., Citigroup, Bank of America Corporation, The
Goldman Sachs Group, Inc., and Morgan Stanley.
3.3. Comparison with European banks
While it is tempting to disentangle the effects of Congressional Acts by comparing the patterns of
the growth of derivatives between U.S. and non-U.S. banks, such comparison might not yield meaningful results because trading derivatives held by big U.S. banks are not independent of the trading
derivatives held by large European banks. Other than the typical global economic relationships, large
European banks are also the main counterparties of derivative contracts held by large U.S. banks, and
vice versa. This particular feature arises from the fact that financial derivatives are bilateral contracts
with a dynamic counterparty risk and are unlike any other financial instruments. Therefore, an
increase in the use of derivatives by U.S. bank holding companies may be accompanied by a similar
growth of derivatives at large European banks such as Barclays, Royal Bank of Scotland, Deutsche
Bank, UBS and others. In addition to this obvious interrelationship, European banks did not have
consistent or coherent financial disclosure policies.
Nevertheless, we still chose to examine whether non-U.S. banks experienced a similar pattern of
growth in derivatives during the period under study. To do so, we hand-collected data for the largest
European banks in four different countries and compared them with the corresponding data of the
Big5 bank holding companies in USA. We are limited to the period beginning in 1999 and to five banks
because of inconsistency, the diversity of reporting, and unavailability of derivatives data in different
European countries. Fig. 5 shows the behavior of average total notional amounts for total derivatives
between Big5 U.S. BHCs and Big5 European banks. The average of each of the Big5 BHCs was about
$8.00 trillion in 1999, while it was about $2.00 trillion for the large European banks. Both groups
had the same average in the period 2008–2010. During the period of 2001–2008, the growth pattern
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A.R. Abdel-khalik, P.-C. Chen / J. Account. Public Policy 34 (2015) 291–318
Average Noonal Amount of Total Derivaves, 1999 -2012
Trillion
60.0
50.0
40.0
30.0
20.0
10.0
0.0
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Foreign Big5
U.S. Big5
Fig. 5. Comparison of derivatives used by large U.S. and European Bank holding companies (in USD).
of derivatives in the Big5 European banks appeared to be similar to that of the Big5 U.S. BHCs. Overall,
Fig. 5 confirms our conjecture that the growth of derivatives after the passage of the two U.S.
legislations had crossed physical boundaries to also impact other countries.
4. Part I summary and conclusions
The growth in OTC derivatives has been phenomenal, especially since currency and interest rate
swaps were not invented until several years post the well-known IBM-World Bank currency swap
in 1981.26 Additionally, credit default swaps were invented in mid-1990s as a consequence of the Exxon
Valdez disaster in 1994.27
The International Swap and Derivatives Association, the Bank for International Settlements, and the
U. S. Office of the Comptroller of the Currency publish periodical aggregate statistics on both
exchange-traded and OTC financial derivatives. We collected data from these sources for the period
1995–2012. The OTC market for the largest 25 bank holding companies in the United States has grown
from $240 billion in 1987 to over $308 trillion dollars or 44% of total global notional amounts of
derivatives as of the end of June 2012. Of these amounts, $274 trillion were held by the top five bank
holding companies.
The recent history of over-the-counter financial derivatives in the USA has two landmark
legislations in 1999 and 2000:
1. The enactment of the Financial Services Modernization Act in 1999 (GLB). GLB repealed critical
features of the 1933 Glass Steagall Act to permit commercial and investment banking to affiliate
and merge their activities.
2. The enactment of the Commodity Futures Modernization Act in 2000 (CFMA), which preempted
and cancelled all laws that prohibit gaming or gambling in securities and making bets on price
movements. (i.e., bucket shops).
26
In 1981, the Swiss government had restricted World Bank borrowing in Swiss markets. In the meantime, IBM had to make debt
payments in Swiss Franc and Deutsche Marks. A broker at Salmon Brothers organized a swap between IBM and World Bank—
World Bank gives IBM funds in Swiss Franc and Deutsche Mark and receives U.S. Dollar from IBM. Chase Manhattan Bank
introduced the first commodity swap in 1986, and Bankers Trust introduced the first equity swap in 1989 http://kimsuk.facility.
udmercy.edu/SGUIDE/CH07sguide.doc.
27
To reduce its credit risk exposure to Exxon after the Valdez crisis, Blythe Masters at JP Morgan sold that risk to the European
Bank of Reconstruction and Development. Shortly thereafter, credit default swaps were born Lancaster (2009) http://www.
newyorker.com/arts/critics/books/2009/06/01/090601crbo_books_lanchester?currentPage=all.
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Subsequent to the enactment of these legislations, we estimate the rate of growth in derivatives to
be close to 15% annually. However, we are unable to narrow down the exact impact of these two Acts,
and therefore, we are refraining from making a cause and effect conclusion for two reasons:
i. The years of 1999 and 2000 were the finale of several years of lobbying and anticipation in Congress, as evidenced by two major events:
a. Congress request of the GAO in 1992 to study OTC derivatives (GAO, 1994).
b. Apparent confidence in enacting these laws by the year 2000. In particular, the Federal Reserve
Bank, the SEC, and the Department of the Treasury did not contest the then-illegal Citigroup
merger and allowed it to go through in 1998.
ii. While in recent years the big five banks in the Unites States and in Europe equally shared 85% of
global derivatives, the size of the U.S. market was four times the size of the European market in
2000.
Nevertheless, these two legislations have achieved two goals:
(1) Adding economic uncertainty while removing the legal uncertainty in connection with allowing
of OTC markets to continue operating in the dark and completely free of any regulation, even a
call for the slightest transparency.
(2) Freeing bankers and derivatives’ dealers from the penalties of writing and trading in naked
derivatives.
Part II: The Accounting Incentives Arising from Issuing FAS 133
5. Risk transfer and accounting for derivatives
Two main motives for using financial derivatives are (a) reducing risk exposure by transferring risk
from one party to another,28 and (b) managing liquidity, speculation and profit making. Information on
the activities of the firms’ derivatives for both purposes should be of relevance to equity investors as well
as to other capital providers, but confirmation of this assertion remains to be empirically verified.
The issuance of FAS 133 in 1998 aimed at improving the financial reporting disclosure of the use of
derivatives (FASB, 1998). In particular, the standard sets up accounting and reporting based on
classifying derivatives into two main categories: trading derivatives and hedging derivatives. Accounting for trading derivatives is the same as accounting for trading marketable securities—marked to
market and changes in fair values are posted to earnings. Accounting for hedging derivatives would
depend on hedging effectiveness; FAS 133 provides two different accounting treatments for this group
of derivatives:
Successful (effective) hedging: Derivative uses that succeed in hedging risk should be accounted for
in a manner consistent with their economic effects. Success is measured by the degree of hedge
effectiveness—i.e., the extent to which the change in fair value of the derivatives offsets the risk
exposure for which they are designated. In the Appendix A, we briefly describe accounting
treatments for derivatives that exhibit hedge effectiveness.
Unsuccessful (ineffective) hedging: The portion of hedging derivatives that fails to offset the risk
exposure for which they are designated. The accounting for this group reverts to the main treatment of accounting for derivatives as trading—changes in fair values are reported in earnings.
28
This was the reason that swap contracts were invented after the 1981 IBM and World Bank exchange of currency denominated
in Swiss Francs and Deutsche Mark for U.S. Dollars (Williams, 2008). Risk transfer was also the reason that credit default swaps
were invented after Blythe Masters of JP Morgan thought of selling Exxon’s credit risk following the adverse court ruling regarding
Exxon’s liability for the Valdez oil spill of 1994. She convinced the European Bank for Reconstruction and Development to buy
Exxon’s credit risk from JP Morgan for a periodic premium. Masters had the belief that banks could make risk vanish, and this gave
rise to the new world of credit default swaps and securitization (Tett, 2009).
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305
5.1. Accounting for derivatives designated as hedging instruments under FAS 133
FAS 133 (or ASC 815 under the new Accounting Standards Codification) provides a special set of
hedge accounting treatments allowing the management to shelter earnings from the volatility
introduced by changes in fair market values of financial derivatives, assuming these derivatives are
successfully used to hedge risk, and the management elected to adopt hedge accounting. Three
accounting treatments are provided according to type of risk exposure: exposure to loss in value,
exposure to loss due to cash flow volatility, and adverse movement of currency exchange rates of
investment in foreign operations (currency risk in foreign operations). Appendix A presents an
overview of three main buckets of hedge accounting.
5.1. To what extent may hedge accounting drive the growth in derivatives?
Based on the above discussion, it appears that FAS 133 might incentivize firms to use more
derivatives for hedging purposes without concern for introducing unwanted volatility to reported
earnings.29 In addition, the management has considerable latitude in making hedging decisions for
accounting purposes. First, the management could freely shift derivatives from trading to non-trading,
and vice versa. Second, the management could designate and de-designate a derivative instrument as
an accounting hedge based on either hedge accounting requirements or on management intent. For
instance, the firm can designate a derivative instrument first as cash flow hedge and subsequently dedesignate the cash flow hedge and designate the derivative as a fair value hedge.30 Third, for cash flow
hedge, the management can choose to use the hypothetical derivative method to show the desired level
of hedge effectiveness (success) by simply changing the assumptions of the hypothetical derivative.31
Despite the appealing features of FAS 133 with respect to using derivatives for hedging purposes,
hedge accounting treatments apply only to a very small proportion of financial derivatives. According
to OCC’s Quarterly Report on Bank Derivative Activities, close to 97.5% of derivative holdings in 2006
and 98% in 2013 were used for trading (OCC, 2013). Between 2% and 2.5% of the total amounts of
derivatives held by all bank holding companies have been classified as ‘‘non-trading.’’ Non-trading
derivatives include two components: (a) hedge derivatives that qualify for hedge accounting, and
(b) hedge derivatives that do not qualify for hedge accounting, or that qualify for hedge accounting
but the management elected not to use it (often referred to as economic hedging).32 Therefore, hedge
accounting under FAS 133 is applicable to less than 2% of all outstanding financial derivatives, which
places a significant constraint on the impact of the possible effect of hedge accounting on the growth
in using derivatives.
6. Hypotheses development: impact of FAS 133 on the demand for non-trading derivatives
In this section, we posit and test hypotheses about the impact of hedge accounting on the use of
derivatives for non-trading (e.g., hedging) purposes under the assumption that managements enter
29
To the extent that the hedging relationship is effective, the changes in fair values of derivatives designated for hedging
purposes would be completely offset by the changes in fair values of the hedged item (fair value hedge) or be parked in
accumulated other comprehensive income until the future transaction occurs (cash flow hedge).
30
Switching the designation of a derivative between fair value and cash flow hedge treatments could be done at any time and
might be based on failing to meet the hedge accounting requirements or simply because the management wants to do so without
any justification. It is clear from the standard that hedge accounting must be based on management intent. The extant literature
generally does not recognize the flexibility that the management has regarding the designation of hedging derivatives or switching
between different types of accounting hedges. We are under the impression that many authors tend to believe that once a cash
flow or a fair value hedge is designated, it remains in that category to the end.
31
In his book, Accounting for Risk, Hedging and Complex Contracts, Abdel-khalik (2014) refers to the Hypothetical Derivative
Method as ‘‘make believe accounting.’’ For an explanation of the method, see also DIG Issue No. 7 at http://www.fasb.org/
derivatives/issueg7.shtml.
32
Some banks claim that the cost of documentation and continuous testing of hedge effectiveness is too high to justify using
hedge accounting.
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into contracts in order to transfer and reallocate risk. We adopt earnings volatility and market return
volatility as two risk measures reflecting these activities.33
6.1. Earnings volatility
As discussed in the previous section, the primary goal of hedge accounting treatments under FAS
133 as amended (now is ASC 815) is to shield the reported earnings from the volatility introduced
by the derivatives the management intends to use for hedging. Given a maintained assumption that
the management prefers to report smooth earnings, FAS 133 allows the management to increase the
use of non-trading derivatives to manage risk without facing the cost of increased earnings volatility.34
Nonetheless, the literature provides mixed evidence on the proposition of using hedge accounting
treatments to reduce earnings volatility. Of the earlier studies on the subject, using data for the period
1994–1996, Barton (2001) concludes that managers use derivatives to complement discretionary
accruals as tools for smoothing earnings. In a more recent study, Beneda (2013) uses 17,781 firm/year
observations for the period 2003–2010 to examine the association between use of derivatives and
income volatility. Of this sample, 7542 were derivative users. Her results show a negative association
between using derivatives and earnings volatility—i.e., a positive association with income smoothing.
This study differs from Barton in using data from a period of time post the effective date of FAS 133, a
period in which banks became more active in using derivatives, and the volume was increasing steadily. Furthermore, the results of both Barton and Beneda are consistent with the findings of a large survey of insurance companies carried out by the Society of Actuaries (2012) in which ‘‘respondents use
hedging for products with guarantees or options to minimize volatility in economic liabilities and
GAAP earnings, as well as maintain certain levels of statutory surplus.’’
However, finding a positive association between use of derivatives and reduction of earnings
volatility is not shared by other studies. For example, Singh (2004) studied the use of derivatives
during the two years 2000–2001 and found no significant differences in earnings or cash flow volatility between users and non-users of derivatives. He further notes that the use of derivatives has not
increased after implementing FAS 133. Li and Stammerjohan (2005) use data for 178 companies from
Fortune 500 between 1997 and 2002 and find increased earnings volatility for derivatives users. However, these studies covered periods when the amount of outstanding derivatives was small and used
data for only one or two years following the effective date of FAS 133. Kilic et al. (2013) argue that
under FAS 133, the mandatory accounting recognition of hedge ineffectiveness reduces banks’ ability
to smooth earnings and increases their reliance on loan loss provisions to reduce earnings volatility.35
They find results consistent with firms using more loan loss provisions for the income smoothing
purpose.
Given the mixed findings of prior studies, the question regarding the impact of using hedge
accounting on earnings volatility remains unanswered. Therefore, assume that reducing earnings
volatility as a management goal is a sufficient motivator for acquiring and holding more derivatives.
Instead, we test two hypotheses related to derivatives used for cash flow hedge accounting and
non-trading derivatives
H1: The use of derivatives designated as cash flow hedge under FAS 133 reduces earnings volatility.
H2: There is a positive relationship between firms’ earnings volatility and the use of non-trading
derivatives.
33
It is noted, however, that the preference for earnings smoothness is not universally agreed upon. An example is the McKinsey
article by Jiang and Koller (2011), in which they claim that market preference for smooth earnings is a myth. They note
encountering ‘‘the conventional wisdom that investors prefer smoothing earnings growth and shun earnings volatility,’’. . .but
sophisticated investors tell us they get suspicious when earnings growth is too stable, since they know that isn’t how the world
works.’’ Notwithstanding the McKinsey study, much of the research suggests that reducing earnings volatility is a prime goal of
management.
34
Starting June 2012, the FR Y-9C filing combines the reporting of this item with the accumulated other comprehensive income
from pension accounting adjustments.
35
This particular finding requires further investigation, because adoption and use of accounting for derivatives is completely
voluntary at the discretion of management and could be terminated or changed any time at no cost to the enterprise.
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307
The first hypothesis addresses the effect of cash flow hedge accounting treatment on earnings
volatility, while the second hypothesis addresses the extent to which earnings volatility would influence the decisions that managements make regarding the level of non-trading derivatives.
6.2. Equity risk
The impact of derivative uses on equity risk may be direct or indirect. The direct impact would be
the net impact of managing market and credit risks of the firm’s assets and liabilities. Thus, managing
equity risk could be considered the ultimate goal of any program of asset-liability management. The
indirect effect would take place through earnings volatility as a moderating factor. Earnings volatility
could be viewed as a summary risk measure of the firm’s entire activities as captured by accounting
information. However, this summary measure is subject to the specific accounting rules that determine earnings recognition and balance sheet effects that result from compliance with accounting
standards. While these relationships are conceptually intuitive, the extant literature does not provide
a coherent line of thought. Guay (1999) finds that management acquires derivatives first as riskreducing devices, while Schrand and Unal (1998) argue that firms use derivatives as a means of
allocating rather than reducing total risk. In a more general way, Puwalski (2003) suggests that firms
use derivatives to sort out different types of risk and trade to assist the management in adjusting
exposure to specific risks. Guay and Kothari (2003) examine the magnitude of risk exposure hedged
by financial derivatives in 234 non-financial corporations and report that most firms hold derivatives
that are small in magnitude compared to entity-level risks. In more recent studies, Gay et al. (2011)
use data for 1541 firms during two different periods (between 1992–1996 and 2002–2004) to
examine the relationship between the use of derivatives and cost of equity. They report evidence
showing that, on average, firms using derivatives have lower cost of equity as compared to non-users;
the magnitude of difference ranges between 24 and 78 basis points. Similar to some of the research on
earnings volatility, these studies base their findings on data generated in an environment with
relatively low activity in the derivatives’ market.
Few studies used data post the effective date of FAS 133. One such a study is Ahmed et al. (2011),
which offers a negative association between the use of derivatives and fixed-rate bond spreads as
evidence of risk reduction arising from applying FAS 133. However, Keffala et al. (2012) find that using
different financial derivative instruments impacts total equity risk (standard deviation of return) differently; equity risk decreases with the use of forward contracts but increases with the use of options.
Our interest in this study lies in evaluating the effect of cash flow hedge accounting treatment on
equity risk and examining the extent to which such a relationship might be driving the demand for
non-trading derivatives. This is accomplished by testing the following two hypotheses:
H3: The use of derivatives designated as cash flow hedge under FAS 133 reduces equity risk.
H4: There is a positive relationship between firms’ equity risk and the use of non-trading derivatives.
7. Empirical models and data
7.1. Model for earnings volatility
To examine whether the use of cash flow hedge helps achieve the desired goal of reducing earnings
volatility of bank holding companies (H1), we estimate the following model:
EARNVOLi;tþn ¼ a0 þ a1 CFHi;t þ a2 FV TRi;t þ a3 NCCEi;t þ a4 GAP i;t þ a5 SIZEi;t
þ a6 NIIi;t þ a7 LIQUIDi;t þ a8 NPLi;t þ a9 CHAROFF i;t þ ei;t
ð2Þ
, where the variables for bank holding company i at time t are defined as follows: EARNVOL = earnings
volatility measured as the standard deviation of income before extraordinary items (BHCK 4300) over
n quarters starting from time t, scaled by total assets (BHCK 2170)36; CFH = absolute value of the
36
FR Y9-C reports contain year-to-date information. Therefore, we adjust the data item BHCK 4300 to calculate the quarterly
income before extraordinary items.
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accumulated balance Cash Flow Hedge gains and losses deferred in OCI (BHCK 4336), scaled by total
assets; FV_TR = total fair value of derivatives used for trading purposes (the sum of BHCK 8733 to BHCK
8740) scaled by total assets; NCCE = net current credit exposure, measured as the difference between
positive gross values of derivatives (the sum of BHCK 8733 to BHCK 8736 plus the sum of BHCK 8741
to BHCK 8744) less negative gross value (the sum of BHCK 8737 to BHCK 8740 plus the sum of BHCK
8745 to BHCK 8748) scaled by total assets; GAP = absolute value of the difference between interest-sensitive assets (BHCK 3197) and interest-sensitive liabilities (BHCK 3296 + BHCK 3298 + BHCK
3408 + BHCK 3409) scaled by total assets; SIZE = natural logarithm of the bank holding company’s total
assets (BHCK 2170); NII = net interest income (BHCK 4074) scaled by total assets37; LIQUID = sum of cash
and balances due from depository institutions (BHCK 0081 + BHCK 0395 + BHCK 0397), securities (BHCK
1754 + BHCK 1773), and federal funds sold, and securities purchased under agreement to resell (BHDM
B987 + BHCK B989), scaled by total assets; NPL = sum of nonaccrual loans and accruing loans past due
90 days or more (BHCK 5525 + BHCK 5526) scaled by total assets; CHAROFF = total charge-offs on loans
and leases (BHCK 4635) scaled by total loans and leases (BHCK 2122).
We expect to observe a significantly negative a1 coefficient on CFH, the cash flow hedge reserve in
OCI, if designating derivatives as cash flow hedge under FAS 133 helps reduce earnings volatility. We
also include FV_TR as a variable to control for the effect of trading derivatives on earnings volatility.
As for other control variables, we include firm size (SIZE), net interest income (NII), liquidity
(LIQUID), non-performing loans (NPL), and charge-offs (CHAROFF), as they are important metrics of
banks’ performance and/or riskiness and could have impact on earnings volatility. While we expect
earnings volatility to be positively (negatively) correlated with a firm’s riskiness (performance), we
do not form specific predictions on the signs of the coefficients on control variables, because the
dependent variables in Eq. (2) are forward-looking measures.
Next, to examine our second hypothesis of the demand for non-trading (hedging) derivatives when
firms’ earnings volatility is high (H2), we estimate the following model:
FV NTRi;tþ1 ¼ b0 þ b1 EARNVOLi;t þ b2 CFHi;t þ b3 FV NTRi;t þ b4 FV TRi;t þ b5 NCCEi;t
þ b6 SIZEi;t þ b7 NIIi;t þ b8 LIQUIDi;t þ ei;t
ð3Þ
where FV_NTR is the one-period ahead total fair value of derivatives used for non-trading purposes
scaled by total assets, and other variables are the same as defined in model (2). We expect a positive
coefficient on EARNVOL if the level of earnings volatility drives the demand for non-trading derivatives. For control variables, we expect a positive sign for the coefficient on SIZE, as larger firms tend
to use more derivatives.
7.2. Model for equity risk
To examine the association between the use of cash flow hedge and equity risk (H3), we estimate
the following model adapted from Eq. (2):
RETVOLi;t ¼ k0 þ k1 CFHi;t þ k2 FV TRi;t þ ka3 NCCEi;t þ k4 GAP i;t þ k5 SIZEi;t þ k6 NIIi;t
þ k7 LIQUIDi;t þ k8 NPLi;t þ k9 CHAROFF i;t þ k10 VIX i;t þ ei;t
ð4Þ
where RETVOL is the equity risk measured as the standard deviation of daily total return for each quarter in the test period, 1999–2012. In this model, we add VIX, the average implied volatility of S&P 500
as published by the Chicago Board of Option Exchange (CBOE), to control for market risk. All other
variables are the same as defined in Eq. (2). We expect a negative coefficient on CFH if, as stated in
H3, the market perceives the use of cash flow hedge under FAS 133 as a risk-reducing device.
To test our last hypothesis about equity risk being a driver of the demand for the use of non-trading
derivatives (H4), we estimate the following regression:
FV NTRi;tþ1 ¼ h0 þ h1 PRETVOLi;t þ h2 CFHi;t þ h3 FV NTRi;t þ h4 FV TRi;t þ h5 NCCEi;t þ h6 SIZEi;t
þ h7 NIIi;t þ h8 LIQUIDi;t þ ei;t
37
Similar to footnote 36, we adjust the data item BHCK 4704 to calculate the quarterly net interest income.
ð5Þ
A.R. Abdel-khalik, P.-C. Chen / J. Account. Public Policy 34 (2015) 291–318
309
where FV_NTR, as defined in Eq. (3), is the one-period ahead total fair value of derivatives used for nontrading purposes scaled by total assets. The main variable of interest, PRETVOL, is the predicted value
of the dependent variable, RETVOL, from estimating Eq. (4). Essentially, we employ the two-stageleast-squares estimation process here to deal with the potential endogeneity issue arising from the
fact that return volatility appears as the dependent variable in one equation (i.e., Eq. (4)) and the
explanatory variable in another (i.e., Eq. (5)). Observing a positive coefficient on PRETVOL would be
consistent with H4, suggesting that firms use more non-trading derivatives when faced with high
market risk. All other explanatory variables are the same as previously defined.
7.3. Data and sample selection
We collect data on amounts of derivatives at the end of each quarter during our sampling period
(1995–2012) and other financial variables for bank holding companies from FR Y-9C reports filed
by BHCs with the Federal Reserve Bank on a quarterly basis.38 After deleting observations with missing
data, we have a sample of 397 companies that use derivatives for trading and/or non-trading purposes.
Our final sample contains 7645 firm-quarter observations for all variables except the forward-looking
earnings volatility measures, for which there are different observations for different forward horizons:
6676 for measuring earnings volatility over four quarters; 5515 for measuring earnings volatility over
eight quarters; and 4457 for measuring earnings volatility over twelve quarters.39
7.4. Descriptive statistics
Table 3 presents the descriptive statistics of variables used in the empirical tests. Panel A reports
statistics on bank holding companies using derivatives for trading or non-trading purposes. Mean
return volatility, RETVOL, is 2.3%, while average earnings volatility, EARNVOL, is 0.17%. On average,
bank holding companies held derivatives for trading purposes (non-trading purposes) that are equal
to 2.07% (0.20%) of total assets. This indicates a significantly larger magnitude of trading derivatives
held by BHCs as compared to derivatives for hedging purposes.
For other firm characteristics, derivatives users are generally larger bank holding companies, i.e.,
the mean SIZE for this group of bank holding companies is 8.60. In addition, the average difference
between interest-sensitive assets and interest-sensitive liabilities, GAP, is 16.4%. In terms of credit risk,
non-performing loans represent 1.2% of total assets (NPL), and total charge-offs represent 0.5% of total
loans and leases (CHAROFF). On average, 26.8% of the total assets are liquid assets (LIQUID). The means
of NPL, CHAROFF, and LIQUID are 0.012, 0.005, and 0.268, respectively. Finally, the overall market
volatility index, VIX, has a mean of 22.0 over the sample period for both sample sets. The correlations
between these variables are listed in Table 3, Panel B.
8. Estimation results
8.1. The relationship between earnings volatility and hedging derivatives
To examine whether FAS 133 provides banks with volatility-reducing incentives to use more
hedging derivatives, we first test the effect of the use of hedging derivatives on the volatility of
reported earnings. Specifically, we estimate Eq. (2) using earnings volatility as the dependent variable.
Earnings volatility is measured as the standard deviation of income before extraordinary items
38
We end the sample period in March of 2012 in order to properly measure one other key derivative variable, i.e., the
accumulated net gains (losses) on cash flow hedges. In the main analysis, we use bank holding companies that have derivatives for
trading and/or non-trading purposes. For our robustness tests, we include all bank holding companies and have a larger sample
(681 bank holding companies and 16,760 firm-quarter observations).
39
This distribution of derivatives holdings between trading and non-trading purposes is not limited to banks. Manchiraju et al.
(2014) examined derivatives holding by a large sample of oil and gas companies. They find that the majority of derivatives used by
these companies are also for ‘trading’ and conclude that ‘‘the majority of firms (oil and gas companies) in our sample choose to use
non-hedge derivatives, despite their use actually increasing risk, is puzzling.’’
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Table 3
Descriptive statistics and correlations.
N
Panel A: Descriptive statistics
EARNVOL
7645
6676
EARNVOLt+4
EARNVOLt+8
5515
EARNVOLt+12
4457
RETVOL
7645
FV_NTRt+1
7645
FV_NTR
7645
FV_TR
7645
NCCE
7645
CFH
7645
SIZE
7645
GAP
7645
NII
7645
LIQUID
7645
NPL
7645
CHAROFF
7645
VIX
7645
(1)
(2)
Mean
SD
Q1
Q2
Q3
0.002
0.002
0.002
0.002
0.023
2.066
0.196
0.200
0.060
0.058
8.596
0.164
0.008
0.268
0.012
0.005
22.022
0.003
0.004
0.004
0.004
0.019
14.138
0.425
0.431
0.397
0.116
1.748
0.127
0.002
0.126
0.015
0.008
9.333
0.003E1
0.002E1
0.003E1
0.004E1
0.012
0.000
0.007
0.007
0.013
0.000
7.329
0.065
0.007
0.186
0.003
0.001
14.529
0.006E1
0.004E1
0.007E1
0.008E1
0.017
0.000
0.051
0.052
0.000
0.002
8.286
0.137
0.008
0.242
0.006
0.002
20.673
0.001
0.001
0.002
0.002
0.026
0.018
0.196
0.203
0.038
0.064
9.442
0.237
0.009
0.323
0.014
0.006
26.017
(3)
Panel B: Pearson correlation table
EARNVOL (1)
1.000
RETVOL (2)
0.417 1.000
FV_NTR (3)
0.025 0.008 1.000
FV_TR (4)
0.019 0.004 0.239
NCCE (5)
CFH (6)
SIZE (7)
GAP (8)
NII (9)
LIQUID (10)
NPL (11)
CHAROFF (12)
VIX (13)
0.007
0.010
0.038
0.033
0.105
0.035
0.454
0.448
0.183
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
1.000
0.004 0.455 0.513 1.000
0.060 0.199 0.092 0.018 1.000
0.162 0.401 0.418 0.285 0.178 1.000
0.124 0.181 0.053 0.152 0.065 0.266 1.000
0.153 0.074 0.216 0.087 0.060 0.232 0.000 1.000
0.122 0.054 0.072 0.005 0.028 0.115 0.032 0.365 1.000
0.536 0.045 0.006 0.008 0.112 0.006 0.057 0.085 0.204 1.000
0.345 0.127 0.098 0.040 0.133 0.139 0.004 0.017 0.089 0.555 1.000
0.570 0.102 0.038 0.077 0.196 0.011 0.033 0.055 0.054 0.273 0.222 1.000
For FV_NTRt+1, FV_NTR, and FV_TR, the numbers are expressed in percentage. EARNVOL is the earnings volatility measured as the
standard deviation of income over eight quarters ending at time t scaled by total assets. EARNVOLt+4 is the earnings volatility
measured as the standard deviation of income over 4 quarters starting at time t scaled by total assets. EARNVOLt+8 is the
earnings volatility measured as the standard deviation of income over 8 quarters starting at time t scaled by total assets.
EARNVOLt+12 is the earnings volatility measured as the standard deviation of income over 12 quarters starting at time t scaled by
total assets. RETVOL is the equity risk for firm i measured as total daily return volatility over the quarter. FV_NTRt+1 is the total
fair value of derivatives used for non-trading purposes scaled by total assets at time t + 1. FV_NTR is the total fair value of
derivatives used for non-trading purposes scaled by total assets. FV_TR is the total fair value of derivatives used for trading
purposes scaled by total assets. NCCE is the difference between positive fair value of total derivatives and negative fair value of
total derivatives, scaled by total assets. CFH is the absolute value of accumulated OCI as a result of cash flow hedge designations,
scaled by total assets. SIZE is the natural logarithm of the bank holding company’s total assets. GAP is the absolute value of the
difference between interest-sensitive assets and interest-sensitive liabilities scaled by total assets. NII is net interest income
scaled by total assets. LIQUID is the sum of cash and due short-term obligations, federal funds sold, and securities purchased for
resale, scaled by total assets. NPL is the sum of nonaccrual loans and accruing loans past due 90 days or more, scaled by total
assets. CHAROFF is total charge off to loans and leases. VIX is the average quarterly value of Chicago Board of Options Exchange
volatility index.
In Panel B, boldface, italic, and underlined numbers represent significance levels of 1%, 5%, and 10% respectively.
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Table 4
The effect of hedging derivatives on earnings volatility (test of H1).
CFH
FV_TR
NCCE
GAP
SIZE
NII
LIQUID
NPL
CHAROFF
Intercept
Number of observations
Adjusted R2
EARNVOLt+4
EARNVOLt+8
EARNVOLt+12
0.219***
(5.063)
0.001**
(2.256)
0.025*
(1.868)
0.468E3
(0.892)
0.001**
(2.533)
0.051
(0.798)
0.003***
(3.463)
0.058***
(6.951)
0.011
(1.005)
0.004
(1.614)
6676
0.359
0.184***
(4.600)
0.001
(1.624)
0.015
(1.132)
1.018E3**
(2.065)
0.002***
(5.309)
0.011
(0.162)
0.003***
(2.862)
0.048***
(5.513)
0.011
(0.899)
0.012***
(4.427)
5515
0.573
0.122***
(2.874)
0.001
(0.997)
0.003
(0.230)
0.975E3*
(1.662)
0.002***
(6.494)
0.100
(1.513)
0.001
(0.601)
0.049***
(5.225)
0.029**
(2.045)
0.017***
(6.039)
4457
0.616
Year and firm fixed effects are included. Standard errors are adjusted for heteroskedasticity. EARNVOLt+4 is the earnings
volatility measured as the standard deviation of income over 4 quarters starting at time t, scaled by total assets. EARNVOLt+8 is
the earnings volatility measured as the standard deviation of income over 8 quarters starting at time t, scaled by total assets.
EARNVOLt+12 is the earnings volatility measured as the standard deviation of income over 12 quarters starting at time t, scaled
by total assets. CFH is the absolute value of accumulated OCI as a result of cash flow hedge designations, scaled by total assets.
FV_TR is the total fair value of derivatives used for trading purposes scaled by total assets. NCCE is the difference between
positive fair value of total derivatives and negative fair value of total derivatives, scaled by total assets. GAP is the absolute value
of the difference between interest-sensitive assets and interest-sensitive liabilities, scaled by total assets. SIZE is the natural
logarithm of the bank holding company’s total assets. NII is net interest income scaled by total assets. LIQUID is the sum of cash
and due short-term obligations, federal funds sold, and securities purchased for resale, scaled by total assets. NPL is the sum of
nonaccrual loans and accruing loans past due 90 days or more, scaled by total assets. CHAROFF is total charge off to loans and
leases.
*
p < 0.10.
**
p < 0.05.
***
p < 0.01.
starting at time t over three different measurement windows, i.e., four, eight, and twelve quarters,
scaled by total assets.40 The estimation results are presented in Table 4. The results in this table are
based on the observations for bank holding companies that have derivatives for trading and/or nontrading purposes during the test period from 1995 to March 2012.
Table 4, Column 1 reports the results from OLS regression in which earnings volatility is measured
as the standard derivation of income over four quarters scaled by total assets for the sample of
derivative users (n = 6676). The estimated regression relationship has an R2 of 36% and provides
results that should be helpful in testing H1 about the relationship between the cash flow hedge
reserve in OCI and earnings volatility. As predicted, the estimated coefficient on CFH, a1, is negative
and statistically significant at the 1% level (t-statistic = 5.06). This result is consistent with H1: the
use of cash flow hedge helps reduce earnings volatility. In addition, the coefficient on FV_TR is negative
and significant at the 5% level (t-statistic = 2.26), a finding consistent with the notion that bank
holding companies might also use some trading derivatives for economic hedges—i.e., hedging risk
without applying hedge accounting. Lastly, bank holding companies’ liquidity (non-performing loans)
has a negative (positive) effect on future earnings volatility. Table 4, Columns 2 and 3 present similar
40
We use three different measurement windows (four, eight, and twelve quarters) for the earnings volatility estimation to test
whether the results are driven by a particular cutoff. Fiechter (2011) also uses eight quarters to estimate the volatility of bank
earnings.
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A.R. Abdel-khalik, P.-C. Chen / J. Account. Public Policy 34 (2015) 291–318
Table 5
The effect of earnings volatility on the use of non-trading derivatives (test of H2).
Depvar: FV_NTRt+1
(1)
(2)
(3)
EARNVOL
0.039***
(3.140)
0.050***
(3.983)
0.330***
(5.249)
0.008
(1.545)
0.242***
(3.506)
0.463E3*
(1.810)
0.218***
(4.243)
0.003***
(3.569)
0.002
(0.860)
7645
0.535
0.554E3**
(2.502)
0.171***
(3.472)
0.003***
(3.370)
0.003
(1.575)
7645
0.562
0.020***
(3.163)
0.054
(1.186)
0.003
(1.446)
0.108
(1.489)
0.794***
(13.838)
0.219E3*
(1.803)
0.100***
(2.868)
0.001**
(2.088)
0.002*
(1.751)
7645
0.801
CFH
FV_TR
NCCE
FV_NTR
SIZE
NII
LIQUID
Intercept
Number of observations
Adjusted R2
Year and firm fixed effects are included. Standard errors are adjusted for heteroskedasticity. FV_NTRt+1 is the total fair value of
derivatives used for non-trading purposes, scaled by total assets, at t + 1. EARNVOL is the earnings volatility measured as the
standard deviation of income over eight quarters, scaled by total assets. CFH is the absolute value of accumulated OCI as a result
of cash flow hedge designations, scaled by total assets. FV_TR is the total fair value of derivatives used for trading purposes,
scaled by total assets. NCCE is the difference between positive fair value of total derivatives and negative fair value of total
derivatives, scaled by total assets. FV_NTR is the total fair value of derivatives used for non-trading purposes, scaled by total
assets. SIZE is the natural logarithm of the bank holding company’s total assets. NII is the net interest income scaled by total
assets. LIQUID is the sum of cash and due short-term obligations, federal funds sold, and securities purchased for resale, scaled
by total assets.
*
p < 0.10.
**
p < 0.05.
***
p < 0.01.
results when earnings volatility is measured as the standard deviation of quarterly earnings over eight
quarters or over twelve quarters scaled by total assets, respectively. The coefficients on CFH in both
Columns 2 and 3 remain negative and significant at the 1% level, which is also consistent with H1.
In addition, the magnitude of the estimated coefficient on CFH decreases monotonically with the
measurement window of earnings volatility. This is consistent with the notion that the use of
derivatives for hedging has stronger volatility-reducing effect over the nearer term.
Conclusion on H1:
The results are consistent with the hypothesis that the reserve of cash flow hedge accounting
accumulated in other comprehensive income is negatively associated with earnings volatility.
We now turn to test H2 on the relationship of earnings volatility and the demand for non-trading
derivatives. Table 5 reports the results of estimating Eq. (3) using partial and full model specifications.
Column 1 presents the results including the variable of interest, EARNVOL, and other non-derivative
firm characteristics. The estimated coefficient on the test variable, EARNVOL, is positive and significant
at the 1% level (t-statistic = 3.14). This finding supports the second hypothesis: bank holding
companies have incentives to use derivatives for non-trading derivatives when faced with high level
of earnings volatility. Column 2 in Table 5 includes other derivative variables, such as CFH, FV_TR, and
NCCE. With the inclusion of these derivative variables, the estimated coefficient on EARNVOL remains
positive and significant at the 1% level (t-statistic = 3.98). This result suggests that the use of hedging
derivatives by bank holding companies affects earnings volatility beyond the current period. Finally,
Column 3 in Table 5 further controls the level of non-trading derivatives in the current period. As
expected, there is a strong and positive relationship between the current and future levels of
non-trading derivatives (estimated coefficient on FV_NTR = 0.794; t-statistic = 13.84). While other
A.R. Abdel-khalik, P.-C. Chen / J. Account. Public Policy 34 (2015) 291–318
313
control variables become insignificant after including FV_NTR, the coefficient on our main variable of
interest, EARNVOL, remains positive and significant. Therefore, results from both Columns 2 and 3 are
consistent with the second hypothesis.
Conclusion on H2:
The results are supportive of the hypothesis that earnings volatility induces demand for nontrading derivatives.
8.2. The relationship between equity risk and hedging derivatives (2SLS estimation)
Next, we turn to test H3 and H4 on the relationship between equity risk and the demand for derivatives by estimating Eqs. (4) and (5). As discussed in Section 7.2, the return volatility variable is a
dependent variable in Eq. (4) and an explanatory variable in Eq. (5). The endogeneity issue in this
system led us to use the two-stage-least-squares (2SLS) method to estimate Eqs. (4) and (5) for return
volatility using the instrumental variable approach.41
The first-stage results, reported in Table 6, Panel A, show that the estimated regression has an
adjusted R2 of 68%. The coefficient on CFH is negative and statistically significant at the 1% level
(t-statistic = 2.68), consistent with H3. In addition, the signs of the coefficients on control variables
are consistent with expectations—negative signs on firm size (SIZE) and profitability (NII), and positive
signs on non-performing loans (NPL). Given the 2SLS technique, the predicted values PRETVOL will be
the instrumented variable in Eq. (5).
Conclusion on H3:
The results are consistent with the hypothesis that the use of cash flow hedge is negatively associated with equity risk.
The last hypothesis, H4, posits that banks may use non-trading derivatives to reduce equity risk,
which will be reflected in obtaining a positive relationship between equity risk and non-trading
derivatives. The results of the second-stage estimation are presented in Table 6, Panel B. Similar to
the previous earnings volatility analysis, we report results from partial as well as full model
specifications. Table 6, Column 1 shows that the coefficient on PRETVOL is positive and statistically
significant at the 1% level (t-statistic = 4.20). Table 6, Column 2 reports similar results on the estimated
coefficient on PRETVOL when other derivative variables, i.e., CFH, FV_TR, and NCCE, are included in
the model. In addition, the coefficient on CFH is positive and statistically significant at the 1% level.
The coefficients on SIZE and NII are both positive and significant at the 5% or higher level. However,
the coefficients on RETVOL and all other derivative variables become insignificant, as reported in
Table 6, Column 3, when we further control for the current levels of non-trading derivatives, i.e.,
FV_NTR.42 Overall, the results of the second-stage estimation are consistent with H4 that higher equity
risk is associated with larger holding of non-trading derivatives.
Conclusion on H4:
Equity risk has a significant positive association with firms’ use of non-trading derivatives.
8.3. Robustness test
As noted in Part I, the five largest U.S. BHCs have more than 90% of total derivatives in the banking
sector. As a result, we find it necessary to evaluate the impact of these Big5 banks on the results. In our
first robustness test, we re-estimated all regression models excluding these Big5 BHCs. The first
columns in Table 7, Panels A and B report the estimation results for the non-Big5 bank holding
companies that use derivatives for trading and/or non-trading purposes. For ease of presentation,
we show summarized results in this table and report only the main variables of interest and key
41
This system is identified because Eq. (4) has exogenous variables not included in the second-stage regression, Eq. (5).
Given that we have directional predictions for the estimated coefficient on RETVOL in Eq. (5), one could argue that a one-tailed t
test would be suitable for the statistical inferences. The estimated coefficient on RETVOL (t-statistic = 1.32) would be significant at
the 10% level if a one-tailed t test were applied here. To be conservative in making inferences, we choose to use the two-tailed tests
consistently across all the regressions in the paper.
42
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A.R. Abdel-khalik, P.-C. Chen / J. Account. Public Policy 34 (2015) 291–318
Table 6
Two-stage least squares estimation of non-trading derivatives and equity risk.
Depvar: RETVOL
Panel A: First stage
The effect of hedging derivatives on equity risk (test of H3)
EARNVOL
0.764***
(5.280)
0.907***
(2.682)
0.009**
(2.073)
0.060
(0.652)
0.496E3
(0.211)
0.006***
(3.741)
1.508***
(4.623)
0.003
(0.477)
0.349***
(9.339)
0.019
(0.406)
0.001***
(25.281)
0.068***
(4.529)
7645
0.679
CFH
FV_TR
NCCE
GAP
SIZE
NII
LIQUID
NPL
CHAROFF
VIX
Intercept
Number of observations
Adjusted R2
Depvar: FV_NTRt+1
(1)
Panel B: Second stage
The effect of equity risk on the use of non-trading derivatives (test of H4)
PRETVOL
0.028***
(4.196)
CFH
FV_TR
NCCE
(2)
(3)
0.019***
(3.769)
0.325***
(5.179)
0.008
(1.493)
0.236***
(3.422)
0.004
(1.315)
0.058
(1.265)
0.003
(1.414)
0.110
(1.496)
0.794***
(13.837)
0.224E3*
(1.857)
0.109***
(2.660)
0.001*
(1.726)
0.002*
(1.888)
7645
0.801
FV_NTR
SIZE
NII
LIQUID
Intercept
Number of observations
Adjusted R2
1.111E3**
(2.139)
0.371***
(2.975)
0.001
(0.753)
0.012**
(2.320)
7645
0.521
0.586E3***
(2.625)
0.211***
(3.963)
0.002***
(3.156)
0.004**
(1.997)
7645
0.563
Year and firm fixed effects are included. Standard errors are adjusted for heteroskedasticity. For Panel A, RETVOL is the equity
risk for firm i measured as total daily return volatility over the quarter. EARNVOL is the earnings volatility measured as the
standard deviation of income over eight quarters, scaled by total assets. CFH is the absolute value of accumulated OCI as a result
of cash flow hedge designations, scaled by total assets. FV_TR is the total fair value of derivatives used for trading purposes,
scaled by total assets. NCCE is the difference between positive fair value of total derivatives and negative fair value of total
derivatives, scaled by total assets. GAP is the absolute value of the difference between interest-sensitive assets and interestsensitive liabilities, scaled by total assets. VIX is the average quarterly value of the Chicago Board of Options Exchange volatility
index. SIZE is the natural logarithm of the bank holding company’s total assets. NII is the net interest income scaled by total
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A.R. Abdel-khalik, P.-C. Chen / J. Account. Public Policy 34 (2015) 291–318
assets. LIQUID is the sum of cash and due short-term obligations, federal funds sold, and securities purchased for resale, scaled
by total assets. NPL is the sum of nonaccrual loans and accruing loans past due 90 days or more, scaled by total assets. CHAROFF
is total charge off to loans and leases.
For Panel B, FV_NTRt+1 is the total fair value of derivatives used for non-trading purposes, scaled by total assets, at t + 1. PRETVOL
is the predicted value of RETVOL from the first-stage estimation reported in Panel A. FV_NTR is the total fair value of derivatives
used for non-trading purposes, scaled by total assets. Other variables are the same as in Panel A.
*
p < 0.10.
**
p < 0.05.
***
p < 0.01.
Table 7
Robustness checks.
H1: Depar = EARNVOLt+8
Excl. Big5
Panel A: Earnings volatility and non-trading derivatives (H1 and H2)
CFH
0.163***
FV_TR
0.019***
Controls
Yes
H2: Depar = FV_NTRt+1
EARNVOL
CFH
FV_TR
FV_NTR
Controls
H3: Depar = RETVOL
Excl. Big5
0.021***
0.017
0.005E1
0.756***
Yes
Excl. Big5
Panel B: Equity risk and non-trading derivatives (H3 and H4)
EARNVOL
0.751***
CFH
0.963***
FV_TR
0.006
Controls
Yes
H4: Depar = FV_NTRt+1
PRETVOL
CFH
FV_TR
FV_NTR
Controls
Excl. Big5
0.005***
0.024
0.001
0.756***
Yes
All BHC
0.160***
0.001*
Yes
All BHC
0.010***
0.019
0.003
0.797***
Yes
All BHC
0.768***
1.289***
0.009**
Yes
All BHC
0.002***
0.021
0.003*
0.797***
Yes
EARNVOLt+8 is the earnings volatility measured as the standard deviation of income over eight quarters starting at time t, scaled
by total assets. RETVOL is the equity risk for firm i measured as total daily return volatility over the quarter. PRETVOL is the
predicted value of RETVOL from the first-stage estimation. CFH is the absolute value of accumulated OCI as a result of cash flow
hedge designations, scaled by total assets. FV_TR is the total fair value of derivatives used for trading purposes, scaled by total
assets. FV_NTR is the total fair value of derivatives used for non-trading purposes, scaled by total assets. FV_NTRt+1 is the total
fair value of derivatives used for non-trading purposes (i.e., hedging), scaled by total assets at t + 1.
*
p < 0.10.
**
p < 0.05.
***
p < 0.01.
control variables. Comparing the results in these two columns to previous tables shows that the findings related to the test variables are essentially unchanged with or without the Big5.
However, there is one interesting finding based on the comparison of the coefficient on FV_TR for
the test of H3 in Table 7, Panel B to that in Table 6. As reported in Table 6, the coefficient on FV_TR is
statistically significant for the full sample in relation to equity risk. However, the coefficient on the
same variable becomes insignificant in Table 7, Panel B when the Big5 bank holding companies are
excluded. This difference suggests that the market views the use of trading derivatives by Big5 bank
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holding companies as increasing an entity’s risk, which is consistent with Big5 BHCs holding the
majority of derivatives for purposes that are not related to hedging.
To further validate the main results, we conduct another robustness test using the full sample of
bank holding companies including both derivative users and non-users. The results are presented in
the second columns of Table 7, Panels A and B. As expected, the results are essentially similar to those
reported in previous tables. This suggests that our results remain the same if all bank holding
companies (with and without derivatives) are used in estimation. Finally, in an unreported analysis,
we re-estimate Eqs. (4) and (5) using OLS regressions and find similar results to those estimated using
the 2SLS method. In sum, our findings are consistent with the use of cash flow hedge to reduce earnings volatility and equity risk. In addition, both earnings volatility and equity risk have a positive
impact on the use of derivatives for non-trading purposes in next period.
9. Summary and conclusion
We observe with great surprise the rapid growth in financial derivatives to the point that the
worldwide notional amounts of derivative instruments had increased to $700 trillion, which is more
than ten times the size of global GDP. For this notional amount, which is the balance at the end of a
period, the fair market value is estimated to be $21 trillion or about 30% of global GDP. While the U.S.
share of this volume is unknown because of the lack of transparency in OTC markets, regulatory filings
with the Federal Reserve, FDIC and Office of the Comptroller of the Currency reveal that the top 25
financial institutions, banks and thrift companies held derivatives at the end of 2012 amounting to
over $300 trillion, which is more than 40% of global derivatives. Of that amount, about $275 trillion
were in the hands of five large bank holding companies.
In this study, we attempt to examine the potential forces behind such phenomenal growth in the
use of derivatives in the USA. We investigate three main factors that we conjecture as possible driving
forces behind the increase in derivative activities. In Part I, we examine the relationship between two
key legislations, Gram-Leach-Bliley in 1999 and Commodity Futures Modernization Act in 2000, and
the growth of derivatives during the period 1995 –2012. Based on time trend analysis, it appears that
the use of derivatives for both trading and non-trading purposes has increased significantly since the
passage of these two National Acts. However, FAS 133, the accounting standard for derivatives and
hedge accounting, was also issued in 1998 and became effective in 2000. The coincident timing complicates our ability to sort out the impact of accounting and public policy on the growth of derivatives.
To provide further evidence, we devote Part II of this study specifically to investigate the impact of
hedge accounting on the use of non-trading derivatives. The relevance of this analysis emanates from
the management’s ability to designate a significant portion of non-trading derivatives for hedging purposes subject to special accounting treatments.
Our findings show that the use of cash flow hedge accounting under FAS 133 is associated with a
decrease in both earnings volatility and equity risk. This supports the notion that hedge accounting
provides firms with the incentive to use derivatives for hedging purposes if their goal is to reduce
the volatility in reported earnings or the market’s perception of firm risk. We further provide evidence
showing that firms tend to increase the level of derivatives used for non-trading purposes when faced
with high earnings volatility or high equity risk. This finding is consistent with the conjecture that FAS
133 also helped to fuel the growth of derivative use in the USA.
Although we cannot estimate the extent to which the two Congressional Acts and FAS 133
contribute to the growth in the use of derivatives, we note that there is a self-imposed limit for
the impact of accounting standards on the growth of derivatives, in that bank holding companies
use 98% of their derivatives for trading purposes—i.e., speculation and profit making—leaving
nearly 2% or less for hedging risk. Based on the big disparity in the magnitudes of trading and
non-trading derivatives, we believe that the two legislative Acts have a more profound impact
on the rapid growth of derivatives activities than does FAS 133. A more direct comparison between
the effects of these forces on the use of financial derivatives remains an interesting question to be
examined in future research.
A.R. Abdel-khalik, P.-C. Chen / J. Account. Public Policy 34 (2015) 291–318
317
Acknowledgements
We are grateful to the comments of Michael Donohoe, Anne D’Arcy, and the participants in workshops at the University Texas at Dallas, the University of Illinois, National Taiwan University, and the
conference of the Journal of Accounting and Public Policy held in Maryland on May 29, 2014.
Appendix A. A brief overview of hedge accounting treatments in FAS 133 (ASC 815)
Under GAAP, financial derivatives are accounted for in a manner like trading marketable
securities—they are marked to market, and changes in fair values between periods are posted in the
income statement. But the management may elect to adopt hedge accounting for which accounting
standards classify designated hedges into three categories:
1. Fair value hedge: Prior to FAS 133, if firms use financial derivatives to hedge the risk associated with
changes in fair values of financial assets or liabilities,43 the changes in values of the derivative
instruments were recognized as gain/loss in the income statement. In the meantime, the changes
in fair values of hedged items (assets or liabilities) were ignored. As a result, reported earnings were
allowed to fluctuate despite the underlying hedging relationship. Under FAS 133, the changes in the
fair values of the hedged item would be recognized in earnings for the effective portion of the hedge
and thus offset changes in the values of the hedging derivatives. This category of hedging is known as
‘fair value hedge’.44
2. Cash flow hedge: This category applies to the derivatives used for hedging the risk of future changes
in the cash flows associated with financial assets, financial liabilities or forecasted transactions. The
accounting treatment for this hedge results in deferring the earnings recognition of gains and
losses on the effective portion of hedge derivatives in Other Comprehensive Income (OCI) until
one of two conditions occurs: (a) the hedged transaction or event impacts earnings, or (b) the
hedge is cancelled (i.e., de-designated). Therefore, changes in the fair values of the derivatives
designated as cash flow hedge in effective hedging relationships will impact the balance sheet
by accumulating gains/losses in OCI and by reporting the fair values of the derivative assets or
liabilities.
3. Foreign currency hedge: The third category of hedge accounting treatment relates to hedging the
exposure of investment in foreign operations to currency exchange rate risk. If the functional
currency of foreign operations is different from the functional currency of the parent, the entity
could hedge the volatility of investment in foreign operations that emanates from the fluctuations
of currency exchange rates. The effective components of gains or losses on the financial derivatives
used for this hedge are posted to the Accumulated Translation Adjustment and, therefore, do not
affect the income statement.
Financial derivatives that do not fit in any of these three categories would be treated as ‘‘held-fortrading’’ securities for which changes in fair values would flow through the income statement as they
occur. Therefore, unless a special accounting treatment is both permitted by standards and elected by
the management, the reported earnings of any business enterprise having significant derivative
holdings will fluctuate with the changes in fair values of the derivatives portfolio and by a magnitude
commensurate with the size of that portfolio.
In summary, given the income-smoothing role of hedge accounting, its adoption is neither a right
nor a requirement. Rather, it is a privilege that may be elected by management if certain specific
requirements are met.
43
There are alternatives to hedging, however, such as diversification. But many of these alternatives appear to be initially more
costly.
44
Fair value hedge accounting treatment affects the balance sheet by at least 80% (the minimum effectiveness ratio) and the
income statement by no more than 20% (the maximum allowed error) of the changes in fair values of the designated derivatives.
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