Description
Spectacular bankruptcies of the Orange County
Investment Pool in December 1994 and Barings
Bank in February 1995 mounted a pressure on
the U.S. Financial Accounting Standards Board
(FASB) to issue Statement No. 133, Accounting
for Derivatives Instruments and Hedging
Activities (FAS 133). Although measuring
derivatives at fair value is a major improvement
in accounting for derivatives, such type of
accounting falls short of quantifying and
reporting the risk of losses associated with
derivative instruments.
Accounting Research Journal
FASB’s Statement No. 133 on Derivatives and Barings Bank: The Case for Value
at Risk (VAR)
Yass A. Alkafaji Nauzer Balsara J udith N. Aburmishan
Article information:
To cite this document:
Yass A. Alkafaji Nauzer Balsara J udith N. Aburmishan, (2006),"FASB’s Statement No. 133 on
Derivatives and Barings Bank: The Case for Value at Risk (VAR)", Accounting Research J ournal,
Vol. 19 Iss 2 pp. 94 - 104
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ACCOUNTING RESEARCH JOURNAL VOLUME 19 NO 2 (2006)
94
FASB’s Statement No. 133 on Derivatives
and Barings Bank: The Case for Value at
Risk (VAR)
Yass A. Alkafaji
Northeastern Illinois University
Nauzer Balsara
Professor of Finance
Northeastern Illinois University
and
Judith N. Aburmishan
FGMK, LLC
Abstract
Spectacular bankruptcies of the Orange County
Investment Pool in December 1994 and Barings
Bank in February 1995 mounted a pressure on
the U.S. Financial Accounting Standards Board
(FASB) to issue Statement No. 133, Accounting
for Derivatives Instruments and Hedging
Activities (FAS 133). Although measuring
derivatives at fair value is a major improvement
in accounting for derivatives, such type of
accounting falls short of quantifying and
reporting the risk of losses associated with
derivative instruments. The purpose of this paper
is to suggest an alternative approach to market
valuation by integrating quantitative market risk
estimation into the valuation method. The paper
will use the Barings Bank experience to
demonstrate how FAS no.133 disclosure falls
short of disclosing the magnitude of the market
risk held by the bank at the end of 1994. It will
also demonstrate how using a risk-impacted
value would have improved the disclosure of
how much the bank stood to lose from their open
positions.
1. Introduction
Spectacular bankruptcies of the Orange County
Investment Pool in December 1994 and Barings
Bank in February 1995 mounted a new pressure
on the Financial Accounting Standards Board
(FASB) to complete its derivative project that was
in the works since 1986. Derivatives are financial
instruments whose value is derived from the value
of the underlying assets. These financial
instruments can be extremely complex and often
are not understood by investors. “The nature and
terms of the derivative financial instruments and
derivative commodity instruments that gave rise
to these losses varied significantly and are
difficult to summarize based on a common
characteristic. One common characteristic of
these losses, however, was that information about
the instruments and their associated risks was not
well disclosed.” (Linsmeier, 1996).
After much research and discussion, the FASB
finally issued FASB Statement No. 133,
Accounting for Derivatives Instruments and
Hedging Activities (FAS 133), in June of 1998.
This Statement establishes accounting and
reporting standards for derivative instruments,
including certain derivative instruments
embedded in other contracts, (collectively referred
to as derivatives) and for hedging activities.
The cornerstone of FAS 133 is to adopt the fair
value as a basis in measuring and recording
derivatives in the financial statements as assets
and liabilities. The FASB believes that fair value
is the most relevant measure for financial
instruments and the only relevant measure for
derivative instruments. According to FAS 133,
derivative instruments should be measured at fair
value, and adjustments to the carrying amount of
hedged items should reflect changes in their
fair value that are attributable to the risk being
hedged and that arise while the hedge is in effect.
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FASB’s Statement No. 133 on Derivatives and Barings Bank: The Case for Value at Risk (VAR)
95
The objective of this paper is to attempt to
provide an alternative approach to mark-to-
market valuation of derivative instruments by
integrating quantitative market risk estimation
into the valuation method. The paper will use
the Barings Bank example to demonstrate how
FAS 133 would not have alerted the Bank, its
management, depositors and investors as to the
magnitude of the market risk of the positions
held at the end of 1994. It will also demonstrate
how using a risk-impacted value would have
improved our understanding of how much
Barings Bank stood to lose from the open
futures positions adopted by its Singapore-based
rogue trader, Nick Leeson.
2. FAS 133 at a Glance
FAS 133 establishes accounting and reporting
standards for derivative instruments and for
hedging activities. In paragraph 9 of FAS 133, a
derivative instrument is defined to be a financial
instrument or other contract with all three of the
following characteristics:
(a) it has one or more underlying assets and one
or more notional amounts or payment
provisions or both;
(b) it requires no initial net investment or
requires an initial net investment that is
much smaller than would be required for
other contracts that would be expected to
have a similar response to changes in market
forces;
(c) it requires or permits net settlement, or it
provides for delivery of an asset that puts
the recipient in a position not substantially
different from net settlement.
There are four basic types of derivatives
transactions: forward contracts, futures
contracts, swaps, and options on stocks or
futures. They get their name from the fact that
their value is dependent upon and derived from
the value of another underlying asset. FAS 133
requires derivatives to be measured at fair value
and recorded on the balance sheet as assets or
liabilities. Derivative transactions are required
to be identified as either: (i) a cash flow hedge
of a forecasted transaction, (ii) a foreign
currency hedge of a net investment in a foreign
operation, (iii) hedging the exposure to changes
in the fair value of an existing asset, liability, or
firm commitment, or (iv) a derivatives based
transaction not intended to be a hedge.
The gains or losses resulting from fair market
valuation may be recorded in regular earnings
or as part of other comprehensive income as
prescribed by FASB Statement No. 130,
Reporting Comprehensive Income. Gains or
losses on hedges of cash flow exposures would
be reported in other comprehensive income (not
current earnings) until the underlying
transaction hedged against was expected to
occur. Gains or losses on hedges of net
investment in foreign transactions would be
reported in other comprehensive income as part
of the cumulative translation adjustment. The
change in fair value of all other derivatives,
including those that are designated as a hedge of
an existing asset, liability, or firm commitment,
as well as all other transactions not intended as a
hedge would be recognized in current earnings.
The change in fair value of any asset, liability,
or firm commitment being hedged also would
be recognized in earnings to the extent of
offsetting gains or losses on the hedging
instrument. In pursuing convergence with US
GAAP, the International Accounting Standards
Board issued IAS 39 Financial Instruments:
Recognition and Measurement in 1998 and
have since made several amendments to the
standard. In 2004, the Australian Accounting
Standards Board issued AASB 139 Financial
Instruments: Recognition and Measurement
which is identical to the March 2004 version of
lAS 39. Both standards contain very similar
requirements to SFAS N0.133.
3. Goals Met by This Statement
Statement 133 has met many of the Board’s
stated goals. First, this statement clearly
identifies derivatives as potential assets and
liabilities and requires reporting these values on
the balance sheet. This reporting requirement
makes these instruments more visible. In
addition, reporting these transactions on the
financial statements and in footnotes brings the
question of risk management to investors to
include in their analysis.
Second, this statement provides accounting
guidance for hedging derivatives, which is not
difficult to apply and is consistent with tax
treatment. The current literature had been
contradictory and lacked clear guidance on what
constitutes a hedge and what does not. This
pronouncement would clarify the issue and
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ACCOUNTING RESEARCH JOURNAL VOLUME 19 NO 2 (2006)
96
allow for more consistent treatment of similar
products.
Third, the statement would apply evenly
across all derivative instruments, including
newly emerging instruments. This would
increase consistency and comparability of
financial reporting compared to previous
accounting requirement under FAS Statement
No. 119, Disclosure about Derivative Financial
Instruments and Fair Value of Financial
Instruments. Disclosures would include the
contract amount (or notional principal amount),
the nature and terms of the contract, including a
discussion of the credit and market risk, the
average fair value of the instruments, net gains
or losses recorded in the financial statements,
and the purpose of holding the instruments. The
quality of these disclosures was dependent on
management and, thus, was not consistent.
4. Critical Issue Missed by the
Statement
Although defining derivatives and measuring
them at fair value is a major improvement in
accounting for derivatives, FAS 133 has
presented serious problems for financial
statement preparers because of its complexity,
especially as it relates to financial institutions
that are active in hedging interest-rate risks and
use a wide array of derivatives (Phillips and
Lierley, 1999). The increased visibility of
derivatives and hedging activities as introduced
by FAS 133 could pressure CFOs to steer clear
of more exotic derivative instruments (Feay and
Abdullah, 2001). Although FAS 133 is
supposed to have resulted in better financial
disclosure of the risks taken by public
companies, the Financial Accounting Standards
Board does not appear to have attained its twin
goals of increased transparency and
comparability across financial statements of
different companies (Hernandez, 2003).
We believe that FAS 133, and the
subsequent amendment FAS 138, fall short of
quantifying and reporting to the users the risk of
losses associated with the speculative use of
derivative instruments. In other words, fair
value accounting would not detect or quantify
the amount of potential risk an entity stands to
lose from entering into these contracts, such as
the losses that overwhelmed large public
companies such as Barings Bank in 1995 or
Enron in 2001. Enron was a large energy
distribution and derivatives trading firm with
headquarters in Texas. On November 8, 2001,
Enron filed with the SEC a Report of
Unscheduled Material Event, stating that “the
financial activities of a wholly-owned
subsidiary”, based in the Cayman Islands,
“which engaged in derivatives transactions with
Enron enabled to hedge market risks” (Enron
Corp., p.15). This proved that Enron was
heavily involved in derivatives trading, and was
thereby obligated to comply with the
requirements of FAS 133. Buried in the same
Enron filing with the SEC was the following
statement: “The total impact of Enron’s
adoption of SFAS 133 on earnings ….. is
dependent upon certain pending interpretations,
which are currently under consideration….. by
the FASB. While the ultimate conclusions…..
could impact the financial results, Enron does
not believe such a conclusion would have a
material effect on the results.” On December 2,
2001, less than a month after the SEC filing,
Enron filed for the largest bankruptcy in United
States history. Clearly, FAS 133 had not given
Enron stakeholders any early warning signals of
an impending bankruptcy. This is perhaps due
to the fact that Enron was engaged in
speculative transactions involving derivatives,
and FAS 133 is ill-equipped to deal with the
future outcome of such transactions.
Speculative transactions are those in which
risk exposure is increased for a profit. Within
the definition of speculative transactions, there
are three generally identified types of
transactions:
• Scalpers - those traders who hold positions in
the market over the next few minutes.
• Day traders - those traders who hold
positions in the market during different
periods during the day
• Position traders - those traders who hold their
positions overnight. These traders are broken
into two general types:
– Outright positions - an outright position
trader purchases or sells a derivative and
holds the position betting on a particular
movement in the market
– Spread positions - these traders purchase
or sell derivative instruments as noted
above, but offsetting transactions are
taken for different dates or underlying
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FASB’s Statement No. 133 on Derivatives and Barings Bank: The Case for Value at Risk (VAR)
97
assets to mitigate the risk assumed in the
first trade
Since the first two types of speculative
transactions are less than a day, the financial
statement treatment would not be an issue. The
profit or loss from the transaction would be
recorded in the profit and loss statement and no
asset or liability would be necessary to record.
The position trade transactions are more
difficult to record. Speculative traders seldom
hold positions for long periods, unless the profit
on the trade is increasing. Consequently, at the
end of any accounting period, most of the
trades, which would be “marked to market”
would record gains in earnings. A trait of
speculative traders is that they usually hold on
to profitable trades until such time the trade
loses or gives back some of its unrealized profit,
or when the instrument “expires”. At this point
the final income is known, and it is seldom
equal to the amount recorded at the most recent
accounting period end. Considering the traits of
speculative traders, it would seem that including
gains in earnings at the full fair value at the end
of an accounting period would often record
income which did not eventually materialize,
and would unnecessarily add to the perceived
earnings volatility of an enterprise.
Speculative traders, by definition, leverage
their risks. Consequently, income recorded
today can easily be reflected as a loss tomorrow.
The key to proper financial reporting for
speculative investors is in disclosing some
measure of the risk involved in the assets held.
Although the losses of Orange County took
most of a year to fully materialize, the risk was
identifiable while they were still making money.
Similarly, the losses of Barings Bank occurred
during a short time span, with much of the loss
occurring after year-end.
Both Orange County and Barings Bank
recorded significant income for several years
preceding their catastrophic losses. During his
tenure as Orange County Treasurer, Robert
Citron had earned a profit of $750 million by
betting on the term structure of interest rates
(Jorion, 1995 b). Similarly, in 1994, Leeson was
reported to have made $20 million in derivative
profits for Barings Bank, or about one-fifth of
the Bank’s total profits (Rawnsley, 1995). Even
Proctor and Gamble was successful in using
interest rate swaps before they got burnt by the
very same swaps in 1994. As stated in a letter to
the Financial Accounting Standards Board on
October 10, 1996 from the Chicago Board of
Trade, the Chicago Mercantile Exchange, the
Coffee Sugar and Cocoa Exchange, the New
York Mercantile Exchange and the Board of
Trade Clearing Corporation, “even if Proctor &
Gamble had scrupulously observed the
accounting rules in the Exposure Draft, its
potential for losses from the swap transactions it
entered would not be reported until after its
swaps ‘went over the cliff,’ i.e., until the losses
were already incurred.” (Chicago Board of
Trade, 1996). Clearly, what is needed is an early
warning system to alert stakeholders of the risks
associated with speculative transactions. This is
where FAS 133 is found to be wanting.
5. An Early Warning System:
Understanding Value at Risk or
VAR
Consider what would have happened if Robert
Citron, the Orange County Treasurer, had
followed Jorion’s cue (1995 a) and reported to
investors:
Listen, I am implementing a triple-legged repo
strategy that brought you great returns so far.
However, I have to tell you that the risk of the
portfolio is such that, over the coming year, we
could lose at least $1.1 billion in one case out of
twenty.”
Better yet, consider the impact if some
portion of that $1.1 billion risk had been
recorded in a valuation reserve over the past
year so that recent income would have been
reduced by a portion of potential losses. This
information would have been available if the
“Value-at-Risk” (VAR) of the portfolio had
been disclosed.
Value at risk (VAR) is a frequently-used
method for quantifying expected maximum
losses, in normal markets, over a specified time
period, and with a given confidence level. This
method has gained wide acceptance due to the
fact that it can summarize risk over widely
divergent types of investments into one, easily
understandable number. VAR summarizes the
expected maximum loss (or worst loss) in price
risk sensitive instruments over a target horizon
within a given confidence interval (Jorion,
1997). VAR uses probabilistic models to define
an aggregate value of losses likely to be
incurred on a single transaction or a portfolio of
assets. In technical terms, VAR is the loss that is
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ACCOUNTING RESEARCH JOURNAL VOLUME 19 NO 2 (2006)
98
likely to be exceeded with a predefined
probability over a given holding period in the
future. Implicit in VAR is the choice of the
probability or risk tolerance level, and the
holding period over which the calculations are
to be performed.
Losses greater than the VAR number are
considered to be “abnormal” and are likely to be
suffered only a minuscule fraction of times,
given by the predefined (acceptable) probability
mentioned above. If we decide that a loss that is
suffered less than 5 percent of the trading
periods is to be used as the cutoff, the VAR
calculations will ignore portfolio losses that
arise 95 percent of the times as “normal” market
activities, and flag the loss level that occurs no
more than 5 percent of the times. The cutoff
probability need not necessarily be 5 percent. It
can be smaller (say, 1 or 2 percent) or larger
(say, 10 percent), depending on the level of risk
tolerance of the entity in question or the dictates
of a regulatory body, such as the SEC or the
FASB. The smaller the cutoff probability level,
the lower the risk tolerance level, and the higher
the concomitant loss which is likely to be
flagged by VAR calculations.
The choice of the holding period depends on
the nature of the entity’s operations. A trading
house or brokerage firm might have a very
short-term holding period, typically one day.
Non-financial companies might have a much
longer holding period of a month or even three
months. The longer the holding period, the
higher is the resulting VAR. In addition, longer
holding periods tend to produce more stable
VAR results. “In terms of variability over time,
the VAR approaches using longer observation
periods tend to produce less variable results
than using short observation periods.”
(Hendricks, 1996).
6. Methods for Computing Value at
Risk
The three methods commonly mentioned for
computing VAR are (a) historical simulation,
(b) Monte-Carlo simulation, and (c) the
variance-covariance approach. Below is a brief
description of each method.
6.1 The Historical Simulation Approach
The historical simulation approach relies on
actual historical data of the major market factors
to compute the value of the current portfolio
over the past n-days, where n is a reasonably
large look-back period, say 100 days, designed
to get a good feel for historic, and by
implication, future, changes in the current
portfolio value. Let us assume that we have an
open forward position in the Euro. We could
compute the market value of the current
portfolio over each of the past 100 days using
the forward rates prevailing on each of those
100 days. Having done this, we determine the
daily mark-to-market profit or loss, and rank
order the profits from the largest profit to the
largest loss. Finally, we highlight the loss that is
exceeded only 5 percent of the times. Since we
have chosen to work with a look-back period of
100 days, we will select the fifth worst loss over
the past 100 days. This is the VAR at a 95
percent confidence level.
The advantage of the historical simulation
approach is that it is fairly simple and makes no
assumptions as regards the nature of the
statistical distribution of the major market
factors. The only drawback is that the method
makes a simplistic assumption that future
fluctuations will mirror historic changes. This
may not always hold true. For example, the
2003 annual report for the National Australian
Bank disclosed a VAR of $22 million at the 99
percent confidence level. This amount was
arrived at using historical data, and the report
correctly warned investors of the limitations of
VAR analysis based on historical data: “past
history may not be an appropriate proxy for
current market conditions” (National Australian
Bank, p.53). Later that same year, the Bank
suffered foreign exchange options trading losses
of $360 million, far in excess of the $22 million
estimated by VAR based on historical analysis.
However, the huge losses were attributed to the
activities of ‘rogue’ traders who allegedly acted
fraudulently and without any oversight. Given
this background, it may not be accurate to
attribute the entire blame for the gross under-
estimation of trading losses to inherent
shortcomings in the VAR technique based on
historical data from a relatively normal,
scandal-free period.
6.2 The Monte-Carlo Simulation Approach
The Monte-Carlo simulation approach is similar
to the historical simulation approach. There is,
however, one major difference: unlike the
historical simulation approach which uses
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FASB’s Statement No. 133 on Derivatives and Barings Bank: The Case for Value at Risk (VAR)
99
historical data, the Monte-Carlo simulation
approach is based on simulated values given by
a random-number generator. Consequently, the
user has to specify the nature of the statistical
distribution affecting the major market factor(s).
Presumably, the selected statistical distribution
will best approximate the expected changes in
the value of the market factor(s) over the
holding period in question. Having determined
the relevant statistical distribution, the user has
to identify the parameters of the distribution.
For example, if the distribution is assumed to be
normal, the user will have to specify the mean
and standard deviation of the distribution. This
can be determined by a thorough analysis of
past data.
Next, a random-number generator is used to
generate n simulated (hypothetical) values of
the major market factors. The value of n must
be very large, no less than 10,000 iterations and
perhaps as large as 100,000 iterations, to ensure
that all possible outcomes are considered by the
random-number generator. Continuing with our
above example, the Monte-Carlo simulation
approach could be used to generate simulated
values of the dollar/Deutsche Mark forward
rate, based on predefined values for the mean
and standard deviation for the dollar/mark
forward rate.
Once the simulated values are generated,
they are used to compute the profit/loss of the
current portfolio, in much the same way as was
recommended for the historical simulation
approach. Once again, the profits are rank
ordered from the largest profit to the largest
loss, and the cutoff value is the loss which is
exceeded only 5 percent of the times. If 10,000
simulations have been undertaken, the 5 percent
level corresponds to the loss level that is
exceeded no more than 500 times out of 10,000.
This is the VAR number. Assuming the
company wished to employ a 2 percent level
instead of a 5 percent level for VAR
calculations, it would select the loss that is
exceeded no more than 200 times out of 10,000.
This number is likely to be higher than the loss
at the 5 percent level.
The Monte-Carlo simulation approach is
more thorough than the historical simulation
approach, making no simplifying assumptions
about the future being a mirror image of the
past. However, it is also more tedious
computationally, requiring 10,000 simulations
or more in order to come up with meaningful
answers. Besides, the answers generated by the
Monte-Carlo simulation are only as good as the
assumptions initially made about the nature of
the statistical distribution of the underlying
market factor(s). The complaint with this
approach is that although it is computationally
exhaustive, the results are suspect since it is so
very difficult to surmise the exact statistical
nature of the distribution of the underlying
market factor(s).
6.3 The Variance-Covariance Approach
The variance-covariance approach assumes a
normal distribution for the underlying market
factor(s). In case of a single market factor, we
simply have to compute the variance in the
value of the factor in question. In case of
multiple market factors, however, we have to
study how the factors co-vary in addition to
studying their individual variances. The sum of
the variances of the individual market factors
and the covariance between the individual
factors gives us the overall variance for the
portfolio of market factors. The square root of
the portfolio variance gives the standard
deviation of the portfolio. The standard
deviation measures the dispersion of the
distribution: the higher the standard deviation,
the greater the dispersion around the mean or
expected value.
If the two factors are independent, the
covariance between them is zero, leading to a
portfolio variance which is exactly equal to the
sum of the individual factor variances.
However, the case of completely independent
factors is more likely the exception rather than
the rule. It is more likely that factors are not
exactly independent of one another, leading to a
positive or negative covariance term between
factors. If one of the factors increases while the
other decreases, we have a negative covariance
between them. Alternatively, if both factors
move in unison, we have a positive covariance
term. Whereas a negative covariance term leads
to an overall portfolio variance that is less than
the sum of the individual variances, a positive
covariance term leads to an overall portfolio
variance that is higher than the sum of the
individual variances. If there are two factors (A
and B), there is one covariance term between
them (covariance AB). If there are three factors
(A, B, and C), there are three covariance terms
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ACCOUNTING RESEARCH JOURNAL VOLUME 19 NO 2 (2006)
100
between them (covariances AB, AC, and BC).
In general, if there are n market factors, there
are {
*(n-1)}/2 covariance terms between
them. Once the standard deviation is computed,
the resulting value is multiplied by 1.65 to
arrive at the VAR number. This is because a
normal distribution assumes that a deviation
greater than 1.65 times the standard deviation is
likely to occur only 5 percent of the time.
6.4 Current Uses of VAR
Among the many methods of risk quantification
currently in use, VAR has gained wide
acceptance and use. This has been even more
pronounced subsequent to the major financial
losses in 1994 and 1995. Large commercial
banks use the methods to calculate their risk
daily. As stated in a letter to the FASB from
Thomas R. Donovan, President and CEO of the
Chicago Board of Trade to the Board on March
6, 1997, “VAR will become an extremely
important measurement for risk management.”
(Chicago Board of Trade, 1997). In fact, new
Securities and Exchange Commission
regulations require its registrants with market
capitalization in excess of $2.5 billion to
disclose quantitative measures of market risk.
VAR is one of three SEC approved methods for
disclosing risk. (Linsmeier, 1997).
“More recently VAR has made its way into
the hedge fund and managed futures industry.
Individual managers, commodity pool operators
and fund of funds managers for hedge funds are
finding applications for VAR.” (Cavaletti,
1997). “In the next decade, banks may be
required to mark all assets to market on a
regular basis – even ordinary loans and
mortgages. They will need an accepted tool for
making those calculations.” (Coy, 1997).
7. Limitations to the Use of VAR
While VAR is gaining rapid acceptance, it does
have limitations. The six principal limitations
are:
• Most statistical analysis is done based on
historical data, with some modification,
predicting the future. However, history is not
always a good predictor of the future;
• VAR calculations are complex, with no
universally accepted method;
• VAR ignores the dynamic nature of a trading
portfolio. This model works best for traders
that don’t change positions frequently;
• VAR is always measured over a defined risk
horizon. Different horizons result in different
VAR estimates;
• VAR will not deal well with sudden shifts of
volatility since it is a model that is based on
historical volatility;
• VAR has difficulty with options because the
relationship between the change in the
underlying instrument price and the change
in the value of the derivative is not a linear
relationship.
However, VAR is still a practical method of
communicating risk to an investor. Since point
estimates are not as informative as comparisons
across periods, it might be a good idea to
compare the VAR estimate at a given time with
the corresponding VAR for a previous time
period. If a company’s VAR is exceeding past
ranges or is vigorously fluctuating over a wide
range, it will serve as an early warning signal
that positions need to be evaluated.
8. Applying VAR to Barings Bank
In 1994, Nicholas Leeson, head trader for
Barings Bank in Singapore, was betting that the
Nikkei 225, an index of Japanese stocks, would
be moving higher. As shown in Table 1, Leeson
entered into a long position on the Nikkei index
futures to the tune of a staggering $7.7 billion.
He also felt that interest rates in Japan were
likely to move higher, pushing the Japanese
Government Bond (JGB) futures contract
lower. Consequently, he was short the JGB
futures contract, to the tune of $16 billion!
Leeson also sold about 35,000 put and call
options on the Nikkei index, betting that the
volatility of the Japanese stock market would be
diminishing or, at worst, remain relatively
stable. The effects of this strategy, termed a
short straddle, will be ignored in this analysis
since the straddle is unaffected by market
moves in either direction within a certain range
and is therefore considered to be a theoretically
risk-free strategy. As such it would not be
identified by conventional risk management
systems.
In order to analyze the futures trades
correctly, we used the standard deviation of the
Nikkei stock index and the 10-year JGB, and
the correlations between them, as provided by
Jorion (1995 b). This information is then used to
derive the variance-covariance matrix, using the
property that the covariance between two
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FASB’s Statement No. 133 on Derivatives and Barings Bank: The Case for Value at Risk (VAR)
101
Table 1
Nikkei Index Futures and JGB Futures Trades Executed by
Barings Bank in 1994
On August 2, 1994, Nick Leeson of Barings Bank executed the following trades:
A. Long 74,150 Nikkei 225 futures contracts @ Nikkei index value of 20,770
B. Short 32,000 JGB futures contracts @ 108.55
The yen/dollar values of these futures contracts were as follows:
A. 1 Nikkei futures contract is equal to 500 yen times the index value. Therefore, the value of 74,150
contracts purchased at 20,770 was yen 770,047,750,000 or $7,700,477,500 (at yen 100 = $1), or
approximately $7.7 billion.
B. 1 JGB futures contract is worth yen 50,000,000 or $500,000 (at yen 100 = $1). Therefore, the value
of 32,000 contracts was approximately $16 billion.
On December 30, 1994, the Nikkei 225 futures fell to 19,770 and the value of the JGB futures was
unchanged at 108.55, leading to the following unrealized profit/loss:
A. Unrealized yen loss on 74,150 Nikkei futures: (20,770 – 19,770) x 500 x 74,150 or yen
37,075,000,000. At the rate of Yen 100 = $1, the unrealized dollar loss on the open trade is
$370,750,000.
B. No profit/loss on the JGB futures, since the price is unchanged at 108.55.
Table 2
An analysis of the riskiness of Barings Bank’s Futures Position in 1994.
Risk (%)
?
Correlation
JGB
Matrix
Nikkei
JGB 1.18 1.000 -.0114
Nikkei 5.83 -.0114 1.000
Variance – Covariance Matrix
JGB Nikkei
JGB 0.000139 -0.000078
Nikkei -0.000078 0.003397
Variance of JGB (?
1
2
) = (0.0118)
2
= 0.000139
Variance of Nikkei (?
2
2
) = (0.0583)
2
= 0.003397
variables is the product of their standard
deviations and the correlation coefficient
between them. Notice that the correlation
between the Nikkei and the JGB is negative,
suggesting that as one goes up, the other tends
to go down. A long (or short) position in both
assets concurrently would, therefore, constitute
a natural hedge and appear to be risk-free.
Instead, Leeson was long the Nikkei index and
short the JGB, increasing the overall riskiness of
his position!
9. Accounting for the Transactions
Under FAS 133, Barings Bank would record
unrealized losses on the Nikkei futures contract
at December 31, 1994 in the amount of $371
million, and nothing on the JGB futures since
the December 31 value was equal to the value at
the date the contract was executed. No mention
of risk would be made, other than previously
required disclosures in footnotes. On February
26, 1995, only 57 days later, Barings Bank was
bankrupt with a reported $1.3 billion loss from
these very same positions. If the $371 million
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ACCOUNTING RESEARCH JOURNAL VOLUME 19 NO 2 (2006)
102
loss above had been recorded as per FAS 133
guidelines, investors would have been alerted to
a significant problem. Additionally, if the Bank
were required to reflect the negative effects of
its futures and options trading activities using
VAR, this would have alerted the market to the
possible risk of bankruptcy lurking around the
corner.
9.1 An Alternative
In estimating the risk of these contracts, we
recommend using a VAR estimate to replace
the mark-to-market results. This would result in
better disclosure of market risk. For this
example, we will use the 95 percent level of
confidence, which would give us a loss estimate
that would be exceeded, on average, 1 out of
every 20 time periods. We decided to use the
variance-covariance approach to calculate VAR
in our example. This approach was selected
because it was the easiest to apply in our limited
case and the method makes no assumptions
about the normality of the underlying data
distributions.
Using this approach to computing VAR, the
overall riskiness of Leeson’s trades was
determined. The formula and the specific
calculations for this example are shown in
Table 3. The overall portfolio variance has been
calculated as the weighted sum of the individual
variances plus twice the weighted covariance
between the two assets, where the weights are
the amounts betted on the Nikkei index and the
JGB.
The portfolio variance amounts to $256
billion, with the standard deviation risk
amounting to the square root of this figure, or
$506 million. Consequently, at the 95 percent
confidence level, Baring’s VAR was 1.65 times
$506 million or $835 million. The difference
between the $371 million loss recorded under
the requirements of FAS 133 and VAR
alternative loss of $835 million results from the
impact of including market risk consideration in
the valuation model.
Although the VAR does not capture the full
$1.3 billion loss suffered by the bank, it is
significantly more accurate than the FAS 133
market value would have been. A portion of the
losses was due to the fact that one week after
the Kobe earthquake the Nikkei index lost over
6 percent of its value. This was the fallout of an
act of God that no statistical analysis could
capture.
Table 3
Computing the Variance – Covariance based VAR for Barings Bank
?
p
2
= x
1
2
?
1
2
+ x
2
2
?
2
2
+ 2 x
1
x
2
?
12
Where, ?
p
2
= overall portfolio variance
?
1
2
= variance of JGB
?
2
2
= variance of Nikkei
?
12
= covariance between JGB, Nikkei
x
1
= amount invested in JGB futures
x
2
= amount invested in Nikkei futures
?
p
2
= (-16000)
2
(0.000139) + (7700)
2
(0.003397) +
2 (-16000) (7700) (-0.000078)
?
p
2
= 35,584 + 201,408 + 19,219
?
p
2
= 256,211
?
p
2
= ?256,211 = 506 million
? overall portfolio risk = $506 million
At the 95% confidence level VAR = 1.65 x 506 million
= $835 million
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FASB’s Statement No. 133 on Derivatives and Barings Bank: The Case for Value at Risk (VAR)
103
10. Conclusion
FAS No. 133 has improved the accounting for
derivatives by requiring the use of fair value for
all types of derivatives. The gain or loss resulted
from fair value measurement is to be reflected
either in current earnings or in other
comprehensive income, an inconsistent and
politically contentious treatment. However, the
statement fell short of capturing the true essence
of derivatives when it ignored the future risk
associated with these financial instruments.
Investors are interested in future values of
recorded assets/liabilities and not simply in their
present value.
FAS 133 has provided significant
improvement over the previous practice of
ignoring market fluctuations. However, in our
opinion, it did not go far enough to reflect the
fact that the fair value of derivative instruments
is subject to a great deal of uncertainty. This
uncertainty can be quantified through the
application of risk models used in the financial
markets. As this paper demonstrates, Value-at-
Risk, or VAR, can help to quantify the amount
of potential losses, or uncertainty, might result
from holding a derivative financial instrument.
The model may not be exact and it is subject to
the same errors of any statistical model.
Nevertheless, it provides some quantifiable
indicators. The FASB has in the past used
estimation models in anticipating future values
such as the Black-Scholes model for stock
options and actuarial valuation methods for
pension liabilities.
More work is needed to quantify and
disclose speculative investment risk in corporate
financial statements, including the examination
of the impact current risk models such as VAR
would have on the methods of valuation of the
financial instrument. If the FASB is not creative
in its approach to the issue of valuation, the
SEC, or Congress will take over the role of rule-
maker. The central question is whether fair
values are reliable enough to be included in
financial statements. Our approach would be
one method of smoothing the volatility in the
market and allowing an approximation of fair
value to be recorded.
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doc_416371688.pdf
Spectacular bankruptcies of the Orange County
Investment Pool in December 1994 and Barings
Bank in February 1995 mounted a pressure on
the U.S. Financial Accounting Standards Board
(FASB) to issue Statement No. 133, Accounting
for Derivatives Instruments and Hedging
Activities (FAS 133). Although measuring
derivatives at fair value is a major improvement
in accounting for derivatives, such type of
accounting falls short of quantifying and
reporting the risk of losses associated with
derivative instruments.
Accounting Research Journal
FASB’s Statement No. 133 on Derivatives and Barings Bank: The Case for Value
at Risk (VAR)
Yass A. Alkafaji Nauzer Balsara J udith N. Aburmishan
Article information:
To cite this document:
Yass A. Alkafaji Nauzer Balsara J udith N. Aburmishan, (2006),"FASB’s Statement No. 133 on
Derivatives and Barings Bank: The Case for Value at Risk (VAR)", Accounting Research J ournal,
Vol. 19 Iss 2 pp. 94 - 104
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ACCOUNTING RESEARCH JOURNAL VOLUME 19 NO 2 (2006)
94
FASB’s Statement No. 133 on Derivatives
and Barings Bank: The Case for Value at
Risk (VAR)
Yass A. Alkafaji
Northeastern Illinois University
Nauzer Balsara
Professor of Finance
Northeastern Illinois University
and
Judith N. Aburmishan
FGMK, LLC
Abstract
Spectacular bankruptcies of the Orange County
Investment Pool in December 1994 and Barings
Bank in February 1995 mounted a pressure on
the U.S. Financial Accounting Standards Board
(FASB) to issue Statement No. 133, Accounting
for Derivatives Instruments and Hedging
Activities (FAS 133). Although measuring
derivatives at fair value is a major improvement
in accounting for derivatives, such type of
accounting falls short of quantifying and
reporting the risk of losses associated with
derivative instruments. The purpose of this paper
is to suggest an alternative approach to market
valuation by integrating quantitative market risk
estimation into the valuation method. The paper
will use the Barings Bank experience to
demonstrate how FAS no.133 disclosure falls
short of disclosing the magnitude of the market
risk held by the bank at the end of 1994. It will
also demonstrate how using a risk-impacted
value would have improved the disclosure of
how much the bank stood to lose from their open
positions.
1. Introduction
Spectacular bankruptcies of the Orange County
Investment Pool in December 1994 and Barings
Bank in February 1995 mounted a new pressure
on the Financial Accounting Standards Board
(FASB) to complete its derivative project that was
in the works since 1986. Derivatives are financial
instruments whose value is derived from the value
of the underlying assets. These financial
instruments can be extremely complex and often
are not understood by investors. “The nature and
terms of the derivative financial instruments and
derivative commodity instruments that gave rise
to these losses varied significantly and are
difficult to summarize based on a common
characteristic. One common characteristic of
these losses, however, was that information about
the instruments and their associated risks was not
well disclosed.” (Linsmeier, 1996).
After much research and discussion, the FASB
finally issued FASB Statement No. 133,
Accounting for Derivatives Instruments and
Hedging Activities (FAS 133), in June of 1998.
This Statement establishes accounting and
reporting standards for derivative instruments,
including certain derivative instruments
embedded in other contracts, (collectively referred
to as derivatives) and for hedging activities.
The cornerstone of FAS 133 is to adopt the fair
value as a basis in measuring and recording
derivatives in the financial statements as assets
and liabilities. The FASB believes that fair value
is the most relevant measure for financial
instruments and the only relevant measure for
derivative instruments. According to FAS 133,
derivative instruments should be measured at fair
value, and adjustments to the carrying amount of
hedged items should reflect changes in their
fair value that are attributable to the risk being
hedged and that arise while the hedge is in effect.
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The objective of this paper is to attempt to
provide an alternative approach to mark-to-
market valuation of derivative instruments by
integrating quantitative market risk estimation
into the valuation method. The paper will use
the Barings Bank example to demonstrate how
FAS 133 would not have alerted the Bank, its
management, depositors and investors as to the
magnitude of the market risk of the positions
held at the end of 1994. It will also demonstrate
how using a risk-impacted value would have
improved our understanding of how much
Barings Bank stood to lose from the open
futures positions adopted by its Singapore-based
rogue trader, Nick Leeson.
2. FAS 133 at a Glance
FAS 133 establishes accounting and reporting
standards for derivative instruments and for
hedging activities. In paragraph 9 of FAS 133, a
derivative instrument is defined to be a financial
instrument or other contract with all three of the
following characteristics:
(a) it has one or more underlying assets and one
or more notional amounts or payment
provisions or both;
(b) it requires no initial net investment or
requires an initial net investment that is
much smaller than would be required for
other contracts that would be expected to
have a similar response to changes in market
forces;
(c) it requires or permits net settlement, or it
provides for delivery of an asset that puts
the recipient in a position not substantially
different from net settlement.
There are four basic types of derivatives
transactions: forward contracts, futures
contracts, swaps, and options on stocks or
futures. They get their name from the fact that
their value is dependent upon and derived from
the value of another underlying asset. FAS 133
requires derivatives to be measured at fair value
and recorded on the balance sheet as assets or
liabilities. Derivative transactions are required
to be identified as either: (i) a cash flow hedge
of a forecasted transaction, (ii) a foreign
currency hedge of a net investment in a foreign
operation, (iii) hedging the exposure to changes
in the fair value of an existing asset, liability, or
firm commitment, or (iv) a derivatives based
transaction not intended to be a hedge.
The gains or losses resulting from fair market
valuation may be recorded in regular earnings
or as part of other comprehensive income as
prescribed by FASB Statement No. 130,
Reporting Comprehensive Income. Gains or
losses on hedges of cash flow exposures would
be reported in other comprehensive income (not
current earnings) until the underlying
transaction hedged against was expected to
occur. Gains or losses on hedges of net
investment in foreign transactions would be
reported in other comprehensive income as part
of the cumulative translation adjustment. The
change in fair value of all other derivatives,
including those that are designated as a hedge of
an existing asset, liability, or firm commitment,
as well as all other transactions not intended as a
hedge would be recognized in current earnings.
The change in fair value of any asset, liability,
or firm commitment being hedged also would
be recognized in earnings to the extent of
offsetting gains or losses on the hedging
instrument. In pursuing convergence with US
GAAP, the International Accounting Standards
Board issued IAS 39 Financial Instruments:
Recognition and Measurement in 1998 and
have since made several amendments to the
standard. In 2004, the Australian Accounting
Standards Board issued AASB 139 Financial
Instruments: Recognition and Measurement
which is identical to the March 2004 version of
lAS 39. Both standards contain very similar
requirements to SFAS N0.133.
3. Goals Met by This Statement
Statement 133 has met many of the Board’s
stated goals. First, this statement clearly
identifies derivatives as potential assets and
liabilities and requires reporting these values on
the balance sheet. This reporting requirement
makes these instruments more visible. In
addition, reporting these transactions on the
financial statements and in footnotes brings the
question of risk management to investors to
include in their analysis.
Second, this statement provides accounting
guidance for hedging derivatives, which is not
difficult to apply and is consistent with tax
treatment. The current literature had been
contradictory and lacked clear guidance on what
constitutes a hedge and what does not. This
pronouncement would clarify the issue and
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allow for more consistent treatment of similar
products.
Third, the statement would apply evenly
across all derivative instruments, including
newly emerging instruments. This would
increase consistency and comparability of
financial reporting compared to previous
accounting requirement under FAS Statement
No. 119, Disclosure about Derivative Financial
Instruments and Fair Value of Financial
Instruments. Disclosures would include the
contract amount (or notional principal amount),
the nature and terms of the contract, including a
discussion of the credit and market risk, the
average fair value of the instruments, net gains
or losses recorded in the financial statements,
and the purpose of holding the instruments. The
quality of these disclosures was dependent on
management and, thus, was not consistent.
4. Critical Issue Missed by the
Statement
Although defining derivatives and measuring
them at fair value is a major improvement in
accounting for derivatives, FAS 133 has
presented serious problems for financial
statement preparers because of its complexity,
especially as it relates to financial institutions
that are active in hedging interest-rate risks and
use a wide array of derivatives (Phillips and
Lierley, 1999). The increased visibility of
derivatives and hedging activities as introduced
by FAS 133 could pressure CFOs to steer clear
of more exotic derivative instruments (Feay and
Abdullah, 2001). Although FAS 133 is
supposed to have resulted in better financial
disclosure of the risks taken by public
companies, the Financial Accounting Standards
Board does not appear to have attained its twin
goals of increased transparency and
comparability across financial statements of
different companies (Hernandez, 2003).
We believe that FAS 133, and the
subsequent amendment FAS 138, fall short of
quantifying and reporting to the users the risk of
losses associated with the speculative use of
derivative instruments. In other words, fair
value accounting would not detect or quantify
the amount of potential risk an entity stands to
lose from entering into these contracts, such as
the losses that overwhelmed large public
companies such as Barings Bank in 1995 or
Enron in 2001. Enron was a large energy
distribution and derivatives trading firm with
headquarters in Texas. On November 8, 2001,
Enron filed with the SEC a Report of
Unscheduled Material Event, stating that “the
financial activities of a wholly-owned
subsidiary”, based in the Cayman Islands,
“which engaged in derivatives transactions with
Enron enabled to hedge market risks” (Enron
Corp., p.15). This proved that Enron was
heavily involved in derivatives trading, and was
thereby obligated to comply with the
requirements of FAS 133. Buried in the same
Enron filing with the SEC was the following
statement: “The total impact of Enron’s
adoption of SFAS 133 on earnings ….. is
dependent upon certain pending interpretations,
which are currently under consideration….. by
the FASB. While the ultimate conclusions…..
could impact the financial results, Enron does
not believe such a conclusion would have a
material effect on the results.” On December 2,
2001, less than a month after the SEC filing,
Enron filed for the largest bankruptcy in United
States history. Clearly, FAS 133 had not given
Enron stakeholders any early warning signals of
an impending bankruptcy. This is perhaps due
to the fact that Enron was engaged in
speculative transactions involving derivatives,
and FAS 133 is ill-equipped to deal with the
future outcome of such transactions.
Speculative transactions are those in which
risk exposure is increased for a profit. Within
the definition of speculative transactions, there
are three generally identified types of
transactions:
• Scalpers - those traders who hold positions in
the market over the next few minutes.
• Day traders - those traders who hold
positions in the market during different
periods during the day
• Position traders - those traders who hold their
positions overnight. These traders are broken
into two general types:
– Outright positions - an outright position
trader purchases or sells a derivative and
holds the position betting on a particular
movement in the market
– Spread positions - these traders purchase
or sell derivative instruments as noted
above, but offsetting transactions are
taken for different dates or underlying
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assets to mitigate the risk assumed in the
first trade
Since the first two types of speculative
transactions are less than a day, the financial
statement treatment would not be an issue. The
profit or loss from the transaction would be
recorded in the profit and loss statement and no
asset or liability would be necessary to record.
The position trade transactions are more
difficult to record. Speculative traders seldom
hold positions for long periods, unless the profit
on the trade is increasing. Consequently, at the
end of any accounting period, most of the
trades, which would be “marked to market”
would record gains in earnings. A trait of
speculative traders is that they usually hold on
to profitable trades until such time the trade
loses or gives back some of its unrealized profit,
or when the instrument “expires”. At this point
the final income is known, and it is seldom
equal to the amount recorded at the most recent
accounting period end. Considering the traits of
speculative traders, it would seem that including
gains in earnings at the full fair value at the end
of an accounting period would often record
income which did not eventually materialize,
and would unnecessarily add to the perceived
earnings volatility of an enterprise.
Speculative traders, by definition, leverage
their risks. Consequently, income recorded
today can easily be reflected as a loss tomorrow.
The key to proper financial reporting for
speculative investors is in disclosing some
measure of the risk involved in the assets held.
Although the losses of Orange County took
most of a year to fully materialize, the risk was
identifiable while they were still making money.
Similarly, the losses of Barings Bank occurred
during a short time span, with much of the loss
occurring after year-end.
Both Orange County and Barings Bank
recorded significant income for several years
preceding their catastrophic losses. During his
tenure as Orange County Treasurer, Robert
Citron had earned a profit of $750 million by
betting on the term structure of interest rates
(Jorion, 1995 b). Similarly, in 1994, Leeson was
reported to have made $20 million in derivative
profits for Barings Bank, or about one-fifth of
the Bank’s total profits (Rawnsley, 1995). Even
Proctor and Gamble was successful in using
interest rate swaps before they got burnt by the
very same swaps in 1994. As stated in a letter to
the Financial Accounting Standards Board on
October 10, 1996 from the Chicago Board of
Trade, the Chicago Mercantile Exchange, the
Coffee Sugar and Cocoa Exchange, the New
York Mercantile Exchange and the Board of
Trade Clearing Corporation, “even if Proctor &
Gamble had scrupulously observed the
accounting rules in the Exposure Draft, its
potential for losses from the swap transactions it
entered would not be reported until after its
swaps ‘went over the cliff,’ i.e., until the losses
were already incurred.” (Chicago Board of
Trade, 1996). Clearly, what is needed is an early
warning system to alert stakeholders of the risks
associated with speculative transactions. This is
where FAS 133 is found to be wanting.
5. An Early Warning System:
Understanding Value at Risk or
VAR
Consider what would have happened if Robert
Citron, the Orange County Treasurer, had
followed Jorion’s cue (1995 a) and reported to
investors:
Listen, I am implementing a triple-legged repo
strategy that brought you great returns so far.
However, I have to tell you that the risk of the
portfolio is such that, over the coming year, we
could lose at least $1.1 billion in one case out of
twenty.”
Better yet, consider the impact if some
portion of that $1.1 billion risk had been
recorded in a valuation reserve over the past
year so that recent income would have been
reduced by a portion of potential losses. This
information would have been available if the
“Value-at-Risk” (VAR) of the portfolio had
been disclosed.
Value at risk (VAR) is a frequently-used
method for quantifying expected maximum
losses, in normal markets, over a specified time
period, and with a given confidence level. This
method has gained wide acceptance due to the
fact that it can summarize risk over widely
divergent types of investments into one, easily
understandable number. VAR summarizes the
expected maximum loss (or worst loss) in price
risk sensitive instruments over a target horizon
within a given confidence interval (Jorion,
1997). VAR uses probabilistic models to define
an aggregate value of losses likely to be
incurred on a single transaction or a portfolio of
assets. In technical terms, VAR is the loss that is
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likely to be exceeded with a predefined
probability over a given holding period in the
future. Implicit in VAR is the choice of the
probability or risk tolerance level, and the
holding period over which the calculations are
to be performed.
Losses greater than the VAR number are
considered to be “abnormal” and are likely to be
suffered only a minuscule fraction of times,
given by the predefined (acceptable) probability
mentioned above. If we decide that a loss that is
suffered less than 5 percent of the trading
periods is to be used as the cutoff, the VAR
calculations will ignore portfolio losses that
arise 95 percent of the times as “normal” market
activities, and flag the loss level that occurs no
more than 5 percent of the times. The cutoff
probability need not necessarily be 5 percent. It
can be smaller (say, 1 or 2 percent) or larger
(say, 10 percent), depending on the level of risk
tolerance of the entity in question or the dictates
of a regulatory body, such as the SEC or the
FASB. The smaller the cutoff probability level,
the lower the risk tolerance level, and the higher
the concomitant loss which is likely to be
flagged by VAR calculations.
The choice of the holding period depends on
the nature of the entity’s operations. A trading
house or brokerage firm might have a very
short-term holding period, typically one day.
Non-financial companies might have a much
longer holding period of a month or even three
months. The longer the holding period, the
higher is the resulting VAR. In addition, longer
holding periods tend to produce more stable
VAR results. “In terms of variability over time,
the VAR approaches using longer observation
periods tend to produce less variable results
than using short observation periods.”
(Hendricks, 1996).
6. Methods for Computing Value at
Risk
The three methods commonly mentioned for
computing VAR are (a) historical simulation,
(b) Monte-Carlo simulation, and (c) the
variance-covariance approach. Below is a brief
description of each method.
6.1 The Historical Simulation Approach
The historical simulation approach relies on
actual historical data of the major market factors
to compute the value of the current portfolio
over the past n-days, where n is a reasonably
large look-back period, say 100 days, designed
to get a good feel for historic, and by
implication, future, changes in the current
portfolio value. Let us assume that we have an
open forward position in the Euro. We could
compute the market value of the current
portfolio over each of the past 100 days using
the forward rates prevailing on each of those
100 days. Having done this, we determine the
daily mark-to-market profit or loss, and rank
order the profits from the largest profit to the
largest loss. Finally, we highlight the loss that is
exceeded only 5 percent of the times. Since we
have chosen to work with a look-back period of
100 days, we will select the fifth worst loss over
the past 100 days. This is the VAR at a 95
percent confidence level.
The advantage of the historical simulation
approach is that it is fairly simple and makes no
assumptions as regards the nature of the
statistical distribution of the major market
factors. The only drawback is that the method
makes a simplistic assumption that future
fluctuations will mirror historic changes. This
may not always hold true. For example, the
2003 annual report for the National Australian
Bank disclosed a VAR of $22 million at the 99
percent confidence level. This amount was
arrived at using historical data, and the report
correctly warned investors of the limitations of
VAR analysis based on historical data: “past
history may not be an appropriate proxy for
current market conditions” (National Australian
Bank, p.53). Later that same year, the Bank
suffered foreign exchange options trading losses
of $360 million, far in excess of the $22 million
estimated by VAR based on historical analysis.
However, the huge losses were attributed to the
activities of ‘rogue’ traders who allegedly acted
fraudulently and without any oversight. Given
this background, it may not be accurate to
attribute the entire blame for the gross under-
estimation of trading losses to inherent
shortcomings in the VAR technique based on
historical data from a relatively normal,
scandal-free period.
6.2 The Monte-Carlo Simulation Approach
The Monte-Carlo simulation approach is similar
to the historical simulation approach. There is,
however, one major difference: unlike the
historical simulation approach which uses
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historical data, the Monte-Carlo simulation
approach is based on simulated values given by
a random-number generator. Consequently, the
user has to specify the nature of the statistical
distribution affecting the major market factor(s).
Presumably, the selected statistical distribution
will best approximate the expected changes in
the value of the market factor(s) over the
holding period in question. Having determined
the relevant statistical distribution, the user has
to identify the parameters of the distribution.
For example, if the distribution is assumed to be
normal, the user will have to specify the mean
and standard deviation of the distribution. This
can be determined by a thorough analysis of
past data.
Next, a random-number generator is used to
generate n simulated (hypothetical) values of
the major market factors. The value of n must
be very large, no less than 10,000 iterations and
perhaps as large as 100,000 iterations, to ensure
that all possible outcomes are considered by the
random-number generator. Continuing with our
above example, the Monte-Carlo simulation
approach could be used to generate simulated
values of the dollar/Deutsche Mark forward
rate, based on predefined values for the mean
and standard deviation for the dollar/mark
forward rate.
Once the simulated values are generated,
they are used to compute the profit/loss of the
current portfolio, in much the same way as was
recommended for the historical simulation
approach. Once again, the profits are rank
ordered from the largest profit to the largest
loss, and the cutoff value is the loss which is
exceeded only 5 percent of the times. If 10,000
simulations have been undertaken, the 5 percent
level corresponds to the loss level that is
exceeded no more than 500 times out of 10,000.
This is the VAR number. Assuming the
company wished to employ a 2 percent level
instead of a 5 percent level for VAR
calculations, it would select the loss that is
exceeded no more than 200 times out of 10,000.
This number is likely to be higher than the loss
at the 5 percent level.
The Monte-Carlo simulation approach is
more thorough than the historical simulation
approach, making no simplifying assumptions
about the future being a mirror image of the
past. However, it is also more tedious
computationally, requiring 10,000 simulations
or more in order to come up with meaningful
answers. Besides, the answers generated by the
Monte-Carlo simulation are only as good as the
assumptions initially made about the nature of
the statistical distribution of the underlying
market factor(s). The complaint with this
approach is that although it is computationally
exhaustive, the results are suspect since it is so
very difficult to surmise the exact statistical
nature of the distribution of the underlying
market factor(s).
6.3 The Variance-Covariance Approach
The variance-covariance approach assumes a
normal distribution for the underlying market
factor(s). In case of a single market factor, we
simply have to compute the variance in the
value of the factor in question. In case of
multiple market factors, however, we have to
study how the factors co-vary in addition to
studying their individual variances. The sum of
the variances of the individual market factors
and the covariance between the individual
factors gives us the overall variance for the
portfolio of market factors. The square root of
the portfolio variance gives the standard
deviation of the portfolio. The standard
deviation measures the dispersion of the
distribution: the higher the standard deviation,
the greater the dispersion around the mean or
expected value.
If the two factors are independent, the
covariance between them is zero, leading to a
portfolio variance which is exactly equal to the
sum of the individual factor variances.
However, the case of completely independent
factors is more likely the exception rather than
the rule. It is more likely that factors are not
exactly independent of one another, leading to a
positive or negative covariance term between
factors. If one of the factors increases while the
other decreases, we have a negative covariance
between them. Alternatively, if both factors
move in unison, we have a positive covariance
term. Whereas a negative covariance term leads
to an overall portfolio variance that is less than
the sum of the individual variances, a positive
covariance term leads to an overall portfolio
variance that is higher than the sum of the
individual variances. If there are two factors (A
and B), there is one covariance term between
them (covariance AB). If there are three factors
(A, B, and C), there are three covariance terms
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between them (covariances AB, AC, and BC).
In general, if there are n market factors, there
are {
*(n-1)}/2 covariance terms betweenthem. Once the standard deviation is computed,
the resulting value is multiplied by 1.65 to
arrive at the VAR number. This is because a
normal distribution assumes that a deviation
greater than 1.65 times the standard deviation is
likely to occur only 5 percent of the time.
6.4 Current Uses of VAR
Among the many methods of risk quantification
currently in use, VAR has gained wide
acceptance and use. This has been even more
pronounced subsequent to the major financial
losses in 1994 and 1995. Large commercial
banks use the methods to calculate their risk
daily. As stated in a letter to the FASB from
Thomas R. Donovan, President and CEO of the
Chicago Board of Trade to the Board on March
6, 1997, “VAR will become an extremely
important measurement for risk management.”
(Chicago Board of Trade, 1997). In fact, new
Securities and Exchange Commission
regulations require its registrants with market
capitalization in excess of $2.5 billion to
disclose quantitative measures of market risk.
VAR is one of three SEC approved methods for
disclosing risk. (Linsmeier, 1997).
“More recently VAR has made its way into
the hedge fund and managed futures industry.
Individual managers, commodity pool operators
and fund of funds managers for hedge funds are
finding applications for VAR.” (Cavaletti,
1997). “In the next decade, banks may be
required to mark all assets to market on a
regular basis – even ordinary loans and
mortgages. They will need an accepted tool for
making those calculations.” (Coy, 1997).
7. Limitations to the Use of VAR
While VAR is gaining rapid acceptance, it does
have limitations. The six principal limitations
are:
• Most statistical analysis is done based on
historical data, with some modification,
predicting the future. However, history is not
always a good predictor of the future;
• VAR calculations are complex, with no
universally accepted method;
• VAR ignores the dynamic nature of a trading
portfolio. This model works best for traders
that don’t change positions frequently;
• VAR is always measured over a defined risk
horizon. Different horizons result in different
VAR estimates;
• VAR will not deal well with sudden shifts of
volatility since it is a model that is based on
historical volatility;
• VAR has difficulty with options because the
relationship between the change in the
underlying instrument price and the change
in the value of the derivative is not a linear
relationship.
However, VAR is still a practical method of
communicating risk to an investor. Since point
estimates are not as informative as comparisons
across periods, it might be a good idea to
compare the VAR estimate at a given time with
the corresponding VAR for a previous time
period. If a company’s VAR is exceeding past
ranges or is vigorously fluctuating over a wide
range, it will serve as an early warning signal
that positions need to be evaluated.
8. Applying VAR to Barings Bank
In 1994, Nicholas Leeson, head trader for
Barings Bank in Singapore, was betting that the
Nikkei 225, an index of Japanese stocks, would
be moving higher. As shown in Table 1, Leeson
entered into a long position on the Nikkei index
futures to the tune of a staggering $7.7 billion.
He also felt that interest rates in Japan were
likely to move higher, pushing the Japanese
Government Bond (JGB) futures contract
lower. Consequently, he was short the JGB
futures contract, to the tune of $16 billion!
Leeson also sold about 35,000 put and call
options on the Nikkei index, betting that the
volatility of the Japanese stock market would be
diminishing or, at worst, remain relatively
stable. The effects of this strategy, termed a
short straddle, will be ignored in this analysis
since the straddle is unaffected by market
moves in either direction within a certain range
and is therefore considered to be a theoretically
risk-free strategy. As such it would not be
identified by conventional risk management
systems.
In order to analyze the futures trades
correctly, we used the standard deviation of the
Nikkei stock index and the 10-year JGB, and
the correlations between them, as provided by
Jorion (1995 b). This information is then used to
derive the variance-covariance matrix, using the
property that the covariance between two
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FASB’s Statement No. 133 on Derivatives and Barings Bank: The Case for Value at Risk (VAR)
101
Table 1
Nikkei Index Futures and JGB Futures Trades Executed by
Barings Bank in 1994
On August 2, 1994, Nick Leeson of Barings Bank executed the following trades:
A. Long 74,150 Nikkei 225 futures contracts @ Nikkei index value of 20,770
B. Short 32,000 JGB futures contracts @ 108.55
The yen/dollar values of these futures contracts were as follows:
A. 1 Nikkei futures contract is equal to 500 yen times the index value. Therefore, the value of 74,150
contracts purchased at 20,770 was yen 770,047,750,000 or $7,700,477,500 (at yen 100 = $1), or
approximately $7.7 billion.
B. 1 JGB futures contract is worth yen 50,000,000 or $500,000 (at yen 100 = $1). Therefore, the value
of 32,000 contracts was approximately $16 billion.
On December 30, 1994, the Nikkei 225 futures fell to 19,770 and the value of the JGB futures was
unchanged at 108.55, leading to the following unrealized profit/loss:
A. Unrealized yen loss on 74,150 Nikkei futures: (20,770 – 19,770) x 500 x 74,150 or yen
37,075,000,000. At the rate of Yen 100 = $1, the unrealized dollar loss on the open trade is
$370,750,000.
B. No profit/loss on the JGB futures, since the price is unchanged at 108.55.
Table 2
An analysis of the riskiness of Barings Bank’s Futures Position in 1994.
Risk (%)
?
Correlation
JGB
Matrix
Nikkei
JGB 1.18 1.000 -.0114
Nikkei 5.83 -.0114 1.000
Variance – Covariance Matrix
JGB Nikkei
JGB 0.000139 -0.000078
Nikkei -0.000078 0.003397
Variance of JGB (?
1
2
) = (0.0118)
2
= 0.000139
Variance of Nikkei (?
2
2
) = (0.0583)
2
= 0.003397
variables is the product of their standard
deviations and the correlation coefficient
between them. Notice that the correlation
between the Nikkei and the JGB is negative,
suggesting that as one goes up, the other tends
to go down. A long (or short) position in both
assets concurrently would, therefore, constitute
a natural hedge and appear to be risk-free.
Instead, Leeson was long the Nikkei index and
short the JGB, increasing the overall riskiness of
his position!
9. Accounting for the Transactions
Under FAS 133, Barings Bank would record
unrealized losses on the Nikkei futures contract
at December 31, 1994 in the amount of $371
million, and nothing on the JGB futures since
the December 31 value was equal to the value at
the date the contract was executed. No mention
of risk would be made, other than previously
required disclosures in footnotes. On February
26, 1995, only 57 days later, Barings Bank was
bankrupt with a reported $1.3 billion loss from
these very same positions. If the $371 million
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ACCOUNTING RESEARCH JOURNAL VOLUME 19 NO 2 (2006)
102
loss above had been recorded as per FAS 133
guidelines, investors would have been alerted to
a significant problem. Additionally, if the Bank
were required to reflect the negative effects of
its futures and options trading activities using
VAR, this would have alerted the market to the
possible risk of bankruptcy lurking around the
corner.
9.1 An Alternative
In estimating the risk of these contracts, we
recommend using a VAR estimate to replace
the mark-to-market results. This would result in
better disclosure of market risk. For this
example, we will use the 95 percent level of
confidence, which would give us a loss estimate
that would be exceeded, on average, 1 out of
every 20 time periods. We decided to use the
variance-covariance approach to calculate VAR
in our example. This approach was selected
because it was the easiest to apply in our limited
case and the method makes no assumptions
about the normality of the underlying data
distributions.
Using this approach to computing VAR, the
overall riskiness of Leeson’s trades was
determined. The formula and the specific
calculations for this example are shown in
Table 3. The overall portfolio variance has been
calculated as the weighted sum of the individual
variances plus twice the weighted covariance
between the two assets, where the weights are
the amounts betted on the Nikkei index and the
JGB.
The portfolio variance amounts to $256
billion, with the standard deviation risk
amounting to the square root of this figure, or
$506 million. Consequently, at the 95 percent
confidence level, Baring’s VAR was 1.65 times
$506 million or $835 million. The difference
between the $371 million loss recorded under
the requirements of FAS 133 and VAR
alternative loss of $835 million results from the
impact of including market risk consideration in
the valuation model.
Although the VAR does not capture the full
$1.3 billion loss suffered by the bank, it is
significantly more accurate than the FAS 133
market value would have been. A portion of the
losses was due to the fact that one week after
the Kobe earthquake the Nikkei index lost over
6 percent of its value. This was the fallout of an
act of God that no statistical analysis could
capture.
Table 3
Computing the Variance – Covariance based VAR for Barings Bank
?
p
2
= x
1
2
?
1
2
+ x
2
2
?
2
2
+ 2 x
1
x
2
?
12
Where, ?
p
2
= overall portfolio variance
?
1
2
= variance of JGB
?
2
2
= variance of Nikkei
?
12
= covariance between JGB, Nikkei
x
1
= amount invested in JGB futures
x
2
= amount invested in Nikkei futures
?
p
2
= (-16000)
2
(0.000139) + (7700)
2
(0.003397) +
2 (-16000) (7700) (-0.000078)
?
p
2
= 35,584 + 201,408 + 19,219
?
p
2
= 256,211
?
p
2
= ?256,211 = 506 million
? overall portfolio risk = $506 million
At the 95% confidence level VAR = 1.65 x 506 million
= $835 million
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FASB’s Statement No. 133 on Derivatives and Barings Bank: The Case for Value at Risk (VAR)
103
10. Conclusion
FAS No. 133 has improved the accounting for
derivatives by requiring the use of fair value for
all types of derivatives. The gain or loss resulted
from fair value measurement is to be reflected
either in current earnings or in other
comprehensive income, an inconsistent and
politically contentious treatment. However, the
statement fell short of capturing the true essence
of derivatives when it ignored the future risk
associated with these financial instruments.
Investors are interested in future values of
recorded assets/liabilities and not simply in their
present value.
FAS 133 has provided significant
improvement over the previous practice of
ignoring market fluctuations. However, in our
opinion, it did not go far enough to reflect the
fact that the fair value of derivative instruments
is subject to a great deal of uncertainty. This
uncertainty can be quantified through the
application of risk models used in the financial
markets. As this paper demonstrates, Value-at-
Risk, or VAR, can help to quantify the amount
of potential losses, or uncertainty, might result
from holding a derivative financial instrument.
The model may not be exact and it is subject to
the same errors of any statistical model.
Nevertheless, it provides some quantifiable
indicators. The FASB has in the past used
estimation models in anticipating future values
such as the Black-Scholes model for stock
options and actuarial valuation methods for
pension liabilities.
More work is needed to quantify and
disclose speculative investment risk in corporate
financial statements, including the examination
of the impact current risk models such as VAR
would have on the methods of valuation of the
financial instrument. If the FASB is not creative
in its approach to the issue of valuation, the
SEC, or Congress will take over the role of rule-
maker. The central question is whether fair
values are reliable enough to be included in
financial statements. Our approach would be
one method of smoothing the volatility in the
market and allowing an approximation of fair
value to be recorded.
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