2.
Risk Management for Hedge Funds
• Institutions operate in a highly regulated envi- of 10 percent and an annual volatility, SD[R], of 75
ronment and must comply with a number of percent, a rather mediocre fund that few hedge-fund
federal and state laws governing the rights, investors would consider seriously. Now suppose
responsibilities, and liabilities of pension plan that such a manager were to implement a risk man-
sponsors and other fiduciaries. agement protocol on top of his investment strategy,
• Institutions desire structure, stability, and con- a protocol that eliminates the possibility of returns
sistency in a well-defined investment process lower than –20 percent. His return after implement-
that is institutionalized and not dependent on ing this protocol is then R*, where
any single individual. R* = Max[R, –20%]. (1)
While there are, of course, exceptions to these two
Under the assumption of lognormally distributed
sets of views, they do represent the essence of the
returns, it can be shown that the expected value of
gap between hedge-fund managers and institu-
R*, E[R*], is 20.9 percent—by truncating the left tail
tional investors. However, despite these differences,
of the distribution of R below –20 percent, this man-
hedge-fund managers and institutional investors
ager has doubled the expected value of the strategy!
clearly have much to gain from a better understand-
Risk management can be a significant source of
ing of each other’s perspectives, and they do share
alpha. Moreover, the volatility of R*, SD[R*], is 66.8
the common goal of generating superior investment percent, lower than the volatility of R; hence, risk
performance for their clients. management can simultaneously increase alpha
In this article, I hope to contribute to the dia- and decrease risk. Table 1 reports E[R*] and SD[R*]
logue between hedge-fund managers and institu- for various values of E[R], SD[R], and truncation
tional investors by providing an overview of levels and illustrates the potent and direct impact
several key aspects of risk management for hedge that risk management can have on performance.
funds, aspects that any institutional investor must Of course, risk management rarely takes the
grapple with as part of its manager-selection pro- simple form of a guaranteed floor for returns.
cess. While the risk management literature is cer- Indeed, such “portfolio insurance” is often quite
tainly well-developed,3 nevertheless, there are at costly, if it can be obtained at all, and is equivalent
least five aspects of hedge-fund investments that to the premium of a put option on the value of the
pose unique challenges for existing risk manage- portfolio. For example, the Black–Scholes premium
ment protocols and analytics: (1) survivorship bias, for the put option implicit in Equation 1 is equal to
(2) dynamic risk analytics, (3) nonlinearities, (4) 15.4 percent of the value of the portfolio to be
liquidity and credit, and (5) risk preferences. I insured.4 But this only highlights the relevance and
describe each of these aspects in more detail in this economic value of risk management. According to
article, and outline an ambitious research agenda the Black–Scholes formula, the ability to manage
for addressing them. risks in such a way as to create a floor of –20 percent
for annual performance is worth 15.4 percent of
Why Risk Management? assets under management! The more effective a
In contrast to traditional investment vehicles, such manager’s risk management process is, the more it
as stocks, bonds, and mutual funds, hedge funds will contribute to alpha.
have different risk/return objectives. Most hedge-
fund investors expect high returns in exchange for Why Not VaR?
the corresponding risks that they are expected to Given the impact that risk management can have on
bear. Perhaps because it is taken for granted that performance, a natural reaction might be to adopt a
hedge funds are riskier, few hedge-fund investors simple risk management program based on Value-
and even fewer hedge-fund managers seem to at-Risk (VaR), described in J.P. Morgan’s RiskMetrics
devote much attention to active risk management. system documentation in the following way:
Hedge-fund investors and managers often dismiss Value at Risk is an estimate, with a predefined
risk management as secondary, with “alpha” or confidence interval, of how much one can lose
return as the main objective. However, if there is from holding a position over a set horizon.
one lasting insight that modern finance has given Potential horizons may be one day for typical
us, it is the inexorable trade-off between risk and trading activities or a month or longer for port-
expected return; hence, one cannot be considered folio management. The methods described in
without reference to the other. Moreover, it is often our documentation use historical returns to
forecast volatilities and correlations that are
overlooked that proper risk management can, by
then used to estimate the market risk. These
itself, be a source of alpha. This is summarized statistics can be applied across a set of asset
neatly in the old Street wisdom that “one of the best classes covering products used by financial
ways to make money is not to lose it.” institutions, corporations, and institutional
More formally, consider the case of a manager investors. (Morgan Guaranty Trust Company
with a fund that has an annual expected return, E[R], 1995, p. 3)
November/December 2001 17
4.
Risk Management for Hedge Funds
• execution costs (price impact, commissions, 1 T
borrowing rate, short rebate), p = --
ˆ - ∑ It . (3)
T
t =1
• benchmarks and tracking error (T-bill rate ver-
sus S&P 500), This estimator is a binomial random variable;
and compare them with a similar list for a typical hence, its distribution is known, and we can readily
fixed-income hedge fund: compute its mean and standard error:
• yield-curve models (equilibrium versus arbi- E[p ] = p
ˆ (4a)
trage models),
• prepayment models (for mortgage-backed p ( 1 – p)
SD [ p ] =
ˆ -------------------- .
- (4b)
securities), T
• optionality (call, convertible, and put features), Suppose we wish to obtain a standard error of 1
• credit risk (defaults, rating changes, etc.), ˆ
percent for our estimator p (so that we can make the
• inflationary pressures, central bank activity, statement that the true p lies in the interval
• other macroeconomic factors and events. [ p – 2%, p + 2% ] with 95 percent confidence)—
ˆ ˆ
The degree of overlap in these two lists is astonish- how much data would we need? From Equation 4,
ingly small. While such differences also exist among we have:
traditional institutional asset managers, they do not p (1 – p ) p (1 – p)
have nearly the latitude that hedge-fund managers 0.01 = --------------------
- ⇒ T = -------------------- ,
- (5)
T 0.01 2
have in their investment activities; hence, these dif-
ferences are not as consequential for traditional and if we assert that the true probability p is 5 per-
managers. cent, Equation 5 yields a value of 475 years of data!
Second, VaR is a purely statistical measure of Alternatively, VaR is often computed under the
assumption that the distribution is normal; hence,
risk—typically a 95 percent confidence interval or,
estimates of tail probabilities can be obtained by
alternatively, the magnitude of loss corresponding
estimating the mean and variance of the distribu-
to a 5 percent tail probability—with little or no
tion, not just the relative frequency of rare events.
economic structure underlying its computation.
But this apparent increase in precision comes at the
Originally developed by OTC derivatives dealers to expense of the parametric assumption of normality,
evaluate the risk exposure of portfolios of deriva- and it is well known that financial data—especially
tive securities, VaR may not be ideally suited to hedge-fund returns—are highly nonnormal; i.e.,
other types of investments, e.g., emerging market they are asymmetrically distributed, highly
debt, risk arbitrage, or convertible bond arbitrage. skewed, often multimodal, and with fat tails that
In particular, as a static snapshot of the marginal imply many more “rare” events than the normal
distribution of a portfolio’s profit and loss, VaR distribution would predict.
does not capture liquidity risk, event risk, credit Finally, VaR is an unconditional measure of
risk, factor exposures, or time-varying risks due to risk, where “unconditional” refers to the fact that
dynamic trading strategies that may be systemati- VaR calculations are almost always based on the
cally keyed to market conditions, e.g., contrarian, unconditional distribution of a portfolio’s profit-
short-volatility, and credit-spread strategies. and-loss. But for purposes of active risk manage-
Third, without additional economic structure, ment, conditional measures are more relevant,
VaR is notoriously difficult to estimate. By defini- especially for investment strategies that respond
tion, “tail events” are events that happen rarely; actively to changing market conditions. The fact
hence, historical data will contain only a few of that a portfolio’s VaR for the next week is $10
these events, generally too small a sample to yield million may be less informative than a conditional
probability statement in which VaR is $100 million
reliable estimates of tail probabilities. For example,
if the S&P 500 declines by 5 percent or more and $1
suppose we wish to estimate the probability, p, of
million otherwise. Moreover, implicit in VaR calcu-
a rare event occurring in any given year. Denote by
lations are assumptions regarding the correlations
It an indicator function that takes on the value of 1
between components of the portfolio, and these
if the event occurs in year t and 0 otherwise; hence, correlations are also computed unconditionally.
1 with probability p But one of the most important lessons of the sum-
It = . (2) mer of 1998 is the fact that correlations are highly
0 with probability 1 – p
dependent on market conditions and that securities
Now, the usual estimator for p is simply the relative which seem uncorrelated during normal times may
frequency of events in a sample of T observations: become extremely highly correlated during market
November/December 2001 19
6.
Risk Management for Hedge Funds
not unlike Equation 7 has been imposed on the were August and September of 1998. Yet, October
larger set of all hedge funds, both current and and November 1998 were the fund’s two best
defunct. Although this form of survivorship bias months, and for 1998 as a whole, the fund was up
may not be as extreme as the example in Table 2 87.3 percent versus 24.5 percent for the S&P 500! By
for any given fund, it does affect the entire cross- all accounts, this is an enormously successful hedge
section of funds, and its impact is compounded fund with a track record that would be the envy of
over time in the returns of each survivor. The end most managers.11 What is its secret?
result can be enormous for the unwary investor
seeking to construct an optimal portfolio of hedge
funds. Any quantitative approach to hedge-fund Table 3. Capital Decimation Partners, LP,
investments must address this issue explicitly, and January 1992–December 1999
there are several statistical methods ideally suited Statistic CDP S&P 500
to this purpose.9 Monthly mean (%) 3.7 1.4
Monthly standard deviation (%) 5.8 3.6
Dynamic Risk Analytics Minimum month (%) –18.3 –8.9
Maximum month (%) 27.0 14.0
One of the justifications for the unusually rich fee
Annual Sharpe ratio 1.94 0.98
structures that characterize hedge-fund invest-
Number of negative months (out of total) 6/96 36/96
ments is that hedge funds implement highly active
Correlation with S&P 500 59.9 100.0
strategies involving highly skilled portfolio man-
Total return (%) 2,721.3 367.1
agers. Moreover, it is common wisdom that the
most talented managers are drawn first to the The investment strategy summarized in Tables
hedge-fund industry because the absence of regu- 3 and 4 consists of shorting out-of-the-money S&P
latory constraints enables them to make the most of 500 (SPX) put options on each monthly expiration
their investment acumen. With the freedom to date for maturities less than or equal to three
trade as much or as little as they like on any given months, and with strikes approximately 7 percent
day, to go long or short any number of securities out of the money. The number of contracts to be sold
and with varying degrees of leverage, and to each month is determined by the combination of: (1)
change investment strategies at a moment’s notice, Chicago Board Options Exchange margin require-
hedge-fund managers enjoy enormous flexibility ments,12 (2) an assumption that 66 percent of the
and discretion in pursuing performance. But margin is required to be posted as collateral,13 and
dynamic investment strategies imply dynamic risk (3) $10 million of initial risk capital. For concrete-
exposures, and while modern financial economics ness, Table 5 reports the positions and profit/loss
ha s mu ch to say ab out the risk of st atic statement for this strategy for 1992.
investments—the market beta is sufficient in this The track record in Tables 3 and 4 seems much
case—there is currently no single measure of the less impressive in light of the simple strategy on
risk of a dynamic investment strategy.10 which it is based, and few investors would pay
To illustrate the difficulties involved in measur- hedge-fund-type fees for such a fund. However,
ing the risk exposures of a dynamic investment strat- given the secrecy surrounding most hedge-fund
egy, consider the eight-year track record of a strategies and the broad discretion that managers
hypothetical hedge fund, Capital Decimation Part- are given by the typical hedge-fund offering mem-
ners, LP (CDP), summarized in Table 3. This track orandum, it is difficult for investors to detect this
record was obtained by applying a specific invest- type of behavior without resorting to more sophis-
ment strategy, to be revealed below, to actual market ticated risk analytics, analytics that can capture
prices from January 1992 to December 1999. But dynamic risk exposures.
before discussing the particular strategy that gener- Some might argue that this example illustrates
ated these results, consider the strategy’s overall the need for position transparency—after all, it
performance: an average monthly return of 3.7 per- would be apparent from the positions in Table 5 that
cent versus 1.4 percent for the S&P 500; a total return the manager of CDP is providing little or no value-
of 2,721.3 percent over the eight-year period versus added. However, there are many ways of imple-
367.1 percent for the S&P 500; a Sharpe ratio of 1.94 menting this strategy that are not nearly so transpar-
versus 0.98 for the S&P 500; and only 6 negative ent, even when positions are fully disclosed. For
monthly returns out of 96 versus 36 out of 96 for the example, Table 6 reports the weekly positions over
S&P 500. In fact, the monthly performance history— a six-month period in one of 500 securities contained
displayed in Table 4—shows that, as with many in a second hypothetical fund, Capital Decimation
other hedge funds, the worst months for this fund Partners II, LLC. Casual inspection of the positions
November/December 2001 21
8.
Risk Management for Hedge Funds
Table 5. Positions and Profit/Loss of Capital Decimation Partners, LP, 1992 (continued)
Initial Capital Capital
Contract Number Margin plus Cumulative Available for
S&P 500 Status of Puts Strike Price Expiration Required Profits Profits Investments Return
June 19, 1992
403.67 Expired 340 385 $0.000 6/92 $ 0 $ 51,000
403.67 Mark to market 2,200 380 $1.125 7/92 $ 7,866,210 $ 27,500 $13,028,475 $7,848,479 0.6%
Total margin $ 7,866,210
July 17, 1992
415.62 Expired 2,200 380 $0.000 7/92 $ 0 $ 247,500
415.62 New 2,700 385 $1.8125 9/92 $ 8,075,835 $13,275,975 $7,997,575 1.9%
Total margin $ 8,075,835
August 21, 1992
414.85 Mark to market 2,700 385 $1 9/92 $ 8,471,925 $ 219,375 $13,495,350 $8,129,729 1.7%
Total margin $ 8,471,925
September 18, 1992
422.92 Expired 2,700 385 $0 9/92 $ 0 $ 270,000 $13,765,350 $8,292,380 2.0%
422.92 New 2,370 400 $5.375 12/92 $ 8,328,891
Total margin $ 8,328,891
October 16, 1992
411.73 Mark to market 2,370 400 $7 12/92 $10,197,992 ($ 385,125)
411.73 Liquidate 2,370 400 $7 12/92 $ 0 $ 0 $13,380,225 $8,060,377 –2.8%
411.73 New 1,873 400 $7 12/92 $ 8,059,425
Total margin $ 8,059,425
November 20, 1992
426.65 Mark to market 1,873 400 $0.9375 12/92 $ 6,819,593 $1,135,506 $14,515,731 $8,744,416 8.5%
426.65 New 529 400 $0.9375 12/92 $ 1,926,089
Total margin $ 8,745,682
December 18, 1992
441.20 Expired 1,873 400 $0 12/92 $ 0 $ 175,594 $14,691,325 $8,850,196 1.2%
1992 Total return 46.9%
in this one security seems to suggest a contrarian as a corresponding track record that is likely to be
trading strategy—when the price declines, the posi- even more impressive than that of CDP.14 Without
tion in XYZ is increased, and when the price additional analysis that explicitly accounts for the
advances, the position is reduced. A more careful dynamic aspects of the trading strategy described
analysis of the stock and cash positions and the in Table 6, it is difficult for an investor to fully
varying degree of leverage in Table 6 reveals that appreciate the risks inherent in such a fund.
these trades constitute a so-called “delta-hedging” In particular, static methods such as traditional
strategy, designed to synthetically replicate a short mean–variance analysis cannot capture the risks of
position in a two-year European put option on dynamic trading strategies like those of CDP (note
10,000,000 shares of XYZ with a strike price of $25 the impressive Sharpe ratio in Table 3). In the case
(recall that XYZ’s initial stock price was $40; hence, of the strategy of shorting out-of-the-money put
this is a deep-out-of-the-money put). options on the S&P 500, returns are positive most
Shorting deep out-of-the-money puts is a well- of the time, and losses are infrequent, but when
known artifice employed by unscrupulous hedge- they occur, they are extreme. This is a very specific
fund managers to build an impressive track record type of risk signature that is not well summarized
quickly, and most sophisticated investors are able by static measures such as standard deviation. In
to avoid such chicanery. However, imagine an fact, the estimated standard deviations of such
investor presented with position reports such as strategies tend to be rather low; hence, a naive
Table 6, but for 500 securities, not just one, as well application of mean–variance analysis, such as
November/December 2001 23
12.
Risk Management for Hedge Funds
where is not surprising given the highly nonlinear payoff
structures of derivative securities; nevertheless, it
+ Λ t if Λ t > 0
Λt = , (21a) would be a mistake to classify this set of returns as
0 otherwise “market neutral.”
– Λ t if Λ t ≤ 0 These empirical results suggest the need for a
Λt = , (21b) more sophisticated analysis of hedge-fund returns,
0 otherwise
one that accounts for asymmetries in factor expo-
and Λt is the return on the S&P 500. Since Λt = Λ + t sures, phase-locking behavior, and other nonlinear-
+ Λ – , the standard linear model in which fund i’s
t ities that are endemic to high-performance active
market betas are identical in up and down markets investment strategies. In particular, nonlinear risk
is a special case of the more general specification in models must be developed for the various types of
Equation 20 (the case where β + = β – ). However, the
i i securities that hedge funds trade, e.g., equities,
estimates reported in Table 8 for the hedge-fund fixed-income instruments, foreign exchange, com-
index returns of Table 7 show that beta asymmetries modities, and derivatives, and for each type of secu-
can be quite pronounced for certain hedge-fund rity, the risk model should include the following
styles. For example, the emerging-market equities general groups of factors:
index (“EM—equity”) has an up-market beta of • market index returns,
0.16—seemingly close to market neutral; however, • sectors,
its down-market beta is 1.49! For the relative-value • investment style,
option-arbitrage index (“RV—option arb”), the • volatilities,
asymmetries are even more severe—the coeffi- • credit,
cients are of opposite sign, with a beta of –0.78 in • liquidity,
up markets and a beta of 0.33 in down markets. This • macroeconomic indicators.
Table 8. Nonlinearities in Hedge-Fund Index Returns: Monthly Data,
January 1996–November 1999
ˆ ˆ ˆ ˆ
t ( β–)
Style Index α
ˆ t ( α)
ˆ β+ t (β+) β– R2
Currencies 0.93 1.97 0.05 0.34 0.13 0.81 0.01
ED—distress 1.95 7.84 –0.11 –1.50 0.58 6.95 0.36
ED—merger arb 1.35 7.99 0.04 0.91 0.27 4.78 0.27
EM—equity 3.78 2.41 0.16 0.34 1.49 2.84 0.11
EM 2.64 3.20 0.21 0.88 1.18 4.27 0.23
EM—fixed income 1.88 3.99 0.07 0.49 0.56 3.56 0.16
ED 1.61 9.35 –0.01 –0.26 0.43 7.37 0.41
Fund of funds 1.07 6.89 0.08 1.84 0.27 5.13 0.33
Futures trading 0.69 1.35 0.18 1.23 0.13 0.76 0.04
Growth 1.49 3.65 0.69 5.80 0.98 7.13 0.62
High yield 1.11 8.05 –0.08 –1.92 0.19 4.10 0.15
Macro 0.61 1.09 0.30 1.84 0.05 0.28 0.05
Opportunistic 1.35 3.95 0.33 3.31 0.52 4.53 0.37
Other 1.41 5.58 0.23 3.05 0.69 8.19 0.57
RV 1.36 12.22 –0.04 –1.27 0.15 4.02 0.15
RV—convertible 1.25 8.44 –0.01 –0.31 0.18 3.55 0.14
RV—EQLS 0.87 5.64 0.09 2.04 0.14 2.65 0.17
RV—option arb 4.48 4.29 –0.78 –2.56 0.33 0.95 0.07
RV—other—stat arb 1.40 4.38 –0.02 –0.18 0.11 0.99 0.01
Short selling 0.04 0.07 –0.67 –3.94 –1.25 –6.41 0.51
Value 1.46 4.49 0.24 2.54 0.69 6.41 0.45
Note: Regression analysis of monthly hedge-fund index returns with positive and negative returns on
the S&P 500 used as separate regressors. ED = event driven; arb = arbitrage; EM = emerging market;
RV = relative value; EQLS = equity long/short; stat = statistical.
Source: AlphaSimplex Group.
November/December 2001 27
14.
Risk Management for Hedge Funds
There is another, more mundane reason for their managers have very little discretion in mark-
using autocorrelations to proxy for liquidity. For ing such portfolios. Moreover, many of the SEC
portfolios of illiquid securities, i.e., securities that regulations that govern the mutual fund industry,
are not frequently traded and for which there may e.g., detailed prospectuses, daily NAV calculations,
not be a well-established market price, a hedge-fund and quarterly filings, were enacted specifically to
manager has considerable discretion in marking the guard against arbitrary marking, price manipula-
portfolio’s value at the end of each month to arrive tion, and other unsavory investment practices.
at the fund’s net asset value (NAV). Given the In sharp contrast to the mutual-fund sample,
nature of hedge-fund compensation contracts and the hedge-fund sample displays substantial serial
performance statistics, managers have an incentive correlation, with first-order autocorrelation coeffi-
to “smooth” their returns by marking their portfo- cients that range from –20.17 percent to 49.01 per-
lios to less than their actual value in months with cent, with 8 out of 12 funds that have Q-statistics
large positive returns so as to create a “cushion” for with p-values less than 5 percent, and with 10 out of
those months with lower returns. Such return- 12 funds with p-values less than 10 percent. The only
smoothing behavior yields a more consistent set of two funds with p-values not significant at the 5
returns over time with lower volatility and, there- percent or 10 percent levels are the “Risk arbitrage
fore, a higher Sharpe ratio, but it also produces serial A” and “Risk arbitrage B” funds, which have
correlation as a side effect. Of course, if the securities p-values of 74.10 percent and 93.42 percent, respec-
in the manager’s portfolio are actively traded, the tively. This is consistent with the notion of serial
manager has little discretion in marking the portfo- correlation as a proxy for liquidity risk because,
lio; it is “marked to market.” The more illiquid the among the various types of funds in this sample, risk
portfolio, the more discretion the manager has in arbitrage is likely to be one of the most liquid, since,
marking its value and smoothing returns, creating by definition, such funds invest in securities that are
serial correlation in the process.21 exchange-traded and where trading volume is typ-
To obtain a summary measure of the overall ically heavier than usual because of the impending
statistical significance of the autocorrelations, Ljung merger events on which risk arbitrage is based.
and Box (1978) proposed the following statistic: Of course, there are several other aspects of
T( T + 2 ) p liquidity that are not captured by serial correlation,
Q = --------------------- ˆ2
ρk , (22) and certain types of trading strategies can generate
(T – k) ∑
k =1 serial correlation even though they invest in highly
which has an approximate chi-squared distribution liquid instruments.25 In particular, conditioning
with p degrees of freedom in large samples and variables such as investment style, the types of secu-
under the null hypothesis of no autocorrelation.22 rities traded, and other aspects of the market envi-
By forming the sum of squared autocorrelations, the ronment should be taken into account, perhaps
statistic Q reflects the absolute magnitudes of the through the kind of risk model proposed in the
ρ k ‘s irrespective of their signs; hence, funds with
ˆ previous section. However, as a first cut for measur-
large positive or negative autocorrelation coeffi- ing and comparing the liquidity exposures of vari-
cients will exhibit large Q-statistics. ous hedge-fund investments, autocorrelation
To illustrate the potential value of autocorrela- coefficients and Q-statistics provide a great deal of
tions and the Q-statistic for measuring liquidity risk, insight and information in a convenient manner.
I estimated these statistics for a sample of 10 mutual
funds and 12 hedge funds using monthly historical Other Considerations
returns.23 Table 9 reports the means, standard devi- There are at least two other aspects of risk manage-
ations, autocorrelations ρ1 to ρ6, and p-values of the
ˆ ˆ ment for hedge funds that deserve further consid-
Q-statistic using the first six autocorrelations for the eration: risk preferences and operational risks.
sample of mutual and hedge funds. Panel A shows Risk preferences play a major role in the risk
that the 10 mutual funds have very little serial cor- management of hedge funds from both the man-
relation in returns, with first-order autocorrelations ager’s and the investor’s perspectives. Hedge fund
ranging from –3.99 percent to 12.37 percent, and managers are typically compensated with both
with p-values of the corresponding Q-statistics fixed and incentive fees, and this nonlinear payoff
ranging from 10.95 percent to 80.96 percent, imply- scheme can induce excessive risk-taking behavior
ing that none of the Q-statistics is significant at the if it is not properly managed. Imposing hurdle
5 percent level.24 The lack of serial correlation in rates, high-water marks, and other nonlinearities
these 10 mutual-fund returns is not surprising. on the manager’s compensation creates additional
Because of their sheer size, these funds consist pri- complexities that may have a material impact on
marily of highly liquid securities, and, as a result, the manager’s investment decisions, particularly in
November/December 2001 29
16.
Risk Management for Hedge Funds
management of the business. Many of these aspects These considerations are intimately tied to the
are not subject to quantitative analysis, but they are dynamic risk exposures of the pension fund’s
bona fide risks that cannot be ignored and, in some investments, and at least in some cases, hedge funds
cases, can quickly overshadow market risks in may provide the best fit for an institutional inves-
determining fund performance. tor’s optimal risk profile.
For this reason, there is likely to be a double
Conclusion coincidence of desires on the part of managers and
Despite the rapid growth in hedge-fund assets over investors with respect to risk transparency. Manag-
the past decade, the industry is poised for even more ers are unwilling to provide position transparency,
growth as individual and institutional investors and investors usually do not have the time or
become more attuned to its risks and rewards. How- resources to interpret positions (see, for example,
ever, an important catalyst in this next phase of the strategy outlined in Table 6). Instead, both man-
growth will be risk transparency and more sophis- agers and investors seek risk transparency, a hand-
ticated risk management protocols for addressing ful of risk analytics that could provide investors
the issues raised in this article. with a meaningful snapshot of a hedge fund’s risk
A better understanding of the risks that hedge- exposures without compromising the proprietary
fund investments pose for institutional investors is information contained in the manager’s positions.
not just an unavoidable aspect of fiduciary respon- Developing such a set of risk analytics is the next
sibility, but also represents significant business challenge in the evolution of the hedge-fund indus-
opportunities in this growing industry.27 For exam- try. Although this will undoubtedly create more
ple, by the very nature of their assets and liabilities, complexities for investors and managers alike, this
pension funds may be in a natural position to pro- is the price to be paid for access to a richer and
vide the kind of liquidity that many hedge funds potentially more rewarding set of investment alter-
seek. By doing so, they are able to garner more natives. In explaining his philosophy of scientific
attractive returns for their plan participants, using inquiry, Albert Einstein once commented, “Every-
hedge funds as the vehicle. However, hedge-fund thing should be made as simple as possible, but not
managers must develop a deeper appreciation for simpler.” The same can be said for the risk manage-
the types of risks that are consistent with the invest- ment of hedge funds.
ment mandates of institutional investors. Asset/
liability management for pension funds may be a
somewhat arcane discipline, but it involves issues
and insights that are remarkably similar to those of I thank Peter Chan and June Zhang for research assis-
a typical hedge fund. For example, a plan sponsor tance and many stimulating discussions and Leo de
must select and constantly manage the fund’s asset Bever, Arnout Eikeboom, Gifford Fong, Jacob Goldfield,
mix to minimize the risk of defaulting on the plan’s Stephanie Hogue, Tony Kao, Bob Merton, Dan O’Reilly,
liabilities, but completely eliminating such risks is and the conference participants at the 1999 IMN Hedge
typically too costly; i.e., the funding cost for a com- Fund Investors Summit, the Deutsche Bank Securities
pletely “immunized” portfolio of liabilities is too “Bridging the Gap” Conference, the Pricewaterhouse-
high. By maintaining a certain “surplus” of assets Coopers Risk Institute 2000 Conference, the Risk 2001
to liabilities, plan sponsors can control this risk. The Europe Conference, and the Fall 2001 Q Group Confer-
questions they face are how large the surplus ence for many helpful comments and suggestions.
should be and what an acceptable level of default Research support from AlphaSimplex Group is gratefully
risk is over horizons of 1 year, 5 years, and 20 years. acknowledged.
Notes
1. In particular, CalPERS allocated up to $1.5 billion to alter- purposely limiting access to these ideas even within their
native investments in 1999 according to Chernoff (2000). own organizations. As a result, the departure of key person-
2. Of course, many experts in intellectual property law would nel from a hedge fund often causes the demise of the fund.
certainly classify trading strategies, algorithms, and their 3. See, for example, Smithson, Smith, and Wilford (1995), Jorion
software manifestations as intellectual property that, in and Khoury (1996), Head and Horn (1997), Harrington and
some cases, is patentable. However, most hedge-fund man- Niehaus (1999), Saunders (1999), and Shimpi (1999).
agers today (and, therefore, most investors) have elected 4. This assumes a one-year term for the put, with a strike that
not to protect such intellectual property through patents. is 20 percent out of the money, an annual volatility of 75
They have chosen instead to keep them as “trade secrets,” percent, and a risk-free rate of 5 percent.
November/December 2001 31
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