Oct 01 Version

8/14/2019 Oct 01 Version

1/38

Oct-01 Version

10/16/01 4:17 PM - 1 -

Risk Analysis ofHedge Funds versus Long-Only Portfolios

Duen-Li Kao1

Correspondence:General Motors Asset Management767 5

thAvenue

New York, N.Y. 10153E-Mail: [email protected]

Current Draft: October 2001

1 Tony Kao is Managing Director of the Global Fixed Income Group at General Motors AssetManagement. The author would like to thank Pengfei Xie and Kam Chang for their insightful researchassistance. The author is grateful for many useful discussions with colleagues in the Global Fixed IncomeGroup and constructive comments from Stan Kon, Eric Tang and participants at the Q Group Conferencein spring 2001.


2/38

Oct-01 Version

10/16/01 4:17 PM - 2 -

Risk Analysis of Hedge Funds versus Long-Only Portfolios

Introduction

Despite the decade-long bull market in the 1990s and liquidity/credit crises in the late

90s, hedge fund investing has been gaining significant popularity among various types of

investors. Total size of reported hedge funds increased four fold during the period 1994

to 20002. The Internet bubble and valuation concerns for global equity markets,

especially among sectors such as telecommunications, media and technology, have

provided additional catalysts for the soaring interest in hedge funds over the last two

years.

Institutional investors often use hedge funds as part of absolute return strategies in

pursuing capital preservation while seeking high single to low double-digit returns. This

strategy is primarily implemented by absolute return investors (e.g., endowments,

foundations, high net-worth individuals). Allocations by corporate and public pension

plans to hedge funds as a defined asset class is a recent phenomenon. A second

application is to use hedge funds as an alternative to long-only investing through an alpha

transfer process. This often involves combining hedge funds with various derivative

overlays. The pension consulting and hedge fund communities have been advocating this

application in view of long-only managers difficulty in achieving active returns over

benchmarks.

For example, pension plans can overlay an equity market neutral fund with equity index

futures to create a synthetic equity long portfolio. To the extent the hedge fund

component outperforms its funding cost (e.g., LIBOR), the alpha may be transferred back

to a long equity portfolio via derivatives. In theory, one can reverse this process to form

a pseudo-hedge fund. That is, an equity long-only managers alpha over an equity index

can be transferred back to an absolute return fund by shorting equity futures. Most likely,

2 See TASS (2000). Estimated market size of hedge fund industry varies greatly. For example, HennesseeHedge Fund Advisory puts it at $408 billion at the end of 2000 in contrast to $210 billion according toTASS.


3/38

Oct-01 Version

10/16/01 4:17 PM - 3 -

endowments and foundations would not pursue this fantasy strategy. Does a pure

mathematical equivalence fail to convince these institutional investors to expand their

hedge fund manager universe?

Since theoretically one can transfer alphas from either long-only or long/short portfolios

to a desired target investment, we can compare these two types of alphas over their

respective benchmarks (index benchmark or LIBOR) on a common basis. It is a general

perception that as a group, hedge fund managers produce just enough active return to

earn their overall fees while long-only managers fail to do so. How different are these

two types of alpha anyway? Do alphas from long-only and long/short investments

present different return distributions? Do these alphas derive from different risk factors?

This article examines these questions by examining empirical evidence of activeperformance differences in long-only versus long/short investing. It also provides

potential explanations from the standpoint of compensation and investment constraints.

To further gain insight of how hedge funds incur risks, the article reviews the evolution

of methodologies for analyzing hedge fund risk. It first examines return/risk patterns of

various hedge fund investments and issues related to data reliability. Risk factors related

to market returns and financial markets are examined using performance indices of

several popular hedge fund strategies. The article proposes an alternative method of

analyzing investment style as applied to hedge fund investments. It also reviews the

contingent claim approach to hedge fund risk analysis: replicating hedge funds option-

like payoffs or trading strategies.

Classification of Hedge Funds

Conventionally, hedge funds are classified into categories according to their trading

strategies or styles. Sub-sectors of hedge funds include trend following, global/macro

strategies, long-only, arbitrage, long-short, etc. Despite attempts by data vendors,

practitioners and academics, no clear standard of classification currently exists as evident

by diverse categories used by various data vendors. In addition, given a variety of


4/38


5/38

Oct-01 Version

10/16/01 4:17 PM - 5 -

It should be noted that the following simulation results make an implicit assumption of

the alpha transfer process being perfect. That is, financing costs for both hedge funds and

derivatives used in the transferring process are identical. As experienced by many

practitioners in recent years, the violation of this assumption can introduce significant

return variance to the transfer process.

Exhibit 1 compares after-fee quarterly alphas of active U.S. long-only equity accounts

versus the equity market neutral index for the period 1994 to 20004. The 45-degree line

represents even performance of these two universes. Scatter points represent paired

quarterly active performance under different equity market environments during the

period. We use different types of points in the scatter plot to represent activeperformance under different states of equity markets. Solid points (diamond and square

4 Spear and Wiltshire (2000) also investigate the return differences of equity market neutral managers andlong-only equity universe and find similar results.

Exhibit 1: Active U.S. Long-Only Equity vs. Equity Market Neutral for U.S.Equity Asset Class (Data Source: Frank Russell Company, CSFB/Tremont;All figures in %)

-5

-4

-3

-2

-1

0

1

2

3

4

5

-5 -4 -3 -2 -1 0 1 2 3 4 5

Market Neutral Excess Return

< -1 Std dev of S&P 500

> +1 Std dev of S&P 500+/- 1 Std dev of S&P 500

Even Performance Line

After-Fee Quarterly Excess Returns

Over Respective Benchmarks

Q1/94-Q4/00


6/38

Oct-01 Version

10/16/01 4:17 PM - 6 -

shaped) are for large positive or negative equity market movements (observations outside

of one standard deviation of the S&P 500 quarterly return distribution). Triangle/blank

points represent normal equity market conditions. Below the 45-degree line, active return

from equity market neutral strategy is greater than that of active U.S. equity accounts.

Examining from the direction of x or y-axis, one can see that market neutral strategies

had wider active return distributions than long-only accounts with a few observations at

the extreme. Market neutral strategy outperformed its benchmark on an after-fee basis

much more often than active long-only accounts did as indicated by more points below

the 45-degree line. Furthermore, market neutral strategy performed better than the long-

only accounts at extreme equity market conditions as also depicted by more solid points

among them. Another interesting phenomenon is that long-only accounts produced

negative active returns when equity markets are very strong. This is consistent with the

findings of active performance of equity mutual funds from 1965 to 2000 by Mezrich et

al. (2000). Conversely, market neutral funds generated positive alpha over LIBOR under

these situations perhaps due to their positive exposures to the market risk factor (see the

discussion in the later section).

Turning to bond markets, Exhibit 2 shows similar results for fixed income arbitrage funds

as compared with the active U.S. bond manager universe. However, active returns from

bond portfolios produced a substantially narrower distribution as compared to fixed

income arbitrage strategies. The most noticeable outliers for fixed income arbitrage

performance are from the difficult periods for hedge funds: early 1994 and late 1998.

High volatile outcomes should not surprise arbitrage fund investors since those funds

tend to employ leverage that often averages five to ten times of the funds capital. In

general, the investment objective of many fixed income arbitrage funds is to produce

absolute returns comparable to equity markets with lower volatilities or higher return

with comparable volatility. Since potential returns from relative value trades are often

small, leverage is usually employed in order to achieve the return objective. However,

this practice comes with a stiff price during credit or liquidity crises. As such, hedge

funds often incur substantial losses from rapidly rising financial costs of leverage


7/38

Oct-01 Version

10/16/01 4:17 PM - 7 -

positions, forced liquidations stemming from margin calls at the worst market conditions

and demands of true marking-to-market by brokers/dealers or from investors panic

withdrawals.

Another possible reason for fixed income arbitrage funds having a more diverse active

return distribution is attributed to differences in performance benchmarks. Fixed income

arbitrage funds tend to stay within niche market segments where they have substantial

expertise and devise various strategies to exploit investment opportunities. The

performance index reflects various fixed income arbitrage funds employing a variety of

fixed income relative value strategies. When they are measured against a simple and low

volatile return benchmark (e.g., LIBOR, T-bills), the variance of alphas can easily be

magnified. On the other hand, long-only managers tend to emphasize tracking errors

when facing a more diversified and complex market benchmark. In measuring alpha, the

return variance is largely offset by the market benchmark.

-4

-3

-2

-1

0

1

2

3

4

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4

Fixed Income Arbitrage Excess Return

< -1 Std dev of Leh Aggr

> +1 Std dev of Leh Aggr

+/- 1 Std dev of Leh Aggr




Q1/94-Q4/00

Exhibit 2: Active U.S. Long-Only Bonds vs. Fixed Income Arbitrage for U.S. HighQuality Bond Asset Class (Data Source: Frank Russell Company, CSFB/Tremon; Allfigures in %)


8/38

Oct-01 Version

10/16/01 4:17 PM - 8 -

One approach to deal with arbitrage funds volatility is to de-lever the investment.

This can be accomplished by combining arbitrage investments with either short-term cash

portfolios or a bond index fund depending on the objective of the overall portfolio in

achieving absolute return or broad bond market exposures5. Exhibit 3 depicts the result

of active returns of long-only bond portfolios versus the fixed income arbitrage index de-

levered by a ratio of one to ten. The de-levered bond portfolio would invest one-tenth

of the asset in fixed income arbitrage fund with the remaining in a bond index fund. The

hedge fund portion is further overlaid with bond derivatives to create synthetic bond

exposures. As can be seen, a de-levered bond portfolio still offers higher alphas with

comparable volatility. Moreover, negative active returns of this fund are generally not as

severe as those of long-only portfolios during extreme bond market conditions.

5 If the investment objective of the de-levered portfolio is to achieve cash return, it implicitly assumes90% of assets invests in LIBOR-based instruments.

-2

-1.5

-1

-0.5

0

0.5

1

-1 -0.5 0 0.5 1

Fixed Income Arbitrage Excess Return


> +1 Std dev of Leh Aggr

+/- 1 Std dev of Leh Aggr




Q1/94-Q4/00

Exhibit 3: Active U.S. Long-Only Bonds vs. Fixed Income Arbitrage for U.S. HighQuality Bond Asset Class: Risk De-Levered by Ratio of 10 to 1 (DataSource: Frank Russell Company, CSFB/Tremont; All figures in %)


9/38

Oct-01 Version

10/16/01 4:17 PM - 9 -

What type of hedge fund is a better source of alpha for a given asset class?

Exhibit 4 compares excess returns of equity market neutral funds and fixed income

arbitrage funds given equity market performance over the last seven years. The objective

is to evaluate which is the better source of equity alpha if hedge funds alpha is

transferred back to the equity asset class? It appears equity market neutral managers

performed significantly better than fixed income arbitrage managers in most equity

market conditions, even in extreme cases. They also had an active return distribution

slightly tighter and less "fat tailed".

So what if alphas from these two types of hedge funds were transferred to the fixed

income asset class? Exhibit 5 compares these alphas in different U.S. high quality bond

market environments. Similar to the results in Exhibit 4, equity market neutral funds

appear to provide more consistent sources of alpha to the U.S. bond asset class than a

fixed income arbitrage strategy.

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

-5 -4 -3 -2 -1 0 1 2 3 4 5


< -1 Std dev of S&P 500

> +1 Std dev of S&P 500

+/- 1 Std dev of S&P 500




Q1/94-Q4/00

Exhibit 4: Equity Market Neutral vs. Fixed Income Arbitrage for U.S. Equity Asset Class(Data Source: CSFB/Tremont; All figures in %)


10/38

Oct-01 Version

10/16/01 4:17 PM - 10 -

Based on previous exhibits, Exhibit 6 presents statistics of three different sources of after-

fee active returns for equity and bond market asset classes over the last seven years.

Market returns are divided into two states: the top half and bottom half among 28

quarters. A few observations are worth noting:

Equity market neutral funds provided better and more consistent alphas for both

equity and bond asset classes than other funds as evidenced by high average active

returns and information ratios in all market conditions

Fixed income arbitrage funds seem more suitable for the bond asset class than for the

equity asset class although information ratios were extremely low, especially without

de-leveraging.

Both long-only equity and bond portfolios performed poorly compared with hedge

funds, except for long-only bond accounts providing the most consistent alpha for the

bond asset class when the market performed poorly (the bottom-half of market

performance conditions).

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

-5 -4 -3 -2 -1 0 1 2 3 4 5



> +1 Std dev of Leh Aggr+/- 1 Std dev of Leh Aggr




Q1/94-Q4/00

Exhibit 5: Equity Market Neutral vs. Fixed Income Arbitrage for U.S. High QualityBond Asset Class (Data Source: CSFB/Tremon; All figures in %.)


11/38

Oct-01 Version

10/16/01 4:17 PM - 11 -

Active returns of equity market neutral funds were positively correlated with the

equity markets (about 0.5). It confirms the general perception of market neutral funds

exhibiting some market directionality.

Fixed income arbitrage funds had higher correlations with equity markets than with

bond markets. However, they performed poorly when equity market returns were

high.

Active returns from equity and fixed income arbitrage funds were uncorrelated with

bond markets.

Active returns of equity and bond long-only accounts showed negative correlations

with their respective benchmarks in all market conditions, especially for long-only

bond portfolios (-0.73).

Exhibit 6: Sources of After-Fee Active Returns, Q1/1994 to Q4/2000 (Data Source:CSFB/Tremont; All figures in %)

Equity Fixed Inc. Equity Equity Fixed Inc. Bond

Statistics Mkt.Neutral Arbitrage Long-Only Mkt.Neutral Arbitrage Long-Only

Overall

Avg. Excess Ret. 1.43 0.22 0.15 1.43 0.22 -0.11

Volatility 2.13 2.43 1.53 2.13 2.43 0.59

Info. Ratio 0.67 0.09 0.10 0.67 0.09 -0.19

Top Market Returns

Avg. Excess Ret. 2.08 1.03 0.10 1.32 0.19 -0.47

Volatility 2.41 2.27 1.29 1.76 2.59 0.55

Info. Ratio 0.86 0.45 0.08 0.75 0.07 -0.86

Bottom Market Returns

Avg. Excess Ret. 0.78 -0.60 0.19 1.54 0.24 0.25

Volatility 1.65 2.38 1.79 2.51 2.36 0.37

Info. Ratio 0.48 -0.25 0.11 0.62 0.10 0.67

Correlation with Markets

Overall 0.50 0.23 -0.16 0.03 0.01 -0.73

Top Market Returns 0.50 -0.60 -0.30 0.13 0.01 -0.45

Bottom Market Returns 0.33 0.56 -0.14 0.13 0.07 -0.59

Equity Asset Class Bond Asset Class


12/38

Oct-01 Version

10/16/01 4:17 PM - 12 -

In the sections that follow, we will examine potential explanations of market hedge and

arbitrage funds appearing to be better sources of active returns than long-only portfolios.

As for the comparison between hedge funds, why did equity market neutral funds have a

more attractive active risk/return profile than fixed income arbitrage strategies?

First of all, even though CSFB/Tremont indices used in this study are considered superior

than most hedge fund data (Lhabitant, 2001), the time period covers only 1994 onward.

The period examined here is not only short but generally regarded as a tough period for

fixed income arbitrage strategies, e.g., 1994, 1997, 1998 and 1999. As shown above,

positive exposures to market risk by equity market neutral funds further enhanced their

performance advantages over fixed income arbitrage funds during equity bull markets.

Furthermore, it should be noted that equity hedge funds (e.g., market neutral, convertible

arbitrage, risk/merger arbitrage) have significant longer histories than fixed income

funds. Many mistakes have been experienced by equity related hedge funds, especially

during 1990 and 1991. Of course, fixed income related hedge funds learned an expensive

lesson from the recent LTCM episode: the danger of accounting-based leverage, the

power of margin calls, the importance of marking-to-market, and the unreliability of

carry trades without proper downside risk hedges.

Since then, fixed income hedge funds and the broker/dealer community have devised

many remedies (willingly or unwillingly) in an attempt to avoid the same mistakes. For

example, more fixed income arbitrage funds are employing leverage constraints,

downside risk analytics, risk budgeting implementation and fund alliance6. Perhaps fixed

income hedge funds will be able to reduce the performance gap versus equity hedge

funds going forward.

6 I thank Eric Tang for pointing out these issues.


13/38


14/38

Oct-01 Version

10/16/01 4:17 PM - 14 -

hedge funds can be substantial (30%-40%). This is especially problematic for illiquid or

less liquid securities (e.g., high yield and distressed bonds, private securities, over-the-

counter options, structured notes and mortgage derivatives)10

.

Stable pricing/modeling practice is essentially an artificial and costless process to smooth

performance variation and amortize gains and losses11

. It definitely contributes to

hedge funds low return volatility, low correlation with other asset classes which in turn,

enhances the notion of hedge funds being investment vehicles with high information

ratios and great diversifiers. Stale pricing may well be the key factor underlying

quarterly performance persistence of hedge funds found in Agarwal and Naik (2000).

Despite the efforts by numerous studies in documenting and quantifying hedge fund databias, conclusions based on the existing hedge fund databases were diverse and remain

dubious. Thus, it is difficult to conclude the extent or even the direction of performance

differentials between hedge funds and long-only accounts induced by database bias.

Structural Differences

One may argue that what lies beneath performance between hedge funds and long-only

accounts are their differences in compensation structures, investment constraints from

guidelines and regulations, and other structural factors12. These differences may allow

hedge funds to:

Focus on extracting returns related to idiosyncratic risks rather than relying primarily

on taking systematic risks;

Serve as liquidity providers to hedgers;

10Capital Market Risk Advisors, Inc. (2001)

11Arguably, this is similar to the book value accounting used in insurance community.

12 Ackermann (2000) examine these issues as related to differences in performance persistence of mutualfunds versus hedge funds.


15/38

Oct-01 Version

10/16/01 4:17 PM - 15 -

Effectively execute certain investment strategies via various forms of derivatives; and

Customize investment/security structures to explore certain properties of return

distributions.

The following table outlines various factors that may contribute the return differentials of

these two types of alphas.

Compensation Investment Constraints Structural Factors

Management fees Leverage Lockup period

Incentives Short selling Disclosure requirements

Hurdle rate Use of derivatives Asset capacity

High watermark Concentrated positions Simple benchmarkManagement Capital Investment guidelines

One of the common beliefs of hedge funds perceived outperformance is due to their

unique compensation structure, which generally attracts supposedly more skillful

professionals. Arguably, the most important factor is the setting of higher management

fees in addition to potentially large payoffs from the incentive fee schedule (Ackermann

et al., 1999)13. Furthermore, performance hurdle rate, high watermark14 and fund

management contributing their own capital may provide hedge funds with additional

drivers in achieving superior performance.

Investment Constraints

Another factor often cited for hedge funds outperformance is the flexibility they have in

pursuing investment strategies. For example, short selling and the use of leverage are

two of the trademarks of hedge fund management. Short selling allows fund managers totake advantage of their investment views on both sides of factor or security valuation.

13 It is often argued that this feature may encourage managers to take more risk. However, empiricalstudies indicate that this is not necessarily the case unless the implied option is deep out of money(Carpenter, 2000).

14 Incentive fees are earned only if cumulative performance recovers past shortfalls, if any.


16/38

Oct-01 Version

10/16/01 4:17 PM - 16 -

Grinold and Kahn (2000) develop an analytical framework to quantify the efficiency gain

from loosening the short selling restriction. They find that it can have significant impact

on active management especially if they deal with large sets of assets, low volatilities and

high active risk. However, it is questionable whether this flexibility does generate double

alpha. Alexander (2000) empirically shows that if one considers Regulation T restriction,

liquidity haircut and derivatives availability in short selling, abnormal returns from

popular pricing "anomalies" based on zero investment strategies may not be supportive.

With regard to leverage, it is conventionally defined as a discrete, accounting-based

measure and does not give a complete indication of the type or amount of risk taken. It

does not consider market volatilities and possible diversification benefits within

portfolios. In fact, a fund may be able to reduce its leverage while increasing portfoliorisk. In addition to the lack of actual leverage information, researchers have difficulty in

empirically analyzing whether and how leverage improves a hedge fund's risk-adjusted

return. Recent advances in hedge fund risk management call for risk-based definitions of

leverage instead of conventional accounting measures (even if they include on- and off-

balance sheet items)15

. Incorporating value-at-risk and scenario stress tests should help

investors better evaluate the true impact of portfolio leverage. Further research is needed

to understand (1) the relationship between hedge fund return distribution and leverage,

(2) leverage limits and proper leverage for various hedge fund strategies, and (3) leverage

dynamics: what factors influence hedge funds use of leverage over a market cycle.

Since most hedge funds focus on generating absolute returns with a "below-market"

volatility, they are often measured against a simple performance benchmark: the funding

cost. As a result, unlike long-only fund managers, hedge fund managers do not have to

deal with issues related to benchmark style drift (e.g., Brealy and Kaplanis, 2001) and

investment style boxes. In fact, pension sponsors and the consulting community are

increasingly relying on "style" indices to monitor long-only fund managers and to

construct risk/return profiles of overall asset classes. There is a tendency for investors to

15 See Sound Practices (2000), the Presidents Working Group (1999), Norland et al (2000) for excellentdiscussion on this subject.


17/38

Oct-01 Version

10/16/01 4:17 PM - 17 -

end up with a locally optimized asset class since their focus is often a collection of

optimized managers within individual investment styles. Perhaps in response to this

trend in institutional investing, long-only fund managers have shown increasing concern

with tracking error and maverick risk. They tend to stay around the given style

benchmark rather than stay with their supposed investment conviction. Focusing on

"style" products and benchmarking may prove to be detrimental to long-only asset

management going forward.

In sharp contrast to long-only portfolios, hedge funds face few, if any, investment

guideline restrictions. They are not limited by capital markets they can trade, constraints

imposed by the Investment Company Act of 1940, and investment guidelines (e.g.,

sector/security limits and duration/spread duration risk limits) often found in a long-onlyportfolio. This may account for the tendency of hedge funds' extensive use of exotic

securities or derivatives, and holding concentrated positions of what is considered "the

best ideas" rather than overly diversified positions often found in a long-only portfolio.

Finally, most hedge funds have investment lockup periods that allow hedge funds to use

illiquid and restricted securities16

. Anecdotally, all the flexibility discussed above may

contribute to seemingly better risk-adjusted returns earned by hedge funds versus long-

only portfolios.

Other Issues

Recently, questions have been raised regarding practices supposedly used by some hedge

funds and Wall Street that may distort the true picture of hedge fund performance. These

practices include:

Potential conflict of interest from trade allocation by a firm managing both long-only

and hedge funds in view of compensation differentials. The possibility of allocating

16 Lockup period is the time restriction of redeeming hedge fund investments. Ackermann (2000)empirically showed that the provision of lockup period and incentive structure are two of the mostimportant contributors to hedge funds superior performance.


18/38

Oct-01 Version

10/16/01 4:17 PM - 18 -

profitable trades to funds with substantially more profitable compensation structures

has caught regulatory attention17.

Trader order-handling sequence by brokers/dealers for hedge funds versus long-only

(Santini, 2001). The allegedly preferential treatment of hedge funds is perhaps due to

the tendency of hedge funds to have higher portfolio turnover rates and their

willingness to pay higher commissions in order to obtain information flows from

Street traders.

Understanding Hedge Fund Risk

As hedge funds employ diversified and dynamic trading strategies in a rather loosely

defined operating environment, the return generating process of hedge funds can be

complex and hard to analyze. Most studies show that factors based on market returns of

standard asset classes are not sufficient to describe risk taken by hedge funds, especially

those employing market neutral or arbitrage strategies. So, what are additional

systematic risks that hedge funds incur?

Hedge fund risk is a function of quantity (leverage), instruments/markets traded, market

volatility, strategy diversification within the fund and liquidity. One may argue thatinvestors can get a better understanding of risk exposures by a hedge fund from

examining portfolio holdings and trades. Value added from this exercise is generally

considered questionable. Hedge funds tend to dynamically and rapidly shift trading

positions and exposures to risk factors daily or intra-day. Portfolio holdings or

transactions are difficult to piece back together to their original tactical or strategic

purposes.

Perhaps the most important aspect of hedge fund risk analysis is to understand the nature

of trading strategies and underlying risk elements of each strategy. By doing so, the

17 See Financial Times (2001), HedgeWorld (2001).


19/38

Oct-01 Version

10/16/01 4:17 PM - 19 -

investor can develop a more reasonable expected risk/return of the fund. He or she will

better understand how and when trading strategies and funds invested are correlated.

Low correlation is also often found between hedge fund categories focusing on different

"style or markets". However, within each hedge fund category, correlations vary.

Individual funds within market directional hedge fund categories tend to have higher

correlation while non-directional funds often exhibit lower correlations (Brealey and

Kaplanis, 2001; Martin, 2001). Diversification of trading strategies within a hedge fund

is also a powerful tool for delivering consistent performance in various market

conditions. Exhibit 7 shows paired return correlations of six different investment

strategies employed by a successful capital structure arbitrage fund. Monthly correlations

ranged from -0.35 to 0.41 during the period 1998 to 2000. Notwithstanding, in order forhedge funds to be able to perform consistently and survive difficult market environments,

it is important for a manager to dynamically manage the optimal mix of these lowly

correlated strategies.

Exhibit 7: Strategy Diversification within a Fund (Monthly Return Correlations, 3/98-12/00)

Convert

Arb.

Yld-To

C/P

Capital

Str. Arb.

Multi-C

Stk.Arb.

Paired

Trades

Yield-to-Call/Put 0.34

Capital Structure Arb. 0.11 0.41

Multiclass Stock Arb. 0.33 0.06 0.19

Paired Trades 0.23 0.10 -0.35 0.05

Special Situations 0.06 0.23 0.05 -0.14 -0.08


20/38

Oct-01 Version

10/16/01 4:17 PM - 20 -

Performance Measures

Conventionally, the hedge fund community likes to use singular measures to describe

hedge fund performance and risk. For example, hedge fund marketing materials often

present the funds standard deviation of returns, maximum drawdowns (peak-to-trough

performance) and percentage of negative months (or quarters). Various risk adjustment

ratios are also popular -- Sharpe ratio, information ratio (excess returns divided by

volatility of excess returns), efficiency ratio (ex ante risk divided by realized return

volatility) and appraisal ratio (significance of the intercept of a CAPM-type regression).

All these risk/return measures do not express the nature of a fat tail return distribution nor

do they address investors' concern that under certain types of market condition, the true

risk of hedge fund investment will appear.

Risk Factors

Exhibit 8 depicts returns of fixed income arbitrage funds under various bond market

performance levels. Monthly returns of the Lehman Aggregate Bond Index from 1994 to

2000 were classified into seven buckets according to their return rankings. As shown,

fixed income arbitrage funds earned positive active returns in all types of bond market

conditions.

Searching for methods to analyze hedge fund risk beyond exposures to various

market/sector portfolios, researchers attempt to identify economic or financial market

factors as additional systematic risk taken by hedge funds. Financial market factors are

primarily based on publicly traded instruments (e.g., changes in levels and volatilities of

market index, index futures, options, swaps and other forms of derivatives). Unlike

information based on economic conditions (e.g., inflation, GDP growth and industrial

production), financial market factors have advantages of higher pricing frequency and are

directly related to trading strategies used. These factors combined with market factors

provide investors with a better analytical framework and empirically explain higher


21/38

Oct-01 Version

10/16/01 4:17 PM - 21 -

portions of return variance than market risk factors alone18

. Different hedge fund

strategies may require different sets of factors to describe their risk propensity.

Financial Market Risk Factors

Continuing the example in Exhibit 8, we examine the performance of the fixed income

arbitrage funds in different environments of fixed income volatilities during the period of

1995 to 2000. Volatility is represented by the changes in volatilities implied in the

swaption market. As shown in Exhibit 9, the fixed income arbitrage strategy remarkably

performed consistently in all but the highest volatility scenario. In fact, the only regime

in which fixed income arbitrage funds averaged negative returns is when bond markets

experienced their largest increases in implied volatilities (e.g., October 1997 and August

to October 1998).

18 For example, see Martin (1999), Schneeweis and Spurgin (1998)

-2

-1

0

1

2

3

State of Market Performance (lowest to highest)

FI Arb.

Lehman

Aggregate

Exhibit 8: Performance of Fixed Income Arbitrage Funds vs. Bond Market Returns(Monthly, 1/94 to 12/00). All figures in %.


22/38

Oct-01 Version

10/16/01 4:17 PM - 22 -

Exhibit 10 presents other systematic risk factors critical to bond markets: the change in

high yield spreads, Treasury volatility (implied volatility of Treasury options), swap

volatility and equity volatility (implied volatility of the S&P 100 index options).

Monthly excess returns of fixed income arbitrage funds over LIBOR show modest

Most ! Most "Factors 1 2 3 4 5 6

Overall

Correl.

HY Spreads 0.84 0.62 0.18 0.32 -0.04 -1.07 -0.46

Treasury Vol. 0.54 0.52 0.47 0.19 0.09 -0.95 -0.47

Swap Vol. 0.51 0.34 0.38 0.27 0.37 -1.01 -0.50

Equity Vol.

Whole Period -0.72 0.02 0.57 0.56 0.28 0.15 0.23

Excl. 9,10/98 0.31 - - - - - -0.27

Ranks by Changes in Factors

Exhibit 10: Active Returns of Fixed Income Arbitrage Funds Under DifferentRisk Conditions (Monthly, 1/95 to 12/00). All figures in %.

-3

-2

-1

0

1

2

3

4

State of Factor Risk (lowest to highest)

FI Arb

3x10 Swaption Vol

Change

Exhibit 9: Performance of Fixed Income Arbitrage Funds vs. Bond Volatility Risk(Monthly, 1/95 to 12/00). All figures in %.


23/38

Oct-01 Version

10/16/01 4:17 PM - 23 -

negative correlations to the first three fixed income related systematic risk factors (about

0.5). The funds were most vulnerable when systematic risks drastically increased. High

yield spread changes and Treasury volatility had a reasonably linear relationship with

arbitrage funds active returns. As for the equity volatility factor, arbitrage funds

performed the worst during extreme scenarios (both large declines and increases in the

factor). However, excluding large decreases in equity volatilities following the LTCM

episode (September and October of 1998), the correlation changed from a small positive

to a small negative. This indicates that observations from that period (August to October

1998) have a critical impact on the analysis.

Turning to convertible arbitrage funds, the same four systematic risk factors have similar

impacts on active returns as shown in Exhibit 11. The underperformance of convertiblearbitrage was most pronounced in regimes with the largest increases in three fixed

income factors. At the first glance, the overall correlation of convertible funds and the

changes in equity volatilities were virtually zero. At extreme market volatilities (the first

and sixth states), the funds performed poorly as compared to more normal scenarios.

Significantly negative performance from August to October 1998 (the impact is shown at

Most ! Most "Factors 1 2 3 4 5 6

Overall

Correl.

HY Spreads 0.95 1.03 0.55 0.94 0.80 -0.55 -0.46

Treasury Vol. 0.66 1.00 0.57 1.32 0.73 -0.56 -0.49

Swap Vol. 0.90 1.11 0.73 1.05 0.62 -0.69 -0.51

Equity Vol.

Whole Period -0.34 0.59 1.30 0.88 1.20 0.09 0.00

Excl. 8/98 -0.34 - - - - 0.56 0.41

Excl. 9,10/98 0.48 - - - - 0.09 -0.39


Exhibit 11: Active Returns of Convertible Arbitrage Funds Under Different Risk

Conditions (Monthly, 1/95 to 12/00). All figures in %.


24/38

Oct-01 Version

10/16/01 4:17 PM - 24 -

the bottom of Exhibit 11) further demonstrates the vulnerability of convertible hedge

funds during extremely volatile markets. During and after the LTCM debacle,

convertible hedge funds are believed to have suffered significant "mark-to-market" issues

that may have masked the extent of these relationships (Tremont, 2000).

Exhibit 12 examines risk factor exposures of equity market neutral and long/short

(directional) hedge funds. In addition to equity implied volatility, exposures to three

Fama-French return factors are also analyzed. Market neutral funds show insignificant

relationships to the changes in size and value factors. Their active performance was

essentially flat when equity volatility increased the most.

Despite what the name implies, the funds have positive directionality to the market factor

(i.e., positive excess return increases as the equity market performs well). As for

long/short hedge funds, they show strong correlations to all four systematic risk factors:

short equity volatility and value factors while long market and size factors. Examining

across six regimes, active returns of long/short funds had an almost perfect linear

relationship to these factors.

Most ! Most"Market Neutral 1 2 3 4 5 6

Overall

Correl.

Equity Vol. 0.82 0.30 1.36 0.43 0.85 0.06 -0.29

Market Factor -0.04 0.39 0.48 0.57 1.04 1.37 0.52

Size Factor (SML) 0.26 0.69 0.49 0.77 0.87 0.74 0.10

Value Factor (HML) 0.62 1.13 0.72 0.92 -0.20 0.62 -0.12

Long/Short

Equity Vol. 2.60 1.16 2.59 1.53 0.71 -1.62 -0.41

Market Factor -3.33 -1.29 2.06 2.28 3.17 4.07 0.76

Size Factor (SML) -2.67 -0.64 1.58 2.07 2.56 4.06 0.62

Value Factor (HML) 5.35 2.34 1.87 1.34 -0.70 -3.23 -0.77


Exhibit 12: Active Returns of Equity Hedge Funds Under Different Risk Conditions(Monthly, 1/95 to 12/00). All figures in %.


25/38

Oct-01 Version

10/16/01 4:17 PM - 25 -

Style Analysis of Hedge Fund Risk

Style analysis, pioneered by Sharpe (1988 and 1992), uses market/sector benchmark

portfolios as systematic factors to derive the asset mix implied in an active portfolio's

return series. For a long-only portfolio, exposures to these market portfolios are

constrained to be positive and summed to one. Many studies apply style analysis to

analyzing hedge fund risk by relaxing these two constraints (e.g., Fung and Hsieh, 1997;

Brown et al., 1999; Agarwal and Naik, 1999). While most studies employ capital market

or style index portfolios as implied building blocks in style analysis, Lhabitant (2001)

uses hedge fund style indices as risk factors in order to directly derive a funds implied

exposures to conventional hedge fund styles/strategies.

Brown and Goetzmann (2001) further extend hedge fund style analysis by allowing factor

loadings on market portfolios (i.e., coefficients) to vary over time19. Using time varying

factor loadings in style analysis is constructive since the method accommodates dynamic

trading strategies with non-linear payoffs. All these studies found that individual fund

returns have lower correlations to standard asset class returns as compared to mutual

funds. Funds with styles of market neutrality, arbitrage or commodity have significantly

low to nil exposures to these asset classes.

Moreover, one of the criticisms of conventional style analysis is that investment risk as

defined by these styles is too narrow and singular. It fails to recognize that investment

risk is often multi-dimensional, asymmetrical and potentially correlated (Michaud, 1998).

This problem becomes even more severe when analyzing hedge fund risk. Active returns

of hedge funds generally exhibit asymmetric sensitivities to risk factors in different

market environments. For example, it has been shown that hedge funds perform

differently in positive versus negative equity markets (Lo, 2000) and in rising versus

declining interest rate scenarios. Previous sections present empirical evidence of how the

changes in implied volatilities in various capital markets may be of importance in

19 Brown and Goetzmann (1997) first present this methodology in studying mutual fund styles.


26/38

Oct-01 Version

10/16/01 4:17 PM - 26 -

evaluating hedge fund strategies. In summary, to analyze hedge fund risks, we not only

have to incorporate various systematic risk factors beyond conventional market return

factors but also employ a multi-dimensional framework.

Risk Style Analysis Under A Long-Only Framework

Kao (2000a) presents a return-based approach to analyze investment styles of fixed

income managers. It involves identifying several systematic risk factors important to

active performance of a fixed income portfolio; e.g., changes in 10-year Treasury rate,

implied volatility of interest rate options, swap spreads, swap volatility and systematic

risks in equity markets20. Exposures to these risk factors in relation to a bond benchmark

are grouped and summarized in two dimensions: interest rate risk and spread risk.

Exhibit 13 compares two distinct long-only fixed income investment styles. The

construction of this risk factor model follows Kon (1999) in which factors are adjusted

for the variable dependence of prominent risk factors such as the level of interest rates.

For example, the changes in implied volatilities are adjusted for directionality of ten-year

Treasury rates. The changes in swap spreads are adjusted for both the changes in interest

rates and the adjusted changes in volatility.

The exhibit shows how portfolios managed active exposures to two risk dimensions

differently with each point covering a rolling 36-month period. The center point

represents a neutral position of risk exposures versus the benchmark. To illustrate the

changes in exposures over time, the largest point is the most recent observation and the

smallest the earliest.

Manager A is a highly risk controlled bond fund of funds (diversified multiple advisors)

as evident by its stable exposures to both risk dimensions. On the other hand, viewing

from both interest rate and spread risk related to their benchmark, Manager B took more

20 See Kao (2000b) for an application to analyzing determinants of the changes in corporate credit spreads.


27/38

Oct-01 Version

10/16/01 4:17 PM - 27 -

active risks with drastic shifts in exposures than Manager A. The bottom table of Exhibit

13 presents average statistics of risk exposures of the bond index and these two portfolios

according to this risk style model.

The risk factor model explains return variances of these three portfolios very well as

evidenced by the significance of T-statistics and R-squares. Manager B had larger

exposures to all risk factors than Manager A and the benchmark except for exposure to

the changes in 10-year Treasury rates. Obviously, two spread risk factors are very

Exhibit 13: Risk Styles of U.S. High Quality Core Bond Managers (8/99-6/00)

0

0.25

0.5

0.75

1

1.25

1.5

1.75

2

2.25

0.9 1 1.1

Relative Interest Rate Risk

Manager A

Manager B

Benchmark: Salomon BIG Index

Monthly Exposures: 8/99-6/00

Ten-YearRate

Int. RateVolatility

SwapSpread

EquityRisk (t-1)

10-YearRate

Int.RateRisk

FourFacotrs

Bond Index

Coef. -3.92 -0.77 -2.41 1.42 0.91 0.94 0.97

T-Stat -31.65 -6.54 -5.48 2.81

Manager A

Coef. -3.80 -1.40 -2.97 2.80 0.68 0.77 0.83

T-Stat -12.16 -4.71 -2.59 2.25

Manager B

Coef. -3.87 -1.09 -2.01 1.58 0.87 0.93 0.96

T-Stat -24.62 -7.33 -3.62 2.47

Average Statistics R-Square


28/38

Oct-01 Version

10/16/01 4:17 PM - 28 -

important in explaining the return volatility of Manager Bs performance as R-square

increases from 0.77 to 0.83.

We apply the same risk factor model to examine relative risk exposures of fixed income

arbitrage funds versus the long-only bond fund of funds from June 1998 to December

2000 in Exhibit 14. Monthly excess returns of fixed income arbitrage funds over LIBOR

are assumed to transfer to a bond market index in order to make it comparable to the

Exhibit 14: Risk Styles of Fixed Income Arbitrage Overlay Versus Long-OnlyBond Fund (6/98-12/00): Fixed Income Arbitrage de-levered by 10:1

0.5

0.75

1

1.25

1.5

0.8 0.9 1 1.1 1.2

Relative Interest Rate Risk

Long-Only Bond Fund

Fixed Inc. Arb. Overlay

Benchmark: Salomon BIG Index

Quarterly Exposures: 6/98-6/00

Ten-Year

Rate

Int. Rate

Volatility

Swap

Spread

Equity

Risk (t-1)

10-Year

Rate

Int.Rate

Risk

Four

Facotrs

(b.p.) (10 b.p.) (b.p.) (%)

Bond Index

Coef. -3.95 -0.71 -2.42 1.25 0.92 0.95 0.97T-Stat -34.98 -5.59 -4.91 2.58

Fixed Inc. Arb.

Coef. -3.80 -1.05 -2.01 1.58 0.86 0.91 0.94

T-Stat -22.00 -5.40 -2.73 2.11

Long-Only Fund

Coef. -3.94 -1.04 -2.09 1.66 0.88 0.93 0.96

T-Stat -25.45 -6.36 -3.16 2.58



29/38

Oct-01 Version

10/16/01 4:17 PM - 29 -

long-only portfolio. Furthermore, as in the case of Exhibit 3, to make return volatilities

of these two portfolios more comparable, the performance of the arbitrage fund index was

de-levered by investing one-tenth of assets in hedge funds and the remainder in a bond

index fund. Again, the analysis is done on a 36-month rolling basis to explore the funds

changes in risk exposures (only quarter-end observations are displayed).

Exhibit 14 shows these two investments possess similar and rather consistent exposures

to both directional and volatility risks. The de-levered fixed income arbitrage overlay

portfolio had slightly lower relative interest rate and spread risks than the long-only bond

fund. This was achieved through having lower exposures to ten-year interest rate and

equity risk factors. Comparing with the bond market index, however, this portfolio still

had higher exposures to volatility and equity risk factors. Remarkably, hedge fundoverlay and long-only portfolios also changed their exposures over time in a similar

pattern. After the LTCM debacle, both portfolios became more risk neutral versus the

benchmark.

Risk Style Analysis Under A Hedge Fund Framework

If we were to analyze the source of active risk of hedge funds on a stand-alone basis (i.e.,

without an overlay process), the risk factor model requires some modifications. First, we

define risk style dimensions relevant to hedge fund investment risks: directional risk (first

order) and volatility risk (second order). Continuing the example in Exhibit 14,

systematic risk factors important to fixed income arbitrage funds as discussed in previous

sections are categorized into these two dimensions. For example, exposures to the

changes in interest rates and credit spreads are jointly formed to measure directional risk

(correlation of these two factors is considered). Volatility risk combines the changes in

implied volatilities of equity and interest rate options. Again, the model construction

requires the adjustment of variable dependence.

Exhibit 15 compares risk exposures of after-fee active returns of fixed income arbitrage

funds and a long-only bond fund over their respective benchmarks. Fixed income


30/38

Oct-01 Version

10/16/01 4:17 PM - 30 -

arbitrage funds have large and statistically significant active risk exposures to all four

factors, especially to the changes in high yield spreads and equity volatilities as compared

to the long-only fund. Viewing from risk factors important to fixed income arbitrage

funds, volatility risk had significant impact on active returns of the long-only bond fund.

In contrast to fixed income arbitrage funds, the directional risk factor (ten-year rate) had

nil effect on active return variance of the long-only fund.

As indicated by R-square measures in the last three columns of the exhibit, factors related

to directional risk explain about 30% of active return variance of fixed income arbitrage

Exhibit 15: Risk Style of Active Quarterly Returns of Fixed Income ArbitrageVersus Long-Only Bond Fund (12/98-12/00)

5

7.5

10

12.5

15

0 1 2 3 4 5 6 7 8 9 10

Directional Risk

Fixed Income Arb Index

Active US Bond FoF

Benchmarks: 3-Mon. LIBOR and

Salomon BIG Index

Quarterly Exposures: 12/98-12/00

10-Year

Rate

HighYld

Spread

Int.Rate

Volatility

Equity

Volatility

10-Year

Rate

Direct'l

Risk

Four

Facotrs

(b.p.) (b.p.) (10 b.p.) (10 b.p.)

Fixed Inc. Arb.

Coef. 0.18 -0.21 -0.24 0.14 0.08 0.30 0.58

T-Stat 2.28 -2.21 -2.04 3.96 Long-Only Fund

Coef. 0.04 -0.05 -0.33 0.09 0.01 0.16 0.42

T-Stat 0.56 -0.51 -2.76 2.41



31/38

Oct-01 Version

10/16/01 4:17 PM - 31 -

portfolios (ranging from 18% to 41% during the period). This is substantially higher than

the 8% achieved if only the changes in interest rate levels (10-year rates) is used21.

Adding volatility risk factors, average explanatory power increases to 58% for the

arbitrage index. As for the long-only bond fund, directional risk explains 16% of active

return variance and volatility risk factors add another 26%.

During this period, hedge funds as well as the long-only fund generally maintained their

directional risk but decreased exposures to volatility risk.

Mimicking Portfolio/Strategy Approach to Risk Analysis

Recently, several researchers have taken a more direct approach to analyze hedge funds

systematic risk beyond market returns. We call this the mimicking portfolio/strategy

approach since it attempts to replicate either the payoff pattern or explicit trading

strategies of hedge fund activities22. Fung and Hsieh (1997) apply principal component

analysis to extract benchmarks for various trading strategies as implied in hedge fund

return series. When combined with conventional asset class factors, it can effectively

capture the essence of hedge funds' extreme outcomes.

Following the contingent claim concept of performance measurement advocated by

Glosten and Jagannathan (1994), several studies use a series of financial options to

directly replicate the option-like pattern which existed in hedge fund data23. Other

methods involve constructing naive trading strategies actually employed by hedge funds

21 As a reference, if one follows the conventional approach of using a bond market index as the risk factor(e.g., Lehman Aggregate Index), the R-square is only 3%.

22

Broadly speaking, style analysis approach using market/factor portfolios or risk factors can be considereda mimicking portfolio/strategy method for analyzing a funds risk and return.

23Fung and Hsieh (2000b) construct five trend-following mimicking benchmarks that produce straddle

option payoffs commonly observed in hedge fund returns. R-squares were about 48% versus average 7%with standard asset return factors. Agarwal and Naik (2001) also employ a similar methodology tostudying Event Driven and Relative Value Arbitrage funds. Lo (2000) uses a trading strategy of sellingout-of-the-money puts on equity index to demonstrate the illusion of a hypothetical hedge funds superrisk-adjusted performance.


32/38

Oct-01 Version

10/16/01 4:17 PM - 32 -

and thus, provide a more direct and realistic evaluation framework24

. Tang (1999)

extends the framework by simulating hypothetical investment opportunities available to

hedge fund managers, rather than replicating hedge funds trading strategies and

instruments used. The approach attempts to address a difficult task in hedge fund

research: hedge funds (especially arbitrageurs) generally employ multiple investment

strategies within a fund that are seemingly uncorrelated and hard to replicate by trading

unified instruments25.

In all these studies, they found that return patterns from these simulated passive trading

strategies resemble those of actual hedge funds or CTAs. Risk attributes detected from

these time series are generally consistent with what we would expect from specific

trading strategies employed by hedge funds. Return series obtained from this analyticalapproach can be used to:

Evaluate and extract various systematic risks not observed by return series of

conventional asset classes. In the spirit of Sharpes style analysis framework,

mimicking portfolios can be viewed as alternative or additional asset/benchmark style

factors.

Directly model hedge fund's asymmetric return distributions.

Examine how hedge funds manage their risk exposures in extreme market conditions.

Serve as a true hedge fund benchmark26

. Performance in excess of these

benchmark portfolios is considered a better indication of the manager's skill.

24 See Gatev et al (1999) on paired trading (a convergence strategy used to explore relative pricing of closesubstitutes of financial instruments),Mitchell and Pulvino (2000) on risk arbitrage strategy and Richards(1999) on relative value trades, and Liew (1999) on equity long and short of equity risk factors. Returnindices (e.g., Mount Lucas Management Index) based on naive trading strategies in active commodity andfinancial futures is used in analyzing CTA investment risks (see Schneeweis and Spurgin, 1998; Spurgin,

1999).

25 Under this approach composite relative value indices for capital market segments in which hedge fundsoperate are constructed. Each relative value index combines factors related to rich/cheap valuation andtechnical indicators for the market at a given point of time. For example, for yield curve trades, itcalculates relative value opportunities available to carry, butterfly and basis trades. As for technicalfactors, it uses measures such as spreads versus their historical averages popular among practitioners.

26 In fact, recently a few hedge funds replicating index or nave trading strategies are being publicized aspassive alternatives to active hedge fund investing.


33/38

Oct-01 Version

10/16/01 4:17 PM - 33 -

Avoid biases found in most hedge fund databases as discussed previously.

The replication approach to studying hedge fund performance is expected to extend to

other types of trading strategies. This should shed light on the myth surrounding hedge

fund activities.

Investment Style and Performance Evaluation

Both risk style factors and mimicking portfolios can be useful in understanding hedge

fund risk. They serve as better yardsticks for measuring hedge funds performance and

their true active skill beyond nave trading strategies. However, the hedge fund

investment community should keep in mind the experience of improving methods ofmeasuring long-only portfolio performance in recent years. Style analysis was originally

designed to facilitate the evaluation of a money managers active skill in view of their

exposures to some systematic risks. Style indices created from this analytical framework

are not intended to be a primary tool for managing money managers. Investors and

consultants tend to put too much emphasis on the performance tracking error versus a

style benchmark or a customized benchmark based on a set of systematic risk factors.

By doing so, they delegate the responsibility of understanding managers investment

process and what truly drives active performance to a classification scheme based on

singular factor measures. The end result is the danger of further restricting (implicitly or

explicitly) an investment manager in expressing his/her true convictions. This would be

especially troublesome for hedge funds whose active returns rely on multiple, complex

and dynamic trading strategies that may not be easily classified into one particular style

box.

Conclusion

The option-like return pattern of hedge funds presents a challenge for investors in

analyzing risk exposures. Singular measures of risk and return can be misleading

especially in analyzing hedge fund risk. Investors should carefully examine the return


34/38

Oct-01 Version

10/16/01 4:17 PM - 34 -

patterns under various market conditions and other systematic risk factor exposures. Due

to the investors ability of transferring alpha to a desired asset class, it is more appropriate

to evaluate hedge funds and long-only portfolios by comparing them against respective

benchmarks. Hedge funds, especially equity market neutral strategies, seem to provide

more consistent alpha than long-only portfolios for different asset classes under various

market environments. The qualitative assessments of possible explanations are reviewed

here.

Factors derived from asset prices in financial markets are timely and useful for hedge

fund risk analysis. These risk factors depict exposures to market direction, volatility and

valuation that are most relevant to hedge funds risk profiles. This article shows that how

a hedge fund manages its exposures to implied volatilities at extreme market conditionscan be the key to consistent performance. The results highlight the importance of

strategy diversification between funds as well as within a fund in achieving consistent

performance.

An analytical framework incorporating multiple risk factors gives investors a more

complete picture of hedge fund risk taking. In the spirit of equity style analysis popular

among practitioners, this article presents an approach of risk style analysis to evaluate

common risk factors driving the performance of hedge funds and long-only portfolios.

Various financial market risk indicators can be categorized into directional and volatility

risk dimensions to provide a more concise assessment of risk exposures over time.

Another approach to analyze hedge fund risk is to directly replicate the hedge funds

option payoff profile, trading strategies employed or arbitrage opportunities available.

Return series derived from this mimicking approach is particular useful in studying risk

factors and performance attributes underlying hedge fund investing. It also provides a

promising direction for future research of hedge fund asset pricing.


35/38

Oct-01 Version

10/16/01 4:17 PM - 35 -

References

Ackermann, Carl, R. McEnally and D. Ravenscraft. 1999. The Performance of HedgeFunds: Risk, Return, and Incentives. Journal of Finance, 54:833-874.

Ackermann, Carl. 2000. Essays on Hedge Funds. Ph.D. Dissertation, University ofNorth Carolina, Chapel Hill.

Agarwal, Vikas and Narayan Naik. 1999. On Taking the Alternative Route: Risks,Rewards Style and Performance Persistence of Hedge Funds. Working paper,London Business School.

__________. 2000. Multi-Period Performance Persistence Analysis of Hedge Funds.Journal of Financial and Quantitative Analysis, 35:327-342.

__________. 2001. Characterizing Hedge Fund Risks with Buy-and-Hold and Option-Based Strategies. Working paper, London Business School.

Alexander, Gordon. 2000. On Back-Testing Zero Investment Strategies. Journal ofBusiness, vol. 73, no. 2:255-278.

Asness, Clifford, Robert Krail and John Liew. 2000. Do Hedge Funds Hedge?Working paper, AQR Capital Management.

Brealey, Richard and Evi Kaplanis. 2001. Changes in the Factor Exposures of HedgeFunds. Working paper, London Business School.

Brown, Stephen and William Goetzmann. 1997. Mutual Fund Styles. Journal ofFinancial Economics, 43:373-399.

__________. 2001. Hedge Funds with Style. Working paper 00-29, Yale University.

Brown, Stephen, William Goetzmann and Roger Ibbotson. 1999. Offshore HedgeFunds: Survival and Performance1989-1995. Journal of Business, 72:91-118.

Brown, Stephen, William Goetzmann, Roger Ibbotson and Stephen Ross. 1992.Survivorship Bias in Performance Studies.Review of Financial Studies, 5:553-580.

Capital Market Risk Advisors, Inc. 2001. NAV/Fair Value Practices Survey Results.July 9.

Carhart, Mark. 1997. Mutual Fund Survivorship. Working paper, University ofSouthern California.

Carpenter, Jennifer. 2000. Does Option Compensation Increase Managerial RiskAppetite? Journal of Finance, 55:2311-2331


36/38

Oct-01 Version

10/16/01 4:17 PM - 36 -

Financial Times. 2001. More Scrutiny for Hedge Fund and Mutual Fund Managers.March 27.

Fung, William and David Hsieh. 1997. Empirical Characteristics of Dynamic TradingStrategies: The Case of Hedge Funds. Review of Financial Studies, 10:275-302.

__________. 1999. A Primer for Hedge Funds. Journal of Empirical Finance,

__________. 2000a. Performance Characteristics of Hedge Funds and CTA Funds:Natural versus Spurious Biases. Journal of Quantitative and Financial Analysis.

__________. 2000b. The Risk in Hedge Fund Strategies: Theory and Evidence fromTrend Followers. Review of Financial Studies, .

__________. 2001. Benchmarks for Hedge Fund Performance: Information Contentand Measurement Biases. Financial Analysts Journal, Forthcoming.

Gatev, Evan, William Goetzmann and Geert Rouweinhorst. 1999. Pairs Trading:Performance of a Relative Value Arbitrage Rule. Working paper 7032, NationalBureau of Economic Research.

Glosten, Lawrence and Ravi Jagannathan. 1994. A Contingent Claim Approach toPerformance Evaluation. Journal of Empirical Finance, 1:133-160.

Grinold, Richard and Ronald Kahn. 2000. Efficiency Gains of Long-Short Investing.Financial Analysts Journal, November/December:40-53.

HedgeWorld. 2001. Rivalry Gets Uglier: Hedge Funds vs. Mutual Funds. TheAlternative Edge Newsletter, June 4.

Lhabitant, Francois-Serge. 2001. Assessing Market Risk for Hedge Funds and HedgeFunds Portfolios. Research Paper No. 24, International Center for FinancialAsset Management and Engineering.

Liew, Jimmy Kyung-Soo. 1999. Essays in Investment Strategies. Ph.D. Dissertation,Columbia University.

Lo, Andew. 2001. Risk Management for Hedge Funds: Introduction and Overview.

Working paper, presented at the PricewaterhouseCoopers Risk Institute 2000Conference.

Kao, Duen-Li. 2000a. Analyzing Investment Styles of Fixed Income Managers.Journal of Investment Consulting, December.

__________. 2000b. Estimating and Pricing Credit Risk: An Overview.FinancialAnalysts Journal, 56 (July/August): 50-66.


37/38

Oct-01 Version

10/16/01 4:17 PM - 37 -

Kon, Stanley. 1999. Mortgage Portfolio Performance Attribution. Working paper,Smith Breeden Associates.

Martin, George. 2001. Making Sense of Hedge Fund Returns: What Matters and WhatDoesnt. Derivative Strategy.

Mezrich, Jpseph, Qi Zeng and Matthew Rothman. 2000. The Return of Stock Picker.Quantitative Strategies, Morgan Stanley Dean Witter, December.

Michaud, Richard. 1998. Is Value Multidimensional? Implications for StyleManagement and Global Stock Selection. The Journal of Investing, Spring:61-65.

Mitchell, Mark and Todd Pulvino. 2000. Characteristics of Risk and Return in RiskArbitrage. Working paper, Harvard University.

Norland, Erik, Jose Maio Quintana and Sykes Wilford. 2000. Leverage: A VeryMisleading Way to Measure Risk. Derivatives Quarterly, Fall:3-15.

The Presidents Working Group on Financial Markets. 1999. Hedge Funds, Leverageand the Lessons of Long-Term Capital Management. Report of the PresidentsWorking Group on Financial Markets, April 28.

Richards, Anthony. 1999. Idiosyncratic Risk: An Empirical Analysis, withImplications for the Risk of Relative-Value Trading Strategies. Working paper99-148, International Monetary Funds.

Santini, Laura. 2001. Tradings Hidden Costs Hedge Funds, Technophobia andBenchmarking Games Eat Into the Performance of Mutual Funds. TheInvestment Dealers Digest, August 14.

Schneeweis, Thomas and Richard Spurgin. 1998. Multifactor Analysis of Hedge Fund,Managed Futures, and Mutual Fund Return and Risk Characteristics. Journal ofAlternative Investments, 1 (Fall):1-24.

Sharpe, William. 1988. Determining a Funds Effective Asset Mix. InvestmentManagement Review, December:59-69.

Sharpe, William. 1992. Asset Allocation: Management Style and PerformanceMeasurement. Journal of Portfolio Management, Winter:7-19.

Spear Sheena and Steve Wiltshire. 2000. The Long and the Short of Market NeutralInvesting. Russell Research Commentary.

Suprgin, Richard. 1999. A Benchmark for Commodity Trading Advisor Performance.Journal of Alternative Investments, 2 (Summer):11-21.


38/38

Oct-01 Version

Sound Practices for Hedge Fund Managers. 200027

.

Tang, Eric. 1999. AlphaTrack. Working paper, Portfolio Management Technology.

TASS. 2000. The TASS Asset Flows Report: Fourth Quarter 2000.

Tremont. 2000. Convertible Arbitrage: Opportunity & Risk. White paper, September.

27 A ll t t i k di l d t f h d f d th t j i tl d l d b

Oct 01 Version

Documents

Transcript of Oct 01 Version