Taking a Close Look at the European Fund of Hedge Funds Industry

Amenc, Noël; Giraud, Jean René; Martellini, Lionel; Vaissié, Mathieu

doi:10.3905/jai.2004.461458

Cited by 12 publications

(4 citation statements)

References 19 publications

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“…Although most researchers agree that traditional risk management tools cannot capture many of the risk exposures of hedge fund investments, an alternative framework is not yet well established. Traditional risk measures are still dominant among practitioners (see the survey result for the fund of hedge funds industry by Amenc et al (2004)). However, academic research is beginning to examine downside risk, asymmetric volatility, semideviation, extreme value analysis, regime-switching, jump processes, and so on.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Risk Measures for Hedge Funds: a Cross‐sectional Approach

Liang¹,

Park²

2007

Euro Fin Management

118

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"This paper analyses the risk-return trade-off in the hedge fund industry. We compare semi-deviation, value-at-risk (VaR), Expected Shortfall (ES) and Tail Risk (TR) with standard deviation at the individual fund level as well as the portfolio level. Using the""Fama and French (1992)""methodology and the combined live and defunct hedge fund data from TASS, we find that the left-tail risk captured by Expected Shortfall (ES) and Tail Risk (TR) explains the cross-sectional variation in hedge fund returns very well, while the other risk measures provide statistically insignificant or marginally significant results. During the period between January 1995 and December 2004, hedge funds with high ES outperform those with low ES by an annual return difference of 7%. We provide empirical evidence on the theoretical argument by""Artzner et al. (1999)""that ES is superior to VaR as a downside risk measure. We also find the""Cornish-Fisher (1937)""expansion is superior to the nonparametric method in estimating ES and TR." Copyright 2007 The Authors Journal compilation (c) 2007 Blackwell Publishing Ltd.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk Measures for Hedge Funds: a Cross‐sectional Approach

Liang¹,

Park²

2007

Euro Fin Management

118

View full text Add to dashboard Cite

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“…Also, they revealed that most European multi-managers have continued to prefer the traditional mean-variance framework to monitor manager performance. This was confirmed by the fact that 82% of multi-managers adopted the Sharpe ratio as an important indicator (Amenc et al 2004).…”

Section: Introductionmentioning

confidence: 76%

“…Due to the nature of negative skewness and excess kurtosis in HFs and FOHFs returns, any risk estimation which assumes a normal distribution of returns would severely underestimate the actual risk exposure. Nevertheless, according to Amenc et al (2004) only 2 % of European multi-managers have paid attention to the skewness and kurtosis of the return distribution. Also, they revealed that most European multi-managers have continued to prefer the traditional mean-variance framework to monitor manager performance.…”

Section: Introductionmentioning

confidence: 99%

Risk and Return in Hedge Funds and Funds of Hedge Funds: A Cross-Sectional Approach

Lee

2009

SSRN Journal

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The objective of this study is to examine whether the available data on individual hedge funds (HFs) and funds-of-hedge funds (FOHFs) can reveal the risk-return trade-off and, if so, to find an appropriate risk measure that captures the cross-sectional variation in HF and FOHF returns and compare the risk-return relationship in HFs and FOHFs. Using the "live funds" and the "dead funds" datasets provided by Hedge Fund Research Inc. (HFR), we concentrate on alternative risk measures such as semi-deviation, VaR, expected shortfall and tail risk and compare them with standard deviation in terms of their ability to describe the cross-sectional variation in expected returns of HFs and FOHFs. Firstly, the risk measures are analysed at the portfolio level of HFs and FOHFs by adopting the Fama and French (1992) approach. Secondly, the various estimated risk measures are compared at the individual HF and FOHF levels by using univariate and multivariate cross-sectional regressions. The results show that the available data on HFs and FOHFs exhibits different risk-return trade-offs. The Cornish-Fisher expected shortfall or Cornish-Fisher tail risk could be an appropriate risk measure for HF return. Although appropriate alternative risk measures for the HFs are found, it is difficult to determine the risk measures that best capture the cross-sectional variation in FOHF returns. KeywordsHedge funds; funds-of-hedge funds; VaR; expected shortfall; tail risk AbstractThe objective of this study is to examine whether the available data on individual hedge funds (HFs) and funds-of-hedge funds (FOHFs) can reveal the risk-return trade-off and, if so, to find an appropriate risk measure that captures the cross-sectional variation in HF and FOHF returns and compare the risk-return relationship in HFs and FOHFs. Using the "live funds" and the "dead funds" datasets provided by Hedge Fund Research Inc. (HFR), we concentrate on alternative risk measures such as semi-deviation, VaR, expected shortfall and tail risk and compare them with standard deviation in terms of their ability to describe the cross-sectional variation in expected returns of HFs and FOHFs. Firstly, the risk measures are analysed at the portfolio level of HFs and FOHFs by adopting the Fama and French (1992) approach. Secondly, the various estimated risk measures are compared at the individual HF and FOHF levels by using univariate and multivariate cross-sectional regressions. The results show that the available data on HFs and FOHFs exhibits different risk-return trade-offs. The Cornish-Fisher expected shortfall or Cornish-Fisher tail risk could be an appropriate risk measure for HF return. Although appropriate alternative risk measures for the HFs are found, it is difficult to determine the risk measures that best capture the cross-sectional variation in FOHF returns.

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Evaluating Hedge Fund Performance: A Stochastic Dominance Approach

Linton

2010

Handbook of Portfolio Construction

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We introduce a general and ‡exible framework for hedge fund performance evaluation and asset allocation: stochastic dominance (SD) theory. Our approach utilizes statistical tests for stochastic dominance to compare the returns of hedge funds. We form hedge fund portfolios by using SD criteria and examine the out-of-sample performance of these hedge fund portfolios.Compared to performance of portfolios of randomly selected hedge funds and mean-variance e¢ cient hedge funds, our results show that fund selection method based on SD criteria greatly improves the performance of hedge fund portfolio.

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Taking a Close Look at the European Fund of Hedge Funds Industry

Cited by 12 publications

References 19 publications

Risk Measures for Hedge Funds: a Cross‐sectional Approach

Risk Measures for Hedge Funds: a Cross‐sectional Approach

Risk and Return in Hedge Funds and Funds of Hedge Funds: A Cross-Sectional Approach

Evaluating Hedge Fund Performance: A Stochastic Dominance Approach

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