IDEAS home Printed from https://ideas.repec.org/a/kap/fmktpm/v36y2022i4d10.1007_s11408-022-00409-8.html
   My bibliography  Save this article

Beyond mean–variance: assessing hedge fund performance in a non-parametric world

Author

Listed:
  • Afrae Hassouni

    (Université Libre de Bruxelles (ULB), SBS-EM)

  • Hugues Pirotte

    (Université Libre de Bruxelles (ULB), SBS-EM)

Abstract

This paper uses Data Envelopment Analysis (DEA) to compare the performance of hedge funds to that of equities. The analysis covers the period from January 1999 to December 2013 and shows that under a mean–variance DEA, hedge funds significantly outperform equities. However, this outperformance is no longer significant when skewness and kurtosis are integrated. The DEA technique is particularly interesting for assessing hedge fund performance because of its flexibility and its non-parametric property: DEA allows to easily add additional attributes to the analysis and assesses performance relative to the sample under analysis without requiring any benchmark.

Suggested Citation

  • Afrae Hassouni & Hugues Pirotte, 2022. "Beyond mean–variance: assessing hedge fund performance in a non-parametric world," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 36(4), pages 473-488, December.
    • Handle: RePEc:kap:fmktpm:v:36:y:2022:i:4:d:10.1007_s11408-022-00409-8
    DOI: 10.1007/s11408-022-00409-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11408-022-00409-8
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11408-022-00409-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fung, William & Hsieh, David A, 2001. "The Risk in Hedge Fund Strategies: Theory and Evidence from Trend Followers," The Review of Financial Studies, Society for Financial Studies, vol. 14(2), pages 313-341.
    2. Vikas Agarwal, 2004. "Risks and Portfolio Decisions Involving Hedge Funds," The Review of Financial Studies, Society for Financial Studies, vol. 17(1), pages 63-98.
    3. M C A Silva Portela & E Thanassoulis & G Simpson, 2004. "Negative data in DEA: a directional distance approach applied to bank branches," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(10), pages 1111-1121, October.
    4. Carl Ackermann & Richard McEnally & David Ravenscraft, 1999. "The Performance of Hedge Funds: Risk, Return, and Incentives," Journal of Finance, American Finance Association, vol. 54(3), pages 833-874, June.
    5. Turan G. Bali & Stephen J. Brown & K. Ozgur Demirtas, 2013. "Do Hedge Funds Outperform Stocks and Bonds?," Management Science, INFORMS, vol. 59(8), pages 1887-1903, August.
    6. Moshe Leshno & Haim Levy, 2002. "Preferred by "All" and Preferred by "Most" Decision Makers: Almost Stochastic Dominance," Management Science, INFORMS, vol. 48(8), pages 1074-1085, August.
    7. Scott, Robert C & Horvath, Philip A, 1980. "On the Direction of Preference for Moments of Higher Order Than the Variance," Journal of Finance, American Finance Association, vol. 35(4), pages 915-919, September.
    8. Martin Eling, 2006. "Performance measurement of hedge funds using data envelopment analysis," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 20(4), pages 442-471, December.
    9. Rolf Färe & Shawna Grosskopf, 2000. "Theory and Application of Directional Distance Functions," Journal of Productivity Analysis, Springer, vol. 13(2), pages 93-103, March.
    10. Fung, William & Hsieh, David A, 1997. "Empirical Characteristics of Dynamic Trading Strategies: The Case of Hedge Funds," The Review of Financial Studies, Society for Financial Studies, vol. 10(2), pages 275-302.
    11. Glawischnig, Markus & Sommersguter-Reichmann, Margit, 2010. "Assessing the performance of alternative investments using non-parametric efficiency measurement approaches: Is it convincing?," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 295-303, February.
    12. R. G. Chambers & Y. Chung & R. Färe, 1998. "Profit, Directional Distance Functions, and Nerlovian Efficiency," Journal of Optimization Theory and Applications, Springer, vol. 98(2), pages 351-364, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jin Yuan & Xianghui Yuan, 2023. "A Comprehensive Method for Ranking Mutual Fund Performance," SAGE Open, , vol. 13(2), pages 21582440231, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ludwig Chincarini, 2014. "The Impact of Quantitative Methods on Hedge Fund Performance," European Financial Management, European Financial Management Association, vol. 20(5), pages 857-890, November.
    2. Paul Karehnke & Frans de Roon, 2020. "Spanning Tests for Assets with Option-Like Payoffs: The Case of Hedge Funds," Management Science, INFORMS, vol. 66(12), pages 5969-5989, December.
    3. Caporin, Massimiliano & Costola, Michele & Jannin, Gregory & Maillet, Bertrand, 2018. "“On the (Ab)use of Omega?”," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 11-33.
    4. Eling, Martin & Faust, Roger, 2010. "The performance of hedge funds and mutual funds in emerging markets," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1993-2009, August.
    5. Jackwerth, Jens Carsten & Slavutskaya, Anna, 2016. "The total benefit of alternative assets to pension fund portfolios," Journal of Financial Markets, Elsevier, vol. 31(C), pages 25-42.
    6. El Kalak, Izidin & Azevedo, Alcino & Hudson, Robert, 2016. "Reviewing the hedge funds literature II: Hedge funds' returns and risk management characteristics," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 55-66.
    7. Bali, Turan G. & Brown, Stephen J. & Caglayan, Mustafa O., 2019. "Upside potential of hedge funds as a predictor of future performance," Journal of Banking & Finance, Elsevier, vol. 98(C), pages 212-229.
    8. Andrew J. Patton & Tarun Ramadorai, 2013. "On the High-Frequency Dynamics of Hedge Fund Risk Exposures," Journal of Finance, American Finance Association, vol. 68(2), pages 597-635, April.
    9. Agarwal, Vikas & Daniel, Naveen D. & Naik, Narayan Y., 2009. "Role of managerial incentives and discretion in hedge fund performance," CFR Working Papers 04-04, University of Cologne, Centre for Financial Research (CFR).
    10. Benoît Dewaele, 2013. "Leverage and Alpha: The Case of Funds of Hedge Funds," Working Papers CEB 13-033, ULB -- Universite Libre de Bruxelles.
    11. Duarte, Jefferson & Longstaff, Francis A. & Yu, Fan, 2005. "Risk and Return in Fixed Income Arbitage: Nickels in Front of a Steamroller?," University of California at Los Angeles, Anderson Graduate School of Management qt6zx6m7fp, Anderson Graduate School of Management, UCLA.
    12. Nicolas Bollen, 2011. "The financial crisis and hedge fund returns," Review of Derivatives Research, Springer, vol. 14(2), pages 117-135, July.
    13. Antonio Di Cesare & Philip A. Stork & Casper G. de Vries, 2015. "Risk Measures for Autocorrelated Hedge Fund Returns," Journal of Financial Econometrics, Oxford University Press, vol. 13(4), pages 868-895.
    14. Hwang, Inchang & Xu, Simon & In, Francis & Kim, Tong Suk, 2017. "Systemic risk and cross-sectional hedge fund returns," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 109-130.
    15. Douglas Cumming & Na Dai, 2010. "A Law and Finance Analysis of Hedge Funds," Financial Management, Financial Management Association International, vol. 39(3), pages 997-1026, September.
    16. Szabolcs Blazsek & Anna Downarowicz, 2013. "Forecasting hedge fund volatility: a Markov regime-switching approach," The European Journal of Finance, Taylor & Francis Journals, vol. 19(4), pages 243-275, April.
    17. Cao, Charles & Chen, Yong & Liang, Bing & Lo, Andrew W., 2013. "Can hedge funds time market liquidity?," Journal of Financial Economics, Elsevier, vol. 109(2), pages 493-516.
    18. Agarwal, Vikas & Green, T. Clifton & Ren, Honglin, 2018. "Alpha or beta in the eye of the beholder: What drives hedge fund flows?," Journal of Financial Economics, Elsevier, vol. 127(3), pages 417-434.
    19. Weng, Haijie & Trück, Stefan, 2011. "Style analysis and Value-at-Risk of Asia-focused hedge funds," Pacific-Basin Finance Journal, Elsevier, vol. 19(5), pages 491-510, November.
    20. Agarwal, Vikas & Fos, Vyacheslav & Jiang, Wei, 2010. "Inferring reporting biases in hedge fund databases from hedge fund equity holdings," CFR Working Papers 10-08, University of Cologne, Centre for Financial Research (CFR).

    More about this item

    Keywords

    Hedge funds; Directional DEA; Performance; Non-parametric efficient frontier;
    All these keywords.

    JEL classification:

    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:fmktpm:v:36:y:2022:i:4:d:10.1007_s11408-022-00409-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.