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Conditionally fitted Sharpe performance with an application to hedge fund rating

Author

Listed:
  • Serge Darolles

    (DRM-Finance - DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique)

  • Christian Gourieroux

    (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique)

Abstract

We define a battery of Sharpe performance measures, which differ by the information taken into account in their computation, but also by the potential use of the fund by the investor. Four advantages of Sharpe performance based rating are especially important for the investor. First, the performance measures correspond to the standard measures used for mutual funds and known by retail investors. Second, we can compare the numerical results, even if they are obtained with different assumptions. Third, the rankings are based on regression analysis and easy to compute. Fourth, we can easily use these performance measures in the design of an optimal basket of hedge funds. Finally, we can use the performance measures to partition the set of funds into homogenous segments.

Suggested Citation

  • Serge Darolles & Christian Gourieroux, 2010. "Conditionally fitted Sharpe performance with an application to hedge fund rating," Post-Print halshs-00677727, HAL.
    • Handle: RePEc:hal:journl:halshs-00677727
    DOI: 10.1016/j.jbankfin.2009.08.025
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    Citations

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    Cited by:

    1. Guo, Biao & Xiao, Yugu, 2016. "A note on why doesn't the choice of performance measure matter?," Finance Research Letters, Elsevier, vol. 16(C), pages 248-254.
    2. 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.
    3. Brandouy, Olivier & Briec, Walter & Kerstens, Kristiaan & Van de Woestyne, Ignace, 2010. "Portfolio performance gauging in discrete time using a Luenberger productivity indicator," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1899-1910, August.
    4. Antonio Díaz & Carlos Esparcia, 2021. "Dynamic optimal portfolio choice under time-varying risk aversion," International Economics, CEPII research center, issue 166, pages 1-22.
    5. Kerstens, Kristiaan & Mazza, Paolo & Ren, Tiantian & Van de Woestyne, Ignace, 2022. "Multi-Time and Multi-Moment Nonparametric Frontier-Based Fund Rating: Proposal and Buy-and-Hold Backtesting Strategy," Omega, Elsevier, vol. 113(C).
    6. Ozdemir, Huseyin & Ozdemir, Zeynel Abidin, 2021. "A Survey of Hedge and Safe Havens Assets against G-7 Stock Markets before and during the COVID-19 Pandemic," IZA Discussion Papers 14888, Institute of Labor Economics (IZA).
    7. Bussière, Matthieu & Hoerova, Marie & Klaus, Benjamin, 2015. "Commonality in hedge fund returns: Driving factors and implications," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 266-280.
    8. Kerstens, Kristiaan & Mounir, Amine & de Woestyne, Ignace Van, 2011. "Non-parametric frontier estimates of mutual fund performance using C- and L-moments: Some specification tests," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1190-1201, May.
    9. Schuhmacher, Frank & Eling, Martin, 2012. "A decision-theoretic foundation for reward-to-risk performance measures," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2077-2082.
    10. Davide Venturelli & Alexei Kondratyev, 2018. "Reverse Quantum Annealing Approach to Portfolio Optimization Problems," Papers 1810.08584, arXiv.org, revised Oct 2018.
    11. Sadefo Kamdem, J. & Mbairadjim Moussa, A. & Terraza, M., 2012. "Fuzzy risk adjusted performance measures: Application to hedge funds," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 702-712.
    12. Giannikis, Dimitrios & Vrontos, Ioannis D., 2011. "A Bayesian approach to detecting nonlinear risk exposures in hedge fund strategies," Journal of Banking & Finance, Elsevier, vol. 35(6), pages 1399-1414, June.
    13. Schuhmacher, Frank & Eling, Martin, 2011. "Sufficient conditions for expected utility to imply drawdown-based performance rankings," Journal of Banking & Finance, Elsevier, vol. 35(9), pages 2311-2318, September.
    14. Badrinath, S.G. & Gubellini, S., 2011. "On the characteristics and performance of long-short, market-neutral and bear mutual funds," Journal of Banking & Finance, Elsevier, vol. 35(7), pages 1762-1776, July.
    15. Christian Gouriéroux, 2008. "Bon ou mauvais usage des notations," Revue d'Économie Financière, Programme National Persée, vol. 7(1), pages 259-263.
    16. Rachida Hennani & Michel Terraza, 2012. "Value-at-Risk stressée chaotique d’un portefeuille bancaire," Working Papers 12-23, LAMETA, Universtiy of Montpellier, revised Sep 2012.
    17. Angelidis, Timotheos & Tessaromatis, Nikolaos, 2010. "The efficiency of Greek public pension fund portfolios," Journal of Banking & Finance, Elsevier, vol. 34(9), pages 2158-2167, September.

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