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Nonparametric Assessment of Hedge Fund Performance

Author

Listed:
  • Caio Almeida

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Kim Ardison

    (FGV-EPGE - Universidad de Brazil)

  • René Garcia

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

We propose a new class of performance measures for Hedge Fund (HF) returns based on a family of empirically identifiable stochastic discount factors (SDFs). The SDF-based measures incorporate no-arbitrage pricing restrictions and naturally embed information about higher-order mixed moments between HF and benchmark factors returns. We provide a full asymptotic theory for our SDF estimators to test for the statistical significance of each fund's performance and for the relevance of individual benchmark factors within each proposed measure. We apply our methodology to a panel of 4815 individual hedge funds. Our empirical analysis reveals that fewer funds have a statistically significant positive alpha compared to the Jensen's alpha obtained by the traditional linear regression approach. Moreover, the funds' rankings vary considerably between the two approaches. Performance also varies between the members of our family because of a different fund exposure to higher-order moments of the benchmark factors, highlighting the potential heterogeneity across investors in evaluating performance. (C) 2019 Elsevier B.V. All rights reserved.

Suggested Citation

  • Caio Almeida & Kim Ardison & René Garcia, 2020. "Nonparametric Assessment of Hedge Fund Performance," Post-Print hal-02550789, HAL.
    • Handle: RePEc:hal:journl:hal-02550789
    DOI: 10.1016/j.jeconom.2019.08.002
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    Cited by:

    1. Milad Nozari, 2021. "Information content of the risk-free rate for the pricing kernel bound," Journal of Asset Management, Palgrave Macmillan, vol. 22(4), pages 267-276, July.
    2. Elisa Becker‐Foss, 2024. "Performance and reporting predictability of hedge funds," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2257-2278, September.
    3. Ghosh, Anisha & Julliard, Christian & Taylor, Alex. P, 2025. "An information-theoretic asset pricing model," LSE Research Online Documents on Economics 126155, London School of Economics and Political Science, LSE Library.
    4. Jin Yuan & Xianghui Yuan, 2023. "A Comprehensive Method for Ranking Mutual Fund Performance," SAGE Open, , vol. 13(2), pages 21582440231, May.
    5. Ardia, David & Barras, Laurent & Gagliardini, Patrick & Scaillet, Olivier, 2024. "Is it alpha or beta? Decomposing hedge fund returns when models are misspecified," Journal of Financial Economics, Elsevier, vol. 154(C).
    6. Yahyaei, Hamid & Singh, Abhay & Smith, Tom, 2025. "How does the smart money feel? Hedge fund sentiment, returns, and the business cycle," Journal of Behavioral and Experimental Finance, Elsevier, vol. 47(C).
    7. Fletcher, Jonathan, 2021. "Evaluating the performance of U.S. international equity closed-end funds," Journal of Multinational Financial Management, Elsevier, vol. 60(C).

    More about this item

    Keywords

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    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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