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Omega and Sharpe ratio

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  • Eric Benhamou

    (LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique)

  • Beatrice Guez
  • Nicolas Paris

    (AP-HP - Assistance publique - Hôpitaux de Paris (AP-HP))

Abstract

Omega ratio, defined as the probability-weighted ratio of gains over losses at a given level of expected return, has been advocated as a better performance indicator compared to Sharpe and Sortino ratio as it depends on the full return distribution and hence encapsulates all information about risk and return. We compute Omega ratio for the normal distribution and show that under some distribution symmetry assumptions , the Omega ratio is oversold as it does not provide any additional information compared to Sharpe ratio. Indeed, for returns that have elliptic distributions , we prove that the optimal portfolio according to Omega ratio is the same as the optimal portfolio according to Sharpe ratio. As elliptic distributions are a weak form of symmetric distributions that generalized Gaussian distributions and encompass many fat tail distributions, this reduces tremendously the potential interest for the Omega ratio.

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  • Eric Benhamou & Beatrice Guez & Nicolas Paris, 2020. "Omega and Sharpe ratio," Working Papers hal-02886481, HAL.
  • Handle: RePEc:hal:wpaper:hal-02886481
    Note: View the original document on HAL open archive server: https://hal.science/hal-02886481
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    References listed on IDEAS

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    1. Jaume Belles‐Sampera & Montserrat Guillén & Miguel Santolino, 2014. "Beyond Value‐at‐Risk: GlueVaR Distortion Risk Measures," Risk Analysis, John Wiley & Sons, vol. 34(1), pages 121-134, January.
    2. Michael R. Metel & Traian A. Pirvu & Julian Wong, 2017. "Risk Management under Omega Measure," Risks, MDPI, vol. 5(2), pages 1-14, May.
    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.
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    9. Michael R. Metel & Traian A. Pirvu & Julian Wong, 2015. "Risk management under Omega measure," Papers 1510.05790, arXiv.org, revised Apr 2017.
    10. Bernard, Carole & Vanduffel, Steven & Ye, Jiang, 2019. "Optimal strategies under Omega ratio," European Journal of Operational Research, Elsevier, vol. 275(2), pages 755-767.
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    Cited by:

    1. Eric Benhamou & David Saltiel & Serge Tabachnik & Sui Kai Wong & François Chareyron, 2021. "Distinguish the indistinguishable: a Deep Reinforcement Learning approach for volatility targeting models," Working Papers hal-03202431, HAL.
    2. Carole Bernard & Massimiliano Caporin & Bertrand Maillet & Xiang Zhang, 2023. "Omega Compatibility: A Meta-analysis," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 493-526, August.
    3. Eric Benhamou & David Saltiel & Serge Tabachnik & Sui Kai Wong & Franc{c}ois Chareyron, 2021. "Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting," Papers 2104.10483, arXiv.org, revised Apr 2021.

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    Keywords

    Omega ratio; Sharpe ratio; normal distribution; elliptical distribution;
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