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On the (Ab)Use of Omega?

Author

Listed:
  • Massimiliano Caporin

    (Unipd - Università degli Studi di Padova = University of Padua)

  • Michele Costola

    (Goethe University Frankfurt = Goethe-Universität Frankfurt am Main)

  • Gregory Mathieu Jannin

    (UP1 - Université Paris 1 Panthéon-Sorbonne)

  • Bertrand Maillet

    (EM - EMLyon Business School, CEMOI - Centre d'Économie et de Management de l'Océan Indien - UR - Université de La Réunion)

Abstract

Several recent finance articles employ the Omega measure, proposed by Keating and Shadwick (2002) - defined as a ratio of potential gains out of possible losses - for gauging the performance of funds or active strategies (e.g. Eling and Schuhmacher, 2007; Farinelli and Tibiletti, 2008; Annaert et al., 2009; Bertrand and Prigent, 2011; Zieling et al., 2014; Kapsos et al., 2014; Hamidi et al., 2014), in substitution of the traditional Sharpe ratio (1966), with the arguments that return distributions are not Gaussian and volatility is not, always, the relevant risk metric. Other authors also use the same criterion for optimizing (non-linear) portfolios with important downside risk. However, we wonder in this article about the relevance of such approaches. First, we show through a basic illustration that the Omega ratio is inconsistent with the Strict Inferior Second-order Stochastic Dominance criterion. Furthermore, we observe that the trade-off between return and risk, corresponding to the Omega measure, may be essentially influenced by the mean return. Next, we illustrate in static and dynamic frameworks that Omega-based optimal portfolios can be associated with traditional optimization paradigms depending on the chosen threshold used in the computation of Omega. Finally, we present some robustness checks on long-only asset and hedge fund databases that all confirm our general results.

Suggested Citation

  • Massimiliano Caporin & Michele Costola & Gregory Mathieu Jannin & Bertrand Maillet, 2016. "On the (Ab)Use of Omega?," Working Papers hal-01697640, HAL.
  • Handle: RePEc:hal:wpaper:hal-01697640
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    Cited by:

    1. is not listed on IDEAS
    2. Monica Billio & Bertrand Maillet & Loriana Pelizzon, 2024. "Correction to: A meta-measure of performance related to both investors and investments characteristics," Annals of Operations Research, Springer, vol. 332(1), pages 1271-1271, January.
    3. Hamidi, Benjamin & Maillet, Bertrand & Prigent, Jean-Luc, 2014. "A dynamic autoregressive expectile for time-invariant portfolio protection strategies," Journal of Economic Dynamics and Control, Elsevier, vol. 46(C), pages 1-29.
    4. Balbás, Alejandro & Serna, Gregorio, 2024. "Selling options to beat the market: Further empirical evidence," Research in International Business and Finance, Elsevier, vol. 67(PB).
    5. 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.
    6. Balder, Sven & Schweizer, Nikolaus, 2017. "Risk aversion vs. the Omega ratio: Consistency results," Finance Research Letters, Elsevier, vol. 21(C), pages 78-84.
    7. Eric Benhamou & Beatrice Guez & Nicolas Paris1, 2019. "Omega and Sharpe ratio," Papers 1911.10254, arXiv.org.
    8. Taylor, James W., 2022. "Forecasting Value at Risk and expected shortfall using a model with a dynamic omega ratio," Journal of Banking & Finance, Elsevier, vol. 140(C).
    9. Bernard, Carole & Vanduffel, Steven & Ye, Jiang, 2019. "Optimal strategies under Omega ratio," European Journal of Operational Research, Elsevier, vol. 275(2), pages 755-767.
    10. Sehgal, Ruchika & Sharma, Amita & Mansini, Renata, 2023. "Worst-case analysis of Omega-VaR ratio optimization model," Omega, Elsevier, vol. 114(C).
    11. Xu Guo & Xuejun Jiang & Wing-Keung Wong, 2017. "Stochastic Dominance and Omega Ratio: Measures to Examine Market Efficiency, Arbitrage Opportunity, and Anomaly," Economies, MDPI, vol. 5(4), pages 1-16, October.
    12. Xu Guo & Cuizhen Niu & Wing-Keung Wong, 2019. "Farinelli and Tibiletti ratio and stochastic dominance," Risk Management, Palgrave Macmillan, vol. 21(3), pages 201-213, September.
    13. Balter, Anne G. & Chau, Ki Wai & Schweizer, Nikolaus, 2024. "Comparative risk aversion vs. threshold choice in the Omega ratio," Omega, Elsevier, vol. 123(C).
    14. Yu, Jing-Rung & Paul Chiou, Wan-Jiun & Hsin, Yi-Ting & Sheu, Her-Jiun, 2022. "Omega portfolio models with floating return threshold," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 743-758.

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    Keywords

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    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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