'On the (Ab)use of Omega?'
Autor: | Bertrand Maillet, Michele Costola, Gregory Mathieu Jannin, Massimiliano Caporin |
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Přispěvatelé: | emlyon business school, Universita degli Studi di Padova, Goethe-University Frankfurt am Main, Université Paris 1 Panthéon-Sorbonne (UP1), Centre d'Économie et de Management de l'Océan Indien (CEMOI), Université de La Réunion (UR), Social Science Research Network, business school, emlyon |
Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
Risk
Settore SECS-P/11 - Economia degli Intermediari Finanziari Economics and Econometrics Stochastic dominance Downside risk Settore SECS-P/05 - Econometria Performance measure Settore SECS-P/02 - Politica Economica Omega JEL: C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General/C.C1.C11 - Bayesian Analysis: General Hedge fund OmegaReturn distribution 0502 economics and business Econometrics Return distribution 050207 economics [SHS.ECO] Humanities and Social Sciences/Economics and Finance Mathematics Finance 050208 finance business.industry Omega ratio Sharpe ratio 05 social sciences Risk metric JEL: C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General/C.C1.C10 - General jel:C10 jel:C11 jel:G12 [SHS.ECO]Humanities and Social Sciences/Economics and Finance [SHS.GESTION]Humanities and Social Sciences/Business administration Performance Measure Omega Return Distribution Risk Stochastic Dominance Volatility (finance) [SHS.GESTION] Humanities and Social Sciences/Business administration business JEL: G - Financial Economics/G.G1 - General Financial Markets/G.G1.G12 - Asset Pricing • Trading Volume • Bond Interest Rates |
Zdroj: | Journal of Empirical Finance Journal of Empirical Finance, 2018, 11-33 p [Research Report] ID 2559216, Social Science Research Network. 2016 Journal of Empirical Finance, Elsevier, 2018, 46, pp.11-33. ⟨10.1016/j.jempfin.2017.11.007⟩ |
ISSN: | 0927-5398 |
DOI: | 10.1016/j.jempfin.2017.11.007⟩ |
Popis: | 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. |
Databáze: | OpenAIRE |
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