'On the (Ab)use of Omega?'

Autor: Bertrand Maillet, Michele Costola, Gregory Mathieu Jannin, Massimiliano Caporin
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