Robust portfolio optimization with Value-at-Risk-adjusted Sharpe ratios

Geng Deng (), Tim Dulaney, Craig McCann and Olivia Wang
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Geng Deng: Geng Deng, Securities Litigation and Consulting Group, 3998 Fair Ridge Dr.

Journal of Asset Management, 2013, vol. 14, issue 5, No 3, 293-305

Abstract: Abstract We propose a robust portfolio optimization approach based on Value-at-Risk (VaR)-adjusted Sharpe ratios. Traditional Sharpe ratio estimates using a limited series of historical returns are subject to estimation errors. Portfolio optimization based on traditional Sharpe ratios ignores this uncertainty and, as a result, is not robust. In this article, we propose a robust portfolio optimization model that selects the portfolio with the largest worse-case-scenario Sharpe ratio within a given confidence interval. We show that this framework is equivalent to maximizing the Sharpe ratio reduced by a quantity proportional to the standard deviation in the Sharpe ratio estimator. We highlight the relationship between the VaR-adjusted Sharpe ratios and other modified Sharpe ratios proposed in the literature. In addition, we present both numerical and empirical results comparing optimal portfolios generated by the approach advocated here with those generated by both traditional and alternative optimization approaches.

Keywords: Sharpe ratio; portfolio optimization; robust optimization; VaR (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (17)

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DOI: 10.1057/jam.2013.21

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