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Mean-Variance Optimization Is a Good Choice, But for Other Reasons than You Might Think

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  • Andrea Rigamonti

    (Faculty of Economics and Management, Free University of Bozen-Bolzano, 39100 Bolzano, Italy)

Abstract

Mean-variance portfolio optimization is more popular than optimization procedures that employ downside risk measures such as the semivariance, despite the latter being more in line with the preferences of a rational investor. We describe strengths and weaknesses of semivariance and how to minimize it for asset allocation decisions. We then apply this approach to a variety of simulated and real data and show that the traditional approach based on the variance generally outperforms it. The results hold even if the CVaR is used, because all downside risk measures are difficult to estimate. The popularity of variance as a measure of risk appears therefore to be rationally justified.

Suggested Citation

  • Andrea Rigamonti, 2020. "Mean-Variance Optimization Is a Good Choice, But for Other Reasons than You Might Think," Risks, MDPI, vol. 8(1), pages 1-16, March.
    • Handle: RePEc:gam:jrisks:v:8:y:2020:i:1:p:29-:d:332644
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    References listed on IDEAS

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    1. Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
    2. Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1683, August.
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    4. Yamai, Yasuhiro & Yoshiba, Toshinao, 2002. "On the Validity of Value-at-Risk: Comparative Analyses with Expected Shortfall," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 20(1), pages 57-85, January.
    5. Rui Albuquerque, 2012. "Skewness in Stock Returns: Reconciling the Evidence on Firm Versus Aggregate Returns," The Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1630-1673.
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    Cited by:

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    3. Matteo Foglia & Maria Cristina Recchioni & Gloria Polinesi, 2021. "Smart Beta Allocation and Macroeconomic Variables: The Impact of COVID-19," Risks, MDPI, vol. 9(2), pages 1-25, February.

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