Portfolio Risk Management with CVaR-like Constraints

A current research stream in the portfolio allocation literature develops models that take into account the asymmetric nature of asset return distributions. Our paper contributes to this research stream by extending the Krokhmal, Palmquist, and Uryasev approach. We add CVaR-like constraints in the traditional portfolio optimization problem to reshape the tails of the portfolio return distribution while not significantly affecting its mean and variance. We illustrate how to apply this approach, called the ‘‘MV CVaR approach,’’ to manage tail risk of an insurer’s asset-liability portfolio. Finally, we compare the MV CVaR approach with the traditional Markowitz method and a method recently introduced by Boyle and Ding. Our numerical analysis provides empirical support for the effectiveness of the MV CVaR approach in controlling downside risk. Moreover, we find that the MV CVaR approach may improve skewness of mean-variance portfolios, especially for high-variance portfolios.