Enhancing mean–variance portfolio selection by modeling distributional asymmetries

• Model-based estimates that incorporate return asymmetries are applied to 18 meanvariance optimization rules.
• Model-based estimates are a significant improvement over use of historical-based estimates.
• Model-based estimates result in out-performance of the basic mean–variance optimization strategy after transaction costs.
• Outperforming the 1/N portfolio after transaction costs remains an elusive task even with model-based estimates.

Why do mean–variance (MV) models perform so poorly? In searching for an answer to this question, we estimate expected returns by sampling from a multivariate probability model that explicitly incorporates distributional asymmetries. Specifically, our empirical analysis shows that an application of copulas using marginal models that incorporate dynamic features such as autoregression, volatility clustering, and skewness to reduce estimation error in comparison to historical sampling windows. Using these copula-based models, we find that several MV-based rules exhibit statistically significant and superior performance improvements even after accounting for transaction costs. However, we find that outperforming the naïve equally-weighted (1/N) strategy after accounting for transactions costs still remains an elusive task.