Robust mean-to-CVaR optimization under ambiguity in distributions means and covariance

We develop a robust mean-to-CVaR portfolio optimization model under interval ambiguity in returns means and covariance. The robust model satisfies second-order stochastic dominance consistency and is formulated as a semi-definite cone program. We use two controlled experiments to document the sensitivity of the optimal allocations to the ambiguity when asset correlation varies, and to the ambiguity intervals. We find that means ambiguity has a higher impact than covariance ambiguity. We apply the model to US equities data to corroborate works showing that ambiguity in mean returns induces a home bias; it can explain the puzzle in a two-country setting but not with three countries. We further establish that covariance ambiguity also induces bias, but with lower impact that can not explain the puzzle. Our results suggest what is needed for the ambiguity channel to provide a full explanation of the puzzle. The findings are robust to alternative model specifications and outliers.