Optimal Portfolio Choice with Fat Tails and Parameter Uncertainty
研究发现，当收益呈厚尾分布时，基于正态假设的投资组合规则表现更差，因此应减少对样本均值-方差和全局最小方差组合的配置，增加无风险资产，从而显著提升样本外表现。
Abstract Existing portfolio combination rules that optimize the out-of-sample performance under parameter uncertainty assume multivariate normally distributed returns. However, we show that this assumption is not innocuous because fat tails in returns lead to poorer out-of-sample performance of the sample mean–variance and sample global minimum-variance (GMV) portfolios relative to normality. Consequently, when returns are fat-tailed, portfolio combination rules should allocate less to the sample mean–variance and sample GMV portfolios, and more to the risk-free asset, than the normality assumption prescribes. Empirical evidence shows that accounting for fat tails in the construction of optimal portfolio combination rules significantly improves their out-of-sample performance.