This study investigates expectile Value-at-Risk (EVaR) as a risk measure in dynamic copula-based portfolio optimization, compared with the common variance and CVaR. To estimate the dependence structure between asset returns, the canonical vine copula augmented with the generalized additive models (GAMC-vine) is used. Applying multivariate conditional distributions from the GAMC-vine model, step-ahead asset return forecasts are obtained and used to construct dynamic copula-based EVaR portfolios. Using ten S&P 500 industry sectors, EVaR leads to a min-risk dynamic GAMC-vine portfolio that achieves higher out-of-sample average return and risk-adjusted ratios. Furthermore, EVaR shows a better portfolio ranking than CVaR. Moreover, the copula-based variance and EVaR portfolios show higher-order stochastic dominance compared to CVaR strategies. Finally, a subsample stochastic dominance analysis reveals that, in overall, the risk minimization does not benefit from the choice of risk modeling. However, the dynamic copula model leads to optimal portfolios that dominate the equally weighted benchmark more often compared to those from historical approach.

这项研究调查了预期风险价值（EVaR）作为基于动态copula的投资组合优化中的一种风险度量，并与普通方差和CVaR进行了比较。为了估计资产收益率之间的依存关系结构，使用了用通用加性模型（GAMC-vine）增强的规范藤蔓系。应用来自GAMC-vine模型的多元条件分布，获得超前资产收益预测，并将其用于构建基于动态copula的EVaR投资组合。EVaR使用十个标准普尔500行业板块，形成了具有最小风险的动态GAMC-vine产品组合，可实现更高的样本外平均回报率和风险调整率。此外，EVaR的投资组合排名比CVaR更好。此外，与CVaR策略相比，基于copula的方差和EVaR投资组合显示出更高阶的随机优势。最后，子样本随机优势分析表明，总体而言，最小化风险不会从风险模型的选择中受益。然而，与历史方法相比，动态copula模型可导致最优投资组合在同等加权的基准中占主导地位。