报告题目：Distributionally Robust Downside Risk Optimization
时 间：2026年4月11日（星期六）11：30
地 点：科研楼18号楼1102
主 办：数学与统计学院，分析数学及应用教育部重点实验室，福建省分析数学及应用重点实验室
参加对象：相关专业师生
报告摘要：We introduce a portfolio framework that jointly integrates investors' aversion to downside risk and distributional ambiguity. We measure downside risk with the lower partial moment and capture ambiguity with a distributionally robust formulation using a Wasserstein ball centered at a reference distribution of asset returns. Our problem is general, allowing for various downside risk measures, orders of the Wasserstein distance, and choices of the cost function. We provide a convex dual formulation of the distributionally robust problem and characterize its Slater condition analytically. When the cost function is the Mahalanobis distance and the reference distribution is elliptical, we derive an analytical solution for the optimal portfolio and show it is located on the sample mean-variance efficient frontier. For other cost functions, the optimal portfolio can be located outside of the frontier. We also establish formal relations between our framework and two classical portfolio paradigms, which show that the risk-aversion and ambiguity-aversion coefficients can be linked to a more intuitive downside return threshold. An empirical analysis on datasets of characteristic-sorted portfolios and individual stocks shows that our portfolio strategies deliver gains in terms of downside risk, risk-return tradeoff, and turnover relative to distributionally robust meanvariance strategies, non-robust strategies, and naive diversification.
报告人简介：姚经，苏州大学金融工程研究中心教授、博导。教育部海外引才计划专家，江苏省特聘教授，重庆市巴渝学者讲座教授。研究方向包括量化金融风险管理，衍生品定价，随机控制与鲁棒优化，绿色金融、统计机器学习方法及其在金融保险中的应用等。