报告题目：Model Aggregation for Risk Evaluation and Robust Optimization

报告所属学科：管理科学与工程

报告人：毛甜甜（中国科学技术大学管理学院）

报告时间：2022年12月23日 10:30-12:00

报告地点：#腾讯会议：584-249-3538

报告摘要：

We introduce a new approach for prudent risk evaluation based on stochastic dominance, which will be called the model aggregation (MA) approach. In contrast to the classic worst-case risk (WR) approach, the MA approach produces not only a robust value of risk evaluation but also a robust distributional model which is useful for modeling, analysis and simulation, independent of any specific risk measure. The MA approach is easy to implement even if the uncertainty set is non-convex or the risk measure is computationally complicated, and it provides great tractability in distributionally robust optimization. Via an equivalence property between the MA and the WR approaches, new axiomatic characterizations are obtained for a few classes of popular risk measures. In particular, the Expected Shortfall (ES, also known as CVaR) is the unique risk measure satisfying the equivalence property for convex uncertainty sets among a very large class. The MA approach for Wasserstein and mean-variance uncertainty sets admits explicit formulas for the obtained robust models, and the new approach is illustrated with various risk measures and examples from portfolio optimization.

报告人简介：

毛甜甜，中国科学技术大学管理学院副教授。主要研究方向有应用概率、风险度量、量化金融/保险风险管理、极值理论和随机占优及在决策理论中的应用等。曾获得首届国家博士学术新人奖及国际精算协会2020年度Bob Alting von Geusau奖。近年来在 Mathematical Programming, Mathematics of Operations Research, Mathematical Finance, Stochastics and Finance等期刊上发表科研论文40余篇。