报 告 人: 刘鹏 （University of Essex）
报告时间：2025年7月11 号上午10:30-11:30
报告地点：苏州大学览秀楼201；#腾讯会议：362-683-9628
报告摘要： We establish sharp upper and lower bounds for distortion risk metrics under distributional uncertainty. The uncertainty sets are characterized by four key features of the underlying distribution: mean, variance, unimodality, and Wasserstein distance to a reference distribution.
We first examine very general distortion risk metrics, assuming only finite variation for the underlying distortion function and without requiring continuity or monotonicity. This broad framework includes notable distortion risk metrics such as range value-at-risk, glue value-at-risk, Gini deviation, mean-median deviation and inter-quantile difference. In this setting, when the uncertainty set is characterized by a fixed mean, variance and a Wasserstein distance, we determine both the worst- and best-case values of a given distortion risk metric and identify the corresponding extremal distribution. When the uncertainty set is further constrained by unimodality with a fixed reflection point, we establish for the case of absolutely continuous distortion functions the extremal values along with their respective extremal distributions.
We apply our results to robust portfolio optimization and model risk assessment offering improved decision-making under model uncertainty.
(This talk is based on a joint work with Steven Vanduffel and Yi Xia.)
报告人简介： Dr Peng Liu is currently a lecturer in the University of Essex. He received his PhD from Nankai University and worked as Postdocs in the University of Lausanne and University of Waterloo respectively. His research focuses on topics in quantitative risk management and actuarial mathematics, including risk measures, risk sharing, portfolio optimization, model uncertainty and extreme value theory. His papers appear in the leading journals in the fields such as MOR (x2) and MF(x2). Moreover, he serves as an associate editor of the International Journal of Financial Engineering and co-editor of the book: Quantitative risk management in agricultural business in Springer Actuarial Series.
