报告人：Silvana Pesenti Associate Professor in Insurance Risk Management at the Department of Statistical Sciences at the University of Toronto

报告时间：2026年10月13日，晚上8：00-9:00

报告地点：览秀楼105学术报告厅 #腾讯会议：362-683-9628

We introduce a framework for preference-robust decision making when preferences over risk are modelled through generalised distortion risk measures. Unlike distributional robustness, our approach addresses ambiguity in the risk functional itself. We construct ambiguity sets on distortion (weight) functions using  the Wasserstein distance and Bregman divergences, and derive closed-form expressions for the worst- and best-case distortion risk measures. We further extend the framework to rank-dependent utility, yielding preference-robust behavioural models. In particular, rank-dependent utility appears as a robustification of the expected utility model, yielding a novel way to address the Allais paradox.

报告人简介：Silvana Pesenti is an Associate Professor in Insurance Risk Management at the Department of Statistical Sciences at the University of Toronto. Previously she was an Assistant Professor in Insurance Risk Management at the University of Toronto. Silvana was named the 2022 Rising Star in Quant Finance by Risk.net for the paper Portfolio Optimisation within a Wasserstein Ball. She received the 2020 Peter Clark Best Paper Prize for her research paper  “Reverse Sensitivity Testing: what does it take to break the model?” from the Institute and Faculty of Actuaries (IFoA). In 2019, Silvana was awarded the Dorothy Shoichet Women Faculty Science Award of Excellence.