报告地点：行健楼学术活动室526

摘要：This paper studies two optimal insurance contracting problems under distributional uncertainty from the perspective of a potential policyholder, with the ambiguity set of loss distributions characterized by a Bregman-Wasserstein ball. Unlike the p-Wasserstein distance, the Bregman-Wasserstein (BW) divergence allows for asymmetric penalization of deviations from the benchmark distribution. The first problem considers an insurance demand model under an αmaxmin preference with Value-at-Risk (VaR). We derive the optimal indemnity function in closed form and explore, both analytically and numerically, how the asymmetry inherent in the BW divergence shapes the structure of the optimal indemnity. The second problem involves a robust optimization framework in which the policyholder minimizes the worst-case convex distortion risk measure subject to a guaranteed VaR constraint. In this setting, we obtain explicit characterizations of both the optimal indemnity and the worst-case distribution. A concrete example based on Tail Value-at-Risk (TVaR) is provided to illustrate the practical implications of our theoretical findings.

报告人简介：姜文骏，加拿大卡尔加里大学数学统计系副教授，于2019 年在加拿大西安大略大学取得统计学博士学位（精算方向）。主要从事最优（再）保险和风险分担的研究。主要成果发表于 EJOR, IME, SIFIN, ASTIN, SAJ, NAAJ, FRL 上。