勷勤数学•专家报告

题      目：Robust Stackelberg reinsurance game with Bayesian learning

报  告  人： 陈树敏 教授  (邀请人：杨舟)

                                              广东工业大学

时      间： 10月13日  10：30-11:30

地     点：数科院西楼二楼会议室

报告人简介:

          广东工业大学教授、博士生导师，主要从事金融工程、保险精算等领域的研究，目前已在Journal of Banking & Finance、Siam Journal on Financial Mathematics、Journal of Economic Dynamics and Control、Insurance: Mathematics and Economics、ASTIN Bulletin、Scandinavian Actuarial Journal、系统工程理论与实践、管理科学学报等期刊上发表论文三十余篇，主持国家自然科学基金青年/面上项目、广东省自然科学基金，中国博士后基金面上/特别资助项目等，参与多项国家自然科学基金项目及省部级项目。

摘      要：

          This paper investigates a robust stochastic Stackelberg reinsurance differential game between an insurer and a reinsurer under Bayesian learning and ambiguity aversion. In the reinsurance contracting problem, the insurer's claim risk follows a diffusion model, and the premium consists of an observable factor and an unobservable factor that is dynamically estimated via Bayesian learning. Under the expected utility maximization criterion, we derive closed-form solutions for the insurer's robust optimal risk retention level, the reinsurer's robust optimal reinsurance price, and the corresponding optimal value functions. The effects of learning and ambiguity aversion on the equilibrium strategies are then analyzed. Several numerical examples are provided to illustrate the theoretical results and their economic implications. We show that Bayesian learning generates an additional hedging demand, the sign of which depends on the interplay among Bayesian filtering, claim–factor correlations, and ambiguity aversion. The ambiguity aversion of the insurer and the reinsurer affects equilibrium risk sharing asymmetrically.

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