报告题目：Optimal insurance contract design with self-protection under asymmetric information: Signaling and Screening

报告时间：2026年11月6日 9：00-10：00

地点：疯av 106会议室

报告人：梁志彬

摘要：This paper investigates optimal insurance contract design with self-protection under asymmetric information through two information-revelation mechanisms: signaling and screening. Specifically, the insurance market consists of high-risk and low-risk policyholders with distinct claim-size distributions, while the risk-neutral insurer only knows the population shares of the two types. The optimal contract menu under complete information is characterized by solving a Stackelberg game, revealing that high-risk policyholders may prefer the low-risk contract, whereas low-risk ones have no incentive to mimic. Therefore, in the signaling game, low-risk policyholders may need to credibly signal their type by reducing self-protection expenditure, resulting in a costly separating equilibrium, while greater risk heterogeneity increases the likelihood of costless separation. To achieve self-selection, the insurer lowers the loading for high-risk policyholder while raising it for low-risk one, offering the former a high-premium contract with high or full indemnity and the latter a low-premium contract with low indemnity. Notably, numerical results show that signaling benefits low-risk policyholder more when the proportion of high-risk policyholders or risk heterogeneity is greater; otherwise, screening menu is more profitable. Furthermore, more effective self-protection reduces both signaling and screening costs, thereby mitigating adverse selection.

报告人简介：梁志彬，南京师范大学数学科学学院教授，博士生导师。主要研究方向：风险管理与精算，数理金融与定价，随机最优风险控制。目前感兴趣的研究领域是：金融保险市场不确定环境下的博弈与优化；去中心化最优风险共担决策；深度学习算法下的量化金融与随机最优控制。在EJOR，JEDC，IME，SAJ，AMO等数理金融与精算以及优化相关期刊发表学术论文70余篇，主持和完成国家自然科学基金及省部级基金项目多项。08年以来，先后访问过英国London Imperial College的Tanaka商学院；美国University of Michigan的数学系（先后三年半）；加拿大Concordia University的数学与统计系；美国北卡州立大学数学系；以及多次访问香港大学的统计与精算系等。