报告人：张艺赢 副研究员
报告题目：Insurance demand under government interventions and distorted probabilities
报告时间：2025年11月4日（周二）下午16:00-17:00
报告地点：云龙校区6号楼304会议室
主办单位：数学与统计学院、数学研究院、科学技术研究院
报告人简介：
张艺赢，南方科技大学数学系副研究员、助理教授、博士生导师。曾赴鲁汶大学和阿姆斯特丹大学进行联合学术访问。主要开展最优（再）保险设计、巨灾保险、风险减量、风险度量、系统性风险等研究。在Insurance: Mathematics and Economics、SIAM Journal on Financial Mathematics、Quantitative Finance、European Journal of Operational Research、Reliability Engineering & System Safety等期刊发表学术论文约80篇，谷歌学术显示总引用1120次，h-index为20。正在主持国自然面上1项、深圳市面上2项，完成国自然青年1项、广东省面上1项、天津市青年项目1项。担任国际SCIE期刊《Hacettepe Journal of Mathematics and Statistics》编委会成员（统计学Area Editor）。
报告摘要：
In this article, we investigate the optimal insurance demand for an individual under risk-adjusted distorted probabilities, considering the participation of government interventions, such as premium subsidies and disaster relief. We model the premium subsidy as a non-decreasing function ranging from 0 to 1, representing the percentage of government support, whereas the relief assistance is characterized by a 1-Lipschitz relief scheme function, reflecting the government's effort in post-disaster recovery. When the expected value premium principle is employed, the general form of the optimal retained loss function for the policyholder is derived by jointly applying the calculus of variations and the marginal indemnification function approach when the relief scheme function is concave. We demonstrate that the optimal retained loss function takes a layered form, shaped by the trade-off between government premium subsidies and relief assistance, and can be further characterized by an ordinary integro-differential equation. In particular, explicit solutions are obtained for VaR and general convex distortion risk measures. To provide further insights, we explore two nontrivial extensions: one investigates the design of the optimal safety loading from the insurer's perspective, while the other examines the impact of the government's budget constraint. Finally, we present numerical examples to illustrate and validate the main findings of the paper.
