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发布时间:2022-05-16文章来源: 浏览次数:

报告题目:Distributionally Robust Goal-Reaching Optimization in the Presence of Background Risk  


地点:腾讯会议 ID501 219 396


In this talk, we examine the effect of background risk on portfolio selection and optimal reinsurance design under the criterion of maximizing the probability of reaching a goal. Following the literature, we adopt dependence uncertainty to model the dependence ambiguity between financial risk (or insurable risk) and background risk. Because the goal-reaching objective function is nonconcave, these two problems bring highly unconventional and challenging issues for which classical optimization techniques often fail. Using a quantile formulation method, we derive the optimal solutions explicitly. The results show that the presence of background risk does not alter the shape of the solution but instead changes the parameter value of the solution. Finally, numerical examples are given to illustrate the results and verify the robustness of our solutions. (This is a joint work with Zuo Quan Xu and Shengchao Zhuang)


池义春,中央财经大学保险学院、中国精算研究院研究员。现主要从事精算学与风险管理中的风险理论、最优保险/再保险设计以及变额年金的定价和对冲等研究,主持过三项国家自然科学基金项目和一项教育部人文社科重点研究基地重大课题,在国际著名的精算学杂志Insurance: Mathematics and Economics、ASTIN Bulletin、North American Actuarial Journal、Scandinavian Actuarial Journal,金融数学杂志Finance and Stochastics,运筹学杂志European Journal of Operational Research上发表三十多篇学术论文。2012年荣获北美产险精算学会Charles A. Hachemeister奖,2015年破格晋升为研究员。

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