报告题目：Generative Adversarial Network Stopping for Robust Surrendering

报告时间：2026年10月30日 16：00-17：00

地点：动漫a片 106会议室

报告人：王玲

摘要：Accurate estimation of robust surrender behavior under model ambiguity is crucial for insurance companies. Existing analytical approaches are largely restricted to the Black–Scholes framework and low-dimensional settings. In this paper, we propose a generative adversarial network (GAN)–based approach to determine robust stopping rules for lapsing insurance products, with a particular focus on variable annuities. Our method is fully data-driven and readily extends to high-dimensional problems. We demonstrate its effectiveness using both simulated data and a real-world application.

报告人简介：王玲，中央财经大学保险学院、中国精算研究院教授。2022年7月博士毕业于香港中文大学统计系。研究方向为保险精算中的随机控制问题及机器学习在保险精算中的应用，主持国家自然科学基金青年项目和面上项目各一项，在国际权威期刊Journal of Banking & Finance，European Journal of Operational Research，Journal of Risk and Insurance，Insurance: Mathematics and Economics，Scandinavian Actuarial Journal等发表多篇学术论文。