时间：2019年5月14日15:00-16:30
地点：南校区院系办公楼401室
报告人：杨海亮 教授 （香港大学统计与精算学系）
题目：Optimal Insurance Strategies: A Hybrid Deep Learning Markov Chain Approximation Approach
主办单位：广东外语外贸大学金融学院
内容介绍：This paper studies deep learning approaches to find optimal reinsurance and dividend strategies for insurance companies. Due to the randomness of the financial ruin time
to terminate the control processes, a Markov chain approximation-based iterative deep learning algorithm is developed to study this type of infinite-horizon optimal control problems. The optimal controls are approximated as deep neural networks in both cases of regular and singular types of dividend strategies. The framework of Markov chain approximation plays a key role in building the iterative equations and initialization of the algorithm. We implement this self-learning approach to approximate the optimal strategies and compare the learning results with existing analytical solutions.
Satisfactory computation efficiency and accuracy are achieved as presented in numerical examples.
报告人简介：Hailiang Yang, Ph.D., ASA, HonFIA, received his PhD degree from University of Alberta and Master in Actuarial Science from University of Waterloo. He joined the University of Hong Kong in 1996 and is currently a Professor in the Department of Statistics and Actuarial Science. Hailiang Yang’s research is on actuarial science and mathematical finance. He has worked with many leading figures in the field. He has supervised more than 20 research students, his graduate students are, in many cases, now well-known researchers in their own right. He is an editor of Insurance; Mathematics and Economics and associate editor of five other journals. He is an Associate of Society of Actuaries, and he was elected as an Honorary Fellow of the Institute and Faculty of Actuaries and a Corresponding Member of the Swiss Association of Actuaries in 2014. He is an Elected Member of the International Statistical Institute (ISI). He received an Outstanding Researcher Award from The University of Hong Kong in 2013-2014.
