5月14日下午，香港大学统计与精算学系杨海亮教授受邀做客广外金融论坛第三十八讲，在公司南校区院系办公楼401室做了主题为“Optimal Insurance Strategies: A Hybrid Deep Learning Markov Chain Approximation Approach”的学术讲座。本次讲座由基地研究员、青年珠江学者、博士生导师姚海祥教授主持，基地研究员邓超博士、张琰博士也参加了本次讲座。
杨海亮教授做学术报告
杨海亮教授介绍了保险公司寻找最优再保险和分红策略的深度学习方法。针对金融破产终止控制过程的随机性，他提出了一种基于马尔可夫链逼近的迭代深度学习算法来研究这类无限视界最优控制问题。他指出，在常规和奇异类型的股利分配策略中，最优控制近似为深层神经网络。马尔可夫链近似的框架在迭代方程的建立和算法的初始化中起着关键作用。他向我们展示了用这种自学习方法来得到最优策略，其计算的效率和精度相较于现有的分析解都有着更好的表现。
讲座现场
杨海亮教授的讲座给了公司师生诸多启发，并在现场与公司师生进行了深入的探讨与交流，获得现场的热烈掌声。
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.
