报告人：周超 博士

时间：2019年11月6日（星期三）下午3:30-4:30

地点：T2-102 

语言：英语

报告摘要：

We establish a Nash equilibrium in a market with N agents with CARA utility and relative performance criteria, when model parameter is partially observed. Each investor has a Gaussian prior belief on the return rate of the risky asset. The prior belief can be heterogeneous. We characterize the optimal investment strategy for stochastic return rate by a forward-backward stochastic differential equations (FBSDE). We solve the FBSDEs using a deep learning method and demonstrate the efficiency and accuracy by comparing with the numerical solution from PDE for linear filter case. We find that while investors trade more aggressively under relative performance, the effect is mitigated by partial information. 

讲者简介：

Dr. Zhou Chao is an Assistant Professor in the Department of Mathematics at National University of Singapore (NUS). He is also an affiliated researcher in the Institute of Operations Research & Analytics and Suzhou Research Institute at NUS.  He received his M.Sc. in Financial Mathematics from Paris Dauphine University. He obtained the Engineering degree and the Ph.D. in Applied Mathematics from École Polytechnique Paris. His research interests are quantitative finance, stochastic control, backward stochastic differential equations (BSDE) and deep learning methods in finance. He published several papers in The Annals of Applied Probability, The Annals of Probability, and Mathematical Finance.