报告题目：Unified Framework of Mean-Field Formulations for Optimal Multi-period Mean-Variance Portfolio Selection

报 告 人：Dr. Xun Li（香港理工大学）

报告时间：2014年4月25日（周五）下午16：00

报告地点：览秀楼211

报告简介（Abstract）：

When a dynamic optimization problem is not decomposable by a stage-wise backward recursion, it is nonseparable in the sense of dynamic programming. The classical dynamic programming-based optimal stochastic control methods would fail in such nonseparable situations as the principle of optimality no longer applies.

Among these notorious nonseparable problems, the dynamic mean-variance portfolio selection formulation had posed a great challenge to our research community until recently. Different from the existing literature that invokes embedding schemes and auxiliary parametric formulations to solve the dynamic mean-variance portfolio selection formulation, we propose in this paper a novel mean-field framework that offers a more efficient modeling tool and a more accurate solution scheme in tackling directly the issue of nonseparability and deriving the optimal policies analytically for the multi-period mean-variance-type portfolio selection problems.

报告人简介：

Dr. Xun Li received received the B.S., M.S. degrees in 1992, 1995, respectively, from the Department of Mathematics, Shanghai University of Science and Technology, and the Department of Mathematics, Shanghai University, China. He completed Ph.D. degree in Operations Research and Financial Engineering in November 2000 from  Department of Systems Engineering and Engineering Management at the Chinese University of Hong Kong, and he stayed with the same department as a postdoctoral research fellow until 2001. From 2001 to 2003, he was a postdoctoral fellow in the Mathematical and Computational Finance Laboratory at University of Calgary. From 2003 to 2007, he was a visiting fellow in the Department of Mathematics at the National University of Singapore.  He joined the Department of Applied Mathematics at the Hong Kong Polytechnic University as an Assistant Professor in 2007 and is currently an Associate Professor. His main research areas are applied probability and stochastic control with financial applications and has published in journals such as SIAM Journal on Control and Optimization, Annals of Applied Probability, IEEE Transactions on Automatic Control, Automatica and Annals of Finance.