报 告 人：郑星华

　　报告地点：数学与统计学院515室

　　报告时间：2014年05月29日星期四16:00-17:00

　　报告简介：

　　We consider the high-dimensional Markowitz optimization problem. A new approach combining regression and estimation of optimal returns is proposed to solve the problem. We prove that under some sparsity assumptions on the underlying optimal portfolio, our solution asymptotically yields the theoretical optimal returns, and in the meanwhile satisfies the risk constraint. To the best of our knowledge, this is the first time that both these goals are achieved. We further conduct simulation and empirical studies to compare our method with some benchmark methods, including the equally weighted portfolio, the bootstrap-corrected estimator by Bai, Liu and Wong (2009) and the covariance-shrinkage method by Ledoit and Wolf (2003). The results demonstrate substantial advantage of our method, which attains high level of returns while keeping the risk well controlled by the given constraint. Based on joint work with Mengmeng Ao and Yingying Li.

　　主讲人简介：

　　Dr.Xinghua Zheng is an Assistant Professor in the Department of Information Systems, Business Statistics and Operations Management at the Hong Kong University of Science and Technology. He received his Ph.D. in Statistics from the University of Chicago in 2008, and worked as a postdoctoral fellow at the University of British Columbia in 2009. Dr. Zheng’s research interests lie in high dimensional statistics, stochastic processes, and financial econometrics. His research has been published in leading statistics, probability and econometrics journals, including Annals of Statistics, Annals of Probability, Annals of Applied Probability, Probability Theory and Related Fields, and Econometric Theory. Dr.Zheng has been invited to present his research at many universities and international conferences.