统计与管理学院2015年学术报告第49期
【主 题】Solving the High-dimensional Markowitz Optimization Problem: When Sparse Regression Meets Random Matrix Theory
【报告人】Xinghua Zheng
香港科技大学
【时 间】 2015年11月13日（星期五）15:30－16:30
【地 点】 上海财经大学统计与管理学院大楼1208室
【语 言】 英文
【摘 要】To solve the high-dimensional Markowitz optimization problem, a new approach combining sparse regression and estimation of maximum expected return for a given risk level based on random matrix theory is proposed. We prove that under some sparsity assumptions on the underlying optimal portfolio, our estimated portfolio, the Response-estimated Sparse Regression Portfolio (ReSReP), asymptotically reaches the maximum expected return and meanwhile satisfies the risk constraint. To the best of our knowledge, this is the first time that these two goals are simultaneously achieved in the high-dimensional setting. The superior properties of ReSReP are demonstrated via simulation and extensive empirical studies. Based on joint work with Mengmeng AO and Yingying Li
【邀请人】冯兴东
