第十九讲——Evolution of Portfolio Optimization
2017年12月11日，星期一下午15:00在大学城771771威尼斯.cm大全行政东楼前座412会议室。澳门大学舒连杰教授为各位学子带来了精彩的学术讲座--《Evolution of Portfolio Optimization》——暨羊城讲坛第十九讲。此次讲座由771771威尼斯.cm大全张兴发副教授主持，相关专业的师生也参加了此次讲座。在这次报告中，舒连杰教授给我们讲述了传统方法和一些现代高维统计方法。此外，还提出了一种基于样本特征值收缩的新方法，旨在减少样本特征值的过分散问题。实证研究表明，在大多数实际数据集中，所提出的方法通常可以实现比现有组合策略更低的样本外方差和更高的夏普指数。
个人介绍
Dr. Lianjie Shu is currently Professor in Faculty of Business Administration at University of Macau. He received his Bachelor degree in Mechanical Engineering and Automation from Xi'an Jiao Tong University in 1998, and his Ph.D. in Industrial Engineering and Engineering Management from the Hong Kong University of Science and Technology (HKUST) in 2002. He currently serves an Associate Editor on Journal of Statistical Computation and Simulation and a Senior Editor on Journal of Industrial and Production Engineering. He is a senior member of American Society for Quality (ASQ), and also a senior member of Institute of Industrial Engineers (IIE). His recent research interests include portfolio optimization, high-dimensional statistics and monitoring, quality control and management, and statistical computing.
摘要:
The classical mean-variance portfolio model was originally proposed by Markowitz (1952). It has now undergone 65 years of development. In the mean-variance portfolio model, the mean and the covariance matrix of asset returns are often unknown and need to be estimated. However, the sampling errors have adverse effects on portfolio performance, leading to sub-optimal and unstable portfolio weights. Various strategies have been proposed to reduce the sampling errors. In this talk, both the traditional methods and some modern high-dimensional statistical approaches are widely reviewed. Moreover, a new approach based on the shrinkage of the sample eigenvalues is proposed, aimed at reducing the over-dispersion issue of the sample eigenvalues. The empirical studies show that the proposed approach can often achieve a lower out-of-sample variance and higher Sharpe ratio than the existing portfolio strategies in most real data ets.
