A Synthetic Regression Model for Large Portfolio Allocation
发布时间: 2022-04-07 浏览次数: 10

报告标题:A Synthetic Regression Model for Large Portfolio Allocation

主讲人:李高荣 教授

专家简介:李高荣,北京师范大学统计学院教授、博导。2007年在北京工业大学获得了博士学位,2007-2009年在华东师范大学金融与统计学院做博士后研究,2016-2017年在美国南加州大学(USC)商学院做博士后研究,多次到香港浸会大学、新加坡南洋理工大学、美国南加州大学(USC)和香港城市大学进行学术访问和交流。目前,已经主持多项国家自然科学基金项目和北京市自然科学基金项目。

主要从事高维统计、非参数统计和复杂数据分析、模型和变量选择、统计学习、因果推断、纵向数据分析、测量误差和经验似然等方面的研究。出版著作《纵向数据半参数模型》、《现代测量误差模型》、《多元统计分析》共三部,在《The Annals of Statistics》、《Journal of the American Statistical Association》、《Journal of Business & Economic Statistics》、《Statistics and Computing》、《Statistica Sinica》、《CSDA》、《中国科学:数学》和《统计研究》等国内外重要学术期刊发表学术论文90多篇。

报告摘要:Portfolio allocation is an important topic in financial data analysis. In this article, based on the mean-variance optimization principle, we propose a synthetic regression model for construction of portfolio allocation, and an easy to implement approach to generate the synthetic sample for the model. Compared with the regression approach in existing literature for portfolio allocation, the proposed method of generating the synthetic sample provides more accurate approximation for the synthetic response variable when the number of assets under consideration is large. Due to the embedded leave-one-out idea, the synthetic sample generated by the proposed method has weaker within sample correlation, which makes the resulting portfolio allocation more close to the optimal one. This intuitive conclusion is theoretically confirmed to be true by the asymptotic properties established in this article. We have also conducted intensive simulation studies in this article to compare the proposed method with the existing ones, and found the proposed method works better. Finally, we apply the proposed method to real datasets. The yielded returns look very encouraging.

腾讯会议:503-379-130

报告时间:2022414号(周四),上午900-1000