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


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.