A Synthetic Regression Model for Large Portfolio Allocation

主讲人：张文扬 英国约克大学首席教授

时间：2022年10月24日18：00

地点：腾讯会议 540 862 352

举办单位：数理学院

主讲人介绍：张文扬教授是英国一流大学约克大学的统计学首席教授，统计学三大国际顶尖期刊之一 the Annals of Statistics 的副主编，商务和经济统计方面的国际顶尖期刊 Journal of Business & Economic Statistics 的副主编。张文扬教授主要从事大数据分析，金融数据分析，高维数据分析，非参数建模、时间序列分析、空间数据分析，多层次建模，生存分析，结构方程模型等方向的研究。他在国际顶尖学术期刊发表了很多非常有影响的学术论文，他明升体育,明升m88备用ABC方法的一篇论文被引用超过3000多次。他曾先后在英国伦敦政治经济学院、英国 Kent 大学、英国 Bath 大学、英国 York 大学任教，现为英国 York 大学统计学首席教授。他曾是英国皇家统计学会科研委员会委员(历史上第三位华人担任该委员会委员)，曾经连续担任三届统计学三大国际顶尖期刊之一 Journal of the American Statistical Association 的副主编。

内容介绍：Portfolio allocation is an important topic in financial data analysis. In this talk, based on the mean-variance optimization principle, I will present asynthetic 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. I will show this intuitive conclusion is theoretically confirmed to be true by the asymptotic properties established. I will also show intensive simulation studies to compare the proposed method with the existing ones, and illustrate the proposed method works better. Finally, I will apply the proposed method to real data sets, and show very encouraging yielded returns.