A Unified Framework for Estimation of High-dimensional Conditional Factor Models
Holder： Qihui Chen（Chinese University of Hong Kong (Shenzhen)）
Time：2024-03-28 15:10-17:00
Location：Room 217, Guanghua Building 2
Abstract:
This paper develops a general framework for estimation of high-dimensional conditional factor models via nuclear norm regularization. We establish large sample properties of the estimators, and provide an efficient computing algorithm for finding the estimators as well as a cross validation procedure for choosing the regularization parameter. The general framework allows us to estimate a variety of conditional factor models in a unified way and quickly deliver new asymptotic results. We apply the method to analyze the cross section of individual US stock returns, and find that imposing homogeneity may improve the model's out-of-sample predictability.
About the Speaker:
陈齐辉博士现任香港中文大学（深圳）经管学院经济学副教授，他于2017年从加利福尼亚大学圣地亚哥分校获得经济学博士学位。他主要研究领域为计量经济学、机器学习等。
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