论文标题
估计高维条件因子模型的统一框架
A Unified Framework for Estimation of High-dimensional Conditional Factor Models
论文作者
论文摘要
本文开发了一个通用框架，用于通过核规范正规化估算高维条件因子模型。我们建立了估计器的较大样本属性，并提供了用于查找估计器的有效计算算法以及选择正则化参数的交叉验证程序。一般框架使我们能够以统一的方式估算各种条件因素模型，并迅速提供新的渐近结果。我们应用了分析美国各个股票回报的横截面的方法，并发现施加同质性可以改善模型的样本外可预测性。
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.
