标题： 高维条件因子模型的统一估计框架 显示英文标题

标题： A Unified Framework for Estimation of High-dimensional Conditional Factor Models

摘要： 本文提出了一种通过核范数正则化估计高维条件因子模型的通用框架。 我们建立了估计量的大样本性质，并提供了一种高效的计算算法来寻找估计量，以及一种交叉验证过程来选择正则化参数。 通用框架使我们能够以统一的方式估计各种条件因子模型，并快速得出新的渐近结果。 我们将该方法应用于分析个别美国股票收益的横截面，并发现施加同质性可能提高模型的样本外预测能力。 显示英文摘要

摘要： 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.