论文标题
有条件因素模型的强大估计
Robust Estimation of Conditional Factor Models
论文作者
论文摘要
本文开发了条件分位数因子模型的估计和推理方法。我们首先引入一个简单的筛估计，并在$ n $下建立估计器的渐近性能。然后，我们提供一个自举程序来估计估计器的分布。我们还为因素数量提供了两个一致的估计器。这些方法不仅可以估计利用特征的资产回报分布的条件因素结构，而且还可以对条件因子模型进行强大的推断，从而使我们能够分析具有沉重尾巴的资产回报的横截面。我们应用了分析美国各个股票收益的横截面的方法。
This paper develops estimation and inference methods for conditional quantile factor models. We first introduce a simple sieve estimation, and establish asymptotic properties of the estimators under large $N$. We then provide a bootstrap procedure for estimating the distributions of the estimators. We also provide two consistent estimators for the number of factors. The methods allow us not only to estimate conditional factor structures of distributions of asset returns utilizing characteristics, but also to conduct robust inference in conditional factor models, which enables us to analyze the cross section of asset returns with heavy tails. We apply the methods to analyze the cross section of individual US stock returns.
