Robust Estimation of Conditional Factor Models

TL;DR

This paper introduces robust estimation and inference methods for conditional quantile factor models, enabling analysis of asset return distributions with heavy tails using characteristics.

Contribution

It develops a simple sieve estimation approach, provides bootstrap inference, and offers consistent estimators for the number of factors in conditional models.

Findings

Effective estimation of conditional factor structures in heavy-tailed asset returns

Bootstrap methods for inference on factor models

Application to US stock return data

Abstract

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.