Estimation in Semiparametric Quantile Factor Models

TL;DR

This paper introduces a new estimation method for semiparametric quantile factor models that is robust to moments and dependence, demonstrated through application to stock return data.

Contribution

The paper develops a novel estimation approach for semiparametric quantile factor models with robust inference tools for complex dependence structures.

Findings

Effective estimation in stock return data

Robust inference under weak dependence

Applicability to financial data analysis

Abstract

We propose an estimation methodology for a semiparametric quantile factor panel model. We provide tools for inference that are robust to the existence of moments and to the form of weak cross-sectional dependence in the idiosyncratic error term. We apply our method to daily stock return data.