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Robust Estimation of Conditional Factor Models

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  • Qihui Chen

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.

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  • Qihui Chen, 2022. "Robust Estimation of Conditional Factor Models," Papers 2204.00801, arXiv.org, revised Apr 2022.
    • Handle: RePEc:arx:papers:2204.00801
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    References listed on IDEAS

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    1. Qihui Chen & Nikolai Roussanov & Xiaoliang Wang, 2021. "Semiparametric Conditional Factor Models: Estimation and Inference," Papers 2112.07121, arXiv.org, revised Sep 2023.
    2. Belloni, Alexandre & Chernozhukov, Victor & Chetverikov, Denis & Fernández-Val, Iván, 2019. "Conditional quantile processes based on series or many regressors," Journal of Econometrics, Elsevier, vol. 213(1), pages 4-29.
    3. Seung C. Ahn & Alex R. Horenstein, 2013. "Eigenvalue Ratio Test for the Number of Factors," Econometrica, Econometric Society, vol. 81(3), pages 1203-1227, May.
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