Robust covariance estimation for approximate factor models

Jianqing Fan, Weichen Wang and Yiqiao Zhong

Journal of Econometrics, 2019, vol. 208, issue 1, 5-22

Abstract: In this paper, we study robust covariance estimation under the approximate factor model with observed factors. We propose a novel framework to first estimate the initial joint covariance matrix of the observed data and the factors, and then use it to recover the covariance matrix of the observed data. We prove that once the initial matrix estimator is good enough to maintain the element-wise optimal rate, the whole procedure will generate an estimated covariance with desired properties. For data with bounded fourth moments, we propose to use adaptive Huber loss minimization to give the initial joint covariance estimation. This approach is applicable to a much wider class of distributions, beyond sub-Gaussian and elliptical distributions. We also present an asymptotic result for adaptive Huber’s M-estimator with a diverging parameter. The conclusions are demonstrated by extensive simulations and real data analysis.

Keywords: Robust covariance matrix; Approximate factor model; M-estimator (search for similar items in EconPapers)
JEL-codes: C13 C38 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:208:y:2019:i:1:p:5-22

DOI: 10.1016/j.jeconom.2018.09.003

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