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Robust Covariance Estimation for Approximate Factor Models.

Author(s): Fan, Jianqing; Wang, Weichen; Zhong, Yiqiao

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dc.contributor.authorFan, Jianqing-
dc.contributor.authorWang, Weichen-
dc.contributor.authorZhong, Yiqiao-
dc.identifier.citationFan, Jianqing, Wang, Weichen, Zhong, Yiqiao. (2019). Robust Covariance Estimation for Approximate Factor Models.. Journal of econometrics, 208 (1), 5 - 22. doi:10.1016/j.jeconom.2018.09.003en_US
dc.description.abstractIn 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 only bounded fourth moment, 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.en_US
dc.format.extent5 - 22en_US
dc.relation.ispartofJournal of econometricsen_US
dc.rightsAuthor's manuscripten_US
dc.titleRobust Covariance Estimation for Approximate Factor Models.en_US
dc.typeJournal Articleen_US

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