High dimensional minimum variance portfolio estimation under statistical factor models

Yi Ding, Yingying Li and Xinghua Zheng

Journal of Econometrics, 2021, vol. 222, issue 1, 502-515

Abstract: We propose a high dimensional minimum variance portfolio estimator under statistical factor models, and show that our estimated portfolio enjoys sharp risk consistency. Our approach relies on properly integrating ℓ1 constraint on portfolio weights with an appropriate covariance matrix estimator. In terms of covariance matrix estimation, we extend the theoretical results of POET (Fan et al., 2013) to a setting that is coherent with principal component analysis. Simulation and extensive empirical studies on S&P 100 Index constituent stocks demonstrate favorable performance of our MVP estimator compared with benchmark portfolios.

Keywords: Minimum variance portfolio; High dimension; Principal component analysis; Factor model (search for similar items in EconPapers)
JEL-codes: C13 C55 C58 G11 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:222:y:2021:i:1:p:502-515

DOI: 10.1016/j.jeconom.2020.07.013

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