High dimensional minimum variance portfolio estimation under statistical factor models

By Yi Ding, Yingying Li, Xinghua Zheng. Journal of Econometrics, 2020

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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 l 1 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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/1139594cb4a7a86309a4bd98f63f686d1aea7f06