@article{f0e72c01ea8f431ba6a049899951c9cd,
title = "High dimensional minimum variance portfolio estimation under statistical factor models",
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 = "Factor model, High dimension, Minimum variance portfolio, Principal component analysis",
author = "Yi Ding and Yingying Li and Xinghua Zheng",
note = "Funding Information: Research is supported in part by grants RGCGRF16518716, GRF16502118, T31-604/18-N and NSFC19BM03.Research is supported in part by RGC grants GRF163053115, GRF16304317, GRF16304019 and T31-604/18-N.We thank the guest editor and two anonymous referees for their thoughtful and constructive suggestions. We also thank the participants of SoFiE 2017 annual conference for helpful discussions on a preliminary version of this paper. Research is partially supported by grants RGC, Hong KongGRF163053115, GRF16518716, GRF16304317, GRF16502118, GRF16304019, T31-604/18-N and NSFC19BM03. Funding Information: We thank the guest editor and two anonymous referees for their thoughtful and constructive suggestions. We also thank the participants of SoFiE 2017 annual conference for helpful discussions on a preliminary version of this paper. Research is partially supported by grants RGC, Hong Kong GRF163053115 , GRF16518716 , GRF16304317 , GRF16502118 , GRF16304019 , T31-604/18-N and NSFC19BM03 . Publisher Copyright: {\textcopyright} 2020 Elsevier B.V.",
year = "2021",
month = may,
doi = "10.1016/j.jeconom.2020.07.013",
language = "English",
volume = "222",
pages = "502--515",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier B.V.",
number = "1",
}