High dimensional minimum variance portfolio estimation under statistical factor models

Yi Ding, Yingying Li, Xinghua Zheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

23 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)502-515
Number of pages14
JournalJournal of Econometrics
Issue number1
Publication statusPublished - May 2021
Externally publishedYes


  • Factor model
  • High dimension
  • Minimum variance portfolio
  • Principal component analysis

ASJC Scopus subject areas

  • Economics and Econometrics


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