High dimensional covariance matrix estimation using a factor model

Jianqing Fan, Yingying Fan, Jinchi Lv

Research output: Contribution to journalArticle

247 Scopus citations

Abstract

High dimensionality comparable to sample size is common in many statistical problems. We examine covariance matrix estimation in the asymptotic framework that the dimensionality p tends to ∞ as the sample size n increases. Motivated by the Arbitrage Pricing Theory in finance, a multi-factor model is employed to reduce dimensionality and to estimate the covariance matrix. The factors are observable and the number of factors K is allowed to grow with p. We investigate the impact of p and K on the performance of the model-based covariance matrix estimator. Under mild assumptions, we have established convergence rates and asymptotic normality of the model-based estimator. Its performance is compared with that of the sample covariance matrix. We identify situations under which the factor approach increases performance substantially or marginally. The impacts of covariance matrix estimation on optimal portfolio allocation and portfolio risk assessment are studied. The asymptotic results are supported by a thorough simulation study.

Original languageEnglish (US)
Pages (from-to)186-197
Number of pages12
JournalJournal of Econometrics
Volume147
Issue number1
DOIs
StatePublished - Nov 1 2008

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

Keywords

  • Asymptotic properties
  • Covariance matrix estimation
  • Diverging dimensionality
  • Factor model
  • Portfolio management

Fingerprint Dive into the research topics of 'High dimensional covariance matrix estimation using a factor model'. Together they form a unique fingerprint.

  • Cite this