Incorporating Global Industrial Classification Standard Into Portfolio Allocation: A Simple Factor-Based Large Covariance Matrix Estimator With High-Frequency Data

Jianqing Fan, Alex Furger, Dacheng Xiu

Research output: Contribution to journalArticlepeer-review

69 Scopus citations

Abstract

We document a striking block-diagonal pattern in the factor model residual covariances of the S&P 500 Equity Index constituents, after sorting the assets by their assigned Global Industry Classification Standard (GICS) codes. Cognizant of this structure, we propose combining a location-based thresholding approach based on sector inclusion with the Fama-French and SDPR sector Exchange Traded Funds (ETF’s). We investigate the performance of our estimators in an out-of-sample portfolio allocation study. We find that our simple and positive-definite covariance matrix estimator yields strong empirical results under a variety of factor models and thresholding schemes. Conversely, we find that the Fama-French factor model is only suitable for covariance estimation when used in conjunction with our proposed thresholding technique. Theoretically, we provide justification for the empirical results by jointly analyzing the in-fill and diverging dimension asymptotics.

Original languageEnglish (US)
Pages (from-to)489-503
Number of pages15
JournalJournal of Business and Economic Statistics
Volume34
Issue number4
DOIs
StatePublished - Oct 1 2016

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics
  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Statistics, Probability and Uncertainty

Keywords

  • Big data
  • Concentration inequality
  • GICS
  • High-frequency factor model
  • Location-based thresholding
  • Low rank plus sparse
  • Positive-definite
  • Precision matrix
  • SDPR sector ETF’s

Fingerprint

Dive into the research topics of 'Incorporating Global Industrial Classification Standard Into Portfolio Allocation: A Simple Factor-Based Large Covariance Matrix Estimator With High-Frequency Data'. Together they form a unique fingerprint.

Cite this