Using principal component analysis to estimate a high dimensional factor model with high-frequency data

Yacine Aït-Sahalia, Dacheng Xiu

Research output: Contribution to journalArticle

44 Scopus citations

Abstract

This paper constructs an estimator for the number of common factors in a setting where both the sampling frequency and the number of variables increase. Empirically, we document that the covariance matrix of a large portfolio of US equities is well represented by a low rank common structure with sparse residual matrix. When employed for out-of-sample portfolio allocation, the proposed estimator largely outperforms the sample covariance estimator.

Original languageEnglish (US)
Pages (from-to)384-399
Number of pages16
JournalJournal of Econometrics
Volume201
Issue number2
DOIs
StatePublished - Dec 2017

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

Keywords

  • High-dimensional data
  • High-frequency data
  • Latent factor model
  • Portfolio optimization
  • Principal components

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