Principal Component Analysis of High-Frequency Data

Yacine Aït-Sahalia, Dacheng Xiu

Research output: Contribution to journalArticlepeer-review

135 Scopus citations


We develop the necessary methodology to conduct principal component analysis at high frequency. We construct estimators of realized eigenvalues, eigenvectors, and principal components, and provide the asymptotic distribution of these estimators. Empirically, we study the high-frequency covariance structure of the constituents of the S&P 100 Index using as little as one week of high-frequency data at a time, and examines whether it is compatible with the evidence accumulated over decades of lower frequency returns. We find a surprising consistency between the low- and high-frequency structures. During the recent financial crisis, the first principal component becomes increasingly dominant, explaining up to 60% of the variation on its own, while the second principal component drives the common variation of financial sector stocks. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)287-303
Number of pages17
JournalJournal of the American Statistical Association
Issue number525
StatePublished - Jan 2 2019

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


  • Eigenvalue
  • Eigenvector
  • High frequency
  • Itô semimartingale
  • Principal components
  • Spectral function


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