Principal Component Analysis of High Frequency Data

53 Pages Posted: 28 Sep 2015

See all articles by Yacine Ait-Sahalia

Yacine Ait-Sahalia

Princeton University - Department of Economics; National Bureau of Economic Research (NBER)

Dacheng Xiu

University of Chicago - Booth School of Business

Multiple version iconThere are 2 versions of this paper

Date Written: September 2015

Abstract

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. The explanatory power of the high frequency principal components varies over time. 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.

Suggested Citation

Ait-Sahalia, Yacine and Xiu, Dacheng, Principal Component Analysis of High Frequency Data (September 2015). NBER Working Paper No. w21584. Available at SSRN: https://ssrn.com/abstract=2666352

Yacine Ait-Sahalia (Contact Author)

Princeton University - Department of Economics ( email )

Fisher Hall
Princeton, NJ 08544
United States
609-258-4015 (Phone)
609-258-5398 (Fax)

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Dacheng Xiu

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

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