Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data

43 Pages Posted: 7 Oct 2015 Last revised: 11 Oct 2016

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

Date Written: October 7, 2016

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.

Keywords: High-dimensional data, high-frequency, latent factor model, principal components, portfolio optimization

JEL Classification: C13, C14, C55, C58, G01

Suggested Citation

Ait-Sahalia, Yacine and Xiu, Dacheng, Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data (October 7, 2016). Chicago Booth Research Paper No. 15-43, Available at SSRN: https://ssrn.com/abstract=2669506 or http://dx.doi.org/10.2139/ssrn.2669506

Yacine Ait-Sahalia

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 (Contact Author)

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

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