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
Date Written: October 7, 2016
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
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