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

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

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Year of publication:	2016
Authors:	Aït-Sahalia, Yacine
Other Persons:	Xiu, Dacheng (contributor)
Publisher:	[2016]: [S.l.] : SSRN
Subject:	Faktorenanalyse \| Factor analysis \| Hauptkomponentenanalyse \| Principal component analysis \| Theorie \| Theory \| CAPM \| Zeitreihenanalyse \| Time series analysis

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