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:	Ait-Sahalia, Yacine
Other Persons:	Xiu, Dacheng (contributor)
Publisher:	[2016]: [S.l.] : SSRN
Subject:	Faktorenanalyse \| Factor analysis \| Zeitreihenanalyse \| Time series analysis \| Volatilität \| Volatility \| Theorie \| Theory \| Schätzung \| Estimation

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Extent:	1 Online-Ressource (43 p)
Series:	Chicago Booth Research Paper ; No. 15-43
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments October 7, 2016 erstellt
Other identifiers:	10.2139/ssrn.2669506 [DOI]
Classification:	C13 - Estimation ; C14 - Semiparametric and Nonparametric Methods ; c55 ; c58 ; G01 - Financial Crises
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10013003349