Universität zu Köln

Gribisch, Bastian and Stollenwerk, Michael (2020). Dynamic principal component CAW models for high-dimensional realized covariance matrices. Quant. Financ., 20 (5). S. 799 - 822. ABINGDON: ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD. ISSN 1469-7696

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Abstract

We propose a new dynamic principal component CAW model (DPC-CAW) for time-series of high-dimensional realized covariance matrices of asset returns (up to 100 assets). The model performs a spectral decomposition of the scale matrix of a central Wishart distribution and assumes independent dynamics for the principal components' variances and the eigenvector processes. A three-step estimation procedure makes the model applicable to high-dimensional covariance matrices. We analyze the finite sample properties of the estimation approach and provide an empirical application to realized covariance matrices for 100 assets. The DPC-CAW model has particularly good forecasting properties and outperforms its competitors for realized covariance matrices.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Gribisch, Bastian UNSPECIFIED UNSPECIFIED UNSPECIFIED Stollenwerk, Michael UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-348049
DOI:	10.1080/14697688.2019.1701197
Journal or Publication Title:	Quant. Financ.
Volume:	20
Number:	5
Page Range:	S. 799 - 822
Date:	2020
Publisher:	ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
Place of Publication:	ABINGDON
ISSN:	1469-7696
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language ECONOMETRIC-ANALYSIS; LONG-MEMORY; MULTIVARIATE; VOLATILITY; REGRESSION Multiple languages Business, Finance; Economics; Mathematics, Interdisciplinary Applications; Social Sciences, Mathematical Methods Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/34804