Universität zu Köln

Gribisch, Bastian (2018). A latent dynamic factor approach to forecasting multivariate stock market volatility. Empir. Econ., 55 (2). S. 621 - 652. HEIDELBERG: PHYSICA-VERLAG GMBH & CO. ISSN 1435-8921

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Abstract

This paper proposes a latent dynamic factor model for high-dimensional realized covariance matrices of stock returns. The approach is based on the matrix logarithm and combines common latent factors driven by HAR processes and idiosyncratic autoregressive dynamics. The model accounts for positive definiteness of covariance matrices without imposing parametric restrictions. Simulated Bayesian parameter estimates are obtained using basic Markov chain Monte Carlo methods. An empirical application to 5-dimensional and 30-dimensional realized covariance matrices shows remarkably good forecasting results, in-sample and out-of-sample.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Gribisch, Bastian UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-175640
DOI:	10.1007/s00181-017-1278-6
Journal or Publication Title:	Empir. Econ.
Volume:	55
Number:	2
Page Range:	S. 621 - 652
Date:	2018
Publisher:	PHYSICA-VERLAG GMBH & CO
Place of Publication:	HEIDELBERG
ISSN:	1435-8921
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language APPROXIMATE FACTOR MODELS; STOCHASTIC VOLATILITY; REALIZED VOLATILITY; NUMBER; ARCH; COVARIANCE Multiple languages Economics; Social Sciences, Mathematical Methods Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/17564