Factor State-Space Models for High-Dimensional Realized Covariance Matrices of Asset Returns

46 Pages Posted: 13 Nov 2018 Last revised: 16 Nov 2018

See all articles by Bastian Gribisch

Bastian Gribisch

University of Cologne - Department of Econometrics and Statistics

Jan Patrick Hartkopf

University of Cologne

Roman Liesenfeld

University of Cologne, Department of Economics

Date Written: November 13, 2018

Abstract

We propose a dynamic factor state-space model for high-dimensional covariance matrices of asset returns. It uses observed risk factors and assumes that the latent covariance matrix of assets and factors is observed through their realized covariance matrix with a Wishart measurement density. The imposed strict factor structure allows for dynamics in the covariances of the factors and the residual components as well as in the factor loadings. The model structure facilitates inference using simple Bayesian MCMC procedures making the approach scalable w.r.t. the number of assets. An empirical application shows that the model performs very well in-and-out-of-sample.

Keywords: factor model, realized covariance, state-space model, bayesian inference, wishart distribution

JEL Classification: C32, C38, C51, C58, G17

Suggested Citation

Gribisch, Bastian and Hartkopf, Jan Patrick and Liesenfeld, Roman, Factor State-Space Models for High-Dimensional Realized Covariance Matrices of Asset Returns (November 13, 2018). Available at SSRN: https://ssrn.com/abstract=3283742 or http://dx.doi.org/10.2139/ssrn.3283742

Bastian Gribisch (Contact Author)

University of Cologne - Department of Econometrics and Statistics ( email )

Albertus-Magnus-Platz
Cologne, 50923
Germany

Jan Patrick Hartkopf

University of Cologne ( email )

Albertus Magnus Platz
Cologne, GA NRW 50923
Germany

Roman Liesenfeld

University of Cologne, Department of Economics ( email )

Albertus-Magnus-Platz
D-50931 Köln
Germany

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