How predictable are equity covariance matrices? Evidence from high frequency data for four markets

Buckle, M.; Chen, J.; Williams, J.

doi:10.1002/for.2310

How predictable are equity covariance matrices? Evidence from high frequency data for four markets

Buckle, M.; Chen, J.; Williams, J.

Authors

M. Buckle

J. Chen

Professor Julian Williams julian.williams@durham.ac.uk
Head of Department

Abstract

Most pricing and hedging models rely on the long-run temporal stability of a sample covariance matrix. Using a large dataset of equity prices from four countries—the USA, UK, Japan and Germany—we test the stability of realized sample covariance matrices using two complementary approaches: a standard covariance equality test and a novel matrix loss function approach. Our results present a pessimistic outlook for equilibrium models that require the covariance of assets returns to mean revert in the long run. We find that, while a daily first-order Wishart autoregression is the best covariance matrix-generating candidate, this non-mean-reverting process cannot capture all of the time series variation in the covariance-generating process.

Citation

Buckle, M., Chen, J., & Williams, J. (2014). How predictable are equity covariance matrices? Evidence from high frequency data for four markets. Journal of Forecasting, 33(7), 542-557. https://doi.org/10.1002/for.2310

Journal Article Type	Article
Publication Date	Nov 1, 2014
Deposit Date	Jul 2, 2014
Publicly Available Date	Sep 8, 2016
Journal	Journal of Forecasting
Print ISSN	0277-6693
Electronic ISSN	1099-131X
Publisher	Wiley
Peer Reviewed	Peer Reviewed
Volume	33
Issue	7
Pages	542-557
DOI	https://doi.org/10.1002/for.2310
Keywords	Realized covariance, Microstructure, Wishart distribution.
Public URL	https://durham-repository.worktribe.com/output/1424120

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Copyright Statement
This is the peer reviewed version of the following article: Buckle M. Chen J. and Williams J. (2014), How Predictable Are Equity Covariance Matrices? Evidence from High-Frequency Data for Four Markets, Journal of Forecasting, 33 (7): 542–557 which has been published in final form at http://dx.doi.org/10.1002/for.2310. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.