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

Buckle, Mike, Chen, Jing ORCID: https://orcid.org/0000-0001-7135-2116 and Williams, Julian 2014. How predictable are equity covariance matrices? Evidence from high-frequency data for four markets. Journal of Forecasting 33 (7) , pp. 542-557. 10.1002/for.2310

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Publisher:	Wiley
ISSN:	0277-6693
Date of Acceptance:	27 May 2014
Last Modified:	28 Oct 2022 10:20
URI:	https://orca.cardiff.ac.uk/id/eprint/77914

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