How Predictable are Equity Covariance Matrices? Evidence from High Frequency Data for Four Markets

48 Pages Posted: 31 Jul 2012

See all articles by Jing Chen

Jing Chen

Cardiff University - School of Mathematics

Julian M. Williams

Durham Business School

Mike Buckle

University of Wales, Swansea - School of Business and Economics

Date Written: July 24, 2012

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 US, UK, Japan and Germany, we test the rolling 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 pessmistic outlook for equilibrium models that require the covariance of assets returns to mean revert in the long run. We find that whilst, 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.

Keywords: Realized Covariance, Microstructure, Wishart Distribution

JEL Classification: G14, G15, G17

Suggested Citation

Chen, Jing and Williams, Julian M. and Buckle, Mike J., How Predictable are Equity Covariance Matrices? Evidence from High Frequency Data for Four Markets (July 24, 2012). Available at SSRN: https://ssrn.com/abstract=2120094 or http://dx.doi.org/10.2139/ssrn.2120094

Jing Chen

Cardiff University - School of Mathematics ( email )

Senghennydd Road
Cardiff, CF24 4AG
United Kingdom

Julian M. Williams (Contact Author)

Durham Business School ( email )

Mill Hill Lane
Durham, Durham DH1 3LB
United Kingdom

Mike J. Buckle

University of Wales, Swansea - School of Business and Economics ( email )

Singleton Park
Swansea, Wales SA2 8PP
United Kingdom

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