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

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

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