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

Buckle, M. and 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). pp. 542-557.

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
Keywords:Realized covariance, Microstructure, Wishart distribution.
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:http://dx.doi.org/10.1002/for.2310
Publisher 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.
Record Created:10 Jul 2014 13:05
Last Modified:18 Sep 2016 00:42

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