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.
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.
|Keywords:||Realized covariance, Microstructure, Wishart distribution.|
|Full text:||(AM) Accepted Manuscript|
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|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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