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How Predictable Are Equity Covariance Matrices? Evidence from High‐Frequency Data for Four Markets
Author(s) -
Buckle Mike,
Chen Jing,
Williams Julian
Publication year - 2014
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2310
Subject(s) - econometrics , covariance , wishart distribution , covariance matrix , estimation of covariance matrices , economics , covariance function , vector autoregression , covariance intersection , equity (law) , mathematics , statistics , multivariate statistics , political science , law
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. Copyright © 2014 John Wiley & Sons, Ltd.