How Predictable Are Equity Covariance Matrices? Evidence from High‐Frequency Data for Four Markets. (8th September 2014)

• Record Type: Journal Article
• Authors: Buckle, Mike; Chen, Jing; Williams, Julian
• Is Part Of: Journal of forecasting. Volume 33:Number 7(2014:Nov.)
• Page Start: 542
• Page End: 557
• Publication Date: 2014-09-08
• Subjects: Forecasting -- Periodicals; Forecasting -- Mathematical models -- Periodicals; 003.2
• DOI: 10.1002/for.2310
• Languages: English
• ISSNs: 0277-6693

Abstract

<abstract abstract-type="main" id="for2310-abs-0001"> <title>ABSTRACT</title> <p id="for2310-para-0004">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 &amp; Sons, Ltd.</p> </abstract>