Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

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

Buckle, Mike, Chen, Jing and Williams, Julian 2014. How predictable are equity covariance matrices? Evidence from high-frequency data for four markets. Journal of Forecasting 33 (7) , pp. 542-557. 10.1002/for.2310

Full text not available from this repository.

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
Date Type: Publication
Status: Published
Schools: Mathematics
Publisher: Wiley Blackwell
ISSN: 0277-6693
Date of Acceptance: 27 May 2014
Last Modified: 09 Sep 2019 14:37
URI: http://orca.cf.ac.uk/id/eprint/77914

Citation Data

Cited 1 time in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item