Table of Contents
Economics Research International
Volume 2011, Article ID 564952, 15 pages
Research Article

Long Memory Process in Asset Returns with Multivariate GARCH Innovations

GREQAM, Université de la Méditerranée, Morseilles, France

Received 27 February 2011; Accepted 6 June 2011

Academic Editor: Paresh Kumar Narayan

Copyright © 2011 Imène Mootamri. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The main purpose of this paper is to consider the multivariate GARCH (MGARCH) framework to model the volatility of a multivariate process exhibiting long-term dependence in stock returns. More precisely, the long-term dependence is examined in the first conditional moment of US stock returns through multivariate ARFIMA process, and the time-varying feature of volatility is explained by MGARCH models. An empirical application to the returns series is carried out to illustrate the usefulness of our approach. The main results confirm the presence of long memory property in the conditional mean of all stock returns.