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Long Memory Process in Asset Returns with Multivariate GARCH innovations

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  • Imene Mootamri

    () (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - ECM - Ecole Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique - AMU - Aix Marseille Université - EHESS - École des hautes études en sciences sociales)

Abstract

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 confi rm the presence of long memory property in the conditional mean of all stock returns.

Suggested Citation

  • Imene Mootamri, 2011. "Long Memory Process in Asset Returns with Multivariate GARCH innovations," Working Papers halshs-00599250, HAL.
  • Handle: RePEc:hal:wpaper:halshs-00599250
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00599250
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    References listed on IDEAS

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    1. Gil-Alana, L. A., 2003. "A fractional multivariate long memory model for the US and the Canadian real output," Economics Letters, Elsevier, vol. 81(3), pages 355-359, December.
    2. John Barkoulas & Christopher Baum & Nickolaos Travlos, 2000. "Long memory in the Greek stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 10(2), pages 177-184.
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    Keywords

    Forecasting; Long memory; Multivariate GARCH; Stock Returns;

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