IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this paper

Long Memory Process in Asset Returns with Multivariate GARCH innovations

  • Imene Mootamri

    ()

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - ECM - Ecole Centrale de Marseille - AMU - Aix Marseille Université - EHESS - École des hautes études en sciences sociales - Université Paul Cézanne - Aix-Marseille 3 - Université de la Méditerranée - Aix-Marseille 2 - CNRS - Centre National de la Recherche Scientifique)

Registered author(s):

    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.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL: https://halshs.archives-ouvertes.fr/halshs-00599250/document
    Download Restriction: no

    Paper provided by HAL in its series Working Papers with number halshs-00599250.

    as
    in new window

    Length:
    Date of creation: 09 Jun 2011
    Date of revision:
    Handle: RePEc:hal:wpaper:halshs-00599250
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00599250
    Contact details of provider: Web page: https://hal.archives-ouvertes.fr/

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    as in new window
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:hal:wpaper:halshs-00599250. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CCSD)

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.