IDEAS home Printed from https://ideas.repec.org/a/alk/jmss00/v2y2021i2id1802.html

On Mixture GARCH Models: Long, Short Memory and Application in Finance

Author

Listed:
  • Halim Zeghdoudi

    (LaPS laboratory, Faculty of Sciences, University of Badji-Mokhtar University, BP12 Annaba 23000-Algeria)

  • Madjda Amrani

    (LaPS laboratory, Faculty of Sciences, University of Badji-Mokhtar University, BP12 Annaba 23000-Algeria)

Abstract

In this work, we study the famous model of volatility; called model of conditional heteroscedastic autoregressive with mixed memory MMGARCH for modeling nonlinear time series. The MMGARCH model has two mixing components, one is a GARCH short memory and the other is GARCH long memory. the main objective of this search for finds the best model between mixtures of the models we made (long memory with long memory, short memory with short memory and short memory with long memory) Also, the existence of its stationary solution is discussed. The Monte Carlo experiments demonstrate we discovered theoretical. In addition, the empirical application of the MMGARCH model (1, 1) to the daily index DOW and NASDAQ illustrates its capabilities; we find that for the mixture between APARCH and EGARCH is superior to any other model tested because it produces the smallest errors.

Suggested Citation

  • Halim Zeghdoudi & Madjda Amrani, 2021. "On Mixture GARCH Models: Long, Short Memory and Application in Finance," Journal of Mathematics and Statistics Studies, Al-Kindi Center for Research and Development., vol. 2(2), pages 01-05, June.
    • Handle: RePEc:alk:jmss00:v:2:y:2021:i:2:id:1802
    DOI: 10.32996/jmss.2021.2.2.1
    as

    Download full text from publisher

    File URL: https://al-kindipublisher.com/index.php/jmss/article/view/1802
    File Function: Abstract page
    Download Restriction: no
    File URL: https://al-kindipublisher.com/index.php/jmss/article/download/1802/1502
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32996/jmss.2021.2.2.1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:alk:jmss00:v:2:y:2021:i:2:id:1802. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Al-Kindi Center for Research and Development (email available below). General contact details of provider: https://al-kindipublisher.com/index.php/jmss .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.