IDEAS home Printed from https://ideas.repec.org/a/bpj/sndecm/v26y2022i4p529-539n1.html
   My bibliography  Save this article

Forecasting transaction counts with integer-valued GARCH models

Author

Listed:
  • Aknouche Abdelhakim
  • Almohaimeed Bader S.

    (Department of Mathematics, College of Science, Qassim University, P.O. Box 707, Buraydah, 51431, Saudi Arabia)

  • Dimitrakopoulos Stefanos

    (Economics Division, Leeds University Business School, University of Leeds, LS2 9JT, Leeds, UK)

Abstract

Using numerous transaction data on the number of stock trades, we conduct a forecasting exercise with INGARCH models, governed by various conditional distributions; the Poisson, the linear and quadratic negative binomial, the double Poisson and the generalized Poisson. The model parameters are estimated with efficient Markov Chain Monte Carlo methods, while forecast evaluation is done by calculating point and density forecasts.

Suggested Citation

  • Aknouche Abdelhakim & Almohaimeed Bader S. & Dimitrakopoulos Stefanos, 2022. "Forecasting transaction counts with integer-valued GARCH models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 26(4), pages 529-539, September.
  • Handle: RePEc:bpj:sndecm:v:26:y:2022:i:4:p:529-539:n:1
    DOI: 10.1515/snde-2020-0095
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/snde-2020-0095
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.1515/snde-2020-0095?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:bpj:sndecm:v:26:y:2022:i:4:p:529-539:n:1. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.