IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-03257268.html
   My bibliography  Save this paper

Forecasting the intra-day effective bid ask spread by combining density forecasts

Author

Listed:
  • Malick Fall

    (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique)

  • Waël Louhichi

    (ESSCA Research Lab - ESSCA - Ecole Supérieure des Sciences Commerciales d'Angers)

  • Jean-Laurent Viviani

    (CREM - Centre de recherche en économie et management - UNICAEN - Université de Caen Normandie - NU - Normandie Université - UR - Université de Rennes - CNRS - Centre National de la Recherche Scientifique)

Abstract

The bid-ask spread refers to the tightness dimension of liquidity and can be used as a proxy for transaction costs. Despite the importance of the bid-ask spread in the financial literature, few studies have investigated its forecastability. We propose a new methodology to predict the bid ask spread by combining density forecasts of two types of models: Multiplicative Errors Models and ARMA-GARCH models. Our method is employed to predict the effective intra-day bid-ask spread series of all shares pertaining to the CAC40 index. Using a one-step-ahead out-of-sample framework, we resort on the Model Confidence Set procedure of Hansen et al. (2004) to classify models and we found that the proposed model appears to beat all the benchmark specifications.

Suggested Citation

  • Malick Fall & Waël Louhichi & Jean-Laurent Viviani, 2021. "Forecasting the intra-day effective bid ask spread by combining density forecasts," Post-Print hal-03257268, HAL.
    • Handle: RePEc:hal:journl:hal-03257268
    DOI: 10.1080/00036846.2021.1929821
    Note: View the original document on HAL open archive server: https://hal.science/hal-03257268
    as

    Download full text from publisher

    File URL: https://hal.science/hal-03257268/document
    Download Restriction: no
    File URL: https://libkey.io/10.1080/00036846.2021.1929821?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

    Effective bid-ask spread; High-Frequency; Multiplicative Errors Models; Forecasting;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:hal:journl:hal-03257268. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.