IDEAS home Printed from https://ideas.repec.org/p/hal/journl/halshs-00601428.html
   My bibliography  Save this paper

Nonlinear Shift Contagion Modeling: Further Evidence from High Frequency Stock Data

Author

Listed:
  • M.H. Arouri
  • F. Jawadi

    (EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique, CERAG - Centre d'études et de recherches appliquées à la gestion - UPMF - Université Pierre Mendès France - Grenoble 2 - CNRS - Centre National de la Recherche Scientifique)

  • Waël Louhichi

    (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)

  • D. K. Nguyen

Abstract

This paper investigates the contagion hypothesis for the French and German stock markets using a combination of a Switching Transition Error Correction model and a Generalized Autoregressive Conditional Heteroscedasticity (STEC-GARCH) model. The main advantage of this double nonlinear error-correction modeling is to specify a time-varying process that apprehends the dynamic evolution of the contagion and reproduces its speed, its extreme regimes as well as its intermediate states, by taking into account the possible linkages between these markets. More importantly, these techniques capture two kinds of nonlinearity: nonlinearity in the mean and nonlinearity in the variance. Applying this modeling on the intraday data of the CAC40 and DAX100 indices over the pre-crisis period (2004-2006) and the post-crisis period (2007-2009), our results indicate significant shift contagion between studied markets. There is also evidence of nonlinear time-varying error correcting-mechanism toward the long-run equilibrium.

Suggested Citation

  • M.H. Arouri & F. Jawadi & Waël Louhichi & D. K. Nguyen, 2011. "Nonlinear Shift Contagion Modeling: Further Evidence from High Frequency Stock Data," Post-Print halshs-00601428, HAL.
  • Handle: RePEc:hal:journl:halshs-00601428
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Okorie, David Iheke & Lin, Boqiang, 2021. "Stock markets and the COVID-19 fractal contagion effects," Finance Research Letters, Elsevier, vol. 38(C).

    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:halshs-00601428. 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.