IDEAS home Printed from https://ideas.repec.org/a/spr/eurphb/v87y2014i10p1-1410.1140-epjb-e2014-41085-6.html
   My bibliography  Save this article

Bayesian log-periodic model for financial crashes

Author

Listed:
  • Carlos Vladimir Rodríguez-Caballero

    ()

  • Oskar Knapik

Abstract

This paper introduces a Bayesian approach in econophysics literature about financial bubbles in order to estimate the most probable time for a financial crash to occur. To this end, we propose using noninformative prior distributions to obtain posterior distributions. Since these distributions cannot be performed analytically, we develop a Markov Chain Monte Carlo algorithm to draw from posterior distributions. We consider three Bayesian models that involve normal and Student’s t-distributions in the disturbances and an AR(1)-GARCH(1,1) structure only within the first case. In the empirical part of the study, we analyze a well-known example of financial bubble – the S&P 500 1987 crash – to show the usefulness of the three methods under consideration and crashes of Merval-94, Bovespa-97, IPCMX-94, Hang Seng-97 using the simplest method. The novelty of this research is that the Bayesian models provide 95% credible intervals for the estimated crash time. Copyright EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Carlos Vladimir Rodríguez-Caballero & Oskar Knapik, 2014. "Bayesian log-periodic model for financial crashes," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 87(10), pages 1-14, October.
  • Handle: RePEc:spr:eurphb:v:87:y:2014:i:10:p:1-14:10.1140/epjb/e2014-41085-6
    DOI: 10.1140/epjb/e2014-41085-6
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1140/epjb/e2014-41085-6
    Download Restriction: Access to full text is restricted to subscribers.

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

    More about this item

    Keywords

    Statistical and Nonlinear Physics;

    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:spr:eurphb:v:87:y:2014:i:10:p:1-14:10.1140/epjb/e2014-41085-6. 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: (Sonal Shukla) or (Rebekah McClure). General contact details of provider: http://www.springer.com .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.