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Bayesian log-periodic model for financial crashes

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  • 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
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    Cited by:

    1. Molina-Muñoz, Jesús & Mora-Valencia, Andrés & Perote, Javier, 2020. "Market-crash forecasting based on the dynamics of the alpha-stable distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    2. C. Vladimir Rodríguez-Caballero & Mauricio Villanueva-Domínguez, 2022. "Predicting cryptocurrency crash dates," Empirical Economics, Springer, vol. 63(6), pages 2855-2873, December.

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    Keywords

    Statistical and Nonlinear Physics;

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