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Bayesian log-periodic model for financial crashes

Listed author(s):
  • Carlos Vladimir Rodríguez-Caballero


  • Oskar Knapik

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

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Article provided by Springer & EDP Sciences in its journal The European Physical Journal B.

Volume (Year): 87 (2014)
Issue (Month): 10 (October)
Pages: 1-14

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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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