Bayesian log-periodic model for financial crashes

Carlos Vladimir Rodríguez-Caballero () and Oskar Knapik
Authors registered in the RePEc Author Service: Carlos Vladimir Rodriguez Caballero

The European Physical Journal B: Condensed Matter and Complex Systems, 2014, vol. 87, issue 10, 1-14

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

Keywords: Statistical and Nonlinear Physics (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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DOI: 10.1140/epjb/e2014-41085-6

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