Rodríguez-Caballero, Carlos Vladimir4; Knapik, Oskar4
1 Department of Economics and Business Economics, Aarhus BSS, Aarhus University2 Aarhus University3 Department of Economics and Business Economics - Center for Research in Econometric Analysis of Time Series (CREATES), Department of Economics and Business Economics, Aarhus BSS, Aarhus University4 Department of Economics and Business Economics, Aarhus BSS, Aarhus University
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.
European Physical Journal B. Condensed Matter and Complex Systems, 2014, Vol 87, Issue 10