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Neglected chaos in international stock markets: Bayesian analysis of the joint return–volatility dynamical system

Mike Tsionas and Panayotis Michaelides

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We use a novel Bayesian inference procedure for the Lyapunov exponent in the dynamical system of returns and their unobserved volatility. In the dynamical system, computation of largest Lyapunov exponent by traditional methods is impossible as the stochastic nature has to be taken explicitly into account due to unobserved volatility. We apply the new techniques to daily stock return data for a group of six countries, namely USA, UK, Switzerland, Netherlands, Germany and France, from 2003 to 2014, by means of Sequential Monte Carlo for Bayesian inference. The evidence points to the direction that there is indeed noisy chaos both before and after the recent financial crisis. However, when a much simpler model is examined where the interaction between returns and volatility is not taken into consideration jointly, the hypothesis of chaotic dynamics does not receive much support by the data (“neglected chaos”).

Keywords: Neglected chaos; Lyapunov exponent; Neural networks; Bayesian analysis; Sequential Monte Carlo; Global economy (search for similar items in EconPapers)
JEL-codes: C1 F3 G3 (search for similar items in EconPapers)
Date: 2017-09
New Economics Papers: this item is included in nep-cmp and nep-ore
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Citations: View citations in EconPapers (1)

Published in Physica A: Statistical Mechanics and Its Applications, September, 2017, 482, pp. 95-107. ISSN: 0378-4371

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