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Symbol Dynamics, Information theory and Complexity of Economic time series

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  • Geoffrey Ducournau

Abstract

We propose to examine the predictability and the complexity characteristics of the Standard&Poor500 dynamics behaviors in a coarse-grained way using the symbolic dynamics method and under the prism of the Information theory through the concept of entropy and uncertainty. We believe that experimental measurement of entropy as a way of examining the complexity of a system is more relevant than more common tests of universality in the transition to chaos because it does not make any prior prejudices on the underlying causes associated with the system dynamics, whether deterministic or stochastic. We regard the studied economic time series as being complex and propose to express it in terms of the amount of information this last is producing on different time scales and according to various scaling parameters.

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  • Geoffrey Ducournau, 2021. "Symbol Dynamics, Information theory and Complexity of Economic time series," Papers 2105.04131, arXiv.org.
    • Handle: RePEc:arx:papers:2105.04131
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    1. Liudas Giraitis & Peter M. Robinson & Alexander Samarov, 1997. "Rate Optimal Semiparametric Estimation Of The Memory Parameter Of The Gaussian Time Series With Long‐Range Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 18(1), pages 49-60, January.
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    Cited by:

    1. Matthieu Garcin, 2023. "Complexity measure, kernel density estimation, bandwidth selection, and the efficient market hypothesis," Papers 2305.13123, arXiv.org.
    2. Matthieu Garcin, 2023. "Complexity measure, kernel density estimation, bandwidth selection, and the efficient market hypothesis," Working Papers hal-04102815, HAL.

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