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Measuring the correlation complexity between return series by multiscale complex analysis on Potts dynamics

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Abstract

Financial market is a complex evolving dynamic system with high volatilities and noises, and the modeling and the analyzing of financial time series are regarded as the rather challenging tasks in financial research. In this work, by applying the Potts dynamic system, a random financial time series model is developed in an attempt to uncover the empirical laws in finance, where the particles in the Potts model are introduced to imitate the attitudes of market traders. Through the computer simulation on the selected data, we present the numerical research in conjunction with statistical analysis and nonlinear analysis to investigate the volatilities of financial time series. The fluctuation behavior of logarithmic returns of the proposed model is investigated by MCID, q-MCID and EMD-MCID analyses. Furthermore, in order to obtain a robust conclusion, the complex dynamic behaviors of return time series of the model are investigated by using multiaspect chaos-exploring methods, phase space reconstruction, correlation dimension analysis, largest Lyapunov exponent, density of Kolmogorov–Sinai entropy and Kolmogorov–Sinai entropy universality. The corresponding daily return behaviors of SSE, SZSE, S&P500, IXIC, FTSE, Nikkei and AEX are considered for comparison.

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Acknowledgements

The authors were supported by National Natural Science Foundation of China, Grant No. 71271026.

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  1. School of Science, Beijing Jiaotong University, Beijing, 100044, People’s Republic of China

    Jie Wang & Jun Wang

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  1. Jie Wang
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Correspondence to Jie Wang.

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Wang, J., Wang, J. Measuring the correlation complexity between return series by multiscale complex analysis on Potts dynamics. Nonlinear Dyn 89, 2703–2721 (2017). https://doi.org/10.1007/s11071-017-3619-6

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