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

Measuring the correlation complexity between return series by multiscale complex analysis on... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nonlinear Dynamics Springer Journals

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

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Publisher
Springer Netherlands
Copyright
Copyright © 2017 by Springer Science+Business Media B.V.
Subject
Engineering; Vibration, Dynamical Systems, Control; Classical Mechanics; Mechanical Engineering; Automotive Engineering
ISSN
0924-090X
eISSN
1573-269X
D.O.I.
10.1007/s11071-017-3619-6
Publisher site
See Article on Publisher Site

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.

Journal

Nonlinear DynamicsSpringer Journals

Published: Jun 26, 2017

References

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