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Fluctuation complexity of agent-based financial time series model by stochastic Potts system

Author

Listed:
  • Weijia Hong

    (School of Science, Beijing Jiaotong University, Beijing 100044, P. R. China)

  • Jun Wang

    (School of Science, Beijing Jiaotong University, Beijing 100044, P. R. China)

Abstract

Financial market is a complex evolved dynamic system with high volatilities and noises, and the modeling and 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 agent-based financial time series model is developed in an attempt to uncover the empirical laws in finance, where the Potts model is introduced to imitate the trading interactions among the investing agents. Based on the computer simulation in conjunction with the statistical analysis and the nonlinear analysis, we present numerical research to investigate the fluctuation behaviors of the proposed time series model. Furthermore, in order to get a robust conclusion, we consider the daily returns of Shanghai Composite Index and Shenzhen Component Index, and the comparison analysis of return behaviors between the simulation data and the actual data is exhibited.

Suggested Citation

  • Weijia Hong & Jun Wang, 2015. "Fluctuation complexity of agent-based financial time series model by stochastic Potts system," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(11), pages 1-19.
    • Handle: RePEc:wsi:ijmpcx:v:26:y:2015:i:11:n:s0129183115501235
    DOI: 10.1142/S0129183115501235
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