Nonlinear dynamical complexity of agent-based stochastic financial interacting epidemic system

In an attempt to investigate the nonlinear dynamic mechanism of financial market microstructure, a stochastic interacting epidemic system is applied to establish an agent-based financial price dynamics, in which the spread of viruses and the physical condition of humans in the interacting epidemic system are, respectively, utilized to imitate the dispersal of the information and the investment attitude of the investors in a stock market. Combined with the ensemble empirical mode decomposition, the composite multiscale entropy analysis is applied to analyze the fluctuation complexity of financial time series, including the proposed model data and seven real stock indices. Further, the Zipf fluctuation behaviors of these time series are also investigated. The comparatively empirical results of the real indices and the simulation data show the similar fluctuation behaviors, indicating that this agent-based financial model can imitate some important properties of stock markets.

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