Multiscale complexity fluctuation behaviours of stochastic interacting cryptocurrency price model

The price dynamics of the emerging cryptocurrency market become an important factor affecting the current global investment market. To investigate its microscopic nonlinear dynamics mechanism, we develop a novel financial agent-based price model by combining the stochastic interacting system and two opposite directions of random jump processes, in which the attribute interactions between particles in the interacting system are utilized to imitate the influence of information transmission on investor's investment attitude in the cryptocurrency market. Return series of the model, generated by Monte Carlo simulations, can reproduce a number of “stylized facts” of real cryptocurrency data. Additionally, we apply the composite multiscale fuzzy entropy algorithm and the matching energy method to detecting the complexity of price volatility behaviours in both actual and simulated cryptocurrency markets. The similarity of empirical results indicates that the proposed model can replicate the properties of price volatility in actual cryptocurrency market to some extent.