To model the nonlinear and complex dynamics of financial systems, a new model for the formation of financial prices is developed, taking into account heterogeneity in the communication range of market agents. Specifically, one type of agents can potentially gather and disseminate information via additional long-distance contacts compared to the other type, and interactions among these agents are imitated by the contact process. The financial price series of the model are simulated, analysed, and compared with multiple major stock indices in nonlinear fluctuation behaviours. To better investigate the complexity structure of the financial time series, a generalization of the multiscale entropy method is developed to consider various moments in coarse graining. Overall, the modelled series are found to follow a fat-tail distribution and a pattern of complexity structure over both moments and time scales similar to real market data. This similarity is also shown by applying alternative complexity measure, matching energy method. Moreover, the wealth inequality among agents is found to increase over time within each type as well as across two types, further revealing nonlinear price and welfare dynamics of the model.

为了对金融系统的非线性和复杂动态建模，考虑到了市场代理人沟通范围的异质性，开发了一种新的金融价格形成模型。具体而言，与另一种类型的代理相比，一种类型的代理可以通过其他远程联系来潜在地收集和传播信息，并且这些代理之间的交互作用是通过联系过程来模仿的。对模型的金融价格序列进行仿真，分析，并与非线性波动行为中的多个主要股票指数进行比较。为了更好地研究金融时间序列的复杂性结构，开发了一种多尺度熵方法的泛化，以考虑粗粒度中的各个矩。总体，发现建模的序列在类似于真实市场数据的时刻和时间尺度上遵循胖尾分布和复杂性结构的模式。通过应用替代性的复杂性度量，匹配能量方法也显示出这种相似性。此外，发现代理商之间的财富不平等在每种类型以及两种类型之间都随着时间而增加，从而进一步揭示了该模型的非线性价格和福利动态。