Effects of modularity in financial markets on an agent-based model

We investigate an agent-based model of financial markets that is made up of numerous interacting agents with respect to local and global coupling and intrinsic randomness. The prices or return time series are constructed by using the essential mechanism of non-trivial interactions between agents. The characteristics that can describe a financial time series are the fat-tail distribution in the return time series and the long-term memory and clustering behavior in the volatility data. We find that for the proper parameter values, the probability distribution function of the return time series follows a power-law distribution with various scaling exponents that vary with the control parameter set. In particular, the local coupling strength considered as the modularity in financial markets plays an important role in terms of price formation.