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In complex financial systems, the sector structure and volatility clusteringare respectively important features of the spatial and temporal correlations.However, the microscopic generation mechanism of the sector structure is notyet understood. Especially, how to produce these two features in one modelremains challenging. We introduce a novel interaction mechanism, i.e., themulti-level herding, in constructing an agent-based model to investigate thesector structure combined with volatility clustering. According to the previousmarket performance, agents trade in groups, and their herding behaviorcomprises the herding at stock, sector and market levels. Further, we proposemethods to determine the key model parameters from historical market data,rather than from statistical fitting of the results. From the simulation, weobtain the sector structure and volatility clustering, as well as theeigenvalue distribution of the cross-correlation matrix, for the New York andHong Kong stock exchanges. These properties are in agreement with the empiricalones. Our results quantitatively reveal that the multi-level herding is themicroscopic generation mechanism of the sector structure, and provide newinsight into the spatio-temporal interactions in financial systems at themicroscopic level.

Agent-based model with multi-level herding for complex financial systems