  In complex financial systems, the sector structure and volatility clustering
are respectively important features of the spatial and temporal correlations.
However, the microscopic generation mechanism of the sector structure is not
yet understood. Especially, how to produce these two features in one model
remains challenging. We introduce a novel interaction mechanism, i.e., the
multi-level herding, in constructing an agent-based model to investigate the
sector structure combined with volatility clustering. According to the previous
market performance, agents trade in groups, and their herding behavior
comprises the herding at stock, sector and market levels. Further, we propose
methods to determine the key model parameters from historical market data,
rather than from statistical fitting of the results. From the simulation, we
obtain the sector structure and volatility clustering, as well as the
eigenvalue distribution of the cross-correlation matrix, for the New York and
Hong Kong stock exchanges. These properties are in agreement with the empirical
ones. Our results quantitatively reveal that the multi-level herding is the
microscopic generation mechanism of the sector structure, and provide new
insight into the spatio-temporal interactions in financial systems at the
microscopic level.
