标题： 基于多级从众行为的智能体模型用于复杂金融系统 显示英文标题

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

摘要： 在复杂的金融系统中，行业结构和波动率聚类分别是空间和时间相关性的重要特征。 然而，行业结构的微观生成机制尚未被理解。 特别是，如何在一个模型中产生这两个特征仍然是一个挑战。 我们引入了一种新的交互机制，即多层次从众行为，在构建基于代理的模型中研究与波动率聚类相结合的行业结构。 根据以前的市场表现，代理以群体形式交易，其从众行为包括股票、行业和市场层面的从众行为。 此外，我们提出了从历史市场数据中确定关键模型参数的方法，而不是通过结果的统计拟合。 从模拟中，我们获得了纽约和香港证券交易所的行业结构和波动率聚类，以及交叉相关矩阵的特征值分布。 这些特性与实证结果一致。 我们的结果定量地揭示了多层次从众行为是行业结构的微观生成机制，并为金融系统在微观层面上的空间-时间相互作用提供了新的见解。 显示英文摘要

摘要： 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.