英文标题：
《New approaches in agent-based modeling of complex financial systems》
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作者：
T. T. Chen, B. Zheng, Y. Li, and X. F. Jiang
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最新提交年份：
2017
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英文摘要：
Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of the agent-based models from empirical data instead of setting them artificially was suggested. We first review several agent-based models and the new approaches to determine the key model parameters from historical market data. Based on the agents\' behaviors with heterogenous personal preferences and interactions, these models are successful to explain the microscopic origination of the temporal and spatial correlations of the financial markets. We then present a novel paradigm combining the big-data analysis with the agent-based modeling. Specifically, from internet query and stock market data, we extract the information driving forces, and develop an agent-based model to simulate the dynamic behaviors of the complex financial systems.
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中文摘要：
基于Agent的建模是理解复杂金融系统中的集体行为和微观交互的强大仿真技术。最近，有人提出了从经验数据中确定基于代理的模型的关键参数的概念，而不是人工设置这些参数。我们首先回顾了几种基于代理的模型以及根据历史市场数据确定关键模型参数的新方法。这些模型基于具有异质个人偏好和交互作用的代理行为，成功地解释了金融市场时空相关性的微观起源。然后，我们提出了一种将大数据分析与基于agent的建模相结合的新范式。具体而言，我们从互联网查询和股票市场数据中提取信息驱动力，并开发基于agent的模型来模拟复杂金融系统的动态行为。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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