Linking agent-based models and stochastic models of financial markets
Linking agent-based models and stochastic models of financial markets
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DOI：
10.1073/pnas.1205013109
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发表时间：
2012-05-29
影响因子：
11.1
通讯作者：
Stanley, H. Eugene
中科院分区：
文献类型：
--
作者：
Feng, Ling;Li, Baowen;Stanley, H. Eugene
众所周知，金融资产回报表现出肥尾分布和长期记忆。这些经验特征是使用（i）随机过程定量再现这些特征和（ii）基于代理的模拟来理解潜在的微观相互作用的建模工作的主要目标。在审查了记录交易者行为的选定经验和理论证据后，我们构建了一个基于代理的模型，以定量证明当交易者共享相似的技术交易策略和决策时，收益分布中会出现“肥尾”。将我们的行为模型扩展到随机模型，我们从个体市场参与者的经验行为中推导出并解释了一组长期记忆的定量标度关系。我们的分析提供了对绝对价格回报和平方价格回报的长期记忆的行为解释：它们与投资者通过在不同投资期限应用技术策略来评估其投资的方式直接相关，并且这种定量关系与实证研究结果一致。我们的方法为一般金融系统的随机模型提供了一种可能的行为解释，并提供了一种根据市场数据而不是统计拟合来参数化此类模型的方法。
It is well-known that financial asset returns exhibit fat-tailed distributions and long-term memory. These empirical features are the main objectives of modeling efforts using (i) stochastic processes to quantitatively reproduce these features and (ii) agent-based simulations to understand the underlying microscopic interactions. After reviewing selected empirical and theoretical evidence documenting the behavior of traders, we construct an agent-based model to quantitatively demonstrate that "fat" tails in return distributions arise when traders share similar technical trading strategies and decisions. Extending our behavioral model to a stochastic model, we derive and explain a set of quantitative scaling relations of long-term memory from the empirical behavior of individual market participants. Our analysis provides a behavioral interpretation of the long-term memory of absolute and squared price returns: They are directly linked to the way investors evaluate their investments by applying technical strategies at different investment horizons, and this quantitative relationship is in agreement with empirical findings. Our approach provides a possible behavioral explanation for stochastic models for financial systems in general and provides a method to parameterize such models from market data rather than from statistical fitting.