一个基于Agent的模型来解释日志返回中风格化事实的出现
An Agent-Based Model to Explain the Emergence of Stylised Facts in Log Returns
摘要：
本文概述了一个简单的金融市场，其中一个单一的资产可供三种不同类型的交易者交易的基于代理的模型。该模型首次在一位作者的博士论文中介绍，参见参考文献[1]。检查模拟日志返回是否存在金融数据的风格化事实。重点研究了尖峰态、波动率聚集和聚合高斯性特征。以下成分被认为是必不可少的生产这些程式化的事实：噪声交易者的记忆谁作出随机的贸易决定;包括技术贸易商的贸易符合价格趋势和包括基本面贸易商谁知道的“基本价值”的股票和贸易。当这三种基本类型的交易者包括日志收益产生的尖峰分布和波动率聚集以及一些进一步的统计特征的经验数据。这增强和拓宽了我们对市场产生经验数据的基本过程的理解。
英文摘要：
This paper outlines an agent-based model of a simple financial market in which a single asset is available for trade by three different types of traders. The model was first introduced in the PhD thesis of one of the authors, see reference [1]. The simulated log returns are examined for the presence of the stylised facts of financial data. The features of leptokurtosis, volatility clustering and aggregational Gaussianity are especially highlighted and studied in detail. The following ingredients are found to be essential for the production of these stylised facts: the memory of noise traders who make random trade decisions; the inclusion of technical traders that trade in line with trends in the price and the inclusion of fundamental traders who know the "fundamental value" of the stock and trade accordingly. When these three basic types of traders are included log returns are produced with a leptokurtic distribution and volatility clustering as well as some further statistical features of empirical data. This enhances and broadens our understanding of the fundamental processes involved in the production of empirical data by the market.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
