An Agent-Based Model to Explain the Emergence of Stylised Facts in Log Returns

TL;DR

This paper presents an agent-based model of a financial market demonstrating how specific trader behaviors lead to stylised facts like leptokurtosis and volatility clustering in log returns, enhancing understanding of market dynamics.

Contribution

The paper introduces an agent-based model incorporating noise, technical, and fundamental traders to replicate key stylised facts of financial data.

Findings

Log returns exhibit leptokurtosis and volatility clustering when all trader types are included.

Memory of noise traders is crucial for generating realistic stylised facts.

Technical and fundamental trading strategies contribute to the emergence of empirical data features.

Abstract

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.