Volatility clustering and scaling for financial time series due to attractor bubbling

TL;DR

This paper presents a microscopic agent-based model that reproduces key statistical features of financial time series, such as volatility clustering and power-law tails, through attractor bubbling phenomena.

Contribution

It introduces a novel interacting agent model with dynamic couplings that captures chaotic bursts and scaling behaviors observed in real financial markets.

Findings

Model reproduces volatility clustering observed in empirical data.

Returns exhibit power-law tails with realistic scaling exponents.

Chaotic bursts emerge from attractor bubbling in the model dynamics.

Abstract

A microscopic model of financial markets is considered, consisting of many interacting agents (spins) with global coupling and discrete-time thermal bath dynamics, similar to random Ising systems. The interactions between agents change randomly in time. In the thermodynamic limit the obtained time series of price returns show chaotic bursts resulting from the emergence of attractor bubbling or on-off intermittency, resembling the empirical financial time series with volatility clustering. For a proper choice of the model parameters the probability distributions of returns exhibit power-law tails with scaling exponents close to the empirical ones.