Large-Scale Simulation of Multi-Asset Ising Financial Markets

TL;DR

This paper presents a large-scale simulation of an Ising-based financial market model with 300 assets, revealing key stylized facts like fat tails, volatility clustering, and systemic risk dynamics during high volatility periods.

Contribution

It introduces a comprehensive large-scale simulation of a multi-asset Ising model, analyzing systemic risk and correlation measures during different market volatility regimes.

Findings

The model reproduces fat-tailed return distributions.

Volatility clustering is observed in the simulated data.

Systemic risk measures like Cumulative Risk Fraction change during high volatility periods.

Abstract

We perform a large-scale simulation of an Ising-based financial market model that includes 300 asset time series. The financial system simulated by the model shows a fat-tailed return distribution and volatility clustering and exhibits unstable periods indicated by the volatility index measured as the average of absolute-returns. Moreover, we determine that the cumulative risk fraction, which measures the system risk, changes at high volatility periods. We also calculate the inverse participation ratio (IPR) and its higher-power version, IPR6, from the absolute-return cross-correlation matrix. Finally, we show that the IPR and IPR6 also change at high volatility periods.