Self-organised criticality in high frequency finance: the case of flash crashes

TL;DR

This paper explores the occurrence of flash crashes in high-frequency finance, demonstrating that volume distributions exhibit heavy tails indicative of self-organised criticality, which has significant implications for risk modeling and regulation.

Contribution

It links flash crashes to self-organised criticality and highlights the importance of heavy-tailed modeling in financial risk assessment.

Findings

Volume distributions during crashes have tail exponents less than 2.

The divergence of variance suggests criticality in market dynamics.

Regulatory implications for managing liquidity and risk constraints.

Abstract

With the rise of computing and artificial intelligence, advanced modeling and forecasting has been applied to High Frequency markets. A crucial element of solid production modeling though relies on the investigation of data distributions and how they relate to modeling assumptions. In this work we investigate volume distributions during anomalous price events and show how their tail exponents < 2 indicate a diverging second moment of the distribution, i.e. variance. We then tie the dynamics of flash crashes to self-organised criticality. The findings are of great relevance for regulators and market makers as they advocate for rigorous heavy-tailed modeling of risk and changes in regulation to avoid simultaneous liquidity withdrawals and hard risk constraints which lead to synchronisation and critical events.