Aftershock prediction for high-frequency financial markets' dynamics

TL;DR

This paper introduces a stochastic model for predicting aftershocks in high-frequency financial markets, improving upon traditional empirical methods by leveraging the scaling symmetry of financial assets and validated with S&P data.

Contribution

The paper presents a novel stochastic model that enhances prediction of market aftershocks based on main shock magnitude, surpassing the limitations of empirical approaches.

Findings

Model successfully predicts aftershocks in high-frequency data.

Validation with S&P data confirms predictive potential.

Highlights the importance of scaling symmetry in financial dynamics.

Abstract

The occurrence of aftershocks following a major financial crash manifests the critical dynamical response of financial markets. Aftershocks put additional stress on markets, with conceivable dramatic consequences. Such a phenomenon has been shown to be common to most financial assets, both at high and low frequency. Its present-day description relies on an empirical characterization proposed by Omori at the end of 1800 for seismic earthquakes. We point out the limited predictive power in this phenomenological approach and present a stochastic model, based on the scaling symmetry of financial assets, which is potentially capable to predict aftershocks occurrence, given the main shock magnitude. Comparisons with S&P high-frequency data confirm this predictive potential.