Are Crises Predictable? A Review of the Early Warning Systems in Currency and Stock Markets

TL;DR

This paper reviews early warning systems for currency and stock market crises, highlighting the effectiveness of SWARCH models and machine learning, and analyzing key predictive factors across markets and timeframes.

Contribution

It synthesizes and compares various crisis prediction models, emphasizing the superior performance of SWARCH and machine learning approaches in different market contexts.

Findings

SWARCH model with elastic thresholding improves crisis classification accuracy

Machine learning models offer higher prediction precision and practical benefits

Different markets and periods are influenced by diverse leading predictive factors

Abstract

The study efforts to explore and extend the crisis predictability by synthetically reviewing and comparing a full mixture of early warning models into two constitutions: crisis identifications and predictive models. Given empirical results on Chinese currency and stock markets, three-strata findings are concluded as (i) the SWARCH model conditional on an elastic thresholding methodology can most accurately classify crisis observations and greatly contribute to boosting the predicting precision, (ii) stylized machine learning models are preferred given higher precision in predicting and greater benefit in practicing, (iii) leading factors sign the crisis in a diversified way for different types of markets and varied prediction periods.