  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.
