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A bi-annual forecasting model of currency crises

ABSTRACT This study proposes a novel approach that combines random forests and discrete wavelet transform (DWT) to construct a bi-annual forecasting model of currency crises. The proposed model can achieve a reasonably high level of accuracy in predicting crises and demonstrates that the DWT of monthly real exchange rates and foreign reserves can serve as reliable predictors. The predicted probability of crises in individual countries is visualized through a map, which indicates that the risk of crises has increased substantially across regions in the second half of 2018.