Abstract
We propose a novel approach that combines random forests and the wavelet transform to model the prediction of currency crises. Our classification model of random forests, built using both standard predictors and wavelet predictors, and obtained from the wavelet transform, achieves a demonstrably high level of predictive accuracy. We also use variable importance measures to find that wavelet predictors are key predictors of crises. In particular, we find that real exchange rate appreciation and overvaluation, which are measured over a horizon of 16–32 months, are the most important.
Highlights
Severe economic collapse in developing countries often involves currency crises triggered by speculative attacks on the currency and sudden stops to capital inflows
We propose a novel approach that combines a machine learning technique of random forests and a signal processing method of the wavelet transform to model the prediction of currency crises
We used a modified version of discrete wavelet transform (DWT) known as the maximal overlap DWT (MODWT) because its sample size need not be restricted to a power of two
Summary
Severe economic collapse in developing countries often involves currency crises triggered by speculative attacks on the currency and sudden stops to capital inflows. Kaminsky et al (1998) propose a signaling approach, which seeks to identify the threshold values for individual predictors They find that exports, real exchange rate overvaluation, GDP growth, foreign exchange reserves, and equity prices are the most reliable predictors of crises. Frankel and Frankel and Saravelos (2012) investigated whether traditional indicators can help explain why some countries were badly impacted by the global financial crisis and found that foreign exchange reserves and real exchange rate overvaluation are the most useful predictors. We propose a novel approach that combines a machine learning technique of random forests and a signal processing method of the wavelet transform to model the prediction of currency crises. We use the wavelet transform to extract key features of exchange rate behavior that may signal the risk of currency crises.
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