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J. Risk Financial Manag. 2018, 11(4), 86;

Predicting Currency Crises: A Novel Approach Combining Random Forests and Wavelet Transform

Graduate School of Economics, Kobe University, 2-1, Rokkodai, Nada-Ku, Kobe 657-8501, Japan
Author to whom correspondence should be addressed.
Received: 1 November 2018 / Revised: 27 November 2018 / Accepted: 1 December 2018 / Published: 4 December 2018
(This article belongs to the Special Issue Empirical Finance)
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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. View Full-Text
Keywords: currency crisis; random forests; wavelet transform; predictive accuracy currency crisis; random forests; wavelet transform; predictive accuracy

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Xu, L.; Kinkyo, T.; Hamori, S. Predicting Currency Crises: A Novel Approach Combining Random Forests and Wavelet Transform. J. Risk Financial Manag. 2018, 11, 86.

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J. Risk Financial Manag. EISSN 1911-8074 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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