Predicting Currency Crises: A Novel Approach Combining Random Forests and Wavelet Transform
Lei Xu (),
Takuji Kinkyo () and
Shigeyuki Hamori ()
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Lei Xu: Graduate School of Economics, Kobe University, 2-1, Rokkodai, Nada-Ku, Kobe 657-8501, Japan
Takuji Kinkyo: Graduate School of Economics, Kobe University, 2-1, Rokkodai, Nada-Ku, Kobe 657-8501, Japan
Journal of Risk and Financial Management, 2018, vol. 11, issue 4, 1-11
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.
Keywords: currency crisis; random forests; wavelet transform; predictive accuracy (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:11:y:2018:i:4:p:86-:d:187697
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