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

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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)

  • Shigeyuki Hamori

    (Graduate School of Economics, Kobe University, 2-1, Rokkodai, Nada-Ku, Kobe 657-8501, Japan)

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.

Suggested Citation

  • Lei Xu & Takuji Kinkyo & Shigeyuki Hamori, 2018. "Predicting Currency Crises: A Novel Approach Combining Random Forests and Wavelet Transform," JRFM, MDPI, vol. 11(4), pages 1-11, December.
    • Handle: RePEc:gam:jjrfmx:v:11:y:2018:i:4:p:86-:d:187697
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    References listed on IDEAS

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    Cited by:

    1. Wenting Zhang & Shigeyuki Hamori, 2020. "Do Machine Learning Techniques and Dynamic Methods Help Forecast US Natural Gas Crises?," Energies, MDPI, vol. 13(9), pages 1-22, May.
    2. Ashwin Madhou & Tayushma Sewak & Imad Moosa & Vikash Ramiah & Florian Gerth, 2021. "Towards Full-Fledged Inflation Targeting Monetary Policy Regime in Mauritius," JRFM, MDPI, vol. 14(3), pages 1-18, March.
    3. Shigeyuki Hamori, 2020. "Empirical Finance," JRFM, MDPI, vol. 13(1), pages 1-3, January.

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