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

• Xu, Lei

• Kinkyo, Takuji

• Hamori, Shigeyuki

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.

Journal

• Journal of Risk and Financial Management

  Journal of Risk and Financial Management 11 (4), 86-86, 2018-12

  MDPI

Keywords

• currency crisis
• random forests
• wavelet transform
• predictive accuracy

Details

• CRID: 1050856995323276032
• NII Article ID: 120006551684
• DOI: 10.3390/jrfm11040086
• ISSN: 19118074
• ISSN: 19118066
• HANDLE: 20.500.14094/90005541
• Web Site: http://www.mdpi.com/1911-8074/11/4/86/pdf
• Text Lang: en
• Article Type: journal article
• Data Source:
  • IRDB
  • Crossref
  • CiNii Articles
  • KAKEN