Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/238903 
Year of Publication: 
2018
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 11 [Issue:] 4 [Publisher:] MDPI [Place:] Basel [Year:] 2018 [Pages:] 1-11
Publisher: 
MDPI, Basel
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.
Subjects: 
currency crisis
random forests
wavelet transform
predictive accuracy
JEL: 
F31
F37
F47
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

Files in This Item:
File
Size
373.59 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.