RT Journal Article
T1 Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques
A1 Alaminos, David
A1 Ignacio Peliez, Jose
A1 Belen Salas, M.
A1 Fernandez-Gamez, Manuel A.
K1 sovereign debt crisis prediction
K1 currency crisis prediction
K1 deep learning neural decision trees
K1 fuzzy decision trees
K1 extreme gradient boosting
K1 country reputation
K1 Early warning systems
K1 Artificial neural-networks
K1 Banking crises
K1 Performance
K1 Countries
K1 Indicator
K1 Industry
AB Sovereign debt and currencies play an increasingly influential role in the development of any country, given the need to obtain financing and establish international relations. A recurring theme in the literature on financial crises has been the prediction of sovereign debt and currency crises due to their extreme importance in international economic activity. Nevertheless, the limitations of the existing models are related to accuracy and the literature calls for more investigation on the subject and lacks geographic diversity in the samples used. This article presents new models for the prediction of sovereign debt and currency crises, using various computational techniques, which increase their precision. Also, these models present experiences with a wide global sample of the main geographical world zones, such as Africa and the Middle East, Latin America, Asia, Europe, and globally. Our models demonstrate the superiority of computational techniques concerning statistics in terms of the level of precision, which are the best methods for the sovereign debt crisis: fuzzy decision trees, AdaBoost, extreme gradient boosting, and deep learning neural decision trees, and for forecasting the currency crisis: deep learning neural decision trees, extreme gradient boosting, random forests, and deep belief network. Our research has a large and potentially significant impact on the macroeconomic policy adequacy of the countries against the risks arising from financial crises and provides instruments that make it possible to improve the balance in the finance of the countries.
PB Mdpi
YR 2021
FD 2021-04-01
LK http://hdl.handle.net/10668/19332
UL http://hdl.handle.net/10668/19332
LA en
DS RISalud
RD Apr 8, 2025