RT Journal Article
T1 Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques.
A1 Alaminos, David
A1 Peláez-Sánchez, José Ignacio
A1 Salas-Compás, María Belén
A1 Fernández-Gámez, Manuel Ángel
K1 Crisis financieras - Modelos econométricos
K1 Deuda pública - Modelos econométricos
AB Sovereign debt and currencies play an increasingly influential role in the development ofany country, given the need to obtain financing and establish international relations. A recurringtheme in the literature on financial crises has been the prediction of sovereign debt and currency crisesdue to their extreme importance in international economic activity. Nevertheless, the limitationsof the existing models are related to accuracy and the literature calls for more investigation on thesubject and lacks geographic diversity in the samples used. This article presents new models for theprediction of sovereign debt and currency crises, using various computational techniques, whichincrease their precision. Also, these models present experiences with a wide global sample of themain geographical world zones, such as Africa and the Middle East, Latin America, Asia, Europe,and globally. Our models demonstrate the superiority of computational techniques concerningstatistics 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 impacton the macroeconomic policy adequacy of the countries against the risks arising from financial crisesand provides instruments that make it possible to improve the balance in the finance of the countries.
PB MDPI
YR 2021
FD 2021-04-12
LK https://hdl.handle.net/10630/32285
UL https://hdl.handle.net/10630/32285
LA eng
NO Alaminos, D.; Peláez, J.I.; Salas, M.B.; Fernández-Gámez, M.A. Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques. Symmetry 2021, 13, 652.
NO This research was funded by Cátedra de Economía y Finanzas Sostenibles, Universidad de Málaga, Spain.Partial funding for open access charge: Universidad de Málaga
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026