RT Journal Article
T1 Global patterns and extreme events in sovereign risk premia: a fuzzy vs deep learning comparative.
A1 Alaminos, David
A1 Salas-Compás, María Belén
A1 Fernández-Gámez, Manuel Ángel
K1 Riesgo país - Modelos econométricos
K1 Teoría de grafos
K1 Aprendizaje automático
AB Investment in foreign countries has become more common nowadays and this im-plies that there may be risks inherent to these investments, being the sovereign risk premiumthe measure of such risk. Many studies have examined the behaviour of the sovereign riskpremium, nevertheless, there are limitations to the current models and the literature calls forfurther investigation of the issue as behavioural factors are necessary to analyse the investor’srisk perception. In addition, the methodology widely used in previous research is the regres-sion model, and the literature shows it as scarce yet. This study provides a model for a newof the drivers of the government risk premia in developing countries and developed coun-tries, comparing Fuzzy methods such as Fuzzy Decision Trees, Fuzzy Rough Nearest Neighbour,Neuro-Fuzzy Approach, with Deep Learning procedures such as Deep Recurrent ConvolutionNeural Network, Deep Neural Decision Trees, Deep Learning Linear Support Vector Machines.Our models have a large effect on the suitability of macroeconomic policy in the face of foreigninvestment risks by delivering instruments that contribute to bringing about financial stabilityat the global level.
PB Vilnius Gediminas Technical University
YR 2024
FD 2024
LK https://hdl.handle.net/10630/32301
UL https://hdl.handle.net/10630/32301
LA eng
NO Alaminos, D., Salas, M. B., & Fernández-Gámez, M. A. (2024). Global patterns and extreme events in sovereign risk premia: a fuzzy vs deep learning comparative. Technological and Economic Development of Economy, 30(3), 753–782. https://doi.org/10.3846/tede.2024.20488
NO This research received funding from the University of Málaga, and from the Cátedra deEconomía y Finanzas Sostenibles (University of Málaga). Additionally, we also appreciate thefinancial support from the University of Barcelona (under the grant UB-AE-AS017634).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 25 ene 2026