Brújula Home

Institutional repository of the Universidad Loyola

View Item 
  •   Brújula Home
  • PRODUCCIÓN CIENTÍFICA Y TRANSFERENCIA
  • Departamento Métodos Cuantitativos
  • Artículos
  • View Item
  •   Brújula Home
  • PRODUCCIÓN CIENTÍFICA Y TRANSFERENCIA
  • Departamento Métodos Cuantitativos
  • Artículos
  • View Item
    • español
    • English
JavaScript is disabled for your browser. Some features of this site may not work without it.

Browse

All of BrújulaCommunities and CollectionsAuthorsTitlesKeywordsAuthor profilesThis CollectionAuthorsTitlesKeywords

My Account

Login

Statistics

View Usage Statistics

Añadido Recientemente

Novedades
Repository
How to publish
Visibility
FAQs

Revisiting the determinants of sovereign debt ratings in Europe through artificial intelligence techniques

Author:
Galnares Jiménez-Placer, Carlos; Martínez Estudillo, Alfonso CarlosUniversidad Loyola Authority; Carbonero Ruz, MarianoUniversidad Loyola Authority; Campoy Muñoz, María Del PilarUniversidad Loyola Authority
URI:
https://hdl.handle.net/20.500.12412/7183
ISSN:
0165-1765
Date:
2022-06-06
Keyword(s):

Sovereign credit ratings

credit rating agencies

EU-15

artificial intelligence

Abstract:

In papers using artificial intelligence (AI) techniques, little attention has been paid to the determinants of sovereign debt ratings. We propose a reduced set of variables regarding the economic performance of a country that are consistent with the idea of debt sustainability. The robustness of this set is supported by the results obtained with different well-known AI techniques using data from EU-15 countries during the 2002–2017 period as the experimental setting. The variables are publicly available, allowing a quick and reliable assessment of the creditworthiness of a sovereign and providing useful information for decision-makers and investors.

In papers using artificial intelligence (AI) techniques, little attention has been paid to the determinants of sovereign debt ratings. We propose a reduced set of variables regarding the economic performance of a country that are consistent with the idea of debt sustainability. The robustness of this set is supported by the results obtained with different well-known AI techniques using data from EU-15 countries during the 2002–2017 period as the experimental setting. The variables are publicly available, allowing a quick and reliable assessment of the creditworthiness of a sovereign and providing useful information for decision-makers and investors.

Show full item record
Collections
  • Artículos
Files in this item
Thumbnail
3. Aportación. Revisiting the determinants of sovereign debt ratings in Europe through artificial intelligence techniques.pdf (648.1Kb)
Share
Export to Mendeley
Statistics
Usage statistics
Metrics and citations
Go to Brújula home

Universidad Loyola

Library

Contact

Facebook Loyola BibliotecaTwitter Loyola Biblioteca

The content of the Repository is protected with a Creative Commons license:

Attribution-NonCommercial-NoDerivatives 4.0 Internacional

Creative Commons Image