Addressing the EU sovereign ratings using an ordinal regression approach
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- Áreas de investigación:
- Año:
- 2013
- Tipo de publicación:
- Artículo
- Palabras clave:
- country risk detection, negative correlation learning, neural networks, ordinal regression
- Autores:
-
- Fernandez-Navarro, Francisco
- Campoy-Muñoz, Pilar
- de la Paz Marín, Mónica
- Hervás-Martínez, César
- Yao, Xin
- Journal:
- IEEE Transactions on Cybernetics
- Volumen:
- 43
- Número:
- 6
- Páginas:
- 2228-2240
- Mes:
- December
- ISSN:
- 2168-2291
- Nota:
- JCR 2011
- Abstract:
- The current European debt crisis has drawn considerable attention to credit rating agencies’ news about sovereign ratings. From a technical point of view, credit rating constitutes a typical ordinal regression problem because credit rating agencies generally present a scale of risk composed several categories. This fact motivated the use of an ordinal regression approach for addressing the problem of sovereign credit-rating in this paper. Therefore, the ranking of different classes will be taken into account for the design of the classifier. To do so, a novel model is introduced in order to replicate sovereign rating, based on the Negative Correlation Learning framework. The methodology is fully described in the paper, and applied to the classification of the 27 European countries’ sovereign rating during the 2007-2010 period based on Standard and Poor’s reports. The proposed technique seems to be competitive and robust enough to classify the sovereign ratings reported by this agency when compared to other existing well-known ordinal and nominal methods.
