Addressing the EU sovereign ratings using an ordinal regression approach

Francisco FERNÁNDEZ-NAVARRO, Pilar CAMPOY-MUÑOZ, Mónica-De LA PAZ-MARÍN, César HERVÁS-MARTÍNEZ, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

26 Citations (Scopus)

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 of several categories. This fact motivated the use of an ordinal regression approach to address 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 this 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 with other existing well-known ordinal and nominal methods. © 2013 IEEE.
Original languageEnglish
Article number6532312
Pages (from-to)2228-2240
Number of pages13
JournalIEEE Transactions on Cybernetics
Volume43
Issue number6
Early online date14 Jun 2013
DOIs
Publication statusPublished - Dec 2013
Externally publishedYes

Keywords

  • Country risk detection
  • Negative correlation learning (NCL)
  • Neural networks
  • Ordinal regression

Fingerprint

Dive into the research topics of 'Addressing the EU sovereign ratings using an ordinal regression approach'. Together they form a unique fingerprint.

Cite this