Predicting Sovereign Debt Crises Using Artificial Neural Networks: A Comparative Approach

Marco Fioramanti

No 72, ISAE Working Papers from ISTAT - Italian National Institute of Statistics - (Rome, ITALY)

Abstract: Recent episodes of financial crises have revived the interest in developing models that are able to timely signal their occurrence. The literature has developed both parametric and non parametric models to predict these crises, the so called Early Warning Systems. Using data related to sovereign debt crises occurred in developing countries from 1980 to 2004, this paper shows that a further progress can be done applying a less developed non-parametric method, i.e. Artificial Neural Networks (ANN). Thanks to the high flexibility of neural networks and to the Universal Approximation Theorem an ANN based early warning system can, under certain conditions, outperform more consolidated methods.

Keywords: Early Warning System; Financial Crisis; Sovereign Debt Crises; Artificial Neural Network. (search for similar items in EconPapers)
JEL-codes: C14 C45 F34 F37 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2006-10
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ict and nep-neu
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Journal Article: Predicting sovereign debt crises using artificial neural networks: A comparative approach (2008)
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