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

Posted: 20 Oct 2008

See all articles by Marco Fioramanti

Marco Fioramanti

Parliamentary Budget Office (PBO)

Multiple version iconThere are 2 versions of this paper

Date Written: February 23, 2008

Abstract

Recent episodes of financial crisis have revived interest in developing models able to signal their occurrence in timely manner. The literature has developed both parametric and non-parametric models, the so-called Early Warning Systems, to predict these crises. Using data related to sovereign debt crises which occurred in developing countries from 1980 to 2004, this paper shows that further progress can be achieved by applying a less developed non-parametric method based on artificial neural networks (ANN). Thanks to the high flexibility of neural networks and their ability to approximate non-linear relationship, 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

JEL Classification: F34, F37, C45, C14

Suggested Citation

Fioramanti, Marco, Predicting Sovereign Debt Crises Using Artificial Neural Networks: A Comparative Approach (February 23, 2008). Journal of Financial Stability, Vol. 4, No. 2, 2008, Available at SSRN: https://ssrn.com/abstract=1286150

Marco Fioramanti (Contact Author)

Parliamentary Budget Office (PBO) ( email )

Palazzo del Seminario (S. Macuto)
via del Seminario, 76
Roma, 00186
Italy

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