Predicting Sovereign Debt Crises Using Artificial Neural Networks: A comparative approach

by Marco Fioramanti of the Istituto di Studi e Analisi Economica - (ISAE)

October 2006

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.

JEL Classification: F34, F37, C45, C14.

Keywords: Early Warning System, Financial Crisis, Sovereign Debt Crises, Artificial Neural Network.

Published in: Journal of Financial Stability, Vol. 4, No. 2, (June 2008), pp. 149-164.

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