Predicting sovereign debt crises using artificial neural networks: A comparative approach

Marco Fioramanti

Journal of Financial Stability, 2008, vol. 4, issue 2, 149-164

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.

Date: 2008
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Working Paper: Predicting Sovereign Debt Crises Using Artificial Neural Networks: A Comparative Approach (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finsta:v:4:y:2008:i:2:p:149-164

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