Predicting sovereign debt crises using artificial neural networks: A comparative approach
AbstractRecent 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Financial Stability.
Volume (Year): 4 (2008)
Issue (Month): 2 (June)
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Web page: http://www.elsevier.com/locate/jfstabil
Other versions of this item:
- Marco Fioramanti, 2006. "Predicting Sovereign Debt Crises Using Artificial Neural Networks: A Comparative Approach," ISAE Working Papers 72, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
- F34 - International Economics - - International Finance - - - International Lending and Debt Problems
- F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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