Predicting Sovereign Debt Crises Using Artificial Neural Networks: A Comparative Approach
AbstractRecent 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.
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Bibliographic InfoPaper provided by ISTAT - Italian National Institute of Statistics - (Rome, ITALY) in its series ISAE Working Papers with number 72.
Length: 32 pages
Date of creation: Oct 2006
Date of revision:
Early Warning System; Financial Crisis; Sovereign Debt Crises; Artificial Neural Network.;
Other versions of this item:
- Fioramanti, Marco, 2008. "Predicting sovereign debt crises using artificial neural networks: A comparative approach," Journal of Financial Stability, Elsevier, vol. 4(2), pages 149-164, June.
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-02-24 (All new papers)
- NEP-CMP-2007-02-24 (Computational Economics)
- NEP-ECM-2007-02-24 (Econometrics)
- NEP-ICT-2007-02-24 (Information & Communication Technologies)
- NEP-NEU-2007-02-24 (Neuroeconomics)
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