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

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  • Fioramanti, Marco

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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Financial Stability.

Volume (Year): 4 (2008)
Issue (Month): 2 (June)
Pages: 149-164
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Handle: RePEc:eee:finsta:v:4:y:2008:i:2:p:149-164

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  1. Kaminsky, Graciela & Lizondo, Saul & Reinhart, Carmen M., 1997. "Leading indicators of currency crises," Policy Research Working Paper Series 1852, The World Bank.
  2. Ciarlone, Alessio & Trebeschi, Giorgio, 2005. "Designing an early warning system for debt crises," Emerging Markets Review, Elsevier, vol. 6(4), pages 376-395, December.
  3. Paolo Manasse & Axel Schimmelpfennig & Nouriel Roubini, 2003. "Predicting Sovereign Debt Crises," IMF Working Papers 03/221, International Monetary Fund.
  4. Abdul Abiad, 2003. "Early Warning Systems: A Survey and a Regime-Switching Approach," IMF Working Papers 03/32, International Monetary Fund.
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Cited by:
  1. Peter Sarlin & Tuomas A. Peltonen, 2011. "Mapping the state of financial stability," Working Paper Series 1382, European Central Bank.
  2. Sarlin, Peter & Peltonen, Tuomas A., 2011. "Mapping the State of Financial Stability," BOFIT Discussion Papers 18/2011, Bank of Finland, Institute for Economies in Transition.

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