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Predicting Sovereign Debt Crises Using Artificial Neural Networks: A Comparative Approach

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  • Marco Fioramanti

    (ISAE - Institute for Studies and Economic Analyses
    University of Pescara, Faculty of Economics)

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.

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File URL: http://lipari.istat.it/digibib/Working_Papers/WP_72_2006_Fioramanti.pdf
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Bibliographic Info

Paper provided by ISTAT - Italian National Institute of Statistics - (Rome, ITALY) in its series ISAE Working Papers with number 72.

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Length: 32 pages
Date of creation: Oct 2006
Date of revision:
Handle: RePEc:isa:wpaper:72

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

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  1. Chamberlain, Gary, 1980. "Analysis of Covariance with Qualitative Data," Review of Economic Studies, Wiley Blackwell, vol. 47(1), pages 225-38, January.
  2. Graciela Kaminsky & Saul Lizondo & Carmen M. Reinhart, 1998. "Leading Indicators of Currency Crises," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 1-48, March.
  3. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "Currency crashes in emerging markets: An empirical treatment," Journal of International Economics, Elsevier, vol. 41(3-4), pages 351-366, November.
  4. Ciarlone, Alessio & Trebeschi, Giorgio, 2005. "Designing an early warning system for debt crises," Emerging Markets Review, Elsevier, vol. 6(4), pages 376-395, December.
  5. Abdul Abiad, 2003. "Early Warning Systems," IMF Working Papers 03/32, International Monetary Fund.
  6. Axel Schimmelpfennig & Nouriel Roubini & Paolo Manasse, 2003. "Predicting Sovereign Debt Crises," IMF Working Papers 03/221, International Monetary Fund.
  7. Graciela L. Kaminsky, 2003. "Varieties of Currency Crises," NBER Working Papers 10193, National Bureau of Economic Research, Inc.
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Cited by:
  1. Makram El-Shagi & Gregor von Schweinitz, 2012. "Qual VAR Revisited: Good Forecast, Bad Story," IWH Discussion Papers 12, Halle Institute for Economic Research.
  2. Ceyhun Elgin & Burak R. Uras, 2012. "Public Debt, Sovereign Default Risk and Shadow Economy," Working Papers 2012/10, Bogazici University, Department of Economics.
  3. Sebastián Nieto-Parra, 2009. "Who Saw Sovereign Debt Crises Coming?," JOURNAL OF LACEA ECONOMIA, LACEA - LATIN AMERICAN AND CARIBBEAN ECONOMIC ASSOCIATION.
  4. Mioara CHIRITA, 2012. "Usefulness of Artificial Neural Networks for Predicting Financial and Economic Crisis," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 61-66.
  5. Ayşe Özmen & Gerhard-Wilhelm Weber & Zehra Çavuşoğlu & Özlem Defterli, 2013. "The new robust conic GPLM method with an application to finance: prediction of credit default," Journal of Global Optimization, Springer, vol. 56(2), pages 233-249, June.
  6. Sarlin, Peter & Peltonen, Tuomas A., 2013. "Mapping the state of financial stability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 46-76.
  7. Eleftherios Giovanis, 2010. "Application of logit model and self-organizing maps (SOMs) for the prediction of financial crisis periods in US economy," Journal of Financial Economic Policy, Emerald Group Publishing, vol. 2(2), pages 98-125, June.
  8. Eleftherios Giovanis, 2012. "Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA," Economic Analysis and Policy (EAP), Queensland University of Technology (QUT), School of Economics and Finance, vol. 42(1), pages 79-96, March.
  9. Bandiera, Luca & Cuaresma, Jesus Crespo & Vincelette, Gallina A., 2010. "Unpleasant surprises : sovereign default determinants and prospects," Policy Research Working Paper Series 5401, The World Bank.
  10. Sevim, Cuneyt & Oztekin, Asil & Bali, Ozkan & Gumus, Serkan & Guresen, Erkam, 2014. "Developing an early warning system to predict currency crises," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1095-1104.
  11. Mioara CHIRITA & Daniela SARPE, 2011. "Usefulness of Artificial Neural Networks for Predicting Financial and Economic Crisis," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 44-48.
  12. Petr Hájek & Michal Střižík & Pavel Praks & Petr Kadeřábek, 2009. "Possibilities of Financial Crises Forecasting with Latent Semantic Indexing," Politická ekonomie, University of Economics, Prague, vol. 2009(6), pages 754-768.

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