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

Author

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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.

Suggested Citation

  • 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).
  • Handle: RePEc:isa:wpaper:72
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Ciarlone, Alessio & Trebeschi, Giorgio, 2005. "Designing an early warning system for debt crises," Emerging Markets Review, Elsevier, vol. 6(4), pages 376-395, December.
    4. André Fourçans & Raphaël Franck, 2003. "Currency Crises," Books, Edward Elgar Publishing, number 3124.
    5. Graciela L. Kaminsky, 2003. "Varieties of Currency Crises," NBER Working Papers 10193, National Bureau of Economic Research, Inc.
    6. Axel Schimmelpfennig & Nouriel Roubini & Paolo Manasse, 2003. "Predicting Sovereign Debt Crises," IMF Working Papers 03/221, International Monetary Fund.
    7. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," Review of Economic Studies, Oxford University Press, vol. 47(1), pages 225-238.
    8. Abdul d Abiad, 2003. "Early Warning Systems; A Survey and a Regime-Switching Approach," IMF Working Papers 03/32, International Monetary Fund.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Dawood, Mary & Horsewood, Nicholas & Strobel, Frank, 2017. "Predicting sovereign debt crises: An Early Warning System approach," Journal of Financial Stability, Elsevier, vol. 28(C), pages 16-28.
    2. Makram El-Shagi & Gregor von Schweinitz, 2016. "Qual VAR revisited: Good forecast, bad story," Journal of Applied Economics, Universidad del CEMA, vol. 19, pages 293-322, November.
    3. 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.
    4. 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.
    5. Petr Hájek & Michal Střižík & Pavel Praks & Petr Kadeřábek, 2009. "Možnosti využití přístupu latentní sémantiky při předpovídání finančních krizí
      [Possibilities of Financial Crises Forecasting with Latent Semantic Indexing]
      ," Politická ekonomie, University of Economics, Prague, vol. 2009(6), pages 754-768.
    6. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents' expectations. Different patterns of anticipation of the 2008 financial crisis”," IREA Working Papers 201511, University of Barcelona, Research Institute of Applied Economics, revised Mar 2015.
    7. 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.
    8. Sebastián Nieto-Parra, 2009. "Who Saw Sovereign Debt Crises Coming?," ECONOMIA JOURNAL, THE LATIN AMERICAN AND CARIBBEAN ECONOMIC ASSOCIATION - LACEA, vol. 0(Fall 2009), pages 125-169, August.
    9. 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, Elsevier, vol. 42(1), pages 79-96, March.
    10. Kim Ristolainen, 2015. "Were the Scandinavian Banking Crises Predictable? A Neural Network Approach," Discussion Papers 99, Aboa Centre for Economics.
    11. 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.
    12. Elgin, Ceyhun & Uras, Burak R., 2013. "Public debt, sovereign default risk and shadow economy," Journal of Financial Stability, Elsevier, vol. 9(4), pages 628-640.
    13. 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.
    14. 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.
    15. Markus Holopainen & Peter Sarlin, 2015. "Toward robust early-warning models: A horse race, ensembles and model uncertainty," Papers 1501.04682, arXiv.org, revised Apr 2016.
    16. 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.
    17. Carlos León & José Fernando Moreno & Jorge Cely, 2016. "Whose Balance Sheet is this? Neural Networks for Banks’ Pattern Recognition," Borradores de Economia 959, Banco de la Republica de Colombia.
    18. 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.
    19. Fuat SEKMEN & Murat KURKCU, 2014. "An Early Warning System for Turkey: The Forecasting Of Economic Crisis by Using the Artificial Neural Networks," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 4(4), pages 529-543, April.
    20. repec:spr:fininn:v:1:y:2015:i:1:d:10.1186_s40854-015-0005-6 is not listed on IDEAS

    More about this item

    Keywords

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

    JEL classification:

    • 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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