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Fitting and Forecasting Sovereign Defaults Using Multiple Risk Signals

Author

Listed:
  • Roberto Savona

    () (Department of Business Studies, University Of Brescia)

  • Marika Vezzoli

    (Department of Quantitative Methods, University Of Brescia)

Abstract

In this paper we face the fitting versus forecasting paradox with the objective of realizing an optimal Early Warning System to better describe and predict past and future sovereign defaults. We do this by proposing a new Regression Tree-based model that signals a potential crisis whenever preselected indicators exceed specific thresholds. Using data on 66 emerging markets over the period 1975-2002, our model provides an accurate description of past data, although not the best description relative to existing competing models (Logit, Stepwise logit, Noise-to-Signal Ratio and Regression Trees), and produces the best forecasts accomodating to different risk aversion targets. By modulating in- and out-of sample model accuracy, our methodology leads to unambiguous empirical results, since we find that illiquidity (short-term debt to reserves ratio), insolvency (reserve growth) and contagion risks act as the main determinants/predictors of past/future debt crises.

Suggested Citation

  • Roberto Savona & Marika Vezzoli, 2012. "Fitting and Forecasting Sovereign Defaults Using Multiple Risk Signals," Working Papers 2012_26, Department of Economics, University of Venice "Ca' Foscari".
  • Handle: RePEc:ven:wpaper:2012_26
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    References listed on IDEAS

    as
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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. repec:wly:ijfiec:v:22:y:2017:i:1:p:44-67 is not listed on IDEAS
    3. Gilles Dufrénot & Anne-Charlotte Paret, 2016. "Power-Law Distribution in the Debt-to-Fiscal Revenue Ratio: Empirical Evidence and a Theoretical Model," Working Papers halshs-01357797, HAL.
    4. repec:eee:finsta:v:35:y:2018:i:c:p:215-225 is not listed on IDEAS
    5. repec:vrs:demode:v:4:y:2016:i:1:p:26:n:15 is not listed on IDEAS
    6. P. Manasse & R. Savona & M. Vezzoli, 2013. "Rules of Thumb for Banking Crises in Emerging Markets," Working Papers wp872, Dipartimento Scienze Economiche, Universita' di Bologna.
    7. repec:eee:joecas:v:13:y:2016:i:c:p:100-113 is not listed on IDEAS
    8. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    9. Mark Joy & Marek Rusnák & Kateřina Šmídková & Bořek Vašíček, 2017. "Banking and Currency Crises: Differential Diagnostics for Developed Countries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 22(1), pages 44-67, January.
    10. Alessi, Lucia & Detken, Carsten, 2018. "Identifying excessive credit growth and leverage," Journal of Financial Stability, Elsevier, vol. 35(C), pages 215-225.
    11. Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions in Germany With Boosted Regression Trees," Macroeconomics and Finance Series 201505, University of Hamburg, Department of Socioeconomics.

    More about this item

    Keywords

    Data mining; Evaluating forecasts; Model selection; Panel data; Probability forecasting.;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • G01 - Financial Economics - - General - - - Financial Crises
    • H63 - Public Economics - - National Budget, Deficit, and Debt - - - Debt; Debt Management; Sovereign Debt

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