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Towards an early warning system for sovereign defaults leveraging on machine learning methodologies

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  • Anastasios Petropoulos
  • Vasilis Siakoulis
  • Evangelos Stavroulakis

Abstract

In this study, we address the topic of credit risk stemming from central governments from a technical point of view. First, we explore various econometric and machine learning techniques to build an enhanced sovereign rating system that effectively differentiates the risk of default among countries. Our empirical results indicate that the machine learning method of XGBOOST has a superior out‐of‐sample and out‐of‐time predictive performance. Then, we use the models developed to calibrate a sovereign rating system and provide useful insights into the set‐up of a parsimonious early warning system. Our results provide a more concise view of the most robust method for classifying countries’ default risk with significant regulatory implications, given that the efficient assessment of sovereign debt is crucial for effective proactive risk measurement.

Suggested Citation

  • Anastasios Petropoulos & Vasilis Siakoulis & Evangelos Stavroulakis, 2022. "Towards an early warning system for sovereign defaults leveraging on machine learning methodologies," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(2), pages 118-129, April.
    • Handle: RePEc:wly:isacfm:v:29:y:2022:i:2:p:118-129
    DOI: 10.1002/isaf.1516
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    References listed on IDEAS

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    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. Chen, Sheng-Syan & Chen, Hsien-Yi & Chang, Chong-Chuo & Yang, Shu-Ling, 2016. "The relation between sovereign credit rating revisions and economic growth," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 90-100.
    3. Manasse, Paolo & Roubini, Nouriel, 2009. ""Rules of thumb" for sovereign debt crises," Journal of International Economics, Elsevier, vol. 78(2), pages 192-205, July.
    4. Rick van de Ven & Shaunak Dabadghao & Arun Chockalingam, 2018. "Assigning Eurozone sovereign credit ratings using CDS spreads," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 19(5), pages 478-512, August.
    5. Viral Acharya & Itamar Drechsler & Philipp Schnabl, 2014. "A Pyrrhic Victory? Bank Bailouts and Sovereign Credit Risk," Journal of Finance, American Finance Association, vol. 69(6), pages 2689-2739, December.
    6. Raymond A.K. Cox & Grace W.-Y. Wang, 2014. "Predicting the US bank failure: A discriminant analysis," Economic Analysis and Policy, Elsevier, vol. 44(2), pages 202-211.
    7. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    8. Bedendo, Mascia & Colla, Paolo, 2015. "Sovereign and corporate credit risk: Evidence from the Eurozone," Journal of Corporate Finance, Elsevier, vol. 33(C), pages 34-52.
    9. Graciela Laura Kaminsky & Pablo Vega-García, 2016. "Systemic and Idiosyncratic Sovereign Debt Crises," Journal of the European Economic Association, European Economic Association, vol. 14(1), pages 80-114.
    10. Veni Arakelian & Petros Dellaportas & Roberto Savona & Marika Vezzoli, 2019. "Sovereign risk zones in Europe during and after the debt crisis," Quantitative Finance, Taylor & Francis Journals, vol. 19(6), pages 961-980, June.
    11. De Moor, Lieven & Luitel, Prabesh & Sercu, Piet & Vanpée, Rosanne, 2018. "Subjectivity in sovereign credit ratings," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 366-392.
    12. Roberto Savona & Marika Vezzoli, 2015. "Fitting and Forecasting Sovereign Defaults using Multiple Risk Signals," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 66-92, February.
    13. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    14. Carmen M. Reinhart & Kenneth S. Rogoff, 2011. "From Financial Crash to Debt Crisis," American Economic Review, American Economic Association, vol. 101(5), pages 1676-1706, August.
    15. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    16. Mr. Andrew J Tiffin, 2019. "Machine Learning and Causality: The Impact of Financial Crises on Growth," IMF Working Papers 2019/228, International Monetary Fund.
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