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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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