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

Anastasios Petropoulos, Vasilis Siakoulis and Evangelos Stavroulakis

Intelligent Systems in Accounting, Finance and Management, 2022, vol. 29, issue 2, 118-129

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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:wly:isacfm:v:29:y:2022:i:2:p:118-129

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