Empirical Models of Sovereign Credit Ratings: A Critical Systematic Review and Future Research Directions

Abstract

Research Question: What are the prevailing modelling approaches, determinants, and limitations in empirical sovereign credit rating models? Motivation: Sovereign credit ratings occupy a central position in capital markets, both as a transmission mechanism through which sovereign credit risk is reflected in bond yields and other financial asset prices, and as an institutional input embedded within investment mandates and risk management frameworks. This dual role has generated an expanding body of empirical research, warranting a structured synthesis to reconcile modelling approaches and assess their limitations. Idea: This paper undertakes a critical, structured review of the empirical sovereign credit rating literature, with the objective of synthesising existing research, clarifying prevailing modelling conventions, and identifying methodological and empirical gaps. Data: The analysis draws on the existing body of empirical studies on sovereign credit ratings, spanning multiple countries, time periods, and data constructions documented in the academic literature. Method/Tools: A structured taxonomic framework is applied to classify prior studies along key dimensions, including modelling methodologies, the composition of explanatory variables, and sample characteristics. Findings: The classification reveals clear regularities alongside systematic gaps. First, the literature exhibits a pronounced reliance on conventional regression-based approaches, while alternative methodologies, including dimension reduction techniques and artificial intelligence methods, remain comparatively underutilised despite evidence of their ability to capture latent structures and enhance predictive performance. Second, most studies rely on broad pooled samples of sovereigns, with limited stratification by economic development, credit quality, or geographic scope, even though model estimates are sensitive to sample composition. Third, there is a structural imbalance in variable selection, with a dominant emphasis on macroeconomic fundamentals, while financial market indicators, particularly fixed income variables, receive comparatively limited attention despite their direct relevance to sovereign credit risk. Contributions: The paper provides a structured synthesis that enhances comparability across studies and clarifies points of convergence and divergence in existing findings. It also outlines a forward-looking research agenda, emphasising the need for more granular sample construction, broader adoption of alternative modelling techniques, and deeper integration of financial market variables to strengthen the explanatory and contextual validity of sovereign credit rating models.