The calibration of rating models

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Across Europe, a significant drama unfolds as the debt crisis shakes the global financial landscape. Amid rising insecurity, the public, policymakers, scientists, and academics seek independent analyses to navigate this unusual situation. For over a century, rating agencies have established methods to evaluate companies, projects, and sovereign nations. However, their outdated processes have raised questions about their independence, leading to conflicts of interest that undermine the sector's credibility. Stakeholders discuss the vast economic and political ramifications of these assessments, including the opaque methodologies, questionable timing of announcements, accuracy, and focus on profitability. This work delves into the statistical tools used in credit rating and their validation. It situates the research within its institutional and historical context, presenting both standard and innovative techniques for understanding the statistical approach in credit rating. A new method for validating the Probability of Default, a key output of the rating model, is introduced. Theoretical discussions are supported by an extensive empirical study, illustrating practical applications. The methods developed here allow banks and regulators to statistically assess the consistency of rating methodologies concerning discriminatory power and calibration quality.