Exploring Bayesian Hierarchical Models for Multi-Level Credit Risk Assessment: Detailed Insights

In this paper, we examine the use of Bayesian Hierarchical Models (BHMs) for multi-level credit risk assessment while focusing on their advantages compared to conventional valuation approaches of single-level models. Unlike most traditional methodologies, which consider events either separately or condition on an aggregate measure, each of the BHMs systematically incorporates data from different levels — loan or obligor level and institution level — to provide a more holistic view of credit risk under numerous uncertainties and dependencies. The paper reviews basic theoretical underpinnings of BHMs, such as Bayesian inference and hierarchical Modeling, while giving examples on how these mechanisms work in practice within the context of estimating default risk. In addition, the paper outlines computational challenges, highlights the role of prior distributions, and explains that BHMs could potentially be combined with machine learning for dynamic risk assessments. The paper highlights a real-world application, and provides detailed insights into how BHMs can help improve both the accuracy and interpretability of credit risk assessments.