Introduction to Credit Risk Modeling, Second Edition

Credit risk modeling brings probability, portfolio analysis, and financial decision-making together. In Introduction to Credit Risk Modeling, Second Edition, Christian Bluhm, Ludger Overbeck, and Christoph Wagner build from core measures of credit loss toward the models used to analyze correlated defaults, allocate capital, and assess complex credit products.

The book is organized as a progression through the subject: foundational concepts lead into portfolio models and loss distributions, then into risk measures, credit derivatives, and collateralized debt obligations. Its second edition updates key discussions and adds material on topics including spectral risk measures, nonhomogeneous Markov chains, and multi-period credit models.

From expected loss to portfolio models

The opening chapters establish the components of credit loss: probability of default, exposure at default, and loss given default. From there, the authors consider unexpected loss, economic and regulatory capital, and ways to represent dependence between defaults. Bernoulli and Poisson models, factor and sector approaches, copulas, and methods for generating portfolio loss distributions give readers several modeling frameworks to compare.

Connect credit risk theory with applications

Further chapters introduce asset-value models, including Merton’s approach, and develop the CreditRisk+ model. The discussion then turns to coherent risk measures, expected shortfall, and methods for allocating capital across contributors to portfolio risk.

Explore credit through time and across products

The later sections address default-probability term structures, credit derivatives, and collateralized debt obligations. Topics include survival functions and hazard rates, migration and multi-period models, and the modeling of structured credit. Taken together, these chapters show how credit risk analysis extends beyond estimating whether a borrower may default to examining how losses behave across a portfolio and over time.

For readers working with quantitative finance

This book may suit students and practitioners who want a structured introduction to the mathematical approaches behind credit portfolio modeling. Its breadth is especially useful for readers comparing modeling assumptions and techniques across risk measurement, capital allocation, and credit products.