Credit Risk Modeling with R
A Step-by-Step Guide for Business, Finance, and Risk Analysts
Preface
This book is written for readers who want to close the gap between credit-risk models, the logic behind those models, and the code required to implement them. It is designed for business, finance, economics, and risk-management readers who want to understand quantitative credit risk through explicit calculations, reproducible R code, and careful financial interpretation.
A central feature of the book is its deliberately explicit style. It keeps intuition, mathematical structure, data work, graphics, tables, and R code in the same conversation. The computational details remain visible so the reader can see what the computer is being asked to do, why that operation is useful for the credit-risk problem, and how the numerical output should be interpreted.
The organizing principle follows the literate programming spirit of Knuth (1984). Code appears as part of the explanation and stays close to the financial question it answers. A table is introduced because it answers a credit question. A figure is included because it makes a model mechanism easier to see. A formula is connected to the R object that implements it. This is the reason the book often walks slowly through steps that more advanced texts may compress.
This is an online book under active revision. The publication metadata above records the first online publication, the current publication number, and a content-based book edition. Those markers are included so readers can tell which version they are using and so revisions remain traceable over time. The current emphasis is clarity, reproducibility, practical interpretation, and a steady connection between credit-risk theory and applied financial decisions.
What’s new in this edition
- Stronger book framing: The book now has a clearer introduction and a separate conclusion focused on credit decisions, default consequences, reproducibility, and model interpretation.
- Credit-scoring workflow: Chapter 1 connects logistic PDs with cutoff rules, bad rates, calibration, ROC/AUC, Brier score, and a lending payoff exercise.
- Tree-based credit scoring: Chapter 2 compares logistic regression, a single decision tree, and XGBoost, with a more transparent explanation of boosting rounds and individual PD updates.
- Structural default modeling: Chapter 3 gives a clearer Merton workflow, replicates Hull’s equations, checks the numerical solution, and links firm value, debt, equity, recovery, and risk-neutral default probability.
- Credit spreads and CDS valuation: Chapter 4 connects default probabilities with corporate bond spreads, recovery assumptions, Hull-style CDS valuation, CDS sensitivity, and a relative-value interpretation.
- Portfolio credit risk: Chapter 5 develops Gaussian and t-copula credit simulations, correlated defaults, concentration risk, portfolio loss distributions, Credit VaR, expected shortfall, and economic capital.
- Climate credit risk: Chapter 6 uses public NGFS/CLIMACRED scenario data to connect climate-adjusted PDs with expected loss, sector contributions, spread adjustments, CDS-style compensation, and simulated portfolio losses.