Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS
Credit Risk Analytics provides a targeted training guide for risk managers looking to efficiently build or validate in-house models for credit risk management. Combining theory with practice, this book walks you through the fundamentals of credit risk management and shows you how to implement these concepts using the SAS credit risk management program, with helpful code provided. Coverage includes data analysis and preprocessing, credit scoring; PD and LGD estimation and forecasting, low default portfolios, correlation modeling and estimation, validation, implementation of prudential regulation, stress testing of existing modeling concepts, and more, to provide a one-stop tutorial and reference for credit risk analytics. The companion website offers examples of both real and simulated credit portfolio data to help you more easily implement the concepts discussed, and the expert author team provides practical insight on this real-world intersection of finance, statistics, and analytics.
SAS is the preferred software for credit risk modeling due to its functionality and ability to process large amounts of data. This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate credit risk management models.
- Understand the general concepts of credit risk management
- Validate and stress-test existing models
- Access working examples based on both real and simulated data
- Learn useful code for implementing and validating models in SAS
Despite the high demand for in-house models, there is little comprehensive training available; practitioners are left to comb through piece-meal resources, executive training courses, and consultancies to cobble together the information they need. This book ends the search by providing a comprehensive, focused resource backed by expert guidance. Credit Risk Analytics is the reference every risk manager needs to streamline the modeling process.
About the Authors xiii
Chapter 1 Introduction to Credit Risk Analytics 1
Chapter 2 Introduction to SAS Software 17
Chapter 3 Exploratory Data Analysis 33
Chapter 4 Data Preprocessing for Credit Risk Modeling 57
Chapter 5 Credit Scoring 93
Chapter 6 Probabilities of Default (PD): Discrete-Time Hazard Models 137
Chapter 7 Probabilities of Default: Continuous-Time Hazard Models 179
Chapter 8 Low Default Portfolios 213
Chapter 9 Default Correlations and Credit Portfolio Risk 237
Chapter 10 Loss Given Default (LGD) and Recovery Rates 271
Chapter 11 Exposure at Default (EAD) and Adverse Selection 315
Chapter 12 Bayesian Methods for Credit Risk Modeling 351
Chapter 13 Model Validation 385
Chapter 14 Stress Testing 445
Chapter 15 Concluding Remarks 475
BART BAESENS is a professor at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom).
DANIEL RÖSCH is a professor in business and management and chair in statistics and risk management at the University of Regensburg (Germany).
HARALD SCHEULE is an associate professor of finance at the University of Technology Sydney (Australia) and a regional director of the Global Association of Risk Professionals.