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Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS



Risk managers who want to stay competitive in today's marketplace need Credit Risk Analytics to streamline their modeling processes. Despite the high demand for in-house models, this pioneering guidebook is the only complete, focused resource of expert guidance on building and validating accurate, state-of-the-art credit risk management models. Written by a proven author team with international experience, this hands-on road map takes you from the fundamentals of credit risk management to implementing proven strategies in a real-world environment using SAS® software. With the same dependability, clarity, and commitment to excellence books in the Wiley and SAS Business Series are known for, this latest addition enables you to:

  • Exercise proficiency in credit risk management, from applied theory to various real-life case studies
  • Build models from the ground up, as well as validate and stress-test existing models
  • Access exclusive, online materials and a supportive community on a companion website

Spend less time searching for answers and more time exploiting observable and unobservable information in the most efficient ways with Credit Risk Analytics.

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.

Acknowledgments xi

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

Index 481