Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS
Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS
Bart Baesens, Daniel Roesch, Harald Scheule
ISBN: 9788126567027
512 pages
Description
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.
About the Author
Harald (Harry) Scheule , is Associate Professor of Finance at the University of Technology, Sydney. He is a regional director of the Global Association of Risk Professionals. His award-winning research has been widely cited and published in leading journals. He currently serves on the editorial board of the Journal of Risk Model Validation. Scheule has worked with prudential regulators of financial institutions and undertaken consulting work for a wide range of financial institutions and service providers in Asia, Australia, Europe and North America.
Bart Baesens is an associate professor at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on analytics, customer relationship management, web analytics, fraud detection and credit risk management. He regularly advises and provides consulting support to international firms with respect to their analytics and credit risk management strategy.
Daniel Roesch, Ph.D, holds the chair in Statistics and Risk Management at the University of Regensburg. Prior to joining the University of Regensburg in 2013 he was Professor of Finance and Director of the Institute of Banking of Finance at the Leibniz University of Hannover from 2007 to 2013. He is the current President of the German Finance Association, co-founder and member of the board of directors of the Hannover Center of Finance, and deputy managing director of the workgroup Finance and Financial Institutions of the Operations Research Society.
Table of Contents
Acknowledgments
About the Authors
Chapter 1 Introduction to Credit Risk Analytics
Chapter 2 Introduction to SAS Software
Chapter 3 Exploratory Data Analysis
Chapter 4 Data Preprocessing for Credit Risk Modeling
Chapter 5 Credit Scoring
Chapter 6 Probabilities of Default (PD): Discrete-Time Hazard Models
Chapter 7 Probabilities of Default: Continuous-Time Hazard Models
Chapter 8 Low Default Portfolios
Chapter 9 Default Correlations and Credit Portfolio Risk
Chapter 10 Loss Given Default (LGD) and Recovery Rates
Chapter 11 Exposure at Default (EAD) and Adverse Selection
Chapter 12 Bayesian Methods for Credit Risk Modeling
