TY  - BOOK
AU  - Baesens, Bart; Rosch, Daniel; Scheule, Harald
TI  - Credit risk analytics: measurement techniques, applications, and examples in SAS
SN  - 978-8126567027
U1  - 332.102855 BAE 
PY  - 2017///
CY  - New Delhi
PB  - Wiley
KW  - Credit risk
KW  - Economics
N1  - Rs.999/-  Gratis received from the publisher; 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 
Chapter 13 Model Validation  
Chapter 14 Stress Testing 
Chapter 15 Concluding Remarks
N2  - 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.-
ER  -