000			03604nam a22002417a 4500
999			_c237 _d237
005			20190829173331.0
008			190829b ||||| |||| 00| 0 eng d
020			_a9788126567027
082			_a332.10285555 _bBAE
100			_aBaesens, Bart _9663
245			_aCredit risk analytics: measurement techniques, applications, and examples in SAS
260			_bWiley India Pvt. Ltd. _aNew Delhi _c2019
300			_axiv, 498 p.
365			_aINR _b1099.00
504			_aTABLE OF CONTENTS 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
520			_aDESCRIPTION The long-awaited, comprehensive guide to practical credit risk modeling 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.
650			_aRisk management--Data processing _9664
650			_aBank loans--Data processing _9665
650			_aCredit--Management--Data processing _9667
700			_aRosch, Daniel _9668
700			_aScheule, Harald _9669
942			_2ddc _cBK