000			04219nam a2200277Ia 4500
003			OSt
005			20260216152639.0
008			210716s2018 xx 000 0 und d
020			_a9788126567027
040			_cAIMIT LIBRARY
041			_aeng
082			_a658.4038011 _21 _bBAEB
100			_aBaesens, Bart. _9255029
245			_aCredit risk analytics : _bmeasurement techniques applications and examples in SAS / _cBy Bart Baesens, Daniel Rosch and Harald Scheule.
250			_a1st ed.
260			_aNew Delhi : _bWiley India Pvt Ltd , _c2018.
300			_axiv,498p. ; _bPB _c24.2 cm
500			_aThe 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.
505			_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 Index 481 _rAbout the Author 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.
650			_aExploratory data analysis _9255030
650			_aLow default portfolios _9255031
650			_aModel validation _9255032
700			_aRosch, Daniel. _9255033
700			_aScheule, Harald. _9255034
942			_2ddc _cBK _e1st _k658.4038011 BABE
999			_c210829 _d210829