}

PayPal accepted MORE INFO

Close Notification

Your cart does not contain any items

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

Bart Baesens Daniel Roesch Harald Scheule

$133.95

Hardback

We can order this in for you
How long will it take?

QTY:

John Wiley & Sons Inc
23 September 2016
Credit & credit institutions; Mathematical modelling
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.
By:   Bart Baesens, Daniel Roesch, Harald Scheule
Imprint:   John Wiley & Sons Inc
Country of Publication:   United States
Dimensions:   Height: 242mm,  Width: 184mm,  Spine: 29mm
Weight:   984g
ISBN:   9781119143987
ISBN 10:   1119143985
Series:   Wiley and SAS Business Series
Pages:   512
Publication Date:   23 September 2016
Audience:   Professional and scholarly ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active
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

BART BAESENS is a professor at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom). DANIEL ROESCH 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.

See Also

Too Big to Ignore: The Business...

Phil Simon

Hardback

Statistical Thinking: Improving...

Roger W. Hoerl

Hardback

Human Capital Analytics: How to...

Gene Pease

Hardback

Analytics in a Big Data World: The...

Bart Baesens

Hardback

Big Data, Big Innovation: Enabling...

Evan Stubbs

Hardback

Predictive Analytics for Human Resources...

Jac Fitz-enz

Hardback