Credit Risk Analytics

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

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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.
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Product details

  • Hardback | 512 pages
  • 185 x 240 x 30mm | 975.22g
  • New York, United States
  • English
  • 1. Auflage
  • 1119143985
  • 9781119143987
  • 392,437

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Credit risk analytics is undoubtedly one of the most crucial players in the field of financial risk management. With the recent financial downturn and the regulatory changes introduced by the Basel accords, credit risk analytics has been attracting greater attention from the banking and finance industries worldwide.

Now, risk professionals have an inclusive, targeted training guide to producing quality, standardized, and scalable in-house models for credit risk management. Credit Risk Analytics begins with a complete primer on SAS, including how to explicitly program and code the various data steps and models, extract information from data without having to rely on programming, compute basic statistics, and pre-process data. Whether you're building a model from scratch or validating an existing one, this single resource gives you all the insight and practical advice you need on such critical issues as regulatory requirements and stress-testing of credit risk models, including marginal loss given default (LGD) and exposure at default (EAD) models.

A state-of-the-art companion website expedites real-world implementation with clarifying examples of both actual and simulated credit portfolio data, as well as added practical guidance from the author team. This expert resource enables you to: Master the critical probability of default parameter of risk management, including converting credit scores and other information into default probabilities using discrete-time and continuous-time hazard models Estimate default and asset correlations and create loss distributions using analytical methods and Monte Carlo simulation Build on various models throughout the book with capstone modeling strategies, including Bayesian models

No other solutions package provides the depth of coverage and level of expertise on aligning risk management theory with the latest code. Keep Credit Risk Analytics at your fingertips for everything you need to analyze credit risk of loans and loan portfolios in the commercial banking industry.
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Back cover copy


Risk managers who want to stay competitive in today's marketplace need Credit Risk Analytics to streamline their modeling processes. Despite the high demand for in-house models, this pioneering guidebook is the only complete, focused resource of expert guidance on building and validating accurate, state-of-the-art credit risk management models. Written by a proven author team with international experience, this hands-on road map takes you from the fundamentals of credit risk management to implementing proven strategies in a real-world environment using SAS(R) software. With the same dependability, clarity, and commitment to excellence books in the Wiley and SAS Business Series are known for, this latest addition enables you to: Exercise proficiency in credit risk management, from applied theory to various real-life case studies Build models from the ground up, as well as validate and stress-test existing models Access exclusive, online materials and a supportive community on a companion website

Spend less time searching for answers and more time exploiting observable and unobservable information in the most efficient ways with Credit Risk Analytics.
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Table 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
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About Harald Scheule

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