Credit Risk Analytics
Measurement Techniques, Applications, and Examples in SAS
Bart Baesens, Daniel Roesch, Harald Scheule
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Credit Risk Analytics
Measurement Techniques, Applications, and Examples in SAS
Bart Baesens, Daniel Roesch, Harald Scheule
About This Book
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.
Frequently asked questions
Chapter 1
Introduction to Credit Risk Analytics
- 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
- Exploit the capabilities of this high-powered package to create clean and accurate credit risk management models
WHY THIS BOOK IS TIMELY
Current Challenges in Credit Risk Analytics
- Implementation of Basel III: The Basel rules concern capital increases in terms of quantity and quality, leverage ratios, liquidity ratios, and impact analysis. We will discuss the Basel rules in more detail later.
- Stress testing: Regulators require annual stress tests for all risk models.
- Consistency across financial institutions and instruments: Regulators are currently identifying areas where regulation is applied in inconsistent ways.
- Reinvigoration of financial markets (securitization): A number of markets, in particular the private (i.e., non-government-supported) securitization market, have declined in volume.
- Transparency: Central transaction repositories and collection of loan-level data mean more information is collected and made available to credit risk analysts.
- Increase of bank efficiency, competition, deregulation, and simplification: The precise measurement of credit risk is a central constituent in this process.