Credit risk analytics : measurement techniques, applications, and examples in SAS / Bart Baesens, Daniel Rösch, Harald Scheule.

Format:

Book

Author/Creator:

Baesens, Bart, author.

Rösch, Daniel, 1968- author.

Scheule, Harald, author.

Series:

Wiley and SAS business series.

Wiley & SAS Business Series

Language:

English

Subjects (All):

Credit--Management--Data processing.

Credit.

Risk management--Data processing.

Risk management.

Bank loans--Data processing.

Bank loans.

SAS (Computer file).

Physical Description:

1 online resource (583 pages).

Edition:

1st ed.

Place of Publication:

Hoboken, New Jersey : Wiley, 2016.

Language Note:

English

Summary:

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.

Contents:

Intro

Title Page

Copyright

Table of Contents

Dedication

Acknowledgments

About the Authors

Chapter 1: Introduction to Credit Risk Analytics

Why This Book Is Timely

The Current Regulatory Regime: Basel Regulations

Introduction to Our Data Sets

Housekeeping

Chapter 2: Introduction to SAS Software

SAS versus Open Source Software

Base SAS

SAS/STAT

Macros in Base SAS

SAS Output Delivery System (ODS)

SAS/IML

SAS Studio

SAS Enterprise Miner

Other SAS Solutions for Credit Risk Management

Reference

Chapter 3: Exploratory Data Analysis

Introduction

One-Dimensional Analysis

Two-Dimensional Analysis

Highlights of Inductive Statistics

Chapter 4: Data Preprocessing for Credit Risk Modeling

Types of Data Sources

Merging Data Sources

Sampling

Types of Data Elements

Visual Data Exploration and Exploratory Statistical Analysis

Descriptive Statistics

Missing Values

Outlier Detection and Treatment

Standardizing Data

Categorization

Weights of Evidence Coding

Variable Selection

Segmentation

Default Definition

Practice Questions

Notes

References

Chapter 5: Credit Scoring

Basic Concepts

Judgmental versus Statistical Scoring

Advantages of Statistical Credit Scoring

Techniques to Build Scorecards

Credit Scoring for Retail Exposures

Reject Inference

Credit Scoring for Nonretail Exposures

Big Data for Credit Scoring

Overrides

Evaluating Scorecard Performance

Business Applications of Credit Scoring

Limitations

Chapter 6: Probabilities of Default (PD): Discrete-Time Hazard Models

Discrete-Time Hazard Models

Which Model Should I Choose?

Fitting and Forecasting

Formation of Rating Classes

References.

Chapter 7: Probabilities of Default: Continuous-Time Hazard Models

Censoring

Life Tables

Cox Proportional Hazards Models

Accelerated Failure Time Models

Extension: Mixture Cure Modeling

Discrete-Time Hazard versus Continuous-Time Hazard Models

Chapter 8: Low Default Portfolios

Developing Predictive Models for Skewed Data Sets

Mapping to an External Rating Agency

Confidence Level Based Approach

Other Methods

LGD and EAD for Low Default Portfolios

Chapter 9: Default Correlations and Credit Portfolio Risk

Modeling Loss Distributions with Correlated Defaults

Estimating Correlations

Extensions

Chapter 10: Loss Given Default (LGD) and Recovery Rates

Marginal LGD Models

PD-LGD Models

Chapter 11: Exposure at Default (EAD) and Adverse Selection

Regulatory Perspective on EAD

EAD Modeling

Chapter 12: Bayesian Methods for Credit Risk Modeling

The Bayesian Approach to Statistics

PD Estimation with Bayesian Statistics

Correlation Estimation with Bayesian Statistics

PD Estimation for Low Default Portfolios

Chapter 13: Model Validation

Regulatory Perspective

Basic Concepts of Validation

Quantitative Validation

Qualitative Validation

Chapter 14: Stress Testing

Integration with the Basel Risk Model

Stress Testing Applications in SAS

Chapter 15: Concluding Remarks

Other Credit Risk Exposures.

Limitations of Credit Risk Analytics

Guiding Principles for Building Good Credit Risk Models

Index

End User License Agreement.

Notes:

Includes index.

Includes bibliographical references at the end of each chapters and index.

Description based on print version record.

ISBN:

9781119449560

1119449561

9781119278344

1119278341

9781119278283

1119278287

OCLC:

954720033