Developing Credit Risk Models Using SAS Enterprise Miner and SASSTAT Theory and Applications - Dr. Iain Brown.pdf

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Developing Credit Risk Models
Using SAS Enterprise Miner
™
and SAS/STAT
®
®
Theory and Applications
Iain L. J. Brown, PhD
support.sas.com/bookstore
The correct bibliographic citation for this manual is as follows: Brown, Iain. 2014.
Developing Credit Risk Models
Using
SAS
®
Enterprise Miner
TM
and SAS/STAT
®
: Theory and Applications.
Cary, NC: SAS Institute Inc.
Developing Credit Risk Models Using SAS
®
Enterprise Miner
TM
and SAS/STAT
®
: Theory and Applications
Copyright © 2014, SAS Institute Inc., Cary, NC, USA
ISBN 978-1-61290-691-1 (Hardcopy)
ISBN 978-1-62959-486-6 (EPUB)
ISBN 978-1-62959-487-3 (MOBI)
ISBN 978-1-62959-488-0 (PDF)
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Contents
About this Book ........................................................................................ ix
About the Author .................................................................................... xiii
Acknowledgments ....................................................................................xv
Chapter 1 Introduction .............................................................................. 1
1.1 Book
Overview..........................................................................................................................
1
1.2 Overview of Credit Risk Modeling .......................................................................................... 2
1.3 Regulatory Environment .......................................................................................................... 3
1.3.1 Minimum Capital Requirements.................................................................................... 4
1.3.2 Expected Loss................................................................................................................. 5
1.3.3 Unexpected Loss ............................................................................................................ 6
1.3.4 Risk Weighted Assets .................................................................................................... 6
1.4 SAS Software Utilized .............................................................................................................. 7
1.5 Chapter Summary .................................................................................................................. 11
1.6 References and Further Reading ......................................................................................... 11
Chapter 2 Sampling and Data Pre-Processing .......................................... 13
2.1 Introduction ............................................................................................................................ 13
2.2 Sampling and Variable Selection .......................................................................................... 16
2.2.1 Sampling ........................................................................................................................ 17
2.2.2 Variable Selection ......................................................................................................... 18
2.3 Missing Values and Outlier Treatment ................................................................................. 19
2.3.1 Missing Values .............................................................................................................. 19
2.3.2 Outlier Detection ........................................................................................................... 21
2.4 Data Segmentation ................................................................................................................ 22
2.4.1 Decision Trees for Segmentation ............................................................................... 23
2.4.2 K-Means Clustering ...................................................................................................... 24
iv
Contents
2.5 Chapter Summary .................................................................................................................. 25
2.6 References and Further Reading ......................................................................................... 25
Chapter 3 Development of a Probability of Default (PD) Model ................. 27
3.1 Overview of Probability of Default ........................................................................................ 27
3.1.1 PD Models for Retail Credit ......................................................................................... 28
3.1.2 PD Models for Corporate Credit ................................................................................. 28
3.1.3 PD Calibration ............................................................................................................... 29
3.2 Classification Techniques for PD ......................................................................................... 29
3.2.1 Logistic Regression ...................................................................................................... 29
3.2.2 Linear and Quadratic Discriminant Analysis ............................................................. 31
3.2.3 Neural Networks ........................................................................................................... 32
3.2.4 Decision Trees .............................................................................................................. 33
3.2.5 Memory Based Reasoning ........................................................................................... 34
3.2.6 Random Forests ........................................................................................................... 34
3.2.7 Gradient Boosting ......................................................................................................... 35
3.3 Model Development (Application Scorecards) ................................................................... 35
3.3.1 Motivation for Application Scorecards....................................................................... 36
3.3.2 Developing a PD Model for Application Scoring ....................................................... 36
3.4 Model Development (Behavioral Scoring) ........................................................................... 47
3.4.1 Motivation for Behavioral Scorecards ........................................................................ 48
3.4.2 Developing a PD Model for Behavioral Scoring ........................................................ 49
3.5 PD Model Reporting ............................................................................................................... 52
3.5.1 Overview ........................................................................................................................ 52
3.5.2 Variable Worth Statistics ............................................................................................. 52
3.5.3 Scorecard Strength ...................................................................................................... 54
3.5.4 Model Performance Measures .................................................................................... 54
3.5.5 Tuning the Model .......................................................................................................... 54
3.6 Model Deployment ................................................................................................................. 55
3.6.1 Creating a Model Package .......................................................................................... 55
3.6.2 Registering a Model Package ..................................................................................... 56
3.7 Chapter Summary .................................................................................................................. 57
3.8 References and Further Reading ......................................................................................... 58
Contents
v
Chapter 4 Development of a Loss Given Default (LGD) Model................... 59
4.1 Overview of Loss Given Default ............................................................................................ 59
4.1.1 LGD Models for Retail Credit ...................................................................................... 60
4.1.2 LGD Models for Corporate Credit ............................................................................... 60
4.1.3 Economic Variables for LGD Estimation .................................................................... 61
4.1.4 Estimating Downturn LGD ........................................................................................... 61
4.2 Regression Techniques for LGD........................................................................................... 62
4.2.1 Ordinary Least Squares – Linear Regression ............................................................ 64
4.2.2 Ordinary Least Squares with Beta Transformation .................................................. 64
4.2.3 Beta Regression ........................................................................................................... 65
4.2.4 Ordinary Least Squares with Box-Cox Transformation ........................................... 66
4.2.5 Regression Trees .......................................................................................................... 67
4.2.6 Artificial Neural Networks ............................................................................................ 67
4.2.7 Linear Regression and Non-linear Regression ......................................................... 68
4.2.8 Logistic Regression and Non-linear Regression....................................................... 68
4.3 Performance Metrics for LGD ............................................................................................... 69
4.3.1 Root Mean Squared Error ............................................................................................ 69
4.3.2 Mean Absolute Error .................................................................................................... 70
4.3.3 Area Under the Receiver Operating Curve ................................................................ 70
4.3.4 Area Over the Regression Error Characteristic Curves ........................................... 71
4.3.5 R-square ........................................................................................................................ 72
4.3.6 Pearson’s Correlation Coefficient............................................................................... 72
4.3.7 Spearman’s Correlation Coefficient ........................................................................... 72
4.3.8 Kendall’s Correlation Coefficient ................................................................................ 73
4.4 Model Development ............................................................................................................... 73
4.4.1 Motivation for LGD models .......................................................................................... 73
4.4.2 Developing an LGD Model ........................................................................................... 73
4.5 Case Study: Benchmarking Regression Algorithms for LGD ............................................ 77
4.5.1 Data Set Characteristics .............................................................................................. 77
4.5.2 Experimental Set-Up .................................................................................................... 78
4.5.3 Results and Discussion ................................................................................................ 79
4.6 Chapter Summary .................................................................................................................. 83
4.7 References and Further Reading ......................................................................................... 84

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