Guidelines on Credit Risk Management - Rating Models and Validation

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Guidelines on Credit Risk Management

Rating Models and Validation

I INTRODUCTION 7

II ESTIMATING AND VALIDATING PROBABILITY

OF DEFAULT (PD) 8

1 Defining Segments for Credit Assessment 8

2 Best-Practice Data Requirements for Credit Assessment 11

2.1 Governments and the Public Sector 12

2.2 Financial Service Providers 15

2.3 Corporate Customers — Enterprises/Business Owners 17

2.4 Corporate Customers — Specialized Lending 22

2.4.1 Project Finance 24

2.4.2 Object Finance 25

2.4.3 Commodities Finance 26

2.4.4 Income-Producing Real Estate Financing 26

2.5 Retail Customers 28

2.5.1 Mass-Market Banking 28

2.5.2 Private Banking 31

3 Commonly Used Credit Assessment Models 32

3.1 Heuristic Models 33

3.1.1
Classic Rating Questionnaires 33

3.1.2 Qualitative Systems 34

3.1.3 Expert Systems 36

3.1.4 Fuzzy Logic Systems 38

3.2 Statistical Models 40

3.2.1 Multivariate Discriminant Analysis 41

3.2.2 Regression Models 43

3.2.3 Artificial Neural Networks 45

3.3 Causal Models 48

3.3.1 Option Pricing Models 48

3.3.2 Cash Flow (Simulation) Models 49

3.4 Hybrid Forms 50

3.4.1 Horizontal Linking of Model Types 51

3.4.2 Vertical Linking of Model Types Using Overrides 52

3.4.3 Upstream Inclusion of Heuristic Knock-Out Criteria 53

4 Assessing the Models
Suitability for Various Rating

Segments 54

4.1 Fulfillment of Essential Requirements 54

4.1.1 PD as Target Value 54

4.1.2 Completeness 55

4.1.3 Objectivity 55

4.1.4 Acceptance 56

4.1.5 Consistency 57

4.2 Suitability of Individual Model Types 57

4.2.1 Heuristic Models 57

4.2.2 Statistical Models 58

4.2.3 Causal Models 60

5 Developing a Rating Model 60

5.1 Generating the Data Set 62

5.1.1 Data Requirements and Sources 62

5.1.2 Data Collection and Cleansing 64

5.1.3 Definition of the Sample 72

5.2 Developing the Scoring Function 82

5.2.1 Univariate Analyses 75

5.2.2 Multivariate Analysis 80

5.2.3 Overall Scoring Function 82

5.3 Calibrating the Rating Model 84

5.3.1 Calibration for Logistic Regression 85

5.3.2 Calibration in Standard Cases 86

5.4 Transition Matrices 88

5.4.1 The One-Year Transition Matrix 88

5.4.2 Multi-Year Transition Matrices 91

6 Validating Rating Models 94

6.1 Qualitative Validation 96

6.2 Quantitative Validation 98

6.2.1 Discriminatory Power 98

6.2.2 Back-Testing the Calibration 115

6.2.3 Back-Testing Transition Matrices 132

6.2.4 Stability 134

6.3 Benchmarking 128

6.4 Stress Tests 130

6.4.1 Definition and Necessity of Stress Tests 130

6.4.2 Essential Factors in Stress Tests 131

6.4.3 Developing Stress Tests 133

6.4.4 Performing and Evaluating Stress Tests 137

III ESTIMATING AND VALIDATING LGD/EAD

AS RISK COMPONENTS 139

7 Estimating Loss Given Default (LGD) 139

7.1 Definition of Loss 140

7.2 Parameters for LGD Calculation 140

7.2.1 LGD-Specific Loss Components in Non-Retail Transactions 140

7.2.2 LGD-Specific Loss Components in Retail Transactions 143

7.3 Identifying Information Carriers for Loss Parameters 144

7.3.1 Information Carriers for Specific Loss Parameters 144

7.3.2 Customer Types 146

7.3.3 Types of Collateral 148

7.3.4 Types of Transaction 149

7.3.5 Linking of Collateral Types and Customer Types 150

7.4 Methods of Estimating LGD Parameters 151

7.4.1 Top-Down Approaches 151

7.4.2 Bottom-Up Approaches 153

7.5 Developing an LGD Estimation Model 157

8 Estimating Exposure at Default (EAD) 162

8.1 Transaction Types 162

8.2 Customer Types 163

8.3 EAD Estimation Methods 165

IV REFERENCES 167

V FURTHER READING 170

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