Rating Models and Validation - Oesterreichische Nationalbank

Contents
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
2.4.1 Project Finance
2.4.2 Object Finance
2.4.3 Commodities Finance
2.4.4 Income-Producing Real Estate Financing
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2.5 Retail Customers
2.5.1 Mass-Market Banking
2.5.2 Private Banking
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3 Commonly Used Credit Assessment Models 32
3.1 Heuristic Models
3.1.1 ÒClassicÓ Rating Questionnaires
3.1.2 Qualitative Systems
3.1.3 Expert Systems
3.1.4 Fuzzy Logic Systems
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3.2 Statistical Models
3.2.1 Multivariate Discriminant Analysis
3.2.2 Regression Models
3.2.3 Artificial Neural Networks
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3.3 Causal Models
3.3.1 Option Pricing Models
3.3.2 Cash Flow (Simulation) Models
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3.4 Hybrid Forms
3.4.1 Horizontal Linking of Model Types
3.4.2 Vertical Linking of Model Types Using Overrides
3.4.3 Upstream Inclusion of Heuristic Knock-Out Criteria
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4 Assessing the ModelsÕ Suitability for Various Rating
Segments 54
4.1 Fulfillment of Essential Requirements
4.1.1 PD as Target Value
4.1.2 Completeness
4.1.3 Objectivity
4.1.4 Acceptance
4.1.5 Consistency
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4.2 Suitability of Individual Model Types
4.2.1 Heuristic Models
4.2.2 Statistical Models
4.2.3 Causal Models
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4 Guidelines on Credit Risk Management