- Presents an accessible framework for bank managers, students and quantitative analysts, combining strategic issues, management needs, regulatory requirements and statistical bases.
- Discusses available methodologies to build, validate and use internal rate models.
- Demonstrates how to use statistical packages for building statistical-based credit rating systems.
- Evaluates sources of model risks and strategic risks when using statistical-based rating systems in lending.
This book will prove to be of great value to bank managers, credit and loan officers, quantitative analysts and advanced students on credit risk management courses.
2 Classifications and key concepts of credit risk
2.1 A classification
2.2 Key concepts
3 Rating assignment methodologies
3.1 Experts based approaches
3.2 Statistical based models
3.3 Heuristic and numerical approaches
3.4 Involving qualitative information
4 Developing a statistical based rating system
4.1 The process
4.2 Setting model’s objectives and generating the dataset
4.3 Case study: dataset and preliminary analysis
4.4 Defining an analysis sample
4.5 Univariate and bivariate analyses
4.6 Estimating a model and assessing its discriminatory power
4.7 From scores to ratings and from ratings to probabilities of default
5 Validating rating models
5.1 Validation profiles
5.2 Roles of internal validation units
5.3 Qualitative and quantitative validation
6 Case Study. Validating PanalpBank’s statistical based rating system for Financial Institutions 211
6.1 Case study objectives and context
6.2 The ‘Development report’ for the validation unit
6.3 The ‘Validation report’ by the validation unit
7 Conclusions. Ratings usage opportunities and warnings.
7.1 Internal ratings are critical to credit risk management
7.2 Internal ratings assignment trends
7.3 Statistical based ratings and regulation: conflicting objectives?
7.4 Statistical based ratings and customers: needs and fears
7.5 Limits of statistical based ratings
7.6 Statistical based ratings and the theory of financial intermediation
7.7 Statistical based ratings usage: guidelines
Renato Maino Bocconi University, Italy.
Luca Molteni University of Bocconi.