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Informationen zum Autor GIACOMO DE LAURENTIS, Department of Finance and SDA Bocconi School of Management, Bocconi University, Italy. RENATO MAINO, Lecturer, Bocconi University and Turin University, Italy. LUCA MOLTENI, Department of Economics and SDA Bocconi School of Management, Bocconi University, Italy. Klappentext This book provides a thorough analysis of internal rating systems. two case studies are devoted to building and validating statistical-based models for borrowers' ratings, using SPSS-PaSW and SaS statistical packages. Mainstream approaches to building and validating models for assigning counterpart ratings to small and medium enterprises are discussed, together with their implications on lending strategy. Key Features: 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. Zusammenfassung *Introduces statistical tools for credit risk analysis Inhaltsverzeichnis Preface xi About the authors xiii 1 The emergence of credit ratings tools 1 2 Classifications and key concepts of credit risk 5 2.1 Classification 5 2.1.1 Default mode and value-based valuations 5 2.1.2 Default risk 6 2.1.3 Recovery risk 7 2.1.4 Exposure risk 8 2.2 Key concepts 8 2.2.1 Expected losses 8 2.2.2 Unexpected losses, VAR, and concentration risk 9 2.2.3 Risk adjusted pricing 13 3 Rating assignment methodologies 17 3.1 Introduction 17 3.2 Experts-based approaches 19 3.2.1 Structured experts-based systems 19 3.2.2 Agencies' ratings 22 3.2.3 From borrower ratings to probabilities of default 26 3.2.4 Experts-based internal ratings used by banks 31 3.3 Statistical-based models 32 3.3.1 Statistical-based classification 32 3.3.2 Structural approaches 34 3.3.3 Reduced form approaches 38 3.3.4 Statistical methods: linear discriminant analysis 41 3.3.5 Statistical methods: logistic regression 54 3.3.6 From partial ratings modules to the integrated model 58 3.3.7 Unsupervised techniques for variance reduction and variables' association 60 3.3.8 Cash flow simulations 73 3.3.9 A synthetic vision of quantitative-based statistical models 76 3.4 Heuristic and numerical approaches 77 3.4.1 Expert systems 78 3.4.2 Neural networks 81 3.4.3 Comparison of heuristic and numerical approaches 85 3.5 Involving qualitative information 86 4 Developing a statistical-based rating system 93 4.1 The process 93 4.2 Setting the model's objectives and generating the dataset 96 4.2.1 Objectives and nature of data to be collected 96 4.2.2 The time frame of data 96 4.3 Case study: dataset and preliminary analysis 97 4.3.1 The dataset: an overview 97 4.3.2 Duplicate cases analysis 103 4.3.3 Missing values analysis 104 4.3.4 Missing value treatment 107 4.3.5 Other preliminary overviews 109 4.4 Defining an analysis sample 114 4.4.1 Rationale for splitting the dataset into an analysis sample and a validation sample 114 4.4.2 How to split the dataset into an analysis sample and a validation sample 114 4.5 Univariate and bivariate analyses 116
