Renato Maino – författare

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.

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.