A process model to develop an internal rating system: Sovereign credit ratings

The Basel II capital accord encourages financial institutions to develop rating systems for assessing the risk of default of their credit portfolios in order to better calculate the minimum regulatory capital needed to cover unexpected losses. In the internal ratings based approach, financial instit...

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Bibliographic Details
Published in:Decision Support Systems 2006-11, Vol.42 (2), p.1131-1151
Main Authors: Van Gestel, Tony, Baesens, Bart, Van Dijcke, Peter, Garcia, Joao, Suykens, Johan A.K., Vanthienen, Jan
Format: Article
Language:English
Subjects:
Algebra
Applied sciences
Computer science
control theory
systems
Credit ratings
Data processing. List processing. Character string processing
Exact sciences and technology
Financial institutions
Internal rating system
Logistics
Memory organisation. Data processing
Operational research and scientific management
Operational research. Management science
Portfolio theory
Process model
Risk theory. Actuarial science
Software
Sovereign ratings
Studies
Support vector machines
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Summary:The Basel II capital accord encourages financial institutions to develop rating systems for assessing the risk of default of their credit portfolios in order to better calculate the minimum regulatory capital needed to cover unexpected losses. In the internal ratings based approach, financial institutions are allowed to build their own models based on collected data. In this paper, a generic process model to develop an advanced internal rating system is presented in the context of country risk analysis of developed and developing countries. In the modelling step, a new, gradual approach is suggested to augment the well-known ordinal logistic regression model with a kernel based learning capability, hereby yielding models which are at the same time both accurate and readable. The estimated models are extensively evaluated and validated taking into account several criteria. Furthermore, it is shown how these models can be transformed into user-friendly and easy to understand scorecards.
ISSN:0167-9236
1873-5797
DOI:10.1016/j.dss.2005.10.001