Publication alert: EBA - Consultation paper on the supervisory handbook on the validation of IRB rating systems
We inform of the publication by the European Banking Authority (EBA) of the Consultation paper on the supervisory handbook on the validation of IRB rating systems.
1. Context
In 2010, it was published the Regulation establishing a European Supervisory Authority, the EBA. This Regulation stipulates that the EBA shall develop and maintain an up-to-date Union supervisory handbook on the supervision of financial institutions which is to set out supervisory best practices and high-quality methodologies and processes. Therefore, in 2016 it was published the roadmap to repair internal models used to calculate own funds requirements for credit risk under the Internal Ratings Based (IRB) approach and later in 2019, a progress report of this roadmap.
In this context, the EBA has published a Consultation Paper (CP) on the supervisory handbook on the validation of IRB rating systems, which complements the roadmap and provides some general guidance on the expectations relative to the validation function without presenting any specific methodology to be used.
2. Main points
General requirements. This requirements are applicable to the validation function:
- Scope. The internal validation should be conducted at each level where a Competent Authority (CA) has granted an approval for a rating system.
- Validation policy and validation report. The validation policy is expected to describe the validation framework, i.e. the roles, responsibilities, processes and content of the validation activities that need to be performed.
- Validation tasks. Institutions shall have robust systems in place to validate the accuracy and consistency of rating systems, processes and the estimation of all relevant risk parameters. The techniques that are expected to be used should include quantitative as well as qualitative methods.
Validation content. A differentiation must be made between the tasks related to the pure model performance assessment and the ones dealing with on the modelling environment:
- Assessment of the core model performance. One of the objectives of the validation function is to assess the core performance of the rating system and this assessment can be broken down in:
- Risk differentiation: its dimensions are: i) the consistency and comprehensiveness of the rating assignment process; and ii) the accuracy of the rating assignment in the model development.
- Risk quantification: its dimensions are: i) the accuracy of the best estimates; ii) the conservatism of the risk estimates; and iii) for the Loss given default (LGD) and Credit conversion factor (CF) parameters, the appropriateness of the estimates.
- Specificities: there are several specificities related to the validation of: i) defaulted exposures; ii) credit risk mitigation; and iii) Exposures risk weighted according to the slotting approach.
- Assessment of the modelling environment. To ensure a proper assessment of the data quality and maintenance, the data quality framework should clearly define policies, roles and responsibilities in data processing and data quality management. Furthermore, the validation function is expected to verify the adequacy of the implementation of internal ratings and risk parameters in IT systems.
First validation of newly developed rating systems. It refers to the validation of either a newly introduced models or the validation of changes or extensions to changed models. In the first validation it is important to address the model design and risk quantification choices and in case of a model change, the validation function is expected to compare the performance of the new models with the previous ones.
Subsequent validation. It refers to the validation of either an unchanged model or the validation of unchanged aspects. The subsequent validation differs from the first validation in two ways: i) It benefits from additional data and observations; and ii) it has at its disposal previous conclusions from the first validation.
In both the first and the subsequent validation there are certain specificities in relation to the performance of the core model and the modelling environment.
Validation challenges.
- Use of external data in the model development. The validation of a rating system built on external data is expected to follow five principles: i) representativeness; ii) access to data; iii) methodological choices’ assessment; iv) performance assessment; v) data quality.
- Outsourcing of validation task. It is expected that the institution perform a comprehensive analysis of its compliance with all the regulatory requirements on outsourcing.
- Validation in the context of data scarcity. The validation of ratings systems in a context of data scarcity brings some additional challenges, for example, the adaptation of the validation policy.
3. Next steps
Comments to this CP can be sent before 28 October 2022.