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CP10 (Full Document) - European Banking Authority

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274. The documentation must also reflect the fact that different rating<br />

systems can be used for different portfolios. Supervisors would<br />

expect institutions to maintain different sets of documentation on<br />

development, validation, and operations of each rating system or<br />

combination of rating systems used and of the portfolios to which<br />

they are applied.<br />

275. All documentation relating to an institution’s rating systems should<br />

be made available to the competent authority upon request, and to a<br />

third party acting as its agent.<br />

276. Section 3.3.4. applies to all documentation regarding model<br />

development and validation, including rating systems and PD, LGD,<br />

and CF estimation.<br />

277. The documentation of statistical models and other mechanical<br />

methods should cover at least the following areas:<br />

1. <strong>Document</strong>ation of model design:<br />

· Delineation criteria for the rating segment<br />

· Description of the rating method, the type of model used,<br />

and the architecture of the model<br />

· Model architecture and fundamental assumptions, including<br />

the definitions of default, the rationale for and analysis<br />

supporting the choice of rating criteria, typical circumstances<br />

under which the model does not work effectively, and the<br />

general strength and weaknesses of the model<br />

· The reason for selecting a specific model type<br />

· <strong>Document</strong>ation of all model functions<br />

· Description of the rating process<br />

· Duties and responsibilities with regard to the rating model<br />

· Model change policy<br />

2. <strong>Document</strong>ation of development and analyses<br />

· Relevance of the criteria used in the model<br />

· Dataset used in rating development<br />

· Quality assurance for the dataset<br />

· Model development procedure<br />

· Selection of model input factors and assessment of model<br />

parameters<br />

· Quality assurance/validation during model development,<br />

including at least out­of­time and/or out­of­sample<br />

performance tests.<br />

· Calibration of model output to default probabilities<br />

· Procedure for validation/regular review<br />

· Use of human judgement to complement the model<br />

278. Tests for quality assurance/validation should be performed only if the<br />

data needed to set aside for them do not unduly reduce the data set<br />

for model development. In cases of scarce data, it might be more<br />

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