The development of internal models must follow a documented and structured process with sequential and logical steps, supporting the construction of the most appropriate models to meet the objectives assigned to these models. At a minimum, institutions must consider the following components. More components can be added depending on the type of model. If a component is not addressed, then clear justification must be provided.
(i)
Data preparation,
(ii)
Data exploration (for statistical models),
(iii)
Data transformation,
(iv)
Sampling (for statistical models),
(v)
Choice of methodology,
(vi)
Model construction,
(vii)
Model selection,
(viii)
Model calibration (for statistical models),
(ix)
Pre-implementation validation, and
(x)
Impact analysis.
6.1.2
This process must be iterative, in that, if one step is not satisfactory, some prior steps must be repeated. For instance, if no model can be successfully constructed, additional data may be needed or another methodology should be explored.
6.1.3
Each of these steps must be fully documented to enable an independent assessment of the modelling choices and their execution. This requirement is essential to support an adequate, independent model validation. Mathematical expressions must be documented rigorously to enable replication if needed.
6.1.4
For the purpose of risk models, a sufficient degree of conservatism must be incorporated in each of the development step to compensate for uncertainties. This is particularly relevant in the choice of data and the choice of methodology.