Response to consultation Paper on draft RTS on criteria for assessing risk factors modellability under the IMA

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Q5. Do you see any problems with requiring that institutions are allowed to use data from external data providers as input to the modellability assessment only where the external data providers are regularly subject to an independent audit (independent of whether the price is shared with the institution or not)? If so, please describe them thoroughly (i.e. for which data providers and the reasons for it).


Q6. Do you have any proposals on additional specifications that could be included in the legal text in order to ensure that verifiable prices provided by third-party vendors meet the requirements of this Regulation?


Q7. How relevant are the provisions outlined above for your institution? How many and which curves, surfaces or cubes are (planned to be) represented by a mathematical function with function parameters chosen as risk factors in your (future) internal model?

The provisions outlined are relevant for our institution and we intend to model a volatility surfaces and curves across a variety of asset classes with parametric functions.

Q8. Do you have a preference for any of the options outlined above? For which reasons? Please motivate your response.

We have a preference for OPTION 2. The requirement for two calibrations in OPTION 1 leads to a more complex implementation. For some combinations of modellable and non-modellable buckets the parameter calibration may not even be possible with OPTION 1 (see the response to Q9).We request feedback on whether the following interpretation of OPTION 2 is acceptable:
When we use parametric function parameters as risk factors in the internal risk-measurement model, two aspects of this choice are important:
a. The risk-measurement model will use perturbations to these parameters to measure portfolio risk. A single perturbation will produce a single P&L result.
b. Perturbations to parameters can be converted into equivalent perturbations to each of the points in the curve, surface, or cube where were used to calibrate the function parameters.
To determine the modellable or non-modellable P&L from a single parameter perturbation, we require an appropriate methodology to allocate the P&L to modellable and non-modellable buckets. The ES calculation then consists of perturbing the parameters using ES shocks and calculating the P&L due to modellable points on the curve, surface, or cube. In a similar fashion, the SES calculation consists of perturbing the parameters using SES shocks and calculating the P&L due to non-modellable points on the curve, surface, or cube.

Q9. Do you consider any of the options outlined above as impossible or impractical? For which reasons? Please motivate your response.

OPTION 1 could result in a requirement to fit a parametric model with an incomplete data set. As an example, if the parametric model has three parameters, but fewer than three data inputs belong to modellable buckets, then a set of possible calibrations are possible. The parameter fitting methodology would require a heuristic for picking a single calibration from this set which adds extra complexity to the parametric model specification and validation.

Q10. Do you have alternative proposals to define the consequence on the modellability of the parameters where some buckets of a curve, surface or cube are modellable whilst others are nonmodellable?

No specific proposals.

Q11. Do you intend to apply paragraph 4? If so, for which risk factors will it be relevant? Do you expect any implementation issues related to it? Please explain expected issues thoroughly.


Q12. Do you agree with the outlined methodology for the assessment of modellability of risk factors? If not, please explain why.


Q13. Do you expect any problems for the modellability assessment arising from the upcoming benchmark rate transition that could be addressed via this regulation? If so, please provide a thorough description and potential solutions if any


Q14. How do you intend to integrate the risk factor modellability assessment (i.e. RFET) into the processes of your institution? Do you expect those data to be used for the purpose of the RFET only or do you think those data would increase the data availability used e.g. for the calibration of your internal model (under para 31.26 of 2019 Basel rules)? What percentage of data used for the RFET do you think will be used also for the calibration of your internal model?


Name of organisation

Credit Suisse