Response to consultation Paper on draft RTS on criteria for assessing risk factors modellability under the IMA
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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.
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).
N/AQ6. 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?
N/AQ7. 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.