Response to consultation on Guidelines PD estimation, LGD estimation and treatment of defaulted assets
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Therefore, they will have to modify their methodology and implement new models considerably. The
significant evolution of the institutions’ models will require constant validation processes by the
regulators.
In particular, the LGD estimation (in default and non -default) part of the proposed guidelines would
have the biggest impact on us, as it requires the collection of new statistical series of data and the
establishment of new models, which, above the main points of operational cost on IT systems, it raises
the issue of planning and complying with the deadlines, considering implementation and validation
delays.
While we support the EBA efforts to harmonise IRB models in Europe it should be emphasised that the
EBA proposals will require an extra burden for our industries both in terms of cost and time for our
companies. The proposed deadline for implementing the new framework by 2020 seems constringent,
given the amount of work required to implement the new IT systems, therefore we would advocate for
a more flexible and proportionate framework to comply with the new proposed requirements.
requirement to calculate the one-year default rates at least quarterly. Given that parameters are backtested
and updated on an annual basis, we would propose to have an annual review, but based on
quarterly data.
relevant for operations quitting the status of default.
For example, in the case of a 3-month unpaid instalment loan, which is repaid in the following month,
we observe the following:
A recuperation would equal 0;
An economic loss would equal EAD at the date of entry in default (if there is no fees);
The LGD would be a 100% while there is eventually no loss.
PD levels as regard to the heterogeneity of risk profiles and business models across the EU. This
approach will penalise low risk institutions.
To reduce heterogeneity through benchmarks, they would have to be applied by type of exposure,
business models or localisation. This will be too complex to put it into practice.
Specialised business models such as consumer credit or leasing might be significantly impacted by the
introduction of benchmarks of pools and grades and maximum PD levels. If the same grades apply to all
business lines, specialised credit activities will be concerned by very few grades. Therefore, it would be
difficult to duly monitor the large scale of institutions business models or activities with a low number of
grades.
Finally, we would like to highlight that there is no definition of homogeneity in the EBA paper. It is
important to have a clear definition in order to achieve homogeneity.
calculation of the long-run average of default rates.
introduce undue divergence and variability in the models for this industry compared to other credit
activity.
different risk pools.
means that there is only one type of portfolio or type of exposure. For example, retail portfolio for
consumer credit. The rating philosophy is mostly the same for all the exposures.
included in the institution’s estimations and the appropriate adjustments are applied. We therefore
question the necessity to quantify margin of conservatism (MoC) by applying an ‘unbiased’ adjustment
and a conservative one.
The overlapping of two levels of MoC (by type and global), in the case there are several layers of MoC
required, would be complex to apply, and could reintroduce variability in the different models. In
addition, there could be a potential duplicative effect of applying several conservative adjustments.
The proposed approach with a very granular and analytical vision on MoC may lead to the aggregation
of several MoC, which would have a significant impact on capital requirements. It will also question the
value of operationality of risk parameter. A wider application and definition of the MoC will not lead to
less variability in RWA.
Therefore, to avoid duplicates institutions should have to possibility to assess and apply a global MoC.
We also suggest that if an institution can demonstrate that the deficiency itself leads to a conservative
outcome, it should be exempted from applying a MoC.
The proposed methodology for the estimation of the MoC raise operational concerns since we
understand that two calculations will be required: the estimation with the available data and the
estimation with the corrected data. This would be burdensome, time consuming and difficult to
implement.
We would also welcome further clarification of the Category C (general estimation errors).
Finally, we would like to emphasise that not every deficiency has a material impact on modelled
estimates.
Question 1: Do you agree with the proposed requirement with regard to the application of appropriate adjustments and margin of conservatism? Do you have any operational concern with respect to the proposed categorization?
For our companies the impact of the proposed regulation on current models would be very material.Therefore, they will have to modify their methodology and implement new models considerably. The
significant evolution of the institutions’ models will require constant validation processes by the
regulators.
In particular, the LGD estimation (in default and non -default) part of the proposed guidelines would
have the biggest impact on us, as it requires the collection of new statistical series of data and the
establishment of new models, which, above the main points of operational cost on IT systems, it raises
the issue of planning and complying with the deadlines, considering implementation and validation
delays.
While we support the EBA efforts to harmonise IRB models in Europe it should be emphasised that the
EBA proposals will require an extra burden for our industries both in terms of cost and time for our
companies. The proposed deadline for implementing the new framework by 2020 seems constringent,
given the amount of work required to implement the new IT systems, therefore we would advocate for
a more flexible and proportionate framework to comply with the new proposed requirements.
Question 2: Do you see any operational limitations with respect to the monitoring requirement proposed in paragraph 53?
It will be difficult for our industries, from an operational point of view, to comply with the proposedrequirement to calculate the one-year default rates at least quarterly. Given that parameters are backtested
and updated on an annual basis, we would propose to have an annual review, but based on
quarterly data.
Question 3: Do you agree with the proposed policy for calculating observed average default rates? How do you treat short term contracts in this regard?
Regarding paragraph 113, the proposed formula for the calculation of economic loss does not seemrelevant for operations quitting the status of default.
For example, in the case of a 3-month unpaid instalment loan, which is repaid in the following month,
we observe the following:
A recuperation would equal 0;
An economic loss would equal EAD at the date of entry in default (if there is no fees);
The LGD would be a 100% while there is eventually no loss.
Question 4: Are the requirements on determining the relevant historical observation periods sufficiently clear? Which adjustments (downward or upward), and due to which reasons, are currently applied to the average of observed default rates in order to estimate the long-run average default rate? If possible, please order those adjustments by materiality in terms of RWA.
We believe that it is not necessary to apply benchmarks for number of pools and grades and maximumPD levels as regard to the heterogeneity of risk profiles and business models across the EU. This
approach will penalise low risk institutions.
To reduce heterogeneity through benchmarks, they would have to be applied by type of exposure,
business models or localisation. This will be too complex to put it into practice.
Specialised business models such as consumer credit or leasing might be significantly impacted by the
introduction of benchmarks of pools and grades and maximum PD levels. If the same grades apply to all
business lines, specialised credit activities will be concerned by very few grades. Therefore, it would be
difficult to duly monitor the large scale of institutions business models or activities with a low number of
grades.
Finally, we would like to highlight that there is no definition of homogeneity in the EBA paper. It is
important to have a clear definition in order to achieve homogeneity.
Question 5: How do you take economic conditions into account in the design of your rating systems, in particular in terms of: d. definition of risk drivers, e. definition of the number of grades f. definition of the long-run average of default rates?
It is currently common practices for our industries to take into account economic conditions only in thecalculation of the long-run average of default rates.
Question 6: Do you have processes in place to monitor the rating philosophy over time? If yes, please describe them.
Most consumer credit contracts are short term, therefore the requirement of an additional MoC wouldintroduce undue divergence and variability in the models for this industry compared to other credit
activity.
Question 7: Do you have different rating philosophy approaches to different types of exposures? If yes, please describe them.
It is currently common practices for our industries to conduct studies to observe migration between thedifferent risk pools.
Question 8: Would you expect that benchmarks for number of pools and grades and maximum PD levels (e.g. for exposures that are not sensitive to the economic cycle) could reduce unjustified variability?
For most specialised financial services providers, the types of credit granted are usually monoline, whichmeans that there is only one type of portfolio or type of exposure. For example, retail portfolio for
consumer credit. The rating philosophy is mostly the same for all the exposures.
Question 9: Do you agree with the proposed principles for the assessment of the representativeness of data?
We would like to point out that most of the deficiencies identified in the proposed guidelines areincluded in the institution’s estimations and the appropriate adjustments are applied. We therefore
question the necessity to quantify margin of conservatism (MoC) by applying an ‘unbiased’ adjustment
and a conservative one.
The overlapping of two levels of MoC (by type and global), in the case there are several layers of MoC
required, would be complex to apply, and could reintroduce variability in the different models. In
addition, there could be a potential duplicative effect of applying several conservative adjustments.
The proposed approach with a very granular and analytical vision on MoC may lead to the aggregation
of several MoC, which would have a significant impact on capital requirements. It will also question the
value of operationality of risk parameter. A wider application and definition of the MoC will not lead to
less variability in RWA.
Therefore, to avoid duplicates institutions should have to possibility to assess and apply a global MoC.
We also suggest that if an institution can demonstrate that the deficiency itself leads to a conservative
outcome, it should be exempted from applying a MoC.
The proposed methodology for the estimation of the MoC raise operational concerns since we
understand that two calculations will be required: the estimation with the available data and the
estimation with the corrected data. This would be burdensome, time consuming and difficult to
implement.
We would also welcome further clarification of the Category C (general estimation errors).
Finally, we would like to emphasise that not every deficiency has a material impact on modelled
estimates.