Very material, as a matter of fact. One ESBG member explains that a large portion of models would require material changes, particularly in respect of the LGD modelling.
With regard to retail and SMEs, we don’t see any operational limits. Regarding large corporates/low default portfolios (LDP), we don’t see operational limits either, but detect obviously unstable measurements.
ESBG believes that benchmarks may not be representative if a different approach is optional when it comes to additional drawings. LGD and EAD should be viewed together for benchmarking purposes if this optionality remains.
d) One ESBG member states that financial ratios are used directly without adjusting them for current economic conditions, hence giving a point-in-time (PIT) effect. With regard to large corporates, they use non-financial information that tends to be through-the-cycle (TTC).
e) This is not applicable to one ESBG member. They use a common master scale (PD-bands into grades) across all their portfolios.
f) They are weighted between normal periods and downturn periods and almost always higher than the observed average default rates, as one ESBG member reports. They typically use 10-20% weight on severe downturn periods.
One ESBG member indicates that, as of now, they use non-overlapping windows. Short term contracts are not included.
One ESBG member points out that they partially carry it out in the validation processes.
Yes, mortgage and qualifying revolving retail exposure (QRRE) portfolios rely heavily on behaviour information, including current accounts and days past due.
Yes, we agree.
Furthermore, we believe that without guidance on principles for the quantification of the margin of conservatism (MoC), it is unlikely that there will be consistency across banks. However, we would also like to point out that quantifying and documenting the MoC requires great effort. In some cases, there remain doubts whether it will be feasible to carry out the modifications regarding the estimation of the LGD. In other cases, some information may not be available to institutions, be it that they do not receive it or be it that it does not exist, and burdensome deficiencies assessment, analysis and documentation are to be expected. Moreover, important IT developments are required. In short, there are some operational concerns.
Apart from that, the draft Guidelines require a specific margin for each deficiency as defined in the Article 30: “Any occurrence of any of the triggers referred to in paragraph 25 should result in the application of a margin of conservatism (MoC). Where more than one trigger occurs, a higher aggregate MoC should be applied […]”. We think that it should be possible to apply one MoC if the identified deficiencies are related. In this case a separate evaluation of the MoC would lead to double cover of the deficiencies which are interconnected. It also might be difficult to find an appropriate methodology to estimate the impact of a particular deficiency separately from other deficiencies and other factors impacting the estimation. Hence, ESBG thinks that a common margin for related deficiencies should be allowed.
Considering all this, ESBG is not entirely convinced that the costs and benefits of quantifying and documenting the MoC are perfectly balanced.
We would like to point out the following three aspects, which could require further clarification:
Exclusion rules: on one hand, the draft Guidelines define that all data should be included in the Reference Data Set (RDS) with possible representativeness adjustment, e.g. in the Representativeness of data explanatory box: “As in accordance with Article 181(1)(a) of Regulation (EU) 575/2013 institutions are required to use all observed defaults it is not possible to remove the observations that are not fully representative from the estimation sample”. In Article 21(c) a possibility to introduce some exclusions is introduced: “The rationale and scale of data exclusions broken down by reason for exclusion, using statistics of the share of total data covered by each exclusion, where certain data were excluded from the model development sample”. The intention is not clear to us.
Outlier rules: in reference to the representativeness of data, the draft Guidelines might be understood as if no outlier rules are allowed: e.g. in the section on the representativeness of data: “As the purpose of the own funds requirements is to address the unexpected loss even if extreme events were observed in the past these should not be excluded from the estimation sample.”. On the other hand Article 21(d) supposes the possibility of outlier rules: “The procedures for dealing with erroneous and missing data and treatment of outliers and categorical data, and the procedures for ensuring that, where there has been a change in the type of categorisation, this did not lead to decreased data quality or structural breaks in the data”. It is not clear if outlier treatment is allowed and if some limits are imposed. This should, in ESBG’s opinion, be clarified. Following the reasoning in Article 21(d), we suggest accepting the exclusion of some marginal amount of outliers.
Open defaults: it is required that all observed defaults have to be used for the purpose of LGD estimation, which indicates that also all defaults, where the recovery process is not completed (‘open defaults’) have to be used, independent of the time in default. As an accurate estimation of future recoveries is hardly possible at the beginning of default, because the workout strategy is mostly fixed after a certain observation period, we propose allowing the option to exclude open defaults with a duration shorter than a reasonable period.