Guidelines on PD estimation, LGD estimation and treatment of defaulted assets (EBA/GL/2017/16)

The first chapter is based on a standardised template for EBA guidelines which sets out the ‘comply or explain’ procedure. After the publication of the GL, and until the specified date, competent authorities will have to notify the EBA of whether or not they intend to comply with the GL. If a competent authority decides not to comply with the whole or part of the GL it has to justify its decision. The notifications of the competent authorities will be made public on the EBA website.

These GL apply to all models for which an institution received permission to use under the IRB Approach. Where institutions do not use own estimates of LGD (in accordance with the ‘Foundation’ IRB Approach) Chapters 6 and 7 do not apply, but all other requirements, including those specified in Chapters 4, 5, 8 and 9, apply with regard to PD estimates. The applicable requirements are expected to be assessed by the competent authorities within the process of granting initial permission to use the IRB Approach, assessment of material changes to the rating systems in accordance with Article 143(3) of the CRR, or ongoing reviews of the use of the IRB Approach.

The definitions specified in this section are used throughout the GL. The terms ‘PD model’ and ‘LGD model’ have been defined in a broad manner, referring to all data and methods related to both risk differentiation and risk quantification. In the case of LGD models, it has to be noted that the definition encompasses not only the data and methods used to derive LGD estimates for non-defaulted exposures, but also the risk parameters applicable to defaulted exposures such as EL BE and LGD in-default. This is based on the CRR requirements, which have also been defined jointly for performing and defaulted exposures.

The definitions also specify the main stages of the estimation of risk parameters, including model development and calibration, as well as the application of risk parameters. These definitions are particularly relevant for understanding which parts of the GL apply to which processes, as also presented in the overview section. These definitions do not refer to the design of grades or pools, as this aspect may be part of model development or of calibration, depending on the applied methodology.

The date of application of these GL is set consistently as for all other regulatory products developed as part of the EBA’s regulatory review of the IRB Approach. The implementation date is relatively distant, as it is expected that the implementation of these products, including these GL, will lead to material changes in many rating systems currently in use. To facilitate the implementation process as well as the assessment and approval of these changes by competent authorities, for instance by including multiple aspects in one change request, the implementation deadline is the same for all required changes. To ensure sufficient time for supervisory assessment and implementation within the deadline, institutions should agree with their competent authorities the latest date for submitting the application for the necessary changes in the rating systems.

Principles for specifying the range of application of the rating systems

The segmentation principles aim to provide guidance on the highest level of rating system design. These principles are particularly relevant to changes in the range of application of certain rating systems, for example where an existing rating system is rolled out to an acquired portfolio, or a portfolio that is otherwise not yet treated under the IRB Approach. Among other things, the GL require in this regard the availability of fundamentally comparable credit-related information, meaning that, with respect to the obligors or exposures to which the rating system is extended, the relevant information has a similar nature and is available for the purpose of rating assignment, or is at least possible to obtain. For instance, as the information available for business clients and for natural persons is fundamentally different, these should not be covered by the same rating system.

A rating system is a broad concept which includes both PD and LGD models. Obligors and exposures covered by a rating system should be homogeneously managed, including in particular the use of common obligor and facility rating scales. Although a rating system may comprise more than one PD and LGD model, and although these models may comprise multiple calibration segments, the results of all calibrations within a rating system have to be reflected in the single obligor rating scale and a single facility rating scale applicable within the rating system. It is also crucial that the definition of default is used identically for the purpose of calibration of all models within a rating system. Data requirements

Good quality of data is a fundamental condition for developing a robust rating system. The data requirements in this general part apply to model development, risk quantification and application of all risk parameters and contain clarifications regarding the assessment of accuracy, completeness and appropriateness of data. More detailed requirements, especially regarding the scope of required data specific to PD or LGD estimation, and to LGD-in-default or EL BE estimation, are described within the relevant chapters of these GL.

Representativeness of data

Representativeness of data may influence the accuracy of the estimates; where the underlying historical data is less representative of the current portfolio the estimates may be less adequate. To ensure good performance of the models and their good predictive power, institutions should have adequate policies, processes and methods for assessing the representativeness of data used for the purpose of estimation of risk parameters, and they should pay particular attention to situations where data from different sources are used. While the methods of assessment may in some cases differ for data from different sources, especially because of the structure and availability of information, the applied standards should be the same for all data used in the estimation process.

The dimensions of representativeness specified in the GL include the scope of application of the model, definition of default, distribution of the relevant risk drivers, current and foreseeable economic and market conditions, and lending standards and recovery policies. The requirements for data representativeness are split into two sub-sections, namely requirements for data used in model development and for data used in calibration of risk parameters (i.e. for the data used to calculate the long-run average default rate and the long-run average LGD). This split is consistent with the structure of the CRR, where data requirements regarding model development are treated in sub-section 1 of Section 6 in Chapter 3 of Part Three, Title II of the CRR, and data requirements for risk quantification (calibration) are treated in sub-section 2 of the same section. This split has also been consistently followed in Chapters 7 and 8 of the RTS on IRB assessment methodology respectively.

The data requirements for risk differentiation and risk quantification differ both in terms of the methods for analysis and with regard to required actions where the analysis reveals insufficient data representativeness. In particular, for the purpose of model development, institutions may, subject to certain requirements, use a definition of default other than that specified in Article 178 of the CRR. However, they should closely analyse the ranges and distributions of key risk characteristics within the historical observations as compared with the current portfolio, because, for the purpose of model development, material observed differences in these characteristics should be avoided by selecting an appropriate sample of data, for instance by using a shorter observation period. On the other hand, for the purpose of calculating long-run average default rate and long-run average LGD, the definition of default has to strictly reflect the requirements of Article 178 of the CRR, whereas the comparability of the ranges of the key risk characteristics is necessary only to a required degree. Furthermore, requirements regarding the representativeness of current and foreseeable market and economic conditions are included only for data used in calibration, and are related to the requirements for estimating long-run average default rate for PD models, and long-run average LGD as well as downturn considerations for LGD. The steps that should be taken where significant non-representativeness of data is identified are also different in terms of model development and calibration. Whereas in model development non-representativeness is normally addressed by the adequate choice of a sample, the non-comparability of the historical data underlying risk quantification should not lead to any data exclusions, but should trigger an appropriate adjustment and increased MoC. The same principle should be followed where adjustments to the observed average default rate are necessary to meet the requirements laid down in Section 5.4. Thus data exclusions may be a tool to quantify adjustments to the observed average default and loss rates where these are necessary; however, they should never be taken into account for the purpose of calculating the observed average default rate.

The principles described above also apply to the specific case of the disposal of the portfolio of non-performing exposures and to the way these cases are reflected in the LGD estimation. This means that, while all defaulted observations, including those subject to the sale, have to be included in the calculation of long-run average LGD for the purpose of LGD calibration, institutions may decide not to include these observations in the sample used for the purpose of risk differentiation, for instance in the design of relevant pools. As specified in the GL, the LGD estimation methodology should be consistent with the collection and recovery policies adopted by the institution and adequate to the type of exposures to which LGDs are applied. One of the possible risk estimation methods includes specification of possible recovery scenarios and their probabilities, where the probability of each scenario would be a component of the model and would be determined taking into account relevant risk drivers, which may influence the frequency of use of a certain scenario. Another possibility would be to discard (some of) the default observations of sold credit obligations in the model development, whereas all default observations, including those related to sold credit obligations, should be included directly in the LGD calibration. In other words, where (some of) the observations subject to the sale would not be included in the phase of model development, they should provide the basis for the adequate adjustment in the calibration phase in order to reflect the calibration target, i.e. the long-run average LGD, which should always be based on all observed defaults.

Human judgement in estimation of risk parameters

Development of a robust rating system cannot be a purely statistical process, but to some extent also has to involve human judgement, to make sure that the models are appropriate for current and foreseeable portfolios and conditions, and that the models are acceptable for business users. Expert judgement may be necessary in particular with respect to the verification of model assumptions, and whether or not these are in line with economic expectations, to the design of the model, to the choice of risk drivers, etc. However, to ensure high quality of the models, expert judgement has to be appropriately documented and justified. This way, the judgemental elements of the model can be appropriately challenged and verified both by the validation function and by competent authorities. Therefore, this part of the GL clarifies the requirements regarding human judgement, including its documentation, in the estimation of risk parameters.

Treatment of deficiencies and margin of conservatism

When estimating risk parameters, institutions should identify any deficiencies that may lead to a bias in the quantification of risk parameters, or to increased uncertainty that is not fully captured by the general estimation error. These deficiencies may be related to data and methodology issues (defined in the GL as category A), or to the changes in relevant processes or external environment which may lead to additional uncertainty in the quantification of risk parameters (specified as category B). Within category B, institutions should also consider whether or not any changes in legal environment may lead to changes in default or loss rates, in particular changes to bankruptcy law and any regulations related to legal collection processes. However, this does not refer to changes in legal regulations on internal models or definition of default. Any data deficiencies related to the definition of default or any weaknesses in methods used in internal models should be classified under category A. Categories A and B are expected to be non-overlapping, i.e. each identified deficiency should be classified in only one of the categories.

The same rules should also be applied to any deficiencies related to the representativeness of data. The classification will therefore depend on the nature of the identified deficiency. Any missing data, including missing information on certain risk drivers in historical observations should be classified under category A. However, any changes in the distribution of risk characteristics in the application portfolio due to, for instance, changes in the underwriting standards, will be classified under category B.

As a general principle, institutions are required to address the identified deficiencies via appropriate adjustments and MoC. As an example, adjusting the data where deficiencies have been identified may involve rectifying the identified errors, for instance where missing data points are filled in with the most probable information, or the inaccuracies in data are corrected. Appropriate adjustment in the case of changes in the underwriting standards may take the form of an adequate shift of the estimated risk parameters. In any case, the objective of the appropriate adjustment is to achieve the most accurate estimates possible. To avoid excessive use of adjustments, institutions should be able to demonstrate that this objective has actually been achieved by a certain adjustment. They should also document any adjustments that have been applied, and regularly monitor their adequacy.

MoC should be applied on top of the best estimate of the risk parameter (i.e. a parameter after applying all appropriate adjustments). This also applies to situations where a predefined master scale is used; in this case, MoC is applied on top of the parameters defined in the master scale. While for internal purposes institutions may use best estimates of risk parameters if this is more appropriate, for own funds requirements calculation the final risk parameters, including MoC, should be taken into account.

MoC should be quantified for all deficiencies that could not be rectified by appropriate adjustment. However, even if the appropriate adjustment has been applied, the estimates are subject to additional uncertainty resulting from the application of the adjustments and its potential inaccuracy. Institutions should quantify MoC related to the identified deficiencies in the same categories used to classify deficiencies (categories A and B). In addition, they should quantify a general estimation error and present it in a separate category (category C). In future, this categorisation will help increase transparency with regard to the levels and underlying reasons for MoC.

The quantification of MoC related to categories A and B should reflect the additional uncertainty resulting either from the application of the adjustments or, where no adjustments are possible, from the uncertainty driven by the deficiencies in the relevant category. The quantification of the MoC for the general estimation error should reflect the dispersion of the distribution of the statistical estimator. The reference to statistical variance has been avoided in order to not impose a fixed methodology, which might for example lead to disproportionate MoC for low default portfolios.

The GL require that the final MoC to be added to the best estimate of the risk parameter be the sum of MoC for categories A, B and C. This proposal is based on an expectation that the categories will in general be non-overlapping. Nevertheless, the assumption of independence between MoC related to these categories would not be correct, as the appropriate adjustments which are made in relation to deficiencies stemming from categories A and B may influence the general estimation error. Therefore, for the purpose of harmonisation and in order not to impose over sophistication, it has been decided to require the aggregation of MoC between the categories based on a simple sum. However, different aggregation techniques may be used within each of the categories. Here, flexibility is left for the institutions to address in an adequate manner different types of deficiencies and their potential interrelations. All methods used for quantification and aggregation of MoC should be documented and regularly monitored.

Example of appropriate adjustment and margin of conservatism

Assume a change in regulatory requirements regarding the materiality threshold for detecting defaults triggered by the 90 days past due criterion. Assume the institution under consideration has stored the information regarding outstanding exposures monthly rather than daily. Thus the date of default and the exposure value cannot be retrieved historically. Even though some calculations could be made based on the monthly data, the 90 days may have been reached during a particular month and the default may not be visible based on monthly data. Therefore the institution has decided to set up a parallel default detection according to the new trigger, and to estimate MoC starting from the difference of the amount of defaults detected according to the old trigger and the new trigger.

An appropriate adjustment could be derived as follows: calculating the relative change in the number of defaults triggered according to the old and the new 90 days past due criterion compared with all defaults (i.e. taking into account all triggers) on a monthly basis. Thus an average correction factor can be estimated and applied retrospectively. As a result, the number of defaults according to the new 90 days past due trigger can be estimated for the available historical data.

The additional MoC could be derived, for example, from the 90% confidence interval around the average of the new default rates. However, this method is only an example, and institutions may use other methods for deriving MoC if these are deemed more appropriate.

The GL on PD estimation aim to provide, among other things, more detailed guidance on the calculation of observed default rates and on the estimation of the long run average default rate. Moreover, they clarify how risk drivers and rating criteria should be chosen, and which requirements should be fulfilled where ratings serve as input to the PD estimation. Other aspects touched upon are the rating philosophy and how the long-run average default rates relate to the final PD estimation of a grade or pool. General requirements specific to PD estimation

It is clarified that each natural or legal person that has exposures within the scope of the IRB Approach should be rated including where there is unfunded credit protection. The rationale for this is that, for the purpose of model development, risk quantification and validation the assignment to a pool or grade after the application of unfunded credit protection would bias risk differentiation and default rate calculation. Even in the case of a rating transfer from a third party to the obligor, therefore, the initial rating of the obligor without taking into account the support of a third party should be available.

Model development in PD estimation

The requirements for model development include clarifications regarding data used for this purpose, as well as requirements for risk drivers and rating criteria, to be taken into account in statistical models or other mechanical methods to assign exposures to obligor grades. These risk drivers may include in particular the rating of a third party which has certain organisational or contractual relations with the obligor.

The data requirements for building the model for assigning exposures or obligors to grades or pools clarify that the reference points in time at which risk drivers are evaluated for the purpose of developing the model should be considered taking into account the dynamics and the update frequency of the information in question. The rationale for this is to avoid unjustified burden as well as to avoid biases in analysis due to autocorrelation. For example, obligor characteristics might be fairly static over time, whereas financial data from balance sheets is usually updated annually.

Risk drivers and rating criteria

The GL clarify that the selection of certain risk drivers and rating criteria should be based not only on statistical analysis, but that the relevant business experts should be consulted on the business rationale and risk contribution of the risk drivers under consideration. The background of this requirement is to ensure that the proper risk drivers and rating criteria are taken into account in the model.

An additional characteristic that should be considered as a potential risk driver is the number of loans of the same type of facility that have been granted to an obligor. In particular, where an institution chooses for its retail portfolios to assign PDs at exposure level and to identify default at facility level, it should be ensured that the PDs are adequate for PD estimates by obligor grade or pool, as required in Article 180(1)(a) and 180(2)(a) of the CRR. The rationale is that, in the latter case, the underlying one-year default rates in a grade or pool may vary significantly if based on largely one-to-one or one-to-x relations (where one-to-x relations reflect obligors carrying multiple (x) facilities of the same type in a certain grade or pool). However, if the number of loans of the same type of facility which have been granted to one obligor does not constitute a risk driver, there may as well be no difference in risk.

Another aspect, which should be taken into account when selecting and designing risk drivers and rating criteria, relates to the loss of reliability as information ages. The rationale for this requirement is that the loss of information value over time with respect to certain risk drivers or rating criteria, and in particular information which stems from the credit application and which is usually not updated during the life time of the credit products under consideration, leads to increased uncertainty, which should be appropriately reflected in the design of grades and pools, and the corresponding PD estimates.

The last requirement specified in the GL with respect to risk drivers and rating criteria relevant for PD estimation is that risk drivers and rating criteria should be used consistently with respect to the relevant time horizon in model development, model calibration and model application. This is because any inconsistency in the time horizons could lead to significant bias in the resulting PD estimates. If, for example, an institution considers days-past-due buckets as pools in its rating model, it nevertheless has to ensure that the ranking model for the pool of obligors without any past due obligations provides PD estimates appropriate for a one-year time horizon, as this is the relevant time horizon for the application portfolio.

Treatment of ratings of third parties

Regarding the use of third party ratings in obligors’ PD estimates, the GL specify the principles for the use of third party internal or external ratings in the estimates of the PD of an obligor. It is clarified that institutions may use an internal rating of a guarantor instead of a rating of an obligor for the purposes of own funds requirements calculation where there is no difference in risk between the relevant obligor or exposure and the relevant third party, given the existence of explicit guarantees (this is referred to as a rating transfer). Furthermore, the rating of a related third party may be taken into account as a risk driver or rating criterion in the ranking method, to reflect the support from a third party to the obligor. Finally, the third party’s rating may be considered for the purpose of overriding the assignment of an obligor to a pool or grade provided by a ranking method.

Among the important aspects clarified in this chapter is the provision that the considered obligor or exposure in whose PD estimate the third party rating is considered should remain in its exposure class. Even where the PD of the obligor is fully substituted by the PD of the third party because there is no difference in risk, the exposure should remain in the exposure class applicable to the obligor. Moreover, it is required that any changes to the rating of the third party should be reflected in the PD estimate incorporating the third party rating in a timely manner.

Rating philosophy

Depending on the methods and risk drivers used to assign obligors or exposures to grades or pools, changes in the portfolio’s default rate caused by changes in economic conditions will be reflected through either, or a combination, of the following:

•  migrations across risk grades or pools;
•  changes in the yearly default rates of each grade or pool.

Where the rating assignment process is highly sensitive to economic conditions, grade assignment will change significantly, while default rates of each grade will re-main relatively stable. In contrast, when the assignment is less sensitive to economic conditions, the yearly default rates per grade component will capture the cyclicality of the global default rate.

The requirements in this section aim mainly at increasing institutions awareness and understanding of the relationship between the definition of risk drivers, the number of grades, migrations across risk grades, changes in the one-year default rates over time, and the dynamics and volatility of capital requirements. To achieve that objective, the GL allow institutions to decide on the philosophy underlying the grade or pool assignment and the risk drivers, but require that a number of criteria be taken into account, including, among others, that institutions need to monitor their choice of rating philosophy, to apply that philosophy consistently over time, to understand its impact on the dynamics and volatility of capital requirements, and to take the rating philosophy into account for back-testing purposes.

PD calibration

Data requirements for observed default rates calculation

For the data for calculating default rates to be complete, it has to include the criteria to identify the relevant type of exposures covered by the rating system in. The rationale for this is that the institution should take into account all defaults with respect to the relevant type of exposures, independently of the existence of a rating assignment. Moreover, data covering the risk drivers and rating criteria need to be available for the purpose of calculating default rates per grade and pool. Where relevant risk drivers or rating criteria are missing, for example because the relevant information has not been collected from the obligors in the past, institutions should apply appropriate adjustments and increase MoC. Moreover, banks should make efforts to minimise the proportion of missing data over time. The rationale for this is that the missing information increases uncertainty around the default rates per grade in the past, so institutions should ensure that obligors or exposures are appropriately assigned to grades and pools, taking into account the identified risk drivers and rating criteria.

The GL also clarify that all data relevant for identifying the non-defaulted exposures at the beginning of a one-year observation period has to be available, in addition to all relevant default information as required in Article 178 of the CRR. This clarification was considered necessary because emphasis is often put on the data collection related to defaulted exposures, but for the purpose of default rate calculation, the accurate and complete identification and collection of data for non-defaulted exposures are equally important.

Calculation of the one-year default rates

The section on the calculation of observed one-year default rates clarifies in more detail which obligors should be taken into account in the numerator and denominator for the purpose of calculating a one-year default rate.

While the general calculation of a one-year default rate is already outlined in the CRR and the RTS on IRB assessment methodology, the GL clarify this calculation for a number of specific situations. In particular, the denominator should contain the obligors of the relevant model or calibration segment, with any credit obligation at the beginning of the observation period. The rationale is to ensure accurate measurement of one-year default rates, as an obligor can also default if there is no repayment obligation during the observation period, for example because of bankruptcy filing. Moreover, it should be noted that guarantors which do not carry a credit obligation are excluded from this specification of one-year default rate calculation⁶ , as by definition they cannot be subject to the same default identification process as the institution’s obligors. However, the information stemming from defaults of guarantors or any other relevant data on external default rates should be taken into account in the estimation of the long-run average default rates if this is necessary to meet the requirements in Section 5.3.4. Where obligors whose obligations stem solely from non-credit products fall under the scope of application of the relevant model and are treated in accordance with the institution’s internal default definition, these should form a separate calibration segment in the rating system, in order not to bias the default rate of obligors with credit facilities. Similarly, with regard to obligors or facilities only with committed but undrawn credit lines, these might have to be treated in a separate calibration segment in the rating system, to avoid lowering unduly the default rate of obligors with drawn credit lines.

It is clarified that, for the purpose of calculation of the one-year default rate by grade or pool, institutions should refer to the obligors assigned to grades or pools at the beginning of the one-year observation period, where the assignment to a grade may be based on an override. However, where the assignment to a grade is the effect of a rating transfer from a provider of unfunded credit protection, the obligor and potential default should be taken into account with respect to the grade to which the obligor was assigned before the rating transfer. The rationale for this is that the default rate of lower rating grades would be biased if the defaults of those obligors that received credit protection were not taken into account for the respective default rate calculation. The same rationale holds true for the requirement that ex post conservative adjustments should not be taken into account for the purpose of one-year default rate calculations, as obligors that are downgraded for the purpose of capital requirements calculation, for example because of a missing re-rating, could unduly lower the default rate of the resulting (worse) grade.

Finally, and with respect to one-year default rate calculation, it is clarified that all obligors carrying a credit obligation at the beginning of the observation period should be included in the denominator and, if applicable, the numerator of the default rate, calculated for the grade the obligor has been assigned to at the beginning of the observation period This also holds true where obligors migrated to a different grade or pool, or to a different rating system, during the observation period, and also where the credit obligation was sold, written off, repaid or otherwise closed during the observation period. The rationale for this is to achieve a harmonised and well-defined notion of one-year default rate. However, in case of concerns that the inclusion of these obligors could lead to a biased observed average default rate, for example because of a missing information on the rating at the beginning of the observation period, appropriate adjustments may be applied and MoC should be added to the best estimate of the risk parameter. Calculation of the observed average default rate

The GL require institutions to justify their approach to calculating the average of one-year default rates taking into account, in particular, analysis of the effect of short-term contracts or specific calculation dates. The rationale for the required analysis is to ensure that institutions are aware of the impact of the chosen calculation method and of fast-growing or shrinking portfolios on the observed average default rate and that they apply appropriate adjustments and MoC where necessary. Moreover, it is clarified that the observed average of one-year default rates should be calculated as the arithmetic average of all one-year default rates, except for retail portfolios, where the observed average default rate may be calculated as a weighted average of one-year default rates if more recent data is a better predictor of losses.

Long-run average default rate

Regarding the long-run average default rate, the GL clarify that this should be calculated as the average of observed one-year default rates if the historical observation period is representative of the likely range of variability of one-year default rates and, in particular, if the historical observation period contains an appropriate mix of good and bad years. If the one-year default rates are not representative of the likely range of variability, institutions should estimate the long-run average default rate by estimating an appropriate adjustment to the average of observed one-year default rates.

For the purpose of clarity the existence, lack or prevalence of bad years should be considered with respect to economic indicators which are relevant for the considered type of exposure. In particular it is clarified that, where bad years are over-represented in the historical observation period, institutions may consider downward adjustments of the observed average of default rates, based on an appropriate method taking into account the correlation between economic indicators relevant for the considered portfolio and the underlying observed default rates.

To limit possible variability stemming from the application of downward adjustments, a benchmark is proposed, namely the maximum of the average of one-year default rates over the most recent five years and the average of one-year default rates over the whole available observation period. Institutions may still estimate long-run average default rates below this benchmark, but this should be duly justified and eventually trigger additional MoC.

Calibration to the long-run average default rate

The process of PD calibration is performed on a calibration sample, namely the sample to which the ranking or scoring method is applied to assign the long-run average default rate estimates to the grades and pools provided by the ranking or scoring method. One of the issues clarified in this context is that the calibration sample should be comparable to the current portfolio in terms of obligor and transaction characteristics, but should reflect at the same time the likely range of variability of default rates.

The choice and understanding of the rating philosophy underlying the methods and drivers used to assign obligors or exposures to grades or pools is closely related to the calibration and re-calibration process and methodology an institution has chosen in that regard. The objectives behind the chosen calibration and re-calibration process and methodology, including the design of the calibration sample, can be referred to as calibration philosophy. The extent to which capital requirements are sensitive to changes in the economic environment depends on both the rating as well as the calibration philosophy.

Institutions that have a ranking method in place which is very sensitive to macroeconomic conditions, reflecting point-in-time (PIT) rating philosophy but also aiming to obtain more stable through-the-cycle (TTC) PD estimates in their calibration and recalibration process (to ensure a certain degree of stability of capital requirements over the credit cycle), might show the same cyclicality of capital requirements as institutions that incorporate risk drivers less sensitive to the economic environment into their ranking method. The extent to which calibration influences the sensitivity of PD estimates to the economic environment depends in particular on:

•  whether institutions aim to ensure that the long-run average default rate equals the PD estimates at calibration segment or portfolio level or at rating grade level; and
•  the design of the calibration sample.

Table 1 outlines various combinations of rating and calibration philosophy including an indication of whether or not such combinations would be compliant with the requirements set out in these GL and other relevant regulations (i.e. the CRR and the RTS on IRB assessment methodology). For example, if the grades or pools of a model were based predominantly on risk drivers that are insensitive to economic conditions (such as the obligors jurisdiction or sector), thus constituting a TTC rating philosophy, and at the same time the PD estimates were based on the last available (‘current’) one-year default rate (‘PIT A’) these estimates would not be applicable for the purpose of own funds requirements calculation. This is because the requirements of Section 5.3.4 of these GL would not be met. For the same reason, a ranking or scoring method reflecting a PIT rating philosophy where the PD estimates by grade or pool were based on the current default rate (‘PIT B’) would not be compliant with the relevant requirements for the IRB Approach.

On the other hand, with a model based on a TTC rating philosophy where the PD estimates per grade or pool reflect long-run average default rates (‘TTC A’), the resulting PD estimates may be applicable for own funds requirements calculation if all other requirements are met, in particular those set out in Articles 170(4) and 179(1)(a) of the CRR, where the incorporation of all relevant information (including, for example, on delinquency) is required.

An institution might use a more complex combination, for example by using risk drivers that are quite sensitive to economic conditions in a statistical default prediction model providing PD estimates for individual obligors, (i.e. a PIT rating philosophy), but applying a two-step approach for the purpose of calibration, namely by evaluating these PD estimates on a more recent point in time and subsequently adjusting them such that the average of these estimates reflects the long-run average default rate at the level of the relevant calibration segment (or even portfolio – if there is only one calibration segment). This would be an example of a ‘TTC B’ calibration, as indicated in Table 1, and would be applicable for own funds requirements calculation if all other requirements are met, in particular those on the calibration sample (the chosen point in time would need to reflect a sample comparable to the current portfolio and representative of the likely range of variability of one-year default rates).

The difference between calibration approaches ‘TTC B’ and ‘TTC C’ in Table 1 is that, for the latter, the calibration sample would consist of multiple points in time covering the whole historical observation period. Another example would be if an institution estimates PDs per grade but is able to assess these grades historically for only a part of the historical observation period (‘TTC D’); for example, it might not be possible to assess retrospectively the correct grades for all obligors over the whole observation period. In this case, the ranking method would require expert-based qualitative assessments, and experts would need to historically review every rating of every obligor subject to the ranking method within the historical observation period. The PD estimates based on the available grades and pools would be adjusted to reflect a long-run average default rate of the relevant pool, covering the whole historical observation period.

Finally, the approach ‘TTC E’ reflects an example where the grades and pools are evaluated on only one or a few points in time, and the PD estimation of a grade or pool incorporates the use of migration matrices for the purpose of estimating long-run average default rates covering the full historical observation period.

⁶ And thus also from the calculation of the observed average default rate

General requirements specific to LGD estimation

LGD estimation methodologies

General requirements for LGD estimation outline the scope of methodologies that can be used for the purpose of LGD estimation. In this context, workout LGD based on the institution’s experience in terms of recovery processes and losses is considered to be the main, superior methodology that should be used by institutions. It is essential that LGD estimates be based on the institutions’ own loss and recovery experience, to ensure that estimates are adequate for the institutions’ portfolios and policies, and in particular that they are consistent with recovery processes. Therefore, although internal experience may be supplemented with external data, institutions should not use methodologies that are based only on external data, such as so called market LGD and market-implied LGD, which are based on the market prices of financial instruments such as marketable loans, bonds or credit default instruments.

An alternative methodology that is available for retail exposures and purchased corporate receivables is that of deriving LGD estimates from realised losses and appropriate estimates of PD. However, in this case too, institutions should ensure that the estimates are sufficiently robust. This is ensured where both realised losses and PD estimates meet all relevant requirements. In particular, to ensure comparability of LGD estimates based on this methodology with other LGD estimates, the calculation of total losses has to be consistent with the concept of economic loss used for the purpose of workout LGD.

In accordance with point (b) of Article 52 of the RTS on IRB assessment methodology, and to achieve correct LGD estimates, institutions should assess whether two defaults related to the same exposure are independent. The less time between defaults, the less likely it is that the default events were independent. In particular, it was specified that institutions cannot consider a new default on an exposure, occurring within nine months from the return to non-defaulted status, independent from with the first default. For the purpose of LGD estimation, they should treat this situation as if the exposure was constantly defaulted from the first moment when default occurred. These nine months should not be confused with the probation period defined in the EBA Guidelines on the application of the definition of default, which ensures a prudent ongoing management of defaulted exposures. The minimum independence period should be considered in addition to the probation period (not including it). This independence period does not impact the default rate computation, not to bias PD estimates.

Data requirements for LGD estimation

As the LGD estimates should be based on the institution’s own experience, it is important that all relevant data be properly recorded and stored. The scope of data necessary for proper LGD estimation is very broad, and entails not only the date of default and all cash flows and events after default, but also all relevant information on the obligors and transactions that could be used as risk drivers in the model development. The reference data set (RDS) should include all information necessary for the model development as well as for calibration. For the purpose of LGD estimation, it should include complete information for all defaulted facilities observed during the historical observation period.

To ensure that the estimates are adequate for the existing portfolio, it is essential that the information about risk drivers be used consistently in the LGD estimation and in the application of LGD estimates. Hence, as the LGD estimates are applied to non-defaulted exposures, institutions should use the values of risk drivers from before the moment of default in the estimation of LGD.

One of the most important risk drivers in the LGD estimation is the existence of collaterals. The RDS should include all relevant information about the collaterals and the process of their realisation. In this context, it should be added that, as the observed and estimated recovery rates relate to the value of the collateral, the timing and type of valuation is a key aspect that may significantly influence the estimates. Although a more up-to-date valuation that was performed after default or in relation to default may be available, this information should not be used in the estimation of LGD because this valuation is not comparable to the valuation of collateral for non-defaulted exposures. Valuations performed after default are often more conservative, so the use of such information could lead to overestimation of recovery rates.

In some cases, the information necessary for the LGD estimation may be collected by institutions on a portfolio rather than on an individual basis. This may take place in particular in the case of indirect costs and sales of portfolios of credit obligations. The relevant information about costs and recoveries has to be adequately allocated to individual exposures whenever this is a necessary step for the purpose of LGD model development and, in any case, for the calculation of the long-run average in the calibration phase. In any case, the allocation methodology should not lead to a bias in the estimation of LGD. The simplest methodologies used for this purpose may be based on the exposure values. However, in some cases more sophisticated approaches may be necessary. In particular, in the case of the sale of credit obligations, the methodology for the allocation of recoveries should take into account possible significant heterogeneity of exposures subject to the sale, and the need to preserve their specificities as relevant drivers for the expected recoveries. In addition, in the case of the sale of secured credit obligations, institutions may allocate the value of recoveries (i.e. part of the sale price) to the existing collaterals. However, the need to use this additional allocation of recoveries to collaterals will depend on the regularity of occurrence of the sales of credit obligations as a recovery scenario, as well as on the estimation methodology chosen by the institution.

Recoveries from collaterals

It has been clarified that, for the purpose of LGD estimation, the recoveries realised with the use of collaterals have to be treated as such regardless of the form of the realisation of the collateral. This might include not only workout processes through court proceedings but also sale of the collateral by the obligor, normally with the consent of the institution, or the sale of the credit obligation where the collateral is reflected in the price. The list of possible ways of realising the collateral provided in the GL is non-exhaustive and includes only the most common forms. However, depending on the type of collateral and on the applicable legal framework, other eligible methods should also be recognised by institutions to identify cash flows stemming from realising the collaterals.

Broader clarification has been provided for a specific case where the collateral is repossessed by the institution, in exchange for decreasing the credit obligation. It is proposed that this event should be treated as recovery, as from an economic perspective such an event is equivalent to receiving a cash payment and investing it in an asset. However, as the value of repossession does not always reflect accurately the market value of the asset, this uncertainty should be addressed by applying an appropriate haircut to the value of repossession. Although institutions may have different strategies with regard to the repossessed assets, and in particular in some cases they may decide to keep the asset on their balance sheet for speculative purposes, these different strategies should not influence the value of the recovery. Therefore, it has been specified that the haircut should be estimated on the assumption that the institution intends to sell the repossessed asset as soon as reasonably possible. Wherever sufficient past experience with regard to repossession of collaterals exists, the haircuts should be supported by historical observations and regularly back-tested. In the absence of such experience, the assessment will have to be performed on a case-by-case basis, but this will require more conservatism, as the assessment will be less reliable.

Furthermore, clarification has been provided with regard to the treatment of situations where institutions sell credit obligations, and these obligations may be secured by collaterals. As it is often not possible to allocate part of the price received for the credit obligation to the value of collateral in a reliable manner, it is possible under certain circumstances to exclude these observations from the sample used for the purpose of model development. However, such allocation is not necessary for the purpose of calculation of realised LGD. In any case, realised LGD has to be calculated for each defaulted observation, and all observations have to be taken into account in calculating long-run average LGD.

Model development in LGD estimation

Risk drivers

The GL do not prescribe any specific methodology that should be used in the estimation of LGD. It has been recognised that various methodologies may be valid, depending on specific circumstances, portfolios and processes. However, it was considered appropriate to specify certain principles that should be adhered to regardless of the methodology that is chosen.

As part of these general principles, it has been specified which types of potential risk drivers should be taken into account by institutions. These include factors related to transactions and obligors but also to institutions themselves, in particular in terms of the organisation of recovery processes, as well as external factors such as legal frameworks, especially where models apply to exposures in various countries. It is important that institutions duly analyse potential risk drivers and choose those that meaningfully differentiate risk of transactions. In addition, the risk drivers should be analysed at an appropriate reference date that is representative of the realisations of the given risk driver within a year before default. This has the purpose of ensuring consistency between the estimation and the application of LGD, where the estimates will apply to non-defaulted exposures.

Eligibility of collaterals

Based on Article 108(2) of the CRR, it has been clarified that, for the purpose of LGD estimation, institutions may take into account any type of collateral as long as the requirement of Article 181(1)(f) of the CRR is met. It has been further clarified in the RTS on IRB assessment methodology that, to meet this requirement, the institution’s internal policies should be at least fully consistent with the requirements of Section 3 of Chapter 4, Part Three, Title II of the CRR with regard to legal certainty and regular valuation of collateral. It is also envisaged that, for the purpose of LGD estimation, institutions may use specific types of collaterals that are not explicitly described in the mentioned Chapter 4 of the CRR. In these cases, the policies and procedures relating to internal requirements for valuation and legal certainty should be appropriate to the type of collateral in question.

The existence of collateral is one of the main aspects that affect the recovery processes and their results. As institutions are required to incorporate in their LGD estimates all relevant data, information and methods in accordance with Article 179(1)(a) of the CRR, it has been specified that information on at least the main types of collaterals used for a given type of exposures should be considered relevant and included in the LGD estimates. This means, however, that for the main types of collaterals the requirement of Article 181(1)(f) of the CRR will have to be met as specified above.

Inclusion of collaterals in the LGD estimation

The GL specify general principles for reflecting the effect of collaterals in the LGD estimates, without prescribing any specific methodology. These principles include avoiding bias in the LGD estimates, which may stem from inappropriate treatment of cash flows realised with the use of collaterals, or from inappropriate valuation of the collateral.

LGD calibration

Calculation of economic loss and realised LGD

General requirements specific to EL BE and LGD in-default

Estimation methodologies for EL BE and LGD in-default

The treatment of defaulted assets was identified as one of the major drivers of variability of the own funds requirements across institutions. Clarification has already been provided in the RTS on IRB assessment methodology, in particular in Article 54(2)(c), that direct estimation of LGD in-default should be consistent with the LGD for non-defaulted exposures, to avoid potential cliff effects. Following this approach, it has been further clarified in the GL that, for the purpose of estimating EL BE and LGD in-default, institutions should use the same estimation methods used for estimating LGD for non-defaulted exposures, as they are in fact part of the LGD model. Thus, Chapter 7 generally refers to the requirements on LGD estimation set out in Chapter 6, as well as general estimation requirements set out in Chapter 4, and the requirements on the application of risk parameters specified in Chapter 8. Chapter 7 provides guidance only on those specific aspects where different treatment for defaulted exposures in terms of loss rate estimation is justified.

Reference dates

The difference between the LGD in-default and the EL BE is used for computing the risk weight in accordance with Article 153(1)(ii) of the CRR, which is then applied to the current outstanding exposure value to obtain the risk-weighted exposure amount. Moreover, the EL BE is compared with credit risk adjustments for the purpose of calculating IRB shortfall / excess, where credit risk adjustments are again computed with respect to the current value of exposures. Thus, for the purpose of computing realised LGDs for defaulted exposures, institutions should use reference points in time that will be relevant for the current outstanding obligations of defaulted exposures. The concept of current outstanding exposure is clearly defined in Article 166(1) of the CRR and should be used also for defaulted exposures in the application of the EL BE and LGD in-default. However, given data limitations, the continuous concept of current exposure amount may not be suitable for estimation purposes. The GL therefore suggest that institutions should set discrete relevant reference dates at which the realised LGDs should be computed. This way, it should be feasible to estimate the parameters for defaulted exposures that are appropriate for their current status. To ensure adequacy of the estimates, institutions should set the reference dates according to the recovery patterns observed on a specific type of exposures. Such reference dates may either be event based, e.g. linked with the realisation of collateral, or reflect certain time periods during which exposures have been in default.

For the purpose of application of the estimated LGD in-default and EL BE to a given defaulted exposure in the current portfolio, institutions should first evaluate which reference date is relevant for the exposure under consideration. In accordance with Article 158(5) of the CRR, the amount of expected loss to be assigned to the defaulted exposure under consideration should be calculated as the product of the EL BE relevant at the selected reference date, in percentage terms, and the current exposure value.

Data requirements for EL BE and LGD in-default

As for non-defaulted exposures, EL BE and LGD in-default estimates should be based on the institutions’ own experience. The scope of data necessary for proper EL BE and LGD in-default estimation includes not only those required for LGD for non-defaulted exposures, but also all relevant information obtained during the recovery process and, in particular, at each reference date used in the estimation. This implies that, for the purpose of the treatment of defaulted exposures, institutions should additionally store data including relevant risk drivers, which may become relevant after default, and outstanding exposure amounts and values of collaterals at each reference date.

Model development in the estimation of EL BE and LGD in-default

This section specifies which types of potential risk drivers should be taken into account in estimating EL BE and LGD in-default on top of those used for non-defaulted exposures. Article 54(2) of RTS on IRB assessment methodology prescribes that the LGD in-default and EL BE estimation methods take into account the information on the time in default and recoveries realised so far. In this respect, the GL clarify that the information on recoveries realised so far and on time in default may be taken into account either directly, as risk drivers, or indirectly, in setting the reference dates for estimation purposes. Moreover, to ensure that the information after default is taken into account in a timely manner, it is clarified that the relevance of risk drivers should be re-evaluated for the relevant periods after default, until the date of termination of the recovery process. This implies, for example, that new risk drivers might become relevant after the date of default. Calibration of EL BE and LGD in-default

Calculation of realised LGD and long-run average LGD for defaulted exposures

One major difference between the calculation of realised LGDs for the purpose of estimation of risk parameters for defaulted and non-defaulted exposures is that the former should be performed at each relevant reference date, rather than at the date of default, as described above. Other than this, to calculate realised LGDs for defaulted exposures institutions should follow the same requirements as those set out in Chapter 6. This implies that institutions should calculate for each defaulted exposure in the RDS the realised LGDs according to each reference date that is relevant for estimation purposes.

Another important aspect clarified in this section concerns the treatment of incomplete recovery processes for the purposes of calculation of long-run average LGD for defaulted exposures. The approach to the long-run average LGD is aligned with that prescribed in Section 6.4, with the exception that incomplete recovery processes should be used only for those reference dates for which observations of recoveries and costs are available at least until the next reference date. The rationale for this exception is to avoid a circular reference in the estimates and to ensure that each observation included in the calculation brings additional information. In fact, incomplete recovery processes on which EL BE and LGD in-default are estimated should not take part in the estimation itself and will be therefore excluded, if only those incomplete recovery processes for which later reference dates are relevant are taken into account.

The application of the same requirements for the treatment of incomplete recovery processes, as specified in Section 6.4, also means that no further recoveries should be estimated beyond the maximum length of the recovery processes specified for the specific type of exposures. However, relevant exposure-specific information may be taken into account in the process of application of the risk parameters, and may provide the basis for an override of the model output where justified.

Similarly in the case of partial write-offs performed on a defaulted exposure, while the estimation of the risk parameters should be based on a full outstanding credit obligation, the information on the partial write-off on a specific exposure may provide the basis for an override of the output of the model. Such an override would have the purpose of achieving consistency between the estimation and the application of the risk parameters, taking into account the fact that the risk weight and the expected loss amount will be calculated on the basis of the accounting value of the exposure, determined in accordance with Article 166 of the CRR.

Example on the treatment of incomplete recovery processes

70

) s 60

h t n

• o 50 m

( t l 40

u a

f e d 30

n i e 20 m

i T 10

0 A B C D Incomplete workouts

70

) s 60

h t n

• o 50 m

( t l 40

u a

f e d 30

n i e 20 m

i T 10

0 A B C D Incomplete workouts

70

) s 60

h t n o 50 m

( t l 40

u a

f e d 30

n

i e 20 m

i T 10

0 A B C D Incomplete workouts

Specific requirements for EL BE estimation

Conservatism in the application of risk parameters

While the MoC described in Chapter 4 addresses any weaknesses in data or methods in the process of model development and risk quantification, additional conservatism referred to in Chapter 8 is meant to address any weaknesses in the implementation of the models and application of the risk estimates to the currently existing exposures. These weaknesses may include, in particular, missing or outdated information necessary for the rating assignment in accordance with the model (for example missing update of the financial statement of the obligor), missing rating or update of the rating of the obligor. Institutions should be able to detect and monitor these situations to make sure that the risk is reflected correctly, including additional conservatism where necessary.

Human judgement in the application of risk parameters

The proposed rules for the use of human judgement in the application of risk parameters are based on Article 172(3) of the CRR, which allows the overriding of both inputs to and outputs from the process of assignment of obligors or exposures to grades or pools. The requirements for overrides apply equally to PD and LGD models, including EL BE and LGD in-default. In any case, where institutions want to apply the overrides, this should be based on an appropriate internal framework to make sure that the weaknesses of the assignment are identified consistently, that the overrides are applied within certain limits and that they are appropriately justified, approved and monitored. As a large number of overrides may indicate certain weaknesses of the model, institutions should analyse these situations carefully, taking into account the reasons for overrides. Where necessary, such analysis should result in the improvement of the model, for instance by including additional risk drivers, increasing granularity of categorisation or changing the weights of risk drivers, or in the improvement of data collection or data quality management processes.

Use of internal ratings and default and loss estimates

The concept of use test was introduced in the IRB Approach to ensure high quality of risk parameters, under the assumption that institutions would not use the estimates of risk parameters for internal risk management if they did not believe that these estimates appropriately reflect the actual level of risk. However, for the purpose of own funds requirements it is important not only to accurately assess the obligors and exposures, and to rank them according to the level of risk, but also to ensure certain level of conservatism. These additional prudential requirements may lead to the parameters less accurately reflecting the actual level of risk under certain economic circumstances. It is therefore specified in the GL that, for internal purposes, institutions may decide, where this is justified, to use the estimates of the risk parameters without MoC, without regulatory floors or, in the case of LGD, without downturn adjustment. Furthermore, where this is justified for a specific area of use, it may be necessary to use a different calibration method for internal purposes. This different method may in particular involve specifying a different calibration target (for instance, the use of lifetime horizon instead of a one-year time horizon on PD estimates, where this is required by the applicable accounting standards), or may aim to have the parameters more accurately reflect the current economic circumstances. The calibration method may also include additional risk drivers applied at the stage of calibration. In any case, however, the rank ordering of obligors or exposures should remain the same as used for the purpose of own funds requirements, apart from possibly different grouping of exposures into grades. The change in rank ordering would mean that not only different estimates but also different internal ratings are used internally, so the use test requirement would not be met.

The requirements for use test specified in these GL should be read together with the RTS on IRB assessment methodology, which in Articles 18 to 21 specify obligatory and optional areas of use test. While the GL provide an exhaustive list of possible deviations from the parameters used for own funds requirements calculation, the use of parameters in both obligatory and optional areas of use test does not preclude taking into account the information from other sources as well. The combination of the information on internal ratings or risk parameters and any other relevant information should lead to an appropriate management decision in accordance with the institution’s policy.

Calculation of IRB shortfall or excess

Apart from the calculation of risk-weighted exposure amounts the risk parameters are also used for regulatory purposes in the calculation of the expected loss and so-called IRB shortfall or IRB excess, which is then used to correct the value of own funds used in the calculation of capital adequacy ratios. Article 159 of the CRR requires institutions to calculate the difference between, on one hand, credit risk adjustments, additional value adjustments, and, on the other hand, own funds reductions and expected loss amounts. The GL refer to IRB excess where this calculation results in a positive amount, i.e. where provisions are in excess of expected loss, and to IRB shortfall where it results in a negative amount, i.e. there is a shortfall of provisions given the expected loss. It has been clarified in Article 73(h) of the RTS on assessment methodology that this difference should be calculated at an aggregate level separately for the portfolio of defaulted exposures and the portfolio of exposures that are not in default. This separation is necessary to ensure that the IRB excess resulting from the calculation performed for the defaulted portfolio is not used to offset IRB shortfall resulting from the calculation performed for the portfolio of exposures that are not in default, as prescribed in Article 159 of the CRR. However, the IRB excess from the overall non-defaulted portfolio may be used to cover any IRB shortfall from the overall defaulted portfolio. It is furthermore clarified that, if the calculation required by Article 159 of the CRR results in an IRB excess for both the defaulted and the non-defaulted portfolio, the limit for adding the overall IRB excess to Tier 2 capital set out in Article 62(d) of the CRR, i.e. up to 0.06% of risk-weighted exposure amounts, should be applied to the sum of the two IRB excesses.

Furthermore, the treatment of partial write-offs has been clarified in accordance with the interpretation provided earlier in the Q&A Question ID 2014_1064. In accordance with Annex V, Part 2, paragraphs 49 and 50 of Regulation (EU) No 680/2014 (Implementing Technical Standards (ITS) on supervisory reporting), write-off should be understood as the amount of principal and past due interest of any debt instrument that an institution no longer recognises because it is considered uncollectible. These ITS also clarify that write-offs can be caused both by reductions of the carrying amount of financial assets recognised directly in profit or loss, and by reductions in the amounts of the allowance accounts for credit losses taken against the carrying amount of financial assets. Such partial write-offs do not constitute impairment, irrespective of the method (specific loan loss provision or direct reduction of the carrying amount) chosen to book impairment in the financial statements of the asset, because any amounts written-off following a derecognition will not affect the carrying amount of the financial asset (unlike a reversal of impairment losses). For that reason, a partial write-off would not be included in the calculation of general and specific credit risk adjustments.

This chapter provides additional guidance for institutions with regard to their policies on the maintenance of the models, including regular review of estimates. To ensure that the deterioration of the model performance is detected and addressed in a timely manner, the GL clarify what institutions should consider in their internal frameworks for annual reviews, as required by Article 179(1)(c) of the CRR, and what should be the minimum scope of analysis that institutions conduct during this annual review. The review of estimates is required when new information comes to light, and at least on an annual basis. Moreover, institutions are asked to define a cycle for a fundamental review of models depending on the materiality of the models considered. In accordance with the GL, the scope of annual review includes the analysis of representativeness, the performance of the model, its stability over time and its predictive power. Full reviews including model design may be performed on a less frequent basis.

While the annual reviews referred to in Article 179(1)(c) of the CRR may be performed either by the model development function, which is part of the credit risk control unit or units (CRCU) or by the validation function, the required scope and frequency of validation is not within the scope of these GL. The role of independent validation has been further clarified in the RTS on IRB assessment methodology, in particular in Article 10 of these RTS, which specifies criteria for the independence of the validation function from model development, and in Article 11, which clarifies the minimum frequency and scope of validation. Therefore the requirements included in the GL are focused on the reviews of estimates in accordance with Article 179(1)(c) of the CRR.

The more fundamental reviews of models which in accordance with the GL should be performed on a regular basis may also be carried out as part of the validation function, or by the CRCU, which retains overall responsibility for the oversight and performance of the rating systems, in accordance with Article 190 of the CRR. In this sense, both the annual reviews and full reviews of estimates can be seen as part of the ongoing reviews by the CRCU as specified in Article 190(2)(h) of the CRR, even if partly or fully performed by the validation function, as long as the CRCU is provided with the results of validation. The appropriate remediation of any of the identified weaknesses may lead to changes in the models. These changes should be appropriately classified and approved in accordance with Commission Delegated Regulation (EU) No 529/2014.

3. Guidelines on PD estimation, LGD estimation and the treatment of defaulted exposures

1 Compliance and reporting obligations

2 Subject matter, scope and definitions

3 Implementation

4 General estimation requirements

5 PD estimation

6 LGD estimation

7 Estimation of risk parameters for defaulted exposures

8 Application of risk parameters

9 Review of estimates