| Guidelines This chapter contains specific guidance on issues related to the internal ratings-based approach. Specifically, on: (i) the estimation of loss given default; (ii) the validation of low-default portfolios; (iii) the use test; and (iv) the use of vendor products. The contents of this chapter are based on:
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Banks which have received supervisory approval to use internal ratings-based (IRB) approaches for credit risk may rely on their own internal estimates of risk components to determine regulatory capital requirements for a given exposure. The Basel Framework sets out detailed requirements for IRB approval and implementation; this chapter provides additional guidance on certain aspects of the IRB approach set out in the following 4 sections.
The following terms are used throughout this chapter and have the meaning given below:
| 1 | The concept of economic loss referred to here is defined in CRE36.76. |
| 2 | The Basel Framework contains cases in which a bank is required to first transform or adjust its risk estimates (eg by applying floors) before their use in the IRB approach. Use test compliance generally concerns the internal use of these estimates prior to their transformation or adjustment for regulatory capital purposes. |
This section aims to further clarify the quantification of LGD parameters for the purpose of Pillar 1 capital calculations, including further guidance on identifying downturn conditions and incorporating these conditions into LGD estimates where appropriate.
Principle 1: The bank should have a rigorous and well documented process for assessing the effects, if any, of economic downturn conditions on recovery rates and for producing LGD estimates consistent with downturn conditions.
CRE36.83 of the Basel Framework requires that the LGD parameters used in Pillar 1 capital calculations must “reflect economic downturn conditions where necessary to capture the relevant risks.” The purpose of this requirement is to ensure that LGD parameters will embed forward-looking forecasts of recovery rates on exposures that default during conditions where credit losses are expected to be substantially higher than average. Under such conditions default rates are expected to be high so that if recovery rates are negatively related to default rates, LGD parameters should embed forecasts of future recovery rates that are lower than those expected during more neutral conditions. In those cases where future recovery rates are expected to be independent of future default rates there is no supervisory expectation that the forward-looking forecasts of recovery rates embedded in LGD parameters will differ from those expected during more neutral conditions.
To meet the standard set forth in CRE36.83 a bank’s quantification and validation system should comply with Principle 1. The process referred to in the principle should consist of the following integrated components:
Appropriate downturn conditions might be characterised, for example, by the following:
At a minimum, the bank’s quantification process should identify separate downturn conditions for each supervisory asset class, and with some exceptions, within each jurisdiction. Since, all else equal, greater granularity in defining downturn conditions will tend to result in more conservative LGD estimates, the bank may identify downturn conditions at a more granular level if such an approach is more risk sensitive. Appropriate downturn conditions are those in which the relevant drivers of default rates are consistent with conditions where credit losses for the supervisory asset class are expected to be substantially higher than average.
Where recovery rates of exposures are sensitive to local economic conditions, the bank should identify separate downturn conditions for each jurisdiction. However, in those cases where a bank can demonstrate that exposures in the same asset classes in different jurisdictions exhibit strong co-movement in recovery rates, the bank can group those jurisdictions together for the purpose of defining downturn conditions. Where recovery rates of exposures are not sensitive to local economic conditions (eg exposures to internationally diversified obligors), the bank may identify downturn conditions appropriate to the exposures, which may span national boundaries.
Those adverse dependencies might be identified, for example, by some or all of the following:
For example, for those exposures for which adverse dependencies between default rates and recovery rates have been identified through analysis consistent with CRI30.6b, the LGD estimates may be based on averages of observed loss rates during downturn periods identified according to CRI30.6a. or they may be derived from forecasts based on stressing appropriate risk drivers in a manner consistent with downturn conditions identified according to CRI30.6a. If no material adverse dependencies between default rates and recovery rates have been identified through analysis consistent with [6.b], the LGD estimates may be based on long-run default-weighted averages of observed loss rates, or they may be derived from forecasts that do not involve stressing appropriate risk drivers.
Most approaches to quantifying LGDs either implicitly or explicitly involve the discounting of streams of recoveries received after a facility goes into default to compare the net present value of recovery streams as of a default date with a measure of exposure at default. Discount rates reflected in estimates of LGD should comply with Principle 2.
When recovery streams are uncertain and involve risk that cannot be diversified away, net present value calculations should reflect the time value of money and a risk premium appropriate to the undiversifiable risk. In establishing appropriate risk premiums for the estimation of LGDs consistent with economic downturn conditions, the bank should focus on the uncertainties in recovery cash flows associated with defaults that arise during the economic downturn conditions identified under Principle 1. When there is no uncertainty in recovery streams (eg recoveries derived from cash collateral), net present value calculations need only reflect the time value of money, and a risk-free discount rate is appropriate.
These measures of recovery rates can be computed in several ways, for example by:
| 4 | A certainty-equivalent cash flow is defined as the cash payment required to make a risk averse investor indifferent between receiving the cash payment with certainty at the payment date and receiving an asset yielding an uncertain payout whose distribution at the payment date is equal to that of the uncertain cash flow. |
| 5 | A bank may use an “effective interest rate” in accordance with IFRS9 as the discount rate, but in that case should adjust the stream of net recoveries in a way that is consistent with this principle. |
Given the substantial flexibility in identifying downturn conditions and incorporating the effects of identified downturn conditions in LGD estimates and the requirement of CRE36.83 that LGD estimates be no lower than the long-run default-weighted average loss rate given default for a facility type, it is important that banks and their supervisors be able to compare long-run default-weighted average loss rates given default with LGD estimates.
For each exposure to which an estimated LGD is assigned as part of the Pillar 1 capital calculations, banks also should be prepared to provide an estimate of long-run default-weighted average loss rate given default to supervisors if requested.
Supervisors may not wish to request this information if the bank can demonstrate that its estimates of loss rates given default under downturn conditions are consistent with the principles articulated above and that reporting separate estimates of long-run default-weighted average loss rates given default would not be practical. In no case may the LGD for an exposure used for Pillar 1 calculations be lower than the corresponding long-run default-weighted average loss rate given default for that exposure, but in some cases the two parameters may be the same.
Banks are expected to meet the principles described in this section to be eligible to use own-estimates of LGDs for regulatory purposes. However, in some circumstances, certain banks may temporarily not be able to comply with the principles above to the satisfaction of their supervisors for certain asset classes but may be able to estimate the long-run default-weighted average loss rates given default for that asset class and be otherwise compliant with the minimum requirements of the IRB approaches. If supervisory estimates of LGDs are available for the relevant asset class in that jurisdiction, these banks should use the supervisory estimated parameters for the entire asset class. For asset classes for which supervisory LGDs are not provided in that jurisdiction, supervisors may choose, at national discretion, to establish conservative and temporary measures for these banks. These measures should be conservative so that banks will have a strong incentive to work towards meeting the principles above. Any bank that is allowed to use these temporary measures will be required to produce a plan to become fully compliant with these principles, and have that plan approved by its supervisor.
There is no expectation that the stress tests referred to in CRE36.50 or CRE36.51 will necessarily produce an LGD that is either lower than or higher than the LGD estimated according to CRE36.83. To the extent that the identification of downturn periods under CRE36.83 coincides with the stress tests in CRE36.50 or CRE36.51, the calculation might turn out to be similar. More generally, some stress test calculations under CRE36.50 or CRE36.51 may function as one tool for assessing the robustness of the LGD estimation under CRE36.83.
This section sets out principles that are intended to support banks and supervisors in interpreting the key use test provisions set out in CRE36.60 of the Basel Framework.
The IRB use test is based on the conception that supervisors can take additional comfort in the IRB components where such components “play an essential role” in how banks measure and manage risk in their businesses. If the IRB components are solely used for regulatory capital purposes, there could be incentives to minimise capital requirements rather than produce accurate measurement of the IRB components and the resultant capital requirement. Moreover, the employment of IRB components in internal decision making creates an automatic incentive to ensure sufficient quality and adequate robustness of the systems that produce such data.
The universal usage of the IRB components for all internal purposes, however, is not necessary. In some cases, differences between IRB components and other internal risk estimates can result from mismatches between prudential requirements in the Basel Framework and reasonable risk management practices, business considerations or other regulatory and legal considerations. Examples include different regulatory and accounting requirements for downturn LGD, PD and LGD floors, annualised PDs and provisioning. Other examples of where differences could occur include pricing practices and default definitions. In such cases, supervisors may take a flexible position with respect to the principles below, although the institution should still be able to elaborate on the reasonableness of the differences between the IRB and internal parameter estimates, and demonstrate the relationships between them.
In general, there are three main areas where the use of IRB components for internal risk management purposes should be observable:
If IRB components are not used in some of these areas, the supervisor may require an explanation for such non-use or may raise concerns about the quality of the IRB components. In many instances, supervisors will need to exercise considerable judgement in assessing the use of IRB components.
Principle 3: Banks are responsible for demonstrating their compliance with the use test.
According to CRE36.122, banks have the responsibility for validating their rating system and associated IRB parameter estimates. The use test is no exception to this principle: banks are responsible for complying with the use test requirement and for demonstrating compliance by providing relevant documentation and evidence of use of IRB components.
Banks should demonstrate to their supervisors the processes where IRB components play an essential role and provide the relevant supporting evidence for compliance with the use test. In line with CRE36.61, banks should illustrate how these internal uses confirm management’s belief in the validity of the IRB components and contribute towards meeting the use test objectives. Banks should clarify whether the IRB components are used directly in risk management processes, or whether they are used in a derived form or in a partial way. Banks should also demonstrate how risk management processes support the accuracy, robustness and timeliness of the IRB components.
Banks and supervisors may rely on existing internal documentation for the purpose of demonstrating use test compliance. To a large extent, the obligations implied by this principle will be met through normal documentation of the banks’ overall validation and governance frameworks and internal operating processes.
Principle 4: Internal use of IRB components should be sufficiently material to result in continuous pressure on the quality of the IRB components.
To make the use of the IRB approach credible, IRB components should be entrenched in the bank’s internal risk management processes. While IRB components should play an essential role in risk management and decision making, this does not necessarily mean an exclusive or primary role in all relevant processes. In addition, as elaborated upon in Principle 5, there may be differences between the internal risk measures used for risk management and the IRB components.
One of the aims of the use test is to promote adequate and appropriate incentives internal to banks so that the banks have a strong belief and interest in the accuracy of their IRB components and the quality of the processes that generate those components. The following are examples of situations where a lack of quality in the IRB components or their underlying processes may give rise to supervisory concern:
In a bank that meets the use test, supervisors would expect to see evidence of the occurrence of internal challenges to the accuracy, robustness, and timeliness of IRB components resulting from any direct or indirect employment of IRB components along the lines mentioned in the introductory section, ie strategy and planning processes, credit exposure management, and reporting.
As a quality check of IRB components and underlying processes, the use test is a necessary supplement to the overall validation process. It represents a very important supervisory tool and a fundamental component of the case that banks should put to their supervisors to demonstrate that they initially meet the IRB minimum requirements and will continue to do so, on an ongoing basis.
The use test plays a key role in ensuring and encouraging the accuracy, robustness, and timeliness of a bank’s IRB components, confirms the bank’s trust in those components and allows supervisors to place more reliance on their robustness and thus on the adequacy of regulatory capital. The evaluation of the use test in banks’ risk management processes and the focus on continuous quality assurance for risk estimates may also encourage improved risk management, which is an overarching objective of the Basel Framework.
Principle 5: Demonstrating consistency and explaining differences between IRB components and internal measures can establish sufficient comfort that principles 3 and 4 are met.
Measures used for internal processes may reasonably differ from IRB components in some instances. Such differences may arise from legitimate mismatches between the prudential requirements of the IRB framework and a bank’s own risk management practices. Where such differences exist, banks should demonstrate good reasons for use of parameters that do not match IRB components. The supervisory objectives of the use test could be met if banks demonstrate that the degree of consistency between the IRB components and the internal estimates is sufficiently high as to contribute to continuous quality pressure on the IRB components. In this context, consistency might be demonstrated by establishing clear linkages between the internal inputs and the IRB components, showing that any differences reflect legitimate risk management needs.
A combination of multiple features could provide comfort to supervisors that sufficient linkage exists. Such features could include use of the same underlying data for computations, reliance on the same IT systems, application of similar quality checks and similar validation techniques, or use of common methodologies or similar models.
Principle 6: The importance of an internal process to the bank’s decision making influences the extent to which that process contributes to an assessment of use test compliance. Banks should take a holistic approach when assessing overall compliance of their institution with the use test requirements.
Any processes in which significant use is made of IRB components or where incentives to ensure the quality of IRB components are sufficiently strong can contribute to a bank’s overall self-assessment of use test compliance. Certain uses in certain processes could potentially provide higher comfort than others. Generally, the more important, pervasive and granular the use of the IRB components in a bank’s decision-making processes, the greater is the likelihood of meaningful internal challenge and the stronger are the incentives to ensure the accuracy and robustness of IRB components, giving greater confidence that management is committed to the validity of the IRB components. Supervisors should adopt a similar approach when assessing factors supporting a bank’s compliance with the use test.
If, on the other hand, ratings, retail segmentation and estimates used in internal processes differ from respective IRB components without convincing explanation as to the reasons for the lack of consistency, bank management’s commitment to the importance of the IRB components and thus compliance with the use test may be in doubt. However, shortfalls in use test compliance in individual processes do not in and of themselves imply a negative overall evaluation of an institution’s compliance with the use test.
A group with subsidiaries in more than one country may conduct much of its risk management and business management activities on a group basis using processes, procedures and IRB and internal components defined at the group level. In such cases both home and host supervisors may need to be flexible in determining whether the purposes of the use test are met on a holistic basis, generally with reference to such group policies, procedures and components.6
The section provides guidance regarding how banks might satisfy IRB validation requirements when vendor products, which frequently introduce information transparency issues, are used within banks’ IRB processes. It focuses on vendor developed models and datasets used within the context of banks’ IRB processes to assign exposures to rating grades, or segments, or to estimate IRB risk parameters.
The Basel Framework does not make any exceptions from minimum requirements when vendor products are used within banks’ IRB processes (CRE36.37). The proprietary nature of certain aspects of vendor products does not necessarily disqualify their use in the bank’s IRB quantification and validation processes; however, when full and complete details concerning aspects of a vendor product are lacking, it will be necessary for banks to rely more heavily on other validation techniques or methods designed to compensate for the lack of access to full information. As examples:
The principles set out in this section balance the need for banks to be transparent in developing their IRB risk estimates and the need for vendors to protect the intellectual property that accompanies their proprietary models. For supervisors it is appropriate to scale supervisory expectations by the relative importance of vendor models or data within the bank’s IRB processes.7
| 7 | For instance, if results produced by a vendor model rely heavily on external data inputs, and that model in turn plays a material role in estimating a bank’s IRB parameters, then Principles 7 to 10 outlined below should be applied to the fullest extent possible to both the vendor model and the external data inputs used by the model as they relate specifically to a bank’s IRB risk quantification and validation processes. If, in another instance, external data are used only to provide broad benchmarks for certain IRB risk parameters, a bank’s validation efforts might be limited to processes that ensure the integrity of the data and their applicability to the bank’s exposures. |
Principle 7: Banks should be able to document and explain the role of vendor products and the extent to which they are used within their IRB processes.
Vendor products can play several roles within a bank’s IRB processes. It is the responsibility of the bank to demonstrate and document how its risk estimates are derived and validated. When vendor products play a material role in either deriving or validating these risk estimates, it is important that banks clearly articulate what role these products play in the estimation process and the extent to which these products are used in arriving at IRB parameter estimates. At a minimum, banks need to describe the particular portfolios to which the vendor products are applied as well as how these products are applied. To improve both supervisory and internal understanding of the bank’s IRB processes, banks should be prepared to explain the underlying rationale for choosing third-party products over internally developed models and data (eg lack of default data or internal resources). Banks should be able to explain what alternative solutions it has considered, and, if possible, how results using the vendor products compare to those of alternative products or solutions.
Principle 8: Banks should be able to demonstrate a thorough understanding of vendor products used in their IRB processes.
In general, when banks use vendor products in their IRB processes, they should be able to demonstrate a thorough understanding of those products. Additionally, if banks integrate vendor models within their IRB risk quantification processes, banks need to demonstrate how those models effectively contribute to IRB risk quantification. This in-house knowledge of vendor products might be demonstrated by the following:
Principle 9: Vendor products should be appropriate to the bank’s exposures and risk rating methodologies and suitable for use within the IRB framework.
Banks using the IRB approach should be able to demonstrate clear linkages between vendor model inputs, datasets, and estimates and the bank’s own portfolio characteristics and risk rating methodologies. This requirement does not imply that vendor model inputs and data need to mirror those of the bank’s portfolio in detail. Nevertheless, there should be a reasonable degree of consistency between model inputs and the risk drivers of the bank’s internal portfolio as well as a reasonable comparability between the data that were used for building the model and the bank’s internal portfolio characteristics to produce meaningful risk rating assignments or risk parameter estimates. As examples:
Banks should also ensure that vendor products are consistent with the requirements for use in an IRB context. The output of the vendor products may be consistent with the Basel standards and requirements but, in themselves, may not achieve full compliance. That is, the vendor product outcomes may require adjustment or supplementation in the form of additional information (eg qualitative information not included in the vendor model) or a mathematical adjustment or transformation (eg conversion to logarithms). The bank should recognize the need for such supplementation and incorporate the combined results in its IRB processes to achieve full compliance. As examples:
Principle 10: Banks should have clearly articulated strategies for regularly reviewing the performance of vendor model results and the integrity of external data used in their IRB risk quantification processes.
A fundamental difference between internally and externally developed models is the degree to which banks are able to provide transparent descriptions of a model’s development. When the developmental evidence is less than fully transparent in the case of vendor models, the bank will have to rely more heavily on alternative validation approaches. Accordingly, banks should implement clear strategies designed to periodically (at least once a year) assess the performance of any vendor models used in the bank’s IRB processes to ensure the models continue to function as intended. Since vendor model parameters and weights may have been calibrated using external data, it is critical for banks to test the performance of vendor models against the bank’s own portfolio of exposures. Where there is a scarcity of internal performance data (eg low-default portfolios) with which to perform backtesting or outcomes analysis, bank’s performance reviews will have to rely more heavily on alternative performance measurement techniques. In addition, banks should develop and implement strategies designed to verify the accuracy and consistency of any external data used within the bank’s IRB risk quantification processes. This can be done, among other ways, by comparing the results obtained using the external data to the results obtained using a bank’s own portfolio data in the same risk rating, segmentation, or parameter estimation models or methods.
First and foremost, banks should ask vendors to follow good model validation practices. However, it is expected that the scope of these validation activities will focus on the vendor model itself and not on IRB processes. Vendors should be willing to demonstrate these practices to their clients and should always be willing to furnish documentation and reports relating to the validation of their models and the mapping (applicability) of developmental data used in those models to a client bank’s portfolio. When new versions or releases of their products are released, banks should obtain documentation that describes the recalibration or conversion from the old product to the new product as well as information on the validation of the new versions. Banks should obtain from vendors descriptions of key model parameters and the sensitivity of model results to changes in these parameters and their statistical weights. Perhaps most critically, banks should be able to test the performance of the vendor model against the bank’s own portfolio when those models are used in the IRB processes.
In the case of sensitive, proprietary data and model information that is not disclosed, vendors should nonetheless provide descriptions of the general nature, model characteristics, and sources of development data. Proprietary elements often include technical model characteristics, such as the equation specifications and statistical weights; in some cases, details of the datasets used in the formulation of the model are also not disclosed. While the evaluation of such developmental information is a key component of banks’ validation processes, banks, in most cases, could compensate for the non-disclosure of proprietary information by applying the principles outlined in this section. Banks should be able to demonstrate to their supervisors that they have taken sufficient measures to ensure that proprietary elements of vendor models do not inhibit them from meeting these principles. The extent of the measures required will depend on the relative importance of a vendor product within a bank’s IRB processes and may be material when a bank relies heavily on vendor products with non-disclosed elements.
This section sets out guidance regarding the appropriate treatment within the foundation and advanced IRB approaches of portfolios where banks may have limited loss data.
Portfolios for which bank’s internal data systems include relatively few loss events (ie low default portfolios (LDP)), present challenges for risk quantification and validation. A straightforward calculation based on historic losses for a given wholesale rating or retail segment would not be sufficiently reliable to form the basis of a PD estimate, let alone an estimate of LGD or EAD. In addition, backtesting realised outcomes against estimates may not provide strong evidence to support the accuracy of the rating system.
Several types of portfolios may have low numbers of defaults, eg low risk portfolios, small portfolios, or portfolios with a short history due to entry in a new market. Other portfolios may not have incurred recent losses, but historical experience or other analysis might suggest that there is a greater likelihood of losses than is captured in recent data.
A relative lack of historic data does not automatically preclude portfolios from use of the IRB approaches. Relatively sparse data might require increased reliance on alternative data sources and data-enhancing tools for quantification and alternative techniques for validation. The choice of specific tools and techniques will depend on the particular circumstances of the individual bank and the specific portfolio. Nevertheless, banks are strongly encouraged to consider the tools and techniques listed below and to utilise those that are most appropriate to their particular circumstances to improve their risk assessments.
Parameter estimates should be forward-looking and predictive.
While parameter estimates must be grounded in historical experience and empirical evidence, and not based purely on subjective or judgmental considerations, they are intended to be predictive for all portfolios. Relative scarcity of historical loss data in some circumstances may not be a serious impediment to developing PD, LGD and EAD estimates. Where, for example, there is a lack of recent loss data but historical experience or other analysis suggests that this is unlikely to be representative of probable long-term outcomes, it should be possible to base risk estimates not solely on recent loss data, but also on additional information about the drivers of losses.
The qualitative requirements for the IRB approaches apply to all portfolios.
There is a range of IRB qualifying criteria in the Basel Framework, of which data and statistical elements are only one part. Regardless of whether or not a bank has relatively scarce internal loss data in a particular portfolio, supervisors should expect the bank to satisfy all of the qualitative criteria set forth in CRE36.
A relative lack of loss data can at times be compensated for by other methods for assessing risk parameters.
For some portfolios (eg sovereign and bank), there may be limited loss data. According to the quality of loss data that might be available internally and/or externally, there may be tools that banks can utilise to enhance data richness (see CRI30.65 to CRI30.71).
Where scarce internal loss data makes it difficult to test risk estimates against actual experience, a variety of validation tools are available.
In some cases, even where tools such as those described below have been used to enhance data richness, banks and supervisors may find that backtesting risk rating system predictions against realised defaults cannot be done in a manner that strongly demonstrates predictiveness. In such cases, a bank may find that employing a variety of validation tools, including review of developmental evidence, process verification and benchmarking, can satisfy both itself and its supervisor that its rating estimates are reasonable see CRI30.65 to CRI30.71.
Supervisors will review bank estimates and evaluate whether the bank has taken reasonable steps to address the scarcity of internal loss data consistent with the minimum requirements set forth in the Basel Framework (CRE36) and the guidance in this section. For example, supervisors will have to be satisfied with any additional conservatism in loss estimates used by the bank as per CRE36.67 and CRE36.78. Where a bank does not, in the judgement of the supervisor, take adequate steps to address its lack of historic loss data, there is a range of options open to supervisors that will depend on the circumstances of the bank, the jurisdiction and the specific portfolio.
While a relative lack of loss data may make it more difficult to use quantitative methods to assess risk parameters, there are nevertheless some tools that could potentially be used to enhance data richness or to determine the degree of uncertainty to be addressed through conservatism. This sub-section outlines several possible data-enhancing and validation tools that can be used in the quantification and validation of all portfolios, but that might be especially relevant for the treatment of LDPs. These tools are more applicable to estimation of PDs rather than LGDs or EADs. The suitability and most appropriate combination of individual tools and techniques will depend on the nature of the bank and the characteristics of the specific portfolio.
Pooling of data with other banks or market participants, the use of other external data sources, and the use of market measures of risk can be effective methods to complement internal loss data. While a bank would need to satisfy itself and its supervisor that these sources of data are relevant to its own situation, in principle, data pooling, external data and market measures can be effective means to augment internal data in appropriate circumstances. This can be especially relevant for small portfolios or for portfolios where a bank is a recent market entrant.
Internal portfolio segments with similar risk characteristics might be combined. For example, a bank might have a broad portfolio with adequate default history that, if narrowly segmented, could result in the creation of a number of LDPs. While such segmentation might be appropriate from the standpoint of internal use (eg pricing), for purposes of assigning risk parameters for regulatory capital purposes it might be more appropriate to combine subportfolios.
In some circumstances, different rating categories might be combined and PDs analysed for the combined category. A bank using a rating system that maps to rating agency categories might find it useful, for example, to combine AAA, AA and A-rated credits, provided this is done in a manner that is consistent with CRE36.19 to CRE36.20. This could enhance default data without necessarily sacrificing the predictiveness or risk-sensitivity of the bank’s rating system.
The upper bound of the PD estimate can be used as an input to the formula for risk-weighted assets for those portfolios where the PD estimate itself is deemed to be too unreliable to warrant direct inclusion in capital adequacy calculations.
Banks may derive PD estimates from data with a horizon that is different from one year. Where defaults are spread out over several years, a bank may calculate a multi-year cumulative PD and then annualise the resulting figure. Where intra-year rating migrations contain additional information, these migrations could be analysed as separate rating movements to infer PDs, which may be especially useful for the higher-quality rating grades.
If low default rates in a particular portfolio are the result of credit support, the lowest non-default rating could be used as a proxy for default (eg banks, investment firms, thrifts, pension funds, insurance firms) to develop ratings that differentiate risk. When such an approach is taken, calibration of such ratings to a PD consistent with the Basel definition of default would still be necessary.
In addition, where a scarcity of internal historical data makes it difficult to meaningfully backtest risk rating predictions against realised defaults, it may be possible to make greater use of various benchmarking tools for validation. Among the tools that could potentially be used are the following:
This list is not intended to be exhaustive but rather is intended to provide examples of some benchmarking tools that may be useful in the case of scarce internal loss data. It is important that banks utilise as many tools and techniques, including those other than benchmarking, as they deem necessary to build confidence and demonstrate the predictive ability of their risk rating systems.
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