Credit Risk – The Standardised Approach

50. The Committee permits banks a choice between two broad methodologies for calculating their capital requirements for credit risk.

One alternative, the Standardised Approach, will be to measure credit risk in a standardised manner, supported by external credit assessments

51. The other alternative, the Internal Ratings-based Approach, which is subject to the explicit approval of the bank’s supervisor, would allow banks to use their internal rating systems for credit risk.

52. The following section sets out revisions to the 1988 Accord for risk weighting banking book exposures. Exposures that are not explicitly addressed in this section will retain the current treatment; however, exposures related to securitisation are dealt with in Section IV.

In determining the risk weights in the standardised approach, banks may use assessments by external credit assessment institutions recognised as eligible for capital purposes by national supervisors in accordance with the criteria defined in paragraphs 90 and 91.

Exposures should be risk-weighted net of specific provisions.

Off-balance sheet items
82. Off-balance-sheet items under the standardised approach will be converted into credit exposure equivalents through the use of credit conversion factors (CCF).

Counterparty risk weightings for OTC derivative transactions will not be subject to any specific ceiling.

83. Commitments with an original maturity up to one year and commitments with an original maturity over one year will receive a CCF of 20% and 50%, respectively. However, any commitments that are unconditionally cancellable at any time by the bank without prior notice, or that effectively provide for automatic cancellation due to deterioration in a borrower’s creditworthiness, will receive a 0% CCF.

83(i). Direct credit substitutes, e.g. general guarantees of indebtedness (including standby letters of credit serving as financial guarantees for loans and securities) and acceptances (including endorsements with the character of acceptances) will receive a CCF of 100%.

83(ii). Sale and repurchase agreements and asset sales with recourse, where the credit risk remains with the bank will receive a CCF of 100%.

84. A CCF of 100% will be applied to the lending of banks’ securities or the posting of securities as collateral by banks, including instances where these arise out of repo-style transactions (i.e. repurchase/reverse repurchase and securities lending/securities borrowing transactions).

84(i). Forward asset purchases, forward forward deposits and partly-paid shares and securities, which represent commitments with certain drawdown will receive a CCF of 100%.

(ii). Certain transaction-related contingent items (e.g. performance bonds, bid bonds, warranties and standby letters of credit related to particular transactions) will receive a CCF of 50%.

84(iii). Note issuance facilities (NIFs) and revolving underwriting facilities (RUFs) will receive a CCF of 50%.

85. For short-term self-liquidating trade letters of credit arising from the movement of goods (e.g. documentary credits collateralised by the underlying shipment), a 20% CCF will be applied to both issuing and confirming banks.

86. Where there is an undertaking to provide a commitment on an off-balance sheet item, banks are to apply the lower of the two applicable CCFs.

87. The credit equivalent amount of OTC derivatives and SFTs that expose a bank to counterparty credit risk is to be calculated under the rules set forth in Annex 4 of this Framework.

88. Banks must closely monitor securities, commodities, and foreign exchange transactions that have failed, starting the first day they fail. A capital charge to failed transactions must be calculated in accordance with Annex 3 of this Framework.

89. With regard to unsettled securities, commodities, and foreign exchange transactions, the Committee is of the opinion that banks are exposed to counterparty credit risk from trade date, irrespective of the booking or the accounting of the transaction.

Therefore, banks are encouraged to develop, implement and improve systems for tracking and monitoring the credit risk exposure arising from unsettled transactions as appropriate for producing management information that facilitates action on a timely basis.

Studies on credit risk concentration: an overview of the issues and a synopsis of the results from the Research Task Force project

BCBS Publications No 15 November 2006

Historical experience shows that concentration of credit risk in asset portfolios has been one of the major causes of bank distress.

This is true both for individual institutions as well as banking systems at large. It is therefore important to measure concentration risk in credit portfolios of banks that arises from two sources, systematic and idiosyncratic. Systematic risk represents the effect of unexpected changes in macroeconomic and financial market conditions on the performance of borrowers. Idiosyncratic risk represents the effects of risks that are peculiar to individual firms.

The model framework for the internal ratings-based (IRB) approach of the Basel II Framework assumes that (a) there is only a single source of systematic risk, and (b) bank portfolios are perfectly fine-grained in the sense that as the largest individual exposures account for a smaller and smaller share of total portfolio exposure, idiosyncratic risk is diversified away at the portfolio level. To the extent that either assumption is violated, IRB capital requirements may understate the true economic capital requirement.

Concentration of exposures in credit portfolios is an important aspect of credit risk. It may arise from two types of imperfect diversification.

The first type, name concentration, relates to imperfect diversification of idiosyncratic risk in the portfolio either because of its small size or because of large exposures to specific individual obligors.

The second type, sector concentration, relates to imperfect diversification across systematic components of risk, namely sectoral factors. The existence of concentration risk violates one or both of two key assumptions of the Asymptotic Single-Risk Factor (ASRF) model that underpins the capital calculations of the internal ratings-based (IRB) approaches of the Basel II Framework. Name concentration implies less than perfect granularity of the portfolio, while sectoral concentration implies that risk may be driven by more than one systematic component (factor).

The Concentration Risk Group of the Research Task Force of the Basel Committee on Banking Supervision undertook a principally analytical project with the following objectives:

(i) to provide an overview of the issues and current practice in a sample of the more advanced banks as well as highlight the main policy issues that arise in this context;

(ii) to assess the extent to which "real world" deviations from the "stylised world" behind the ASRF assumptions can result in important deviations of economic capital from Pillar 1 capital charges in the IRB approach of the Basel II Framework; and

(iii) to examine and further develop fit-for-purpose tools that can be used in the quantification of concentration risk.

The work of the group was divided into three workstreams.

The first workstream collected information about the current "state of the art" both in terms of industry best practice and in terms of the developments in the academic literature. A workshop organised in November 2005 was an occasion to exchange views among experts from the supervisory, academic and industry areas. These contacts revealed that there is a great deal of diversity in the way banks measure and treat concentration risk. Some employ sophisticated portfolio credit risk models that incorporate interactions between different types of exposures while some rely on simpler, ad hoc indicators of such risk. Multi-factor vendor models are also used as inputs or benchmarks to internal models. Management of concentration risk typically depends on a variety of tools including limits on single entity exposures either in terms of overall credit limits or economic capital, and pricing tools that are used by a minority of banks. Typical stress tests employed by banks include a concentration risk component although this is not always studied separately. The availability of the necessary bank-level data for the analysis of concentration risk remains an important practical issue especially when it comes to producing stable and reliable estimates of asset correlation across exposures.

The second workstream focused on gauging the impact of departures from the ASRF model assumptions on economic capital and examined various methodologies that can help to bridge the gap between underlying risk and risk measured by the specific model. The workstream had two sub-themes that focused on name concentration risk (imperfect portfolio granularity) and sector concentration risk (imperfect diversification across risk factors).

The empirical studies conducted by the group, all of which used data only on corporate portfolios, suggest that name concentration risk, albeit important in its own sake, is likely to represent a smaller marginal contribution to economic capital than sector concentration for a typical commercial bank with a medium to large sized loan portfolio. For these portfolios, name concentration could add anywhere between 2 and 8% to the credit value-at-risk while sector concentration can increase economic capital by 20-40%. The patterns of asset correlations both across and within sectors are key determinants of this impact. While single-factor credit risk frameworks tend to produce higher measures of risk in certain circumstances because they generally do not account for diversification across credit portfolio types (eg between wholesale and retail) or do not fully allow for diversification gains within portfolio types, there are also situations in which single-factor credit risk models produce lower measures of risk because they do not capture name and sectoral concentrations.

The notion of name concentration risk is generally better understood than sectoral concentration risk and a number of analytical measurement tools have been proposed in the literature. Some are based on ad hoc measures of concentration (such as the Herfindahl-Hirschman index of portfolio exposures) while others are more firmly embedded in formal models of credit risk. The latter are preferred to the former whenever the needed data requirements are met because they represent a more consistent approach to the measurement and management of all dimensions of credit risk for the portfolio. The group elaborated on an adjustment for imperfect portfolio granularity which had been proposed as part of an earlier version of Basel II. The revised method incorporates analytical advancements that have occurred in the meantime and deals with some practical complications of the earlier proposal.

Sector concentration arises from the violation of the single systematic risk factor assumption which represents an elementary departure from the IRB model framework. It arises because business conditions and hence default risk may not be fully synchronised across all business sectors or geographical regions within a large economy. A bank’s portfolio may be more or less concentrated on some of these risk factors leading to a discrepancy between the measured risk from a single-factor model and a model that allows for a richer factor structure. Given the calibration of the ASRF model for the IRB formulae, this discrepancy can be positive as well as negative.

The group examined various methods that can deal with sector concentration. Some represent tools that can be considered as extensions of more elementary models while others start from a more general multi-factor structure. An example of the former group of tools is a multiplicative adjustment to the ASRF model which uses a more general calibration to a multi-factor model to incorporate concentration risk and was found to perform quite well. In terms of tools that rely explicitly on multi-factor frameworks the group studied the performance of a simplified version of a model originally proposed by Pykhtin and obtained very favourable results. Overall, the choice of approach depends very much on the purpose of the exercise and the availability of the necessary inputs (such as estimates of differentiated probability of default, loss-given-default and asset correlations for various sectors). All approaches require considerable care and judgment by the analyst.

The third workstream focused mostly on the ability of stress tests to detect excessive concentration (of either type) and to provide estimates of economic capital in stress scenarios. Plausibility, consistency with the credit portfolio model, being adapted to the portfolio under consideration and being reportable to senior management were identified as desirable properties for stress tests. A methodology based on the idea of stressing core factors while other factors move conditional on them demonstrates that it is possible to derive stress tests on the basis of a consistent model and a close link between the model and the real world.

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