Studies on credit risk concentration: an overview

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:

• 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.
• 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.
• To examine and further develop fit-for-purpose tools that can be used in the quantification of concentration risk.

State-of-the-art

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.

Departures from ASRF

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 two and eight percent to the credit value-at-risk while sector concentration can increase economic capital by 20-40 percent. 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 (e.g. 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 that 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.

Stress tests

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.

Finally the group highlighted a number of technical issues that while outside the scope of the project, are nonetheless important in dealing with the overall issue of concentration risk in credit portfolios.

• The choice of an adequate sector scheme for the purpose of concentration risk assessment.
• The definition of a “benchmark” for concentration risk correction.
• Data-related issues.

Studies on credit risk concentration

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. The failures of large borrowers like Enron, Worldcom and Parmalat were the source of sizeable losses in a number of banks. Large exposures to less-developed countries’ debt were one of the reasons of protracted weakness of major US banks in the 1980s, demonstrating that the stability of entire systems can be undermined by the excessive exposure to a single asset class. More intriguing, banks in Texas and Oklahoma suffered severe losses in both corporate and commercial real estate lending in the 1980s. The reason being that in addition to very significant concentrations of lending in the energy industry, the regional dependence on oil implied a strong correlation between the health of the energy industry and local demand for commercial real estate.

These examples illustrate the importance of measuring concentration risk in credit portfolios of banks that arises not only from exposures to a single credit, or asset class, but also from linkages between asset classes. The Asymptotic Single-Risk Factor (ASRF) model [1] that underpins the IRB approach in the new Basel capital framework [2] does not allow for the explicit measurement of concentration risk. A group of researchers from the Research Task Force (RTF) of the Basel Committee on Banking Supervision undertook a project with the goal of analysing the ability of various methods to account for concentration risk in bank loan portfolios and to survey current best-practice in the industry.

The complete results of the project are to be found in individual research papers and reports listed at the end of the complete working paper.

The views expressed in the Working Papers are those of their authors and do not represent the official views of the Basel Committee, its member institutions or the BIS.

All material is copyright Bank for International Settlements 2006. All rights reserved.