Applying the method in a policy context: choices and trade-offs

BIS Quarterly Review  | 
14 September 2009

(Extract from page 84 of BIS Quarterly Review, September 2009)

This box addresses the policy choices and practical issues that have to be confronted when implementing the methodology as an element in a macroprudential approach to regulation and supervision.

The definition of the appropriate “system”, as a precondition for calibration, is not straightforward. This is less of an issue in current regulatory arrangements which focus on individual institutions but becomes critical when the prudential framework focuses on systemic risk. At least two aspects need to be addressed. The first relates to the institutional coverage of regulation – its so-called “perimeter”. A systemic approach would need to take account of the risks generated by all financial institutions that are capable, on their own and as a group, of causing material system-wide damage. This is so regardless of their legal form. The second aspect relates to the geographical coverage of regulation. Should the approach be applied at a domestic level or at a more global level, say to internationally active institutions? And if the answer is to both, how would the adjustments be reconciled? Clearly, a large dose of pragmatism is necessary. And the precise answers will also depend on the extent of cooperation across regulatory jurisdictions.

The definition of the size of the institutions also merits attention, and partly overlaps with that of the system. One question is whether to include only domestic exposures or both domestic and international ones. Another question is whether the appropriate measure refers to the assets (presumably including off-balance sheet items) or to the liabilities (excluding equity) of the institutions. Total assets better reflect the potential overall losses incurred by all the claimants on the institution; liabilities are a better measure of the direct losses linked to its failure.

Having defined the system and the size of the institutions, the next practical question is how to estimate the additional parameters, notably the probabilities of default and the factor loadings on the systematic risk factors. The sources of information range from market inputs, at one end, to supervisory inputs, at the other. Combinations of the two are also possible.

Market inputs have a number of attractive features but also limitations. On the plus side: they summarise the considered opinion of market participants based on the information at their disposal; they should reflect market participants’ views of all potential sources of risk, regardless of their origin (eg poor asset quality, bank runs, counterparty linkages); and they are easily available on a timely basis. On the minus side: they may not be available for all institutions (eg equity prices for savings banks); they require the use of “models” to either filter out extraneous information (eg risk premia, expectations of bailouts) or complete the information they contain (eg to derive probabilities of default from equity prices), giving rise to “model” uncertainty; and they may contain systematic biases: for example, it is well known that market prices tend to be especially buoyant as financial vulnerabilities build up during booms (Borio and Drehmann (2009)).

Supervisory estimates have their own strengths and weaknesses. On the plus side, they can be based on more granular and private information, to which market participants do not have access; on the minus side, they may simply not be available, or may be hard to construct for certain inputs. For example, supervisors have a long tradition in producing measures of the soundness of individual financial institutions, such as rating systems. However, they have as yet not developed tools to derive measures of exposures to systematic risk factors and correlations across institutions based on balance sheet data. The available techniques are in their early stages of development.

All this suggests that, in practice, it might be helpful to rely on a combination of sources and to minimise their individual limitations. For example, currently market prices appear to be especially suited for the estimation of exposures to common factors. And long-term averages of such prices would help to address the biases in the time dimension. This would be especially appropriate if the tool is used to calculate relative contributions of institutions to systemic risk and to avoid procyclicality (Borio (2009)).

These difficulties highlight the need to deal with the margin of error that will inevitably surround the estimates of systemic risk and hence, by implication, of institutions’ contributions to it (Tarashev (2009)). Fortunately, as noted above, the linearity property of the allocation procedure makes it possible to address this issue in a formal, simple and transparent way. This property allows one to combine alternative estimates, weighting them by the degree of confidence that one attaches to them (Tarashev et al (2009)). In addition, it may be advisable for policymakers not to rely too heavily on the resulting point estimates. One possibility would be to allocate institutions into a few buckets, each of them comprising an interval of point estimates – akin to a rating system. This grouping has the added advantage of reducing the computational burden of assessing risk at the level of subgroups of institutions.