For one, it runs to four, with one bank’s model suggesting $ of capital, another’s over $ for an identical exposure. These inconsistencies are likely to be an under-statement of the true problem. Because they cover only a sub-set of the banks using internal models, these exercises may significantly under-estimate variability across the bank population. The portfolios covered by existing HPE  1  Market-based measures of bank riskiness, such as CDS spreads or credit ratings, are also consistent with this interpretation. If anything, these suggest the panel of banks is as risky or riskier today than 20 years ago. 2  The European Banking Authority (EBA) has also looked at the degree of consistency of European banks’ risk weights, though not using a HPE methodology. It too found large cross-bank discrepancies. BIS central bankers’ speeches  3  exercises have also tended to be relatively simple. More complex portfolios would probably result in wider cross-bank variability. The Basel Committee is currently undertaking an equivalent exercise for banking book assets. There is a delicate line to tread between useful diversity in model outputs on the one hand and useless inconsistency on the other. It is clear we are currently on the wrong side of this line. If a regulatory regime can generate capital ratios of 5%, 10% and 20% for three identical banks, it is not a robust basis for assessing capital adequacy. In short, the objectives of the original Basel Accord are at risk.
One possible reason why risk-shifting may have been difficult to detect is that risk-based capital standards coincided with the shift to a much more complex regulatory framework. Complexity has an important impact on risk-shifting incentives - and not in the ways some have suggested. For example, some have argued that simpler rules are easier to arbitrage. The evidence suggests the exact opposite. Take the tax system. Simple linear tax schedules are typically found to be more robust to problems of tax arbitrage than complex rules (Hindriks, Keen and Muthoo (1999)). Why? Because complexity increases the number of loopholes through which the tax-avoider can slip. Indeed, evidence suggests that complexity of the tax system may be the single largest determinant of tax avoidance across countries (Richardson (2006)). The same logic carries across to financial regulation. Regulatory complexity creates wormholes. At a macro level, cross-country studies suggest that regulatory complexity, in particular the use of internal models, appears to have had an important bearing on bank failure (Cihak et al (2012)). At a micro level, the parts of the regulatory framework which have been most prone to arbitrage are those where complexity and opacity has been greatest – for example, the trading book. Incentives will always exist to shift risk to where it is cheapest. No tax or regulatory system can fully avoid those incentives. But some regimes may be better at constraining those incentives than others. The current mix of complexity and self-regulation may provide too few constraints.