This suggests that, at least in this sample, imposing a small number of regulatory constraints can achieve the same hit rate as any singular constraint, but at a materially lower societal and regulatory cost, as measured by levels of capital and liquidity and/or regulatory false alarms. Intuitively, these results broadly accord with the conceptual discussion. A regulatory portfolio can help when insuring against multiple sources of risk and myriad sources of uncertainty. It also accords with what we know from various individual case studies of bank failure during the global financial crisis: some banks failed because they were over-leveraged, others because their assets were excessively risky, others still because they undertook too much maturity transformation. Because these fault-lines were, for some banks, reasonably well-correlated – their risk management was poor across all dimensions – individual regulatory metrics performed fairly well in identifying these banks prior to them failing. But some banks’ risk-management failings were singular, not plural. Their risk blind-spots were idiosyncratic and uncorrelated. By using a portfolio of regulatory stress metrics, it is possible to isolate those banks which were risk-management outliers in one, but not all, dimensions. Consider two very different banks which failed during the global financial crisis. American bank Countrywide had a leverage ratio of 7.7% and a risk-weighted capital ratio of 11.6% at the end of 2006. Even if capital regulation had been much tougher in 2006, it may not have been required to raise capital. But its NSFR was just 0.76, indicative of the structural liquidity risk it was undertaking.

It also raises the severity of a crisis, with real GDP per capita almost 1% lower after five years.12 Colleagues at the Bank of England have considered whether it is the level of credit, or its growth, prior to a crisis that matters most for subsequent economic performance (Bridges et al (2017)). They find that credit growth has historically been a significant predictor of crisis severity, whereas the level of indebtedness appears less important. Second, not all forms of credit are equal. In the post-WWII era, mortgage credit growth has been the dominant driver of financial crisis risk. And growth in mortgages, rather than in other forms of credit, is the key determinant of the drag in the recovery phase from crisis (Jordà et al (2017)). Third, asset prices are also important with ‘leveraged bubbles’ – synchronised house price and mortgage credit booms - particularly dangerous (Jordà et al (2015)). Taken together, this evidence is consistent with the probability (γ) and output costs of credit crises (∆) being at least as large as assumed in the original LEI study, perhaps larger, given the still-high levels of the credit-to-GDP ratio in most countries, and the monetary and fiscal space available to the authorities at present relative to the average of the past – a recent paper by Romer and Romer (2017) presents evidence that this factor is a significant determinant of crisis severity. The still-accumulating output losses during the recovery phase from this time’s crisis would also point in this direction (Chart 1).