If financial market participants are driven by crowd psychology, emotion and narratives, as much as by economic fundamentals and rational calculation, then risks are unlikely to be well captured by standard models (Tuckett and Taffler (2008), Tuckett (2011), Shiller (2017), Bailey et al (2016)). These risks are likely to be highly non-linear, heavily state and time-dependent and thus significantly fat-tailed. In a world of such Knightian uncertainty, it may be difficult to estimate risk weights on individual assets with any degree of precision. Indeed, attempts to do so may result in “over-fitting”, increasing the potential fragility of these model estimates out-of-sample. In uncertain settings, simpler weighting schemes have been found, in a variety of different environments, to offer a better defence against “unknown unknowns” (Gigerenzer (2014)). For example, a 1/N or unweighted asset allocation heuristic (Benartzi and Thaler (2001)), which allocates an equal amount of wealth to each of the assets in one’s portfolio, has been found to outperform more complex strategies such as Markowitz’s (1952) mean-variance optimisation in out-of-sample tests, unless the sample size is very large.19 That logic is one rationale for the use - and, in some settings, predictive superiority – of the LR in capturing solvency risks. It is a variation of the Brainard (1967) portfolio argument. In this vein, Aikman et al (2014) conduct simulations which demonstrate how simple methods, akin to a leverage ratio, can sometimes dominate complex, risk-weighted approaches to calculating banks’ capital requirements when guarding against solvency problems out of sample.

In Europe, without a leverage ratio but with risk-based capital standards, there were incentives for banks to expand balance sheets and shade downwards risk weights. Canadian banks’ incentives sat somewhere in between. Some recent studies have looked at these behavioural shifts in greater detail. During the euro-area crisis, banks increased their exposure toward higher risk government bonds, which carried no capital requirement (Acharya and Steffen (2015)). And following the Lehman crisis, German banks reduced their corporate lending less when the capital requirement was set under the standardised approach (Behn, Haselmann, Wachtel (2016)). In the UK, higher risk mortgages shifted towards lenders whose capital requirements were less risk-sensitive after the introduction of Basel II (Benetton et al (2017)). Recent research has considered how the leverage ratio announcement affected behaviour among a panel of over 650 European banks (Acosta-Smith et al (2017)). It finds a significant increase in risk-taking among those banks for whom the new regime was a binding constraint. This risk-taking was greater, the further these banks were from meeting the new 3% threshold: banks with leverage ratios of 1.5%, 2% and 2.5% were found to increase their risk-taking by 3.4, 2.3 and 1.1 percentage points of risk-weighted assets respectively. This is clear empirical evidence of the risk-shifting channel at work. This is only, however, one side of the risk equation. There were two mitigating factors on the other side. First, a rise in the leverage ratio also boosts these banks’ capital.