Granularity may not improve risk-sensitivity; it could even worsen it by amplifying model risk. And given that uncertainty, simpler metrics may often provide a more robust signal of solvency. (c) Risk-shifting and robustness A third argument used to justify risk-based frameworks is that they help defuse incentives to engage in “risk-shifting” or other types of regulatory arbitrage (Kahane (1977), Kim and Santomero (1988)). Risk-shifting refers to incentives to shift portfolios towards higher-risk, higher-return assets. These incentives are likely to be strongest when risk is not well aligned with return – for example, under an equally-weighted leverage ratio. Indeed, this powerful logic justified moving to risk-based capital standards in the first place. Historically, there have certainly been episodes when risk-shifting appears to have been important. For example, the movement by banks into Latin American debt in the 1980s; the loading-up on zero risk-weighted OECD sovereign debt in the 1990s; and the extension of zero-weighted 364-day lines of credit in the 2000s. Yet in these cases, the problems seem to have been generic - mis-calibration of risk weights. A few studies have looked at the evidence on risk-shifting more systematically. For example, Furlong (1988) looked at the behaviour of around 100 US bank holding companies after the introduction of the leverage ratio in 1981. While banks’ average riskiness increased, there was no difference in behaviour between regulatory-constrained and unconstrained banks. This suggests risk-shifting was not too potent a factor. Sheldon (1996) reaches the same conclusion when considering the move by international banks to Basel I.

Over-fitting the past makes for fragile predictions about the future. Unweighted measures, without noise pollution, have often been shown to do better. Whether they do so in bank regulation is, ultimately, an empirical question. The noise around estimated risk weights cannot be observed directly, but empirical evidence is illustrative. For example, if estimated risk weights can differ across banks by factors ranging from three to a thousand, this suggests a high, sometimes deafening, amount of model noise. Historical errors in estimated risk weights are also illuminating. For example, trading book risk weights during the crisis were found to be mis-calibrated by factors of up to 50. A common driver behind these fragilities may be the shortness of the samples used for model estimation. It is well-known that small sample problems are especially acute when the underlying distribution of outcomes is tail-heavy, as with financial time-series. The Basel regime imposes constraints on so-called “look back” periods. For example, the minimum sample period for VaR models in the trading book is one year, though most models in practice use between one and two years. For the banking book, the minimum look-back period is between five and seven years, with actual samples lying between five and twenty years. From a robustness perspective, samples this short are, quite literally, a recipe for disaster. At most, they would encompass one cycle or extreme event. Some would fail to cover even that. This would leave the tail of the risk distribution empirically under-fitted and the body over-fitted.