As elsewhere, a regulatory regime of constrained discretion has given way to one with too much unconstrained indiscretion. This calls for regulatory repair. Without change, the current regulatory system risks suffering, like the Chicago teachers and the LIBOR fixers, reputational damage. Fortunately, there are early signs that regulatory change is afoot to place tighter constraints on this (in)discretion. The emergence of self-regulation To understand how we ended up here, it is useful to explore the historical contours of the regulatory debate. This is a history in roughly four chapters. Chapter 1 covers the period prior to the agreement of the first Basel Accord in 1988. Until then, a patchwork of national regulatory frameworks for capital adequacy operated. Some countries set capital adequacy standards based on simple measures of bank equity to assets – a leverage ratio. Others, including in the US, used risk-based standards with risk weights set by regulators for a small set of asset categories. Chapter 2 begins with the introduction of the Basel Accord. This was a landmark agreement: the first-ever genuinely international banking accord, based around an 8% bank capital ratio, with internationally-set risk-weights applied to a small set of banks’ assets. The Accord was explicitly designed to lean against an international “race to the bottom” in capital adequacy standards (Goodhart (2011)). It also helped ensure a level international playing field. Chapter 3 commences with the Market Risk Amendment to Basel I in 1996 and continues through to the Basel II agreement of 2004.

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.