Zahid Amadxarif,Paula Gallego Marquez and Nic Garbarino
“We’ve done a lot to lower prudential barriers to entry into the banking sector […] but have we done enough to lower the equivalent barriers to growth?” asked PRA CEO Sam Woods in a recent speech. To make regulation proportionate, policymakers adapt regulatory requirements to the risks posed by each firm. But regulators face a trade-off between addressing systemic risks in a proportionate way and limiting regulatory complexity. New thresholds can create complexity and cliff-edge effects that can discourage healthy firms from growing. We identify regulatory thresholds for UK banks and building societies using textual analysis on a new dataset that contains the universe of prudential rules.
For some years, financial regulations have been becoming more complex. This has led some prominent commentators, regulators and regulatory bodies, to set out the case for simplicity, including Adrian Blundell–Wignall, Andy Haldane, Basel Committee and Dan Tarullo. In his contribution, Haldane illustrates how simple rules can achieve complex tasks: by simply adjusting its speed to keep its angle of gaze fixed, a dog can manage the complex task of catching a Frisbee. In this post, however, we argue that some financial risks are hard to catch with simple rules – they are more like a boomerang’s flight path than that of a Frisbee. Complex rules can sometimes do a better job at catching risk; and simple rules can be less prudent.
The financial system is complex and highly interconnected. Indeed, interactions between agents are key to its functioning. But these interconnections have the potential to turn small shocks into systemic crises. Understanding the complex nature of these interconnections is important, but can also be difficult. In this post we introduce new tools designed to analyse the financial network and help analysts build a better understanding of risks posed by interconnectedness.
Most large banks assess the capital they need for regulatory purposes using ‘internal models’. The idea is that banks are in a better position to judge the risks on their own balance sheets. But there are two fundamental problems that can arise when it comes to modelling. The first is complexity. We live in a complex world, but does that mean a complex model is always the best way of dealing with it? Probably not. The second problem is a lack of ‘events’ (eg defaults). If we cannot observe an event, it is difficult to model it credibly, so internal models may not work well.