Capitalising climate risks: what are we weighting for?

David Swallow and Chris Faint

Policymakers have been investing heavily, to an accelerated timeline, to better understand the financial risks from climate change and to ensure that the financial system is resilient to those risks. Against that background, some commentators have observed that the most carbon-intensive sectors may be subject to the greatest increase in transition risk. They argue that these risks are not currently included within risk weights in the banking prudential framework and that regulators should adjust the framework to include them. Conceptually, this argument sounds credible – so how might UK regulators approach whether to adjust the risk-weighted asset (RWA) framework to include potential increases in risks? This post updates on some of the latest thinking to help answer this question.

Continue reading →

Do large and small banks need different prudential rules?

Austen Saunders and Matthew Willison

Banks come in different shapes and sizes. Do prudential regulations that work well for big banks work as well for small ones? To help us find out, we measure the effectiveness of some key regulatory ratios as predictors of bank failure. We do so using ‘receiver operating characteristic’ – or ‘ROC’ – analysis of simple threshold rules. When we do this, we find that we can use the ratios we test to make better predictions for large banks than for small ones. This provides evidence that an efficient set of regulations for large banks might not be as efficient for small ones.

Continue reading →

How to see it coming: predicting bank distress with machine learning

Joel Suss and Henry Treitel

The need to see it coming

The great American baseball sage, Yogi Berra, is thought to have once remarked: ‘It’s tough to make predictions, especially about the future’. That is certainly true, but thankfully the accelerating development and deployment of machine learning methodologies in recent years is making prediction easier and easier. That is good news for many sectors and activities, including microprudential regulation. In this post, we show how machine learning can be applied to help regulators. In particular, we outline our recent research that develops an early warning system of bank distress, demonstrating the improved performance of machine learning techniques relative to traditional approaches.

Continue reading →