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.
Capital flows are fickle. In the UK, the largest and most volatile component of inflows from foreign investors are so-called ‘other investment flows’ – the foreign capital which flows into banks and other financial institutions. But where do these funds ultimately go and which sectors are particularly exposed to fickle capital inflows? Do capital inflows allow domestic firms to borrow more? Or does capital from abroad ultimately finance mortgages of UK households? Some of the foreign capital could also get passed on to the financial sector or flow back abroad.
The Payment Protection Insurance (PPI) mis-selling scandal has rumbled on for years. But how did PPI impact loan margins pre-crisis?
This post argues
that income from cross-selling PPI substantially offset lenders’ margins on
personal loans between 2004 and 2009, and compares the pre-crisis PPI-adjusted
margin to loan spreads today.
How do banks adjust when faced with a sudden rise in capital requirements? The most frequent response, in the theoretical literature, is that they reduce lending or “deleverage” (see, e.g., Aiyagari and Gertler (1999); Gertler and Kiyotaki (2010). This is particularly true in crisis episodes when raising equity can be costly. However, in a new paper co-authored with Hans Degryse and Artashes Karapetyan, I show this is only part of the story. Banks may also ask borrowers to provide more collateral; collateralised exposures carry lower risk weights on average and hence enhance capital ratios. This requirement can adversely affect young and new borrowers that typically lack collateral to pledge and are also unlikely to have longstanding banking relationships.
Qun Harris, Analise Mercieca, Emma Soane and Misa Tanaka.
The bonus regulations were introduced based on the consensus amongst financial regulators that compensation practices were a contributing factor to the 2008-9 financial crisis. But little is known about how they affect behaviour in practice. So we conducted a lab experiment to examine how different bonus structures affect individuals’ risk and effort choices. We find that restrictions on bonuses, such as a bonus cap, can incentivise people to take less risk. But their risk-mitigating effects weaken or disappear once bonus payment is made conditional on hitting a high performance target. We also find some evidence that bonus cap discourages effort to search for better projects.
Sebastian de-Ramon, Bill Francis and Michael Straughan.
There is a debate in the regulatory and academic community about whether competition is good or bad for bank stability, particularly following the financial crisis (see Chapter 6 of the Independent Commission on Banking final report). The debate tends to be seen as a head-to-head argument between two camps: those that see competition as bad for stability (competition-fragility) versus those that see competition as good (competition-stability). In new research, we look at how competition affects the stability of banks in the UK. We find that competition affects less stable firms differently than more stable firms and that focussing on what happens to the average firm may not be sufficient.
Will people in 2030 buy goods, get mortgages or hold their pension pots in bitcoin, ethereum or ripple rather than central bank issued currencies? I doubt it. Existing private cryptocurrencies do not seriously threaten traditional monies because they are afflicted by multiple internal contradictions. They are hard to scale, are expensive to store, cumbersome to maintain, tricky for holders to liquidate, almost worthless in theory, and boxed in by their anonymity. And if newer cryptocurrencies ever emerge to solve these problems, that’s additional downside news for the value of existing ones.
A well-insulated house reduces heat loss during cold winter periods and it keeps outdoor heat from entering during hot summer conditions. Hence, effective insulation can reduce the need for households to use cooling and heating systems. While this can lower greenhouse gas emissions by households, it also reduces homeowners’ energy bills, which can free up available income. This can protect households from unexpected decreases in income (e.g. reduced overtime payments) or increases in expenses (e.g. healthcare costs). It could also help homeowners to make their mortgage payments even if such shocks occurred. But does this also imply that mortgages against energy-efficient properties are less credit-risky?
Cross-border bank lending fell dramatically in the aftermath of Lehman Brothers’ failure as funding constraints forced banks to reduce their foreign exposures. While this decline was partly driven by lower demand for international bank credit, it was substantially aggravated by a retrenchment of international banks from cross-border lending. But banks did not cut their cross-border lending in a uniform manner. Instead, they reallocated their foreign portfolios towards countries that were geographically close, in which they had more experience, in which they had close connections with domestic banks or in which they operated a subsidiary. The crisis thus showed that deeper financial integration is associated with more stable cross-border credit when large global banks are hit by a funding shock.