Alex Ntelekos, Dimitris Papachristou and Juan Duan
The 2017 Atlantic hurricane season was the fifth most active in 168 years. It was also one of only six seasons to see multiple Cat 5 hurricanes (Irma & Maria). These two hurricanes, followed similar tracks and, together with Hurricane Harvey, occurred close together. This situation can hinder relief efforts. For insurers it may also lead to resource strain, disputes and unhedged risks, if insurers do not have enough ‘sideways’ reinsurance cover. Our post asks whether three major hurricanes occurring in the US in close succession really was exceptional or, as our analysis of recent data suggests, it might happen more often in future. Is the insurance industry underestimating the likely ‘clustering’ of major hurricanes?
In a recent research paper, we show that the way supervisors write to banks and building societies (hereafter ‘banks’) has changed since the financial crisis. Supervisors now adopt a more directive, forward-looking, complex and formal style than they did before the financial crisis. We also show that their language and linguistic style is related to the nature of the bank. For instance, banks that are closest to failure get letters that have a lot of risk-related language in them. In this blog, we discuss the linguistic features that most sharply distinguish different types of letters, and the machine learning algorithm we used to arrive at our conclusions.
The interest-only product has undergone tremendous evolution, from its mass-market glory days in the run-up to the crisis, to its rebirth as a niche product. However, since reaching a low-point in 2016, the interest-only market is starting to show signs of life again as lenders re-enter the market.
Consumer credit growth has raised concern in some quarters. This type of borrowing – which covers mainstream products such as credit cards, motor finance, personal loans and less mainstream ones such as rent-to-own agreements – has been growing at a rapid 10% a year. What’s been driving this credit growth, and how worried should policymakers be?
Rapid advances in analytical modelling and information processing capabilities, particularly in machine learning (ML) and artificial intelligence (AI), combined with ever more granular data are currently transforming many aspects of everyday life and work. In this blog post we give a brief overview of basic concepts of ML and potential applications at central banks based on our research. We demonstrate how an artificial neural network (NN) can be used for inflation forecasting which lies at the heart of modern central banking. We show how its structure can help to understand model reactions. The NN generally outperforms more conventional models. However, it struggles to cope with the unseen post-crises situation which highlights the care needed when considering new modelling approaches.
(Northern Rock image – Lee Jordan – Flickr, reproduced from wikimedia commons under CCA licence)
Ten years ago this month, queues of people started to form early in the morning outside Northern Rock branches across the UK, to withdraw their money out of fear that their bank would soon collapse. As the day wore on panic spread, and the run continued until when the government stepped in to guarantee all Northern Rock deposits. It was the UK’s first retail bank run since the 19th century and one of the first symptoms of the global financial crisis. This anniversary is an appropriate time to reflect on those events, but also to look forward and assess how things have moved on in the last decade, and whether something similar could ever happen again.
The recently proposed liquidity regulations for banks under Basel III emphasize the importance of deposit insurance and well-established customer relationships for the stability of bank funding. However, little is known about which clients withdraw their deposits from distressed banks. New survey data covering the behaviour of households in Switzerland during the 2007-2009 crisis suggest that well-established customer relationships are indeed crucial for mitigating withdrawal risk when a bank is in distress.
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.
Sebastian de-Ramon, Bill Francis and Kristoffer Milonas.
Navigational aids are helpful when visibility is poor or when landmarks are unfamiliar, especially when journeying to new destinations. In a recent working paper, we introduce a new regulatory dataset, the ‘Historical Banking Regulatory Database’ (HBRD), that provides a clearer view of the UK banking sector and helps navigate issues difficult to explore with other datasets. This post describes the HBRD, its benefits for research and policy analyses, and what can be learned from it.
A key feature of the post-crisis regulatory reform agenda has been the introduction of a leverage ratio to complement the risk-weighted framework. The FPC designed the UK leverage ratio to mirror risk-weighted capital requirements so the two frameworks move in lock-step over time and across firms. For the sake of simplicity however, the FPC did not reflect Pillar 2 capital charges, which aim to capture risks that cannot be modelled adequately in the risk-weighted framework, in the leverage ratio framework. In this post we explore what happens to leverage and risk-weighted requirements once Pillar 2 are taken into account. We find that in keeping the leverage ratio simple, the perfect lock-step interaction with risk-weighted requirements no longer holds, which could prompt riskier banks to take on more risk.