On 16 June 1933, as the nationwide banking crisis was reaching a new peak, freshly elected US President Franklin D. Roosevelt put his signature at the bottom of a 37-page document: the Glass-Steagall Act. Eight decades later, the debate still rages on: should retail and investment banking be separated, as Glass-Steagall required? In a recent paper, we shed new light on the consequences of this type of regulation by examining the recent UK ‘ring-fencing’ legislation. We show that ring-fencing has an important impact on banking groups’ funding structures, and find that this incentivises banks to rebalance their activities towards retail mortgage lending and away from capital markets, with important knock-on effects for competition and risk-taking across the wider banking system.
This post contributes to our occasional series of guest posts by external researchers who have used the Bank of England’s archives for their work on subjects outside traditional central banking topics.
In 1944, the Bank of England’s historian, John Clapham, looked back at the ways in which the Bank had changed since 1914 and remarked:
‘ . . . it would not be fantastic to argue that the Bank in 1944 was further . . . from 1914 than 1914 was from 1714.’
Since the tumultuous events of 2007, much work has suggested that financial shocks are the main driver of economic fluctuations. In a recent paper, I propose a novel strategy to identify financial disturbances. I use the evolution of loan finance relative to bond finance to proxy for firms’ credit conditions and single out the shocks born in the financial sector. I apply and test the method for the US economy. I obtain three key results. First, financial shocks account for around a third of the US business cycle. Second, these shocks occur around precise events such as the Japanese crisis and the Great Recession. Third, the financial shocks I obtain are predictive of the corporate bond spread.
What was the root cause of the financial crisis? Ask any economist or banker and undoubtedly they will at some point mention leverage (see e.g. here, here and here). Yet when a capital requirement based on leverage — the leverage ratio requirement — was introduced, fierce criticism followed (see e.g. here and here). Drawing on the insights from a working paper, and thinking about the main criticism — that a leverage ratio requirement could cause excessive risk-taking — this seems not to have been the case.
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.
Policymakers have put forward proposals to ensure that banks do not underestimate long-term risks from climate change. To examine how lenders account for extreme weather, we compare matched repeat mortgage and property transactions around a severe flood event in England in 2013-14. We find that lender valuations do not ‘mark-to-market’ against local price declines. As a result valuations are biased upwards. We also show that lenders do not offset this valuation bias by adjusting interest rates or loan amounts. Overall, these results suggest that lenders do not track closely the impact of extreme weather ex-post.
Many commentators on the global financial crisis identified ‘fire sales’ as one of the key mechanisms by which shocks to banks were amplified and transmitted across the wider financial system. When firms in distress sell assets held by other institutions at discounted prices, losses can spread through the financial system as prices fall, amplifying the initial stress. In a working paper published last year, we explored this mechanism by presenting a new model of fire sales. In doing so, we answer the following questions: Which types of financial shocks combine to produce fire sales? How can banks optimally liquidate their portfolios when forced to do so? How big a risk are bank fire sales?
Faced with unprecedented declines in corporate revenue, the Covid-19 shock represents a loss of cash flow of indeterminate duration for many firms. It is too early to tell how exactly firms will be affected by this crisis and how scarring it will be, but the crisis will likely have a significant impact on most corporates. This post reviews the literature on factors affecting firms’ ability to withstand the Covid-19 shock and what large corporates did to shore up their finances.
Matteo Benetton, Philippe Bracke, João F Cocco and Nicola Garbarino
Academics have made the case for mortgage products with equity features, so that gains and losses due to fluctuations in house values are shared between the household and an outside investor. In theory, the equity component expands the set of affordable properties, without increasing household debt, and default risk. These products have not become mainstream, but in a recent paper, we study a large UK experiment with equity-based housing finance — the Help To Buy Equity Loan scheme. We find that equity loans are mainly used to overcome credit constraints, rather than to reduce investment risk. Unconstrained household prefer mortgage debt over equity loans, suggesting optimism about house price risk. Equity loans could still contribute to house price inflation: we don’t find evidence that houses purchased with equity loans are overpriced, but an assessment of the aggregate effects is beyond the scope of the paper.
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.