Systemic financial crises occur infrequently, giving relatively few crisis observations to feed into the models that try to warn when a crisis is on the horizon. So how certain are these models? And can policymakers trust them when making vital decisions related to financial stability? In this blog, I build a Bayesian neural network to predict financial crises. I show that such a framework can effectively quantify the uncertainty inherent in prediction.
Central banks don’t just care about what is expected to happen. They also care about what could happen if things turn out worse than expected. In line with this, an emerging literature has developed models for measuring and predicting overall levels of macroeconomic risk. This body of work has focused on estimating the level of ‘tail risk‘ in a country by monitoring a range of domestic developments. But this misses a key part of the picture. In a recent Staff Working Paper, we show that monitoring developments abroad is as important as monitoring developments at home when assessing the vulnerability of the economy to a severe downturn.
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.
Kristina Bluwstein, Marcus Buckmann, Andreas Joseph, Miao Kang, Sujit Kapadia and Özgür Şimşek
Financial crises are recurrent events in economic history. But they are as rare as a Kraftwerk album, making their prediction challenging. In a recent study, we apply robots — in the form of machine learning — to a long-run dataset spanning 140 years, 17 countries and almost 50 crises, successfully predicting almost all crises up to two years ahead. We identify the key economic drivers of our models using Shapley values. The most important predictors are credit growth and the yield curve slope, both domestically and globally. A flat or inverted yield curve is of most concern when interest rates are low and credit growth is high. In such zones of heightened crisis vulnerability, it may be valuable to deploy macroprudential policies.
Financial markets process orders faster now than ever before. However, they remain prone to occasional dysfunction where prices move away from fundamentals. One important type of market fragility is flash events. Identifying such events is crucial to understanding them and their effects. This post displays the results from a new methodology to identify these, but also longer lasting V-shaped events, as we show here with an application to three sovereign bond markets.
Every minute of the day, Google returns over 3.5 million searches, Instagram users post nearly 50,000 photos, and Tinder matches about 7,000 times. We all produce and consume data, and financial firms are key contributors to this trend. Indeed, the global business models of many firms have amplified the data-intensity of the financial services industry. But potential fragmentation of the global data supply chain now poses a novel risk to financial services. In this blog post, we first discuss the importance of data flows for financial services, and then potential risks from blockages to these flows.
In August 2007 problems were emerging in the US sub-prime mortgage market. Rising numbers of borrowers were getting behind on their repayments, and some investors exposed to the mortgages were warning that they were difficult to value. But projected write-downs were small: less than half a percent of GDP. Just over a year later, Lehman Brothers had failed, the global financial system was on the brink of collapse and the world was plunged into recession. So how did a seemingly small corner of the US mortgage market unleash a global crisis? And what lessons did the turmoil of autumn 2008 reveal about the financial system?
Does the introduction of a central bank digital currency (CBDC) crowd out bank funding? Does it open the door to runs on the aggregate banking system? In a recent Staff Working Paper we provide insights on these questions. We find that some of the major risks to financial stability posed by CBDC can be addressed by a set of four core design principles for a CBDC system. Implementing these principles, however, is non-trivial and risks would remain.
What could falls in sterling mean for UK firms’ ability to sustain foreign currency (FX) debt obligations? The value of sterling began falling around two years ago and dropped further after the EU referendum – remaining around these lower values ever since. There is every possibility that sterling may stay low for the foreseeable future – creating both potential winners and losers. In this piece, I investigate one particular channel for losses related to sterling weakness: whether UK firms could find meeting their FX debt obligations more challenging. By reviewing market intelligence, market prices and derivatives databases, I find limited evidence that sterling weakness has yet produced any significant changes to UK firms’ ability to manage their FX debt obligations.
Marco Bardoscia, Paolo Barucca, Adam Brinley Codd and John Hill
The failure of Lehman Brothers on 15 September 2008 sent shockwaves around the world. But the losses at Lehman Brothers were only the start of the problem. The price of their bonds halved, almost overnight. Other institutions that held Lehman’s debt faced huge losses, and markets feared that those losses could trigger further failures. The good news is that our latest research suggests that risks within the UK banking system from one such contagion channel, “solvency contagion”, have declined sharply since 2008. We have developed a new model which quantifies risk from this channel, and helps us understand why it has fallen. Regulators are using the model to monitor this particular source of risk as part of the Bank’s annual concurrent stress test exercise.