Artificial intelligence (AI) is an increasingly important feature of the financial system with firms expecting the use of AI and machine learning to increase by 3.5 times over the next three years. The impact of bias, fairness, and other ethical considerations are principally associated with conduct and consumer protection. But as set out in DP5/22, AI may create or amplify financial stability and monetary stability risks. I argue that biased data or unethical algorithms could exacerbate financial stability risks, as well as conduct risks.
Álvaro Fernández-Gallardo, Simon Lloyd and Ed Manuel
Since the 2007–09 Global Financial Crisis, central banks have developed a range of macroprudential policies (‘macropru’) to address fault lines in the financial system. A key aim of macropru is to reduce ‘left-tail risks‘ – ie, minimise the probability and severity of future economic crises. However, building this resilience could influence other parts of the GDP-growth distribution and so may not always be costless. In our Working Paper, we gauge these potential costs and benefits by estimating the effects of macropru on the entire GDP-growth distribution, and explore its transmission channels. We find that macropru is effective at reducing the variance of GDP growth, and that it does so by reducing the probability and severity of excessive credit booms.
Lydia Henning, Simon Jurkatis, Manesh Powar and Gian Valentini
Autumn 2022 saw some of the largest intraday moves in gilt yields in history. It was then that jargon normally confined to financial stability papers entered into mainstream commentary – ‘LDI’, ‘doom loop’, ‘deleveraging’. And it was then that the Bank of England engaged in an unprecedented financial stability motivated government bond market intervention. What happened and why has been set out in detail in official Bank communications. This article instead hovers a magnifying glass over transaction-level regulatory data on derivative, repurchase agreements (repo) and bond markets to quantify liability-driven investment (LDI) and pension fund behaviour and enrich our understanding of these exceptional few weeks of stress.
How concerned should policymakers be as UK business insolvencies have soared to 60-year highs? This phenomenon has been extensively covered in the media; with media outlets attributing the record-breaking numbers to a ‘perfect storm’ of energy prices, supply-chain disruptions and the cost of living squeeze. Insolvencies are a popular measure of economic distress because they have implications for both the financial system and the real economy. For the financial system, an insolvency generally means creditors will incur losses. Insolvent firms will have to cease trading and lay off workers, which affects the real economy. In this blog post, I assess the evolution of corporate insolvencies over time, including the post-Covid surge to understand what these record numbers mean for the UK economy.
Kristina Bluwstein, Sudipto Karmakar and David Aikman
Inflation reached almost 9% in July 2022, its highest reading since the early 1990s. A large proportion of the working age population will never have experienced such price increases, or the prospect of higher interest rates to bring inflation back under control. In recent years, many commentators have been concerned about risks to financial stability from the prolonged period of low rates, including the possibility of financial institutions searching for yield by taking on riskier debt structures. But what about the opposite case? What financial stability risks do high inflation and increasing interest rates pose?
In less than two decades, the system of market-based finance (MBF) – which involves mainly non-bank financial institutions (NBFIs) providing credit to the economy through bonds rather than loans – has both mitigated and amplified the economic effects of financial crises. It mitigated effects after the global financial crisis (GFC), when it substituted for banks in providing credit. But it amplified effects at the outbreak of the Covid pandemic, when NBFIs propagated a dash for cash (DFC), and more recently when pension fund gilt sales exacerbated increases in yields. This post outlines five different aspects of MBF that contribute to such amplification and summarises some policy proposals – suggested and debated internationally by regulators, academics and market participants – to make MBF more resilient.
Open-ended funds (OEFs) offer daily redemptions to investors, often while holding illiquid assets that take longer to sell. There is evidence that this mismatch creates an incentive for investors to redeem ahead of others, which could lead to large redemptions from OEFs and asset price falls. Some research has suggested that ‘swing pricing’ can help to moderate these redemptions, but until now, no-one has considered the impact of its use on the wider economy. In a recent paper, we carry out a financial stability cost-benefit analysis of more widespread and consistent usage of swing pricing by OEFs, finding that enhanced swing pricing could reduce amplification of shocks to corporate bond prices, providing benefits to the financial system and economy.
Alina Barnett, Sinem Hacioglu Hoke and Simon Lloyd
Since 2007, macroprudential policymakers have grappled with a broad set of vulnerabilities. While regulators cannot be sure what risks the next decade will feature, they can be sure that the set of issues will continuously evolve. In this post, we explore threetimely challenges that financial stability policymakers are likely to face in the coming years, including risks associated with: non-bank financial intermediation, cryptoassets and decentralised finance (DeFi), and climate change. These challenges have been noted by many, and are already stimulating development of macroprudential frameworks. But while some of this development can build on well-grounded principles for financial stability policy, other aspects are likely to come up against threetimeless challenges, requiring novel and innovative thinking to overcome.
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.