Author: BankUnderground

Attention to the tail(s): global financial conditions and exchange rate risks

Fernando Eguren-Martin and Andrej Sokol

Asset prices tend to co-move internationally, in what is often described as the ‘global financial cycle’. However, one such asset class, exchange rates, cannot by definition all move in the same direction. In this post we show how the ‘global financial cycle’ is associated with markedly different dynamics across currencies. We enrich traditional labels such as ‘safe haven’ and ‘risky’ currencies with an explicit quantification of exchange rate tail risks. We also find that several popular ‘risk factors’, such as current account balances and interest rate differentials, can be linked to these differences.

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Tell me why! Looking under the bonnet of machine learning models

Carsten Jung and Philippe Bracke

Whether in case of a breakup (Backstreet Boys), wondering why a relationship isn’t working (Mary J. Blige) or bad weather (Travis) – humans really care about explanations. The same holds in the world of finance, where firms increasingly deploy artificial intelligence (AI) software. But AI is often so complex that it becomes hard to explain why exactly it made a decision in a certain way. This issue isn’t purely hypothetical. Our recent survey found that AI already impacts customers – whether it’s calculating the price of an insurance policy or assessing a borrower’s credit-worthiness. In our new paper, we argue that so-called ‘explainability methods’ can help address this problem. But we also caution that, perhaps as with humans, gaining a deeper understanding of such models remains very hard.

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Build-your-own fancharts in R

Andrew Blake

Central banks the world over calculate and plot forecast fancharts as a way of illustrating uncertainty. Explaining the details of how this is done in a single blog post is a big ask, but leveraging free software tools means showing how to go about it isn’t. Each necessary step (getting data, building a model, forecasting with it, creating a fanchart) is shown as R code. In this post, a simple data-coherent model (a vector auto-regression or VAR) is used to forecast US GDP growth and inflation and the resulting fanchart plotted, all in a few self-contained chunks of code.

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Handel and the Bank of England

Ellen T. Harris

This guest post is the third of an 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.

George Frideric Handel was a master musician — an internationally renowned composer, virtuoso performer, and music director of London’s Royal Academy of Music, one of Europe’s most prestigious opera houses. For musicologists, studying his life and works typically means engaging with his compositional manuscripts at The British Library, as well as the documents, letters, and newspapers that describe his interaction with royalty, relationships to others, and contemporary reaction to his music. But when I began to explore Handel’s personal accounts at the Bank of England twenty years ago, I was often asked why. For me the answer was always ‘follow the money’. Handel’s financial records provide a unique window on his career, musical environments, income, and even his health.

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Bitesize: What can the MPC minutes tell us about the policymaker’s uncertainty?

Michal Stelmach

Policymaking is invariably uncertain. I created a new index of ‘policymaker’s uncertainty’ based on a textual search of the minutes of the MPC meetings since 1997. The index is constructed by simply calculating the number of references to the word ‘uncertainty’ (and its derivatives, including ‘not certain’ and ‘far from certain’) as a share of the total word count. To avoid double-counting, it also excludes the Monetary Policy Summary that was introduced in 2015. One caveat of this approach is that it doesn’t distinguish instances of low or falling uncertainty from those where uncertainty was high. That aside, this measure can offer a new insight into uncertainty compared to indicators based on media references or business surveys.

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The ownership of central banks

David Bholat and Karla Martinez Gutierrez

Around the world, central banks have a number of different ownership structures. At one end of the spectrum are central banks, like the Bank of England, that are wholly owned by the public sector. At the other end are central banks, like the Banca d’Italia, whose shareholders are wholly private sector entities. And there are central banks, like the Bank of Japan, that lie in-between. But do these differences matter?

In this blog post, we explore the variety of central bank ownership structures, both historically and globally.  We also suggest areas for future research on the topic.

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What does population ageing mean for net foreign asset positions?

Noëmie Lisack, Rana Sajedi and Gregory Thwaites

How sound is the argument that current account balances are driven by demographics? Our multi-country lifecycle model explains 20% of the variation in observed net foreign asset positions among advanced economies through differences in population age structure. These positions should expand further as countries continue to age at varying speeds.

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Perceiving risk wrong: what happens when markets misprice risk?

Kristina Bluwstein and Julieta Yung

Financial markets provide insightful information about the level of risk in the economy. However, sometimes market participants might be driven more by their perception rather than any fundamental changes in risk. In a recent Staff Working Paper we study the effect of changes in risk perceptions that can lead to a mispricing of risk. We find that when agents over-price risk, banks adjust their bank lending policies, which can lead to depressed investment and output. On the other hand, when agents under-price risk, excessive lending creates a ‘bad’ credit boom that can lead to a severe recession once sentiment is reversed.

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Bitesize: Fixing ideas – The Slowing of Interest-rate Pass-through to Mortgagors

Fergus Cumming

When choosing a mortgage, a key question is whether to choose a fixed or variable-rate contract. By choosing the former, households are unaffected by official interest-rate decisions for the length of the fixation period. We can use transaction data on residential mortgages to get a sense of how long it takes interest-rate decisions to filter through to people’s finances.

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