Fluttering and falling: banks’ capital requirements for credit valuation adjustment (CVA) risk since 2014

Giulio Malberti and Thom Adcock

The financial crisis exposed banks’ vulnerability to a type of risk associated with derivatives: credit valuation adjustment (CVA) risk. Despite being a major driver of losses – around $43 billion across 10 banks according to one estimate – there had been no capital requirement to cushion banks against these losses. New rules in 2014 changed this.

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Does accepting a broader set of collateral in central bank operations incentivise the use of riskier collateral by riskier counterparties?

Calebe de Roure and Nick McLaren

Central banks accept a wide range of assets from participants as collateral in their liquidity operations – but can this lead to undesired side effects? Such an approach can enhance overall liquidity in the financial sector, by allowing participants to transform illiquid collateral into more liquid assets. But, as a result, the central bank then needs to manage the greater potential risks of holding these riskier assets on its own balance sheet. Financially weaker participants may be encouraged to hold these assets if they can benefit from the higher returns, which compensate for the greater risk. In our recent paper we investigate whether central banks’ acceptance of a broad set of collateral incentivises the concentration of risk by examining the experience of the Bank of England (BoE).

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What’s been driving long-run house price growth in the UK?

David Miles and Victoria Monro

Since the mid-1980s, the average real (RPI-adjusted) UK house price has more than doubled, rising around one and a half times as fast as incomes. Economists’ diagnoses of the root cause varies – from anaemic supply, to the consequences of financial deregulation, or even a bubble. In our recent paper, we explore the role of the long-run decline in the real risk-free rate in driving up house prices. Low interest rates push up asset prices and reduce borrowing costs. We find the decline in the real risk-free rate can account for all of the rise in house prices relative to incomes.

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Bitesize: What might pension funds do when bond yields fall?

Matt Roberts-Sklar

Government bond yields fell sharply mid-2019, especially at longer maturities. For defined benefit pension funds, lower yields tend to mean deficits widen as discounted liabilities increase by more than the value of their assets. How might pension funds respond to this?

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Do emerging market prudential policies lessen the spillover effects of US monetary policy?

Andra Coman and Simon Lloyd

Prudential policies have grown in popularity as a tool for addressing financial stability risks since the 2007-09 global financial crisis. Yet their effects are still debated, with sanguine and more pessimistic viewpoints. In a recent Bank of England Staff Working Paper, we assess the extent to which emerging market (EM) prudential policies can partially insulate their domestic economies against the spillovers from US monetary policy. Using a database of prudential policies implemented by EMs since 2000, our estimates indicate that each additional prudential policy tightening can dampen the decline in total credit following a US monetary policy tightening by around 20%. This suggests that domestic prudential policies allow EMs to insulate themselves somewhat from global shocks.

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Our top 5 posts of 2019

As another year draws to an end, we wanted to take a look back at the blog in 2019. In case you missed any of them the first time round, the five most viewed posts for the year were:

  1. Handel and the Bank of England
  2. Houses are assets not goods: part 1 and part 2
  3. The ownership of central banks
  4. Opening the machine learning black box
  5. What happens when ‘angels fall’?

We hope you enjoyed the blog in 2019. Happy New Year and we look forward to you reading our posts in 2020!

Belinda Tracey, Managing Editor

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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