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.
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).
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.
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.
Before Bank Underground goes off on its Christmas holidays, it’s time for the Annual Bank Underground Christmas Quiz! We hope you enjoy testing your knowledge on our festive themed questions on economics, finance and all things central banking…
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.
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.
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.