GDP-linked bonds are sovereign debt instruments with repayments linked to the evolution of a country’s GDP. Originally proposed by Shiller in the 90s, they have recently been re-invoked in the debate around the policy response to the Covid-19 pandemic. These instruments present an obvious attraction for issuers: repayments are lower at times when the economy is growing relatively slowly, which typically coincides with lower tax earnings. A greater share of the risk of weak growth is then transferred to investors, who will require a compensation given they are typically risk-averse. Therefore, while the design is attractive ex-ante, a relevant question facing sovereigns willing to issue this type of instrument is `at what cost?’. In this post (and in an underlying Staff Working Paper) we provide some tentative answers.
Speculative buying can drive cryptocurrency prices down. This is contrary to the usual laws of economics. Blockchain technology limits how quickly transactions can be settled. This constraint creates competition for priority between different users. The more speculative activity there is, the longer it takes to make a payment. But the future value of cryptocurrency depends on its usefulness as a means of payment. Speculation therefore affects price formation through a channel that does not exist for other asset classes. This can explain the high price volatility of cryptocurrencies, and is consistent with the low adoption rate so far.
Meteorologists and insurers talk about the “1-in-100 year storm”. Should regulators do the same for financial crises? In this post, we argue that false confidence in people’s ability to calculate probabilities of rare events might end up worsening the crises regulators are trying to prevent.
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).
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.
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.
In yesterday’s post we argued that housing is an asset, whose value should be determined by the expected future value of rents, rather than a textbook demand and supply for physical dwellings. In this post we develop a simple asset-pricing model, and combine it with data for England and Wales. We find that the rise in real house prices since 2000 can be explained almost entirely by lower interest rates. Increasing scarcity of housing, evidenced by real rental prices and their expected growth, has played a negligible role at the national level.
A tulip bulb produces flowers. Those flowers are what people actually enjoy consuming, not the bulb. Whilst that’s blindingly obvious for tulips, the equivalent is also true for housing. The physical dwelling is the asset, but it’s the actual living there (aka “housing services”) that people consume. The two things sound very similar and are often lumped together as “housing”. But in today’s post, we argue they are as different as bulbs and flowers. Sketching out a simplified framework of houses as assets we show how this can radically change how one views the “housing market”. Tomorrow, we use this to develop a toy model and bring it to the data to shed light on house price growth in England and Wales.