Dollar shortages in funding markets outside the United States have been a recurrent feature of the last three major crises, including the turmoil associated with the ongoing Covid-19 pandemic. The Federal Reserve has responded by improving conditions and extending the reach of its network of central bank swap lines, with the aim of channelling US dollars to non-US financial systems. Despite the recurrence of this phenomena, little is known about the macroeconomic consequences of both dollar shortage shocks and central bank swap lines. In this post (and in an underlying Staff Working Paper) I provide some tentative answers.
Starting today, Bank Underground (BU) is launching a special series of ‘Covid-19 Briefings’. These posts are different to our regular posts – rather than containing primary analysis or the author’s own research, they instead aim to summarise key lessons from the early literature on a particular area of the economics of Covid-19. Each post focuses on a different area, and aims to provide an introduction to key papers, rather than a comprehensive overview of all the literature. As with any BU post, they are the views of the authors, not necessarily those of the Bank. We hope our readers find them helpful in understanding the new, rapidly developing and fast growing body of work on the economics of Covid-19.
Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.
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.
Will people in 2030 buy goods, get mortgages or hold their pension pots in bitcoin, ethereum or ripple rather than central bank issued currencies? I doubt it. Existing private cryptocurrencies do not seriously threaten traditional monies because they are afflicted by multiple internal contradictions. They are hard to scale, are expensive to store, cumbersome to maintain, tricky for holders to liquidate, almost worthless in theory, and boxed in by their anonymity. And if newer cryptocurrencies ever emerge to solve these problems, that’s additional downside news for the value of existing ones.
Would removing the 1p and 2p coins from circulation cause inflation? Or deflation? Or neither? Our analysis, and the overwhelming weight of literature and experience, suggests it would have no significant impact on prices because price rounding would be applied at the total bill level, not on individual items and it would only affect cash transactions, which make up a low proportion of spending by value. Even if individual prices were rounded on all payments, analysis of UK price data suggests no economically significant impact on inflation.
Does the introduction of a central bank digital currency (CBDC) crowd out bank funding? Does it open the door to runs on the aggregate banking system? In a recent Staff Working Paper we provide insights on these questions. We find that some of the major risks to financial stability posed by CBDC can be addressed by a set of four core design principles for a CBDC system. Implementing these principles, however, is non-trivial and risks would remain.
Francis Breedon, Louisa Chen, Angelo Ranaldo and Nicholas Vause
Most academic studies find that algorithmic trading improves the quality of financial markets in normal times by boosting market liquidity (so larger trades can be executed more quickly at lower cost) and enhancing price efficiency (so market prices better reflect all value-relevant information). But what about in times of market stress? In a recent paper looking at the removal of the Swiss franc cap, we find that algorithmic trading provided less liquidity than usual, at worse prices, and that its contribution to efficient pricing dropped to near zero. Market quality benefits from a diversity of participants pursuing different trading strategies, but it seems this was undermined in this episode by commonalities in the way algorithms responded.