How do banks adjust when faced with a sudden rise in capital requirements? The most frequent response, in the theoretical literature, is that they reduce lending or “deleverage” (see, e.g., Aiyagari and Gertler (1999); Gertler and Kiyotaki (2010). This is particularly true in crisis episodes when raising equity can be costly. However, in a new paper co-authored with Hans Degryse and Artashes Karapetyan, I show this is only part of the story. Banks may also ask borrowers to provide more collateral; collateralised exposures carry lower risk weights on average and hence enhance capital ratios. This requirement can adversely affect young and new borrowers that typically lack collateral to pledge and are also unlikely to have longstanding banking relationships.
Thomas Mathae, Stephen Millard, Tairi Room, Ladislav Wintr and Robert Wyszynski
How do firms respond to shocks? Do they first change the hours worked by their employees? Or the number of employees? Or wages? Or a combination? Does the shock matter? And the firm’s country? One way of answering these questions is to ask the managers within firms themselves. And this is exactly what the Wage Dynamics Network did, surveying firms in 25 European countries. Our research used this survey to answer these questions. We found that in response to negative shocks firms were most likely to reduce employment, then wages and then hours, regardless of the source of the shock. But, in response to positive shocks, firms were most likely to raise wages, then employment and then hours.
To consider Bitcoin volatility, we
look at 10-day returns (capital standards typically estimate market risk over a
10-day period) since 19 July 2010, when Bloomberg’s Bitcoin data start. We
compare Bitcoin with assets in three categories – currency pairs, commodities
and equities – and for each we have picked one low-volatility asset and one
more volatile asset. For currency pairs and commodities, we chose the most and
least volatile ones (in terms of standard deviation of 10-day returns) out of
the most liquid in each category. And we chose the most and least volatile FTSE
100 equities (again, in terms of standard deviation of 10-day returns).
For stable assets we expect a peaked distribution with short tails, as returns cluster near 0%. Figure 1 shows that Bitcoin has been more volatile than any other asset in our sample.
But people are often interested in the downside risk of assets. We therefore consider how Bitcoin’s Value at Risk (VaR) compares to other assets. VaR is the maximum loss over a given time interval under normal market conditions at a given confidence interval (eg 99%). A 10-day 99% VaR of -10% tells you that 99% of the time your 10-day return on the asset would be no worse than a 10% loss.
Figure 2 shows Bitcoin’s VaR is high, but the VaR of the other most liquid crypto-assets is higher. TRON’s VaR to date (-84%) is almost twice Bitcoin’s (-44%).
Giulio Malberti and Thom Adcock work in the Bank’s Banking Policy Division.
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.
Cryptoassets (or ‘cryptocurrencies’) are notoriously volatile. For example, in November 2018, Bitcoin – one of the more stable cryptoassets – lost 43% of its value in just 11 days. This kind of volatility makes it difficult for cryptoassets to function as money: they’re too unstable to be a good store of value, means of exchange or unit of account. But could so-called ‘stablecoins’ solve this problem and finally provide a price-stable cryptoasset?
Cristiano Cantore, Filippo Ferroni and Miguel León-Ledesma.
How do monetary policy shocks affect the distribution of income between workers and owners of capital? Do workers benefit relatively more when policy changes? Tackling this question empirically requires technical econometric methods, but we are able to show that the share of output allocated to wages (the labor share) temporarily increases following a positive shock to the interest rate. This means that the slice of the pie enjoyed by those whose earnings are mostly made up of wages increases at the expense of profits and capital income. Strikingly, this redistribution channel that shows up in the data runs precisely in the opposite direction to the predictions of standard New Keynesian models commonly used to study the effects of monetary policy.
As the UK economy went into recession in 2008, the Monetary Policy Committee responded with a 400 basis point reduction in Bank Rate between October 2008 and March 2009. Although this easing lessened the impact of the recession across the whole economy, its cash-flow effect would have initially benefited some households more than others. Those holding large debt contracts with repayments closely linked to policy rates immediately received substantial boosts to their disposable income. Cheaper mortgage repayments meant more pounds in peoples’ pockets, and this supported both spending and employment in 2009. In this article I explore one element of the monetary transmission mechanism that works through cash-flow effects associated with the mortgage market, and show that it can vary across both time and space.
Short-time work (STW) schemes are an important fiscal stabiliser in many countries. In the Great Recession, 25 out of 33 OECD countries used short-time work schemes (Balleer et al. 2016). STW schemes aim to preserve employment in firms temporarily experiencing weak demand. This is achieved by providing subsidies to firms to reduce number of hours worked by each employee, instead of reducing the number of workers. As well as being paid for actual hours worked, the subsidy is used to pay workers for hours not worked – albeit not completely compensating the loss of income due to reduced hours. In most countries, the bulk of the subsidy is paid by the state, although companies can also contribute.
Arthur Turrell, Nikoleta Anesti and Silvia Miranda-Agrippino.
As the American playwright Arthur Miller wrote, “A good newspaper, I suppose, is a nation talking to itself.” Using text analysis and machine learning, we decided to put this to test – to find out whether newspaper copy could tell us about the national economy, and in particular, whether it can help us predict GDP growth.
Qun Harris, Analise Mercieca, Emma Soane and Misa Tanaka.
The bonus regulations were introduced based on the consensus amongst financial regulators that compensation practices were a contributing factor to the 2008-9 financial crisis. But little is known about how they affect behaviour in practice. So we conducted a lab experiment to examine how different bonus structures affect individuals’ risk and effort choices. We find that restrictions on bonuses, such as a bonus cap, can incentivise people to take less risk. But their risk-mitigating effects weaken or disappear once bonus payment is made conditional on hitting a high performance target. We also find some evidence that bonus cap discourages effort to search for better projects.