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.
relationship between financial conditions and risks to growth in an economy?
And, in a world of highly integrated financial markets, to what extent are
these “local” risks rather than reflections of global developments? In this
post we offer some tentative answers. Financial conditions, measured across a
broad range of asset classes and countries, display an important common
component reflecting global developments. Loose financial conditions today
increase the likelihood of a growth boom over the following few quarters, but
when global financial conditions are loose, they increase the chances of a
sharp contraction further ahead, highlighting some of the challenges of
managing risks to growth across time from a policy maker’s perspective.
Sebastian de-Ramon, Bill Francis and Michael Straughan.
There is a debate in the regulatory and academic community about whether competition is good or bad for bank stability, particularly following the financial crisis (see Chapter 6 of the Independent Commission on Banking final report). The debate tends to be seen as a head-to-head argument between two camps: those that see competition as bad for stability (competition-fragility) versus those that see competition as good (competition-stability). In new research, we look at how competition affects the stability of banks in the UK. We find that competition affects less stable firms differently than more stable firms and that focussing on what happens to the average firm may not be sufficient.
Certain policy actions require a high level of precision to be successful. In a recent paper, we find that using margins on derivative trades as a macroprudential tool would require such precision. Such a policy could force derivative users to hold more liquid assets. This would help them to meet larger margin calls and avoid fire-selling their derivatives, which could affect other market participants by moving prices. We find that perfect calibration of such a policy would completely eliminate this fire-sale externality and achieve the best possible outcome, while simple rules are almost as effective. However, calibration errors in any rule could amplify fire-sales and leave the financial system worse off than if there had been no policy at all.
Defaults on sovereign debt – the term commonly used to denote debt issued by national governments and other fiscally autonomous territories – are a recurring feature of public finance. They are more widespread than is often appreciated, since 1960 involving 145 governments, over half the current sovereign universe. Examples include the many governments ensnared in the Latin American and Eastern European debt crises of the 1980s. More recently, there have been big bond defaults by Russia (1998), Argentina (2001), Greece (2012), and Puerto Rico (2015). On a smaller scale, scores of sovereign defaults can occur each year on one or more types of debt. Some, such as Sudan’s, have dragged on for decades and remain unresolved (Chart 1).
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.
A well-insulated house reduces heat loss during cold winter periods and it keeps outdoor heat from entering during hot summer conditions. Hence, effective insulation can reduce the need for households to use cooling and heating systems. While this can lower greenhouse gas emissions by households, it also reduces homeowners’ energy bills, which can free up available income. This can protect households from unexpected decreases in income (e.g. reduced overtime payments) or increases in expenses (e.g. healthcare costs). It could also help homeowners to make their mortgage payments even if such shocks occurred. But does this also imply that mortgages against energy-efficient properties are less credit-risky?
In a recent post, my co-author and I showed some charts suggesting that investors have been accepting less compensation for bearing credit risk. This type of risk can be very costly when it materialises, but the probability of that happening is typically very low. A similar risk is inherent in deeply out-of-the-money options. Here too, investors seem to be accepting less compensation for risk.
Earlier this year, a number of financial market participants, commentators and regulators suggested that investors have been accepting less compensation for bearing given amounts of credit risk. This short post presents two charts in support of that view.