Most of us think we understand the term “volatility.”
We digest headlines about tense political situations around the world; we are wary of explosive chemical compounds; some of us have had relationships with their fair share of ups and downs.
“Volatility” implies sharp and unpredictable changes, and usually has negative connotations. Even when it comes to financial markets, we intuitively shy away from investments that would produce wild swings in our wealth.
But volatility, in finance, is usually misunderstood. Even the most commonly accepted calculation is often incorrectly applied.
Its desirability is also confusing. Investors hate it unless it makes them money. Traders love it unless it means too high a risk premium.
And few of us understand where it comes from. Many think that it’s the result of low liquidity*. This intuitively makes sense: with thin trading volume, a large order can push prices sharply up or down. But empirical studies show that it’s actually the other way around: volatility leads to low liquidity, through the wider spread market makers apply to compensate the additional risk of holding a volatile asset in their inventory.
(*The misconception also stems from our mistaken conflation of low liquidity and low volume – it is possible to have high volume and low liquidity, but that’s for another post.)
This confusion matters in the crypto sector.
Bitcoin’s volatility has often been cited as the reason why it will never make a good store of value, a reliable payment token or a solid portfolio hedge. Many of us fall into the trap of assuming that as the market matures, volatility will decrease. This leads us to believe in use cases that may not ever be appropriate; it can also lead us to apply incorrect crypto asset valuation methods, portfolio weightings and derivative strategies that could have a material impact on our bottom line.
So it’s worth picking apart some of the assumptions and looking at why bitcoin’s unique characteristics can help us better understand market fundamentals more broadly.
Changing uncertainty
First, there are different types of market volatility. Academic literature provides an array of variations, each with its distinct formula and limitations. Jump-diffusion models used to value assets hint at a helpful differentiation. “Jump” volatility results, as its name implies, from a sudden event. “Diffuse” volatility, however, is part of the standard trading patterns of an asset, its “usual” variation.
With this we can start to see that, when we assume that greater liquidity will dampen price swings, we’re talking about “jump” volatility.
“Diffuse” volatility, however, is a more intrinsic concept.
The standard deviation calculation – the most commonly applied measure of volatility – incorporates the destabilizing effect of sharp moves by using the square of large deviations (otherwise they could be offset and masked by small ones). But this exaggerates the effect of outliers, which are often the result of “jump” volatility. These are likely to diminish as transaction volume grows, leading to a misleadingly downward-sloping volatility graph.
JP Koning proposes an alternative calculation that uses the deviation from the middle value rather than the average, which reduces the effect of outliers and shows a more intrinsic volatility measure. As the below chart shows, this has not noticeably decreased over the years.
coindesk.com/September 16, 2019/Noelle Acheson
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