Price volatility definition, how to use it in crypto
Volatility is a term you hear every day in the crypto media, and yet it’s often misunderstood. In this post we review what volatility is, how it’s measured, and how to use it in analysis and trading.
- Volatility explained
- How volatility is measured
- How to use volatility in market forecasting
- Volatility and leverage
- Data sources for volatility
Price volatility explained
In plain terms, price volatility is a measure of how much prices move up and down over a given period. For volatile assets, prices swing a lot (think any altcoin). For less volatile assets, prices are more stable (e.g. stable coins).
For traders, volatility can be both good and bad:
- Volatility creates opportunities for high returns. Prices can go up a lot – just compare any crypto asset to major fiat currencies. When volatility is high, you can buy assets cheaply and sell when overpriced;
- Volatility increases losses. Similarly, prices can drop sharply, resulting in greater losses. And the bigger the price swings, the stronger the emotions: both the fear when prices crash and the greed when they soar.
While volatility is a very important concept, there is no dominant theory to explain it. From a rational perspective, prices should react to fundamental news. Yet, in practice, behavioural factors and emotions likely play a bigger role. This is particularly true for crypto assets, for which pricing frameworks are still in their infancy.
What does the word volatility mean?
The word volatility in finance means the degree of variation in an asset's trading price over a specific amount of time. The more volatile a crypto asset has been, the more that it varied in trading price over that timeframe. Volatility is a critical aspect of trading to understand and harness as you place focused effort into building your financial portfolio.
Examples shown below:
How volatility is measured
Volatility is about rates of return rather than actual prices. In financial mathematics, volatility is usually defined as the standard deviation of returns.
Returns are assumed to be normally distributed, although actual distribution might be different. In a normal distribution, 68% of observations fall within one standard deviation and 95% of observations fall within two standard deviations.
What does this mean in practice?
If we have 30-day volatility of 5% (the current figure for Bitcoin), then on 20 of those days (i.e. 68%) the next day’s price should differ by less than 5% (one standard deviation). On about 28 of the days (i.e. 95%), the daily price difference should be less than 10% (two standard deviations). In reality, the returns do not always have a normal distribution, but it’s still a useful approximation.
There are two kinds of volatility: historical and implied.
Historical volatility – volatility based on past asset prices over a given period (usually the last 30 or 90 days). This is also known as “realised” or “actual” volatility because it’s based on actual prices for trades that have already been realised. This shows how much prices have changed in the past.
Implied volatility – volatility of assets derived from current prices of options and other market-traded derivatives. To find this volatility (σ) you need to plug the asset’s current price and other inputs into an option pricing model, such as Black–Scholes. In a way this can be understood as expected volatility as reflected in the prices of financial derivatives.
If the options perfectly capture all the information, then implied volatility serves as a perfect predictor of actual volatility. Of course, this is rarely the case.
How to use volatility in market forecasting
Why should you care? As a market watcher and (hopefully) an analyst on our platform, you should consider past volatility and trends.
Knowing and understanding volatility is particularly important for range questions about min and max prices. If there is no major news, an asset will move within its average volatility. So there is no point in selecting extreme values if you don’t expect any important events. If an asset moves ±1% a day, then it’s unlikely that it will move ±3% over the next few days – such moves are relatively rare.
Here are several ideas and observations for you to consider:
- Low volatility now. If the order book is balanced, the price won’t change much as long as volume stays the same. Yet if there is a sudden increase in sellers or buyers, the price might change sharply;
- High volatility now. Illiquid assets with little trading activity will typically have higher volatility as each large order changes the price;
- Decreasing volatility. Low and decreasing volatility is common for bull runs when prices go up. If volatility continues to decline, this could be a bullish sign;
- Increasing volatility. Volatility tends to revert to mean, with increases after periods of low volatility and decreases after periods of high volatility;
Volatility and leverage
Usually, less volatile assets are more liquid, i.e. there is more trading going on and it’s easier to sell and buy. For these assets, leverage is more readily available. For example, most stockbrokers can provide you with 3:1 leverage. Yet many currency trading platforms (real forex used by banks) can provide leverage of 100:1 and even higher.
Leverage for low volatility assets
Leverage can make a less interesting asset more attractive for traders. Everything else being equal, the higher the volatility the higher the potential profits for traders. Of course, the risks are also higher. Yet even more stable assets, such as major currency pairs (EUR/USD, USD/JPY, etc.) moving ±0.3%, could be made more attractive by adding leverage. This way, a trader can generate short-term returns comparable to equities.
Leverage for assets with high volatility
A word of caution. You should be very careful with high leverage for volatile assets. With 100:1 leverage, a 1% movement is enough to wipe out your whole deposit! And what is a 1% move in the turbulent crypto world…
Data sources for volatility
You can find historical and implied volatility for many assets, which could be helpful in your analysis. Below we have summarised some data sources for you.
- The Bitcoin Volatility Index for 30-day and 60-day periods;
- 30-day annualised volatility from BitMEX (NB: divide it by the square root of the number of periods in a year to get the volatility for a given period).
- The Ethereum Volatility Index for 30-day and 60-day periods.
Bitfinex also has annualised seven-day volatility for all its trading pairs. For other crypto assets, there are no ready-made historical volatility estimates, but you can calculate them yourself in Excel. If you’re interested in finding out more, please leave a comment.
For traditional assets, in addition to historical volatility, you can also find implied volatility from the Chicago Board Options Exchange (CBOE).
S&P 500 volatility
- Historical: S&P 500 1-Month Realized Volatility Index - note that it is given as “daily levels” rather than daily returns;
- Implied: VIX, the infamous “fear index” often cited by the media. It’s quoted in annualised percentage points. So 18 means an annualised change of ±18%. To get the volatility for a specific period, divide this by the square root of the number of periods in the year. To get the weekly volatility, divide it by √52 or 7.211. So, VIX = 18 implies a weekly change of ±2.4%.
Volatility of individual stocks
- Historical: 10, 20, 30-day from IVolatility;
- Implied: Cboe VIX for Apple, Amazon, Google, Goldman Sachs, and IBM. Like VIX, it’s given in annualised percentage points, so divide it by the square root of the number of periods you need (i.e. √52 for weekly or √365 for daily).
- Implied: Cboe Gold ETF Volatility Index (GVZ).
Crude oil volatility
- Implied: Cboe Crude Oil ETF Volatility Index (OVX).
Foreign exchange volatility
- Historical: OANDA’s currency volatility chart;
- Implied: 30-day Cboe VIX for GBP, EUR, and JPY.
We hope that you now have a better idea of what volatility is and how to apply it.
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