What is volatility: definition, how to use it in trading

Volatility is one of those terms we hear every day in the market, and yet it’s often misunderstood. In this post let us try to understand what volatility is, how it’s measured, and how to use it in analysis and trading.

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. For less volatile assets, prices are more stable.

For traders, volatility can be both good and bad:

• Volatility creates opportunities for high returns. 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.

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) we 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.

How to use volatility in market forecasting?

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 some of my ideas and observations:

• 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;

Usually, less volatile assets are more liquid, i.e. there is more trading going on and it’s easier to sell and buy.

Data sources for volatility

You can find historical and implied volatility for many assets, which could be helpful in your analysis. Below I have summarised some data sources for you.

Crypto assets

BTC/USD

• 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).

ETH/USD

• The Ethereum Volatility Index for 30-day and 60-day periods.

Traditional assets

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).

Gold volatility

• 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.

What’s next

I hope that you now have a better idea of what volatility is and these resources might help you in making better trading decisions.

Let’s gather more insights by sharing our experience, tips and resources in the comments below 🙌.

👏👏👏 if you think this was helpful and encourage me writing better content in future, cheers!