Volatility is more than just market noise; it’s a measure for uncertainty inherent in any ‘risky asset’ (for example a stock).

By forecasting or modelling volatility, we gain insight into potential future fluctuations of asset prices. It provides an insight into ’known unknowns’ and answers the critical question ‘how uncertain is this price’.

Modelling and understanding volatility is important to understand the current value i.e. price of an asset

Below are some characteristics of volatility;

Volatility has predictable qualities

Changes in price are difficult to predict – volatility however has more predictable qualities. These patterns typically depend on the prevailing ‘market regime’. The market regime is the particular set of conditions and characteristics that dominate the market at a given time – for example two that are often thrown around are ‘bull market’ and ‘bear market’.

Typically volatility exhibits a characteristic called long memory, this means that past values influence future values. If volatility is high today, the volatility is more likely to be high tomorrow.

The interesting bit is this: the degree to which volatility is ‘sticky’ changes over time and with the market regime.

The more ‘generally uncertain’ markets are, the more that market relies on shorter term benchmarks for volatility. So in uncertain markets – volatility is increasingly driven by the volatility of the day before and not by longer term volatility levels.

There are different ways to measure volatility and it matters

Volatility is “latent” which means it cannot be directly observed; it must be measured. Why is that important? Because it means that whenever you see any type of volatility value – historical volatility, implied volatility, realised volatility – that value has been calculated using a specific statistical model. The assumptions used in that model matter.

One example is the difference between daily volatility versus realised volatility. Daily volatility is typically what you find on financial websites. To show the difference, consider these two price developments on two separate trading days in red and blue.