I'd like to measure the volatility of my data to know how much values move around.

*Volatility* is a measure of uncertainty (risk) in the price movement of a stock, option, or other financial instrument. An alternate definition is that volatility is the dispersion of individual values around the mean of a set of data.

There are various approaches to calculating volatility, and, in a nutshell, there are two basic types of volatility: historical and implied. *Historical* volatility is easier to measure because the calculation is based on known values. *Implied* volatility is trickier because the purpose here is to guesstimate the level of volatility that will occur in the future (be that tomorrow, next month, or next year). In other words, implied volatility is calculated for forecasting purposes.

One reasonable approach to calculating volatility is to simply use the standard deviation as the measure. A standard deviation measurement requires `>1`

data points to provide a value. Figure 10-16 shows a table of values on the left, and the result of running a query against this data using the `StDev`

aggregate function.

Figure 10-16. Calculating the standard deviation of a set of numbers

The volatility (as interpreted in this approach) is 2.7226562446208. But what does this tell us about the data? As a rule:

The higher the standard deviation, the higher the volatility. ...

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