VolatilityCalc Online

Volatility Moving Average Calculator

Input Parameters

Stock Symbol

Start Date

End Date

Frequency

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Advanced Metrics

Volatility

Skewness

Kurtosis

Autocorrelation

Lomb Periodogram

Calculation Methodology & Glossary

Historical Volatility Calculation

The logarithmic method to calculate stock volatility, often referred to as the log-normal or log return method, is a common approach used in finance to measure the variability or risk associated with the stock price movement over time.

Volatility and Option Pricing

Volatility is a key parameter in pricing options and other derivatives. Higher volatility increases the premium of options due to the greater risk of price movement. Traders and quantitative analysts use volatility charts to identify opportunities and strategies in the options market.

Moving Average

Calculates the volatility over a selected interval of data points. The range of data points then “moves” by one step size, so a newer data point is added to the data point range, and the oldest data point is removed.

Moving average charts attempt to tone down fluctuations into a smooth trend, reducing distortions and providing a clearer picture of the historical volatility. This shows how volatility has changed over time and also helps identify periods of time where the volatility was particularly high. ASC 718 Section 718-10-55-37 allows companies to “disregard an identifiable period of time in which its share price was extraordinarily volatile.”

ASC 718 Section 718-10-55-37

Factors to consider in estimating expected volatility include:

a.) Volatility of the share price, including changes in that volatility and possible mean reversion of that volatility, over the most recent period that is generally commensurate with (1) the contractual term of the option if a lattice model is being used to estimate fair value or (2) the expected term of the option if a closed-form model is being used.

For example, in computing historical volatility, an entity might disregard an identifiable period of time in which its share price was extraordinarily volatile because of a failed takeover bid if a similar event is not expected to recur during the expected or contractual term. If an entity’s share price was extremely volatile for an identifiable period of time, for instance, due to a general market decline, that entity might place less weight on its volatility during that period of time because of possible mean reversion.

Start Date: Volatility is calculated using a specific data range; the start date is the first historical stock price from which the volatility will be computed. Select a start date using the calendar control. For example, if you click on the number 50, you have chosen the first 50 historical stock prices in the data grid as your data range.
End Date: The end date is the last historical stock price from which the volatility will be computed. Select an end date using the calendar control. The drop-down menu next to the end date field allows you to select a specific number of data points traveling up the stock price list from the end date data point.
Data Frequency: For monthly, weekly, and daily data, the assumption is that for any two consecutive stock prices, the corresponding time period is constant.

Autocorrelation: The expected value of the product of a random variable (here, stock prices) with a time-shifted version of itself. It gives a measure of the randomness of data.
Kurtosis: The degree of peakedness of a distribution, defined as a normalized form of the fourth central moment. It determines if the distribution has fat tails or not. It is a parameter to determine if the data is normally distributed.
Lomb Periodogram: An algorithm to find auto-correlation when the data points (here, stock prices) are unevenly distributed.
Skewness: The degree of asymmetry of a distribution. If the distribution is skewed to the left then the skewness is negative; if it is skewed to the right the skewness is positive. If the distribution is symmetrical, the skewness is zero. It is an important parameter to determine whether the data is normally distributed or not.
Volatility: The measure of the amount by which a price has fluctuated (historical volatility) or is expected to fluctuate (expected volatility) during a period. The volatility of a stock is the standard deviation of the continuously compounded rates of return on the stock over a specified period. The higher the volatility, the more the returns on the stock can be expected to vary—up or down. Volatility is typically expressed in annualized terms that are comparable regardless of the time period used in the calculation, for example daily, weekly, or monthly price observations.