# Volatility Historical Realized

All metrics in this category have a default 14 day trailing period (the default of the lookback days in the app), and can be changed by changing the lookback days period in the chart settings gearbox.

A measure of realized volatility that weights nearer samples heavier than further samples in measuring volatility over a given period of time.

Garman Klass is a volatility estimator that incorporates open, low, high and close prices of a security. Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.

Historical volatility is a statistical measure of the dispersion of returns for a given security or market index over a given period of time. Generally, this measure is calculated by determining the average deviation from the average price of a financial instrument in the given time period.

Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day. The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close to close prices could show little difference while large price movements could have happened during the day. Thus Parkinson volatility considered to be more precise and requires less data for calculation than the close-close volatility.

Ratio of a high frequency volatility measurement (Yang Zhang) to a low frequency volatility measurement (Close to Close). A high ratio indicates the market is exhibiting a high degree of mean reversion. A lower ratio indicates a market is trending.

Vol Down is a calculation that is designed to measure and isolate the downside volatility of returns of the asset. The calculation excludes positive returns from the historical realized volatility calculation.

Vol Up is a calculation that is designed to measure and isolate the upside volatility of returns of the asset. The calculation excludes negative returns from the historical realized volatility calculation.

Yang-Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error. We can think of the Yang-Zhang volatility as the combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It considered being 14 times more efficient than the close-to-close estimator.

Last modified 1yr ago