Garman-Klass Volatility is a statistical estimator for asset price volatility that accounts for open, high, low, and closing prices within a given period. Developed by Garman and Klass, this metric offers a more efficient estimation of true price variance compared to simple close-to-close volatility, particularly relevant in continuously trading markets like crypto. It provides a robust measure of price fluctuation.
Mechanism
The mechanism of the Garman-Klass estimator integrates the daily price range (high minus low) and the opening and closing prices into its calculation. This inclusion of intraday price extremes captures more information about the underlying price process than just the end-of-period changes. Specifically, it uses a weighted average of squared log returns based on these four price points to compute volatility.
Methodology
The methodology is rooted in the mathematical theory of Brownian motion, assuming prices follow a diffusion process. It extends upon simpler estimators by extracting additional data from the observed price path, thereby yielding a lower variance estimate for volatility, especially when sampling frequency is low. In crypto trading, this method aids in more accurately pricing options, managing risk, and parameterizing algorithmic trading strategies where precise volatility assessment is critical for institutional operations and smart order routing.
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