Concept
Garman-Klass Estimator
Garman-Klass Estimator, also known as rolling Garman-Klass, is a Volatility concept. The Library holds 2 implementations, each one a working definition you can pull into Quant.
Top Garman-Klass Estimator indicators
2 total
What is the Garman-Klass Estimator?
The Garman-Klass estimator computes historical volatility from each bar's open, high, low, and close rather than closes alone. Published by Mark Garman and Michael Klass in 1980, the per-bar variance estimate is half the squared log of high over low, minus (two times the natural log of two, minus one) times the squared log of close over open; averaging over a window and annualizing gives the volatility figure. Because the intrabar range samples the whole path, the paper's theoretical efficiency gain over close-to-close historical volatility is roughly seven times under idealized assumptions.
Those assumptions are the catch: zero drift, continuous trading, no opening gap. It refines the Parkinson estimator by adding open-to-close information, but anything that happens between one close and the next open is invisible, so gap-prone assets have volatility understated. Strong trends violate zero drift (the Rogers-Satchell estimator was built to fix that), and discretely sampled highs and lows sit slightly inside the true extremes, a small downward bias in thin markets.
How traders use it
- As a lower-noise volatility input: bands, stop distances, and sizing rules settle onto a stable reading with fewer bars, which matters when the lookback must stay short.
- As a diagnostic pair with close-to-close volatility: Garman-Klass running well below it means movement is arriving as overnight gaps the formula cannot see; running above it means turbulent sessions whose closes mask the churn.
- As one vote in an estimator composite, averaged with drift-robust and gap-aware alternatives so each covers the others' blind spots.
Related concepts · Volatility estimators
Concept family
Volatility
56 concepts mapped · 43 in the Library
Garman-Klass Estimator FAQ
Is the Garman-Klass estimator more accurate than standard deviation of returns?
It is more statistically efficient under its assumptions (geometric Brownian motion, zero drift, no gaps), reaching a given precision with far fewer bars because the high-low range is informative. On real data the answer depends on how badly the assumptions fail: heavy overnight gapping or strong trend can bias it where close-to-close volatility is merely noisier.
Does the Garman-Klass estimator account for overnight gaps?
No. The classic formula sees only each bar's open, high, low, and close, so a jump from the prior close to the current open never enters it, and total volatility is understated on gap-prone assets. The Garman-Klass–Yang-Zhang hybrid adds the squared overnight return for exactly this reason, and the full Yang-Zhang estimator handles gaps and drift together.
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