Garman-Klass Volatility Node
OHLC Volatility Estimator with Gap Handling
Garman-Klass Volatility uses open, high, low, and close prices to estimate volatility more efficiently than close-based methods while handling overnight gaps. Simpler than Yang-Zhang but more sophisticated than Parkinson, it's ideal for securities with significant overnight gaps and provides accurate volatility estimates for option pricing.
Formula
Parameters
| Parameter | Default |
|---|---|
| period | 14 |
Use Cases
1. Futures & Forex Volatility
Ideal for 24-hour traded assets with significant overnight gaps.
2. Option Valuation
Superior to close-only methods for option Greek calculations.
3. Realized Volatility Tracking
Daily tracking of true realized volatility including overnight moves.
4. Strategy Volatility Regime
Establish volatility baselines for strategy optimization.
Advantages & Limitations
Advantages
- • 7x more efficient than close-based
- • Handles overnight gaps
- • Simpler than Yang-Zhang
- • Proven for option pricing
Limitations
- • Less accurate than Yang-Zhang
- • Requires OHLC data
- • Assumes lognormal distribution
Tips & Best Practices
📊 Compare Methods
GK usually ~90-100% of Yang-Zhang; significant divergence = data issue.
⚡ Best for Gaps
Garman-Klass excels when open gaps are common; use for futures/crypto.
💰 Historical Baseline
Track 50+ day GK average for establishing normal volatility.
⚠️ Data Quality Matters
Bad OHLC data produces bad estimates; validate data source.
Related Indicators
Yang-Zhang Volatility
More sophisticated OHLC method
Parkinson Volatility
High-low only; what GK extends
Rogers-Satchell Volatility
Gap-aware alternative method
ATR
Traditional true range volatility smoothed