Parkinson Volatility Node
High-Low Range Volatility Estimator
Parkinson Volatility uses only the high-low range to estimate volatility, making it 5x more efficient than close-based standard deviation. It assumes prices follow a geometric Brownian motion and are lognormally distributed, providing rapid volatility assessment useful for option pricing and risk management.
Formula
Parameters
| Parameter | Default |
|---|---|
| period | 14 |
Use Cases
1. Option Valuation Input
Input for Black-Scholes option pricing and risk calculations.
2. Efficient Volatility Estimation
5x more efficient than standard deviation, requires only high-low data.
3. Intraday Market Stress Detection
Rapid volatility changes visible in intrabar high-low ranges.
4. Gap Risk Assessment
Captures realized intrabar volatility including gaps within timeframes.
Advantages & Limitations
Advantages
- • Computationally efficient (only high-low)
- • Theoretically sound (lognormal assumption)
- • 5x better than close-only methods
- • Fast convergence with fewer observations
Limitations
- • Assumes lognormal distribution
- • Ignores close prices entirely
- • Can be distorted by limit moves
Tips & Best Practices
📊 Use for Option Greeks
Input this volatility directly into option pricing models for accuracy.
⚡ Compare with Other Measures
Should be ~75% of close-based standard deviation under normal conditions.
💰 Intrabar Signal
Changes quickly with each new intrabar high-low, good for near-term volatility changes.
⚠️ Watch Limit Moves
Limit up/down days produce artificially high readings that skew measurement.
Related Indicators
Garman-Klass Volatility
Advanced volatility estimator combining open, high, low, close
Rogers-Satchell Volatility
OHLC-based volatility without opening gaps assumption
Standard Deviation
Close-based volatility reference measure
ATR
Dollar-based high-low volatility measure