OHLC Input
Volatility Formulas
Parkinson (~5x efficient)
σ² = (ln(H/L))² / (4 × ln(2))
Garman-Klass (~7.4x efficient)
σ² = 0.5(ln(H/L))² - 0.39(ln(C/O))²
Rogers-Satchell (drift-adjusted)
σ² = ln(H/C)ln(H/O) + ln(L/C)ln(L/O)
Volatility Estimates
Parkinson
~5x efficient
4.14%
Daily
65.8%
Annualized
Garman-Klass
~7.4x efficient
4.52%
Daily
71.8%
Annualized
Rogers-Satchell
Drift-adjusted
4.41%
Daily
70.0%
Annualized
Note: This is a single-day estimate for educational illustration. Multi-day averaging produces more statistically robust volatility estimates.
Formula Breakdown
Interpretation Guide
Annualized Volatility Levels
| Range | Level | Typical Assets |
|---|---|---|
| < 10% | Low | T-bills, stable large-caps |
| 10-20% | Moderate | S&P 500, large-cap stocks |
| 20-30% | Elevated | Individual stocks, small-caps |
| 30-50% | High | Growth stocks, emerging markets |
| > 50% | Extreme | Crypto, speculative assets |
Model Assumptions
- Single-day observation - Multi-day averaging yields more robust estimates
- Same trading session - O, H, L, C from one continuous session
- No overnight gaps - Range estimators don't capture close-to-open jumps
- Log-normal returns - Assumes geometric Brownian motion
- Annualization - Daily vol × √252 (configurable trading days)
Understanding Range-Based Volatility
Why Use Range-Based Estimators?
Traditional close-to-close volatility only uses two price points per day, missing all intraday information. Range-based estimators incorporate High and Low prices to capture more information about true volatility.
Key benefits:
- Parkinson is ~5x more efficient than close-to-close
- Garman-Klass is ~7.4x more efficient
- Fewer observations needed for same accuracy
Choosing an Estimator
Each estimator makes different assumptions:
- Parkinson - Simple, uses only H/L, assumes zero drift
- Garman-Klass - Uses all OHLC, most efficient under no-drift assumption
- Rogers-Satchell - Drift-adjusted, less biased in trending markets
Tip: In trending markets, Rogers-Satchell is typically less biased. In range-bound markets, Garman-Klass is often more efficient under its model assumptions.
Frequently Asked Questions
Range-based volatility estimators use intraday price information (Open, High, Low, Close) to estimate volatility more efficiently than traditional close-to-close methods. By incorporating the trading range, these estimators capture more information about price variability within each trading session. The three main estimators are Parkinson (uses High/Low only), Garman-Klass (uses OHLC), and Rogers-Satchell (drift-adjusted OHLC).
Parkinson volatility is approximately 5x more efficient because it uses the high-low range, which captures the maximum price movement during a session. Close-to-close volatility only uses two data points (yesterday's close and today's close), missing all intraday price action. The high-low range contains more information about the true underlying volatility, requiring fewer observations to achieve the same estimation accuracy.
Garman-Klass (1980) extends Parkinson by incorporating all four OHLC prices, achieving approximately 7.4x efficiency versus close-to-close. The formula combines the high-low range with the open-close relationship, weighting each component to minimize estimation variance. It is considered one of the most efficient volatility estimators for non-trending markets.
"Drift" refers to the directional trend in prices. Parkinson and Garman-Klass assume zero drift (no trend), which can bias estimates in trending markets. Rogers-Satchell (1991) is mathematically constructed to be independent of drift, making it less biased when prices are trending up or down. It achieves this by using the relationships between High/Close and Low/Open rather than just the range.
Each estimator uses different price relationships and makes different assumptions. Parkinson only uses High/Low, making it simpler but less informative. Garman-Klass uses all four prices but assumes zero drift. Rogers-Satchell adjusts for drift but may be more sensitive to unusual OHLC patterns. In trending markets, Rogers-Satchell is typically less biased; in sideways markets, Garman-Klass is often more efficient under its model assumptions.
Multiply daily volatility by the square root of the number of trading days per year. The standard assumption is 252 trading days for US equity markets. For example: Daily vol of 1% multiplied by sqrt(252) equals approximately 15.87% annualized. This scaling assumes volatility is constant and returns are independent across days (geometric Brownian motion assumption).
Disclaimer
This calculator is for educational purposes only. Range-based volatility estimators assume continuous trading without overnight gaps. Single-day estimates are illustrative; multi-day averaging produces more statistically robust results. Actual market volatility depends on many factors not captured here. This tool should not be used for trading decisions. Not financial advice.
