Direct Answer
Parkinson Volatility is a volatility measure used to measure dispersion, range, implied uncertainty, or expansion/contraction in price movement. The production page must define the exact calculation convention before interpreting higher or lower readings, because platform and provider implementations can differ.
Parkinson Volatility: Formula, Meaning, Signals, Examples and How to Use It
How Is Parkinson Volatility Calculated?
The Parkinson estimator measures volatility from the high-low range alone. Because the range of a bar contains more information than a single close, it needs fewer observations than close-to-close volatility, assuming no drift and continuous trading.
Formula: sigma^2 = (1 / (4 x n x ln 2)) x sum of (ln(High / Low))^2; annualize with sqrt(252)
Inputs:
- High and low for each bar
- Window length n
- Annualization factor (commonly 252)
Worked example. Three bars with log high-low ranges of 0.020, 0.025 and 0.018 give an annualized Parkinson volatility of 20.2%. The inputs are illustrative numbers, not market data.
Reading it. Larger values mean wider daily ranges and thus higher volatility. It is about five times as efficient as close-to-close in theory when its assumptions hold, so it is a useful companion measure.
Limits. It ignores overnight gaps and drift, and discrete sampling means the observed high-low is narrower than the true one, so it tends to understate volatility. Bad ticks in highs and lows directly distort it.
Indicator overview
| Attribute | Value |
|---|---|
| Category | Volatility |
| Asset classes | Stocks, ETFs, Indexes, Options, Futures, Forex, Crypto, Commodities |
| Output type | Ratio/index/scalar |
| Parameters | Varies by implementation; document exact settings on use |
What is Parkinson Volatility?
Parkinson Volatility is a volatility measure used to measure dispersion, range, implied uncertainty, or expansion/contraction in price movement. The production page must define the exact calculation convention before interpreting higher or lower readings, because platform and provider implementations can differ.
For Swoopr, the canonical profile separates definition, calculation, interpretation and decision use. That keeps readers from collapsing a descriptive metric into a trading strategy. An indicator can describe trend, momentum, volatility, participation, positioning or risk without providing a complete entry, exit, sizing or portfolio decision.
What does it measure?
Parkinson Volatility measures specific market properties in the Volatility domain. The practical value is repeatability: the same inputs and formula can be applied across a defined dataset, allowing users to compare readings through time or across instruments when the calculation convention is held constant.
How to read Parkinson Volatility
Interpretation depends on the market property being measured. A descriptive reading is not automatically predictive. The same numerical state can lead to different outcomes in a strong trend, quiet range, event-driven gap or illiquid market. Always combine with context from price structure, volume, and broader market regime before drawing conclusions.
Strengths
- Only the high and low are required, so it can be computed from bar data that omits opens and closes.
- The constant 1 over 4 ln 2 makes the squared log range an unbiased variance estimate for driftless continuous price, tying it to a stated model.
- Reproducible when the formula and data convention are fixed.
Weaknesses and limitations
- The squared log range grows with bar length, so estimates from 5-minute and daily bars are on different time scales until annualized.
- Jumps inside the bar are treated as part of continuous variation, so the estimator cannot separate jumps from diffusive movement.
- Correlated indicators can create false confidence by repeating the same underlying information.
Editorial and risk note
This page is educational content, not individualized investment advice. It should not imply guaranteed prediction, accuracy or outperformance. Where multiple valid definitions exist, Swoopr states the alternatives and the reason for selecting its primary convention.
Frequently Asked Questions
Is Parkinson Volatility a buy or sell signal?
No. Larger values mean wider daily ranges and thus higher volatility. It ignores overnight gaps and drift, and discrete sampling means the observed high-low is narrower than the true one, so it tends to understate volatility. A trade built on it still needs a trigger, an invalidation rule, a position size and realistic execution assumptions.
What is the best setting for Parkinson Volatility?
Parkinson Volatility takes these inputs: high and low for each bar; window length n; annualization factor (commonly 252). There is no universal best setting for the tunable ones, so test them across instruments, regimes and out-of-sample periods and favor stable parameter regions over one historical winner.
Can Parkinson Volatility be used by itself?
It ignores overnight gaps and drift, and discrete sampling means the observed high-low is narrower than the true one, so it tends to understate volatility. Bad ticks in highs and lows directly distort it. For that reason it is read next to measures of direction, participation, valuation, liquidity and risk, not alone.
Does Parkinson Volatility work on every timeframe?
The calculation (sigma^2 = (1 / (4 x n x ln 2)) x sum of (ln(High / Low))^2; annualize with sqrt(252)) can be run on any bar size, but behavior is not portable: a reading on a five-minute chart describes a very different market window from the same reading on a daily chart. Validate the exact timeframe before relying on it.
Why does Parkinson Volatility give false signals?
The squared log range grows with bar length, so estimates from 5-minute and daily bars are on different time scales until annualized. Jumps inside the bar are treated as part of continuous variation, so the estimator cannot separate jumps from diffusive movement. Lag, noise and regime changes add to this, because market participants react to new information after the reading is calculated.
How should Parkinson Volatility be backtested?
Rebuild Parkinson Volatility from high and low for each bar using point-in-time data, write the rules before testing, include delisted securities where relevant, model realistic fills and costs, reserve a validation sample and compare with a simpler baseline.