Direct Answer
Yang-Zhang 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.
Yang-Zhang Volatility: Formula, Meaning, Signals, Examples and How to Use It
How Is Yang-Zhang Volatility Calculated?
The Yang-Zhang estimator combines overnight (close-to-open) variance, open-to-close variance and the Rogers-Satchell estimator into one measure. It handles both opening gaps and nonzero drift, which most other range estimators do not.
Formula: sigma^2 = sigma_o^2 + k x sigma_c^2 + (1 - k) x sigma_rs^2, with k = 0.34 / (1.34 + (n + 1) / (n - 1))
Inputs:
- Open, high, low and close for each bar
- Overnight variance of ln(Open / previous Close)
- Open-to-close variance of ln(Close / Open)
- Rogers-Satchell variance
- Window length n
Worked example. With overnight variance 0.00006, open-to-close variance 0.00010, Rogers-Satchell variance 0.00008 and n of 20, the daily volatility is 1.19%. The inputs are illustrative numbers, not market data.
Reading it. It is read like other historical volatility: higher means more movement. Because it covers overnight jumps and drift, it is usually the preferred range-based choice for assets that gap, such as single stocks.
Limits. It needs several variance inputs over a multi-bar window and cannot be computed from a single bar. It assumes no jumps within the trading day, and it is more complex to compute and audit than simpler estimators.
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 Yang-Zhang Volatility?
Yang-Zhang 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?
Yang-Zhang 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 Yang-Zhang 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
- Splitting variance into overnight, open-to-close and range parts lets each component be inspected, so the source of volatility is visible.
- The weight k is chosen to minimize the estimator's variance for a given n, which ties the blend to a stated efficiency criterion.
- Reproducible when the formula and data convention are fixed.
Weaknesses and limitations
- Overnight and intraday terms mix periods of different length and trading intensity, so annualizing each by one factor is an approximation.
- The weight k depends on n, so changing the window changes the blend and not only the sample size.
- 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 Yang-Zhang Volatility a buy or sell signal?
No. It is read like other historical volatility: higher means more movement. It needs several variance inputs over a multi-bar window and cannot be computed from a single bar. 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 Yang-Zhang Volatility?
Yang-Zhang Volatility takes these inputs: open, high, low and close for each bar; overnight variance of ln(Open / previous Close); open-to-close variance of ln(Close / Open). 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 Yang-Zhang Volatility be used by itself?
It needs several variance inputs over a multi-bar window and cannot be computed from a single bar. It assumes no jumps within the trading day, and it is more complex to compute and audit than simpler estimators. For that reason it is read next to measures of direction, participation, valuation, liquidity and risk, not alone.
Does Yang-Zhang Volatility work on every timeframe?
The calculation (sigma^2 = sigma_o^2 + k x sigma_c^2 + (1 - k) x sigma_rs^2, with k = 0.34 / (1.34 + (n + 1) / (n - 1))) 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 Yang-Zhang Volatility give false signals?
Overnight and intraday terms mix periods of different length and trading intensity, so annualizing each by one factor is an approximation. The weight k depends on n, so changing the window changes the blend and not only the sample size. Lag, noise and regime changes add to this, because market participants react to new information after the reading is calculated.
How should Yang-Zhang Volatility be backtested?
Rebuild Yang-Zhang Volatility from open, high, low and close 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.