--- title: "OHLC Volatility: Formula, Meaning" description: "OHLC volatility refers to estimators built from each bar's open, high, low and close rather than closes alone, such as Garman-Klass, Rogers-Satchell and..." canonical: https://www.getswoopr.com/indicators/volatility/ohlc-volatility/ source: "Swoopr Investment: https://www.getswoopr.com" --- ## Direct Answer > OHLC volatility refers to estimators built from each bar's open, high, low and close rather than closes alone, such as Garman-Klass, Rogers-Satchell and Yang-Zhang. Each computes a per-bar variance term and averages it over a window. Higher values mean wider price excursions within the bars. # OHLC Volatility: Formula, Meaning, Signals, Examples and How to Use It ## How Is OHLC Volatility Calculated? OHLC volatility refers to estimators built from each bar's open, high, low and close rather than closes alone, such as Garman-Klass, Rogers-Satchell and Yang-Zhang. Each computes a per-bar variance term and averages it over a window. **Formula:** `sigma = sqrt(252 x (1 / n) x sum of v_t), where v_t is a per-bar variance term built from ln(O), ln(H), ln(L) and ln(C)` **Inputs:** - Open, high, low and close for each bar - The chosen estimator's per-bar formula - Window length n - Annualization factor (commonly 252) **Worked example.** Per-bar variance terms of 0.00010, 0.00014 and 0.00012 average 0.00012, which annualizes to 17.4%. The inputs are illustrative numbers, not market data. **Reading it.** Higher values mean wider price excursions within the bars. These estimators extract more information per bar than close-to-close, so they converge faster with a smaller window when their assumptions hold. **Limits.** The assumptions differ by estimator: several ignore overnight gaps or assume zero drift, which biases results low on gappy assets. Bar data quality (bad highs and lows) feeds directly into the output. ## What is OHLC Volatility? OHLC volatility refers to estimators built from each bar's open, high, low and close rather than closes alone, such as Garman-Klass, Rogers-Satchell and Yang-Zhang. Each computes a per-bar variance term and averages it over a window. ## How to use OHLC Volatility Higher values mean wider price excursions within the bars. These estimators extract more information per bar than close-to-close, so they converge faster with a smaller window when their assumptions hold. A reading alone is never a buy or sell signal; it needs context from price structure, volume, volatility and the broader market regime. ## Strengths - Using all four prices of a bar extracts more information per observation, so a given precision needs fewer bars than close-only estimates. - A per-bar variance term averaged over a window makes each estimator a simple mean, so the choice among them is the choice of formula only. - Reproducible when the formula and data convention are fixed. ## Weaknesses and limitations - Estimators treat the overnight period differently, so two OHLC estimators can give noticeably different values for the same stock. - Estimators that use the open depend on its quality, and a thin or stale opening print distorts the per-bar variance term. - 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. ## Frequently Asked Questions Is OHLC Volatility a buy or sell signal? No. Higher values mean wider price excursions within the bars. The assumptions differ by estimator: several ignore overnight gaps or assume zero drift, which biases results low on gappy assets. 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 OHLC Volatility? OHLC Volatility takes these inputs: open, high, low and close for each bar; the chosen estimator's per-bar formula; window length n. 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 OHLC Volatility be used by itself? The assumptions differ by estimator: several ignore overnight gaps or assume zero drift, which biases results low on gappy assets. Bar data quality (bad highs and lows) feeds directly into the output. For that reason it is read next to measures of direction, participation, valuation, liquidity and risk, not alone. Does OHLC Volatility work on every timeframe? The calculation (sigma = sqrt(252 x (1 / n) x sum of v_t), where v_t is a per-bar variance term built from ln(O), ln(H), ln(L) and ln(C)) 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 OHLC Volatility give false signals? Estimators treat the overnight period differently, so two OHLC estimators can give noticeably different values for the same stock. Estimators that use the open depend on its quality, and a thin or stale opening print distorts the per-bar variance term. Lag, noise and regime changes add to this, because market participants react to new information after the reading is calculated. How should OHLC Volatility be backtested? Rebuild OHLC 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. ## References - [The Journal of Business: On the Estimation of Security Price Volatilities from Historical Data (1980)](https://doi.org/10.1086/296072) S Swoopr Editorial Team The Swoopr Editorial Team produces independent investment education, research, and tools for understanding markets, evaluating opportunities, and managing risk. All content is educational only and does not constitute personalized investment advice. 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