--- title: "Realised volatility: four estimators, windows and factors" description: "Close to close, Parkinson, Garman Klass and Yang Zhang realised volatility explained on Nifty data, with window, annualising factor and overnight gap effects." source_url: "https://www.rupeecase.com/learn/path-16/module-16-5-realised-volatility-estimators" --- [Path 16 5 of 16](/learn/path-16/)Measuring realised volatility: four estimators[Next 16.6](/learn/path-16/module-16-6-iv-crush-around-events) Friday 8 May 2026. The Nifty fell 0.62% that day, a quiet one. Yet the twenty session realised volatility dropped from 18.48 to 13.45 percent a year in one session, a fall of 5.03 points, and nothing that day caused it. A big day from 8 Apr 2026 had simply left the window. That is one reason I never quote a realised volatility without three things next to it: the window, the estimator and the annualising factor. This lesson sets out the four estimators I use on an index, where each one fails, and what changes when you change a choice. Every figure comes from NSE end of day files, so nothing here is live and nothing is a view on where volatility goes next. 3By the end you can 1. Name the four estimators and the part of the day each one reads. 2. Explain why a window figure can fall on a quiet day. 3. State the window, estimator and annualising factor behind any realised volatility. Watch in the terminal: what are the four lead figures? A short tutorial on sample data. ## The idea, plainly Realised volatility measures how much the index actually moved. Take the daily returns of a window, find how spread out they are, and scale the result to a year. The [first volatility lesson](/learn/path-16/module-16-1-historical-vs-implied-volatility) separated three names: historical looks back over a window that ends today, implied reads an option price, and realised is what happened after the option was priced. This lesson is about how the backward looking figure is built. Each estimator uses more of the day than the one before Open, high, low and close are the four prices of an index bar. Yang Zhang also looks at the gap from the previous close to the open. Source: Parkinson (1980), Garman and Klass (1980), Yang and Zhang (2000), RupeeCase. As of 17 Oct 2026. * **Close to close.** The sample standard deviation of the log return from each close to the next, times the square root of the days in a year. It needs closes only. It counts gaps, because the close to the next close includes the overnight jump. It is noisy, because it uses one number a day. * **Parkinson (1980).** Uses the high and the low of each day. A day's range says more about how much the price moved than its close does. The paper shows it extracts the same information from roughly a fifth of the days, under its assumptions: no drift, no gaps, trading all day. * **Garman Klass (1980).** Adds the open and the close to the range. Under the same assumptions it is more efficient again. It shares the weakness: it sees only what happened inside the session. * **Yang Zhang (2000).** Adds the overnight gap and the open to close move to a range term, in a mix that stays sound when the price drifts and when it jumps at the open. * **The window.** Ten sessions react fast and bounce about. A hundred and twenty sessions are steady and late. Twenty sessions, about a month, is the common middle. * **The mean.** Close to close can subtract the average return or assume it is zero. The zero mean version is closer to what an exchange risk file does, and it reads a little differently on a trending window. * **The annualising factor.** The square root of 252 trading days is standard for an index. NSE's own volatility file for F&O underlyings uses a weighted average of past squared returns, which favours recent days, and scales to a year with the square root of 365. Same returns, a figure about a fifth higher. 3 terms this section turns on From the module glossary; every term is in the Learn glossary too. Source: Module 16.5, Measuring realised volatility: four estimators. Read 6 more paragraphs, 272 words **Historical, implied and realised: what is the difference?** Historical and realised use the same arithmetic on different dates. Historical ends on the day you look. Realised starts on the day an option was priced and runs forward. Implied is not measured from returns at all. The [question page](/learn/questions/historical-implied-realised-volatility) gives the short answer. Four estimators cover most needs. Each one uses more of the day than the one before. That weakness matters in India. NSE's index trades from 09:15 to 15:30. Between the close and the next open the index can jump on news from elsewhere, and the high and low of the session know nothing about it. Range estimators that ignore the gap tend to read low in a gappy period. Close to close reads the gap in, and Yang Zhang is built to. Three more choices change any of them. None of these is wrong. They answer slightly different questions, and the answer you need depends on what you will compare the number with. Compare an implied volatility for the next thirty calendar days with a twenty session figure and the same factor, and say so. Use follows purpose. To set an option's implied volatility against what the index has just done, I match the window to the option's life: a one month option, a twenty session window. To look for a change of regime, I read a short and a long window together. When the ten session figure sits well above the hundred and twenty session one, volatility has been rising. When it sits well below, it has been fading. That describes the recent past. It is not a forecast. ## Worked example: the 8 Apr 2026 day, twice On 8 Apr 2026 the Nifty rose 3.78%, from 23,123.65 to 23,997.35. The twenty session realised volatility moved from 26.04 to 29.46 on that day alone. Twenty sessions later, on 8 May 2026, the same day dropped out of the window and the estimate fell from 18.48 to 13.45, on a day the index fell 0.62%. The same day moves the estimate twice, once going in and once leaving Percent a year. Realised is close to close over twenty sessions, computed by RupeeCase from NSE closes. Implied is the 30 day at the money series. The fall on 8 May is the 8 Apr return leaving the window. Source: NSE end of day data, computed by RupeeCase. As of 30 Jun 2026. The shorter the window, the wider the swings in realised volatility Close to close realised volatility on rolling windows from ten to one hundred and twenty sessions, ending 15 Sep 2026. The band runs from the lowest to the highest reading in the file. The dashed line is the 30 day at the money implied volatility of 12.44 percent. Source: NSE end of day data, computed by RupeeCase. As of 15 Sep 2026. Same returns, two factors: 7.27 against 8.75 Both bars use the same daily returns. Only the annualising factor differs, a square root of 252 against a square root of 365. Source: NSE end of day data, computed by RupeeCase. As of 15 Sep 2026. Day Open High Low Close 1 100.0 101.5 99.2 100.8 2 100.5 102.2 100.1 101.9 3 102.6 103.4 101.0 101.4 4 101.2 102.0 99.9 100.3 5 100.9 102.5 100.4 102.1 6 102.0 102.9 100.8 101.3 Four estimators, four answers from the same six invented days The bars are invented for the exercise, so the spread between estimators means nothing about the market. Six days is also far too few for a real reading. Source: RupeeCase engine on six invented days, not market data. As of 17 Oct 2026. Read 7 more paragraphs, 374 words One day carried the estimate for twenty sessions, then vanished. The index did not become calmer on 8 May. The window forgot a day. A slower, weighted estimator softens that cliff. A short window makes it worse. A long window mutes it, but reacts late to a real change. Now the window itself, on one date. The cone below shows, for each window length, the range of close to close readings across the file, from 1 Jul 2025 to 15 Sep 2026, and where each stood at the end. Same index, same date, five different realised numbers, one for each window. Which is right? All of them. The twenty session reading was 7.27. Over the whole file the twenty session figure averaged 11.53 percent, against 12.42 for the 30 day implied volatility, so implied sat above realised on average. A window figure also carries sampling error. Twenty returns give a rough estimate, with an error of roughly one part in six if the returns were independent and normal. On the 7.27 reading that is about 1.2 points either way. A figure from a short window deserves a wide band around it. The factor is a choice that hides in plain sight. The returns behind the 7.27 reading have a daily standard deviation of about 0.46 percent. Annualise them the exchange way and they read 8.75. Last, the four estimators on one set of bars. The data files behind these lessons keep closes only, so I use six invented days of open, high, low and close. The numbers are made up for the exercise and are not market data. A real run would take the daily open, high, low and close from NSE's index end of day files. The four answers sit within a couple of points of each other. Garman Klass reads above Parkinson because on five of the six days the close sat near the open compared with the day's range, and its formula then leans harder on the range. Yang Zhang adds the gaps at the open of the third and fifth days. With six invented days the order says nothing about the market. Real series of hundreds of days narrow that spread and show which estimator suits your use. ## The common mistake The claim: Realised volatility is the true volatility, and implied volatility is only a guess about it. There is no one realised figure to be true to. On 15 Sep 2026 the same twenty sessions of Nifty returns read 7.27 with one annualising factor and 8.75 with another. The next day the window forgets a day and the figure moves with no new news. Four estimators on the same six invented days gave four answers. Realised volatility is a measurement, and a measurement comes with a method. Say the window, the estimator and the factor, and compare like with like. ## Key takeaways * Realised volatility is a measurement, and it comes with a method. * A big day moves a window figure twice: entering and leaving. * Range estimators miss the overnight gap. Close to close and Yang Zhang see it. * Window length trades speed against steadiness. * Calendar days in the factor read about a fifth higher than trading days. * Compare like with like: same window, estimator and factor. Watch in the terminal: what do index, fund and strategy lines show? A short tutorial on sample data. **Before you trade.** Derivatives trading carries a high risk of loss. SEBI's study "Profitability of Individual Traders in the Equity Derivatives Segment (FY25 to FY26)" (August 2026) found that 87.7% of individual traders in equity F&O made net losses in FY26, with aggregate net losses of Rs 91,685 crore. [Read the SEBI study](https://www.sebi.gov.in/reports-and-statistics/research/aug-2026/study-profitability-of-individual-traders-in-the-equity-derivatives-segment-fy25-fy26-_103835.html) What does Measuring realised volatility: four estimators cover?Four ways to measure how much the index moved, and why one day can move a realised volatility figure twice. It works through The idea, plainly, Worked example: the 8 Apr 2026 day, twice, The common mistake, Key takeaways. It is module 16.5 of Path 16, Volatility, about 9 minutes of reading, free. TTry it ## IV rank and percentile, on the same example. Both definitions side by side on a dated series. Dated NSE end of day data, nothing live, no orders. **IV rank and percentile**. [Open the iv rank and percentile](/learn/tools/iv-rank-percentile?series=nifty-atm-iv-30d) AF&O on RupeeCase ## Hedging only, in development F&O at RupeeCase will do one job: protect a portfolio you already own, by a rule published before it runs. No speculation, no naked option selling, no leveraged bets. It is not available yet. [How hedging will work](/hedging)[Sign up for RupeeCase](https://invest.rupeecase.com/signup) [No hedgeFalls with the marketWith a putA floor, for a premiumIllustration with made up numbers, not a product and not a recommendation.](/hedging) QQuick check ## 10 questions, 7 to pass. Each attempt draws 10 of the 15 questions in this module's bank, and a retake draws a fresh set. A pass gives this module's Certificate of Completion. Your result stays on this device and counts towards the Path 16 certificate. 7 of 10 correct passes module 16.5 Every answer is explained when you check; a retake is always free. Source: RupeeCase Learn quiz rules (70% to pass). Passed ### Certificate ID Download PNGPrint or save as PDF[Check the ID](/learn/certificates/verify) Self paced and unproctored. A record of completing free education by RupeeCase; not a NISM certificate and not endorsed by NISM, NSE, BSE or SEBI. It qualifies no one to deal, advise or publish research. CCheck yourself ## Path 16 so far. Your quick check results on this device, stop by stop. Stop 5 of 16 on Path 16 Volatility: every live stop, this module ringed. Source: RupeeCase Learn, Path 16. 1. [16.1Volatility: historical against impliedNot yet](/learn/path-16/module-16-1-historical-vs-implied-volatility) 2. [16.2India VIX for beginners: what it measures and what it does notNot yet](/learn/path-16/module-16-2-india-vix-explained) 3. [16.3Solving for implied volatilityNot yet](/learn/path-16/module-16-3-solving-for-implied-volatility) 4. [16.4IV rank and IV percentile, done properlyNot yet](/learn/path-16/module-16-4-iv-rank-and-iv-percentile) 5. [16.5Measuring realised volatility: four estimatorsNot yet](/learn/path-16/module-16-5-realised-volatility-estimators#quiz) 6. [16.6Events and IV crush: results, Budget, RBI and electionsNot yet](/learn/path-16/module-16-6-iv-crush-around-events) 7. [16.7Smile and skew: a first look at Nifty and stocksNot yet](/learn/path-16/module-16-7-volatility-smile-and-skew) 8. [16.8Skew measures: risk reversal, butterfly and slopeNot yet](/learn/path-16/module-16-8-skew-measures) 9. [16.9Term structure and event varianceNot yet](/learn/path-16/module-16-9-term-structure-and-event-variance) 10. [16.10Forward volatility from two expiriesNot yet](/learn/path-16/module-16-10-forward-volatility) 11. [16.11India VIX rebuilt step by stepNot yet](/learn/path-16/module-16-11-india-vix-rebuilt) 12. [16.12Forecasting volatility: EWMA, GARCH and HARNot yet](/learn/path-16/module-16-12-forecasting-volatility) 13. [16.13The variance risk premium in NiftyNot yet](/learn/path-16/module-16-13-variance-risk-premium) 14. [16.14Variance swaps and replicationNot yet](/learn/path-16/module-16-14-variance-swaps) 15. [16.15Volatility surface models: SVI, SABR, local and stochastic volNot yet](/learn/path-16/module-16-15-volatility-surface-models) 16. [16.16Skew dynamics: sticky strike against sticky deltaNot yet](/learn/path-16/module-16-16-skew-dynamics) 17. [TPath test, 30 questionsNot yet](/learn/path-16/module-16-16-skew-dynamics#pathTestSection) Path 16 in reading time: 159 minutes over 16 modules Minutes of reading per module, this one in amber. Source: RupeeCase Learn, Path 16. Explore next ## Put the module to work. * [ToolOption payoff builderLegs on a dated Nifty chain, breakevens and charges](/learn/tools/payoff-builder) * [Free module 16.1Volatility: historical against implied7 min read](/learn/path-16/module-16-1-historical-vs-implied-volatility) * [Free module 16.3Solving for implied volatility9 min read](/learn/path-16/module-16-3-solving-for-implied-volatility) * [Free module 16.4IV rank and IV percentile, done properly9 min read](/learn/path-16/module-16-4-iv-rank-and-iv-percentile) * [Free module 16.6Events and IV crush: results, Budget, RBI and elections9 min read](/learn/path-16/module-16-6-iv-crush-around-events) * [QuestionHistorical, implied and realised volatility: what is the difference?A short answer with a dated chart](/learn/questions/historical-implied-realised-volatility) Community ## Join my WhatsApp group. Scan the code, fillFill one short form, and join my WhatsApp group. Slides, sources, factsheets and the rebalance logs live there, and members join a daily Google Meet with me. New to RupeeCase? [Getting started](/getting-started) [See the terminal](/terminal) [Sign up for RupeeCase](https://invest.rupeecase.com/signup) [Join the WhatsApp group](https://forms.gle/C6HMtM1Z2GfeocQV7) [One form, one group](https://forms.gle/C6HMtM1Z2GfeocQV7)Scan with your phone camera SSources ## Go to the source. 9 sources, NSE the most cited The publisher of each source listed below. Source: Module 16.5 source list. 1. [NSE, derivatives end of day reports (including the daily volatility file for F&O underlyings)](https://www.nseindia.com/all-reports-derivatives) NSE 2. [NSE, daily volatility file FOVOLT for 15 Sep 2026 (the header gives the weighting and the annualising factor)](https://nsearchives.nseindia.com/archives/nsccl/volt/FOVOLT_15092026.csv) NSE 3. [NSE, end of day index file for 15 Sep 2026 (open, high, low and close of each index)](https://nsearchives.nseindia.com/content/indices/ind_close_all_15092026.csv) NSE 4. [NSE, India VIX: index page and methodology](https://www.nseindia.com/static/products-services/indices-indiavix-index) NSE 5. [NISM, Series VIII Equity Derivatives certification examination: test objectives](https://www.nism.ac.in/revamp-nism/test-objectives-revised-nism-series-viii-equity-derivatives-certification-examination/) NISM 6. [Parkinson, M. (1980), The Extreme Value Method for Estimating the Variance of the Rate of Return, Journal of Business](https://ideas.repec.org/a/ucp/jnlbus/v53y1980i1p61-65.html) Paper 7. [Garman, M. B. and Klass, M. J. (1980), On the Estimation of Security Price Volatilities from Historical Data, Journal of Business](https://ideas.repec.org/a/ucp/jnlbus/v53y1980i1p67-78.html) Paper 8. [Yang, D. and Zhang, Q. (2000), Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices, Journal of Business](https://ideas.repec.org/a/ucp/jnlbus/v73y2000i3p477-91.html) Paper 9. Sinclair, E., Volatility Trading, Wiley (measuring volatility) Book GTerms ## 5 terms from this module. Realised volatilityVolatility measured on returns that have happened, scaled to a year. These lessons keep the word for the window after an option was priced. Close to closeThe standard deviation of the log returns between successive closes, scaled to a year. It sees gaps and needs closes only. Range estimatorA realised volatility built from the daily high and low, and sometimes the open and close. It sees movement inside the session and misses the overnight gap. Annualising factorThe square root of the periods in a year used to scale a daily figure. A trading day year and a calendar day year give figures that differ by about a fifth. WindowThe number of sessions a realised volatility reads. A day that leaves the window can move the figure with no new news. [All 230 terms in the glossary](/learn/glossary) [Question people askHistorical, implied and realised volatility: what is the difference?A short answer with a dated chart](/learn/questions/historical-implied-realised-volatility)[Related module16.1 Volatility: historical against implied7 min, Beginner](/learn/path-16/module-16-1-historical-vs-implied-volatility)[Related module16.3 Solving for implied volatility9 min, Intermediate](/learn/path-16/module-16-3-solving-for-implied-volatility)[Pillar guideDerivatives and Quant115 modules across paths 12 to 19](/learn/derivatives) Education only, not investment advice, a research report or a recommendation to buy, sell or hold any security. RupeeCase (QC Alpha Technologies Pvt Ltd) is not registered with SEBI as an investment adviser or research analyst. Contracts and prices shown are for illustration only, come from NSE end of day files at least 30 days old, and are not recommendatory. Derivatives trading carries a high risk of loss.