--- title: "Skew dynamics: sticky strike vs sticky delta on Nifty" description: "How a Nifty smile moves with the index: sticky strike, sticky delta and local volatility, tested on dated NSE chains and the biggest daily falls and rises." source_url: "https://www.rupeecase.com/learn/path-16/module-16-16-skew-dynamics" --- [Path 16 16 of 16](/learn/path-16/)Skew dynamics: sticky strike against sticky delta[Path 16](/learn/path-16/) Friday 12 Jun 2026. Nifty rose 1.99 percent and the 30 day at the money implied volatility fell 1.12 points. On Thursday 19 Mar 2026 Nifty fell 3.26 percent and the same measure rose 4.40 points. Desks call it the smile moving with the index, and the rule they assume for how it moves decides the hedge. Three rules are common: sticky strike, sticky delta and local volatility. This module sets them side by side, measures the skew that two of them need, and checks all three against the six largest falls and six largest rises of Nifty in an NSE file of 300 sessions. None of the three fits those days. That is the finding. Every figure comes from NSE end of day files, dated, so nothing here is live and nothing is a view on the next move. 3By the end you can 1. Say what sticky strike, sticky delta and local volatility each predict for the at the money volatility. 2. Measure a skew slope from a dated chain and turn each rule into a hedge ratio. 3. Say why two dates cannot settle a regime, and what twelve big days showed. ## The idea, plainly ### What does sticky mean? When the index moves, every implied volatility on the chain can move. A rule says how. Take two things off the chain: the at the money volatility, and the skew, which is the slope of the smile in volatility points for each one percent move of the strike. The skew is negative for Nifty, as the [skew measures module](/learn/path-16/module-16-8-skew-measures) shows. * **Sticky strike.** The volatility at each strike stays where it was. When the index rises, the at the money point slides to a higher strike, so the at the money volatility changes by the skew slope. * **Sticky delta.** The whole smile slides with the index, which is close to saying it is fixed in moneyness. The at the money volatility does not change. * **Local volatility.** The smile moves the opposite way to the index. The at the money volatility changes by about twice the skew slope. Three rules, three answers for the at the money vol A sketch of the rules, not a market number. Source: NSE end of day data, computed by RupeeCase. As of 10 Jun 2026. The maths: the skew stickiness ratio Write R for the ratio of two slopes at the money: how the at the money volatility changes with the log of the index, over how the smile changes with the log of the strike. R = (d vol_ATM / d ln S) / (d vol / d ln K at ATM) For a smile that falls in strike, sticky delta gives R of zero, sticky strike gives one and local volatility about two, to first order. R is a way to measure a regime without adopting a model. A stochastic volatility model gives an R that depends on its parameters, so the class of model does not decide it. For one option at a fixed strike the volatility changes by R minus one skew slopes, because the at the money point itself slides along the smile as the index moves. That is zero for sticky strike, where each strike keeps its own volatility. ### Where does the screen delta come from? An option chain shows a delta beside each strike. It is the Black 76 delta computed with that strike's own implied volatility held fixed. That is the sticky strike rule exactly: the volatility of the strike does not respond to the index, so no vega term enters the hedge. Under the other rules the option's own volatility moves with the index, and a vega term does enter. The number is not wrong. It answers one question, the one sticky strike asks. ### Why does it matter? Because the hedge ratio depends on it. The delta on a screen holds volatility fixed. If volatility moves with the index, the hedge that leaves the least variance is the delta plus the vega times the expected change of the option's own volatility for each point of index. The maths: the minimum variance delta Delta_mv = Delta + vega x (d vol_K / d S) Here vol_K is the volatility of the option itself. With vega in rupees for one volatility point, and d vol over d S in points of volatility for one point of index, the second term is in the same units as delta. Put d vol over d S equal to R minus one, times the skew slope, divided by one percent of the forward, and each rule gives its own hedge ratio: above the screen delta for sticky delta, equal to it for sticky strike and below it for local volatility. Read one more paragraph, 49 words Take the 23,200 call of the 30 Jun expiry on 10 Jun 2026. Its implied volatility was 15.14 percent, its delta 0.525, its vega 21.6 rupees a unit for one volatility point, and the forward 23,240.10. The next sections turn each rule into a delta for it. ### What do the models say? Local volatility, which fits the whole surface at one moment, fixes R near two. A well known study of market smiles found that real markets did not behave that way, and that hedges derived from the model could be unstable and could do worse than plain Black hedges. Stochastic volatility models can give other values, depending on the correlation of the index with its variance and on how fast variance reverts. None of these decides the matter. The data do, and only for the window they cover. Watch in the terminal: where are the tabs, markets and glossary? A short tutorial on sample data. Read one more paragraph, 81 words Why two? In a local volatility model the implied skew is about half the slope of the local volatility itself, and the at the money implied volatility follows the local volatility at the index. Move the index by one percent and the local volatility changes by its own slope, which is twice the implied skew. A stochastic volatility model gets its moves from another place: the correlation between the index and its variance, which makes volatility fall when the index rises. ## Worked example: 10 Jun to 16 Jun 2026 ### What did the at the money volatility do? The skew first. On 10 Jun 2026 the 22,550 put, three percent below the forward, priced 16.13 percent and the 23,950 call three percent above it 14.34. The log distance between the strikes was 0.0602, so the slope was −0.30 vol points for each one percent move of the strike. Then the move: the forward rose from 23,240.10 to 24,001.00, up 3.27 percent, and the at the money volatility of the same expiry went from 15.03 to 12.11 percent. | Points | 10 to 16 Jun | 1 to 2 Feb | | --- | --- | --- | | Days left | 20 to 14 | 23 to 22 | | Skew slope | −0.30 | −0.35 | | Forward, % | 3.27 | 1.16 | | Sticky delta | zero | zero | | Sticky strike | −0.97 | −0.40 | | Local vol | −1.95 | −0.80 | | Observed | −2.92 | −1.90 | Read 3 more paragraphs, 168 words The same exercise runs from Budget Day, Sunday 1 Feb 2026, to the next session, for the 24 Feb expiry. The skew, from wings a log distance of 0.0604 apart, was −0.35 per one percent. The forward rose from 24,853.50 to 25,141.80, up 1.16 percent and the at the money volatility fell from 15.04 to 13.14. In the table, the skew slope is in vol points for one percent of strike, the forward move is in percent, and the last three rows are changes of the at the money volatility in points. In both pairs the at the money volatility moved by more than any rule predicted: −2.92 points against −1.95 at most in June, and −1.90 against −0.80 in February. Neither pair can tell the rules apart. In June four sessions and six days of time passed, and in February the Budget event left the chain, so part of the fall was event variance going out. Two dates cannot settle a regime. Many days can. 2 terms this section turns on From the module glossary; every term is in the Learn glossary too. Source: Module 16.16, Skew dynamics: sticky strike against sticky delta. ### What do the largest days say? Take the six largest one day falls and the six largest one day rises of Nifty in the file, and set the change of the 30 day at the money volatility beside each. The file covers 300 sessions from 1 Jul 2025 to 15 Sep 2026. | Day | Move, % | ATM vol, pts | | --- | --- | --- | | 19 Mar 2026 | −3.26 | 4.40 | | 23 Mar 2026 | −2.60 | 6.46 | | 30 Mar 2026 | −2.14 | 1.76 | | 8 Jul 2026 | −2.12 | 2.92 | | 27 Mar 2026 | −2.09 | 4.12 | | 13 Mar 2026 | −2.06 | 2.01 | | 8 Apr 2026 | 3.78 | −6.17 | | 3 Feb 2026 | 2.55 | −1.22 | | 12 Jun 2026 | 1.99 | −1.12 | | 24 Mar 2026 | 1.78 | −4.11 | | 25 Mar 2026 | 1.72 | −1.24 | | 15 Apr 2026 | 1.63 | −2.20 | On all twelve big days the volatility moved against the index The six largest falls and six largest rises of Nifty in the file, each with the index move beside the date. Close to close, constant maturity at the money volatility from NSE option settlement prices. Source: NSE end of day data, computed by RupeeCase. As of 8 Jul 2026. The big days moved the volatility further than any rule Skew slope from the 10 Jun 2026 chain, the 30 Jun expiry. The last bar is a line through the origin on the twelve days of the table. Points of at the money volatility for a one percent rise of the index. Source: NSE end of day data, computed by RupeeCase. As of 8 Jul 2026. Read one more paragraph, 147 words On all twelve days the volatility moved against the index. A line through the origin fitted to the twelve has a slope of −1.39 vol points for each one percent move of the index. Against the skew slope of −0.30, that makes R about 4.7. It is above sticky strike, above local volatility and a long way above sticky delta. Day by day the response varied by a factor of several, so twelve points give a loose estimate. Read it as what it is: on the big days the index and its own volatility moved together far more than a fixed smile shape explains. Twelve extreme days from one file, mostly from the stress of March and April 2026, are not typical. On all the other days the slope was smaller. R is a descriptive number for a window, not a constant of the market. ### How much does the skew slope itself move? R is a ratio, so a slope that moves makes R move. The same recipe, the put and the call about three percent either side of the forward, gives these slopes, in vol points for each one percent move of the strike, on five dates. | Date, expiry | Slope | | --- | --- | | 10 Jun 2026, 30 Jun | −0.30 | | 16 Jun 2026, 30 Jun | −0.23 | | 1 Feb 2026, 24 Feb | −0.35 | | 2 Feb 2026, 24 Feb | −0.44 | | 11 Jun 2025, 26 Jun | −0.39 | Read 2 more paragraphs, 150 words Divide the twelve day slope by each and R ranges from 3.2 to 6.0. It stays above two whichever date I pick. A slope between two points three percent apart also hides curvature. On 10 Jun 2026 the 23,950 call priced 14.34 percent and the 25,000 call 16.16, so the right side of the smile turns up. One slope at the money does not describe that, which is what the surface models of the [previous module](/learn/path-16/module-16-15-volatility-surface-models) are for. The 30 day measure is a constant maturity interpolation between expiries. As a day passes, weight moves from one expiry to the next, so the measure shifts a little even when no quote changes. On the days in the table that effect is small beside moves of several points. On quiet days it is not, which is one more reason to read the twelve days and not the quiet ones. ### What does each rule do to the hedge? Turn the three rules and the twelve day slope into a delta for the 23,200 call. The option's own volatility moves by R minus one skew slopes for a one percent rise, which is −1.10 points on the twelve day slope. The delta is the screen delta plus vega times that move, over one percent of the forward. The more volatility moves with the index, the smaller the hedge Delta of the 23,200 call of the 30 Jun 2026 expiry on 10 Jun 2026, adjusted by vega times the change of the option's own volatility per one percent of the forward under each rule. Illustrative hedge ratios, not instructions. Source: NSE end of day data, computed by RupeeCase. As of 8 Jul 2026. Appendix: the slope on the twelve days slope = sum of (r x d) / sum of (r x r) Here r is the index move in percent and d the change of the 30 day at the money volatility in points, for each of the twelve days in the table. The sum of products is −95.5 and the sum of squares 68.6, which gives −1.39. A line through the origin is the right shape here, because a day with no index move has no reason to carry a systematic volatility move. The delta hedging simulator under this page hedges an option at its screen delta along a seeded path. Use it to see how the hedge error builds when the volatility assumed differs from the volatility realised. Read one more paragraph, 119 words The ratios are 0.553 for sticky delta, 0.525 for sticky strike, 0.497 for local volatility and 0.423 for the twelve day slope. For a position of 100 option lots the screen delta, which is sticky strike, needs 52.5 lots of the future, sticky delta 55.3, local volatility 49.7 and the twelve day slope 42.3. The screen delta and the twelve day figure are 10.2 lots apart, and the two extremes 12.9. That spread is the size of the question. The future's lot and the option's lot are both 65 units, so lots map one to one. I take it as a measure of how much the assumption matters. I do not take it as an instruction. ## The common mistake The claim: Implied volatility stays put when the index moves, so the delta on the screen is the hedge ratio. It did not stay put on any of those twelve big days. The screen delta holds each strike's volatility fixed, which is exactly the sticky strike rule, and on those days the at the money volatility moved about 4.7 times the skew slope where sticky strike allows one. On the twelve day slope the hedge ratio is 0.423 against the screen's 0.525. The honest statement is that the screen delta is the Black delta, one rule's answer, and that the hedge ratio that leaves the least variance depends on how volatility moves with the index in the window you are hedging through. I measure that window before I trust either number. ## Key takeaways * Sticky delta predicts zero change, sticky strike one skew slope, local vol two. * The screen delta holds volatility fixed, so it is one rule, not the truth. * On twelve large days the volatility moved against the index every time. * The move was larger than any of the three rules predicts. * One pair of dates mixes time and events, so it cannot pick a rule. * Measure the regime in the window you hedge through. Two terms to hold on to from 16.16 From the module glossary. Source: Module 16.16, Skew dynamics: sticky strike against sticky delta. **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 Skew dynamics: sticky strike against sticky delta cover?Three rules say how the smile moves when the index does, and each gives a different hedge ratio. On the largest days in an NSE file, volatility moved further than any of them. It works through The idea, plainly, Worked example: 10 Jun to 16 Jun 2026, The common mistake, Key takeaways. It is module 16.16 of Path 16, Volatility, about 13 minutes of reading, free. TTry it ## Delta hedge simulator, on the same example. Seeded paths, hedge count from 1 to 160, and hedged P&L against realised and implied vol. A model, not market data. Dated NSE end of day data, nothing live, no orders. **Delta hedge simulator**, Nifty, 10 Jun 2026, 30 Jun 2026 expiry. [Open the delta hedge simulator](/learn/tools/delta-hedge-simulator?chain=NIFTY_2026-06-10_2026-06-30) 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 16.16 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. 16Path test ## 30 questions. 21 correct earns the certificate. 30 questions drawn from the 240 in the banks of the 16 live modules of Path 16, 16.1 to 16.16, a fresh set each attempt, with no timer. Your score stays on this device. * **30**questions * **21**to pass (70%) * **16**modules covered Start the testOnly the path test decides the path certificate. The module quizzes are optional: pass them too for each module's own certificate and a tick on every stop. Passed ### Certificate ID Download PNGPrint or save as PDF[Check the ID](/learn/certificates/verify)Retake the test [All your certificates, and the master certificate](/learn/certificates) CCheck yourself ## Path 16 so far. Your quick check results on this device, stop by stop. Stop 16 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) 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#quiz) 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. * [Free module 14.17Second order Greeks: vanna, volga, charm, speed and colour13 min read](/learn/path-14/module-14-17-second-order-greeks) * [ToolOption payoff builderLegs on a dated Nifty chain, breakevens and charges](/learn/tools/payoff-builder) * [Free module 16.8Skew measures: risk reversal, butterfly and slope10 min read](/learn/path-16/module-16-8-skew-measures) * [Free module 14.15Dynamic delta hedging and the P&L identity10 min read](/learn/path-14/module-14-15-delta-hedging) * [Free module 16.15Volatility surface models: SVI, SABR, local and stochastic vol13 min read](/learn/path-16/module-16-15-volatility-surface-models) * [Path 16Volatility16 modules live, 0 coming next](/learn/path-16/) 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? 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[Derman (1999), Regimes of volatility, Risk, April 1999](https://emanuelderman.com/regimes-of-volatility-risk-april-1999/) Paper 6. [Hagan, Kumar, Lesniewski and Woodward (2002), Managing smile risk, Wilmott Magazine](https://www.deriscope.com/docs/Hagan_2002.pdf) Paper GTerms ## 4 terms from this module. Sticky strikeA rule in which the implied volatility at each strike stays where it was when the index moves. The at the money volatility then changes by the skew slope. Sticky deltaA rule in which the whole smile slides with the index, so the at the money volatility does not change. It is close to saying the smile is fixed in moneyness. Skew stickiness ratioThe change of the at the money volatility with the log of the index, over the slope of the smile in log strike. Zero for sticky delta, one for sticky strike, about two for local volatility. Minimum variance deltaThe delta plus vega times the expected change of the option's own volatility for each point of index. It leaves the least variance in a hedged position over a small move. [All 230 terms in the glossary](/learn/glossary) [Related module16.8 Skew measures: risk reversal, butterfly and slope10 min, Advanced](/learn/path-16/module-16-8-skew-measures)[Related module14.15 Dynamic delta hedging and the P&L identity10 min, Advanced](/learn/path-14/module-14-15-delta-hedging)[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.