---
title: "Term structure and event variance in Nifty options"
description: "Why weekly and monthly implied volatility differ, and how to read one event day's variance from NSE option prices, on dated Nifty chains and the Budget of 2026."
source_url: "https://www.rupeecase.com/learn/path-16/module-16-9-term-structure-and-event-variance"
---

[Path 16 9 of 16](/learn/path-16/)Term structure and event variance[Next 16.10](/learn/path-16/module-16-10-forward-volatility)

Monday 7 Apr 2025, a stress session, with India VIX at 22.79. Four Nifty expiries on the same evening, one index. The weekly with 2 days left priced the at the money option at 46.68%. The weekly with 10 days, 28.22%. The monthly with 17 days, 25.04%. The monthly with 52 days, 19.71%. Four vols for one index, and none of them is wrong. Each is an average over its own life, and the early days were far more nervous than the late ones. This module reads that shape, shows how to take the variance of one event day out of it, and says how much of the answer to trust. Every figure comes from NSE end of day files, dated, so nothing here is live and nothing is a view on the next event.

3By the end you can

1. Read the term structure of a dated Nifty chain and name its shape.
2. Compare expiries in total variance, and say why one volatility per expiry hides where the movement sits.
3. Estimate the variance one event day carried from two evenings of prices, and state how far to trust the answer.

Watch in the terminal: how do I set what each page shows? A short tutorial on sample data.

## The idea, plainly

### What is a term structure?

The term structure is the at the money implied volatility of each expiry, laid out by days to expiry. Three shapes cover most of it. Upward, when the market is calm and a long expiry carries more uncertainty than a short one. Flat. Inverted, when the near days are the nervous ones. Three dated chains show all three.

* **11 Jun 2025, upward.** 12.02% at 8 days, 12.83% at 15 days, 13.46% at 50 days.

* **10 Jun 2026, flat.** 14.61% at 6 days, 15.03% at 20 days, 14.77% at 48 days.

* **7 Apr 2025, inverted.** 28.22% at 10 days, 25.04% at 17 days, 19.71% at 52 days.

On the stress day the near expiries priced the most vol

At the money implied volatility by expiry, Nifty, close of 7 Apr 2025. Dashed steps are the forward volatility between neighbouring expiries. At the money is the parity forward for weeklies and the future for monthlies. Source: NSE end of day data, computed by RupeeCase. As of 7 Apr 2025.
Read 2 more paragraphs, 140 words
Why does the shape change? In a calm week the near expiry is the quietest part of the curve and a longer expiry carries more unknowns, so the curve slopes up. After a sharp fall the near days are the nervous ones and the curve turns over. Volatility tends to drift back toward its usual level, so the far end moves least. Those are stories about the cause. The chain shows the shape, not the reason for it.

The chart also carries two weeklies between the monthlies, 30 Apr and 8 May. The 8 May one traded in only a few strikes, so the low dashed step into it is noise and not a forward volatility. A thin expiry is a reason to read a term structure with care, and the four expiries I use here are the liquid ones.

### Why compare variance, not volatility?

Volatility is a rate. Variance, the volatility squared times the time in years, is what adds up. On 10 Jun 2026 the 48 day expiry carried a lower vol than the 20 day one, 14.77% against 15.03%. Yet it held 2.3 times the total variance, 0.002868 against 0.001238, because it is longer.

Total variance =
implied vol^2 x T

Read one more paragraph, 55 words
Two things follow. Total variance has to rise with the expiry, or a calendar spread at the money would be priced below zero, money for a position that cannot lose at the first expiry. And the variance between two expiries is the later total less the earlier one. The [forward volatility module](/learn/path-16/module-16-10-forward-volatility) does that arithmetic.

### What does each expiry say about daily movement?

Divide an implied vol by the square root of the days in a year, 19.105 for 365, and you have the average one day move that expiry prices over its whole life. On 7 Apr 2025 the 2 day expiry averaged 2.44 percent a day, the 10 day expiry 1.48, the 17 day expiry 1.31 and the 52 day expiry 1.03 percent.

The nearer the expiry, the bigger the daily move it priced

At the money implied volatility divided by the square root of the days in a year. Nifty, close of 7 Apr 2025. Implied volatility computed by RupeeCase from NSE settlement prices. Source: NSE end of day data, computed by RupeeCase. As of 7 Apr 2025.
Read one more paragraph, 98 words
The long expiry's average includes the nervous first days, so the days at its far end are calmer still. The forward variance between the 17 and 52 day expiries, annualised, is 0.02725, a forward volatility of 16.51% for those days alone. It matters because a position held for two weeks and one held for two months sit on different parts of this curve. A hedge pays the vol of its own expiry, not the headline number, and a calendar spread is a position on the gap between two points on it, which the [calendar spreads module](/learn/path-17/module-17-9-option-calendar-spreads) prices.

### What is event variance?

One known day, a results day, the Budget, an RBI policy day, adds a lump of variance to every expiry that holds it. The [India VIX module](/learn/path-16/module-16-2-india-vix-explained) shows the index carrying it into the day.

Total variance =
base variance x time
+ event variance

The maths: pulling one day out of two evenings
Take one expiry on two evenings. Let w1 be its total variance on the first evening and w2 on the second, one day later and one day shorter. If the ordinary days did not reprice, then:

Variance of the day =
w1 - w2
= vol1^2 x T1 - vol2^2 x T2

The square root is the implied standard deviation of that day's return. Multiply by the index and you have a move in points. The engine behind the forward volatility tool uses the same identity with one day, 1/365 of a year, taken out of the longer expiry.

The assumption in bold is that the ordinary days did not reprice. They usually do. The surface level moves between two evenings, so the identity attributes the whole change to one day. That is why I compute it on several expiries and read the spread of answers as the error bar.

## Worked example: the day that left after the Budget

### What did the evening of 1 Feb 2026 price for the next session?

The Union Budget was read out on Sunday 1 Feb 2026 in a special session, and Nifty closed at 24,825.45. Monday 2 Feb was the first full session after it. At the close of 1 Feb every Nifty expiry that held Monday still carried its variance. By the close of 2 Feb that day was gone. Take the same expiry on both evenings, subtract total variance, and one day is left.

| Expiry | IV 1 Feb | IV 2 Feb | Move, points |
| --- | --- | --- | --- |
| 3 Feb weekly | 22.01% | 18.02% | 330 |
| 10 Feb weekly | 16.72% | 14.25% | 388 |
| 24 Feb monthly | 15.04% | 13.14% | 486 |
| 30 Mar monthly | 12.67% | 12.28% | 344 |

The term this section turns on: Total variance

From the module glossary; every term is in the Learn glossary too. Source: Module 16.9, Term structure and event variance.
The steps, once, for each expiry:

1. Read the implied volatility at the money on each evening.

2. Turn each into total variance, vol squared times days over a year.

3. Subtract the second evening from the first.

4. Take the square root and multiply by the index.

One day, four answers: the expiries disagree by half

Variance that left between the closes of 1 and 2 Feb 2026, one day shorter the second evening, square rooted and applied to the index. Implied volatility computed by RupeeCase from NSE settlement prices. Source: NSE end of day data, computed by RupeeCase. As of 2 Feb 2026.
Read 3 more paragraphs, 202 words
The variance that left was 0.000176 on the 3 Feb weekly, 0.000244 on the 10 Feb weekly, 0.000384 on the 24 Feb monthly and 0.000192 on the 30 Mar monthly. Its square root, as a share of the index, is 1.33%, 1.56%, 1.96% and 1.39%. Times Nifty at 24825.45, those are the moves in the table, in points.

One day, four answers. The highest is 1.5 times the lowest. The reason is in the assumption: the ordinary days did not stay put. The 24 Feb monthly's implied volatility fell 1.90 points between the two evenings, the 3 Feb weekly's 3.99. If one lump day were all that left, every expiry would give about the same move. They do not, and the 23 day monthly gives the largest, so part of each answer is the whole surface settling.

Nifty then moved 262.95 points, 1.06%, from the close of 1 Feb to the close of 2 Feb. That is below all four numbers. One day says nothing about whether the market priced it too high or too low, and the [VIX module](/learn/path-16/module-16-2-india-vix-explained) and the [events and IV crush module](/learn/path-16/module-16-6-iv-crush-around-events) read the same two evenings from the VIX side.

### How good is the estimate?

Three things would make it cleaner. An evening before the event and one after, for the same expiry, which is what I did. A base vol taken from an expiry the event does not touch. And expiries of similar length, so the tick size noise is similar. Even then it stays a reading under a model, with the price of bearing the risk inside it. The [variance risk premium module](/learn/path-16/module-16-13-variance-risk-premium) asks how large that price is. What the estimate can do is size one day against the ordinary days around it, and show where the disagreement sits.

### Why do weekly and monthly implied volatilities differ?

Mostly because they are different points on one curve. A weekly is a short expiry, a monthly is a longer one, and the term structure above is the reason their vols differ. Three more things add to it.

* **The last day.** The contract with one day left priced more vol than the next expiry on all three dates: 14.11 against 12.02 percent on 11 Jun 2025, 18.02 against 14.25 on 2 Feb 2026 and 15.94 against 13.04 on 29 Jun 2026. Its prices sit near the tick size, so its implied vol is noisy. I read it as a hint, not a number.

* **The forward.** In these files the at the money point of a monthly is read at the future and of a weekly at the parity forward. The two forwards differ a little.

* **Who trades.** After SEBI's circular of 1 Oct 2024, NSE lists weekly index options on Nifty alone. The 2025 weeklies here mostly expire on Thursdays and the 2026 ones on Tuesdays. Liquidity differs by expiry, and so does the quote quality behind each vol.

The one day contract priced more vol than the next expiry each time

Nifty at the money implied volatility, percent a year. The one day contract is a weekly on 11 Jun 2025 and 2 Feb 2026 and the monthly on 29 Jun 2026. Computed by RupeeCase from NSE settlement prices. Source: NSE end of day data, computed by RupeeCase. As of 29 Jun 2026.

## The common mistake

The claim: The option chain tells you the exact move the market expects on the day. It tells you prices. The move is an inference, and it stands on three assumptions: one lump day, ordinary days unchanged, and no price for bearing the risk. On the evening of 1 Feb 2026 four expiries gave four moves for Monday, from 330 to 486 points. Nifty moved 262.95. I quote the range of estimates, never one of them, and I keep the day count and the date beside it.

## Key takeaways

* Compare expiries in total variance, not in volatility.
* A stress day inverts the term structure: the near days carry the nerves.
* An event day adds a lump of variance to every expiry that holds it.
* Four expiries priced the same day four different ways.
* The one day contract priced more vol than the next expiry on all three dates.
* An implied event move is a model reading, not a forecast.

Watch in the terminal: why do the hero, key figure and XIRR differ? 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 Term structure and event variance cover?Every expiry carries its own implied volatility. Read as total variance, the set of them shows where the market expects the movement, and what one day was priced at. It works through The idea, plainly, Worked example: the day that left after the Budget, The common mistake, Key takeaways. It is module 16.9 of Path 16, Volatility, about 10 minutes of reading, free.

TTry it

## Forward vol and event move, on the same example.
The volatility between two expiries and the move an event prices in. Dated NSE end of day data, nothing live, no orders.

**Forward vol and event move**, Nifty, 7 Apr 2025, 24 Apr 2025 expiry.

[Open the forward vol and event move](/learn/tools/forward-vol?chain=NIFTY_2025-04-07_2025-04-24)

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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 16.9

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 9 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#quiz)
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 16.6Events and IV crush: results, Budget, RBI and elections9 min read](/learn/path-16/module-16-6-iv-crush-around-events)
* [Free module 16.7Smile and skew: a first look at Nifty and stocks8 min read](/learn/path-16/module-16-7-volatility-smile-and-skew)
* [Free module 16.1Volatility: historical against implied7 min read](/learn/path-16/module-16-1-historical-vs-implied-volatility)
* [Free module 16.8Skew measures: risk reversal, butterfly and slope10 min read](/learn/path-16/module-16-8-skew-measures)
* [Free module 16.10Forward volatility from two expiries10 min read](/learn/path-16/module-16-10-forward-volatility)

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SSources

## Go to the source.
6 sources, NSE the most cited

The publisher of each source listed below. Source: Module 16.9 source list.
1. [NSE, equity derivatives contract specifications: expiry cycles, lot sizes and cash settlement](https://www.nseindia.com/static/products-services/equity-derivatives-contract-specifications) NSE
2. [NSE, derivatives end of day reports: the F&O bhavcopy these figures are built from](https://www.nseindia.com/all-reports-derivatives) NSE
3. [NSE, India VIX: index page and methodology](https://www.nseindia.com/static/products-services/indices-indiavix-index) NSE
4. [SEBI, circular of 1 Oct 2024, Measures to strengthen the equity index derivatives framework (one weekly expiry per exchange)](https://www.sebi.gov.in/legal/circulars/oct-2024/measures-to-strengthen-equity-index-derivatives-framework-for-increased-investor-protection-and-market-stability_87208.html) SEBI
5. [NISM, Series VIII Equity Derivatives, test objectives (option pricing, volatility and option strategies)](https://www.nism.ac.in/revamp-nism/test-objectives-revised-nism-series-viii-equity-derivatives-certification-examination/) NISM
6. Gatheral, J. (2006), The Volatility Surface: A Practitioner's Guide, Wiley Book

GTerms

## 5 terms from this module.
Term structureThe at the money implied volatility of each expiry, laid out by days to expiry. Upward, flat or inverted.
Total varianceImplied volatility squared times time in years. It is the quantity that adds up across days, so expiries are compared in it.
Event varianceThe extra variance one known day, such as the Budget or a policy day, adds to every expiry that holds it.
Inverted term structureA shape in which near expiries carry a higher implied volatility than far ones, common after a sharp fall.
Expiry day effectThe higher implied volatility the contract with one day left often shows against the next expiry.
[All 230 terms in the glossary](/learn/glossary)

[Question people askTerm structure: why do weekly and monthly IVs differ?A short answer with a dated chart](/learn/questions/weekly-vs-monthly-iv)[Related module16.6 Events and IV crush: results, Budget, RBI and elections9 min, Intermediate](/learn/path-16/module-16-6-iv-crush-around-events)[Related module16.7 Smile and skew: a first look at Nifty and stocks8 min, Intermediate](/learn/path-16/module-16-7-volatility-smile-and-skew)[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.