---
title: "Variance Risk Premium in Nifty: What Selling Volatility Paid"
description: "The variance risk premium measured on Nifty: implied against the realised volatility that followed, fourteen windows and the tail in the average."
source_url: "https://www.rupeecase.com/learn/path-16/module-16-13-variance-risk-premium"
---

[Path 16 13 of 16](/learn/path-16/)The variance risk premium in Nifty[Next 16.14](/learn/path-16/module-16-14-variance-swaps)

Friday 27 Feb 2026. The 30 day at the money implied volatility of the Nifty closed at 11.98. Twenty sessions later, on 1 Apr 2026, the realised volatility of the sessions that followed was 26.17. Someone who sold that volatility gave back 14.19 vol points. Monday 30 Mar 2026 is the mirror: implied read 29.22, the next twenty sessions realised 18.81, and the seller kept 10.41. Those are the worst and the best start dates in the file, a month apart. Selling volatility is paid on average, and the average is where the trouble starts. This lesson measures what the Nifty file paid, what the bad window cost, and why "premium" is a price for risk. Dated NSE data, nothing live, nothing a view on the next month.

3By the end you can

1. Define the variance risk premium and measure it on Nifty with implied and realised volatility.
2. Count windows that do not overlap and read gains and losses in vol points and in variance.
3. Say why a positive average is the price of tail risk and not a free lunch.

Watch in the terminal: what are the four lead figures? A short tutorial on sample data.

## The idea, plainly

### What is the variance risk premium?

The variance risk premium is implied variance less the variance that follows. If options price a month at one volatility and the index then runs at a lower one, the buyer paid for more variance than arrived and the seller kept the gap. Research on equity indices finds the gap positive on average. It is the price of insurance: buyers pay for protection against volatility jumping, and sellers are paid for standing in the way.

Sellers are paid in calm months and pay in the rest

A sketch of the flow of money, not a market number. Source: RupeeCase, after the research named in the sources. As of 17 Oct 2026.
Read one more paragraph, 59 words
Why variance and not volatility? A variance swap, the contract this idea comes from, pays the realised variance against a fixed price, so the premium is the fixed price less the variance that came. India has no listed variance swap. The theory still sits under India VIX, whose formula is a variance one, and under every short option position.

### Why would sellers be paid at all?

Selling variance has a losing tail. Volatility jumps when markets fall, and that is when other losses pile up, margin rises and protection is most wanted. A premium that exists for bearing such a state is a risk premium, not a free lunch. A positive average is the evidence that sellers were paid. The tail is the reason they were.

Read one more paragraph, 58 words
That framing matters in India. SEBI's August 2026 study of individual F&O traders in FY26 found that 87.7% of them lost money. The other side of those trades is not charity. A seller who borrows the average without the capital and the tail management behind it is not collecting the same premium as a firm that has both.

### How do I measure it on the Nifty?

Take the 30 day at the money implied volatility on a date. Then take the realised volatility of the next twenty sessions, close to close and annualised the same way. The gap is implied less realised, in vol points. In variance terms, square both first. Thirty calendar days and twenty sessions are not identical, but they are close enough for this reading.

Implied peaked on 30 Mar and realised peaked on 8 Apr

Percent a year. To see what the implied line of any date was pricing, slide the realised line twenty sessions back. Marks: the worst and best start dates in the file. Source: NSE end of day data, computed by RupeeCase. As of 30 Jun 2026.
The maths: the premium in variance and in vol points
In variance:
VRP = IV^2-RV^2
In vol points:
gap = IV-RV
RV over the next 20 sessions
Variance swap, seller:
  N * (K^2-RV^2)
  K: the strike in vol
India VIX, variance formula:
s^2 = (2/T) * sum(dK/K^2
      * e^(rT) * Q(K))
   -(1/T)*(F/K0-1)^2
Vol points understate the asymmetry. A seller of variance is paid the squared gap, so a month of high realised volatility costs more than an equal month of low realised volatility earns. That curvature is why the next sections count in both units.

Read 2 more paragraphs, 215 words
Read the chart by sliding the realised line twenty sessions back. The implied level on 27 Feb 2026 was priced against the realised line of early April, far higher at 26.17. The implied level on 30 Mar 2026, 29.22, was priced against the realised line of late April, far lower at 18.81. Implied peaked at 29.22 and realised peaked later at 29.46, because a trailing window forgets slowly. That lag is why I always say which window a realised figure is.

I keep both numbers on one page when I look at this: the implied level that was sold and the realised level that arrived, with the date of each. A premium quoted without its dates is a slogan. Two choices decide the answer. The first is the estimator: realised volatility from closes, from ranges or from NSE's weighted file gives different levels for the same days, so use one estimator on both sides of any comparison. The second is the implied side: the at the money figure and India VIX are not the same measure, because VIX weighs the whole strip of out of the money strikes. A premium measured against VIX is usually larger than one measured against the at the money option, for the same index and days.

## Worked example: fourteen months, one gap each

### What did fourteen non overlapping windows show?

I took every twentieth session in the file, fourteen start dates from 1 Jul 2025, so that no two windows share a return. Implied was above what followed in ten of the fourteen. The average gap was 0.89 vol points. The four windows where realised won were 3.28, 8.85, 2.76 and 0.26 points, and the second of them, from 19 Feb 2026, is the largest gap among the fourteen.

Ten gains, four losses, and one loss larger than any gain

Positive means the seller of the 30 day at the money option kept the gap. Each start date is twenty sessions after the one above, so no two windows share a return. Source: NSE end of day data, computed by RupeeCase. As of 15 Sep 2026.
All fourteen windows, with the numbers

| Start | Implied | Next 20 | Gap |
| --- | --- | --- | --- |
| 1 Jul 2025 | 11.98 | 7.39 | 4.59 |
| 29 Jul 2025 | 10.72 | 9.93 | 0.79 |
| 28 Aug 2025 | 10.96 | 6.63 | 4.33 |
| 25 Sep 2025 | 10.01 | 7.98 | 2.03 |
| 27 Oct 2025 | 10.94 | 7.12 | 3.82 |
| 25 Nov 2025 | 10.24 | 8.37 | 1.87 |
| 23 Dec 2025 | 8.76 | 8.38 | 0.38 |
| 22 Jan 2026 | 12.58 | 15.86 | −3.28 |
| 19 Feb 2026 | 11.55 | 20.40 | −8.85 |
| 20 Mar 2026 | 22.23 | 24.99 | −2.76 |
| 23 Apr 2026 | 17.46 | 12.93 | 4.53 |
| 22 May 2026 | 15.71 | 12.81 | 2.90 |
| 22 Jun 2026 | 12.01 | 12.27 | −0.26 |
| 21 Jul 2026 | 12.08 | 9.68 | 2.40 |

Read 2 more paragraphs, 177 words
Counting every start date, not just every twentieth, implied was above what followed on most of them. Overlap makes that count look more certain than it is, which is why I count windows that do not overlap. The whole file points the same way: the mean 30 day implied volatility is 12.42 and the mean twenty session realised volatility is 11.53, a difference of 0.89 vol points. India VIX, which includes the wings of the smile, averages higher still at 13.49.

The losses did not arrive alone. The windows of 22 Jan, 19 Feb and 20 Mar 2026 ran back to back, at 3.28, 8.85 and 2.76 points against the seller. Volatility clusters, as the [forecasting lesson](/learn/path-16/module-16-12-forecasting-volatility) shows, so a seller who is wrong once is often wrong again before a calm month returns. Implied was also not a safe sign. The highest implied level of the file, 29.22 on 30 Mar 2026, paid the seller 10.41 points. The window of 20 Mar 2026, with implied at 22.23, also high, cost 2.76.

### What does the average hide?

In vol points the ten gains sum to 27.64 and the four losses to 15.15, so the losses give back 55% of the gains. Square the numbers, the way a variance seller is paid, and the losses give back 85%. A month of high realised volatility costs more than an equal month of low realised volatility earns.

The worst window is roughly ten times the average window

Thousand rupees for one lot of 65 units, before charges. The straddle on the 30 Jun 2026 expiry has a vega of Rs 2,813 a lot for each vol point. This is a size, not a record: the vega of each past date differed. Source: NSE end of day data, computed by RupeeCase. As of 10 Jun 2026.
Read one more paragraph, 92 words
To see the size in rupees, price each gap at the [vega](/learn/glossary#vega) of one lot of the at the money straddle on the 10 Jun 2026 chain, Rs 2,813 for each vol point. The average window is worth 2.5 thousand rupees, the best 12.9 thousand, and the worst lost 24.9 thousand. The vega of each past date was different, so read the bars as a size and not as a record. The point stands: a loss ten times the average is not an outlier, it is the other side of the premium.

### What does a seller still pay on top?

Costs and capital. The 10 Jun 2026 straddle premium for one lot was Rs 41,841, and STT at 0.15 percent on the premium sold came to Rs 63, plus exchange charges, GST and brokerage. Margin stays blocked for the month. Against an average gap worth 2.5 thousand rupees a lot, the charges are small and the tail is not.

Read 3 more paragraphs, 188 words
Nothing here says the premium will be positive in the next window. Four windows in fourteen show it does not have to be, and the file is only about fifteen months long with one stress episode in it. A longer record would hold more episodes, and a gentler or harsher average. I read the file as a worked example of the shape, not an estimate of the size.

A seller does not have to carry the whole tail. A bought option further out caps the loss, as in the [credit spread lesson](/learn/path-17/module-17-6-credit-spreads), and the [lesson on hedging an option selling book](/learn/path-17/module-17-21-hedging-an-option-selling-book) goes through the trade offs. Capping the tail gives up part of the premium. Whether that is worth it depends on the size of the account, and nothing in this file says which way it falls.

The [margins lesson](/learn/path-12/module-12-5-fo-margins-explained) shows what is blocked. The bigger cost is the risk of the window that returns the year's gains in a month, which a retail account rarely has capital to sit through. A seller who sizes by the average has sized for a month that does not exist.

## The common mistake

The claim: Implied volatility beats realised volatility on average, so selling options pays. The average gap in this file is 0.89 vol points over fourteen windows and it is real. It is also small next to the worst window, −8.85 points from 19 Feb 2026, and in variance terms the four losing windows hand back 85% of the gains. A positive average says sellers were paid for risk. It does not say the next month pays, and it does not say a retail account can carry the tail. Count the windows, size by the worst one, and treat the premium as the price of a risk you hold.

## Key takeaways

* The premium is implied variance less the variance that followed.
* Implied sat above what followed in ten of fourteen non overlapping windows.
* The average gap is small beside the worst window.
* In variance terms the four losing windows hand back most of the gains.
* A positive average is the price of tail risk, not income.
* Count non overlapping windows, never every start date.

The term this section turns on: Non overlapping windows

From the module glossary; every term is in the Learn glossary too. Source: Module 16.13, The variance risk premium in Nifty.

Watch in the terminal: which numbers am I looking at? 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 The variance risk premium in Nifty cover?Implied volatility sat above what followed in ten of fourteen windows, and the four others gave most of it back. It works through The idea, plainly, Worked example: fourteen months, one gap each, The common mistake, Key takeaways. It is module 16.13 of Path 16, Volatility, about 10 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=india-vix)

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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 16.13

Every answer is explained when you check; a retake is always free. Source: RupeeCase Learn quiz rules (70% to pass).

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CCheck yourself

## Path 16 so far.
Your quick check results on this device, stop by stop.

Stop 13 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#quiz)
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 16.4IV rank and IV percentile, done properly9 min read](/learn/path-16/module-16-4-iv-rank-and-iv-percentile)
* [Free module 16.5Measuring realised volatility: four estimators9 min read](/learn/path-16/module-16-5-realised-volatility-estimators)
* [Free module 16.12Forecasting volatility: EWMA, GARCH and HAR11 min read](/learn/path-16/module-16-12-forecasting-volatility)
* [Free module 16.11India VIX rebuilt step by step10 min read](/learn/path-16/module-16-11-india-vix-rebuilt)

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SSources

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

The publisher of each source listed below. Source: Module 16.13 source list.
1. [NSE, India VIX: index page and methodology](https://www.nseindia.com/static/products-services/indices-indiavix-index) NSE
2. [NSE, India VIX white paper](https://nsearchives.nseindia.com/web/sites/default/files/inline-files/white_paper_IndiaVIX.pdf) NSE
3. [NSE, derivatives end of day reports (the bhavcopy these figures are built from)](https://www.nseindia.com/all-reports-derivatives) NSE
4. [SEBI, Profitability of individual traders in the equity derivatives segment, FY25 to FY26 (August 2026)](https://www.sebi.gov.in/sebi_data/attachdocs/aug-2026/1787233506209.pdf) SEBI
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. [Carr, P. and Wu, L. (2009), Variance Risk Premiums, Review of Financial Studies](https://ideas.repec.org/a/oup/rfinst/v22y2009i3p1311-1341.html) Paper

GTerms

## 5 terms from this module.
Variance risk premiumImplied variance less the variance that followed. Positive on average for equity indices, it is what sellers are paid for bearing the risk of a volatility jump.
Realised volatilityVolatility measured on returns that have happened. Here it is the close to close figure of the twenty sessions after a start date, annualised.
Variance swapA contract that pays the realised variance against a fixed strike. It is the textbook form of the premium and is not listed in India.
Non overlapping windowsStart dates a full window apart, so no two share a return. Counting them gives a fairer picture than counting every start date.
[Vega](/learn/glossary#vega)The rupees a position gains or loses for a one point change in implied volatility, other things equal.
[All 230 terms in the glossary](/learn/glossary)

[Question people askIs option selling paid for taking risk (the variance risk premium)?A short answer with a dated chart](/learn/questions/variance-risk-premium)[Related module16.1 Volatility: historical against implied7 min, Beginner](/learn/path-16/module-16-1-historical-vs-implied-volatility)[Related module16.4 IV rank and IV percentile, done properly9 min, Intermediate](/learn/path-16/module-16-4-iv-rank-and-iv-percentile)[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.