Fin Maverick
Foundations VocabularyAccounting & ReportingEconomics & MacroQuant Methods & ProgrammingBusiness & Company AnalysisCorporate Finance & ValuationBehavioural Finance
Banking & Market InfrastructureFixed Income & RatesDerivatives & Structured ProductsPublic EquitiesTransactions & DealsPortfolio ConstructionFunds & AMCs
Private Markets & AlternativesRisk, Treasury & ControlAI & Digital FinanceStochastic Calculus & PricingWealth & Personal FinanceIndian Markets & RegulationProfessional Practice
CalculatorComparison
Frameworks
Explore Bootcamps
Equity ResearchPortfolio ManagementMutual Fund MasteryInvestment Banking Analyst
Private Equity AnalystQuant & Hedge Fund AnalystBreaking Into VCFinancial Analyst Program
Risk Management ProgramPrivate Wealth ManagementDebt Capital MarketsDerivatives Foundation
Explore Free Courses

Equity Research6

Writing an Investment ThesisBuilding a Discounted Cash FlowReading an Annual Report FastReading a Sector Before a CompanySpotting Quality of Earnings Red FlagsBuilding a Revenue Forecast From Drivers

Portfolio Management3

Rebalancing: When, Why and What It CostsStrategic and Tactical Asset AllocationMeasuring Risk in a Portfolio

Mutual Fund Mastery3

Comparing Funds Without Being FooledHow a NAV Is Struck and Which Day You GetReading a Fund Factsheet Properly

Derivatives Unlocked4

Hedging a Real ExposureThe Greeks, PracticallyFutures, the Basis and What Moves ItReading an Option Payoff

AI For Finance2

Retrieval and Grounding for FinanceDocument Extraction in Finance

Breaking Into Quants4

Backtesting a StrategyHypothesis TestingCleaning Financial DataRegression for Finance

Breaking Into VC3

Sizing a MarketReading a Term Sheet as a FounderHow a Venture Round Actually Works

Financial Analyst Program4

Common Size and Trend AnalysisReading a Cash Flow StatementRatio Analysis That Says SomethingBuilding a Working Capital Schedule

Risk Management Program2

Credit Exposure and How It Is ReducedValue at Risk and What It Hides

Investment Banking Analyst3

Precedent Transactions and Why They DifferReading a Term Sheet StructurallyBuilding a Comparable Companies Table

Private Wealth Management3

Tax Aware Portfolio DecisionsBuilding a Client Risk ProfileGoal Based Planning Arithmetic

Debt Capital Markets3

Analysing an Issuer's CreditDuration and What It Does Not Tell YouBond Pricing and Yield Mechanics

Private Equity Analyst2

Fund Waterfalls and CarryThe LBO in Structure

Hedge Funds Analyst2

Short Selling MechanicsLong Short Mechanics
QuarksCourses
Explore Interview Preparation
Investment BankingEquity ResearchVenture CapitalistPrivate EquityHedge Funds
QuantFinancial AnalysisPrivate Wealth ManagementDebt Capital MarketsRisk Management
Derivatives FoundationPortfolio ManagementMutual Fund Mastery
PartnershipsShowdown
Log inSign up
Interview tracksAll
1Investment Banking
Question bankPuzzlesCase studies
2Equity Research
Question bankPuzzlesCase studies
3Venture Capital
Question bankPuzzlesCase studies
4Private Equity
Question bankPuzzlesCase studies
5Hedge Funds
Question bankPuzzlesCase studies
6Quant
Question bankPuzzlesCase studies
7Financial Analysis
Question bankPuzzlesCase studies
8Private Wealth Management
Question bankPuzzlesCase studies
9Debt Capital Markets
Question bankPuzzlesCase studies
10Risk Management
Question bankPuzzlesCase studies
11Derivatives Foundation
Question bankPuzzlesCase studies
12Portfolio Management
Question bankPuzzlesCase studies
13Mutual Fund Mastery
Question bankPuzzlesCase studies
013

Case 013Options and volatility tradingHard

An equally weighted index of ten stocks has implied volatility 18% while each member's implied is 30%. Compute the implied correlation and the sign of P&L for selling index volatility and buying member volatility if realised correlation is 0.25.

1The situation

Taruvara Capital looks at a sector index made of ten stocks in equal weights. Three-month options on the index trade at 18% implied volatility, and options on each of the ten members trade at 30%. To keep the arithmetic clean, treat all members as identical.

The desk proposes a {term('dispersion trade', 'Selling volatility on an index while buying volatility on its members, a position that profits when the members move independently and loses when they move together.')}: sell index volatility with a vega of Rs 10 lakh per vol point, and buy member volatility sized so that the member legs break even if members realise their 30% implied. Its forecast is that realised correlation over the three months will be 0.25.

2Your task

What correlation is the market implying, what is the sign and rough size of the P&L if realised correlation is 0.25, and what is the risk?

Quick check

Roughly what correlation does an 18% index against 30% members imply?

Worked solution

Try it on paper, then open one step at a time.

30-second answerThe answer to give first

The index implies a correlation of about 0.29; if members realise 30% and correlation realises 0.25, the index realises about 17.1% and the trade makes money. Short index vol gains about 0.9 vol points, roughly Rs 9 lakh on Rs 10 lakh of vega. The trade is short correlation: in a sell-off where correlation jumps to 0.6, the index realises 24% and the short leg loses about Rs 60 lakh.

Step 1How do you back out a correlation from two volatilities?

Write the index variance in pieces. With equal weights of one tenth, each member contributes its own variance times one hundredth, and each of the 90 pairs contributes the correlation times the product of their volatilities. Index variance is one tenth of the member variance, plus nine tenths of it times the correlation, so the only unknown is the correlation. Think of ten rowers in one boat: each one's wobble mostly cancels, and how far the boat swings depends on how often they lean the same way.

The relationship
σI2=σ2[1n+(1−1n)ρ]  ⇒  324=90+810 ρ  ⇒  ρ≈0.289\sigma_I^2 = \sigma^2\left[\tfrac{1}{n} + \left(1-\tfrac{1}{n}\right)\rho\right] \;\Rightarrow\; 324 = 90 + 810\,\rho \;\Rightarrow\; \rho \approx 0.289
\sigma_Iindex implied volatility, 18%, so variance 324 in percent squared
\sigmamember implied volatility, 30%, so variance 900
nnumber of equally weighted members, 10
\rhoaverage pairwise correlation
What it says in wordsThe index variance is a fixed piece from the members plus a piece proportional to correlation, and solving gives about 0.29.
Index variance: what the members bring, and what correlation addsIf correlation were 090, vol 9.5%Actual index, 18%90234324If correlation were 1900, vol 30%Green: members on their own, 900 / 10. Lime: the correlation part, 810 x correlation.Implied correlation = 234 / 810 = 0.289
Of the index's variance of 324, only 90 comes from the members on their own; the other 234 must come from correlation, and since full correlation would add 810, the implied correlation is 0.289.
Step 2What happens to the trade if correlation realises at 0.25?

Hold the members at their 30% implied, so the long member legs roughly break even, and recompute the index. At a correlation of 0.25 the index realises 17.1%, below the 18% it was sold at, so the short index leg makes about 0.9 vol points, roughly Rs 9 lakh on Rs 10 lakh of vega. The P&L does not depend on which way the market moves. It depends only on whether the members move together more or less than the 0.29 the index price assumed.

The dispersion trade is a bet on realised correlation10%15%20%25%30%implied index vol 18%breakeven ρ 0.289ρ 0.25: 17.1%, gain 9 lakhsell-off, ρ 0.6: 24%, loss 60 lakhseller of index vol gainsseller of index vol loses0.00.20.40.60.81.0Realised average correlation; member volatility held at 30%
With members realising 30%, the index realises 9.5% at zero correlation and 30% at full correlation, crossing its 18% implied at 0.289; at 0.25 the short index leg gains about Rs 9 lakh, and in a sell-off at 0.6 it loses about Rs 60 lakh.
Step 3Who is on the other side, and why does the gap usually exist?

Index options are bought by institutions protecting whole portfolios, which bids up index volatility. Single-stock options are sold by investors writing calls against holdings and by structured products, which pushes member volatility down. Both flows push implied correlation above what usually realises, so selling it earns a premium most of the time, which is the same insurance logic as selling index skew. The premium exists because the trade loses exactly when correlation spikes, in a sell-off, when every stock falls together.

Close with the risks beyond correlation. Members do not all realise 30%: if one stock has a takeover and others are quiet, the member legs carry their own P&L. Vega is not constant as prices move, so the clean variance arithmetic holds best with variance swaps; with options, the trade needs delta-hedging and rebalancing. And size for the tail: the loss in a correlation spike is about 7 times the gain in the base case.

Where candidates lose it

The common loss is dividing the volatilities or the variances, 0.6 or 0.36, and calling it correlation. That ignores the diversified piece the members contribute even at zero correlation, the 90 of the 324.

The second is describing the trade as long volatility because it buys ten options. It is short correlation: its P&L turns on whether stocks move together, and the candidate who cannot name the sell-off as the losing scenario has missed the point of it.

What the interviewer asks next

  • How would you size the member legs so the trade has no net vega?
  • With unequal weights and member vols, how does the implied correlation formula change?
  • Why might a desk prefer variance swaps to options for this trade?
  • Members realise 35% instead of 30%, correlation still 0.25. What happens to the P&L?
← Case 012In a take-home, a stock's monthly returns are regressed on a factor over 60 months, but one month shows a data error of +250%. Compare the slope with and without it, winsorising at the 1st and 99th percentiles against deleting, and choose.Case 014 →A contract settles at a die roll, doubled if a coin lands heads, plus 5 if a drawn card is red. Quote it, then decide whether to trade with a bot showing 9.5 bid, 10.5 offer, and whether to make or take.

Company names and figures are illustrative.

Fin Maverick Free CoursesExplore Free Courses
Fin Maverick BootcampsExplore Bootcamps
Fin Maverick

Finance education that ends in a job, not a certificate that gathers dust. Built for young India.

LEARN
CalculatorsFrameworksComparisonsInterview RoadmapsShowdown
RESOURCES
All CoursesFree CoursesBootcampsInternships
COMPANY
AboutJob openingPartnership
LEGAL
Privacy PolicyTerms & ConditionsContent LicenseReturn & Refund Policy
© 2026 FIN MAVERICK / BUILT FOR INDIA.DO FINANCE, DO NOT JUST READ ABOUT IT.