Dispersion Trades Using Options

intermediatePublished: 2026-01-01

Dispersion Trades Using Options

Dispersion trading exploits the relationship between index option volatility and single-stock option volatility. When implied correlation is high relative to expected realized correlation, selling index volatility and buying single-stock volatility creates a position that profits from lower-than-expected correlation—stocks moving more independently than priced.

Definition and Key Concepts

Dispersion Trade Structure

Classic dispersion:

  • Sell: Index option volatility (straddles or variance swaps)
  • Buy: Single-stock option volatility (component straddles)
  • Net position: Short implied correlation

Profit condition: Realized correlation < Implied correlation

Mathematical Relationship

Index variance formula: σ²_index = Σᵢ wᵢ² σᵢ² + 2 Σᵢ Σⱼ wᵢ wⱼ σᵢ σⱼ ρᵢⱼ

Simplified (equal weights, identical vols): σ²_index ≈ σ²_stock × [1/n + (1 - 1/n) × ρ]

Implied correlation: ρ_implied ≈ (σ²_index - weighted avg σ²_singles) / (cross terms)

Why Implied Correlation Is Often High

FactorExplanation
Structured product hedgingDealers buy index vol to hedge worst-of
Correlation risk premiumInvestors pay for crisis protection
Supply/demand imbalanceMore index vol sellers than buyers
Mean reversionCorrelation reverts from crisis levels

How It Works in Practice

Trade Setup

S&P 500 dispersion example:

  • Index: S&P 500 at 5,000
  • Components: Top 50 stocks by weight
  • Tenor: 3 months
  • Implied index vol: 16%
  • Weighted avg singles vol: 28%
  • Implied correlation: 0.35

Step 1: Sell index volatility Sell SPX straddle: $250 premium (25 vol points exposure)

Step 2: Buy component volatility Buy straddles on 50 stocks, vega-weighted to match index exposure

Vega matching: Total singles vega = Index vega × (1 / √implied correlation) If index vega = $10,000, singles vega = $10,000 / √0.35 = $16,900

Position Sizing

Per-stock vega allocation: Stock vega = (Stock weight² × Stock vol) / Σ(weights² × vols) × Total singles vega

Example for AAPL (7% weight, 25% vol): AAPL vega = (0.07² × 0.25) / (total) × $16,900 ≈ $600

Buy AAPL straddles with $600 vega exposure

P/L Mechanics

Daily P/L attribution:

SourceCalculation
Index realized moveShort exposure × index move²
Singles realized movesLong exposure × Σ(stock moves²)
Correlation effectNet of above shows correlation
ThetaTime decay (often net positive)

Worked Example

Full Dispersion Trade

Trade parameters:

  • Notional: $1 million vega on singles
  • Index vega sold: $600,000 (adjusted for correlation)
  • Tenor: 60 days
  • Entry implied correlation: 0.38
  • Expected realized correlation: 0.30

Week 1 P/L breakdown:

DaySPX MoveWeighted Singles MoveSingles P/LIndex P/LNet
Mon-1.5%-1.8% avg+$3,200-$1,800+$1,400
Tue+0.8%+0.6% to +1.2%+$1,500-$800+$700
Wed-0.2%-0.5% to +0.3%+$600-$100+$500
Thu+1.0%+0.5% to +1.8%+$2,800-$1,200+$1,600
Fri-0.5%-0.3% to -0.8%+$900-$400+$500
Week+$9,000-$4,300+$4,700

Interpretation: Singles moved more than index (low correlation), generating profits.

Scenario Analysis

Trade outcomes by correlation:

Realized CorrelationIndex Realized VolSingles Avg VolApprox P/L
0.2013%28%+$80,000
0.3015%28%+$40,000
0.3817%28%$0
0.5020%28%-$30,000
0.7023%28%-$70,000
0.90 (crisis)27%30%-$150,000

Maximum loss: When correlation approaches 1, index moves as much as singles but you're short more gamma on index.

Greeks of Dispersion

GreekSingles PositionIndex PositionNet
DeltaApproximately zeroApproximately zero~0
GammaLongShortLong (net)
VegaLongShortDepends on correlation
ThetaNegativePositiveUsually positive

Risks, Limitations, and Tradeoffs

Correlation Spike Risk

Crisis scenario (2008/2020 style):

  • Normal correlation: 0.30
  • Crisis correlation: 0.85
  • Implied was: 0.38

Impact:

  • Index realized vol jumps to match singles
  • Short index position loses more than long singles gains
  • Massive drawdown in short period

Structural Risks

RiskDescriptionMitigation
Correlation spikeSudden increase in correlationSize conservatively
Execution costBid-ask on many optionsTrade liquid names
RebalancingWeights change as prices moveRegular adjustment
Single-stock gapsIndividual stock eventsDiversify, monitor
Model riskWrong correlation estimateUse robust methodology

Operational Challenges

ChallengeDescription
Number of positions50-100 individual stock options
Roll managementMultiple expiration calendars
Delta hedgingMany positions to delta hedge
Margin requirementsLarge gross notional
ReportingComplex P/L attribution

Common Pitfalls

PitfallDescriptionPrevention
Wrong vega ratioMismatched singles to indexCareful calculation
Ignoring tail riskUnderweight crisis scenarioStress test regularly
Over-leverageToo large relative to capitalConservative sizing
Stale weightsIndex composition changesUpdate quarterly
Illiquid namesCan't exit positionsStick to liquid stocks

Implementation Approaches

Variance Swap Dispersion

Alternative to options:

  • Sell index variance swap
  • Buy single-stock variance swaps

Advantages:

  • Cleaner correlation exposure
  • No delta hedging
  • Linear in variance

Disadvantages:

  • Less liquid
  • Larger notionals
  • Counterparty risk

ETF-Based Dispersion

Simplified approach:

  • Sell sector ETF straddles
  • Buy constituent ETF straddles

Example:

  • Short XLK (tech sector) straddle
  • Long AAPL, MSFT, NVDA, GOOGL straddles

Pro: Fewer positions Con: Less precise correlation exposure

Checklist and Next Steps

Pre-trade checklist:

  • Calculate implied correlation from market prices
  • Estimate expected realized correlation
  • Verify premium exists (implied > expected)
  • Size for worst-case correlation spike
  • Select liquid single-stock options
  • Plan execution sequence

Execution checklist:

  • Execute index leg first (most liquid)
  • Build singles portfolio over time
  • Verify vega ratios match target
  • Set up delta hedging
  • Confirm margin requirements

Ongoing management:

  • Monitor correlation daily
  • Rebalance weights as needed
  • Track P/L attribution
  • Manage option rolls
  • Report to risk management

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