Static Portfolio Theory for Variance and Gamma Swaps

Contents

The Theory Behind Static Replication

The theory relies on replicating the payoffs using option pricing mathematics. Here are the key formulas:

Variance Swap Theory

The realized variance of a stock can be replicated by a portfolio weighted by 1/K²:

\[ \text{Variance} \approx \int \frac{1}{K^2} \times \text{Option}(K) \, dK \]

Where:

Why 1/K²? This comes from the mathematical relationship between option prices and realized variance in the Black-Scholes framework.

Gamma Swap Theory

Gamma can be replicated with a different weighting - typically 1/K:

\[ \text{Gamma exposure} \approx \int \frac{1}{K} \times \text{Option}(K) \, dK \]

The Mathematical Intuition:

The Problem in Practice:

You need options at every possible strike, but:

So the "static" theory assumes infinite liquidity across all strikes, which doesn't exist in reality.

How Theory Translates to Actual Trades

Variance Swap - Long Position (receiving realized variance)

What you buy:

Example: Stock at $100

Gamma Swap - Long Position

What you buy:

Example: Stock at $100

Key Points:

In Practice: Dealers approximate this by buying options at available strikes and interpolating.

Quick Comparison

Aspect Variance Swap Gamma Swap
Weighting Formula 1/K² 1/K
Tail Exposure Lower (due to 1/K² weighting) Higher (due to 1/K weighting)
Example Quantity at $95 Strike 0.000111 0.0105
Hedging Approach Static replication possible Static replication possible
Market Liquidity Was popular, collapsed in 2008 Never gained meaningful traction