Quantitative Volatility Lab Demeterfi et al. (1999) Model

Variance Swap & Volatility Derivatives Lab

Model static log-contract replication, fair variance strikes, vega notional scaling, and non-linear payoff profiles.

Market Presets:
Contract Parameters
5%20%60%
Wing tilt (negative = put skew)
Convexity (tail kurtosis)
$
P&L impact per 1 vol point change
5%24%70%
Volatility Analytics
  OTM Put Strip ($K < F$)  |    OTM Call Strip ($K > F$)  |  Weight scales as $2 \Delta K / (T \cdot K^2)$
  Underlying Spot Path ($S_t$)  |    Cumulative Realized Vol  |    Fair Strike Level
  Variance Swap P&L (Linear in $\sigma_R^2$, Convex in $\sigma_R$)  |    Capped Payoff
Fair Variance Strike ($K_{\text{var}}$)

432.50

Equivalent Vol: 20.80%
Variance Risk Premium (VRP)

+32.50

Spread vs ATM²: +0.80% vol
Variance Notional ($N_{\text{var}}$)

$1,201.92

per vol point squared
Swap Net P&L (Long)

+$172,475

Vol P&L: +3.20 vol pts
Institutional Valuation Architecture
1. Terminal Payoff & Notional Scaling

A variance swap pays the difference between the annualized realized variance $\sigma_R^2$ and the delivery variance strike $K_{\text{var}}$:

Payoff = N_var × (σ_R² - K_var)
N_var = N_vega / (2 × √K_var)

Realized variance is calculated from $N$ daily closing log returns: $\sigma_R^2 = \frac{252}{N} \sum_{i=1}^N \left(\ln\frac{S_i}{S_{i-1}}\right)^2 \times 10,000$.

2. Static Log-Contract Replication

Following Demeterfi, Derman, Kamal & Zou (1999), continuous variance is replicated without dynamic rehedging by holding a static portfolio of options:

K_var = (2/T) [rT - (F_0/S_* - 1) - ln(S_*/S_0)] + (2/T) e^{rT} [∫_0^{S_*} P(K)/K² dK + ∫_{S_*}^∞ C(K)/K² dK]

Because of negative equity skew, out-of-the-money puts trade at a premium, inflating the $1/K^2$ weighted integral and driving $K_{\text{var}} > \sigma_{\text{ATM}}^2$.

Swap P&L: Realized Vol ($\sigma_R$) vs Strike Vol ($\sqrt{K_{\text{var}}}$)
Fair Variance Strike ($K_{\text{var}}$): Skew Slope vs ATM Vol ($\sigma$)
Quantitative Audit Export: Sanitized RFC 4180 CSV with formula injection defense.
Variance Swaps & Volatility Mastery Quiz
1. What causes the fair variance strike ($K_{\text{var}}$) to exceed the square of ATM implied volatility ($\sigma_{\text{ATM}}^2$)?
2. Why is the weighting of options in the static replication portfolio proportional to $1/K^2$?
3. By Jensen's Inequality, how does a volatility swap strike compare to a variance swap strike?
4. Why do variance swap sellers often insist on capping the contract payoff?
Frequently Asked Questions

A variance swap is a forward contract on the annualized realized variance of an underlying asset over a specified maturity. At maturity, the buyer receives $N_{\text{var}} \times (\text{Realized Variance} - \text{Fair Variance Strike})$. Variance notional $N_{\text{var}}$ is defined as $\text{Vega Notional} / (2 \cdot \sqrt{\text{Fair Variance Strike}})$.

Following the seminal Demeterfi, Derman, Kamal, and Zou (1999) framework, the log contract $\ln(S_T / S_0)$ is replicated by a static portfolio of out-of-the-money puts and calls with weights inversely proportional to the strike squared ($1 / K^2$). Integrating across the strike surface yields the fair variance strike.

Because equity markets exhibit a pronounced negative volatility skew (expensive out-of-the-money puts due to crash fear), the $1 / K^2$ weighting places disproportionate weight on lower strikes. This skew steepness inflates the integrated fair variance strike above the square of ATM implied volatility.

A variance swap pays linearly on realized variance (volatility squared), whereas a volatility swap pays linearly on realized volatility. By Jensen's Inequality, because the square-root function is strictly concave, the expected volatility is strictly less than the square root of expected variance, creating a convexity premium.