Fig_SupportingData

The provided dataset contains results from Monte Carlo simulations related to variance swaps. The data is organized into multiple sheets, each focusing on different parameters and scenarios.<b>Figure 1</b>:<b>Monte Carlo Simulations</b>: This section presents the results of Monte Carlo simulations for both discretely-sampled and continuously-sampled variance swaps. The values are reported for different sample sizes (N=12 to N=322), showing how the estimated variance swap values converge as the number of samples increases.<b>Sample 1 and Sample 2</b>: These represent two different sets of simulation results, each showing the impact of varying sample sizes on the variance swap values.<b>Figure 2</b>:<b>κθ (Kappa Theta)</b>: This section explores the impact of different values of κθ on the variance swap values. <b>θ̃ (Theta Tilde)</b>: This part examines the effect of varying θ̃ on the variance swap values .<b>σθ (Sigma Theta)</b>: This section analyzes the influence of σθ on the variance swap values .<b>θ₀ (Theta Zero)</b>: This part investigates the impact of different initial volatility levels (θ₀) on the variance swap values .<b>Sheet 3</b>:<b>λ (Lambda)</b>: This section studies the effect of varying λ on the variance swap values .<b>η (Eta)</b>: This part examines the influence of η on the variance swap values .<b>v (Nu)</b>: This section analyzes the impact of v on the variance swap values .<b>δ (Delta)</b>: This part investigates the effect of varying δ on the variance swap values .Overall, the dataset provides a comprehensive analysis of how different parameters and sampling methods affect the valuation of variance swaps, offering insights into the sensitivity and convergence behavior of these financial instruments under various conditions.