VarianceSwapMC: VarianceSwap option valuation via Monte Carlo (MC)...
In QFRM: Pricing of Vanilla and Exotic Option Contracts

Description Usage Arguments Value Author(s) References Examples

Calculates the price of a VarianceSwap Option using 500 Monte Carlo simulations.
Important Assumptions: The option o followes a General Brownian Motion ds = mu * S * dt + sqrt(vol) * S * dW where dW ~ N(0,1). The value of mu (the expected price increase) is assumed to be o$r-o$q.

1 2	VarianceSwapMC(o = OptPx(o = Opt(Style = "VarianceSwap")), var = 0.2, NPaths = 5)

`o`	The `OptPx` Variance Swap option to price.
`var`	The variance strike level
`NPaths`	The number of simulation paths to use in calculating the price,

The option o with the price in the field PxMC based on MC simulations and the Variance Swap option properties set by the users themselves

Huang Jiayao, Risk Management and Business Intelligence at Hong Kong University of Science and Technology, Exchange student at Rice University, Spring 2015

Hull, J.C., Options, Futures and Other Derivatives, 9ed, 2014. Prentice Hall. ISBN 978-0-13-345631-8, http://www-2.rotman.utoronto.ca/~hull/ofod.
http://stackoverflow.com/questions/25946852/r-monte-carlo-simulation-price-path-converging-volatility-issue

(o = VarianceSwapMC())$PxMC #Price = ~0.0245

 (o = VarianceSwapMC(NPaths = 5))$PxMC # Price = ~0.0245

 (o = VarianceSwapMC(var=0.4))$PxMC # Price = ~-0.1565