quantlib.pricingengines.forward.mc_variance_swap_engine.MCVarianceSwapEngine

class MCVarianceSwapEngine(GeneralizedBlackScholesProcess process, Size time_steps=Null[Integer](), Size time_steps_per_year=Null[Integer](), bool brownian_bridge=False, bool antithetic_variate=False, Size required_samples=Null[Integer](), Real required_tolerance=Null[Real](), Size max_samples=Null[Integer](), BigNatural seed=0)

Bases: PricingEngine

Variance-swap pricing engine using Monte Carlo simulation

as described in Demeterfi, Derman, Kamal & Zou, “A Guide to Volatility and Variance Swaps”, 1999 TODO define tolerance of numerical integral and incorporate it in errorEstimate

Test returned fair variances checked for consistency with implied volatility curve.

Calculate variance via Monte Carlo

Parameters:

  • process (GeneralizedBlackScholesProcess)

  • time_steps (Size)

  • time_steps_per_year (Size)

  • brownian_bridge (bool)

  • antithetic_variate (bool)

  • required_samples (Size)

  • required_tolerance (Real)

  • max_samples (Size)

  • seed (BigNatural)

__init__(*args, **kwargs)

Methods

__init__(*args, **kwargs)