Class MCPRVarianceSwapEngine

Namespace: NanoQL.PricingEngines

Assembly: NanoQL.dll

MCVarianceSwapEngine on pseudo-random draws with QuantLib's default statistics: QuantLib-Python's MCPRVarianceSwapEngine, QuantLib's MCVarianceSwapEngine<PseudoRandom>, its calculations closed at PseudoRandom and GeneralStatistics.

public static class MCPRVarianceSwapEngine

Inheritance: object

MCPRVarianceSwapEngine

Inherited Members: object.GetType()

object.MemberwiseClone()

object.ToString()

object.Equals(object)

object.Equals(object, object)

object.ReferenceEquals(object, object)

object.GetHashCode()

Methods

Calculate(VarianceSwap, EvaluationContext, GeneralizedBlackScholesProcess, int?, int?, bool, bool, int?, double?, int?, long)

Prices a variance swap by Monte Carlo.

public static VarianceSwapResults Calculate(VarianceSwap swap, EvaluationContext context, GeneralizedBlackScholesProcess process, int? timeSteps, int? timeStepsPerYear, bool brownianBridge, bool antitheticVariate, int? requiredSamples, double? requiredTolerance, int? maxSamples, long seed)

Parameters

swap VarianceSwap: The variance swap.
context EvaluationContext: The evaluation context.
process GeneralizedBlackScholesProcess: The Black-Scholes process.
timeSteps int?: The number of time steps, or null.
timeStepsPerYear int?: The number of time steps per year, or null.
brownianBridge bool: Whether the draws are ordered by a Brownian bridge.
antitheticVariate bool: Whether antithetic variates are used.
requiredSamples int?: The number of samples, or null.
requiredTolerance double?: The absolute tolerance, or null; it takes precedence.
maxSamples int?: The most samples for a tolerance, or null for no limit.
seed long: The seed, in [1, 4294967295].

Returns

VarianceSwapResults: The swap's results: the NPV, the variance and, where the numbers give one, the error estimate.

Exceptions

ArgumentNullException: The swap, the context or the process is null.
ArgumentException: The time steps are both given, neither, or not positive.
ArgumentOutOfRangeException: A number of samples is negative, or the seed is outside its range.
NanoQLException: Neither a tolerance nor a number of samples is given, or the samples can't reach the tolerance.