Class MCLDDiscreteGeometricAPEngine

Namespace: NanoQL.PricingEngines

Assembly: NanoQL.dll

MCDiscreteGeometricAPEngine on low-discrepancy draws with QuantLib's default statistics: QuantLib-Python's MCLDDiscreteGeometricAPEngine, QuantLib's MCDiscreteGeometricAPEngine<LowDiscrepancy>, its calculations closed at LowDiscrepancy and GeneralStatistics.

public static class MCLDDiscreteGeometricAPEngine

Inheritance: object

MCLDDiscreteGeometricAPEngine

Inherited Members: object.GetType()

object.MemberwiseClone()

object.ToString()

object.Equals(object)

object.Equals(object, object)

object.ReferenceEquals(object, object)

object.GetHashCode()

Methods

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

Prices a discrete geometric average-price Asian option by Monte Carlo.

public static OneAssetOptionResults Calculate(DiscreteAveragingAsianOption option, EvaluationContext context, GeneralizedBlackScholesProcess process, bool brownianBridge, bool antitheticVariate, int? requiredSamples, double? requiredTolerance, int? maxSamples, long seed)

Parameters

option DiscreteAveragingAsianOption: The option.
context EvaluationContext: The evaluation context.
process GeneralizedBlackScholesProcess: The Black-Scholes process.
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

OneAssetOptionResults: The option's results, the NPV, the error estimate and the grid; the expired results when it has expired.

Exceptions

ArgumentNullException: The option, the context or the process is null.
ArgumentOutOfRangeException: A number of samples is negative, or the seed is outside its range.
NanoQLException: The option's past fixings fail its checks, the payoff isn't plain vanilla, the exercise isn't European, every fixing is in the past, or the samples can't reach the tolerance.