//! basic calculate method provided to inherited pricing engines
public void calculate(double requiredTolerance, int requiredSamples, int maxSamples)
{
if (!(requiredTolerance != 0 || requiredSamples != 0))
{
throw new ApplicationException("neither tolerance nor number of samples set");
}
//! Initialize the one-factor Monte Carlo
if (this.controlVariate_)
{
double controlVariateValue = this.controlVariateValue();
if (controlVariateValue == 0)
{
throw new ApplicationException("engine does not provide control-variation price");
}
PathPricer <IPath> controlPP = this.controlPathPricer();
if (controlPP == null)
{
throw new ApplicationException("engine does not provide control-variation path pricer");
}
PathGenerator <IRNG> controlPG = this.controlPathGenerator();
this.mcModel_ = new MonteCarloModel <MC, RNG, S>(pathGenerator(), pathPricer(), new S(), antitheticVariate_,
controlPP, controlVariateValue, controlPG);
}
else
{
this.mcModel_ = new MonteCarloModel <MC, RNG, S>(pathGenerator(), pathPricer(), new S(), antitheticVariate_);
}
if (requiredTolerance != 0)
{
if (maxSamples != 0)
{
value(requiredTolerance, maxSamples);
}
else
{
value(requiredTolerance);
}
}
else
{
valueWithSamples(requiredSamples);
}
}
public MonteCarloModel(IPathGenerator <IRNG> pathGenerator, PathPricer <IPath> pathPricer,
S sampleAccumulator, bool antitheticVariate, PathPricer <IPath> cvPathPricer = null,
double cvOptionValue = 0, IPathGenerator <IRNG> cvPathGenerator = null)
{
pathGenerator_ = pathGenerator;
pathPricer_ = pathPricer;
sampleAccumulator_ = sampleAccumulator;
isAntitheticVariate_ = antitheticVariate;
cvPathPricer_ = cvPathPricer;
cvOptionValue_ = cvOptionValue;
cvPathGenerator_ = cvPathGenerator;
if (cvPathPricer_ == null)
{
isControlVariate_ = false;
}
else
{
isControlVariate_ = true;
}
}
//! basic calculate method provided to inherited pricing engines
public void calculate(double?requiredTolerance, int?requiredSamples, int?maxSamples)
{
Utils.QL_REQUIRE(requiredTolerance != null ||
requiredSamples != null, () => "neither tolerance nor number of samples set");
//! Initialize the one-factor Monte Carlo
if (this.controlVariate_)
{
double?controlVariateValue = this.controlVariateValue();
Utils.QL_REQUIRE(controlVariateValue != null, () => "engine does not provide control-variation price");
PathPricer <IPath> controlPP = this.controlPathPricer();
Utils.QL_REQUIRE(controlPP != null, () => "engine does not provide control-variation path pricer");
IPathGenerator <IRNG> controlPG = this.controlPathGenerator();
this.mcModel_ = new MonteCarloModel <MC, RNG, S>(pathGenerator(), pathPricer(), FastActivator <S> .Create(), antitheticVariate_,
controlPP, controlVariateValue.GetValueOrDefault(), controlPG);
}
else
{
this.mcModel_ = new MonteCarloModel <MC, RNG, S>(pathGenerator(), pathPricer(), FastActivator <S> .Create(), antitheticVariate_);
}
if (requiredTolerance != null)
{
if (maxSamples != null)
{
value(requiredTolerance.Value, maxSamples.Value);
}
else
{
value(requiredTolerance.Value);
}
}
else
{
valueWithSamples(requiredSamples.GetValueOrDefault());
}
}
// constructor
//public MonteCarloModel(PathGenerator<IRNG> pathGenerator, IPathPricer<Path> pathPricer, S sampleAccumulator,
// bool antitheticVariate,
// IPathPricer<Path> cvPathPricer = boost::shared_ptr<path_pricer_type>(),
// result_type cvOptionValue = result_type(),
// PathGenerator<IRNG> cvPathGenerator = path_generator_type()) {
public MonteCarloModel(PathGenerator <IRNG> pathGenerator, PathPricer <IPath> pathPricer, S sampleAccumulator,
bool antitheticVariate)
: this(pathGenerator, pathPricer, sampleAccumulator, antitheticVariate, null, 0, null)
{
}
