/usr/include/ql/experimental/mcbasket/mcamericanpathengine.hpp is in libquantlib0-dev 1.7.1-1.
This file is owned by root:root, with mode 0o644.
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/*
Copyright (C) 2009 Andrea Odetti
This file is part of QuantLib, a free-software/open-source library
for financial quantitative analysts and developers - http://quantlib.org/
QuantLib is free software: you can redistribute it and/or modify it
under the terms of the QuantLib license. You should have received a
copy of the license along with this program; if not, please email
<quantlib-dev@lists.sf.net>. The license is also available online at
<http://quantlib.org/license.shtml>.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the license for more details.
*/
#ifndef quantlib_american_path_montecarlo_engine_hpp
#define quantlib_american_path_montecarlo_engine_hpp
#include <ql/experimental/mcbasket/longstaffschwartzmultipathpricer.hpp>
#include <ql/experimental/mcbasket/mclongstaffschwartzpathengine.hpp>
#include <ql/experimental/mcbasket/pathmultiassetoption.hpp>
#include <ql/processes/blackscholesprocess.hpp>
#include <ql/processes/stochasticprocessarray.hpp>
#include <ql/termstructures/yield/impliedtermstructure.hpp>
#include <boost/function.hpp>
#include <boost/make_shared.hpp>
namespace QuantLib {
//! least-square Monte Carlo engine
/*! \warning This method is intrinsically weak for out-of-the-money
options.
\ingroup basketengines
*/
template <class RNG = PseudoRandom>
class MCAmericanPathEngine
: public MCLongstaffSchwartzPathEngine<
PathMultiAssetOption::engine,MultiVariate,RNG> {
public:
MCAmericanPathEngine(const boost::shared_ptr<StochasticProcessArray>&,
Size timeSteps,
Size timeStepsPerYear,
bool brownianBridge,
bool antitheticVariate,
bool controlVariate,
Size requiredSamples,
Real requiredTolerance,
Size maxSamples,
BigNatural seed,
Size nCalibrationSamples = Null<Size>());
protected:
boost::shared_ptr<LongstaffSchwartzMultiPathPricer>
lsmPathPricer() const;
};
//! Monte Carlo American basket-option engine factory
template <class RNG = PseudoRandom>
class MakeMCAmericanPathEngine {
public:
MakeMCAmericanPathEngine(
const boost::shared_ptr<StochasticProcessArray>&);
// named parameters
MakeMCAmericanPathEngine& withSteps(Size steps);
MakeMCAmericanPathEngine& withStepsPerYear(Size steps);
MakeMCAmericanPathEngine& withBrownianBridge(bool b = true);
MakeMCAmericanPathEngine& withAntitheticVariate(bool b = true);
MakeMCAmericanPathEngine& withControlVariate(bool b = true);
MakeMCAmericanPathEngine& withSamples(Size samples);
MakeMCAmericanPathEngine& withAbsoluteTolerance(Real tolerance);
MakeMCAmericanPathEngine& withMaxSamples(Size samples);
MakeMCAmericanPathEngine& withSeed(BigNatural seed);
MakeMCAmericanPathEngine& withCalibrationSamples(Size samples);
// conversion to pricing engine
operator boost::shared_ptr<PricingEngine>() const;
private:
boost::shared_ptr<StochasticProcessArray> process_;
bool brownianBridge_, antithetic_, controlVariate_;
Size steps_, stepsPerYear_, samples_, maxSamples_, calibrationSamples_;
Real tolerance_;
BigNatural seed_;
};
template <class RNG> inline
MCAmericanPathEngine<RNG>::MCAmericanPathEngine(
const boost::shared_ptr<StochasticProcessArray>& processes,
Size timeSteps,
Size timeStepsPerYear,
bool brownianBridge,
bool antitheticVariate,
bool controlVariate,
Size requiredSamples,
Real requiredTolerance,
Size maxSamples,
BigNatural seed,
Size nCalibrationSamples)
: MCLongstaffSchwartzPathEngine<PathMultiAssetOption::engine,
MultiVariate,RNG>(processes,
timeSteps,
timeStepsPerYear,
brownianBridge,
antitheticVariate,
controlVariate,
requiredSamples,
requiredTolerance,
maxSamples,
seed,
nCalibrationSamples) {}
template <class RNG>
inline boost::shared_ptr<LongstaffSchwartzMultiPathPricer>
MCAmericanPathEngine<RNG>::lsmPathPricer() const {
boost::shared_ptr<StochasticProcessArray> processArray =
boost::dynamic_pointer_cast<StochasticProcessArray>(this->process_);
QL_REQUIRE(processArray && processArray->size()>0,
"Stochastic process array required");
boost::shared_ptr<GeneralizedBlackScholesProcess> process =
boost::dynamic_pointer_cast<GeneralizedBlackScholesProcess>(
processArray->process(0));
QL_REQUIRE(process, "generalized Black-Scholes process required");
const TimeGrid theTimeGrid = this->timeGrid();
const std::vector<Time> & times = theTimeGrid.mandatoryTimes();
const Size numberOfTimes = times.size();
const std::vector<Date> & fixings = this->arguments_.fixingDates;
QL_REQUIRE(fixings.size() == numberOfTimes, "Invalid dates/times");
std::vector<Size> timePositions(numberOfTimes);
Array discountFactors(numberOfTimes);
std::vector<Handle<YieldTermStructure> > forwardTermStructures(numberOfTimes);
const Handle<YieldTermStructure> & riskFreeRate = process->riskFreeRate();
for (Size i = 0; i < numberOfTimes; ++i) {
timePositions[i] = theTimeGrid.index(times[i]);
discountFactors[i] = riskFreeRate->discount(times[i]);
forwardTermStructures[i] = Handle<YieldTermStructure>(
boost::make_shared<ImpliedTermStructure>(riskFreeRate,
fixings[i]));
}
const Size polynomialOrder = 2;
const LsmBasisSystem::PolynomType polynomType = LsmBasisSystem::Monomial;
return boost::shared_ptr<LongstaffSchwartzMultiPathPricer> (
new LongstaffSchwartzMultiPathPricer(this->arguments_.payoff,
timePositions,
forwardTermStructures,
discountFactors,
polynomialOrder,
polynomType));
}
template <class RNG>
inline MakeMCAmericanPathEngine<RNG>::MakeMCAmericanPathEngine(
const boost::shared_ptr<StochasticProcessArray>& process)
: process_(process), brownianBridge_(false), antithetic_(false),
controlVariate_(false),
steps_(Null<Size>()), stepsPerYear_(Null<Size>()),
samples_(Null<Size>()), maxSamples_(Null<Size>()),
calibrationSamples_(Null<Size>()),
tolerance_(Null<Real>()), seed_(0) {}
template <class RNG>
inline MakeMCAmericanPathEngine<RNG>&
MakeMCAmericanPathEngine<RNG>::withSteps(Size steps) {
steps_ = steps;
return *this;
}
template <class RNG>
inline MakeMCAmericanPathEngine<RNG>&
MakeMCAmericanPathEngine<RNG>::withStepsPerYear(Size steps) {
stepsPerYear_ = steps;
return *this;
}
template <class RNG>
inline MakeMCAmericanPathEngine<RNG>&
MakeMCAmericanPathEngine<RNG>::withBrownianBridge(bool brownianBridge) {
brownianBridge_ = brownianBridge;
return *this;
}
template <class RNG>
inline MakeMCAmericanPathEngine<RNG>&
MakeMCAmericanPathEngine<RNG>::withAntitheticVariate(bool b) {
antithetic_ = b;
return *this;
}
template <class RNG>
inline MakeMCAmericanPathEngine<RNG>&
MakeMCAmericanPathEngine<RNG>::withControlVariate(bool b) {
controlVariate_ = b;
return *this;
}
template <class RNG>
inline MakeMCAmericanPathEngine<RNG>&
MakeMCAmericanPathEngine<RNG>::withSamples(Size samples) {
QL_REQUIRE(tolerance_ == Null<Real>(),
"tolerance already set");
samples_ = samples;
return *this;
}
template <class RNG>
inline MakeMCAmericanPathEngine<RNG>&
MakeMCAmericanPathEngine<RNG>::withAbsoluteTolerance(Real tolerance) {
QL_REQUIRE(samples_ == Null<Size>(),
"number of samples already set");
QL_REQUIRE(RNG::allowsErrorEstimate,
"chosen random generator policy "
"does not allow an error estimate");
tolerance_ = tolerance;
return *this;
}
template <class RNG>
inline MakeMCAmericanPathEngine<RNG>&
MakeMCAmericanPathEngine<RNG>::withMaxSamples(Size samples) {
maxSamples_ = samples;
return *this;
}
template <class RNG>
inline MakeMCAmericanPathEngine<RNG>&
MakeMCAmericanPathEngine<RNG>::withSeed(BigNatural seed) {
seed_ = seed;
return *this;
}
template <class RNG>
inline MakeMCAmericanPathEngine<RNG>&
MakeMCAmericanPathEngine<RNG>::withCalibrationSamples(Size samples) {
calibrationSamples_ = samples;
return *this;
}
template <class RNG>
inline
MakeMCAmericanPathEngine<RNG>::operator
boost::shared_ptr<PricingEngine>() const {
QL_REQUIRE(steps_ != Null<Size>() || stepsPerYear_ != Null<Size>(),
"number of steps not given");
QL_REQUIRE(steps_ == Null<Size>() || stepsPerYear_ == Null<Size>(),
"number of steps overspecified");
return boost::shared_ptr<PricingEngine>(new
MCAmericanPathEngine<RNG>(process_,
steps_,
stepsPerYear_,
brownianBridge_,
antithetic_,
controlVariate_,
samples_,
tolerance_,
maxSamples_,
seed_,
calibrationSamples_));
}
}
#endif
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