/usr/include/ql/pricingengines/mclongstaffschwartzengine.hpp is in libquantlib0-dev 1.7.1-1.
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/*
Copyright (C) 2006 Klaus Spanderen
Copyright (C) 2007 StatPro Italia srl
Copyright (C) 2015 Peter Caspers
Copyright (C) 2015 Thema Consulting SA
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.
*/
/*! \file mclongstaffschwartzengine.hpp
\brief Longstaff Schwartz Monte Carlo engine for early exercise options
*/
#ifndef quantlib_mc_longstaff_schwartz_engine_hpp
#define quantlib_mc_longstaff_schwartz_engine_hpp
#include <ql/exercise.hpp>
#include <ql/pricingengines/mcsimulation.hpp>
#include <ql/methods/montecarlo/longstaffschwartzpathpricer.hpp>
namespace QuantLib {
//! Longstaff-Schwarz Monte Carlo engine for early exercise options
/*! References:
Francis Longstaff, Eduardo Schwartz, 2001. Valuing American Options
by Simulation: A Simple Least-Squares Approach, The Review of
Financial Studies, Volume 14, No. 1, 113-147
\test the correctness of the returned value is tested by
reproducing results available in web/literature
*/
template <class GenericEngine, template <class> class MC,
class RNG, class S = Statistics>
class MCLongstaffSchwartzEngine : public GenericEngine,
public McSimulation<MC,RNG,S> {
public:
typedef typename MC<RNG>::path_type path_type;
typedef typename McSimulation<MC,RNG,S>::stats_type
stats_type;
typedef typename McSimulation<MC,RNG,S>::path_pricer_type
path_pricer_type;
typedef typename McSimulation<MC,RNG,S>::path_generator_type
path_generator_type;
MCLongstaffSchwartzEngine(
const boost::shared_ptr<StochasticProcess>& process,
Size timeSteps,
Size timeStepsPerYear,
bool brownianBridge,
bool antitheticVariate,
bool controlVariate,
Size requiredSamples,
Real requiredTolerance,
Size maxSamples,
BigNatural seed,
Size nCalibrationSamples = Null<Size>());
void calculate() const;
protected:
virtual boost::shared_ptr<LongstaffSchwartzPathPricer<path_type> >
lsmPathPricer() const = 0;
TimeGrid timeGrid() const;
boost::shared_ptr<path_pricer_type> pathPricer() const;
boost::shared_ptr<path_generator_type> pathGenerator() const;
boost::shared_ptr<StochasticProcess> process_;
const Size timeSteps_;
const Size timeStepsPerYear_;
const bool brownianBridge_;
const Size requiredSamples_;
const Real requiredTolerance_;
const Size maxSamples_;
const Size seed_;
const Size nCalibrationSamples_;
mutable boost::shared_ptr<LongstaffSchwartzPathPricer<path_type> >
pathPricer_;
};
template <class GenericEngine, template <class> class MC,
class RNG, class S>
inline MCLongstaffSchwartzEngine<GenericEngine,MC,RNG,S>::
MCLongstaffSchwartzEngine(
const boost::shared_ptr<StochasticProcess>& process,
Size timeSteps,
Size timeStepsPerYear,
bool brownianBridge,
bool antitheticVariate,
bool controlVariate,
Size requiredSamples,
Real requiredTolerance,
Size maxSamples,
BigNatural seed,
Size nCalibrationSamples)
: McSimulation<MC,RNG,S> (antitheticVariate, controlVariate),
process_ (process),
timeSteps_ (timeSteps),
timeStepsPerYear_ (timeStepsPerYear),
brownianBridge_ (brownianBridge),
requiredSamples_ (requiredSamples),
requiredTolerance_ (requiredTolerance),
maxSamples_ (maxSamples),
seed_ (seed),
nCalibrationSamples_( (nCalibrationSamples == Null<Size>())
? 2048 : nCalibrationSamples) {
QL_REQUIRE(timeSteps != Null<Size>() ||
timeStepsPerYear != Null<Size>(),
"no time steps provided");
QL_REQUIRE(timeSteps == Null<Size>() ||
timeStepsPerYear == Null<Size>(),
"both time steps and time steps per year were provided");
QL_REQUIRE(timeSteps != 0,
"timeSteps must be positive, " << timeSteps <<
" not allowed");
QL_REQUIRE(timeStepsPerYear != 0,
"timeStepsPerYear must be positive, " << timeStepsPerYear <<
" not allowed");
this->registerWith(process_);
}
template <class GenericEngine, template <class> class MC,
class RNG, class S>
inline
boost::shared_ptr<typename
MCLongstaffSchwartzEngine<GenericEngine,MC,RNG,S>::path_pricer_type>
MCLongstaffSchwartzEngine<GenericEngine,MC,RNG,S>::pathPricer() const {
QL_REQUIRE(pathPricer_, "path pricer unknown");
return pathPricer_;
}
template <class GenericEngine, template <class> class MC,
class RNG, class S>
inline
void MCLongstaffSchwartzEngine<GenericEngine,MC,RNG,S>::calculate() const {
pathPricer_ = this->lsmPathPricer();
this->mcModel_ = boost::shared_ptr<MonteCarloModel<MC,RNG,S> >(
new MonteCarloModel<MC,RNG,S>
(pathGenerator(), pathPricer_,
stats_type(), this->antitheticVariate_));
this->mcModel_->addSamples(nCalibrationSamples_);
this->pathPricer_->calibrate();
McSimulation<MC,RNG,S>::calculate(requiredTolerance_,
requiredSamples_,
maxSamples_);
this->results_.value = this->mcModel_->sampleAccumulator().mean();
this->results_.additionalResults["exerciseProbability"] =
this->pathPricer_->exerciseProbability();
if (RNG::allowsErrorEstimate) {
this->results_.errorEstimate =
this->mcModel_->sampleAccumulator().errorEstimate();
}
}
template <class GenericEngine, template <class> class MC,
class RNG, class S>
inline
TimeGrid MCLongstaffSchwartzEngine<GenericEngine,MC,RNG,S>::timeGrid()
const {
std::vector<Time> requiredTimes;
if (this->arguments_.exercise->type() == Exercise::American) {
Date lastExerciseDate = this->arguments_.exercise->lastDate();
requiredTimes.push_back(process_->time(lastExerciseDate));
} else {
for (Size i = 0; i < this->arguments_.exercise->dates().size();
++i) {
Time t = process_->time(this->arguments_.exercise->date(i));
if (t > 0.0)
requiredTimes.push_back(t);
}
}
if (this->timeSteps_ != Null<Size>()) {
return TimeGrid(requiredTimes.begin(), requiredTimes.end(),
this->timeSteps_);
} else if (this->timeStepsPerYear_ != Null<Size>()) {
Size steps = static_cast<Size>(this->timeStepsPerYear_ *
requiredTimes.back());
return TimeGrid(requiredTimes.begin(), requiredTimes.end(),
std::max<Size>(steps, 1));
} else {
QL_FAIL("time steps not specified");
}
}
template <class GenericEngine, template <class> class MC,
class RNG, class S>
inline
boost::shared_ptr<typename
MCLongstaffSchwartzEngine<GenericEngine,MC,RNG,S>::path_generator_type>
MCLongstaffSchwartzEngine<GenericEngine,MC,RNG,S>::pathGenerator() const {
Size dimensions = process_->factors();
TimeGrid grid = this->timeGrid();
typename RNG::rsg_type generator =
RNG::make_sequence_generator(dimensions*(grid.size()-1),seed_);
return boost::shared_ptr<path_generator_type>(
new path_generator_type(process_,
grid, generator, brownianBridge_));
}
}
#endif
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