/usr/include/ql/experimental/exoticoptions/mchimalayaengine.hpp is in libquantlib0-dev 1.7.1-1.
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/*
Copyright (C) 2008 Master IMAFA - Polytech'Nice Sophia - Université de Nice Sophia Antipolis
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 mchimalayaengine.hpp
\brief Monte Carlo engine for Himalaya options
*/
#ifndef quantlib_mc_himalaya_engine_hpp
#define quantlib_mc_himalaya_engine_hpp
#include <ql/experimental/exoticoptions/himalayaoption.hpp>
#include <ql/pricingengines/mcsimulation.hpp>
#include <ql/processes/blackscholesprocess.hpp>
#include <ql/processes/stochasticprocessarray.hpp>
#include <ql/exercise.hpp>
namespace QuantLib {
template <class RNG = PseudoRandom, class S = Statistics>
class MCHimalayaEngine : public HimalayaOption::engine,
public McSimulation<MultiVariate,RNG,S> {
public:
typedef typename McSimulation<MultiVariate,RNG,S>::path_generator_type
path_generator_type;
typedef typename McSimulation<MultiVariate,RNG,S>::path_pricer_type
path_pricer_type;
typedef typename McSimulation<MultiVariate,RNG,S>::stats_type
stats_type;
MCHimalayaEngine(const boost::shared_ptr<StochasticProcessArray>&,
bool brownianBridge,
bool antitheticVariate,
Size requiredSamples,
Real requiredTolerance,
Size maxSamples,
BigNatural seed);
void calculate() const {
McSimulation<MultiVariate,RNG,S>::calculate(requiredTolerance_,
requiredSamples_,
maxSamples_);
results_.value = this->mcModel_->sampleAccumulator().mean();
if (RNG::allowsErrorEstimate)
results_.errorEstimate =
this->mcModel_->sampleAccumulator().errorEstimate();
}
private:
// McSimulation implementation
TimeGrid timeGrid() const;
boost::shared_ptr<path_generator_type> pathGenerator() const {
Size numAssets = processes_->size();
TimeGrid grid = timeGrid();
typename RNG::rsg_type gen =
RNG::make_sequence_generator(numAssets*(grid.size()-1),seed_);
return boost::shared_ptr<path_generator_type>(
new path_generator_type(processes_,
grid, gen, brownianBridge_));
}
boost::shared_ptr<path_pricer_type> pathPricer() const;
// data members
boost::shared_ptr<StochasticProcessArray> processes_;
Size requiredSamples_;
Size maxSamples_;
Real requiredTolerance_;
bool brownianBridge_;
BigNatural seed_;
};
//! Monte Carlo Himalaya-option engine factory
template <class RNG = PseudoRandom, class S = Statistics>
class MakeMCHimalayaEngine {
public:
MakeMCHimalayaEngine(
const boost::shared_ptr<StochasticProcessArray>&);
// named parameters
MakeMCHimalayaEngine& withBrownianBridge(bool b = true);
MakeMCHimalayaEngine& withAntitheticVariate(bool b = true);
MakeMCHimalayaEngine& withSamples(Size samples);
MakeMCHimalayaEngine& withAbsoluteTolerance(Real tolerance);
MakeMCHimalayaEngine& withMaxSamples(Size samples);
MakeMCHimalayaEngine& withSeed(BigNatural seed);
// conversion to pricing engine
operator boost::shared_ptr<PricingEngine>() const;
private:
boost::shared_ptr<StochasticProcessArray> process_;
bool brownianBridge_, antithetic_;
Size samples_, maxSamples_;
Real tolerance_;
BigNatural seed_;
};
class HimalayaMultiPathPricer : public PathPricer<MultiPath> {
public:
HimalayaMultiPathPricer(const boost::shared_ptr<Payoff>& payoff,
DiscountFactor discount);
Real operator()(const MultiPath& multiPath) const;
private:
boost::shared_ptr<Payoff> payoff_;
DiscountFactor discount_;
};
// template definitions
template<class RNG, class S>
inline MCHimalayaEngine<RNG,S>::MCHimalayaEngine(
const boost::shared_ptr<StochasticProcessArray>& processes,
bool brownianBridge,
bool antitheticVariate,
Size requiredSamples,
Real requiredTolerance,
Size maxSamples,
BigNatural seed)
: McSimulation<MultiVariate,RNG,S>(antitheticVariate, false),
processes_(processes), requiredSamples_(requiredSamples),
maxSamples_(maxSamples), requiredTolerance_(requiredTolerance),
brownianBridge_(brownianBridge), seed_(seed) {
registerWith(processes_);
}
template <class RNG, class S>
inline TimeGrid MCHimalayaEngine<RNG,S>::timeGrid() const {
std::vector<Time> fixingTimes;
for (Size i=0; i<arguments_.fixingDates.size(); i++) {
Time t = processes_->time(arguments_.fixingDates[i]);
QL_REQUIRE(t >= 0.0, "seasoned options are not handled");
if (i > 0) {
QL_REQUIRE(t > fixingTimes.back(), "fixing dates not sorted");
}
fixingTimes.push_back(t);
}
return TimeGrid(fixingTimes.begin(), fixingTimes.end());
}
template <class RNG, class S>
inline
boost::shared_ptr<typename MCHimalayaEngine<RNG,S>::path_pricer_type>
MCHimalayaEngine<RNG,S>::pathPricer() const {
boost::shared_ptr<GeneralizedBlackScholesProcess> process =
boost::dynamic_pointer_cast<GeneralizedBlackScholesProcess>(
processes_->process(0));
QL_REQUIRE(process, "Black-Scholes process required");
return boost::shared_ptr<
typename MCHimalayaEngine<RNG,S>::path_pricer_type>(
new HimalayaMultiPathPricer(arguments_.payoff,
process->riskFreeRate()->discount(
arguments_.exercise->lastDate())));
}
template <class RNG, class S>
inline MakeMCHimalayaEngine<RNG,S>::MakeMCHimalayaEngine(
const boost::shared_ptr<StochasticProcessArray>& process)
: process_(process), brownianBridge_(false), antithetic_(false),
samples_(Null<Size>()), maxSamples_(Null<Size>()),
tolerance_(Null<Real>()), seed_(0) {}
template <class RNG, class S>
inline MakeMCHimalayaEngine<RNG,S>&
MakeMCHimalayaEngine<RNG,S>::withBrownianBridge(bool brownianBridge) {
brownianBridge_ = brownianBridge;
return *this;
}
template <class RNG, class S>
inline MakeMCHimalayaEngine<RNG,S>&
MakeMCHimalayaEngine<RNG,S>::withAntitheticVariate(bool b) {
antithetic_ = b;
return *this;
}
template <class RNG, class S>
inline MakeMCHimalayaEngine<RNG,S>&
MakeMCHimalayaEngine<RNG,S>::withSamples(Size samples) {
QL_REQUIRE(tolerance_ == Null<Real>(),
"tolerance already set");
samples_ = samples;
return *this;
}
template <class RNG, class S>
inline MakeMCHimalayaEngine<RNG,S>&
MakeMCHimalayaEngine<RNG,S>::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, class S>
inline MakeMCHimalayaEngine<RNG,S>&
MakeMCHimalayaEngine<RNG,S>::withMaxSamples(Size samples) {
maxSamples_ = samples;
return *this;
}
template <class RNG, class S>
inline MakeMCHimalayaEngine<RNG,S>&
MakeMCHimalayaEngine<RNG,S>::withSeed(BigNatural seed) {
seed_ = seed;
return *this;
}
template <class RNG, class S>
inline
MakeMCHimalayaEngine<RNG,S>::operator boost::shared_ptr<PricingEngine>()
const {
return boost::shared_ptr<PricingEngine>(new
MCHimalayaEngine<RNG,S>(process_,
brownianBridge_,
antithetic_,
samples_,
tolerance_,
maxSamples_,
seed_));
}
}
#endif
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