/usr/include/ql/pricingengines/asian/mcdiscreteasianengine.hpp is in libquantlib0-dev 1.7.1-1.
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/*
Copyright (C) 2000, 2001, 2002, 2003 RiskMap srl
Copyright (C) 2003, 2004 Ferdinando Ametrano
Copyright (C) 2007, 2008 StatPro Italia srl
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 mcdiscreteasianengine.hpp
\brief Monte Carlo pricing engine for discrete average Asians
*/
#ifndef quantlib_mcdiscreteasian_engine_hpp
#define quantlib_mcdiscreteasian_engine_hpp
#include <ql/pricingengines/mcsimulation.hpp>
#include <ql/instruments/asianoption.hpp>
#include <ql/processes/blackscholesprocess.hpp>
namespace QuantLib {
//! Pricing engine for discrete average Asians using Monte Carlo simulation
/*! \warning control-variate calculation is disabled under VC++6.
\ingroup asianengines
*/
template<class RNG = PseudoRandom, class S = Statistics>
class MCDiscreteAveragingAsianEngine :
public DiscreteAveragingAsianOption::engine,
public McSimulation<SingleVariate,RNG,S> {
public:
typedef
typename McSimulation<SingleVariate,RNG,S>::path_generator_type
path_generator_type;
typedef typename McSimulation<SingleVariate,RNG,S>::path_pricer_type
path_pricer_type;
typedef typename McSimulation<SingleVariate,RNG,S>::stats_type
stats_type;
// constructor
MCDiscreteAveragingAsianEngine(
const boost::shared_ptr<GeneralizedBlackScholesProcess>& process,
bool brownianBridge,
bool antitheticVariate,
bool controlVariate,
Size requiredSamples,
Real requiredTolerance,
Size maxSamples,
BigNatural seed);
void calculate() const {
McSimulation<SingleVariate,RNG,S>::calculate(requiredTolerance_,
requiredSamples_,
maxSamples_);
results_.value = this->mcModel_->sampleAccumulator().mean();
if (this->controlVariate_) {
// control variate might lead to small negative
// option values for deep OTM options
this->results_.value = std::max(0.0, this->results_.value);
}
if (RNG::allowsErrorEstimate)
results_.errorEstimate =
this->mcModel_->sampleAccumulator().errorEstimate();
}
protected:
// McSimulation implementation
TimeGrid timeGrid() const;
boost::shared_ptr<path_generator_type> pathGenerator() const {
TimeGrid grid = this->timeGrid();
typename RNG::rsg_type gen =
RNG::make_sequence_generator(grid.size()-1,seed_);
return boost::shared_ptr<path_generator_type>(
new path_generator_type(process_, grid,
gen, brownianBridge_));
}
Real controlVariateValue() const;
// data members
boost::shared_ptr<GeneralizedBlackScholesProcess> process_;
Size requiredSamples_, maxSamples_;
Real requiredTolerance_;
bool brownianBridge_;
BigNatural seed_;
};
// template definitions
template<class RNG, class S>
inline
MCDiscreteAveragingAsianEngine<RNG,S>::MCDiscreteAveragingAsianEngine(
const boost::shared_ptr<GeneralizedBlackScholesProcess>& process,
bool brownianBridge,
bool antitheticVariate,
bool controlVariate,
Size requiredSamples,
Real requiredTolerance,
Size maxSamples,
BigNatural seed)
: McSimulation<SingleVariate,RNG,S>(antitheticVariate, controlVariate),
process_(process), requiredSamples_(requiredSamples),
maxSamples_(maxSamples), requiredTolerance_(requiredTolerance),
brownianBridge_(brownianBridge), seed_(seed) {
registerWith(process_);
}
template <class RNG, class S>
inline TimeGrid MCDiscreteAveragingAsianEngine<RNG,S>::timeGrid() const {
Date referenceDate = process_->riskFreeRate()->referenceDate();
DayCounter voldc = process_->blackVolatility()->dayCounter();
std::vector<Time> fixingTimes;
Size i;
for (i=0; i<arguments_.fixingDates.size(); i++) {
if (arguments_.fixingDates[i]>=referenceDate) {
Time t = voldc.yearFraction(referenceDate,
arguments_.fixingDates[i]);
fixingTimes.push_back(t);
}
}
return TimeGrid(fixingTimes.begin(), fixingTimes.end());
}
template<class RNG, class S>
inline
Real MCDiscreteAveragingAsianEngine<RNG,S>::controlVariateValue() const {
boost::shared_ptr<PricingEngine> controlPE =
this->controlPricingEngine();
QL_REQUIRE(controlPE,
"engine does not provide "
"control variation pricing engine");
DiscreteAveragingAsianOption::arguments* controlArguments =
dynamic_cast<DiscreteAveragingAsianOption::arguments*>(
controlPE->getArguments());
*controlArguments = arguments_;
controlPE->calculate();
const DiscreteAveragingAsianOption::results* controlResults =
dynamic_cast<const DiscreteAveragingAsianOption::results*>(
controlPE->getResults());
return controlResults->value;
}
}
#endif
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