/usr/include/ql/pricingengines/mcsimulation.hpp is in libquantlib0-dev 1.12-1.
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/*
Copyright (C) 2003 Ferdinando Ametrano
Copyright (C) 2000, 2001, 2002, 2003 RiskMap srl
Copyright (C) 2007 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 mcsimulation.hpp
\brief framework for Monte Carlo engines
*/
#ifndef quantlib_montecarlo_engine_hpp
#define quantlib_montecarlo_engine_hpp
#include <ql/grid.hpp>
#include <ql/methods/montecarlo/montecarlomodel.hpp>
namespace QuantLib {
//! base class for Monte Carlo engines
/*! Eventually this class might offer greeks methods. Deriving a
class from McSimulation gives an easy way to write a Monte
Carlo engine.
See McVanillaEngine as an example.
*/
template <template <class> class MC, class RNG, class S = Statistics>
class McSimulation {
public:
typedef typename MonteCarloModel<MC,RNG,S>::path_generator_type
path_generator_type;
typedef typename MonteCarloModel<MC,RNG,S>::path_pricer_type
path_pricer_type;
typedef typename MonteCarloModel<MC,RNG,S>::stats_type
stats_type;
typedef typename MonteCarloModel<MC,RNG,S>::result_type result_type;
virtual ~McSimulation() {}
//! add samples until the required absolute tolerance is reached
result_type value(Real tolerance,
Size maxSamples = QL_MAX_INTEGER,
Size minSamples = 1023) const;
//! simulate a fixed number of samples
result_type valueWithSamples(Size samples) const;
//! error estimated using the samples simulated so far
result_type errorEstimate() const;
//! access to the sample accumulator for richer statistics
const stats_type& sampleAccumulator(void) const;
//! basic calculate method provided to inherited pricing engines
void calculate(Real requiredTolerance,
Size requiredSamples,
Size maxSamples) const;
protected:
McSimulation(bool antitheticVariate,
bool controlVariate)
: antitheticVariate_(antitheticVariate),
controlVariate_(controlVariate) {}
virtual boost::shared_ptr<path_pricer_type> pathPricer() const = 0;
virtual boost::shared_ptr<path_generator_type> pathGenerator()
const = 0;
virtual TimeGrid timeGrid() const = 0;
virtual boost::shared_ptr<path_pricer_type> controlPathPricer() const {
return boost::shared_ptr<path_pricer_type>();
}
virtual boost::shared_ptr<path_generator_type>
controlPathGenerator() const {
return boost::shared_ptr<path_generator_type>();
}
virtual boost::shared_ptr<PricingEngine> controlPricingEngine() const {
return boost::shared_ptr<PricingEngine>();
}
virtual result_type controlVariateValue() const {
return Null<result_type>();
}
template <class Sequence>
static Real maxError(const Sequence& sequence) {
return *std::max_element(sequence.begin(), sequence.end());
}
static Real maxError(Real error) {
return error;
}
mutable boost::shared_ptr<MonteCarloModel<MC,RNG,S> > mcModel_;
bool antitheticVariate_, controlVariate_;
};
// inline definitions
template <template <class> class MC, class RNG, class S>
inline typename McSimulation<MC,RNG,S>::result_type
McSimulation<MC,RNG,S>::value(Real tolerance,
Size maxSamples,
Size minSamples) const {
Size sampleNumber =
mcModel_->sampleAccumulator().samples();
if (sampleNumber<minSamples) {
mcModel_->addSamples(minSamples-sampleNumber);
sampleNumber = mcModel_->sampleAccumulator().samples();
}
Size nextBatch;
Real order;
result_type error(mcModel_->sampleAccumulator().errorEstimate());
while (maxError(error) > tolerance) {
QL_REQUIRE(sampleNumber<maxSamples,
"max number of samples (" << maxSamples
<< ") reached, while error (" << error
<< ") is still above tolerance (" << tolerance << ")");
// conservative estimate of how many samples are needed
order = maxError(error*error)/tolerance/tolerance;
nextBatch =
Size(std::max<Real>(static_cast<Real>(sampleNumber)*order*0.8 - static_cast<Real>(sampleNumber),
static_cast<Real>(minSamples)));
// do not exceed maxSamples
nextBatch = std::min(nextBatch, maxSamples-sampleNumber);
sampleNumber += nextBatch;
mcModel_->addSamples(nextBatch);
error = result_type(mcModel_->sampleAccumulator().errorEstimate());
}
return result_type(mcModel_->sampleAccumulator().mean());
}
template <template <class> class MC, class RNG, class S>
inline typename McSimulation<MC,RNG,S>::result_type
McSimulation<MC,RNG,S>::valueWithSamples(Size samples) const {
Size sampleNumber = mcModel_->sampleAccumulator().samples();
QL_REQUIRE(samples>=sampleNumber,
"number of already simulated samples (" << sampleNumber
<< ") greater than requested samples (" << samples << ")");
mcModel_->addSamples(samples-sampleNumber);
return result_type(mcModel_->sampleAccumulator().mean());
}
template <template <class> class MC, class RNG, class S>
inline void McSimulation<MC,RNG,S>::calculate(Real requiredTolerance,
Size requiredSamples,
Size maxSamples) const {
QL_REQUIRE(requiredTolerance != Null<Real>() ||
requiredSamples != Null<Size>(),
"neither tolerance nor number of samples set");
//! Initialize the one-factor Monte Carlo
if (this->controlVariate_) {
result_type controlVariateValue = this->controlVariateValue();
QL_REQUIRE(controlVariateValue != Null<result_type>(),
"engine does not provide "
"control-variation price");
boost::shared_ptr<path_pricer_type> controlPP =
this->controlPathPricer();
QL_REQUIRE(controlPP,
"engine does not provide "
"control-variation path pricer");
boost::shared_ptr<path_generator_type> controlPG =
this->controlPathGenerator();
this->mcModel_ =
boost::shared_ptr<MonteCarloModel<MC,RNG,S> >(
new MonteCarloModel<MC,RNG,S>(
pathGenerator(), this->pathPricer(), stats_type(),
this->antitheticVariate_, controlPP,
controlVariateValue, controlPG));
} else {
this->mcModel_ =
boost::shared_ptr<MonteCarloModel<MC,RNG,S> >(
new MonteCarloModel<MC,RNG,S>(
pathGenerator(), this->pathPricer(), S(),
this->antitheticVariate_));
}
if (requiredTolerance != Null<Real>()) {
if (maxSamples != Null<Size>())
this->value(requiredTolerance, maxSamples);
else
this->value(requiredTolerance);
} else {
this->valueWithSamples(requiredSamples);
}
}
template <template <class> class MC, class RNG, class S>
inline typename McSimulation<MC,RNG,S>::result_type
McSimulation<MC,RNG,S>::errorEstimate() const {
return mcModel_->sampleAccumulator().errorEstimate();
}
template <template <class> class MC, class RNG, class S>
inline const typename McSimulation<MC,RNG,S>::stats_type&
McSimulation<MC,RNG,S>::sampleAccumulator() const {
return mcModel_->sampleAccumulator();
}
}
#endif
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