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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2006 Banca Profilo S.p.A.
Copyright (C) 2006 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 mchullwhiteengine.hpp
\brief Monte Carlo Hull-White engine for cap/floors
*/
#ifndef quantlib_mc_hull_white_cap_floor_engine_hpp
#define quantlib_mc_hull_white_cap_floor_engine_hpp
#include <ql/instruments/capfloor.hpp>
#include <ql/pricingengines/mcsimulation.hpp>
#include <ql/processes/hullwhiteprocess.hpp>
#include <ql/models/shortrate/onefactormodels/hullwhite.hpp>
namespace QuantLib {
namespace detail {
class HullWhiteCapFloorPricer : public PathPricer<Path> {
public:
HullWhiteCapFloorPricer(const CapFloor::arguments&,
const boost::shared_ptr<HullWhite>&,
Time forwardMeasureTime);
Real operator()(const Path& path) const;
private:
CapFloor::arguments args_;
boost::shared_ptr<HullWhite> model_;
Time forwardMeasureTime_;
DiscountFactor endDiscount_;
std::vector<Time> startTimes_, endTimes_, fixingTimes_;
};
}
//! Monte Carlo Hull-White engine for cap/floors
/*! \ingroup capfloorengines */
template <class RNG = PseudoRandom, class S = Statistics>
class MCHullWhiteCapFloorEngine
: public CapFloor::engine,
public McSimulation<SingleVariate,RNG,S> {
private:
typedef McSimulation<SingleVariate,RNG,S> simulation;
boost::shared_ptr<HullWhite> model_;
Size requiredSamples_, maxSamples_;
Real requiredTolerance_;
bool brownianBridge_;
BigNatural seed_;
public:
typedef typename simulation::path_generator_type path_generator_type;
typedef typename simulation::path_pricer_type path_pricer_type;
typedef typename simulation::stats_type stats_type;
MCHullWhiteCapFloorEngine(const boost::shared_ptr<HullWhite>& model,
bool brownianBridge,
bool antitheticVariate,
Size requiredSamples,
Real requiredTolerance,
Size maxSamples,
BigNatural seed)
: McSimulation<SingleVariate,RNG,S>(antitheticVariate, false),
model_(model), requiredSamples_(requiredSamples),
maxSamples_(maxSamples), requiredTolerance_(requiredTolerance),
brownianBridge_(brownianBridge), seed_(seed) {
registerWith(model_);
}
void calculate() const {
simulation::calculate(requiredTolerance_,
requiredSamples_,
maxSamples_);
results_.value = this->mcModel_->sampleAccumulator().mean();
if (RNG::allowsErrorEstimate)
results_.errorEstimate =
this->mcModel_->sampleAccumulator().errorEstimate();
}
protected:
boost::shared_ptr<path_pricer_type> pathPricer() const {
Date referenceDate = model_->termStructure()->referenceDate();
DayCounter dayCounter = model_->termStructure()->dayCounter();
Time forwardMeasureTime =
dayCounter.yearFraction(referenceDate,
arguments_.endDates.back());
return boost::shared_ptr<path_pricer_type>(
new detail::HullWhiteCapFloorPricer(arguments_, model_,
forwardMeasureTime));
}
TimeGrid timeGrid() const {
Date referenceDate = model_->termStructure()->referenceDate();
DayCounter dayCounter = model_->termStructure()->dayCounter();
// only add future fixing times...
std::vector<Time> times;
for (Size i=0; i<arguments_.fixingDates.size(); i++) {
if (arguments_.fixingDates[i] > referenceDate)
times.push_back(
dayCounter.yearFraction(referenceDate,
arguments_.fixingDates[i]));
}
// ...and maturity.
times.push_back(
dayCounter.yearFraction(referenceDate,
arguments_.endDates.back()));
return TimeGrid(times.begin(), times.end());
}
boost::shared_ptr<path_generator_type> pathGenerator() const {
Handle<YieldTermStructure> curve = model_->termStructure();
Date referenceDate = curve->referenceDate();
DayCounter dayCounter = curve->dayCounter();
Time forwardMeasureTime =
dayCounter.yearFraction(referenceDate,
arguments_.endDates.back());
Array parameters = model_->params();
Real a = parameters[0], sigma = parameters[1];
boost::shared_ptr<HullWhiteForwardProcess> process(
new HullWhiteForwardProcess(curve, a, sigma));
process->setForwardMeasureTime(forwardMeasureTime);
TimeGrid grid = this->timeGrid();
typename RNG::rsg_type generator =
RNG::make_sequence_generator(grid.size()-1,seed_);
return boost::shared_ptr<path_generator_type>(
new path_generator_type(process, grid, generator,
brownianBridge_));
}
};
//! Monte Carlo Hull-White cap-floor engine factory
template <class RNG = PseudoRandom, class S = Statistics>
class MakeMCHullWhiteCapFloorEngine {
public:
MakeMCHullWhiteCapFloorEngine(const boost::shared_ptr<HullWhite>&);
// named parameters
MakeMCHullWhiteCapFloorEngine& withBrownianBridge(bool b = true);
MakeMCHullWhiteCapFloorEngine& withSamples(Size samples);
MakeMCHullWhiteCapFloorEngine& withAbsoluteTolerance(Real tolerance);
MakeMCHullWhiteCapFloorEngine& withMaxSamples(Size samples);
MakeMCHullWhiteCapFloorEngine& withSeed(BigNatural seed);
MakeMCHullWhiteCapFloorEngine& withAntitheticVariate(bool b = true);
// conversion to pricing engine
operator boost::shared_ptr<PricingEngine>() const;
private:
boost::shared_ptr<HullWhite> model_;
bool antithetic_;
Size samples_, maxSamples_;
Real tolerance_;
bool brownianBridge_;
BigNatural seed_;
};
// inline definitions
template <class RNG, class S>
inline
MakeMCHullWhiteCapFloorEngine<RNG,S>::MakeMCHullWhiteCapFloorEngine(
const boost::shared_ptr<HullWhite>& model)
: model_(model), antithetic_(false),
samples_(Null<Size>()), maxSamples_(Null<Size>()),
tolerance_(Null<Real>()), brownianBridge_(false), seed_(0) {}
template <class RNG, class S>
inline MakeMCHullWhiteCapFloorEngine<RNG,S>&
MakeMCHullWhiteCapFloorEngine<RNG,S>::withSamples(Size samples) {
QL_REQUIRE(tolerance_ == Null<Real>(),
"tolerance already set");
samples_ = samples;
return *this;
}
template <class RNG, class S>
inline MakeMCHullWhiteCapFloorEngine<RNG,S>&
MakeMCHullWhiteCapFloorEngine<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 MakeMCHullWhiteCapFloorEngine<RNG,S>&
MakeMCHullWhiteCapFloorEngine<RNG,S>::withMaxSamples(Size samples) {
maxSamples_ = samples;
return *this;
}
template <class RNG, class S>
inline MakeMCHullWhiteCapFloorEngine<RNG,S>&
MakeMCHullWhiteCapFloorEngine<RNG,S>::withSeed(BigNatural seed) {
seed_ = seed;
return *this;
}
template <class RNG, class S>
inline MakeMCHullWhiteCapFloorEngine<RNG,S>&
MakeMCHullWhiteCapFloorEngine<RNG,S>::withBrownianBridge(bool b) {
brownianBridge_ = b;
return *this;
}
template <class RNG, class S>
inline MakeMCHullWhiteCapFloorEngine<RNG,S>&
MakeMCHullWhiteCapFloorEngine<RNG,S>::withAntitheticVariate(bool b) {
antithetic_ = b;
return *this;
}
template <class RNG, class S>
inline MakeMCHullWhiteCapFloorEngine<RNG,S>::
operator boost::shared_ptr<PricingEngine>() const {
return boost::shared_ptr<PricingEngine>(new
MCHullWhiteCapFloorEngine<RNG,S>(model_,
brownianBridge_, antithetic_,
samples_, tolerance_,
maxSamples_, seed_));
}
}
#endif
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