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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2020 Lew Wei Hao
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
<https://www.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 mclookbackengine.hpp
\brief Monte Carlo lookback fixed engines
*/
#ifndef quantlib_mc_lookback_engines_hpp
#define quantlib_mc_lookback_engines_hpp
#include <ql/exercise.hpp>
#include <ql/instruments/lookbackoption.hpp>
#include <ql/pricingengines/mcsimulation.hpp>
#include <ql/processes/blackscholesprocess.hpp>
#include <utility>
namespace QuantLib {
//! Monte Carlo lookback-option engine
template <class I, class RNG = PseudoRandom, class S = Statistics>
class MCLookbackEngine : public I::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;
// constructor
MCLookbackEngine(ext::shared_ptr<GeneralizedBlackScholesProcess> process,
Size timeSteps,
Size timeStepsPerYear,
bool brownianBridge,
bool antithetic,
Size requiredSamples,
Real requiredTolerance,
Size maxSamples,
BigNatural seed);
void calculate() const override {
Real spot = process_->x0();
QL_REQUIRE(spot > 0.0, "negative or null underlying given");
McSimulation<SingleVariate,RNG,S>::calculate(requiredTolerance_,
requiredSamples_,
maxSamples_);
this->results_.value = this->mcModel_->sampleAccumulator().mean();
if constexpr (RNG::allowsErrorEstimate)
this->results_.errorEstimate =
this->mcModel_->sampleAccumulator().errorEstimate();
}
protected:
// McSimulation implementation
TimeGrid timeGrid() const override;
ext::shared_ptr<path_generator_type> pathGenerator() const override {
TimeGrid grid = timeGrid();
typename RNG::rsg_type gen =
RNG::make_sequence_generator(grid.size()-1,seed_);
return ext::shared_ptr<path_generator_type>(
new path_generator_type(process_,
grid, gen, brownianBridge_));
}
ext::shared_ptr<path_pricer_type> pathPricer() const override;
// data members
ext::shared_ptr<GeneralizedBlackScholesProcess> process_;
Size timeSteps_, timeStepsPerYear_;
Size requiredSamples_, maxSamples_;
Real requiredTolerance_;
bool antithetic_;
bool brownianBridge_;
BigNatural seed_;
};
//! Monte Carlo lookback-option engine factory
template <class I, class RNG = PseudoRandom, class S = Statistics>
class MakeMCLookbackEngine {
public:
explicit MakeMCLookbackEngine(ext::shared_ptr<GeneralizedBlackScholesProcess>);
// named parameters
MakeMCLookbackEngine& withSteps(Size steps);
MakeMCLookbackEngine& withStepsPerYear(Size steps);
MakeMCLookbackEngine& withBrownianBridge(bool b = true);
MakeMCLookbackEngine& withAntitheticVariate(bool b = true);
MakeMCLookbackEngine& withSamples(Size samples);
MakeMCLookbackEngine& withAbsoluteTolerance(Real tolerance);
MakeMCLookbackEngine& withMaxSamples(Size samples);
MakeMCLookbackEngine& withSeed(BigNatural seed);
// conversion to pricing engine
operator ext::shared_ptr<PricingEngine>() const;
private:
ext::shared_ptr<GeneralizedBlackScholesProcess> process_;
bool brownianBridge_ = false, antithetic_ = false;
Size steps_, stepsPerYear_, samples_, maxSamples_;
Real tolerance_;
BigNatural seed_ = 0;
};
// template definitions
template <class I, class RNG, class S>
inline MCLookbackEngine<I, RNG, S>::MCLookbackEngine(
ext::shared_ptr<GeneralizedBlackScholesProcess> process,
Size timeSteps,
Size timeStepsPerYear,
bool brownianBridge,
bool antitheticVariate,
Size requiredSamples,
Real requiredTolerance,
Size maxSamples,
BigNatural seed)
: McSimulation<SingleVariate, RNG, S>(antitheticVariate, false), process_(std::move(process)),
timeSteps_(timeSteps), timeStepsPerYear_(timeStepsPerYear), requiredSamples_(requiredSamples),
maxSamples_(maxSamples), requiredTolerance_(requiredTolerance),
brownianBridge_(brownianBridge), seed_(seed) {
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 I, class RNG, class S>
inline TimeGrid MCLookbackEngine<I,RNG,S>::timeGrid() const {
Time residualTime = process_->time(this->arguments_.exercise->lastDate());
if (timeSteps_ != Null<Size>()) {
return TimeGrid(residualTime, timeSteps_);
} else if (timeStepsPerYear_ != Null<Size>()) {
Size steps = static_cast<Size>(timeStepsPerYear_*residualTime);
return TimeGrid(residualTime, std::max<Size>(steps, 1));
} else {
QL_FAIL("time steps not specified");
}
}
namespace detail {
// these functions are specialized for each of the instruments.
ext::shared_ptr<PathPricer<Path> >
mc_lookback_path_pricer(
const ContinuousFixedLookbackOption::arguments& args,
const GeneralizedBlackScholesProcess& process,
DiscountFactor discount);
ext::shared_ptr<PathPricer<Path> >
mc_lookback_path_pricer(
const ContinuousPartialFixedLookbackOption::arguments& args,
const GeneralizedBlackScholesProcess& process,
DiscountFactor discount);
ext::shared_ptr<PathPricer<Path> >
mc_lookback_path_pricer(
const ContinuousFloatingLookbackOption::arguments& args,
const GeneralizedBlackScholesProcess& process,
DiscountFactor discount);
ext::shared_ptr<PathPricer<Path> >
mc_lookback_path_pricer(
const ContinuousPartialFloatingLookbackOption::arguments& args,
const GeneralizedBlackScholesProcess& process,
DiscountFactor discount);
}
template <class I, class RNG, class S>
inline ext::shared_ptr<typename MCLookbackEngine<I,RNG,S>::path_pricer_type>
MCLookbackEngine<I,RNG,S>::pathPricer() const {
TimeGrid grid = this->timeGrid();
DiscountFactor discount = this->process_->riskFreeRate()->discount(grid.back());
return detail::mc_lookback_path_pricer(this->arguments_,
*(this->process_),
discount);
}
template <class I, class RNG, class S>
inline MakeMCLookbackEngine<I, RNG, S>::MakeMCLookbackEngine(
ext::shared_ptr<GeneralizedBlackScholesProcess> process)
: process_(std::move(process)), steps_(Null<Size>()), stepsPerYear_(Null<Size>()),
samples_(Null<Size>()), maxSamples_(Null<Size>()), tolerance_(Null<Real>()) {}
template <class I, class RNG, class S>
inline MakeMCLookbackEngine<I,RNG,S>&
MakeMCLookbackEngine<I,RNG,S>::withSteps(Size steps) {
steps_ = steps;
return *this;
}
template <class I, class RNG, class S>
inline MakeMCLookbackEngine<I,RNG,S>&
MakeMCLookbackEngine<I,RNG,S>::withStepsPerYear(Size steps) {
stepsPerYear_ = steps;
return *this;
}
template <class I, class RNG, class S>
inline MakeMCLookbackEngine<I,RNG,S>&
MakeMCLookbackEngine<I,RNG,S>::withBrownianBridge(bool brownianBridge) {
brownianBridge_ = brownianBridge;
return *this;
}
template <class I, class RNG, class S>
inline MakeMCLookbackEngine<I,RNG,S>&
MakeMCLookbackEngine<I,RNG,S>::withAntitheticVariate(bool b) {
antithetic_ = b;
return *this;
}
template <class I, class RNG, class S>
inline MakeMCLookbackEngine<I,RNG,S>&
MakeMCLookbackEngine<I,RNG,S>::withSamples(Size samples) {
QL_REQUIRE(tolerance_ == Null<Real>(),
"tolerance already set");
samples_ = samples;
return *this;
}
template <class I, class RNG, class S>
inline MakeMCLookbackEngine<I,RNG,S>&
MakeMCLookbackEngine<I,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 I, class RNG, class S>
inline MakeMCLookbackEngine<I,RNG,S>&
MakeMCLookbackEngine<I,RNG,S>::withMaxSamples(Size samples) {
maxSamples_ = samples;
return *this;
}
template <class I, class RNG, class S>
inline MakeMCLookbackEngine<I,RNG,S>&
MakeMCLookbackEngine<I,RNG,S>::withSeed(BigNatural seed) {
seed_ = seed;
return *this;
}
template <class I, class RNG, class S>
inline MakeMCLookbackEngine<I,RNG,S>::operator ext::shared_ptr<PricingEngine>() const {
QL_REQUIRE(steps_ != Null<Size>() || stepsPerYear_ != Null<Size>(),
"number of steps not given");
QL_REQUIRE(steps_ == Null<Size>() || stepsPerYear_ == Null<Size>(),
"number of steps overspecified");
return ext::shared_ptr<PricingEngine>(
new MCLookbackEngine<I,RNG,S>(process_,
steps_,
stepsPerYear_,
brownianBridge_,
antithetic_,
samples_,
tolerance_,
maxSamples_,
seed_));
}
}
#endif
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