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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
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 mcforwardvanillaengine.hpp
\brief Monte Carlo engine for forward-starting strike-reset vanilla options
*/
#ifndef quantlib_mcforwardvanilla_engine_hpp
#define quantlib_mcforwardvanilla_engine_hpp
#include <ql/instruments/forwardvanillaoption.hpp>
#include <ql/instruments/vanillaoption.hpp>
#include <ql/pricingengines/mcsimulation.hpp>
#include <utility>
namespace QuantLib {
//! Monte Carlo engine for forward-starting vanilla options
/*! \ingroup forwardengines
*/
template<template <class> class MC,
class RNG = PseudoRandom, class S = Statistics>
class MCForwardVanillaEngine : public GenericEngine<ForwardOptionArguments<VanillaOption::arguments>,
VanillaOption::results>,
public McSimulation<MC,RNG,S>
{
public:
typedef typename McSimulation<MC,RNG,S>::path_generator_type
path_generator_type;
typedef typename McSimulation<MC,RNG,S>::path_pricer_type
path_pricer_type;
typedef typename McSimulation<MC,RNG,S>::stats_type
stats_type;
// constructor
MCForwardVanillaEngine(ext::shared_ptr<StochasticProcess> process,
Size timeSteps,
Size timeStepsPerYear,
bool brownianBridge,
bool antitheticVariate,
Size requiredSamples,
Real requiredTolerance,
Size maxSamples,
BigNatural seed,
bool controlVariate = false);
void calculate() const override {
McSimulation<MC,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;
Real controlVariateValue() const override;
ext::shared_ptr<path_generator_type> pathGenerator() const override {
Size dimensions = process_->factors();
TimeGrid grid = this->timeGrid();
typename RNG::rsg_type gen =
RNG::make_sequence_generator(dimensions*(grid.size()-1),seed_);
return ext::shared_ptr<path_generator_type>(
new path_generator_type(process_, grid,
gen, brownianBridge_));
}
// data members
ext::shared_ptr<StochasticProcess> process_;
Size timeSteps_, timeStepsPerYear_, requiredSamples_, maxSamples_;
Real requiredTolerance_;
bool brownianBridge_;
BigNatural seed_;
};
template <template <class> class MC, class RNG, class S>
inline MCForwardVanillaEngine<MC, RNG, S>::MCForwardVanillaEngine(
ext::shared_ptr<StochasticProcess> process,
Size timeSteps,
Size timeStepsPerYear,
bool brownianBridge,
bool antitheticVariate,
Size requiredSamples,
Real requiredTolerance,
Size maxSamples,
BigNatural seed,
bool controlVariate)
: McSimulation<MC, RNG, S>(antitheticVariate, controlVariate), 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");
registerWith(process_);
}
template <template <class> class MC, class RNG, class S>
inline TimeGrid MCForwardVanillaEngine<MC,RNG,S>::timeGrid() const {
Date resetDate = arguments_.resetDate;
Date lastExerciseDate = arguments_.exercise->lastDate();
Time t1 = process_->time(resetDate);
Time t2 = process_->time(lastExerciseDate);
Size totalSteps = Null<Size>();
if (this->timeSteps_ != Null<Size>()) {
totalSteps = timeSteps_;
} else if (this->timeStepsPerYear_ != Null<Size>()) {
totalSteps = static_cast<Size>(this->timeStepsPerYear_*t2);
}
std::vector<Time> fixingTimes;
fixingTimes.push_back(t1);
fixingTimes.push_back(t2);
return TimeGrid(fixingTimes.begin(), fixingTimes.end(), totalSteps);
}
template <template <class> class MC, class RNG, class S>
inline Real MCForwardVanillaEngine<MC,RNG,S>::controlVariateValue() const {
ext::shared_ptr<PricingEngine> controlPE =
this->controlPricingEngine();
QL_REQUIRE(controlPE, "engine does not provide "
"control variation pricing engine");
// Create vanilla option arguments with the same payoff and expiry, but with
// strike-reset equal to initial spot*moneyness, price analytically
ext::shared_ptr<StrikedTypePayoff> payoff =
ext::dynamic_pointer_cast<StrikedTypePayoff>(
this->arguments_.payoff);
QL_REQUIRE(payoff, "non-plain payoff given");
Real spot = this->process_->initialValues()[0];
Real moneyness = this->arguments_.moneyness;
Real strike = moneyness * spot;
ext::shared_ptr<StrikedTypePayoff> newPayoff(new
PlainVanillaPayoff(payoff->optionType(), strike));
auto* controlArguments = dynamic_cast<VanillaOption::arguments*>(controlPE->getArguments());
controlArguments->payoff = newPayoff;
controlArguments->exercise = this->arguments_.exercise;
controlPE->calculate();
const auto* controlResults =
dynamic_cast<const VanillaOption::results*>(controlPE->getResults());
return controlResults->value;
}
}
#endif
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