mcperformanceengine.hpp

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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
 
/*
 Copyright (C) 2008 Master IMAFA - Polytech'Nice Sophia - Université de Nice Sophia Antipolis
 
 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.
*/
 
#ifndef quantlib_mc_performance_engine_hpp
#define quantlib_mc_performance_engine_hpp
 
#include <ql/exercise.hpp>
#include <ql/instruments/cliquetoption.hpp>
#include <ql/pricingengines/mcsimulation.hpp>
#include <ql/processes/blackscholesprocess.hpp>
#include <utility>
 
namespace QuantLib {
 
    //! Pricing engine for performance options using Monte Carlo simulation
    template<class RNG = PseudoRandom, class S = Statistics>
    class MCPerformanceEngine : public CliquetOption::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
        MCPerformanceEngine(ext::shared_ptr<GeneralizedBlackScholesProcess> process,
                            bool brownianBridge,
                            bool antitheticVariate,
                            Size requiredSamples,
                            Real requiredTolerance,
                            Size maxSamples,
                            BigNatural seed);
        void calculate() const override {
            McSimulation<SingleVariate,RNG,S>::calculate(requiredTolerance_,
                                                         requiredSamples_,
                                                         maxSamples_);
            results_.value = this->mcModel_->sampleAccumulator().mean();
            if constexpr (RNG::allowsErrorEstimate)
                results_.errorEstimate =
                    this->mcModel_->sampleAccumulator().errorEstimate();
        }
 
      protected:
        // McSimulation implementation
        TimeGrid timeGrid() const override;
        ext::shared_ptr<path_generator_type> pathGenerator() const override {
 
            TimeGrid grid = this->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 requiredSamples_, maxSamples_;
        Real requiredTolerance_;
        bool brownianBridge_;
        BigNatural seed_;
    };
 
 
    //! Monte Carlo performance-option engine factory
    template <class RNG = PseudoRandom, class S = Statistics>
    class MakeMCPerformanceEngine {
      public:
        MakeMCPerformanceEngine(ext::shared_ptr<GeneralizedBlackScholesProcess>);
        // named parameters
        MakeMCPerformanceEngine& withBrownianBridge(bool b = true);
        MakeMCPerformanceEngine& withAntitheticVariate(bool b = true);
        MakeMCPerformanceEngine& withSamples(Size samples);
        MakeMCPerformanceEngine& withAbsoluteTolerance(Real tolerance);
        MakeMCPerformanceEngine& withMaxSamples(Size samples);
        MakeMCPerformanceEngine& 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 samples_, maxSamples_;
        Real tolerance_;
        BigNatural seed_ = 0;
    };
 
 
 
    class PerformanceOptionPathPricer : public PathPricer<Path> {
      public:
        PerformanceOptionPathPricer(Option::Type type,
                                    Real strike,
                                    std::vector<DiscountFactor> discounts);
        Real operator()(const Path& path) const override;
 
      private:
        Real strike_;
        Option::Type type_;
        std::vector<DiscountFactor> discounts_;
    };
 
 
    // template definitions
 
    template <class RNG, class S>
    inline MCPerformanceEngine<RNG, S>::MCPerformanceEngine(
        ext::shared_ptr<GeneralizedBlackScholesProcess> process,
        bool brownianBridge,
        bool antitheticVariate,
        Size requiredSamples,
        Real requiredTolerance,
        Size maxSamples,
        BigNatural seed)
    : McSimulation<SingleVariate, RNG, S>(antitheticVariate, false), process_(std::move(process)),
      requiredSamples_(requiredSamples), maxSamples_(maxSamples),
      requiredTolerance_(requiredTolerance), brownianBridge_(brownianBridge), seed_(seed) {
        registerWith(process_);
    }
 
 
    template <class RNG, class S>
    inline TimeGrid MCPerformanceEngine<RNG,S>::timeGrid() const {
 
        std::vector<Time> fixingTimes;
        fixingTimes.reserve(arguments_.resetDates.size());
        for (Size i=0; i<arguments_.resetDates.size(); i++)
            fixingTimes.push_back(process_->time(arguments_.resetDates[i]));
        fixingTimes.push_back(process_->time(arguments_.exercise->lastDate()));
 
        return TimeGrid(fixingTimes.begin(), fixingTimes.end());
    }
 
 
    template <class RNG, class S>
    inline
    ext::shared_ptr<typename MCPerformanceEngine<RNG,S>::path_pricer_type>
    MCPerformanceEngine<RNG,S>::pathPricer() const {
 
        ext::shared_ptr<PercentageStrikePayoff> payoff =
            ext::dynamic_pointer_cast<PercentageStrikePayoff>(
                this->arguments_.payoff);
        QL_REQUIRE(payoff, "non-percentage payoff given");
 
        ext::shared_ptr<EuropeanExercise> exercise =
            ext::dynamic_pointer_cast<EuropeanExercise>(
                this->arguments_.exercise);
        QL_REQUIRE(exercise, "wrong exercise given");
 
        std::vector<DiscountFactor> discounts;
 
        discounts.reserve(arguments_.resetDates.size());
        for (Size k=0;k<arguments_.resetDates.size();k++) {
            discounts.push_back(this->process_->riskFreeRate()->discount(
                                                   arguments_.resetDates[k]));
        }
        discounts.push_back(this->process_->riskFreeRate()->discount(
                                            arguments_.exercise->lastDate()));
 
        return ext::shared_ptr<
            typename MCPerformanceEngine<RNG,S>::path_pricer_type>(
                         new PerformanceOptionPathPricer(payoff->optionType(),
                                                         payoff->strike(),
                                                         discounts));
    }
 
 
    template <class RNG, class S>
    inline MakeMCPerformanceEngine<RNG, S>::MakeMCPerformanceEngine(
        ext::shared_ptr<GeneralizedBlackScholesProcess> process)
    : process_(std::move(process)), samples_(Null<Size>()), maxSamples_(Null<Size>()),
      tolerance_(Null<Real>()) {}
 
    template <class RNG, class S>
    inline MakeMCPerformanceEngine<RNG,S>&
    MakeMCPerformanceEngine<RNG,S>::withBrownianBridge(bool brownianBridge) {
        brownianBridge_ = brownianBridge;
        return *this;
    }
 
    template <class RNG, class S>
    inline MakeMCPerformanceEngine<RNG,S>&
    MakeMCPerformanceEngine<RNG,S>::withAntitheticVariate(bool b) {
        antithetic_ = b;
        return *this;
    }
 
    template <class RNG, class S>
    inline MakeMCPerformanceEngine<RNG,S>&
    MakeMCPerformanceEngine<RNG,S>::withSamples(Size samples) {
        QL_REQUIRE(tolerance_ == Null<Real>(),
                   "tolerance already set");
        samples_ = samples;
        return *this;
    }
 
    template <class RNG, class S>
    inline MakeMCPerformanceEngine<RNG,S>&
    MakeMCPerformanceEngine<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 MakeMCPerformanceEngine<RNG,S>&
    MakeMCPerformanceEngine<RNG,S>::withMaxSamples(Size samples) {
        maxSamples_ = samples;
        return *this;
    }
 
    template <class RNG, class S>
    inline MakeMCPerformanceEngine<RNG,S>&
    MakeMCPerformanceEngine<RNG,S>::withSeed(BigNatural seed) {
        seed_ = seed;
        return *this;
    }
 
    template <class RNG, class S>
    inline
    MakeMCPerformanceEngine<RNG,S>::operator ext::shared_ptr<PricingEngine>()
                                                                      const {
        return ext::shared_ptr<PricingEngine>(new
            MCPerformanceEngine<RNG,S>(process_,
                                       brownianBridge_,
                                       antithetic_,
                                       samples_,
                                       tolerance_,
                                       maxSamples_,
                                       seed_));
    }
 
}
 
 
#endif