mc_discr_arith_av_price.hpp

/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
 
/*
 Copyright (C) 2000, 2001, 2002, 2003 RiskMap srl
 Copyright (C) 2003, 2004 Ferdinando Ametrano
 
 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_discrete_arithmetic_average_price_asian_engine_hpp
#define quantlib_mc_discrete_arithmetic_average_price_asian_engine_hpp
 
#include <ql/exercise.hpp>
#include <ql/pricingengines/asian/analytic_discr_geom_av_price.hpp>
#include <ql/pricingengines/asian/mc_discr_geom_av_price.hpp>
#include <ql/processes/blackscholesprocess.hpp>
#include <utility>
 
namespace QuantLib {
 
 
    template <class RNG = PseudoRandom, class S = Statistics>
    class MCDiscreteArithmeticAPEngine
        : public MCDiscreteAveragingAsianEngineBase<SingleVariate,RNG,S> {
      public:
        typedef
        typename MCDiscreteAveragingAsianEngineBase<SingleVariate,RNG,S>::path_generator_type
            path_generator_type;
        typedef
        typename MCDiscreteAveragingAsianEngineBase<SingleVariate,RNG,S>::path_pricer_type
            path_pricer_type;
        typedef typename MCDiscreteAveragingAsianEngineBase<SingleVariate,RNG,S>::stats_type
            stats_type;
        // constructor
        MCDiscreteArithmeticAPEngine(
             const ext::shared_ptr<GeneralizedBlackScholesProcess>& process,
             bool brownianBridge,
             bool antitheticVariate,
             bool controlVariate,
             Size requiredSamples,
             Real requiredTolerance,
             Size maxSamples,
             BigNatural seed);
      protected:
        ext::shared_ptr<path_pricer_type> pathPricer() const override;
        ext::shared_ptr<path_pricer_type> controlPathPricer() const override;
        ext::shared_ptr<PricingEngine> controlPricingEngine() const override {
            ext::shared_ptr<GeneralizedBlackScholesProcess> process =
                ext::dynamic_pointer_cast<GeneralizedBlackScholesProcess>(
                    this->process_);
            QL_REQUIRE(process, "Black-Scholes process required");
            return ext::shared_ptr<PricingEngine>(new
                AnalyticDiscreteGeometricAveragePriceAsianEngine(process));
        }
    };
 
 
    class ArithmeticAPOPathPricer : public PathPricer<Path> {
      public:
        ArithmeticAPOPathPricer(Option::Type type,
                                Real strike,
                                DiscountFactor discount,
                                Real runningSum = 0.0,
                                Size pastFixings = 0);
        Real operator()(const Path& path) const override;
 
      private:
        PlainVanillaPayoff payoff_;
        DiscountFactor discount_;
        Real runningSum_;
        Size pastFixings_;
    };
 
 
    // inline definitions
 
    template <class RNG, class S>
    inline
    MCDiscreteArithmeticAPEngine<RNG,S>::MCDiscreteArithmeticAPEngine(
             const ext::shared_ptr<GeneralizedBlackScholesProcess>& process,
             bool brownianBridge,
             bool antitheticVariate,
             bool controlVariate,
             Size requiredSamples,
             Real requiredTolerance,
             Size maxSamples,
             BigNatural seed)
    : MCDiscreteAveragingAsianEngineBase<SingleVariate,RNG,S>(process,
                                                              brownianBridge,
                                                              antitheticVariate,
                                                              controlVariate,
                                                              requiredSamples,
                                                              requiredTolerance,
                                                              maxSamples,
                                                              seed) {}
 
    template <class RNG, class S>
    inline
    ext::shared_ptr<
            typename MCDiscreteArithmeticAPEngine<RNG,S>::path_pricer_type>
        MCDiscreteArithmeticAPEngine<RNG,S>::pathPricer() const {
 
        ext::shared_ptr<PlainVanillaPayoff> payoff =
            ext::dynamic_pointer_cast<PlainVanillaPayoff>(
                this->arguments_.payoff);
        QL_REQUIRE(payoff, "non-plain payoff given");
 
        ext::shared_ptr<EuropeanExercise> exercise =
            ext::dynamic_pointer_cast<EuropeanExercise>(
                this->arguments_.exercise);
        QL_REQUIRE(exercise, "wrong exercise given");
 
        ext::shared_ptr<GeneralizedBlackScholesProcess> process =
            ext::dynamic_pointer_cast<GeneralizedBlackScholesProcess>(
                this->process_);
        QL_REQUIRE(process, "Black-Scholes process required");
 
        return ext::shared_ptr<typename
            MCDiscreteArithmeticAPEngine<RNG,S>::path_pricer_type>(
                new ArithmeticAPOPathPricer(
                    payoff->optionType(),
                    payoff->strike(),
                    process->riskFreeRate()->discount(exercise->lastDate()),
                    this->arguments_.runningAccumulator,
                    this->arguments_.pastFixings));
    }
 
    template <class RNG, class S>
    inline
    ext::shared_ptr<
            typename MCDiscreteArithmeticAPEngine<RNG,S>::path_pricer_type>
        MCDiscreteArithmeticAPEngine<RNG,S>::controlPathPricer() const {
 
        ext::shared_ptr<PlainVanillaPayoff> payoff =
            ext::dynamic_pointer_cast<PlainVanillaPayoff>(
                this->arguments_.payoff);
        QL_REQUIRE(payoff, "non-plain payoff given");
 
        ext::shared_ptr<EuropeanExercise> exercise =
            ext::dynamic_pointer_cast<EuropeanExercise>(
                this->arguments_.exercise);
        QL_REQUIRE(exercise, "wrong exercise given");
 
        ext::shared_ptr<GeneralizedBlackScholesProcess> process =
            ext::dynamic_pointer_cast<GeneralizedBlackScholesProcess>(
                this->process_);
        QL_REQUIRE(process, "Black-Scholes process required");
 
        // for seasoned option the geometric strike might be rescaled
        // to obtain an equivalent arithmetic strike.
        // Any change applied here MUST be applied to the analytic engine too
        return ext::shared_ptr<typename
            MCDiscreteArithmeticAPEngine<RNG,S>::path_pricer_type>(
            new GeometricAPOPathPricer(
              payoff->optionType(),
              payoff->strike(),
              process->riskFreeRate()->discount(this->timeGrid().back())));
    }
 
    template <class RNG = PseudoRandom, class S = Statistics>
    class MakeMCDiscreteArithmeticAPEngine {
      public:
        explicit MakeMCDiscreteArithmeticAPEngine(
            ext::shared_ptr<GeneralizedBlackScholesProcess> process);
        // named parameters
        MakeMCDiscreteArithmeticAPEngine& withBrownianBridge(bool b = true);
        MakeMCDiscreteArithmeticAPEngine& withSamples(Size samples);
        MakeMCDiscreteArithmeticAPEngine& withAbsoluteTolerance(Real tolerance);
        MakeMCDiscreteArithmeticAPEngine& withMaxSamples(Size samples);
        MakeMCDiscreteArithmeticAPEngine& withSeed(BigNatural seed);
        MakeMCDiscreteArithmeticAPEngine& withAntitheticVariate(bool b = true);
        MakeMCDiscreteArithmeticAPEngine& withControlVariate(bool b = true);
        // conversion to pricing engine
        operator ext::shared_ptr<PricingEngine>() const;
      private:
        ext::shared_ptr<GeneralizedBlackScholesProcess> process_;
        bool antithetic_ = false, controlVariate_ = false;
        Size samples_, maxSamples_;
        Real tolerance_;
        bool brownianBridge_ = true;
        BigNatural seed_ = 0;
    };
 
    template <class RNG, class S>
    inline MakeMCDiscreteArithmeticAPEngine<RNG, S>::MakeMCDiscreteArithmeticAPEngine(
        ext::shared_ptr<GeneralizedBlackScholesProcess> process)
    : process_(std::move(process)), samples_(Null<Size>()), maxSamples_(Null<Size>()),
      tolerance_(Null<Real>()) {}
 
    template <class RNG, class S>
    inline MakeMCDiscreteArithmeticAPEngine<RNG,S>&
    MakeMCDiscreteArithmeticAPEngine<RNG,S>::withSamples(Size samples) {
        QL_REQUIRE(tolerance_ == Null<Real>(),
                   "tolerance already set");
        samples_ = samples;
        return *this;
    }
 
    template <class RNG, class S>
    inline MakeMCDiscreteArithmeticAPEngine<RNG,S>&
    MakeMCDiscreteArithmeticAPEngine<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 MakeMCDiscreteArithmeticAPEngine<RNG,S>&
    MakeMCDiscreteArithmeticAPEngine<RNG,S>::withMaxSamples(Size samples) {
        maxSamples_ = samples;
        return *this;
    }
 
    template <class RNG, class S>
    inline MakeMCDiscreteArithmeticAPEngine<RNG,S>&
    MakeMCDiscreteArithmeticAPEngine<RNG,S>::withSeed(BigNatural seed) {
        seed_ = seed;
        return *this;
    }
 
    template <class RNG, class S>
    inline MakeMCDiscreteArithmeticAPEngine<RNG,S>&
    MakeMCDiscreteArithmeticAPEngine<RNG,S>::withBrownianBridge(bool b) {
        brownianBridge_ = b;
        return *this;
    }
 
    template <class RNG, class S>
    inline MakeMCDiscreteArithmeticAPEngine<RNG,S>&
    MakeMCDiscreteArithmeticAPEngine<RNG,S>::withAntitheticVariate(bool b) {
        antithetic_ = b;
        return *this;
    }
 
    template <class RNG, class S>
    inline MakeMCDiscreteArithmeticAPEngine<RNG,S>&
    MakeMCDiscreteArithmeticAPEngine<RNG,S>::withControlVariate(bool b) {
        controlVariate_ = b;
        return *this;
    }
 
    template <class RNG, class S>
    inline
    MakeMCDiscreteArithmeticAPEngine<RNG,S>::operator ext::shared_ptr<PricingEngine>()
                                                                      const {
        return ext::shared_ptr<PricingEngine>(new
            MCDiscreteArithmeticAPEngine<RNG,S>(process_,
                                                brownianBridge_,
                                                antithetic_, controlVariate_,
                                                samples_, tolerance_,
                                                maxSamples_,
                                                seed_));
    }
 
 
 
}
 
 
#endif