mcvanillaengine.hpp

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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */

/*
 Copyright (C) 2003 Ferdinando Ametrano
 Copyright (C) 2003, 2004, 2005, 2007 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
 <[e-mail]>. 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 mcvanillaengine.hpp
    \brief Monte Carlo vanilla option engine
*/

#ifndef quantlib_mcvanilla_engine_hpp
#define quantlib_mcvanilla_engine_hpp

#include <ql/pricingengines/mcsimulation.hpp>
#include <ql/instruments/vanillaoption.hpp>

namespace QuantLib {

    //! Pricing engine for vanilla options using Monte Carlo simulation
    /*! \ingroup vanillaengines */
    template <template <class> class MC, class RNG, 
              class S = Statistics, class Inst = VanillaOption>
    class MCVanillaEngine : public Inst::engine,
                            public McSimulation<MC,RNG,S> {
      public:
        void calculate() const {
            McSimulation<MC,RNG,S>::calculate(requiredTolerance_,
                                              requiredSamples_,
                                              maxSamples_);
            this->results_.value = this->mcModel_->sampleAccumulator().mean();
            if (RNG::allowsErrorEstimate)
            this->results_.errorEstimate =
                this->mcModel_->sampleAccumulator().errorEstimate();
        }
      protected:
        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;
        typedef typename McSimulation<MC,RNG,S>::result_type 
            result_type;
        // constructor
        MCVanillaEngine(Size timeSteps,
                        Size timeStepsPerYear,
                        bool brownianBridge,
                        bool antitheticVariate,
                        bool controlVariate,
                        Size requiredSamples,
                        Real requiredTolerance,
                        Size maxSamples,
                        BigNatural seed);
        // McSimulation implementation
        TimeGrid timeGrid() const;
        boost::shared_ptr<path_generator_type> pathGenerator() const {

            Size dimensions = this->arguments_.stochasticProcess->factors();
            TimeGrid grid = this->timeGrid();
            typename RNG::rsg_type generator =
                RNG::make_sequence_generator(dimensions*(grid.size()-1),seed_);
            return boost::shared_ptr<path_generator_type>(
                   new path_generator_type(this->arguments_.stochasticProcess,
                                           grid, generator, brownianBridge_));
        }
        result_type controlVariateValue() const;
        // data members
        Size timeSteps_, timeStepsPerYear_;
        Size requiredSamples_, maxSamples_;
        Real requiredTolerance_;
        bool brownianBridge_;
        BigNatural seed_;
    };


    // template definitions

    template <template <class> class MC, class RNG, class S, class Inst>
    inline MCVanillaEngine<MC,RNG,S,Inst>::MCVanillaEngine(Size timeSteps,
                                                      Size timeStepsPerYear,
                                                      bool brownianBridge,
                                                      bool antitheticVariate,
                                                      bool controlVariate,
                                                      Size requiredSamples,
                                                      Real requiredTolerance,
                                                      Size maxSamples,
                                                      BigNatural seed)
    : McSimulation<MC,RNG,S>(antitheticVariate, controlVariate),
      timeSteps_(timeSteps), timeStepsPerYear_(timeStepsPerYear),
      requiredSamples_(requiredSamples), maxSamples_(maxSamples),
      requiredTolerance_(requiredTolerance),
      brownianBridge_(brownianBridge), seed_(seed) {}

    template <template <class> class MC, class RNG, class S, class Inst>
    inline typename MCVanillaEngine<MC,RNG,S,Inst>::result_type 
    MCVanillaEngine<MC,RNG,S,Inst>::controlVariateValue() const {

        boost::shared_ptr<PricingEngine> controlPE =
            this->controlPricingEngine();
        QL_REQUIRE(controlPE,
                   "engine does not provide "
                   "control variation pricing engine");

        typename Inst::arguments* controlArguments =
                dynamic_cast<typename Inst::arguments*>(
                                                   controlPE->getArguments());

        QL_REQUIRE(controlArguments, "engine is using inconsistent arguments");

        *controlArguments = this->arguments_;
        controlPE->calculate();

        const typename Inst::results* controlResults =
                dynamic_cast<const typename Inst::results*>(
                                                     controlPE->getResults());
        QL_REQUIRE(controlResults, 
                   "engine returns an inconsistent result type");

        return result_type(controlResults->value);
    }


    template <template <class> class MC, class RNG, class S, class Inst>
    inline TimeGrid MCVanillaEngine<MC,RNG,S,Inst>::timeGrid() const {
        Date lastExerciseDate = this->arguments_.exercise->lastDate();
        Time t = this->arguments_.stochasticProcess->time(lastExerciseDate);
        if (this->timeSteps_ != Null<Size>()) {
            return TimeGrid(t, this->timeSteps_);
        } else if (this->timeStepsPerYear_ != Null<Size>()) {
            Size steps = static_cast<Size>(this->timeStepsPerYear_*t);
            return TimeGrid(t, std::max<Size>(steps, 1));
        } else {
            QL_FAIL("time steps not specified");
        }
    }

}


#endif