/usr/include/ql/pricingengines/vanilla/mcdigitalengine.hpp is in libquantlib0-dev 1.12-1.
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/*
Copyright (C) 2002, 2003 Ferdinando Ametrano
Copyright (C) 2002, 2003 Sadruddin Rejeb
Copyright (C) 2003 Neil Firth
Copyright (C) 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
<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.
*/
/*! \file mcdigitalengine.hpp
\brief digital option Monte Carlo engine
*/
#ifndef quantlib_digital_mc_engine_hpp
#define quantlib_digital_mc_engine_hpp
#include <ql/exercise.hpp>
#include <ql/termstructures/yieldtermstructure.hpp>
#include <ql/termstructures/volatility/equityfx/blackvoltermstructure.hpp>
#include <ql/methods/montecarlo/mctraits.hpp>
#include <ql/pricingengines/vanilla/mcvanillaengine.hpp>
#include <ql/processes/blackscholesprocess.hpp>
namespace QuantLib {
//! Pricing engine for digital options using Monte Carlo simulation
/*! Uses the Brownian Bridge correction for the barrier found in
<i>
Going to Extremes: Correcting Simulation Bias in Exotic
Option Valuation - D.R. Beaglehole, P.H. Dybvig and G. Zhou
Financial Analysts Journal; Jan/Feb 1997; 53, 1. pg. 62-68
</i>
and
<i>
Simulating path-dependent options: A new approach -
M. El Babsiri and G. Noel
Journal of Derivatives; Winter 1998; 6, 2; pg. 65-83
</i>
\ingroup vanillaengines
\test the correctness of the returned value in case of
cash-or-nothing at-hit digital payoff is tested by
reproducing known good results.
*/
template<class RNG = PseudoRandom, class S = Statistics>
class MCDigitalEngine : public MCVanillaEngine<SingleVariate,RNG,S> {
public:
typedef
typename MCVanillaEngine<SingleVariate,RNG,S>::path_generator_type
path_generator_type;
typedef
typename MCVanillaEngine<SingleVariate,RNG,S>::path_pricer_type
path_pricer_type;
typedef typename MCVanillaEngine<SingleVariate,RNG,S>::stats_type
stats_type;
// constructor
MCDigitalEngine(
const boost::shared_ptr<GeneralizedBlackScholesProcess>&,
Size timeSteps,
Size timeStepsPerYear,
bool brownianBridge,
bool antitheticVariate,
Size requiredSamples,
Real requiredTolerance,
Size maxSamples,
BigNatural seed);
protected:
// McSimulation implementation
boost::shared_ptr<path_pricer_type> pathPricer() const;
};
//! Monte Carlo digital engine factory
template <class RNG = PseudoRandom, class S = Statistics>
class MakeMCDigitalEngine {
public:
MakeMCDigitalEngine(
const boost::shared_ptr<GeneralizedBlackScholesProcess>&);
// named parameters
MakeMCDigitalEngine& withSteps(Size steps);
MakeMCDigitalEngine& withStepsPerYear(Size steps);
MakeMCDigitalEngine& withBrownianBridge(bool b = true);
MakeMCDigitalEngine& withSamples(Size samples);
MakeMCDigitalEngine& withAbsoluteTolerance(Real tolerance);
MakeMCDigitalEngine& withMaxSamples(Size samples);
MakeMCDigitalEngine& withSeed(BigNatural seed);
MakeMCDigitalEngine& withAntitheticVariate(bool b = true);
// conversion to pricing engine
operator boost::shared_ptr<PricingEngine>() const;
private:
boost::shared_ptr<GeneralizedBlackScholesProcess> process_;
bool antithetic_;
Size steps_, stepsPerYear_, samples_, maxSamples_;
Real tolerance_;
bool brownianBridge_;
BigNatural seed_;
};
class DigitalPathPricer : public PathPricer<Path> {
public:
DigitalPathPricer(
const boost::shared_ptr<CashOrNothingPayoff>& payoff,
const boost::shared_ptr<AmericanExercise>& exercise,
const Handle<YieldTermStructure>& discountTS,
const boost::shared_ptr<StochasticProcess1D>& diffProcess,
const PseudoRandom::ursg_type& sequenceGen);
Real operator()(const Path& path) const;
private:
boost::shared_ptr<CashOrNothingPayoff> payoff_;
boost::shared_ptr<AmericanExercise> exercise_;
boost::shared_ptr<StochasticProcess1D> diffProcess_;
PseudoRandom::ursg_type sequenceGen_;
Handle<YieldTermStructure> discountTS_;
};
// template definitions
template<class RNG, class S>
MCDigitalEngine<RNG,S>::MCDigitalEngine(
const boost::shared_ptr<GeneralizedBlackScholesProcess>& process,
Size timeSteps,
Size timeStepsPerYear,
bool brownianBridge,
bool antitheticVariate,
Size requiredSamples,
Real requiredTolerance,
Size maxSamples,
BigNatural seed)
: MCVanillaEngine<SingleVariate,RNG,S>(process,
timeSteps,
timeStepsPerYear,
brownianBridge,
antitheticVariate,
false,
requiredSamples,
requiredTolerance,
maxSamples,
seed) {}
template <class RNG, class S>
inline
boost::shared_ptr<typename MCDigitalEngine<RNG,S>::path_pricer_type>
MCDigitalEngine<RNG,S>::pathPricer() const {
boost::shared_ptr<CashOrNothingPayoff> payoff =
boost::dynamic_pointer_cast<CashOrNothingPayoff>(
this->arguments_.payoff);
QL_REQUIRE(payoff, "wrong payoff given");
boost::shared_ptr<AmericanExercise> exercise =
boost::dynamic_pointer_cast<AmericanExercise>(
this->arguments_.exercise);
QL_REQUIRE(exercise, "wrong exercise given");
boost::shared_ptr<GeneralizedBlackScholesProcess> process =
boost::dynamic_pointer_cast<GeneralizedBlackScholesProcess>(
this->process_);
QL_REQUIRE(process, "Black-Scholes process required");
TimeGrid grid = this->timeGrid();
PseudoRandom::ursg_type sequenceGen(grid.size()-1,
PseudoRandom::urng_type(76));
return boost::shared_ptr<
typename MCDigitalEngine<RNG,S>::path_pricer_type>(
new DigitalPathPricer(payoff,
exercise,
process->riskFreeRate(),
process,
sequenceGen));
}
template <class RNG, class S>
inline MakeMCDigitalEngine<RNG,S>::MakeMCDigitalEngine(
const boost::shared_ptr<GeneralizedBlackScholesProcess>& process)
: process_(process), antithetic_(false),
steps_(Null<Size>()), stepsPerYear_(Null<Size>()),
samples_(Null<Size>()), maxSamples_(Null<Size>()),
tolerance_(Null<Real>()), brownianBridge_(false), seed_(0) {}
template <class RNG, class S>
inline MakeMCDigitalEngine<RNG,S>&
MakeMCDigitalEngine<RNG,S>::withSteps(Size steps) {
steps_ = steps;
return *this;
}
template <class RNG, class S>
inline MakeMCDigitalEngine<RNG,S>&
MakeMCDigitalEngine<RNG,S>::withStepsPerYear(Size steps) {
stepsPerYear_ = steps;
return *this;
}
template <class RNG, class S>
inline MakeMCDigitalEngine<RNG,S>&
MakeMCDigitalEngine<RNG,S>::withSamples(Size samples) {
QL_REQUIRE(tolerance_ == Null<Real>(),
"tolerance already set");
samples_ = samples;
return *this;
}
template <class RNG, class S>
inline MakeMCDigitalEngine<RNG,S>&
MakeMCDigitalEngine<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 MakeMCDigitalEngine<RNG,S>&
MakeMCDigitalEngine<RNG,S>::withMaxSamples(Size samples) {
maxSamples_ = samples;
return *this;
}
template <class RNG, class S>
inline MakeMCDigitalEngine<RNG,S>&
MakeMCDigitalEngine<RNG,S>::withSeed(BigNatural seed) {
seed_ = seed;
return *this;
}
template <class RNG, class S>
inline MakeMCDigitalEngine<RNG,S>&
MakeMCDigitalEngine<RNG,S>::withBrownianBridge(bool brownianBridge) {
brownianBridge_ = brownianBridge;
return *this;
}
template <class RNG, class S>
inline MakeMCDigitalEngine<RNG,S>&
MakeMCDigitalEngine<RNG,S>::withAntitheticVariate(bool b) {
antithetic_ = b;
return *this;
}
template <class RNG, class S>
inline
MakeMCDigitalEngine<RNG,S>::operator boost::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 boost::shared_ptr<PricingEngine>(new
MCDigitalEngine<RNG,S>(process_,
steps_,
stepsPerYear_,
brownianBridge_,
antithetic_,
samples_, tolerance_,
maxSamples_,
seed_));
}
}
#endif
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