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/*
Copyright (C) 2000, 2001, 2002 RiskMap 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 ferdinando@ametrano.net
The license is also available online at http://quantlib.org/html/license.html
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 pagodapathpricer.cpp
\brief path pricer for pagoda options
\fullpath
MonteCarlo/%pagodapathpricer.cpp
*/
// $Id: pagodapathpricer.cpp,v 1.11 2002/01/16 14:42:17 nando Exp $
#include <ql/MonteCarlo/pagodapathpricer.hpp>
#include <ql/dataformatters.hpp>
#include <iostream>
namespace QuantLib {
namespace MonteCarlo {
PagodaPathPricer::PagodaPathPricer(const Array& underlying,
double roof,
DiscountFactor discount, bool useAntitheticVariance)
: PathPricer<MultiPath>(discount, useAntitheticVariance),
underlying_(underlying), roof_(roof) {}
double PagodaPathPricer::operator()(const MultiPath& multiPath) const {
Size numAssets = multiPath.assetNumber();
Size numSteps = multiPath.pathSize();
QL_REQUIRE(underlying_.size() == numAssets,
"PagodaPathPricer: the multi-path must contain "
+ IntegerFormatter::toString(underlying_.size()) +" assets");
Size i,j;
if (useAntitheticVariance_) {
double averageGain = 0.0, averageGain2 = 0.0;
for(i = 0; i < numSteps; i++)
for(j = 0; j < numAssets; j++) {
averageGain += underlying_[j] *
(QL_EXP(multiPath[j].drift()[i]+
multiPath[j].diffusion()[i])
-1.0);
averageGain2 += underlying_[j] *
(QL_EXP(multiPath[j].drift()[i]-
multiPath[j].diffusion()[i])
-1.0);
}
return discount_ * 0.5 *
(QL_MAX(0.0, QL_MIN(roof_, averageGain))+
QL_MAX(0.0, QL_MIN(roof_, averageGain2)));
} else {
double averageGain = 0.0;
for(i = 0; i < numSteps; i++)
for(j = 0; j < numAssets; j++) {
averageGain += underlying_[j] *
(QL_EXP(multiPath[j].drift()[i]+
multiPath[j].diffusion()[i])
-1.0);
}
return discount_ * QL_MAX(0.0, QL_MIN(roof_, averageGain));
}
}
}
}
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