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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2025 Klaus Spanderen
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
<https://www.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.
*/
#include <ql/exercise.hpp>
#include <ql/quotes/simplequote.hpp>
#include <ql/instruments/basketoption.hpp>
#include <ql/pricingengines/blackformula.hpp>
#include <ql/pricingengines/asian/choiasianengine.hpp>
#include <ql/pricingengines/basket/choibasketengine.hpp>
#include <ql/termstructures/yield/flatforward.hpp>
#include <ql/termstructures/volatility/equityfx/blackconstantvol.hpp>
namespace QuantLib {
ChoiAsianEngine::ChoiAsianEngine(
ext::shared_ptr<GeneralizedBlackScholesProcess> process,
Real lambda,
Size maxNrIntegrationSteps)
: process_(std::move(process)),
lambda_(lambda),
maxNrIntegrationSteps_(maxNrIntegrationSteps) {
registerWith(process_);
}
void ChoiAsianEngine::calculate() const {
QL_REQUIRE(arguments_.averageType == Average::Type::Arithmetic,
"must be Average::Type Arithmetic ");
QL_REQUIRE(arguments_.exercise->type() == Exercise::European,
"not a European Option");
const ext::shared_ptr<PlainVanillaPayoff> payoff =
ext::dynamic_pointer_cast<PlainVanillaPayoff>(arguments_.payoff);
QL_REQUIRE(payoff, "non plain vanilla payoff given");
std::vector<Date> fixingDates = arguments_.fixingDates;
std::sort(fixingDates.begin(), fixingDates.end());
Size futureFixings = fixingDates.size();
Size pastFixings = arguments_.pastFixings;
Real runningAccumulator = arguments_.runningAccumulator;
const Date exerciseDate = arguments_.exercise->lastDate();
const Handle<YieldTermStructure> rTS = process_->riskFreeRate();
if ( futureFixings > 0
&& process_->time(fixingDates.front()) == Time(0)) {
// push today fixing to past fixings
fixingDates.erase(fixingDates.begin());
futureFixings--;
pastFixings++;
runningAccumulator += process_->x0();
}
if (futureFixings == 0) {
QL_REQUIRE(pastFixings > 0, "no past fixings given");
results_.value = (*payoff)(runningAccumulator/pastFixings)
* rTS->discount(exerciseDate);
return;
}
QL_REQUIRE(fixingDates.back() <= exerciseDate,
"last fixing date must be before exercise date");
QL_REQUIRE(process_->time(fixingDates.front()) >= 0.0,
"first fixing date is in the past");
QL_REQUIRE(std::adjacent_find(fixingDates.begin(), fixingDates.end())
== fixingDates.end(), "two fixing dates are the same");
const Real accruedAverage = (pastFixings != 0)
? Real(runningAccumulator / (pastFixings + futureFixings))
: 0.0;
const Real strike = payoff->strike() - accruedAverage;
QL_REQUIRE(strike >= 0.0, "effective strike should to be positive");
const Handle<YieldTermStructure> qTS = process_->dividendYield();
const Handle<BlackVolTermStructure> volTS = process_->blackVolatility();
const Date volRefDate = volTS->referenceDate();
const DayCounter volDc = volTS->dayCounter();
if (futureFixings > 1) {
std::vector<Time> fixingTimes(futureFixings), variances(futureFixings);
for (Size i=0; i < futureFixings; ++i) {
const Date& fixingDate = fixingDates[i];
fixingTimes[i] = volDc.yearFraction(volRefDate, fixingDate);
variances[i] = process_->blackVolatility()->blackVariance(fixingDate, strike);
}
Matrix rho(futureFixings, futureFixings);
for (Size i=0; i < rho.rows(); ++i)
for (Size j=i; j < rho.columns(); ++j)
rho[i][j] = rho[j][i] =
variances[std::min(i,j)] / std::sqrt(variances[i]*variances[j]);
const Handle<YieldTermStructure> zeroTS(
ext::make_shared<FlatForward>(rTS->referenceDate(), 0.0, rTS->dayCounter())
);
std::vector<ext::shared_ptr<GeneralizedBlackScholesProcess> > processes;
processes.reserve(futureFixings);
for (Size i=0; i < futureFixings; ++i) {
const Date& fixingDate = fixingDates[i];
const Volatility sig = volTS->blackVol(fixingDate, payoff->strike())
* std::sqrt(fixingTimes[i]/fixingTimes.back());
processes.emplace_back(
ext::make_shared<GeneralizedBlackScholesProcess>(
Handle<Quote>(
ext::make_shared<SimpleQuote>(
process_->x0()*qTS->discount(fixingDate)/rTS->discount(fixingDate)
)
),
zeroTS, zeroTS,
Handle<BlackVolTermStructure>(
ext::make_shared<BlackConstantVol>(
volRefDate, volTS->calendar(),
Handle<Quote>(ext::make_shared<SimpleQuote>(sig)),
volDc
)
)
)
);
}
BasketOption basketOption(
ext::make_shared<AverageBasketPayoff>(
ext::make_shared<PlainVanillaPayoff>(payoff->optionType(), strike),
Array(futureFixings, 1.0/(futureFixings + pastFixings))
),
ext::make_shared<EuropeanExercise>(fixingDates.back())
);
basketOption.setPricingEngine(
ext::make_shared<ChoiBasketEngine>(
processes, rho, lambda_, maxNrIntegrationSteps_)
);
results_.value = basketOption.NPV() * rTS->discount(exerciseDate);
}
else if (futureFixings == 1) {
results_.value = blackFormula(
payoff->optionType(),
strike,
process_->x0()/(pastFixings + futureFixings)
*qTS->discount(fixingDates.back())/rTS->discount(fixingDates.back()),
std::sqrt(volTS->blackVariance(fixingDates.back(), strike)),
rTS->discount(exerciseDate)
);
}
}
}
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