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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2005 Klaus Spanderen
Copyright (C) 2005 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
<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/processes/stochasticprocessarray.hpp>
#include <ql/math/matrixutilities/pseudosqrt.hpp>
namespace QuantLib {
StochasticProcessArray::StochasticProcessArray(
const std::vector<ext::shared_ptr<StochasticProcess1D> >& processes,
const Matrix& correlation)
: processes_(processes),
sqrtCorrelation_(pseudoSqrt(correlation,SalvagingAlgorithm::Spectral)) {
QL_REQUIRE(!processes.empty(), "no processes given");
QL_REQUIRE(correlation.rows() == processes.size(),
"mismatch between number of processes "
"and size of correlation matrix");
for (auto& process : processes_) {
QL_REQUIRE(process, "null 1-D stochastic process");
registerWith(process);
}
}
Size StochasticProcessArray::size() const {
return processes_.size();
}
Array StochasticProcessArray::initialValues() const {
Array tmp(size());
for (Size i=0; i<size(); ++i)
tmp[i] = processes_[i]->x0();
return tmp;
}
Array StochasticProcessArray::drift(Time t,
const Array& x) const {
Array tmp(size());
for (Size i=0; i<size(); ++i)
tmp[i] = processes_[i]->drift(t, x[i]);
return tmp;
}
Matrix StochasticProcessArray::diffusion(Time t,
const Array& x) const {
Matrix tmp = sqrtCorrelation_;
for (Size i=0; i<size(); ++i) {
Real sigma = processes_[i]->diffusion(t, x[i]);
std::transform(tmp.row_begin(i), tmp.row_end(i),
tmp.row_begin(i),
[=](Real x) -> Real { return x * sigma; });
}
return tmp;
}
Array StochasticProcessArray::expectation(Time t0,
const Array& x0,
Time dt) const {
Array tmp(size());
for (Size i=0; i<size(); ++i)
tmp[i] = processes_[i]->expectation(t0, x0[i], dt);
return tmp;
}
Matrix StochasticProcessArray::stdDeviation(Time t0,
const Array& x0,
Time dt) const {
Matrix tmp = sqrtCorrelation_;
for (Size i=0; i<size(); ++i) {
Real sigma = processes_[i]->stdDeviation(t0, x0[i], dt);
std::transform(tmp.row_begin(i), tmp.row_end(i),
tmp.row_begin(i),
[=](Real x) -> Real { return x * sigma; });
}
return tmp;
}
Matrix StochasticProcessArray::covariance(Time t0,
const Array& x0,
Time dt) const {
Matrix tmp = stdDeviation(t0, x0, dt);
return tmp*transpose(tmp);
}
Array StochasticProcessArray::evolve(
Time t0, const Array& x0, Time dt, const Array& dw) const {
const Array dz = sqrtCorrelation_ * dw;
Array tmp(size());
for (Size i=0; i<size(); ++i)
tmp[i] = processes_[i]->evolve(t0, x0[i], dt, dz[i]);
return tmp;
}
Array StochasticProcessArray::apply(const Array& x0,
const Array& dx) const {
Array tmp(size());
for (Size i=0; i<size(); ++i)
tmp[i] = processes_[i]->apply(x0[i],dx[i]);
return tmp;
}
Time StochasticProcessArray::time(const Date& d) const {
return processes_[0]->time(d);
}
const ext::shared_ptr<StochasticProcess1D>&
StochasticProcessArray::process(Size i) const {
return processes_[i];
}
Matrix StochasticProcessArray::correlation() const {
return sqrtCorrelation_ * transpose(sqrtCorrelation_);
}
}
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