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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2005, 2006 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
<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.
*/
#include <ql/legacy/libormarketmodels/lfmcovarparam.hpp>
#include <ql/math/integrals/kronrodintegral.hpp>
namespace QuantLib {
class LfmCovarianceParameterization::Var_Helper {
public:
Var_Helper(const LfmCovarianceParameterization* param, Size i, Size j);
Real operator()(Real t) const;
private:
const Size i_, j_;
const LfmCovarianceParameterization* param_;
};
LfmCovarianceParameterization::Var_Helper::Var_Helper(
const LfmCovarianceParameterization* param,
Size i, Size j)
: i_(i), j_(j), param_(param) {}
Real LfmCovarianceParameterization::Var_Helper::operator()(Real t) const {
const Matrix m = param_->diffusion(t);
return std::inner_product(m.row_begin(i_), m.row_end(i_),
m.row_begin(j_), 0.0);
}
Disposable<Matrix> LfmCovarianceParameterization::covariance(
Time t, const Array& x) const {
Matrix sigma = this->diffusion(t, x);
Matrix result = sigma*transpose(sigma);
return result;
}
Disposable<Matrix> LfmCovarianceParameterization::integratedCovariance(
Time t, const Array& x) const {
// this implementation is not intended for production.
// because it is too slow and too inefficient.
// This method is useful for testing and R&D.
// Please overload the method within derived classes.
QL_REQUIRE(x.empty(), "can not handle given x here");
Matrix tmp(size_, size_,0.0);
for (Size i=0; i<size_; ++i) {
for (Size j=0; j<=i;++j) {
Var_Helper helper(this, i, j);
GaussKronrodAdaptive integrator(1e-10, 10000);
for (Size k=0; k < 64; ++k) {
tmp[i][j]+=integrator(helper, k*t/64.,(k+1)*t/64.);
}
tmp[j][i]=tmp[i][j];
}
}
return tmp;
}
}
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