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getcovariance.cpp

/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */

/*
Copyright (C) 2004, 2007 Ferdinando Ametrano

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/math/matrixutilities/getcovariance.hpp>

namespace QuantLib {

00024     CovarianceDecomposition::CovarianceDecomposition(
                                                const Matrix& cov,
                                                Real tolerance,
                                                SalvagingAlgorithm::Type sa)
    : variances_(cov.diagonal()), stdDevs_(Array(cov.rows())),
      correlationMatrix_(Matrix(cov.rows(), cov.rows()))
    {
        Size size = cov.rows();
        QL_REQUIRE(size==cov.columns(),
                   "input covariance matrix must be square, it is [" <<
                   size << "x" << cov.rows() << "]");

        for (Size i=0; i<size; ++i)
        {
            stdDevs_[i] = std::sqrt(variances_[i]);
            correlationMatrix_[i][i] = 1.0;
            for (Size j=0; j<i; ++j)
            {
                QL_REQUIRE(std::fabs(cov[i][j]-cov[j][i]) <= tolerance,
                           "invalid covariance matrix:" <<
                           "\nc[" << i << ", " << j << "] = " <<
                           cov[i][j] << "\nc[" << j << ", " << i <<
                           "] = " << cov[j][i]);
                correlationMatrix_[i][j] = correlationMatrix_[j][i] =
                    cov[i][j]/(stdDevs_[i]*stdDevs_[j]);
            }
        }
        //Matrix root = pseudoSqrt(correlationMatrix_, sa);
        //Matrix trasposedRoot = transpose(root);
        //correlationMatrix_ = root * trasposedRoot;
    }
}

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