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

/*

 Copyright (C) 2004, 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/eulerdiscretization.hpp>

namespace QuantLib {

    Array EulerDiscretization::drift(const StochasticProcess& process,

                                     Time t0, const Array& x0,

                                     Time dt) const {

        return process.drift(t0, x0)*dt;

    }

    Real EulerDiscretization::drift(const StochasticProcess1D& process,

                                    Time t0, Real x0, Time dt) const {

        return process.drift(t0, x0)*dt;

    }

    Matrix EulerDiscretization::diffusion(const StochasticProcess& process,

                                          Time t0,

                                          const Array& x0,

                                          Time dt) const {

        return process.diffusion(t0, x0) * std::sqrt(dt);

    }

    Real EulerDiscretization::diffusion(const StochasticProcess1D& process,

                                        Time t0, Real x0, Time dt) const {

        return process.diffusion(t0, x0) * std::sqrt(dt);

    }

    Matrix EulerDiscretization::covariance(const StochasticProcess& process,

                                           Time t0,

                                           const Array& x0,

                                           Time dt) const {

        Matrix sigma = process.diffusion(t0, x0);

        Matrix result = sigma*transpose(sigma)*dt;

        return result;

    }

    Real EulerDiscretization::variance(const StochasticProcess1D& process,

                                       Time t0, Real x0, Time dt) const {

        Real sigma = process.diffusion(t0, x0);

        return sigma*sigma*dt;

    }

}