1 /* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */ 2 3 /* 4 Copyright (C) 2004, 2005 StatPro Italia srl 5 6 This file is part of QuantLib, a free-software/open-source library 7 for financial quantitative analysts and developers - http://quantlib.org/ 8 9 QuantLib is free software: you can redistribute it and/or modify it 10 under the terms of the QuantLib license. You should have received a 11 copy of the license along with this program; if not, please email 12 <quantlib-dev@lists.sf.net>. The license is also available online at 13 <http://quantlib.org/license.shtml>. 14 15 This program is distributed in the hope that it will be useful, but WITHOUT 16 ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS 17 FOR A PARTICULAR PURPOSE. See the license for more details. 18 */ 19 20 /*! \file eulerdiscretization.hpp 21 \brief Euler discretization for stochastic processes 22 */ 23 24 #ifndef quantlib_euler_discretization_hpp 25 #define quantlib_euler_discretization_hpp 26 27 #include <ql/stochasticprocess.hpp> 28 29 namespace QuantLib { 30 31 //! Euler discretization for stochastic processes 32 /*! \ingroup processes */ 33 class EulerDiscretization 34 : public StochasticProcess::discretization, 35 public StochasticProcess1D::discretization { 36 public: 37 38 /*! Returns an approximation of the drift defined as 39 \f$ \mu(t_0, \mathbf{x}_0) \Delta t \f$. 40 */ 41 Disposable<Array> drift(const StochasticProcess&, 42 Time t0, const Array& x0, Time dt) const; 43 /*! Returns an approximation of the drift defined as 44 \f$ \mu(t_0, x_0) \Delta t \f$. 45 */ 46 Real drift(const StochasticProcess1D&, 47 Time t0, Real x0, Time dt) const; 48 49 /*! Returns an approximation of the diffusion defined as 50 \f$ \sigma(t_0, \mathbf{x}_0) \sqrt{\Delta t} \f$. 51 */ 52 Disposable<Matrix> diffusion(const StochasticProcess&, 53 Time t0, const Array& x0, Time dt) const; 54 /*! Returns an approximation of the diffusion defined as 55 \f$ \sigma(t_0, x_0) \sqrt{\Delta t} \f$. 56 */ 57 Real diffusion(const StochasticProcess1D&, 58 Time t0, Real x0, Time dt) const; 59 60 /*! Returns an approximation of the covariance defined as 61 \f$ \sigma(t_0, \mathbf{x}_0)^2 \Delta t \f$. 62 */ 63 Disposable<Matrix> covariance(const StochasticProcess&, 64 Time t0, const Array& x0, Time dt) const; 65 /*! Returns an approximation of the variance defined as 66 \f$ \sigma(t_0, x_0)^2 \Delta t \f$. 67 */ 68 Real variance(const StochasticProcess1D&, 69 Time t0, Real x0, Time dt) const; 70 }; 71 72 } 73 74 75 #endif 76 77