yForwardDrift (3) - Linux Manuals

yForwardDrift: Forward G2 stochastic process

NAME

QuantLib::G2ForwardProcess - Forward G2 stochastic process

SYNOPSIS

#include <ql/processes/g2process.hpp>

Inherits QuantLib::ForwardMeasureProcess.

Public Member Functions

G2ForwardProcess (Real a, Real sigma, Real b, Real eta, Real rho)

StochasticProcess interface

Size size () const
returns the number of dimensions of the stochastic process
Disposable< Array > initialValues () const
returns the initial values of the state variables
Disposable< Array > drift (Time t, const Array &x) const
returns the drift part of the equation, i.e., $ mu(t, mathrm{x}_t) $
Disposable< Matrix > diffusion (Time t, const Array &x) const
returns the diffusion part of the equation, i.e. $ igma(t, mathrm{x}_t) $
Disposable< Array > expectation (Time t0, const Array &x0, Time dt) const

Disposable< Matrix > stdDeviation (Time t0, const Array &x0, Time dt) const

Disposable< Matrix > covariance (Time t0, const Array &x0, Time dt) const

Protected Member Functions

Real xForwardDrift (Time t, Time T) const

Real yForwardDrift (Time t, Time T) const

Real Mx_T (Real s, Real t, Real T) const

Real My_T (Real s, Real t, Real T) const

Protected Attributes

Real x0_

Real y0_

Real a_

Real sigma_

Real b_

Real eta_

Real rho_

boost::shared_ptr< QuantLib::OrnsteinUhlenbeckProcess > xProcess_

boost::shared_ptr< QuantLib::OrnsteinUhlenbeckProcess > yProcess_

Detailed Description

Forward G2 stochastic process

Member Function Documentation

Disposable<Array> expectation (Time t0, const Array & x0, Time dt) const [virtual]

returns the expectation $ E(mathrm{x}_{t_0 + Delta t} | mathrm{x}_{t_0} = mathrm{x}_0) $ of the process after a time interval $ Delta t $ according to the given discretization. This method can be overridden in derived classes which want to hard-code a particular discretization.

Reimplemented from StochasticProcess.

Disposable<Matrix> stdDeviation (Time t0, const Array & x0, Time dt) const [virtual]

returns the standard deviation $ S(mathrm{x}_{t_0 + Delta t} | mathrm{x}_{t_0} = mathrm{x}_0) $ of the process after a time interval $ Delta t $ according to the given discretization. This method can be overridden in derived classes which want to hard-code a particular discretization.

Reimplemented from StochasticProcess.

Disposable<Matrix> covariance (Time t0, const Array & x0, Time dt) const [virtual]

returns the covariance $ V(mathrm{x}_{t_0 + Delta t} | mathrm{x}_{t_0} = mathrm{x}_0) $ of the process after a time interval $ Delta t $ according to the given discretization. This method can be overridden in derived classes which want to hard-code a particular discretization.

Reimplemented from StochasticProcess.

Author

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