G2 stochastic process More...

#include <ql/processes/g2process.hpp>

Inheritance diagram for G2Process:

List of all members.

Public Member Functions

 G2Process (Real a, Real sigma, Real b, Real eta, Real rho)
Real x0 () const
Real y0 () const
Real a () const
Real sigma () const
Real b () const
Real eta () const
Real rho () const
StochasticProcess interface
Size size () const
 returns the number of dimensions of the stochastic process
Disposable< ArrayinitialValues () const
 returns the initial values of the state variables
Disposable< Arraydrift (Time t, const Array &x) const
 returns the drift part of the equation, i.e., $ \mu(t, \mathrm{x}_t) $
Disposable< Matrixdiffusion (Time t, const Array &x) const
 returns the diffusion part of the equation, i.e. $ \sigma(t, \mathrm{x}_t) $
Disposable< Arrayexpectation (Time t0, const Array &x0, Time dt) const
Disposable< MatrixstdDeviation (Time t0, const Array &x0, Time dt) const
Disposable< Matrixcovariance (Time t0, const Array &x0, Time dt) const

Detailed Description

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.