G2Process Class Reference
[Stochastic processes]

#include <ql/processes/g2process.hpp>

Inheritance diagram for G2Process:

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List of all members.

Detailed Description

G2 stochastic process


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


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.