expectation (3) - Linux Manuals
expectation: G2 stochastic process
NAME
QuantLib::G2Process - G2 stochastic process
SYNOPSIS
#include <ql/processes/g2process.hpp>
Inherits QuantLib::StochasticProcess.
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< 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
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.
Author
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