StochasticProcessArray Class Reference
[Stochastic processes]

#include <ql/processes/stochasticprocessarray.hpp>

Inheritance diagram for StochasticProcessArray:

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List of all members.

Detailed Description

Array of correlated 1-D stochastic processes


Public Member Functions

 StochasticProcessArray (const std::vector< boost::shared_ptr< StochasticProcess1D > > &, const Matrix &correlation)
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< Arrayexpectation (Time t0, const Array &x0, Time dt) const
Disposable< Matrixdiffusion (Time t, const Array &x) const
 returns the diffusion part of the equation, i.e. $ \sigma(t, \mathrm{x}_t) $
Disposable< Matrixcovariance (Time t0, const Array &x0, Time dt) const
Disposable< MatrixstdDeviation (Time t0, const Array &x0, Time dt) const
Disposable< Arrayapply (const Array &x0, const Array &dx) const
Disposable< Arrayevolve (Time t0, const Array &x0, Time dt, const Array &dw) const
Time time (const Date &) const
const boost::shared_ptr< StochasticProcess1D > & process (Size i) const
Disposable< Matrixcorrelation () const

Protected Attributes

std::vector< boost::shared_ptr<
StochasticProcess1D > > 
processes_
Matrix sqrtCorrelation_


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> 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.

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<Array> apply ( const Array x0,
const Array dx 
) const [virtual]

applies a change to the asset value. By default, it returns $ \mathrm{x} + \Delta \mathrm{x} $.

Reimplemented from StochasticProcess.

Disposable<Array> evolve ( Time  t0,
const Array x0,
Time  dt,
const Array dw 
) const [virtual]

returns the asset value after a time interval $ \Delta t $ according to the given discretization. By default, it returns

\[ E(\mathrm{x}_0,t_0,\Delta t) + S(\mathrm{x}_0,t_0,\Delta t) \cdot \Delta \mathrm{w} \]

where $ E $ is the expectation and $ S $ the standard deviation.

Reimplemented from StochasticProcess.

Time time ( const Date  )  const [virtual]

returns the time value corresponding to the given date in the reference system of the stochastic process.

Note:
As a number of processes might not need this functionality, a default implementation is given which raises an exception.

Reimplemented from StochasticProcess.