org.jquantlib.processes

Class StochasticProcess1D

java.lang.Object

org.jquantlib.processes.StochasticProcess

org.jquantlib.processes.StochasticProcess1D

All Implemented Interfaces:

Observable, Observer

Direct Known Subclasses:

ForwardMeasureProcess1D, GeneralizedBlackScholesProcess, GeometricBrownianMotionProcess, HullWhiteProcess, Merton76Process, OrnsteinUhlenbeckProcess

public abstract class StochasticProcess1D
extends StochasticProcess

1-dimensional stochastic process

This class describes a stochastic process governed by

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static interface	StochasticProcess1D.Discretization1D Discretization of a stochastic process over a given time interval

Nested classes/interfaces inherited from class org.jquantlib.processes.StochasticProcess

StochasticProcess.Discretization

Method Summary

Modifier and Type	Method and Description
Array	apply(Array x0, Array dx) Applies a change to the asset value.
double	apply(double x0, double dx) Applies a change to the asset value.
Matrix	covariance(double t0, Array x0, double dt) Returns the covariance of the process after a time interval according to the given discretization.
Matrix	diffusion(double t, Array x) Returns the diffusion part of the equation, i.e.
abstract double	diffusion(double t, double x) Returns the diffusion part of the equation, i.e.
Array	drift(double t, Array x) Returns the drift part of the equation, i.e.,
abstract double	drift(double t, double x) Returns the drift part of the equation
Array	evolve(double t0, Array x0, double dt, Array dw) Returns the asset value after a time interval according to the given discretization.
double	evolve(double t0, double x0, double dt, double dw) Returns the asset value after a time interval according to the given discretization.
Array	expectation(double t0, Array x0, double dt) Returns the expectation of the process after a time interval according to the given discretization.
double	expectation(double t0, double x0, double dt) Returns the expectation of the process after a time interval according to the given discretization.
Array	initialValues() Returns the initial values of the state variables
int	size() Returns the number of dimensions of the stochastic process
Matrix	stdDeviation(double t0, Array x0, double dt) Returns the standard deviation of the process after a time interval according to the given discretization.
double	stdDeviation(double t0, double x0, double dt) Returns the standard deviation of the process after a time interval according to the given discretization.
double	variance(double t0, double x0, double dt) Returns the variance of the process after a time interval according to the given discretization.
abstract double	x0() Returns the initial value of the state variable

Methods inherited from class org.jquantlib.processes.StochasticProcess

addObserver, countObservers, deleteObserver, deleteObservers, factors, getObservers, notifyObservers, notifyObservers, time, update

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

x0

public abstract double x0()

Returns the initial value of the state variable

drift

public abstract double drift(double t,
                             double x)

Returns the drift part of the equation

diffusion

public abstract double diffusion(double t,
                                 double x)

Returns the diffusion part of the equation, i.e.

expectation

public double expectation(double t0,
                          double x0,
                          double dt)

Returns the expectation of the process after a time interval according to the given discretization. This method can be overridden in derived classes which want to hard-code a particular discretization.

stdDeviation

public double stdDeviation(double t0,
                           double x0,
                           double dt)

Returns the standard deviation of the process after a time interval according to the given discretization. This method can be overridden in derived classes which want to hard-code a particular discretization.

variance

public double variance(double t0,
                       double x0,
                       double dt)

Returns the variance of the process after a time interval according to the given discretization. This method can be overridden in derived classes which want to hard-code a particular discretization.

evolve

public final double evolve(double t0,
                           double x0,
                           double dt,
                           double dw)

Returns the asset value after a time interval according to the given discretization. By default, it returns where is the expectation and the standard deviation.

apply

public double apply(double x0,
                    double dx)

Applies a change to the asset value. By default, it returns .

size

public final int size()

Description copied from class: StochasticProcess

Returns the number of dimensions of the stochastic process

Specified by:

size in class StochasticProcess

initialValues

public final Array initialValues()

Description copied from class: StochasticProcess

Returns the initial values of the state variables

Specified by:

initialValues in class StochasticProcess

drift

public final Array drift(double t,
                         Array x)

Description copied from class: StochasticProcess

Returns the drift part of the equation, i.e.,

Specified by:

drift in class StochasticProcess

diffusion

public final Matrix diffusion(double t,
                              Array x)

Description copied from class: StochasticProcess

Returns the diffusion part of the equation, i.e.

Specified by:

diffusion in class StochasticProcess

expectation

public final Array expectation(double t0,
                               Array x0,
                               double dt)

Description copied from class: StochasticProcess

Returns the expectation of the process after a time interval according to the given discretization. This method can be overridden in derived classes which want to hard-code a particular discretization.

Overrides:

expectation in class StochasticProcess

stdDeviation

public final Matrix stdDeviation(double t0,
                                 Array x0,
                                 double dt)

Description copied from class: StochasticProcess

Returns the standard deviation of the process after a time interval according to the given discretization. This method can be overridden in derived classes which want to hard-code a particular discretization.

Overrides:

stdDeviation in class StochasticProcess

covariance

public final Matrix covariance(double t0,
                               Array x0,
                               double dt)

Description copied from class: StochasticProcess

Returns the covariance of the process after a time interval according to the given discretization. This method can be overridden in derived classes which want to hard-code a particular discretization.

Overrides:

covariance in class StochasticProcess

evolve

public final Array evolve(double t0,
                          Array x0,
                          double dt,
                          Array dw)

Description copied from class: StochasticProcess

Returns the asset value after a time interval according to the given discretization. By default, it returns where is the expectation and the standard deviation.

Overrides:

evolve in class StochasticProcess

apply

public final Array apply(Array x0,
                         Array dx)

Description copied from class: StochasticProcess

Applies a change to the asset value.

Overrides:

apply in class StochasticProcess