org.ojalgo.random.process

Class GeometricBrownianMotion

java.lang.Object

org.ojalgo.random.process.GeometricBrownianMotion

All Implemented Interfaces:

RandomProcess<LogNormal>

public final class GeometricBrownianMotion
extends Object

Diffusion process defined by a stochastic differential equation:

 dX = r X dt + s X dW
 

A stochastic process is said to follow a geometric Brownian motion if it satisfies this stochastic differential equation.

Author:

apete

Nested Class Summary

Nested classes/interfaces inherited from interface org.ojalgo.random.process.RandomProcess

RandomProcess.SimulationResults

Constructor Summary

Constructors

Constructor and Description
GeometricBrownianMotion(double localDrift, double diffusionFunction)

Method Summary

Modifier and Type	Method and Description
boolean	addObservation(Double x, double y)
GeometricBrownianMotion	convert(double convertionFactor)
static GeometricBrownianMotion	estimate(Access1D<?> seriesOfSamples, double samplePeriod)
LogNormal	getDistribution(double evaluationPoint)
double	getExpected() Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getExpected().
double	getLowerConfidenceQuantile(double confidence) The same thing can be achieved by first calling RandomProcess.getDistribution(double) with argumant 1.0, and then ContinuousDistribution.getQuantile(double) (but with different input argument).
protected double	getNormalisedRandomIncrement()
double	getStandardDeviation() Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getStandardDeviation().
double	getUpperConfidenceQuantile(double confidence) The same thing can be achieved by first calling RandomProcess.getDistribution(double) with argumant 1.0, and then ContinuousDistribution.getQuantile(double) (but with different input argument).
double	getValue()
double	getVariance() Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getVariance().
static GeometricBrownianMotion	make(double expected, double variance) Assuming initial value = 1.0 and horizon = 1.0.
static GeometricBrownianMotion	make(double expected, double variance, double horizon) Assuming initial value = 1.0.
static GeometricBrownianMotion	make(double initialValue, double expectedFutureValue, double aVariance, double aHorizon)
protected void	setObservations(Collection<? extends ComparableToDouble<Double>> c)
void	setValue(double newValue)
RandomProcess.SimulationResults	simulate(int numberOfRealisations, int numberOfSteps, double stepSize)
protected double	step(double currentValue, double stepSize, double normalisedRandomIncrement)

Methods inherited from class java.lang.Object

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

Constructor Detail

GeometricBrownianMotion

public GeometricBrownianMotion(double localDrift,
                               double diffusionFunction)

Method Detail

estimate

public static GeometricBrownianMotion estimate(Access1D<?> seriesOfSamples,
                                               double samplePeriod)

Parameters:

seriesOfSamples - A series of samples, evenly spaced in time.

samplePeriod - The amount of time (in which ever unit you prefer) between each sample in the series.

make

public static GeometricBrownianMotion make(double expected,
                                           double variance)

Assuming initial value = 1.0 and horizon = 1.0.

make

public static GeometricBrownianMotion make(double expected,
                                           double variance,
                                           double horizon)

Assuming initial value = 1.0.

make

public static GeometricBrownianMotion make(double initialValue,
                                           double expectedFutureValue,
                                           double aVariance,
                                           double aHorizon)

Parameters:

initialValue - The process initial value.

expectedFutureValue - An expected value (sometime in the future).

aVariance - The variance of that future value.

aHorizon - When do you expect that value?

convert

public GeometricBrownianMotion convert(double convertionFactor)

Parameters:

convertionFactor - A step size change factor.

getDistribution

public LogNormal getDistribution(double evaluationPoint)

Parameters:

evaluationPoint - How far into the future?

Returns:

The distribution for the process value at that future time.

getNormalisedRandomIncrement

protected double getNormalisedRandomIncrement()

step

protected double step(double currentValue,
                      double stepSize,
                      double normalisedRandomIncrement)

addObservation

public final boolean addObservation(Double x,
                                    double y)

getExpected

public final double getExpected()

Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getExpected().

getLowerConfidenceQuantile

public final double getLowerConfidenceQuantile(double confidence)

The same thing can be achieved by first calling RandomProcess.getDistribution(double) with argumant 1.0, and then ContinuousDistribution.getQuantile(double) (but with different input argument).

getStandardDeviation

public final double getStandardDeviation()

Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getStandardDeviation().

getUpperConfidenceQuantile

public final double getUpperConfidenceQuantile(double confidence)

The same thing can be achieved by first calling RandomProcess.getDistribution(double) with argumant 1.0, and then ContinuousDistribution.getQuantile(double) (but with different input argument).

getValue

public final double getValue()

getVariance

public final double getVariance()

Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getVariance().

setValue

public final void setValue(double newValue)

simulate

public final RandomProcess.SimulationResults simulate(int numberOfRealisations,
                                                      int numberOfSteps,
                                                      double stepSize)

Specified by:

simulate in interface RandomProcess<D extends Distribution>

Returns:

An array of sample sets. The array has aNumberOfSteps elements, and each sample set has aNumberOfRealisations samples.

setObservations

protected final void setObservations(Collection<? extends ComparableToDouble<Double>> c)