Class GeometricBrownianMotion

java.lang.Object

org.ojalgo.random.process.AbstractProcess<LogNormal>

org.ojalgo.random.process.SingleValueBasedProcess<LogNormal>

org.ojalgo.random.process.GeometricBrownianMotion

All Implemented Interfaces:

Process1D.ComponentProcess<LogNormal>, RandomProcess<LogNormal>

public final class GeometricBrownianMotion
extends SingleValueBasedProcess<LogNormal>
implements Process1D.ComponentProcess<LogNormal>

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.

Nested Class Summary

Nested classes/interfaces inherited from interface RandomProcess

RandomProcess.SimulationResults

Field Summary

Fields

Modifier and Type	Field	Description
private static final WienerProcess	GENERATOR
private final double	myDiffusionFunction
private final double	myLocalDrift

Constructor Summary

Constructors

Modifier	Constructor	Description
private	GeometricBrownianMotion()
	GeometricBrownianMotion(double localDrift, double diffusionFunction)

Method Summary

Modifier and Type	Method	Description
GeometricBrownianMotion	convert(double convertionFactor)
(package private) double	doStep(double stepSize, double normalisedRandomIncrement)
static GeometricBrownianMotion	estimate(Access1D<?> seriesOfSamples, double samplePeriod)
LogNormal	getDistribution(double evaluationPoint)
private double	getDistributionLocation(double stepSize, double variance)
private double	getDistributionVariance(double stepSize)
(package private) double	getExpected(double stepSize)	Expected future value
(package private) double	getLowerConfidenceQuantile(double stepSize, double confidence)
(package private) double	getNormalisedRandomIncrement()
(package private) double	getStandardDeviation(double stepSize)
(package private) double	getUpperConfidenceQuantile(double stepSize, double confidence)
double	getValue()
(package private) double	getVariance(double stepSize)
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)
void	setValue(double newValue)
double	step(double stepSize, double standardGaussianInnovation)

Methods inherited from class SingleValueBasedProcess

getCurrentValue, setCurrentValue, simulate

Methods inherited from class AbstractProcess

getExpected, getLowerConfidenceQuantile, getStandardDeviation, getUpperConfidenceQuantile, getVariance, step

Methods inherited from class Object

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

Methods inherited from interface RandomProcess

simulate

Field Details

GENERATOR

private static final WienerProcess GENERATOR

myDiffusionFunction

private final double myDiffusionFunction

myLocalDrift

private final double myLocalDrift

Constructor Details

GeometricBrownianMotion

public GeometricBrownianMotion(double localDrift,
 double diffusionFunction)

GeometricBrownianMotion

private GeometricBrownianMotion()

Method Details

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)

Specified by:

getDistribution in interface RandomProcess<LogNormal>

Parameters:

evaluationPoint - How far into the future?

Returns:

The distribution for the process value at that future time.

getValue

public double getValue()

Specified by:

getValue in interface Process1D.ComponentProcess<LogNormal>

setValue

public void setValue(double newValue)

Specified by:

setValue in interface Process1D.ComponentProcess<LogNormal>

step

public double step(double stepSize,
 double standardGaussianInnovation)

Specified by:

step in interface Process1D.ComponentProcess<LogNormal>

getDistributionLocation

private double getDistributionLocation(double stepSize,
 double variance)

getDistributionVariance

private double getDistributionVariance(double stepSize)

doStep

double doStep(double stepSize,
 double normalisedRandomIncrement)

Specified by:

doStep in class AbstractProcess<LogNormal>

getExpected

double getExpected(double stepSize)

Expected future value

Specified by:

getExpected in class AbstractProcess<LogNormal>

getLowerConfidenceQuantile

double getLowerConfidenceQuantile(double stepSize,
 double confidence)

Specified by:

getLowerConfidenceQuantile in class AbstractProcess<LogNormal>

getNormalisedRandomIncrement

double getNormalisedRandomIncrement()

Specified by:

getNormalisedRandomIncrement in class AbstractProcess<LogNormal>

getStandardDeviation

double getStandardDeviation(double stepSize)

Specified by:

getStandardDeviation in class AbstractProcess<LogNormal>

getUpperConfidenceQuantile

double getUpperConfidenceQuantile(double stepSize,
 double confidence)

Specified by:

getUpperConfidenceQuantile in class AbstractProcess<LogNormal>

getVariance

double getVariance(double stepSize)

Specified by:

getVariance in class AbstractProcess<LogNormal>