Same as VarianceGammaProcessDiff, but the two inner. More...

Inheritance diagram for umontreal.ssj.stochprocess.VarianceGammaProcessDiffPCA:

Public Member Functions
	VarianceGammaProcessDiffPCA (double s0, double theta, double sigma, double nu, RandomStream stream)
	Constructs a new VarianceGammaProcessDiffPCA with parameters.
	VarianceGammaProcessDiffPCA (double s0, double theta, double sigma, double nu, GammaProcessPCA gpos, GammaProcessPCA gneg)
	Constructs a new VarianceGammaProcessDiffPCA with parameters.
double	nextObservation ()
	This method is not implemented is this class since the path cannot be generated sequentially.
double[]	generatePath ()
	Generates, returns and saves the path.
double[]	generatePath (double[] uniform01)
	Similar to the usual generatePath(), but here the uniform random numbers used for the simulation must be provided to the method.
Public Member Functions inherited from umontreal.ssj.stochprocess.VarianceGammaProcessDiff
	VarianceGammaProcessDiff (double s0, double theta, double sigma, double nu, RandomStream stream)
	Constructs a new VarianceGammaProcessDiff with parameters.
	VarianceGammaProcessDiff (double s0, double theta, double sigma, double nu, GammaProcess gpos, GammaProcess gneg)
	The parameters of the GammaProcess objects for \(\Gamma^+\) and \(\Gamma^-\) are set to those of ( `dblGammaParams` ) and their initial values \(\Gamma^+(t_0)\) and.
void	resetStartProcess ()
	Sets the observation times on the VarianceGammaProcessDiff as usual, but also applies the resetStartProcess method to the two.
GammaProcess	getGpos ()
	Returns a reference to the GammaProcess object gpos used to generate the \(\Gamma^+\) component of the process.
GammaProcess	getGneg ()
	Returns a reference to the GammaProcess object gneg used to generate the \(\Gamma^-\) component of the process.
void	setObservationTimes (double t[], int d)
	Sets the observation times on the VarianceGammaProcesDiff as usual, but also sets the observation times of the underlying GammaProcess ’es.
RandomStream	getStream ()
	Returns the RandomStream of the \(\Gamma^+\) process.
void	setStream (RandomStream stream)
	Sets the umontreal.ssj.rng.RandomStream of the two.
Public Member Functions inherited from umontreal.ssj.stochprocess.VarianceGammaProcess
	VarianceGammaProcess (double s0, double theta, double sigma, double nu, RandomStream stream)
	Constructs a new VarianceGammaProcess with parameters \(\theta= \mathtt{theta}\), \(\sigma= \mathtt{sigma}\), \(\nu= \mathtt{nu}\) and initial value \(S(t_0) = \mathtt{s0}\).
	VarianceGammaProcess (double s0, BrownianMotion BM, GammaProcess Gamma)
	Constructs a new VarianceGammaProcess.
void	setParams (double s0, double theta, double sigma, double nu)
	Sets the parameters \(S(t_0) =\) s0, \(\theta=\) theta,.
double	getTheta ()
	Returns the value of the parameter \(\theta\).
double	getSigma ()
	Returns the value of the parameter \(\sigma\).
double	getNu ()
	Returns the value of the parameter \(\nu\).
BrownianMotion	getBrownianMotion ()
	Returns a reference to the inner BrownianMotion.
GammaProcess	getGammaProcess ()
	Returns a reference to the inner GammaProcess.
Public Member Functions inherited from umontreal.ssj.stochprocess.StochasticProcess
void	setObservationTimes (double delta, int d)
	Sets equidistant observation times at \(t_j = j\delta\), for.
double[]	getObservationTimes ()
	Returns a reference to the array that contains the observation times.
int	getNumObservationTimes ()
	Returns the number \(d\) of observation times, excluding the time \(t_0\).
double[]	generatePath (RandomStream stream)
	Same as generatePath(), but first resets the stream to stream.
double[]	getPath ()
	Returns a reference to the last generated sample path \(\{X(t_0), ... , X(t_d)\}\).
void	getSubpath (double[] subpath, int[] pathIndices)
	Returns in subpath the values of the process at a subset of the observation times, specified as the times \(t_j\) whose indices.
double	getObservation (int j)
	Returns \(X(t_j)\) from the current sample path.
boolean	hasNextObservation ()
	Returns true if \(j<d\), where \(j\) is the number of observations of the current sample path generated since the last call to resetStartProcess.
int	getCurrentObservationIndex ()
	Returns the value of the index \(j\) corresponding to the time.
double	getCurrentObservation ()
	Returns the value of the last generated observation \(X(t_j)\).
double	getX0 ()
	Returns the initial value \(X(t_0)\) for this process.
void	setX0 (double s0)
	Sets the initial value \(X(t_0)\) for this process to s0, and reinitializes.
int[]	getArrayMappingCounterToIndex ()
	Returns a reference to an array that maps an integer \(k\) to \(i_k\), the index of the observation \(S(t_{i_k})\) corresponding to the.

Detailed Description

Same as VarianceGammaProcessDiff, but the two inner.

GammaProcess ’es are of PCA type. Also, generatePath(double[] uniforms01) distributes the uniform random variates to the GammaProcessPCA ’s according to their eigenvalues, i.e. the GammaProcessPCA with the higher eigenvalue gets the next uniform random number. If one should decide to create a VarianceGammaProcessDiffPCA by giving two GammaProcessPCA ’s to an objet of the class VarianceGammaProcessDiff, the uniform random numbers would not be given this way to the GammaProcessPCA ’s; this might give less variance reduction when used with QMC.

Definition at line 46 of file VarianceGammaProcessDiffPCA.java.

Constructor & Destructor Documentation

◆ VarianceGammaProcessDiffPCA() [1/2]

umontreal.ssj.stochprocess.VarianceGammaProcessDiffPCA.VarianceGammaProcessDiffPCA	(	double	s0,
		double	theta,
		double	sigma,
		double	nu,
		RandomStream	stream )

Constructs a new VarianceGammaProcessDiffPCA with parameters.

\(\theta= \mathtt{theta}\), \(\sigma= \mathtt{sigma}\), \(\nu= \mathtt{nu}\) and initial value \(S(t_0) = \mathtt{s0}\). There is only one umontreal.ssj.rng.RandomStream here which is used for the two inner GammaProcessPCA ’s. The other parameters are set as in VarianceGammaProcessDiff.

Definition at line 60 of file VarianceGammaProcessDiffPCA.java.

◆ VarianceGammaProcessDiffPCA() [2/2]

umontreal.ssj.stochprocess.VarianceGammaProcessDiffPCA.VarianceGammaProcessDiffPCA	(	double	s0,
		double	theta,
		double	sigma,
		double	nu,
		GammaProcessPCA	gpos,
		GammaProcessPCA	gneg )

Constructs a new VarianceGammaProcessDiffPCA with parameters.

\(\theta= \mathtt{theta}\), \(\sigma= \mathtt{sigma}\), \(\nu= \mathtt{nu}\) and initial value \(S(t_0) = \mathtt{s0}\). As in VarianceGammaProcessDiff, the umontreal.ssj.rng.RandomStream of gneg is replaced by the one of gpos to avoid any confusion.

Definition at line 76 of file VarianceGammaProcessDiffPCA.java.

Member Function Documentation

◆ generatePath() [1/2]

double[] umontreal.ssj.stochprocess.VarianceGammaProcessDiffPCA.generatePath ( )

Generates, returns and saves the path.

To do so, the path of

\(\Gamma^+\) is first generated and then the path of \(\Gamma^-\). This is not the optimal way of proceeding in order to reduce the variance in QMC simulations; for that, use generatePath(double[] uniform01) instead.

Reimplemented from umontreal.ssj.stochprocess.VarianceGammaProcessDiff.

Definition at line 91 of file VarianceGammaProcessDiffPCA.java.

◆ generatePath() [2/2]

double[] umontreal.ssj.stochprocess.VarianceGammaProcessDiffPCA.generatePath ( double[] uniform01 )

Similar to the usual generatePath(), but here the uniform random numbers used for the simulation must be provided to the method.

This allows to properly use the uniform random variates in QMC simulations. This method divides the table of uniform random numbers uniform01 in two smaller tables, the first one containing the odd indices of uniform01 are used to generate the path of

\(\Gamma^+\) and the even indices are used to generate the path of \(\Gamma^-\). This way of proceeding further reduces the variance for QMC simulations.

Reimplemented from umontreal.ssj.stochprocess.VarianceGammaProcessDiff.

Definition at line 98 of file VarianceGammaProcessDiffPCA.java.