|
Class Summary |
| BrownianMotion |
This class represents a Brownian motion process
{X(t) : t >= 0},
sampled at times
0 = t0 < t1 < ... |
| BrownianMotionBridge |
Represents a Brownian motion process
{X(t) : t >= 0}
sampled using the bridge sampling technique
(see for example). |
| BrownianMotionPCA |
A Brownian motion process
{X(t) : t >= 0} sampled using the
principal component decomposition (PCA). |
| BrownianMotionPCAEqualSteps |
Same as BrownianMotionPCA, but uses a trick to
speed up the calculation when the time steps
are equidistant. |
| CIRProcess |
This class represents a CIR (Cox, Ingersoll, Ross) process
{X(t) : t >= 0}, sampled at times
0 = t0 < t1 < ... |
| CIRProcessEuler |
. |
| GammaProcess |
This class represents a gamma process
{S(t) = G(t;μ, ν) : t >= 0} with mean parameter μ and
variance parameter ν. |
| GammaProcessBridge |
This class represents a gamma process
{S(t) = G(t;μ, ν) : t >= 0} with mean parameter μ and
variance parameter ν, sampled using the gamma bridge method
(see for example). |
| GammaProcessPCA |
Represents a gamma process sampled using the principal
component analysis (PCA). |
| GammaProcessPCABridge |
Same as GammaProcessPCA, but the generated uniforms
correspond to a bridge transformation of the BrownianMotionPCA
instead of a sequential transformation. |
| GammaProcessPCASymmetricalBridge |
Same as GammaProcessPCABridge, but uses the fast inversion method
for the symmetrical beta distribution, proposed by L'Ecuyer and Simard, to accelerate the generation of the beta random variables. |
| GammaProcessSymmetricalBridge |
This class differs from GammaProcessBridge only in that it requires
the number of interval of the path to be
a power of 2 and of equal size. |
| GeometricBrownianMotion |
. |
| GeometricLevyProcess |
. |
| GeometricNormalInverseGaussianProcess |
. |
| GeometricVarianceGammaProcess |
This class represents a geometric variance gamma process S(t)
(see). |
| InverseGaussianProcess |
The inverse Gaussian process is a non-decreasing process
where the increments are additive and are given by the
inverse gaussian distribution,
InverseGaussianDist. |
| InverseGaussianProcessBridge |
Samples the path by bridge sampling:
first finding the process value at
the final time and then the middle time, etc. |
| InverseGaussianProcessMSH |
Uses a faster generating method (MSH)
than the simple inversion of the distribution function
used by InverseGaussianProcess. |
| InverseGaussianProcessPCA |
Approximates a principal component analysis (PCA)
decomposition of the InverseGaussianProcess. |
| NormalInverseGaussianProcess |
This class represents a normal inverse gaussian process (NIG). |
| OrnsteinUhlenbeckProcess |
This class represents an Ornstein-Uhlenbeck process
{X(t) : t >= 0}, sampled at times
0 = t0 < t1 < ... |
| OrnsteinUhlenbeckProcessEuler |
. |
| StochasticProcess |
Abstract base class for a stochastic process
{X(t) : t >= 0}
sampled (or observed) at a finite number of time points,
0 = t0 < t1 < ... |
| VarianceGammaProcess |
This class represents a variance gamma (VG) process
{S(t) = X(t;θ, σ, ν) : t >= 0}. |
| VarianceGammaProcessDiff |
This class represents a variance gamma (VG) process
{S(t) = X(t;θ, σ, ν) : t >= 0}. |
| VarianceGammaProcessDiffPCA |
Same as VarianceGammaProcessDiff, but the two inner
GammaProcess'es are of PCA type. |
| VarianceGammaProcessDiffPCABridge |
Same as VarianceGammaProcessDiff, but the two
inner GammaProcess'es are of the type PCABridge. |
| VarianceGammaProcessDiffPCASymmetricalBridge |
Same as VarianceGammaProcessDiff, but the two
inner GammaProcess'es are of the PCASymmetricalBridge type. |