Package com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde

Interface Summary

DiscretizedSDE	This interface represents the discretized version of a univariate SDE.
FtAdaptedFunction	This represents a Ft-adapted function that depends on X(t), B(t), or even on the whole past path of B(s), s ≤ t.

Class Summary

Bessel	This implement the Bessel Process, sum of squared Brownian motions, using the 1-dimensional SDE.
Euler	The Euler scheme is the first order approximation of an SDE.
Ft	This represents the concept 'Filtration', the information available at time t.
FtWt	This is a filtration implementation that includes the path-dependent information, e.g., Wt.
GeometricBrownian	A Geometric Brownian motion (GBM) (occasionally, exponential Brownian motion) is a continuous-time stochastic process in which the logarithm of the randomly varying quantity follows a Brownian motion.
Milstein	Milstein scheme is a first-order approximation to a continuous-time SDE.
SDE	This class represents a univariate, continuous-time Stochastic Differential Equation of this form: dX(t) = μ(t, Xt, Zt, ...) * dt + σ(t, Xt, Zt, ...) * dB(t).
XtAdaptedFunction	This represents a Ft-adapted function that depends only on X(t).