Package net.finmath.montecarlo.process

Interfaced for stochastic processes and numerical schemes for stochastic processes (SDEs), like the Euler scheme. The Euler scheme implementation is more generic and can be configured for log-Euler scheme or predictor corrector scheme. The parameters have to be provided by a process model.

Author:

Christian Fries

See Also:

net.finmath.montecarlo.model

Interface Summary

Interface	Description
MonteCarloProcess	The interface for a process (numerical scheme) of a stochastic process X where X = f(Y) and Y is an Itô process \[ dY_{j} = \mu_{j} dt + \lambda_{1,j} dW_{1} + \ldots + \lambda_{m,j} dW_{m} \] The parameters are provided by a model implementing ProcessModel: The value of Y(0) is provided by the method ProcessModel.getInitialState(net.finmath.montecarlo.process.MonteCarloProcess).
Process	The interface for a stochastic process X.
ProcessTimeDiscretizationProvider	An object implementing this interfaces provides a suggestion for an optimal time-discretization associated with this object.

Class Summary

Class	Description
EulerSchemeFromProcessModel	This class implements some numerical schemes for multi-dimensional multi-factor Ito process.
LinearInterpolatedTimeDiscreteProcess	A linear interpolated time discrete process, that is, given a collection of tuples (Double, RandomVariableFromDoubleArray) representing realizations \( X(t_{i}) \) this class implements the Process and creates a stochastic process \( t \mapsto X(t) \) where \[ X(t) = \frac{t_{i+1} - t}{t_{i+1}-t_{i}} X(t_{i}) + \frac{t - t_{i}}{t_{i+1}-t_{i}} X(t_{i+1}) \] with \( t_{i} \leq t \leq t_{i+1} \).
MonteCarloProcessFromProcessModel	This class is an abstract base class to implement a multi-dimensional multi-factor Ito process.

Enum Summary

Enum	Description
EulerSchemeFromProcessModel.Scheme