Module net.finmath.lib

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, RandomVariable) 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