Module net.finmath.lib
Package net.finmath.montecarlo.process
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:
-
ClassDescriptionThis class implements some numerical schemes for multi-dimensional multi-factor Ito process.A linear interpolated time discrete process, that is, given a collection of tuples (
Double,RandomVariable) representing realizations \( X(t_{i}) \) this class implements theProcessand 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 \( t_{i} \leq t \leq t_{i+1} \).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 implementingProcessModel: The value of Y(0) is provided by the methodProcessModel.getInitialState(net.finmath.montecarlo.process.MonteCarloProcess).This class is an abstract base class to implement a multi-dimensional multi-factor Ito process.The interface for a stochastic process X.An object implementing this interfaces provides a suggestion for an optimal time-discretization associated with this object.