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, 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