Module net.finmath.lib
Package net.finmath.montecarlo
package net.finmath.montecarlo
Provides basic interfaces and classes used in Monte-Carlo models (like LIBOR market model or Monte-Carlo simulation
of a Black-Scholes model), e.g., the Monte-Carlo random variable and the Brownian motion.
- Author:
- Christian Fries
-
Interface SummaryInterfaceDescriptionInterface description of a time-discrete n-dimensional Brownian motion W = (W1,...,Wn) where Wi is a Brownian motion.Interface description of a time-discrete n-dimensional stochastic process \( X = (X_{1},\ldots,X_{n}) \) provided by independent increments \( \Delta X(t_{i}) = X(t_{i+1})-X(t_{i}) \).Interface for products requiring an MonteCarloSimulationModel for valuation.The interface implemented by a simulation of an SDE.A factory for creating objects implementing
net.finmath.stochastic.RandomVariable. -
Class SummaryClassDescriptionBase class for products requiring an MonteCarloSimulationModel for valuation.This class implements a Brownian bridge, i.e., samples of realizations of a Brownian motion conditional to a given start and end value.Implementation of a time-discrete n-dimensional Brownian motion W = (W1,...,Wn) where Wi is a Brownian motion and Wi, Wj are independent for i not equal j.Implementation of a time-discrete n-dimensional Brownian motion W = (W1,...,Wn) where Wi is a Brownian motion and Wi, Wj are independent for i not equal j.Deprecated.Refactor rename.A Brownian motion which is defined by some factors of a given Brownian motion, i.e., for a given multi-factorial Brownian motion W, this Brownian motion is given by ( W(i[0]), W(i[1]) W(i[2]), ..., W(i[n-1]) ) where i is a given array of integers.Provides a Brownian motion from given (independent) increments and performs a control of the expectation and the standard deviation.Provides a correlated Brownian motion from given (independent) increments and a given matrix of factor loadings.Implementation of a time-discrete n-dimensional Gamma process \( \Gamma = (\Gamma_{1},\ldots,\Gamma_{n}) \), where \( \Gamma_{i} \) is a Gamma process and \( \Gamma_{i} \), \( \Gamma_{j} \) are independent for i not equal j.Implementation of a time-discrete n-dimensional sequence of independent increments W = (W1,...,Wn) form a given set of inverse cumulative distribution functions.Implementation of a time-discrete n-dimensional jump process J = (J1,...,Jn) where Ji is a Poisson jump process and Ji, Jj are independent for i not equal j.Implementation of the compound Poisson process for the Merton jump diffusion model.A factory (helper class) to create random variables.The class RandomVariableFromDoubleArray represents a random variable being the evaluation of a stochastic process at a certain time within a Monte-Carlo simulation.The class RandomVariableFromFloatArray represents a random variable being the evaluation of a stochastic process at a certain time within a Monte-Carlo simulation.Implements a Monte-Carlo random variable (like
RandomVariableFromDoubleArrayusing late evaluation of Java 8 streams Accesses performed exclusively through the interfaceRandomVariableis thread safe (and does not mutate the class).Implementation of a time-discrete n-dimensional Variance Gamma process via Brownian subordination through a Gamma Process.