finMath.net

Library - finmath lib

finmath lib: Java library with algorithms and methodologies related to mathematical finance.

The library is part of the maven central repository.

Convenient method aliases and implicit classes are available in Scala and Kotlin:

finmath lib opencl extensions: Vector class (RandomVaraible) running via OpenCL on GPUs and CPUs.

Enabling finmath lib with OpenCL (via jocl). - Running finmath lib models on a GPU.

The finmath lib opencl extensions provide an OpenCL implementation of the finmath lib interfaces RandomVariable compatible finmath lib 5.1 or later (tested on GRID GPU Kepler GK105, GeForce GTX 1080, GeForce GT 750M and ATI Radeon Pro 560.

finmath lib cuda extensions: Vector class (RandomVaraible) running on Cuda GPUs.

Enabling finmath lib with Cuda (via jcuda). - Running finmath lib models on a GPU.

The finmath lib cuda extensions provide a Cuda implementation of the finmath lib interfaces RandomVariable and BrownianMotion compatible with finmath lib 5.1 or later (tested on GRID GPU Kepler GK105, GeForce GTX 1080, GeForce GT 750M (Cuda and OpenCL) and ATI Radeon Pro 560 (OpenCL only)).

finmath lib plot extensions: Convenient abstractions of some plotting libraries for lightweight and easy plotting.

This project provides abstractions for some plotting libraries (JFreeChart, JavaFX, JZY3D) and demo usages for finmath lib. It is provided for convenience to test and explore finmath lib.

Laboratory (Experiments, Spreadsheets, Applets and Demos using finmath lib)

finmath experiments: Experiments using the Java shell - JShell.

For details see experiments page. Examples:

Monte-Carlo Simulation: A small experiment creating a Monte-Carlo simulation of the Black-Scholes model and valuing a European option.
Algorithmic Differentiation and Dependency Injection: An experiment performing an algorithmic differentiation via dependency injection on a Monte-Carlo simulation of the Black-Scholes model.
Algorithmic Differentiation for Forward Sensitivities: An experiment performing an algorithmic differentiation via dependency injection on a Monte-Carlo simulation of the Black-Scholes model to derive all forward sensitivities, performing a hedge simulation.

finmath spreadsheets: Spreadsheets providing methodologies from mathematical finance using finmath lib.

For details see spreadsheets page. Examples:

Interest Rate Curve Calibration: The sheet calibrates a set of different curves (including discounting curves (e.g., OIS) and forward curves) from swaps.
LIBOR Market Model: The sheet allows to create a LIBOR market model calibrated to a given forward curve and given swaptions. The parametrized volatility and correlation can be inspected. Generated interest rate scenarios can ben extracted.
More...: More sheets related to methodologies from mathematical finance.

finmath applets: Java Applets illustrating some topics from mathematical finance.

Java Applets illustrating some topics from mathematical finance (using finmath lib).

finmath tutorials: Experiments in Java and Excel.

We provide very basic tutorials for beginners (still under construction).

Documentation

finmath lib API documentation.: Java doc API description.
methodology documentation.: References to documentation of the methodology and theoretical background.