PREMIA : a numerical platform for computational finance
Premia i
s a software designed for option pricing, hedging and financial model calibration. It is provided with it’s C/C++ source code and an extensive scientific documentation
The MathRisk project team develops the software Premia dedicated to pricing and hedging options and calibration of financial models, in collaboration with a consortium of financial institutions.
The Premia project keeps track of the most recent advances in computational finance in a well documented way. It focuses on the implementation of numerical techniques, be they probabilistic or deterministic, to solve financial problems.
Premia contains various numerical algorithms: deterministic methods (Finite difference and finite element algorithms for Partial differential equations, wavelets, Galerkin, sparse grids …), stochastic algorithms (Monte-Carlo simulations, quantization methods, Malliavin calculus based methods), tree methods, approximation methods (Laplace transforms, Fast Fourier transforms…), deep learning algorithms
These algorithms are implemented for the evaluation of vanilla and exotic options on equities, interest rate, credit, energy and insurance products.
Moreover Premia provides a calibration toolbox for Libor Market model and a toolbox for pricing Credit derivatives.
An important feature of the platform Premia is its detailed documentation which provides extended references in computational finance.
A restricted version of Premia software Premia is available on Premia web site and can be downloaded with a special license for academic and evaluation purposes.
Premia releases Premia are registered at the APP agency.
Numerical algorithms:
Deterministic methods: Finite difference and finite element algorithms for PDEs, wavelets, Galerkin, sparse grids …
Stochastic algorithms: Monte-Carlo simulations, quantization methods, Malliavin calculus based methods
Tree methods, approximation methods: Laplace transforms, Fast Fourier transforms…
Premia is supported by the Consortium Premia.
