The package Sim.StableProc provides a simuation of strong approximate solution for stable driven stochastiques differential equations. Under suitable conditions that ensure stability of the Euler-Maruyama scheme.
🌋 For more details, see :
Solym M. Manou-Abi (2025) . Strong rates of approximation for stable-driven stochastic differential equations.
🚀 Local Installation (Developers) and Prerequisites
Python 3.8 ou plus récent
pip (gestionnaire de packages Python)
Installation
git clone https://github.com/YOUR-USERNAME/Sim.StableProc.git cd Sim.StableProc
pip install -r requirements.txt
🚀 🌀 processes.py — Simulation of α-Stable Processes
This module provides core functionality for simulating α-stable Lévy processes, which are frequently used as driving noise in stochastic differential equations (SDEs). It includes:
Generation of strictly stable or symmetric α-stable increments
Construction of sample paths of Lévy processes
Support for varying stability index α, skewness β, and scaling
🚀 🔁 simulation.py — Simulation of SDEs Driven by Stable Processes
This module implements numerical schemes to simulate stable driven stochastic differential equations of the form: It allows flexible specification of Drift functions and Diffusion functions.
Time discretization and sample path resolution
🚀 📊 plots.py — Visualization Utilities
This module contains plotting utilities to visualize simulation results:
🌀 Time series plots of process trajectories
🌀 Comparative plots across parameter settings (e.g. different α or β)
🌀 Stylized plots suitable for academic or professional presentation