ornstein-uhlenbeck-process

High-frequency statistical arbitrage

Python code of commonly used stochastic models for Monte-Carlo simulations

🦀 Scientific Computing Benchmark: Rust 🦀 vs Zig ⚡ vs the father C 👴

Python library for modelling complex multivariate dependencies using stochastic copulas

Hamiltonian Monte Carlo (HMC) sampling method in Python3, based on the original paper: Simon Duane, Anthony D. Kennedy, Brian J. Pendleton and Duncan Roweth (1987). "Hybrid Monte Carlo". Physics Letters B. 195 (2): 216–222.

Variational quantum simulations of stochastic differential equations

Practical activity for the Physics of Biological Systems minicourse at ICTP-SAIFR

Mean Reversion Trend Analysis with the Ornstein–Uhlenbeck Model

Python simulations for CTRWs Ornstein-Uhlenbeck process with different stability index

Ornstein unlenbeck process simulation in python

Brownian motion, Donsker scaling limits, quadratic variation and Ornstein-Uhlenbeck processes with applications to finance. R.

Work in Progress

Verification of a quantitative trading strategy using bootstrap OU calibration and backtesting.

Exploring how changes on the adaptive landscape affect phenotypic evolution within lineages

This repository contains some codes simulating diffusion procceses whose diffusion coefficient has stochastic nature. In particular in the Diffusing diffusivity case the diffusion coefficient is distributed according to the Ornstein-Uhlenbeck process while the Dice Brownian is a Random Walk where the step length varies randomly.

Synthetic Data Generation

Euro SSA repo desk RV framework : market-implied carry & Z-spread analysis (KfW/EIB vs Bund) and synthetic Bund CTD specialness model (Ornstein-Uhlenbeck process) with live Streamlit dashboard.

Stochastic Processes: Basic Examples

A Pyro-PPL implementation of a 2D Ornstein-Uhlenbeck process using stochastic variational inference.

Inference of drift and diffusion parameters in the Ornstein Uhlenbeck (OU) SDE, using Euler-Maruyama scheme, asymptotic analysis and spectral estimating functions. Project for MATH-450 Numerical integration of stochastic differential equations, EPFL, Spring 2026.