fractional-brownian-motion

High-performance quantitative finance in Rust and Python — 130+ stochastic processes, option pricing, calibration, fixed income, risk & copulas, with SIMD/GPU acceleration.

Exact methods for simulating fractional Brownian motion and fractional Gaussian noise in python

Python implementation of fractional brownian motion

Stochastic processes insights from VAE. Code for the paper: Learning minimal representations of stochastic processes with variational autoencoders.

Generate patterns using the fractional Brownian motion

A python library for sampling image textures from the anisotropic fractional Brownian field.

Implementation of NeurIPS 2024 ML4Physics workshop paper "fBm-Based Generative Inpainting for the Reconstruction of Chromosomal Distances"

Fractional brownian motion generator, in C#/dotnet.

Comparative Analysis of Dual Algorithms for high-dimensional Stopping Problems

The 2D noise mixer is designed to make mixing different types noise quick with only a few lines of code. Highly commented and easy to use, this system types to be non-platform specific.

Replication of the research paper : "Discretization of continuous-time arbitrage strategies in financial markets with fBm""

R package for simulating paths of Fractional Brownian Motion and samples of Fractional Gaussian Noise.

KISTEP modular pipeline for the characterization of anomalous diffusion trajectories.

Analysis of point process driven by fractional Brownian motion.

Simulating multi-dimensional bounded fractional Brownian motion with time varying diffusivity and hurst exponent.

JavaScript implementation of simplex noise 2D algorithm with fBm and SplitMix32 in 550 bytes.

Generative model for rough volatility: log-signatures + a learned Besov-wavelet decoder reconstruct high-frequency texture via differentiable IDWT. Pluggable MLP/attention/transformer backbones, scale-weighted wavelet loss, and a 5-dataset multi-domain registry (fBM, rough Bergomi, Burgers turbulence, CHB-MIT EEG, ESC-50 audio).

Neural SDE framework for rough volatility modeling (H ≈ 0.1) with deep hedging. Implements Davies-Harte fBM, signature-based losses, and convergence analysis.

Empirical validation of the Rough Volatility paradigm (H < 0.5) using S&P 500 data, and Monte Carlo options pricing via the Rough Bergomi (rBergomi) model to capture the short-term implied volatility skew.