A Python implementation of the rough Bergomi model.

C Bayer, B Stemper (2018). Deep calibration of rough stochastic volatility models.

C++ implementation of rBergomi model

Bayer, Friz, Gulisashvili, Horvath, Stemper (2017). Short-time near-the-money skew in rough fractional volatility models.

Bayer, Friz, Gassiat, Martin, Stemper (2017). A regularity structure for finance.

Neural network framework for volatility surface approximation and calibration. Supports rough Heston/Bergomi, random grids, multi-regime architectures.

Repository of the 'Pricing under Rough Volatility Models' Student Lab

A high performance pricing and calibration engine for Rough Volatility (rBergomi) models using a hybrid Python/C++ architecture with PyBind11.

Comparative analysis of Value at Risk (VaR) measures using Black-Scholes pricing under different volatility models: jump diffusion, SABR and rough volatility.

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

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).

Regime-Aware Multi-Agent Portfolio Allocator — a five-phase ML pipeline combining HMM regime detection, LightGBM alpha generation, deep rough volatility calibration, and PPO reinforcement learning for dynamic asset allocation.

A Python Package for analytical formula of VIX term structure under rough Heston model via Markovian Approximation

Python framework for volatility models with automatic differentiation.

Numba-accelerated Rough Bergomi volatility model for derivatives pricing. Tested on Tesla (TSLA).