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

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

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.

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

Latent contagion, risk-neutral compression, and option-manifold pricing in Volterra-Perron rough markets.

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

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

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

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.

MSc thesis investigating whether rough volatility models improve out-of-sample volatility forecasting performance relative to HAR-RV

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.

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.