rough-volatility

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

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

Derivatives pricing library: Black-Scholes-Merton (16 Greeks), Monte Carlo with variance reduction, Asian/Barrier/Lookback/Digital exotics, Heston (Fourier pricing, QE simulation, SPX calibration), rough Bergomi (hybrid scheme). 1,013 tests, QuantLib-validated, 97% coverage.

Sixteen option pricers over six stochastic models including Heston, Bates, SABR and rough Bergomi, spanning analytic, lattice, finite-difference, COS Fourier and Monte Carlo methods.

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.

Differentiable option pricing in Julia: Black-Scholes ? Heston ? rough Heston (implicit fractional Riccati solver), calibrated to a real SPX chain with an honest rough-vs-classical verdict.

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

Deep-learning option pricing and hedging: a neural surrogate for Asian options benchmarked against Monte Carlo, a rough Bergomi model for 0DTE, a CVaR deep hedging policy, live calibration, and an interactive dashboard.

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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.