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.

European option pricing in the rough Heston model, with a Taylor expansion in the Hurst exponent H (Python, JAX)

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.

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.

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.

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.

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.

Back up Harvest Moon farm and barn saves before festival patches with a local desktop helper that finds folders, copies files, and writes a keep report.

Differentiable option pricing and volatility surfaces in PyTorch — Heston, Bates, Merton, Variance Gamma via Fourier-COS; SVI/SABR surfaces; rough Bergomi. Greeks from autograd, calibration by gradient descent.

Deep calibration of the rough Bergomi model: hybrid-scheme Monte Carlo on GPU, neural surrogate of the implied-vol surface, Levenberg-Marquardt calibration, Heston COS baseline.