stochastic-volatility

Fourier-transform pricing, Monte Carlo validation, and calibration of European options under stochastic-volatility models in Python

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

Vanilla and exotic option pricing library to support quantitative R&D. Focus on pricing interesting/useful models and contracts (including and beyond Black-Scholes), as well as calibration of financial models to market data.

MATLAB library for large Bayesian VARs: samplers, shrinkage priors, stochastic volatility, marginal likelihoods and forecasting, with examples, tutorials and the replication packages from joshuachan.org.

A UI-friendly program calculating Black-Scholes options pricing with advanced algorithms incorporating option Greeks, IV, Heston model, etc. Reads input from users, files, databases, and real-time, external market feeds (e.g. APIs).

MATLAB library for Bayesian state space models: precision-based samplers that draw the whole state path at once, for unobserved components, time-varying parameter, stochastic volatility and dynamic factor models, with examples and the replication packages from joshuachan.org.

Low-latency options pricing engine in Rust. BSM, Black-76, Heston, Bates (jumps), Local Vol (Dupire), Monte Carlo (Euler/Andersen QE). Adaptive Gauss-Kronrod CF pricers, full analytic Greeks, forward-mode AD (incl. jump sensitivities), Halley IV solver, LM/DE global calibration, no-arbitrage repair, Rayon parallelism. CI + clippy, 0 warnings.

R Finance packages not listed in the Empirical Finance Task View

Bayesian Estimation of Heteroskedastic Structural Vector Autoregressions with Markov-Switching and Time-Varying Identification of the Structural Matrix

Financial analysis and demonstration of the classic algorithmic trading method, pair trading. This analysis compares the portfolio's growth with the underlying assets value and volatility over time.

Q-Variance Challenge: Can any continuous-time stochastic-volatility model reproduce q-variance?

High-fidelity synthetic financial data generator using Heston Stochastic Volatility and Jump Diffusion.

List of books on time series analysis, with links to code where available

Sample chapters and code (MATLAB, R, Python) for 'Bayesian Macroeconometrics: Methods and Applications' by Joshua Chan (Chapman & Hall/CRC, forthcoming)

Heston two-factor stochastic volatility model with Cox-Ingersoll-Ross (CIR) variance and Feller condition checking

Heston two-factor stochastic volatility model with Cox-Ingersoll-Ross (CIR) variance and Feller condition checking

A repo which deals with Computational Methods in Mathematics, mainly applied in the context of Mathematical Finance, even though it can be applied to almost any domain where you need Probability, Partial Differential Equations, Stochastic Differential Equations, Characteristic Functions, Lévy Processes, Stochastic Volatility, FFT, etc.

Stochastic Volatility Estimated by MCMC (Markov Chain Monte Carlo) Method

TVP-GVAR-FSVM model proposed in "Measuring international uncertainty using global vector autoregressions with drifting parameters"