Financial-Models-Numerical-Methods is a collection of Jupyter notebooks covering quantitative finance topics with interactive Python implementations.
The project addresses the scarcity of well-documented, ready-to-use implementations of financial models in Python. It focuses on less mainstream but powerful techniques including partial differential equation methods, Lévy processes, Fourier inversion methods, and Kalman filtering. Rather than explaining foundational concepts, the notebooks assume prior knowledge of stochastic calculus and financial mathematics, then demonstrate practical applications through executable code. Each notebook is largely independent, allowing readers to focus on specific topics of interest.
The notebooks suit advanced students and practitioners in finance, economics, or science who have completed undergraduate coursework in financial mathematics and statistics. The material covers Black-Scholes numerical methods, stochastic differential equation simulation, Heston models, Lévy processes including Merton jump-diffusion, and PDE-based approaches. The interactive format allows readers to run and modify code directly, which the author identifies as superior to static documentation for learning. The README explicitly notes that these notebooks are not for absolute beginners and recommend cloning the repository locally to ensure mathematical formulas display correctly.
Development is ongoing but incremental. The author describes the collection as incomplete and indicates plans to add notebooks on stochastic processes, Kalman filtering, and statistics over time. The project invites user feedback on errors, questions, and improvement suggestions, positioning it as a work in progress rather than a finished reference.
