derivative-pricing

Quantitative Finance tools

Market Data & Derivatives Pricing Tutorial based on Jupyter notebooks

My answers to exercises in Stochastic Calculus for Finance by Steven E. Shreve.

Pricing weather futures using an ARIMA model and 8 years' worth of scraped weather data.

real-time predictive options model - mathematical modeling

Coursework, projects, and datasets from the MSc in Financial Engineering (MScFE) program at WorldQuant University.

Part of the Neutryx Lab ecosystem for differentiable finance.

Repositório com o código-fonte do Derivativos e Risco de Mercado

Note on financial mathematics

An implementation of the Longstaff-Schwartz algorithm, which we use to price a convertible bond.

Financial Engineering in IRFX in C++

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

Numerical convergence validator for finite-difference schemes - verifies O(h) and O(h-squared) orders with log-log regression analysis.

Fullstack Bates (1996) Option Pricing Engine: A high-performance engine utilising Inverse Fourier Transforms for real-time calibration and Euler-Maruyama Monte Carlo for path projections. Optimised for 2026-2027 market volatility regimes and jump-diffusion dynamics.

An advanced Python framework for pricing financial derivatives beyond Black-Scholes using the Heston Stochastic Volatility Model and the Merton Jump Diffusion Model. The project evaluates European, American, and Barrier options, analyzes strike sensitivities, and computes Greeks using Monte Carlo simulations.

An implementation of the Heston model, a stochastic volatility model for options pricing. We compute prices of European call and put options via Monte Carlo simulation, for a variety of strike prices and maturities. We also show that the Heston model captures volatility smiles/smirks/skews.

A hybrid classical-quantum proof-of-concept for pricing European Call Options using Black-Scholes, Monte Carlo, and Iterative Quantum Amplitude Estimation (IAE) via Qiskit. Demonstrates the theoretical quadratic speedup of quantum computing "O(√N) vs O(N)" - over classical Monte Carlo simulations.

Numerical methods for derivative pricing: analytical, Monte Carlo, PDE, and Heston stochastic volatility, cross-validated and tested in parallel Python/C++.

Theoretical foundation of derivative pricing, covering financial markets, bonds, options and models like Black-Scholes.

A high-performance Monte Carlo pricer for Exotic Derivatives (Asian & Barrier Options) featuring a hybrid architecture (C++17, Python/Numba, and Pybind11).