📚SDE research and modelling in Finance📚
Pricing & calibration engine (15+ models; API + CLI + Streamlit UI)
A Python-based Monte Carlo pricing engine for FX derivatives, implementing the Bates Model (Stochastic Volatility + Jump-Diffusion) to account for geopolitical liquidity shocks and fragmented market architecture.
A quantitative trading framework for options pricing, volatility surface calibration, and automated statistical arbitrage using Heston and Bates models.
Differential Machine Learning (Sobolev training) neural surrogate for the Bates SVJD model: 100k-sample training, Carr-Madan FFT pricer, live SPY calibration, self-test suite
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.
Pricing and delta-hedging a Bonus Certificate under the Bates model (FFT calibration + Monte Carlo), MATLAB
A quantitative finance project implementing stochastic volatility models (Heston, Bates) and interest rate diffusion models (CIR) to price Over-The-Counter (OTC) derivatives.
Derivatives pricing and risk models in Python: stochastic volatility, jump diffusion, Monte Carlo VaR, and Black-Scholes Greeks cross-validated with finite differences and automatic differentiation
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