barrier-options

📈 Apply financial engineering techniques to option pricing using Monte Carlo simulations and the Black-Scholes model with clear, documented Python code.

A Python library for options pricing and Greeks computation.

Pricing crypto prediction markets (Polymarket) as one-touch barrier options against the Deribit vol surface.

Extension of the Black-Scholes framework to path-dependent derivatives (Barrier Options) using stochastic calculus, the reflection principle, and PDE replication.

Physics-informed Fourier Neural Operator (FNO) framework for fast pricing and Greeks computation of barrier options under Black–Scholes PDE, developed for an MSc thesis.

Tutorial-based Python experiment for valuing barrier options with Monte Carlo simulation.

Neural-network surrogate for a Monte Carlo FX up-and-out barrier option pricer (TensorFlow/Keras): 1.5e-4 MAE, 100% of test options within 1% absolute error

Pricing models for different types of option contracts.

Dupire local volatility from a real SPY surface, and what flat-vol pricing costs on a barrier option: 32bp of spot. Live in-browser PDE.

Option Pricing with Monte Carlo Simulation — A Python library implementing Black–Scholes analytic pricing, Monte Carlo simulations (with variance reduction, quasi-MC), and advanced derivatives such as Asian, Barrier, and American options. Includes performance acceleration using Numba and comprehensive documentation with visualizations.

MATLAB implementation of European, Barrier (Up&Out), and Bermudan option pricing via CRR Binomial Trees and Monte Carlo simulations. Includes Greeks (Delta, Vega), convergence analysis, and variance reduction via antithetic variables.

Pricing Parisian barrier options with lattice, Monte Carlo and sequential Monte Carlo methods under Black-Scholes dynamics.

Pricing Bonus Certificates under Black–Scholes: closed-form barrier-option replication, Monte Carlo with Brownian-bridge correction, and risk analysis on Deutsche Telekom

SSRN working paper and reproducibility package for barrier option pricing with structurally constrained PINNs and FDM benchmarks.

A modular Python project for computational finance and stochastic modelling.

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.

Retail futures evaluation contracts priced as options on a drawdown barrier. The parameter driving ~80% of economic value is disclosed by no provider; the fee everyone compares is the least material lever. Screens 25 firms from public terms and concludes against the opportunity. Paper (EN/ES), code, falsification protocol.