Options Pricing Models with Python
Implement binomial trees, Black-Scholes-Merton and Monte Carlo option pricers in Python, then test them against each other and against known analytical results.
This course focuses on measuring and managing the sensitivities of options positions. You will compute delta, gamma, vega, theta and rho analytically and by finite differences, aggregate them across a portfolio, and study how they behave through time and across strikes. The second half covers scenario grids, stress testing, P&L attribution and the practical limits of Greek-based risk measures.
6 modules · 24 lessons · 6 weeks. Curriculum preview; the final syllabus may change before enrollment opens.
Build a Greeks engine that computes and aggregates sensitivities for a sample options portfolio
Simulate delta hedging at different rebalancing frequencies and report hedging error distributions
Produce a spot-and-volatility stress report with a P&L attribution breakdown
Quantzivo certificates are completion credentials issued by Quantzivo, not accredited qualifications. Courses teach technical and research skills; they do not promise investment returns or provide investment advice.
Implement binomial trees, Black-Scholes-Merton and Monte Carlo option pricers in Python, then test them against each other and against known analytical results.
Model realized and implied volatility with GARCH, stochastic and local volatility approaches, build arbitrage-aware volatility surfaces, and run disciplined derivatives research.
Combine strategies into portfolios and analyze their risk with allocation methods, risk decomposition, drawdown analysis, stress testing and risk reporting in Python.