I price derivatives and measure the risk they carry, on rates and equity products, in Python.
Each repository below is a self-contained study built around one question. The assumptions are stated up front, the model is checked before any number is produced, and what it leaves out is written down at the end.
equity-option-delta-hedging — Dynamic delta hedging of a short European call. GBM price paths, Black-Scholes Greeks scaled to a $1M notional, daily rebalancing, hedged against unhedged P&L.
fra-sofr-futures-hedge — Hedging a short 3x6 FRA with CME SOFR futures. Forward rate implied from the SOFR curve, DV01-driven hedge ratio rebalanced daily, P&L attribution on the residual.
expected-shortfall-stressed-var-frtb — Expected Shortfall at 97.5% and Stressed VaR under FRTB, computed three ways on the same book to measure what the choice of estimator costs in capital. Kupiec backtest of a calm calibration against a stressed window.
counterparty-exposure-epe-pfe — Exposure profile of an interest rate swap under Hull-White. EE, EPE, Effective EPE, PFE 95% and CVA, and how much netting and a collateral agreement take off each of them.
Python with NumPy, Pandas, SciPy and Matplotlib for the work above. Excel and VBA, SQL and Access, Bloomberg and RiskMetrics from production work.
Market risk at Luxcellence, the CACEIS entity in Luxembourg. Daily historical and Monte Carlo VaR on multi-asset portfolios from 300M to 1.5bn EUR under management, VaR backtesting across 26 portfolios under the Kupiec and Christoffersen tests, and P&L attribution on breaches to identify loss drivers and model limitations.
Master in Management with a quantitative finance major, Toulouse Business School, graduated with honours, alongside an MSc in Banking and Finance awarded with merit.
Teaching derivatives pricing, Black-Scholes, Greeks and VaR, and supervising Python projects, since 2025.
Open to roles across Europe and in Canada — abdel.oubadi@gmail.com