FP&A & Quant | 10+ Years in Financial Modelling, Machine Learning & Strategic Decision-Making.
I'm an FP&A and financial-reporting professional with a decade of experience across budgeting, forecasting, and management accounts for a multi-brand F&B group. Alongside that, I'm pursuing an MSc in Financial Engineering, applying stochastic modelling and quantitative methods to derivatives pricing and systematic asset allocation. This page brings both sides together: structured financial planning work on one hand, and quantitative/data-driven analysis on the other.
A multi-unit FP&A exercise consolidating actuals and budgets for three business units into a single group view, complete with variance analysis (Variance $, Variance %, Favorable/Unfavorable flags), an 8-month rolling forecast (Aug–Mar) building on Q1 actuals, and an executive dashboard summarizing group performance. Demonstrates cross-sheet consolidation, budget-vs-actual variance analysis, and forecasting within a formal FP&A reporting structure.
Stochastic_Modeling_GWP1 — Derivatives Pricing & Calibration (MScFE 600)
Priced and risk-managed a sequence of OTC derivatives for a hypothetical equity client as their requirements evolved: a 20-day Asian call under a calibrated Heston stochastic-volatility model, a 70-day European put under a Bates jump-diffusion extension, and a CIR interest-rate model fitted to the Euribor curve. Calibrated using both Lewis and Carr-Madan Fourier pricing methods for cross-validation, then priced via Monte Carlo simulation with full model diagnostics (calibration error, Feller condition checks, confidence intervals).
Stochastic_Modeling_GWP2 — Regime-Switching Asset Allocation (MScFE 622)
Built a VIX-based regime-detection and asset-rotation strategy across SPY, GLD, and TLT using a 3-state Gaussian Hidden Markov Model (with a discrete Markov chain benchmark), selecting the ETF with the strongest historical return in each volatility regime. Backtested over ~20 years of data with a one-day execution lag, achieving an 18.2% annualized return and 1.16 Sharpe ratio versus 9.0%/0.94 for an equal-weight benchmark and 11.0%/0.65 for SPY buy-and-hold — including sensitivity checks across model specifications and sample windows.
Let's connect Open to FP&A and quantitative finance roles — reach out. 📧 Email | 💼 LinkedIn | 🔗 GitHub