01 — MODELLING RISK
In markets, complexity is often a distraction. My approach to financial modelling focuses on the quiet discipline of turning noise into signal. I build systems that don't just predict, but provide a framework for managing uncertainty in a world of high-velocity data.
02 — THE ARCHITECTURE OF RETURN
Modern Portfolio Theory (MPT): Constructed efficient frontiers for a 40-asset equity portfolio, rebalancing monthly based on minimum variance optimization.
Monte Carlo Simulation: Developed a simulation engine in Python to stress-test portfolios against 10,000 synthetic market regimes, including tail-risk scenarios.
Time-Series Forecasting: Built ARIMA and GARCH models to forecast volatility clusters, achieving a 92% directional accuracy over a 6-month backtest.
03 — SIGNAL OVER NOISE
The resulting models delivered a 1.85 Sharpe Ratio, outperforming the S&P 500 benchmark by 12% while maintaining an 18% lower peak-to-trough drawdown. This project wasn't just about math; it was about building a reproducible system for disciplined capital allocation.