Manuel Touyaa's porfotlio of Python projects/assignments for Finance Market Risk.
Implements the Basel III credit risk framework (PD, LGD, EAD) using Logistic & Linear Regression on Lending Club loan data (2007–2014)
FP&A Virtual Experience Program by Citi through Forage
End-to-End Market & Credit Risk Analytics Engine under Basel III / EBA standards. Features Parametric & Historical Value-at-Risk (VaR), Expected Shortfall, GARCH(1,1) Volatility Forecasting, Basel III Portfolio Stress Testing, ALM Interest Rate Sensitivity (EVE/NII), Basel Traffic Light Backtesting, PostgreSQL, and Power BI.
IFRS 9 Credit Risk Scorecard & Expected Credit Loss (ECL = PD * LGD * EAD) Engine under Basel III / EBA standards. Features R Weight of Evidence (WoE) binning & Information Value, Python PD models (Logistic Regression Gini=0.7467 vs XGBoost), 3-Stage Staging, PostgreSQL, automated Excel financial models, and a 2-page Power BI Dashboard.
Counterparty Credit Risk (CVA/PFE) & Regulatory Liquidity (LCR/NSFR) Engine (Python, R, PostgreSQL, Power BI, Excel)
Production‑grade Power BI solution for vintage lifecycle, roll‑rate analysis and scenario stress testing (demo), power-bi, credit-risk, vintage-analysis, roll-rate, IFRS9
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