Manuel Touyaa's porfotlio of Python projects/assignments for Finance Market Risk.

Simulated 1-day 99% Monte Carlo VaR with Basel III regulatory backtesting

A credit risk scorecard webapp that lets finance teams and analysts run Basel-compliant loan default predictions.

Balance sheet forecasting tool for banks - capital management, liquidity management, and stress testing with Basel III compliance

A Basel III mortgage capital project comparing STD vs IRB RWA/CET1, using Logistic Regression PD modelling.

Calculadora de FPR (Fator de Ponderação de Risco) - Res. BCB 229/2022. Calcula RWACPAD para risco de crédito com suporte completo a todas classes de ativos.

Production-grade Basel III RWA calculation pipeline processing 120M+ records/day with Spark, Airflow, and AWS

Quantitative risk analytics and portfolio construction in Python. Covers Monte Carlo VaR/CVaR (Basel III), Markowitz & Risk Parity optimization, and 20+ quant finance concepts from factor models to backtesting methodology. Built for Quant Risk / ML in Finance roles.

SQL data quality framework and Basel III regulatory calculations for credit risk management

FP&A Virtual Experience Program by Citi through Forage

Basel III-compliant credit risk models: PD, LGD, EAD estimation with explainability and regulatory validation frameworks

A quantitative framework for modeling Operational Risk Capital under Basel III standards using the Loss Distribution Approach (LDA). Implements Monte Carlo convolution of Poisson frequency and Generalized Pareto (Heavy-Tailed) severity distributions to calculate the 99.9% Value at Risk (VaR).

A collection of projects applying mathematical rigor to financial problems, including Basel III Market Risk backtesting, ARIMA-based sales forecasting, and neural networks for credit approval. Developed using Python (TensorFlow, Scikit-learn) and R (astsa, zoo).

Serverless AWS liquidity risk monitoring system - calculates Basel III LCR and alerts on regulatory breaches

This notebook demonstrates a multi-period enterprise-wide stress testing (EWST) framework aligned with OSFI ICAAP expectations.

End-to-end Credit Risk Scorecard development. Implemented Weight of Evidence (WoE) binning and Information Value (IV) analysis for feature selection. Validated via Basel-III standards using Gini and AUC-ROC metrics.

Production-grade credit risk modelling platform — Probability of Default, Loss Given Default, Exposure at Default models, Monte Carlo Value at Risk, Basel III Risk-Weighted Assets, and EBA stress testing. Built with Python, Streamlit, and SQLite.

📊 Model operational risk capital using the Loss Distribution Approach (LDA) and Monte Carlo methods for accurate economic risk assessment.