Manuel Touyaa's porfotlio of Python projects/assignments for Finance Market Risk.
This is about calculating Market Risk capital charge under Standardised Approach(SBM) in FRTB
Advanced Approach under FRTB(IMA) requires institutions to meet quantitative tests namely Backtesting and PnL attribution..
End-to-end sell-side market-risk engine: VaR/ES across four methods, FRTB Expected Shortfall with liquidity horizons, Basel III vs FRTB capital, Kupiec/Christoffersen backtesting, stress testing and component-VaR attribution.
FRTB IMA-compliant daily VaR/Expected Shortfall risk monitor: stress calibration, liquidity-horizon scaling, NMRF checks, Acerbi-Szekely/Kupiec/Christoffersen backtesting, a Plotly Dash dashboard, and Claude-generated narratives.
FRTB-aligned VaR & Expected Shortfall risk engine with GARCH volatility modeling, regulatory backtesting, and Streamlit dashboard — built for Swedish equities.
FRTB IMA engine: VaR, ES, full backtesting suite, stressed calibration, and PLAT test in Python.
Multi-asset market risk framework: VaR, Expected Shortfall, stress testing, and backtesting across equity, IG/HY credit, and US Treasury instruments.
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