expected-shortfall

Portfolio level (un)conditional risk measure estimation for backtesting using Vine Copula and ARMA-GARCH models.

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

R Finance packages not listed in the Empirical Finance Task View

Expected Shortfall Backtesting

Comparisons of financial metrics (e.g. VaR vs CVaR/ES, simple vs log returns, etc.).

A package for evaluating tail probabilities and partial moments for random vectors in multivariate generalized hyperbolic random vectors.

R package providing functions for computing Expected shortfall (ES) and Value at risk (VaR)

A collection of approaches for forecasting VaR and ES, based on both autoregressive approaches and neural networks.

This repository consits of: projects and homeworks connected with research area such as Risk Management.

[R] Statistical analysis of financial data conducted in R

A library for the calculation of tail risk measures

Market risk analytics dashboard in Python and Streamlit that computes portfolio volatility, drawdowns, VaR/ES, rolling correlations, and stress tests (shocks + COVID‑style crisis window) for equity/ETF portfolios. ​

Production-grade open-source Market Risk Engine 🚀💹 – Full-stack FastAPI (Python) + React 19/TypeScript with a sleek fintech dark-theme dashboard.Compute VaR & CVaR via multiple methods, advanced stress testing (historical crises + custom), VaR backtesting (Kupiec test), and rich portfolio analytics.

Backtesting my current US stocks portfolio

Code for the case studies and theoretical visualizations for the master thesis 'Estimation and Backtesting of the Expected Shortfall and Value at Risk using Vine Copulas'

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.

Curso ministrado por mim na Financial Risk Academy (FRA) sobre Introdução ao Risco de Mercado com Python

Repository represents python usability of measuring and managing risks (practice tasks and real cases)

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.

Multi-asset market risk engine in Python: Value at Risk (VaR) and Expected Shortfall by 4 methods (historical, parametric, Cornish-Fisher, Monte Carlo), EWMA + GARCH(1,1) volatility by MLE, extreme value theory (EVT) tail risk, component VaR, stress testing, and Kupiec/Christoffersen/Basel traffic-light backtesting.