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

Portfolio Value-at-Risk (VaR) and Conditional VaR (Expected Shortfall) engine calculating tail risk across parametric and historical loss distributions.

Portfolio Value-at-Risk (VaR) and Conditional VaR (Expected Shortfall) engine calculating tail risk across parametric and historical loss distributions.

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)

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.

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. ​

Backtesting my current US stocks portfolio

Out-of-sample VaR and expected-shortfall audit for Gaussian, historical, and EVT models

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.

Portfolio risk an AI agent can drive and a person can read. Historical VaR and Expected Shortfall, signed linear exposure, explicit stress scenarios, counterparty exposure and XVA through a local ORE project. 4 MCP tools, 5 skills, a local dashboard and a hosted service. Research software, not investment advice. Noncommercial licence.