Estimates potential future portfolio losses using Value-at-Risk (VaR), Monte Carlo simulations, and stress testing scenarios.
Provides advanced financial risk modeling capabilities directly within Claude Code, allowing users to quantify tail risk and project potential losses across diverse asset classes. It supports multiple methodologies including parametric VaR, Expected Shortfall (CVaR), and Monte Carlo simulations to handle non-normal distributions and complex instruments like options. Whether you are performing factor-based risk decomposition to isolate systematic exposures or conducting hypothetical stress tests for market crashes, this skill offers the rigorous mathematical frameworks and implementation patterns required for institutional-grade portfolio risk management.
Características Principales
0118 GitHub stars
02Hypothetical and Historical Scenario Stress Testing
03Component and Marginal VaR Risk Decomposition
04Factor-Based Systematic Risk Modeling
05Parametric and Monte Carlo VaR Calculation
06Expected Shortfall (CVaR) Tail Risk Analysis
Casos de Uso
01Estimating the maximum expected daily loss for a diversified investment portfolio at a 99% confidence level
02Identifying which specific holdings are the primary drivers of portfolio risk using Component VaR
03Simulating portfolio resilience under extreme scenarios such as interest rate shocks or equity market crashes