Compute VaR, CVaR, and maximum drawdown from return series.
npx skills add https://github.com/GGwujun/SigmX --skill risk-analysis-ggwujun
Please help me install this Agent Skill. Skill: risk-analysis Source: https://github.com/GGwujun/SigmX/tree/main/agent/src/skills/risk-analysis Command: npx skills add https://github.com/GGwujun/SigmX --skill risk-analysis-ggwujun
Quantifies and communicates downside risk for portfolios using standardized risk metrics to support informed decision-making.
Run a full risk analysis on the provided return data to generate a report with VaR, CVaR, max drawdown, Monte Carlo paths, and tail-risk metrics.
High-intent search queries and answers about installing and using this skill.
To calculate VaR and CVaR from a return series, you need historical return data and portfolio weights to compute downside risk using historical, parametric, and Monte Carlo methods, generating a structured risk report.
The best way to perform stress testing on a monthly rebalanced portfolio is by applying predefined market shock scenarios to your return data, evaluating portfolio resilience and downside risk impact.
Monte Carlo simulation for portfolio tail risk works by generating random return paths based on historical parameters, calculating VaR and expected shortfall to quantify extreme downside risk probabilities.
Yes, EVT tail distribution fitting can be used alongside maximum drawdown analysis to model extreme market movements, providing deeper insight into tail risk and recovery periods for your portfolio.
Running a full risk analysis requires input return data and portfolio weights, plus an optional benchmark, to produce clear risk recommendations and reproducible VaR, CVaR, and drawdown results.
Use Monte Carlo VaR when you need to simulate hypothetical market shocks and model future risk distributions, whereas historical VaR relies strictly on past return data for downside risk quantification.
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