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risk-analysis

Calculate VaR, CVaR, and stress test portfolio risks using statistical methods.

15|2|Updated May 1, 2026
Search Tags:#stress-testing#Monte Carlo#asset management#risk#VaR#tail-risk
One-click install
npx skills add https://github.com/OpenSucker/OpenSucker --skill risk-analysis-opensucker
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: risk-analysis
Source: https://github.com/OpenSucker/OpenSucker/tree/main/skills/vibe_skills/risk-analysis
Command: npx skills add https://github.com/OpenSucker/OpenSucker --skill risk-analysis-opensucker

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scipy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables detailed measurement and evaluation of financial risk, assisting users in quantifying and understanding potential losses and tail risks.

Core Features & Use Cases

  • Risk Quantification: Calculates VaR, CVaR, maximum drawdown, and tail characteristics to assess portfolio risk levels.
  • Stress Testing: Simulates historical and hypothetical market scenarios to evaluate portfolio resilience during crises.
  • Use Case: A fund manager reviews the VaR and stress test outputs to adjust allocations and implement risk mitigation measures.

Quick Start

Request risk analysis with historical return data and portfolio weights to generate a comprehensive risk report.

Frequently Asked Questions about risk-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate VaR and CVaR for portfolio asset management?▼

To calculate VaR and CVaR for portfolio asset management, provide historical return data and portfolio weights to generate a comprehensive risk report quantifying potential losses, maximum drawdown, and tail characteristics using statistical and simulation methods.

What is the best way to run Monte Carlo stress testing on historical returns?▼

Running Monte Carlo stress testing on historical returns is best handled by simulating hypothetical and historical market scenarios to evaluate portfolio resilience and quantify potential losses during financial crises using statistical modeling.

How does tail-risk measurement evaluate portfolio vulnerabilities?▼

Tail-risk measurement evaluates portfolio vulnerabilities by applying statistical and simulation methods, using scipy's genpareto for statistical modeling to quantify maximum drawdown and extreme potential losses to assess overall risk resistance.

Can I use numpy and pandas for financial risk quantification?▼

Yes, you can use numpy and pandas for financial risk quantification, as this approach requires them alongside scipy to process historical return data, calculate VaR, and simulate stress testing scenarios for asset management.

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