Agent skill

value-at-risk-calculator

Value at Risk (VaR) and related risk metrics calculation skill for financial and operational risk assessment

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/decision-intelligence/skills/value-at-risk-calculator

Metadata

Additional technical details for this skill

domain
business
category
risk
priority
medium
specialization
decision-intelligence
tools libraries 
[
    "scipy.stats",
    "numpy",
    "arch",
    "riskfolio-lib"
]

SKILL.md

Value at Risk Calculator

Overview

The Value at Risk Calculator skill provides comprehensive capabilities for calculating VaR and related risk metrics using multiple methodologies. It supports financial risk assessment, operational risk quantification, and regulatory compliance through parametric, historical, and simulation-based approaches.

Capabilities

  • Historical simulation VaR
  • Parametric VaR (variance-covariance)
  • Monte Carlo VaR
  • Conditional VaR (CVaR/Expected Shortfall)
  • Incremental and component VaR
  • Stress testing
  • Backtesting and validation
  • Regulatory reporting support

Used By Processes

  • Monte Carlo Simulation for Decision Support
  • Risk Assessment
  • Decision Quality Assessment

Usage

Historical Simulation VaR

python
# Historical VaR configuration
historical_var_config = {
    "method": "historical_simulation",
    "data": {
        "returns": portfolio_returns,  # historical return series
        "period": "daily",
        "history_length": 252  # 1 year of trading days
    },
    "confidence_levels": [0.95, 0.99],
    "holding_period": 1,  # days
    "options": {
        "age_weighting": {
            "enabled": True,
            "decay_factor": 0.97
        }
    }
}

Parametric VaR

python
# Parametric (variance-covariance) VaR
parametric_var_config = {
    "method": "parametric",
    "portfolio": {
        "positions": {
            "Asset_A": {"value": 1000000, "weight": 0.4},
            "Asset_B": {"value": 750000, "weight": 0.3},
            "Asset_C": {"value": 750000, "weight": 0.3}
        }
    },
    "parameters": {
        "volatilities": {"Asset_A": 0.20, "Asset_B": 0.15, "Asset_C": 0.25},
        "correlation_matrix": [
            [1.0, 0.3, 0.2],
            [0.3, 1.0, 0.5],
            [0.2, 0.5, 1.0]
        ]
    },
    "confidence_level": 0.99,
    "holding_period": 10  # days
}

Monte Carlo VaR

python
# Monte Carlo VaR configuration
monte_carlo_var_config = {
    "method": "monte_carlo",
    "simulations": 100000,
    "model": {
        "type": "geometric_brownian_motion",
        "parameters": {
            "drift": "historical",
            "volatility": "garch"
        }
    },
    "portfolio_valuation": "full_revaluation",
    "confidence_levels": [0.95, 0.99],
    "holding_period": 10
}

Conditional VaR (Expected Shortfall)

CVaR represents the expected loss given that VaR is exceeded:

  • CVaR at 95% = Average loss in worst 5% of scenarios
  • Required by Basel III/IV for market risk capital
  • More coherent risk measure than VaR

Stress Testing

python
# Stress test scenarios
stress_tests = {
    "scenarios": [
        {
            "name": "2008 Financial Crisis",
            "shocks": {"equity": -0.40, "credit_spreads": 0.03, "rates": -0.02}
        },
        {
            "name": "COVID-19 March 2020",
            "shocks": {"equity": -0.30, "volatility": 0.50, "credit_spreads": 0.02}
        },
        {
            "name": "Interest Rate Spike",
            "shocks": {"rates": 0.03, "equity": -0.15}
        }
    ],
    "output": ["portfolio_loss", "position_contributions"]
}

Input Schema

json
{
  "method": "historical|parametric|monte_carlo",
  "portfolio": {
    "positions": "object",
    "total_value": "number"
  },
  "data": {
    "returns": "array or path",
    "period": "string"
  },
  "parameters": {
    "confidence_levels": ["number"],
    "holding_period": "number",
    "volatility_model": "string"
  },
  "stress_testing": {
    "scenarios": ["object"]
  },
  "backtesting": {
    "enabled": "boolean",
    "test_period": "string"
  }
}

Output Schema

json
{
  "var_results": {
    "confidence_level": {
      "VaR": "number",
      "VaR_percent": "number",
      "CVaR": "number",
      "CVaR_percent": "number"
    }
  },
  "component_var": {
    "position": {
      "marginal_var": "number",
      "component_var": "number",
      "contribution_percent": "number"
    }
  },
  "stress_test_results": {
    "scenario_name": {
      "portfolio_loss": "number",
      "loss_percent": "number"
    }
  },
  "backtesting": {
    "exceptions": "number",
    "exception_rate": "number",
    "traffic_light": "green|yellow|red",
    "kupiec_test": "object",
    "christoffersen_test": "object"
  },
  "risk_attribution": "object"
}

Best Practices

  1. Use multiple methods and compare results
  2. Validate with backtesting regularly
  3. Include fat tails (t-distribution or historical for parametric)
  4. Update parameters (volatility, correlations) frequently
  5. Complement VaR with stress testing
  6. Report CVaR alongside VaR for tail risk
  7. Document all assumptions and limitations

VaR Interpretation

Metric Meaning
VaR 95% = $1M 95% confident loss won't exceed $1M
CVaR 95% = $1.5M If loss exceeds VaR, average loss is $1.5M
Incremental VaR Change in portfolio VaR from adding position
Component VaR Position's contribution to total VaR

Backtesting Standards

Exceptions (250 days) Zone Interpretation
0-4 Green Model is acceptable
5-9 Yellow Model may have issues
10+ Red Model needs review

Integration Points

  • Receives simulations from Monte Carlo Engine
  • Connects with Risk Register Manager for risk assessment
  • Supports Risk Analyst agent
  • Integrates with Decision Visualization for risk charts

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