--- name: value-at-risk-calculator-4e719edc1cd6 description: Value at Risk (VaR) and related risk metrics calculation skill for financial and operational risk assessment allowed-tools: - Read - Write - Glob - Grep - Bash metadata: specialization: decision-intelligence domain: business category: risk priority: medium tools-libraries: - scipy.stats - numpy - arch - riskfolio-lib graph: domains: [domain:business-intelligence] skillAreas: [skill-area:quantitative-modeling, skill-area:financial-risk-modeling, skill-area:statistical-analysis] roles: [role:quantitative-analyst, role:risk-analyst, role:financial-analyst] --- # 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 --- Source attribution: a5c-ai Original project: https://github.com/a5c-ai/babysitter License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Gabriel Moreira Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: a5c-ai Original project: https://github.com/a5c-ai/babysitter Attribution basis: Upstream repository explicitly recorded in package mirror path Fields of work: Finance & Accounting, Sales Roles: Accountant, Business Development, CFO / FP&A, Financial Analyst Specialties: API, Backend Tools: Arch, Numpy, Riskfolio Lib, Scipy.Stats Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: a3db1d4cb24d13eaad9b0be859f0418533a342e10737477de0cd0a8fc2ace921