Validate VaR models with the Kupiec POF test and Basel traffic light system.
npx skills add https://github.com/Vaibhavkkm/vkkm-aegis-plugin --skill vkkm-backtest
Please help me install this Agent Skill. Skill: /vkkm:backtest Source: https://github.com/Vaibhavkkm/vkkm-aegis-plugin/tree/main/skills/backtest Command: npx skills add https://github.com/Vaibhavkkm/vkkm-aegis-plugin --skill vkkm-backtest
This Skill automates the validation of Value at Risk (VaR) models, ensuring they meet regulatory standards and accurately reflect potential financial losses.
Validate my 99% 1-day VaR model which estimated €50,000 daily VaR, given 250 trading days with 8 exceptions.
High-intent search queries and answers about installing and using this skill.
To validate a VaR model using the Kupiec test, you compare your Value at Risk estimates against actual P&L time series data. This Skill performs the Proportion of Failures test to assess model accuracy and classifies results into Basel traffic light zones based on exception rates.
The Basel traffic light system categorizes VaR model performance into Green, Yellow, or Red zones based on the number of exceptions. This Skill analyzes your P&L time series to identify exception clustering and consecutive VaR breaches, indicating potential volatility clustering issues.
Yes, you can backtest a 99% 1-day VaR model using 250 trading days of data. The Skill accepts your VaR estimates and actual P&L data to calculate the exception rate, providing a clear pass or fail verdict with regulatory context for compliance.
Yes, this Skill supports both online Python MCP calculations and offline parametric calculations for VaR backtesting. It provides data-grounded or illustrative estimates depending on your available inputs and environment setup.
Consecutive VaR breaches indicate exception clustering, often caused by volatility clustering issues in your P&L time series. Failing the Proportion of Failures test means your model inaccurately reflects potential financial losses and requires recalibration to meet regulatory standards.
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