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Checklist, in Backtesting & Validation

Risk Model Validation Checklist

A risk model that has never been backtested is a number that feels safe. This checklist validates whether the model's stated losses match reality, using the standard coverage and independence tests.

12 items. Published May 26, 2026. By AI Fin Hub Research.

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Sections

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Section 1

Phase 1: Coverage testing

3 items

Use the VaR Backtest — Kupiec & Christoffersen: Paste P&L + VaR series and run Kupiec POF, Christoffersen independence, and joint conditional-coverage tests. Likelihood-ratio χ² p-values.

Section 2

Phase 2: Distribution assumptions

3 items

Use the Returns Distribution Analyzer: Paste a returns CSV. Histogram, normal QQ plot, skewness, excess kurtosis, Jarque-Bera test, tail-weight index. See why Sharpe alone misleads.

Section 3

Phase 3: Correlation and aggregation

3 items

Use the Portfolio Correlation Matrix: Paste a multi-asset returns CSV. See the Pearson correlation heatmap, condition number, average absolute correlation, and eigenvalue concentration.

Section 4

Phase 4: Stress and governance

3 items

Tips

Small moves that make the checklist easier to finish

Independence failures matter more than frequency failures. A model that is right on average but wrong in clusters will be wrong exactly when you can least afford it.
Gaussian VaR on fat-tailed returns is a comfortable lie. If the returns reject normality, the tail loss is larger and more frequent than the model claims.
Diversification that depends on a correlation matrix is diversification that can evaporate. Stress-test what the book looks like when everything moves together.

Run the numbers next

  • Calculators Risk-Adjusted Returns Calculator

    Paste a returns CSV. Sharpe, Sortino, Calmar, Omega, alpha, beta, tracking error, information ratio, max drawdown, and tail moments — plus.

Sources

  1. Evaluating Interval Forecasts , Peter F. Christoffersen, International Economic Review (1998)
  2. Techniques for Verifying the Accuracy of Risk Measurement Models , Paul H. Kupiec, Journal of Derivatives (1995)

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