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Backtesting & Validation Checklist

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.

By AI Fin Hub Research · AI Fin Hub Team

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Checklist Sections

Work in focused batches instead of one long wall

Section 1

Phase 1: Coverage testing

3 items
Use The ToolPlaygrounds

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.

ToolOpen ->

Section 2

Phase 2: Distribution assumptions

3 items
Use The ToolCalculators

Returns Distribution Analyzer

Paste a returns CSV. Histogram, normal-overlay, QQ plot, skewness, excess kurtosis, Jarque-Bera test, tail-weight index. See why Sharpe alone misleads.

ToolOpen ->

Section 3

Phase 3: Correlation and aggregation

3 items
Use The ToolCalculators

Correlation Matrix Visualizer

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

ToolOpen ->

Section 4

Phase 4: Stress and governance

3 items

Pro 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.

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Planning estimates only — not financial, tax, or investment advice.