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FRM Part II · FRM Exam Part II · Beyond Exceedance-Based Backtesting of Value-at-Risk Models

A validator wants to address the weakness that exceedance-based tests ignore the full predicted distribution. Which approach most directly responds to this?

Testing whether realised returns passed through the model's predicted cumulative distribution are uniformly distributed uses the entire forecast distribution, not just one quantile. This probability integral transform approach addresses the single-threshold limitation of exceedance counting.

  1. AEvaluating whether portfolio returns transformed by the model's predicted CDF are uniformly distributedCorrect
  2. BIncreasing the VaR confidence level from 99% to 99.9% only
  3. CCounting exceedances over a shorter window to raise sensitivity
  4. DReplacing the backtest with a comparison of VaR to the prior day's VaR

Explanation

The probability integral transform maps realised returns through the model's forecast CDF; if the model is correct, results should be uniform and independent. This uses the whole distribution rather than a single quantile. The other options still rely on a single threshold or do not test calibration.

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