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FRM Part II · FRM Exam Part II · Case Study: Model Risk and Model Validation

In the London Whale case, the modified VaR model computed a hedge-ratio-like volatility measure using a spreadsheet in which a calculation divided by a sum of rates rather than their average. What is the most likely effect and the best lesson?

Dividing by a sum rather than an average halved the volatility estimate, so VaR was understated. The lesson is that spreadsheet-based, end-user computing components of models need the same validation, change control and review as formal production systems.

  1. AVolatility was effectively halved, understating VaR; end-user computing tools used in models need controls equal to formal systemsCorrect
  2. BVolatility was doubled, overstating VaR; models should be simplified
  3. CCorrelations were set to one, so VaR was unchanged; documentation is unnecessary
  4. DVolatility was unaffected; the error only affected reporting format

Explanation

Dividing by the sum instead of the average of two rates cuts the result by a factor of two, which lowered volatility and hence VaR. This shows manual spreadsheets inside models need version control, review and validation. The other options misstate the direction or deny the impact.

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