hard · FRM Part 2 Operational Risk

A bank models a single high-severity operational loss cell with a lognormal severity (μ= 10, σ= 2.5, in log-dollars) and a Poisson frequency with annual intensity λ= 20. The risk team computes the 99.9% aggregate capital using the single-loss approximation (SLA): VaR_α ≈ F^-1big(1-(1-α)/(λ)big). A reviewer argues this materially understates the true 99.9% aggregate VaR.

Which statement best identifies the reason the SLA is biased here and the direction of the bias?

  1. The SLA omits the mean-aggregate offset entirely; because the body of this compound Poisson-lognormal loss distribution contributes a genuine positive shift, the SLA thus understates true 99.9% VaR by roughly the full expected aggregate annual loss.
  2. The SLA is asymptotically exact only as αto1 for subexponential severities, but at finite λ and high σ it should be corrected upward by adding the mean of the aggregate loss, λ,e^μ+σ^2/2, which the first-order SLA drops.
  3. The SLA overstates the true 99.9% VaR because it attributes the entire capital charge to a single extreme loss, whereas genuine diversification benefit across the λ= 20 expected annual events lowers the aggregate quantile by a factor tied to √(λ).
  4. The SLA is essentially unbiased for any subexponential severity distribution once α= 99.9% is reached, so the reviewer's concern is mistaken; the apparent capital gap here is merely an artifact of computing F^-1 in log-dollar space rather than in raw dollars.

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