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Limitations of Value at Risk

Value at Risk reports a threshold loss: the most you expect to lose over a horizon at a chosen confidence level. Its great weakness is that it says nothing about how bad the tail beyond that threshold can be. A 99% one-day VaR of $10m is consistent with a worst-case loss of $11m or of $500m. VaR is also not subadditive in general, so the VaR of a combined book can exceed the sum of its parts, which contradicts the idea that diversification reduces risk. Estimates are highly model-dependent (normal, historical, Monte Carlo each give different numbers), and a single VaR figure can lull a desk into ignoring the rare but ruinous event.

Why it matters

VaR draws a line in the loss distribution and tells you the size of a "bad but not unusual" day. It is silent about the catastrophe lurking past that line. Think of it as a fence height that the flood will clear 1 day in 100; it never tells you how deep the water gets once it does. That blind spot is precisely where institutions fail, which is why VaR needs a tail-aware companion measure and a backtest.

Formulas

VaR as a quantile of the loss distribution
Pr(L>VaRα)=1α\Pr(L > \mathrm{VaR}_{\alpha}) = 1 - \alpha
At confidence α\alpha (say 99%), losses exceed VaRα\mathrm{VaR}_{\alpha} with probability (1α)(1-\alpha). The measure fixes the probability of breaching the threshold but is silent on the magnitude of the breach.

Worked examples

Scenario

Two trading books each have a one-day 99% VaR of $5m. The head of desk claims the combined VaR must be $10m or less because "diversification always helps." Is the claim safe?

Solution

No. VaR is not generally subadditive, so the combined 99% VaR can exceed $10m when the books share concentrated tail exposures (for example both short deep out-of-the-money options on the same underlying). The intuition that risk measures fall when you pool positions holds for a coherent measure such as expected shortfall, but it is not guaranteed for VaR.

Common mistakes

  • VaR is the worst possible loss. VaR is only the threshold the loss exceeds with a fixed small probability; the loss beyond it can be far larger and is exactly what VaR ignores.
  • A lower portfolio VaR always means lower risk. Because VaR can violate subadditivity, merging positions can raise it, and two books with identical VaR can have very different tail severity.
  • VaR is an objective number. The figure depends heavily on the method (parametric, historical, Monte Carlo), the window, and the assumed distribution, so reported VaR is a modelling choice as much as a fact.

Revision bullets

  • VaR is a threshold loss, not the maximum loss
  • It is silent about the severity of the tail beyond the threshold
  • VaR is not subadditive in general, so it can penalise diversification
  • Numbers are model-dependent (normal vs historical vs Monte Carlo)
  • A single VaR figure can mask catastrophic, low-probability risk

Quick check

What does a one-day 99% VaR of $8m fail to tell you?

Why can combining two portfolios produce a VaR larger than the sum of the individual VaRs?

Connected topics

Sources

  1. Jorion (2007), Ch. 5
    Jorion, P. Value at Risk: The New Benchmark for Managing Financial Risk. 3rd ed. McGraw-Hill, 2007.
    Discusses what VaR measures and its principal shortcomings as a single-number risk summary.
  2. Artzner, P., Delbaen, F., Eber, J.-M., & Heath, D. "Coherent Measures of Risk." Mathematical Finance, 9(3), 203-228, 1999.
    Shows VaR can fail subadditivity, the formal basis for the diversification critique.
How to cite this page
Dr. Phil's Quant Lab. (2026). Limitations of Value at Risk. Derivatives Atlas. https://phucnguyenvan.com/concept/frm-var-limitations