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What is Value at Risk (VaR)?

By the FES team · Published 1 June 2026

In brief: Value at Risk (VaR) is a statistical measure that quantifies the maximum expected loss of a portfolio over a given time period with a given confidence level. A "1-day 99% VaR of £1 million" means there is a 1% chance of losing more than £1 million in a single day. It's the most widely used risk metric in banking — and also one of the most widely criticised.

Every bank's risk management team calculates VaR daily. Regulators require it. Traders monitor it. And yet, VaR famously failed to prevent the 2008 crisis — the very models designed to capture risk systematically underestimated the losses that were actually possible. Understanding VaR means understanding both its power and its profound limitations.

The three parameters

  • Confidence level: Typically 95% or 99%. At 99%, you're saying: "I'm 99% confident losses won't exceed X."
  • Time horizon: Usually 1 day (for trading) or 10 days (for regulatory capital). Longer = bigger VaR.
  • Portfolio: VaR depends on the specific mix of positions held.
VaR on a Return Distribution 1% tail VaR cutoff 99% of outcomes better than VaR Worst 1% of outcomes VaR tells you the threshold — not the size of losses beyond it

Methods for calculating VaR

Historical simulation: Apply today's portfolio to historical returns over the past 250 days; the 99% VaR is the 2.5th worst day's loss. Simple, but limited to scenarios that actually occurred.
Parametric (variance-covariance): Assumes returns are normally distributed; uses historical volatility and correlations. Fast but blind to fat tails.
Monte Carlo simulation: Simulates thousands of random scenarios from assumed distributions. Flexible but computationally intensive and model-dependent.

VaR's critical flaw: it says nothing about tail losses

The most dangerous thing about VaR is what it doesn't tell you. A 99% VaR of £1m says you'll lose more than £1m in 1% of days. But it says nothing about how much more on those worst days. You might lose £1.1m or £100m — VaR is silent on this. This is why risk managers now supplement VaR with "Expected Shortfall" (CVaR) — the average loss in the worst scenarios beyond VaR.

25σThe event Goldman Sachs's models said occurred for several consecutive days in August 2007 — a mathematical impossibility under normal distributions

What this means for you

When a bank says its "daily VaR is £50m," they're not saying they can't lose more than £50m. They're saying they expect to lose more than £50m about 2.5 times per year. In a crisis — when correlations spike, markets become illiquid, and the "fat tails" materialise — VaR-based risk management can catastrophically understate actual risk. Post-2008, stress testing (simulating specific scenarios like 2008 itself) is now required alongside VaR precisely because VaR fails when it matters most.

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