Set out the strengths and weaknesses of VaR
The idea
VaR condenses risk into a single money figure that non-specialists grasp, can be compared across desks and asset classes, supports limits and capital allocation, and is widely accepted by regulators. Its limitations are serious. It says nothing about how large losses beyond the threshold can be; estimates depend heavily on method, look-back period and assumptions such as normality; it tends to understate risk when correlations rise in a crisis; and it is not always subadditive, so for some non-normal positions a combined portfolio's VaR can exceed the sum of the parts. It focuses on the left tail and ignores upside. The trap is treating a low VaR as proof of low risk when the tail beyond it is fat.
In the app: 2 practice questions on this outcome, a narrated walkthrough with diagrams, spaced review that brings it back before you forget it.
Take the free diagnostic →More in Measuring and Managing Market Risk
- Explain how value at risk (VaR) is applied to gauge the risk of a portfolio
- Contrast three ways to estimate VaR: Monte Carlo simulation, historical simulation, and the parametric (variance–covariance) method
- Estimate VaR by Monte Carlo simulation, by historical simulation, and by the parametric method, and explain what each result means
- Set out the ways VaR has been extended
- Explain sensitivity and scenario risk measures and weigh them against VaR
- Show how exposure measures for equities, fixed income, and options can help gauge and control market risk and volatility risk
- Set out how sensitivity and scenario risk measures are applied
- Set out the strengths and weaknesses of sensitivity and scenario risk measures
- Explain the limits used to control market risk, among them stop-loss limits, scenario limits, position limits, and risk budgeting
- Explain how risk measures can guide decisions on allocating capital
- Set out the risk measures relied on by insurers, pension funds, asset managers, and banks