Also referred to as

VaR. An application of normal

probability theory and the concepts of duration and

yield volatility can be tied together to provide insight into the

risk of a portfolio or

position. It's the measure of

potential loss from an unlikely, adverse event in a normal

market environment. Specifically, suppose that a manager wants to make the following

statement:

"There is a Y%

probability that the

loss in value from a position will be less than $A in the next T days."

The $A in this statement are popularly referred to as the

Value at

Risk.

The VaR can be exhibited graphically on a

normal distribution curve of a change in the value of a position over the next T days. The VaR would be the

z-score where the area (probability) to the left of that value is equal to 1-Y%.

The general approaches to VaR computation have fallen into three classes called

parametric,

historical simulation, and

Monte Carlo.

Though VaR is very

popular among risk managers these days, no

theory exists to show that VaR is the appropriate measure upon which to build optimal decision rules. VaR does not measure "event" (e.g.,

market crash) risk. That is why portfolio

stress tests are recommended to supplement VaR. VaR does not readily capture liquidity differences among instruments. That is why limits on both tenors and option

greeks are still useful. VaR doesn't readily capture

model risks, which is why model reserves are also necessary.

There's an interesting

paradox involving

VaR and the

Heisenberg Uncertainty Principle. You can predict what sort of

economic crisis will occur next, or you can predict

when it will happen-- but you will never

predict both.