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Other Voices: Uncovering hidden insights from your VAR engine data

Friday, July 22, 2011

The following post was written by John Doran, Principal, SunGard Global Services’ Energy Practice. Value at Risk (VaR) is a popular metric used in risk management. While it gained popularity in the 1980s after the market crash of 1987, today it is being scrutinized after the market crash of 2008 due to extreme tail events that were not properly identified or mitigated.

Visibility into the behavior of the tail or the distribution of the simulated paths is generally not communicated to the stakeholders that use the metric. These simulated paths, while having low probability, have the potential to cause serious harm to trading operations. However, new visualization techniques are emerging that make it possible to increase transparency around risk, enhance process efficiencies, and boost revenue opportunity.

But first, let’s look at two main challenges. One of the primary problems with VaR is the fact that it is just a single number. Nothing about the behavior on the extreme tail or the distribution of the simulated paths is generally communicated to the stakeholders that use the metric.

The other major challenge with VaR is the fact that it is non-additive. VaR can be broken down into component levels, but one cannot add and subtract these values. However, detailed simulation trials from Monte Carlo can be added and subtracted from one a......................

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