principal, SunGard Global Services’ energy practice

19
Jul
2011

Uncovering hidden insights from your VaR engine data

Contributor:

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 another. This is not commonly done today, since the volume of data would exceed the threshold with which most organizations are comfortable working.

Methods and capabilities associated with large data sets (big data) are beginning to gain momentum. In fact, some of the techniques available today can be applied to the simulation results from Monte Carlo. Using advanced visualization techniques, one can begin to observe and analyze VaR in new and unique ways.

This diagram displays the simulation paths associated with VaR from a typical risk engine. The attribution will depend on the level of detail that VaR is calculated. In this example, one can easily see the dispersion and contribution to VaR by desk, instrument and commodity. The control tabs on the right-side allow the analyst to isolate and observe distinct views with a click of a button.

There are many possibilities that can be leveraged with this approach. Establishing a visualization approach provides new levels of transparency into risk currently not communicated by the existing VaR metric. One of the more interesting concepts is to observe the other end of the spectrum, which is profit. It is likely that this type of view could help both risk management teams and the front-office leadership increase transparency as well as opportunity to generate more revenue.

Lastly, this approach to visualizing and analyzing VaR provides an excellent approach to model validation. Typically, a model validation team will review a model over a period of months. This approach streamlines the model validation process down to weeks rather than months, which can help firms their reduce time to market on new trading strategies.

Is your firm using a visualization approach to VaR? Are you realizing greater success in terms of transparency, efficiency and opportunity, particularly for large data sets? What challenges do you face in gaining greater insight into your VaR data?

10 Responses to “Uncovering hidden insights from your VaR engine data”

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  10. Interesting points you raise John. In Capital Markets we’ve been working with some of our clients to solve these, and have developed the Adaptiv Risk Cube in response. This lets people store all the simulation detail – each scenario, each trade valuation, each time point – and then aggregate risk metrics such as VaR on the fly. This gives a great deal of insight into how the risk profile is built up across the multiple dimensions most firms operate on and, to your example, allows analysts to view the scenarios that make up the tail of the distribution. As you say – there’s an awful lot of data!

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