Value-at-risk analyses use basic statistical principles to model the volatility of a wide range of financial products. Currently, VaR is being used by institutions and regulators to assess and monitor risk on all levels from individual trades, to trader portfolios, to enterprise-wide risk exposure. This TowerGroup Research Note provides a basic understanding of how value at risk (VaR) is calculated, its strengths and weaknesses, and how it is applied in the world of financial modeling. It also provides an overview of the key technology issues and the leading technology vendors that support VaR analysis.
Additional Information
Highlights
When defined, implemented, and used properly, value at risk (VaR) can and should be used for many risk management functions within a financial institution. However, because there are a number of risk management issues that it does not address, VaR analysis alone is not a complete measure of overall risk.
One of the key weaknesses in many VaR models is the use of the normal distribution as it assumes that share prices exhibit completely random behavior. In reality, the normal distribution does a poor job of modeling extreme market conditions such as dramatic drops in share price.
There are many different ways to calculate VaR, and different methods often yield different values. The three most common methods are the variance covariance method, the historical method, and the stochastic method.
Two types of applications support the VaR methodology. Front-end systems are operational systems that support day-to-day trading activities. Middle-office and risk-oversight systems are informational systems that offer regulatory reporting and asset liability management functionality.
Currently the leading vendors of operational VaR solutions are Algorithmics, Infinity, SunGard, Reuters, Midas-Kapiti, Summit, and Kamakura. The leading vendors of informational VaR applications include Oracle, Bancware, Quantitative Risk Management, Risk Management Technologies, and IPS-Sendero.
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