Stress Test and VaR   05-11-2007   RiskWave has launched a research project around the integration of Stress Test scenarios in the calculation of Montecarlo Var and Market Risk Modeling. While VaR models have proven themselves to be very useful risk management tools, recent financial debacles like the sub-prime turmoil have also highlighted their limitations, in particular their backward looking nature and their excessive dependency on unrealistic statistical assumptions. The natural response to these limitations is for firms to resort to Stress Tests to complement the results of their VaR analyses. Stress tests are exercises to determine the losses that might occur under unlikely but plausible circumstances, and there has been a dramatic increase in the importance given to stress testing since the 1997 East Asian crisis and the 1998 LTCM affair. Indeed, many firms and regulators now regard stress tests as no less important than VAR methods for assessing a firm’s risk exposure. Stress test can provide useful information about a firm’s risk exposure that VaR methods can easily miss, particularly if VaR models focus on “Normal” market risks rather than the risks associated with rare or extreme events. However, traditional stress testing is done on a stand-alone basis, and the results of stress tests are evaluated side-by-side with the results of traditional market Risk (or VaR) models. This creates problems for risk managers, who then have to choose which set of risk exposures to believe. There is also the related problem that risk managers often don’t know whether to believe to stress tests results, because the stress tests exercises give them no idea of how likely or unlikely stress test scenarios might be. Objectives of the project: Our research project aims to find a solution to this problem by integrating stress testing into formal risk modeling by assigning probabilities to stress test scenarios. The resulting risk estimates incorporate traditional market risk estimates and the outcomes of stress tests, as well as the probabilities of each. They therefore give risk managers a single, integrated set of risk estimates to work with.   Read More : Risk management     News1 News2