Value-at-risk performance model update

As a financial professional, it’s essential to stay up-to-date on the latest risk management techniques and tools. One such tool is Value-at-Risk (VaR) modeling, which is commonly used to measure capital, fund exposures, and limit trading risk. Robert Thorén, partner at Algorithmica Research, conducted an analysis of VaR models in light of a war and interest rate hikes.

The results showed that while VaR is a valuable tool for measuring risk, it should be used with caution. The analysis revealed that the Filtered Historical Simulation model was the only model that passed statistical tests for the number of exceptions and clustering independence. Additionally, it was found that the Unweighted Historical Simulation model performed well for the equity index future position, but the credit risky mortgage bond position had a large number of VaR breaches, indicating that the model was not well calibrated for this position and time frame.

The study highlights the importance of properly calibrating and evaluating VaR models during times of stress and to remember that VaR is a statistical measure and not a definite prediction of future events. It serves as a speed gauge of risk taking and can provide a rough estimate of potential loss in different scenarios.

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