Explanation:
A loss function quantifies the discrepancy between predicted VaR values and actual observed losses (or other benchmark outcomes). Common loss functions include the Mean Squared Error (MSE) or other measures designed to evaluate model accuracy in predicting tail risk.
A is incorrect. Visual comparison is insufficient for rigorous benchmarking.
C is incorrect. Computational speed is a practical consideration but doesn't assess model accuracy.
D is incorrect. A higher VaR estimate doesn't necessarily mean a model is more accurate; it could simply be more conservative.
Things to Remember
Ultimate access to all questions.
No comments yet.
Q.6439 A risk manager at Gamma Investments is comparing two different VaR models: a historical simulation model and a Monte Carlo simulation model. They have plotted the VaR outputs of both models over a period of time. However, they are unsure how to formally assess which model is more accurate. Which of the following approaches would be MOST appropriate for Gamma Investments to rigorously compare the two VaR models?
A
Observing the visual differences between the plotted VaR outputs.
B
Using a loss function-based comparison.
C
Comparing the computational speed of the two models.
D
Evaluating which model produces a higher VaR estimate.