Explanation:
Bootstrapping GARCH models requires re-estimating the model parameters for each generated bootstrap sample. This process can be highly computationally intensive, especially for large datasets or complex models.
Option A is incorrect because bootstrapping is generally used to reduce bias or estimate uncertainty, rather than introducing bias. Option C is incorrect because bootstrapping resamples historical data, which inherently includes extreme events if they are present in the original sample. Option D is incorrect because bootstrap methods (like filtered historical simulation) do not rely on a specific parametric distributional assumption like normality for standardized residuals; instead, they draw from their empirical distribution.
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Q.1 A bank is using bootstrap techniques to estimate the confidence intervals for its GARCH VaR model. Which of the following is a significant challenge associated with this approach?
A
The potential for biased VaR estimates due to the resampling process.
B
The computational intensity of re-estimating the GARCH model for each bootstrap sample
C
The difficulty in capturing the impact of extreme market events present in the historical data.
D
The reliance on the assumption of normally distributed standardized residuals.
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