Explanation:
One of the primary challenges of using bootstrap techniques with GARCH models is the need to re-estimate the model parameters for each bootstrap sample. This iterative process can be computationally intensive, especially for large datasets or complex models, making it a significant practical limitation of the approach.
A is incorrect. Bootstrapping is designed to reduce bias, not introduce it.
C is incorrect. The bootstrap uses the historical data, so it captures the extreme events that are present within it. The problem is that it cannot create new extremes that are not present in the historical data.
D is incorrect. One of the advantages of bootstrapping is that it does not require the assumption of normally distributed standardized residuals. It is a non-parametric approach.
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Q.6445 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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