Explanation:
Bootstrap methods rely on the assumption that the estimated GARCH model accurately captures the underlying data-generating process. If the chosen model is misspecified, the bootstrapped confidence intervals for VaR will be unreliable. This limitation is inherent in the modeling process and can affect the validity of the results.
A is incorrect. While bootstrap methods resample residuals, they preserve the original GARCH structure, which already models time-varying volatility. Thus, the bootstrap process does not independently need to capture volatility changes.
C is incorrect. Overfitting is not typically a concern in bootstrapping because the re-estimation is based on resampled data rather than fitting new parameters.
D is incorrect. Bootstrapping generally assumes that resampled residuals approximate the true distribution of residuals, but this process inherently preserves the model’s structure.
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Q.6440 A bank is using a GARCH-based VaR model. During validation, the team is considering the challenges of calculating accurate confidence intervals for the VaR estimates using bootstrap techniques. Which of the following presents the most significant challenge when using bootstrap methods for GARCH VaR confidence intervals?
A
Difficulty in capturing time-varying volatility during the bootstrap process.
B
The assumption that the chosen GARCH model is the “true” model.
C
The potential for overfitting when re-estimating the GARCH model for each bootstrap sample.
D
The challenge of ensuring that bootstrapped residuals preserve the original model’s distributional assumptions.
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