The correct answer is B.

Bootstrapping relies on resampling from the available historical dataset to estimate the distribution of possible outcomes. When the dataset is small, the likelihood of capturing rare, extreme tail events diminishes because these events occur infrequently in the historical data. This limitation affects the reliability of the confidence intervals for VaR, especially for portfolios sensitive to tail risk.

A is incorrect: Bootstrapping is designed to be a bias-free resampling method, as it uses the existing dataset to replicate the original distribution. While small datasets may inherently contain bias, bootstrapping itself does not introduce new bias into the estimates.

C is incorrect: Bootstrapped confidence intervals reflect the variability in the original data. With small datasets, the intervals may not capture the full range of potential outcomes, particularly in the tails, but this does not necessarily make them overly narrow. Instead, the main issue is the lack of representation of extreme events.

D is incorrect: While bootstrapping involves resampling, it does not inherently amplify noise. It replicates the structure of the original dataset, so the noise level remains constant. However, this distractor is plausible because candidates might associate resampling with an exaggerated effect on variability or noise.