Explanation:
Bootstrapping is a statistical method that involves resampling a dataset with replacement to estimate the distribution of a statistic (e.g., VaR or ES). By drawing from the historical dataset, the bootstrapping method inherently assumes that the empirical distribution of past returns is representative of the true distribution of returns, and that this distribution will remain the same in the future.
Choice A is incorrect because bootstrapping involves resampling with replacement, not without.
Choice C is incorrect because bootstrapping relies on historical data acting as a proxy for the future. If the future were assumed to be markedly different, using historical data for bootstrapping would not be appropriate.
Choice D is incorrect because while bootstrapping can be used to construct a distribution of sample VaRs to find standard errors or confidence intervals around a VaR estimate, it does not result in a VaR estimate that is the sum of sample VaRs. Instead, one might look at the mean, median, or percentiles of the bootstrapped VaR estimates.
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Q.1492 Bootstrapping is a common technique in financial modeling and statistics, used to construct yield curves or to estimate the distribution of a statistic. Understanding its correct application is crucial for accurate analysis. Which one of the following statements is most likely correct? A bootstrapping exercise:
A
resampling from our existing data set without replacement.
B
assumes that the distribution of returns will remain the same in the past and in the future.
C
assumes that the distribution of returns in future will be markedly different from past distributions.
D
results in a VaR estimate that is a sum of sample VaRs after repeated sampling.