Explanation:
The correct answer is B.
Bootstrapping is a technique used in historical simulation and statistical modeling that involves resampling data from an existing historical dataset. Because the simulated future scenarios are drawn directly from past historical returns, an inherent assumption of the bootstrapping exercise is that the past distribution of returns is stationary and will remain the same in the future.
Choice A is incorrect. Bootstrapping involves resampling with replacement, not without replacement. Resampling without replacement would simply recreate the exact same historical sample, completely defeating the purpose of the exercise.
Choice C is incorrect. Bootstrapping relies heavily on historical data. If one assumed future returns would be markedly different from past distributions, bootstrapping historical data would be an inappropriate method.
Choice D is incorrect. A bootstrapping exercise typically provides a distribution of VaR estimates, and the final VaR estimate is usually derived from the average, median, or a specific quantile of these sample VaRs, not their sum. Summing sample VaRs would result in a nonsensical and exponentially large value.
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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.
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