Explanation:
If outliers exist in the data, the inferences drawn from parameter estimates may not be accurate, depending on how many times the outliers are included in the bootstrapped sample. Because replacement is used in the bootstrap method, outliers could be drawn more often, causing the bootstrap distribution to have fatter tails. If autocorrelation exists in the original sample data, then the original historical data are not independent of one another. An advantage of the bootstrapping approach is that no assumptions are made regarding the true distribution of the parameter estimate that is being examined.
(Book 2, Module 24.2, LO 24.f)
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Question 63
An asset manager is using the bootstrapping method as an alternative to traditional Monte Carlo simulation. He draws random return data from a sample of historical data and makes sure to replace the data so the values can potentially be drawn again. The manager realizes that this simulation method may not always be effective. Which of the following statements correctly identifies a situation that may cause the bootstrapping method to be ineffective?
A
When sampling from historical observations, the data does not follow a standard normal distribution.
B
If autocorrelation exists in the original sample data, then the original historical data are independent of one another.
C
When replacement is used, outliers could be drawn more often, causing the bootstrap distribution to have fatter tails.
D
Given randomly generated inputs drawn from a pre-specified probability distribution, assumptions about the probability distribution of the sampled data are required.
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