Answer: Unlike Monte Carlo simulation, bootstrapping does not require the specification of a model to estimate the confidence interval.

## Explanation **Correct Answer: C** Bootstrapping differs from Monte Carlo simulation in that it does **not** require the specification of a model to estimate confidence intervals. Here's why: ### Key Differences: **Monte Carlo Simulation:** - Requires explicit assumptions about the underlying distribution of returns - Needs to specify parameters for the distribution (mean, variance, etc.) - If the assumed distribution is incorrect, the results will be biased **Bootstrapping:** - Uses the empirical distribution of the actual historical data - Does not require assumptions about the underlying distribution - Resamples from the observed data to create new datasets - Makes no parametric assumptions about the data ### Why Other Options Are Incorrect: - **A:** Incorrect - Bootstrapping does NOT require a normal distribution assumption; it uses the empirical distribution of the actual data. - **B:** Incorrect - Bootstrapping does not make distributional assumptions, so there is no "incorrect assumption" about the distribution to produce inaccurate results. - **D:** Incorrect - While bootstrapping can be useful, it doesn't necessarily increase accuracy compared to Monte Carlo; both methods have their strengths depending on the context. ### Practical Implication: The manager would find bootstrapping advantageous because it allows for estimating confidence intervals directly from historical data without needing to assume a specific distribution, making it particularly useful when the true distribution of returns is unknown or non-normal.

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