Financial Risk Manager Part 1

Explanation

Bootstrapping is particularly preferred over pure simulation when:

• Option B is correct: When the distribution of the actual data is unknown, bootstrapping uses resampling from the actual data to create empirical distributions, making it more robust than assuming a specific parametric distribution.

• Option A is incorrect: Bootstrapping typically requires a reasonably sized sample to be effective. Very small samples may not provide enough information for meaningful resampling.

• Option C is incorrect: If the distribution of the data is known exactly, pure simulation would be more appropriate as you can directly sample from the known distribution without the need for resampling.

Bootstrapping is most valuable when dealing with unknown distributions or when you want to avoid making strong parametric assumptions about the underlying data generating process.