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The Monte Carlo simulation method:
A
uses regression and distribution-fitting techniques to estimate distributional parameter inputs.
B
employs bootstrapping so that the number of simulations is independent of the size of the historical dataset.
C
requires the decision whether to sample from the record of past returns with replacement or without replacement.
