Answer-first summary for fast verification
Answer: uses regression and distribution-fitting techniques to estimate distributional parameter inputs.
## Explanation **Correct Answer: A** Monte Carlo simulation uses regression and distribution-fitting techniques to estimate distributional parameter inputs. This is a key step in calibrating the statistical distributions used in the simulation. **Analysis of Other Options:** - **B**: Incorrect - Bootstrapping is used in **historical simulation**, not Monte Carlo simulation. Monte Carlo uses parametric distributions and the number of simulations is independent of historical data size. - **C**: Incorrect - The decision about sampling with/without replacement applies to **bootstrapping methods** (historical simulation), not Monte Carlo simulation. **Key Points:** - Monte Carlo requires parameter estimation for assumed distributions - It uses statistical techniques to fit distributions to data - The method generates scenarios from parametric distributions rather than resampling historical data
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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.