Answer-first summary for fast verification
Answer: allows estimating a smaller number of parameters.
## Explanation The correct answer is **A** because: - **Multivariate normal distribution** (Simulation 1) requires estimating only the **mean vector** and **covariance matrix** parameters. - **Multivariate Student's-t-distribution** (Simulation 2) requires estimating **additional parameters** - specifically the **degrees of freedom** parameter that controls the tail thickness, in addition to the mean and covariance parameters. - Option B is incorrect because the number of Monte Carlo simulation steps is typically determined by the desired precision and convergence criteria, not by the choice of distribution. - Option C is incorrect because the normal distribution cannot account for skewness and excess kurtosis; it assumes symmetric distributions with normal kurtosis, while Student's-t-distribution can capture fat tails (excess kurtosis). Therefore, Simulation 1 with the multivariate normal distribution requires estimating fewer parameters than Simulation 2 with the multivariate Student's-t-distribution.
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A portfolio manager is backtesting a multifactor investment strategy and performs additional sensitivity analyses using two Monte Carlo simulations with different factor return assumptions:
In comparison to Simulation 2, Simulation 1 most likely:
A
allows estimating a smaller number of parameters.
B
requires fewer steps in the Monte Carlo simulation process.
C
accounts for skewness and excess kurtosis observed in the factor return data.