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Answer: Simulation 2 can account for skewness and excess kurtosis often observed in factor and asset return data
## Explanation The correct answer is **C** because: - **Multivariate Student's-t-distribution** can capture **fat tails** (excess kurtosis) and potentially **skewness** in financial return data, which are common characteristics observed in real-world financial markets. - **Multivariate normal distribution** assumes symmetric, bell-shaped distributions with no excess kurtosis, which often fails to capture the extreme events and tail risk present in financial data. - Option A is incorrect because Student's-t-distribution typically requires estimating additional parameters (degrees of freedom) compared to the normal distribution. - Option B is incorrect because both simulations would generally require similar computational steps in the Monte Carlo process; the distribution choice doesn't significantly affect the number of simulation steps. This makes Simulation 2 more realistic for modeling financial returns where extreme events occur more frequently than predicted by normal distributions.
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An investor wants to determine the sensitivity of returns of the benchmark and risk parity portfolios. The investor performs two different Monte Carlo simulations:
Compared to Simulation 1, which of the following is most likely an advantage of Simulation 2?
A
Simulation 2 requires fewer parameters to be estimated from historical data
B
Simulation 2 significantly reduces the number of required steps in the Monte Carlo simulation process
C
Simulation 2 can account for skewness and excess kurtosis often observed in factor and asset return data
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