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Answer: the ability to accommodate skewness and excess kurtosis in factor return data.
## Explanation **Correct Answer: C** The model most likely has the ability to accommodate skewness and excess kurtosis in factor return data. The Student's t-distribution can capture fat tails (excess kurtosis) better than the normal distribution. **Analysis of Other Options:** - **A**: Incorrect - With limited historical data and complex models, estimation error is likely to be **high**, not low. - **B**: Incorrect - While specification error could be present, the use of Student's t-distribution is actually a better specification choice than normal distribution for accommodating fat tails. **Key Points:** - Student's t-distribution can model excess kurtosis (fat tails) - Limited historical data increases estimation error - Complex models with many factors can suffer from overfitting with limited data
Author: LeetQuiz Editorial Team
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An analyst uses Monte Carlo simulation to measure Conditional Value at Risk (CVaR) on a model that is calibrated to a multivariate Student's t-distribution. The model is complex and several factors have limited historical data. The model most likely has:
A
low estimation error.
B
high specification error.
C
the ability to accommodate skewness and excess kurtosis in factor return data.
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