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Answer: Skewness in the distribution
**Explanation:** - **Non-parametric approaches** (like historical simulation) make no assumptions about the underlying distribution - **Parametric approaches** assume a specific distribution (usually normal) - **Skewness** in the distribution violates the normal distribution assumption - When data exhibits skewness, parametric methods may produce inaccurate VaR estimates - Non-parametric methods are preferred when: - Distribution is non-normal - Data exhibits skewness or fat tails - No specific distributional assumptions can be made - Scarcity of high magnitude loss events (A) actually makes parametric methods more reliable - Unusually high or low volatility (C, D) can affect both methods but doesn't specifically favor non-parametric approaches
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A risk manager is comparing the use of parametric and non-parametric approaches for calculating VaR and is concerned about some of the characteristics present in the loss data. Which of the following conditions would make non-parametric approaches the favored method to use?
A
Scarcity of high magnitude loss event
B
Skewness in the distribution
C
Unusually high volatility during the data period
D
Unusually low volatility during the data period
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