FRM真题解析，送给备考5月FRM考试的你！

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Extreme value theory (EVT) can assist with value at risk (VaR) calculations by providing better probability estimates of observing extreme losses than that indicated by a standard normal distribution because empirical distributions exhibit fat tails. If one uses the generalized Pareto distribution (GPD method to generate parameter estimates for the shape parameter, fat tails will indicate a:》2022年新版FRM一二级内部资料免费领取！【精华版】

A) positive parameter estimate and VaR calculations that are too large

B) negative parameter estimate and VaR calculations that are too small

C) positive parameter estimate and VaR calculations that are too small

D) negative parameter estimate and VaR calculations that are too large

答案：C

解析：Fat tails will generate a positive shape parameter, which indicates that VaR estimates are probably too small.

The generalized extreme value (GEV) generally requires:

A) Fewer estimated parameters than the POT approach and does not share any parameters with the POT approach.

B) Fewer estimated parameters than the POT approach and shares one parameter with the POT.

C) More estimated parameters than the POT approach and shares one parameter with the POT.

D) More estimated parameters than the POT approach and does not share any parameters with the POT approach.

答案：C

解析：The POT approach generally has fewer parameters, but GEV approaches share the tail parameter。

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