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Roseline · 2020年12月21日

问一道题：NO.PZ2018122701000002

  1. FRM II
  2. Market Risk Measurement And Management

问题如下：

A risk analyst 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 distribution characteristics would make parametric approaches the favored method to use?

选项：

A.

Skewness in the distribution

B.

Fat tails in the distribution

C.

Scarcity of high magnitude loss events

D.

Heteroskedasticity in the distribution

解释：

C is correct.

考点 ： non-parametric method

解析 ： Non-parametric approaches can accommodate fat tails, skewness, and any other non-normal features that can cause problems for parametric approaches. However, if the data period that is used in estimation includes few losses or losses with low magnitude, non-parametric methods will often produce risk measures that are too low. Hence parametric methods would be more appropriate in those situations.

老师好，能否讲解一下D选项，谢谢！

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小刘_品职助教 · 2020年12月22日

同学你好，

对于参数估计法来说是假设数据符合某一种分布来求解，选项D中描述的是异方差，出现异方差有可能会导致估计的参数出现偏差（这个在假设检验的课程有讲过），所以这种情况下不太适合用参数法进行估计。

水瓶公主 · 2023年06月30日

所以，异方差只适合非参数法的估计？

李坏_品职助教 · 2023年07月03日

嗨，努力学习的PZer你好：


非参数估计可以很好的解决异方差问题，但是某些参数估计法也可以解决异方差。


比如我们可以用GARCH模型来针对时间序列的方差进行建模，对GARCH模型进行参数估计。

GARCH可以有效地拟合具有长期记忆性的异方差函数。

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努力的时光都是限量版，加油！

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