真題就是對歷年考試試題的集合整理，考試可以通過做題來進行備考。FRM真題也是這樣的，也是考試的重難點地方。FRM真題練習，在FRM考試中真的很重要嗎？

小編建議一定要做歷年的FRM真題，在考試中也很重要的，因此建議考生在考前能夠進行至少三套真題的練習，并對真題的知識點進行總結，幫助自己進行提升！》》》戳：各科視頻講義+歷年真題+21年原版書（PDF版）免·費領取

A CRO is concerned that existing internal risk models of a firm, which are governed mainly by the central limit theorem, are not adequate in addressing potential random extreme losses of the firm. The CRO then recommends the use of extreme value theory (EVT). When applying EVT and examining distributions of losses exceeding a threshold value, which of the following is correct?

A) As the threshold value is increased, the distribution of losses over a fixed threshold value converges to a generalized Pareto distribution.》》》想了解更多21年FRM備考技巧的點我咨詢?

B) If the tail parameter value of the generalized extreme-value (GEV) distribution goes to infinity, then the GEV essentially becomes a normal distribution.

C) To apply EVT, the underlying loss distribution must be either normal or lognormal.

D) The number of exceedances decreases as the threshold value decreases, which causes the reliability of the parameter estimates to increase.

答案：A

進群領資料

解析：A key foundation of EVT Is that as the threshold value is increased, the distribution of loss exceedances converges to a generalized Pareto distribution.【資料下載】FRM一級思維導圖PDF版

Assuming the threshold is high enough, excess losses can be modeled using the generalized Pareto distribution. Thus, Ais correct. B is incorrect. If the tail parameter value of the generalized extreme-value (GEV) distribution goes to zero, and not infinity, then the distribution of the original data {not the GEV) could be a light-tail distribution such as normal or log-normal. In other words, the corresponding GEV distribution is a Gumbel distribution. C is incorrect. To apply EVT, the underlying loss distribution can be any of the commonly used distributions: normal, lognormal, t,etc. D is incorrect. As the threshold value is decreased, the number of exceedances

increases.

如果想要獲得更多關于FRM考試的真題解析，點擊在線咨詢或者添加融躍老師微信（rongyuejiaoyu）！