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Which of the following statements accurately describe filtered historical simulation? Filtered historical simulation:

A) Is the most comprehensive, and hence most complicated, of the parametric estimators.》》》戳：免费领取FRM各科视频讲义+历年真题+21年原版书（PDF版）

B) Is not flexible enough to capture conditional volatility and volatility clustering.

C) Is only reasonable for small portfolios, and empirical evidence does not support its predictive ability.

D) Combines the historical simulation model with conditional volatility models.

答案：D

解析：The filtered historical simulation is the most comprehensive, and hence most complicated, of the non-parametric estimators. The process combines the historical simulation model with conditional volatility models (like GARCH or asymmetric GARCH). Thus, the method contains both the attractions of the traditional historical simulation approach with the sophistication of models that incorporate changing volatility.

In simplified terms, the model is flexible enough to capture conditional volatility and volatility clustering as well as a surprise factor that could have an asymmetric effect on volatility. From a computational standpoint, this method is very reasonable even for large portfolios, and empirical evidence supports its predictive ability.【资料下载】点击下载FRM二级思维导图PDF版

Johanna Roberto has collected a data set of 1,000 daily observations on equity returns. She is concerned about the appropriateness of using parametric techniques as the data appears skewed. Ultimately, she decides to use historical simulation and bootstrapping to estimate the 5% VaR. Which of the following steps is most likely to be part of the estimation procedure?

A) Filter the data to remove the obvious outliers.

B) Repeated sampling with replacement.

C) Identify the tail region from reordering the original data.

D) Apply a weighting procedure to reduce the impact of older data.

答案：B

解析：Bootstrapping from historical simulation involves repeated sampling with replacement. The 5% VaR is recorded from each sample draw. The average of the VaRs from all the draws is the VaR estimate. The bootstrapping procedure does not involve filtering the data or weighting observations. Note that the VaR from the original data set is not used in the analysis.

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