Explanation:
The main advantage of the historical simulation approach to estimating Value-at-Risk (VaR) or Expected Shortfall (ES) is that it inherently uses the empirical distribution of historical returns. Therefore, it automatically accounts for non-normal features typically found in financial data, such as fat tails, skewness, and kurtosis, without requiring explicit statistical assumptions about the underlying distribution (as parametric methods do).
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A
Since the data reflect actual historical events, an ES estimate derived from this data is more accurate than an estimate derived from using parametric methods.
B
The precision of ES estimates and their responsiveness to new market observations can typically be enhanced by increasing the length of the sample period.
C
Major shifts in markets, their structure, or the factors that influence them can be ignored since any effects of these shifts will be included in historical datasets.
D
Features such as fat tails and skewness are easily accommodated without making assumptions about the distribution.