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).
Ultimate access to all questions.
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.
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