Explanation

The key weakness of Value at Risk (VaR) is that it does not consider the severity of losses in the tail of the returns distribution.

Why Option A is correct:

• VaR measures the maximum loss at a given confidence level over a specified time horizon
• However, it does not provide information about losses beyond the VaR threshold
• This means VaR ignores the magnitude and probability of extreme losses in the tail of the distribution
• For example, a VaR of $1 million at 95% confidence tells us we won't lose more than $1 million 95% of the time, but it doesn't tell us what could happen in the remaining 5% of cases

Why other options are incorrect:

• Option B: VaR is not particularly difficult to compute with modern computational methods
• Option C: VaR is actually not subadditive - this is one of its mathematical weaknesses, but the question specifically asks for a "key weakness" and the tail risk issue is more fundamental
• Option D: The amount of backtesting data depends on the specific implementation, not an inherent weakness of VaR itself

This limitation led to the development of Expected Shortfall (ES) or Conditional VaR (CVaR), which measures the average loss in the tail beyond the VaR threshold.