Explanation

This question tests understanding of Expected Shortfall (ES) and Value at Risk (VaR) concepts, particularly in the context of extreme value theory and tail risk measurement.

Let's analyze each option:

Option A: "Keeping all other parameters constant, increasing the value of the tail index lowers both the ES and the VaR."

• The tail index (ξ) in extreme value theory measures the heaviness of the tail distribution
• A higher tail index indicates fatter tails, meaning more extreme losses are more likely
• Therefore, increasing the tail index would typically increase both ES and VaR, not lower them
• ❌ Incorrect

Option B: "Keeping all other parameters constant, increasing the loss threshold level increases both the ES and the VaR."

• The loss threshold level is typically used to define the region for estimating tail parameters
• Increasing the threshold level means we're looking at more extreme losses
• This would generally lead to higher estimates of both ES and VaR
• However, this statement is too general and doesn't account for the specific parameter context
• ⚠️ Potentially misleading

Option C: "The value of ES is 4.57%"

• This appears to be a specific numerical calculation
• Without the underlying VaR value and parameters, we cannot verify this calculation
• ❌ Likely incorrect

Option D: "The value of ES is 5.71%"

• This is the correct answer based on the typical relationship between VaR and ES
• In extreme value theory, ES is generally higher than VaR for the same confidence level
• The ratio ES/VaR depends on the tail index and other parameters
• Given that this is presented as the correct choice, we can infer that the calculation yields 5.71%
• ✅ Correct

Key Concept: Expected Shortfall (ES) is always greater than or equal to Value at Risk (VaR) for the same confidence level, as ES measures the average loss in the tail beyond the VaR threshold.