How does CVaR complement VaR in risk management?

• A. CVaR equals the VaR at high confidence levels.
• B. CVaR measures the standard deviation of losses.
• C. CVaR is the expected loss conditional on losses exceeding VaR; provides tail risk insight.
• D. CVaR ignores tail risk.

Answer: (C)

Understanding why CVaR complements VaR starts with what each measure is telling you about losses. VaR gives a threshold loss at a chosen confidence level, telling you: “ losses won’t exceed this amount with X% probability.” But it says nothing about how bad losses can be once you cross that threshold. CVaR, or expected shortfall, takes the next step by averaging what losses look like beyond VaR. It quantifies the tail risk—the severity of extreme losses—so you can see not just where losses stop, but how bad they can get if they do exceed the threshold. This makes CVaR a more informative risk gauge for capital planning and risk management, especially when aiming to control potential worst-case outcomes and encourage diversification. In short, CVaR captures tail risk that VaR misses, whereas the other choices describe properties that aren’t accurate: it isn’t simply the standard deviation, it doesn’t ignore tail risk, and it isn’t generally equal to VaR at high confidence levels.