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CAIA Risk Management Flashcards

6 cards from real CAIA practice questions. Tap to flip, then mark Knew It or Still Learning โ€” missed cards come back until you master them.

Read the first 6 CAIA Risk Management flashcards as text
  1. Value at Risk (VaR) at the 95% confidence level over a 1-day horizon means:

    Answer: Losses will not exceed this amount on 95% of trading days, or equivalently will exceed it on 5% of days

    95% 1-day VaR states that losses will exceed the VaR threshold only 5% of trading days (1 in 20 days).

  2. Which risk measure addresses the main limitation of VaR by estimating the expected loss in the tail beyond the VaR threshold?

    Answer: Expected Shortfall (CVaR)

    Expected Shortfall (also called Conditional VaR or CVaR) measures the average loss in scenarios that exceed the VaR, capturing tail risk that VaR ignores.

  3. Liquidity risk in alternative investments is best described as:

    Answer: The risk of being unable to sell or exit a position without significant price impact or within a required timeframe

    Liquidity risk is the inability to transact at a fair price quickly, which is especially acute in illiquid alternatives like private equity and real estate.

  4. Counterparty risk in the context of OTC derivatives refers to:

    Answer: The risk that the other party to a contract defaults before settlement

    Counterparty risk arises when one party to an OTC contract may fail to fulfill its obligations, exposing the surviving party to replacement cost risk.

  5. Stress testing in risk management is designed primarily to:

    Answer: Assess portfolio behavior under extreme but plausible adverse scenarios beyond normal VaR

    Stress tests apply hypothetical or historical extreme scenarios (e.g., 2008 crisis, COVID crash) to evaluate portfolio vulnerability beyond statistical VaR.

  6. Which of the following best describes 'fat tails' (leptokurtosis) in the return distribution of hedge funds?

    Answer: Extreme returns (both gains and losses) occur more frequently than predicted by a normal distribution

    Leptokurtic distributions have excess kurtosis, meaning extreme outcomes occur more often than a normal distribution predicts, making standard deviation an underestimate of true risk.