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Review and Assessment Flashcards

7 cards from real FRM practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

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  1. A risk assessment review finds a portfolio's 1-day 99% VaR is $2M. What does this figure represent?

    Answer: The loss expected to be exceeded on only 1% of days

    A 1-day 99% VaR of $2M means losses should exceed $2M on roughly 1% of trading days.

  2. During backtesting review, VaR breaches occur far more often than the model predicts. What is the most likely conclusion?

    Answer: The model understates risk and underestimates VaR

    Excessive breaches indicate the model underestimates risk and produces VaR figures that are too low.

  3. Which limitation of VaR does Expected Shortfall (ES) specifically address in a model review?

    Answer: It says nothing about the size of losses beyond the VaR threshold

    Expected Shortfall captures the average loss in the tail beyond VaR, which VaR itself does not quantify.

  4. An assessment of a bank's capital adequacy under Basel III focuses on which primary ratio?

    Answer: Common Equity Tier 1 (CET1) ratio

    Basel III emphasizes the CET1 capital ratio as the core measure of a bank's loss-absorbing capacity.

  5. A review of stress testing reveals scenarios only use historical events. What weakness should the assessor flag?

    Answer: It may miss plausible but unprecedented (hypothetical) shocks

    Relying solely on historical scenarios can overlook severe but previously unobserved events.

  6. In assessing operational risk, which approach links capital to internal loss data and scenario analysis?

    Answer: Advanced Measurement Approach (AMA)

    The AMA uses internal loss data, external data, scenario analysis, and business environment factors to estimate operational risk capital.

  7. A model validation review checks for model risk. Which is a key source of model risk?

    Answer: Incorrect assumptions or implementation errors in the model

    Model risk arises from flawed assumptions, data, or implementation that lead to inaccurate outputs.