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FRM Part II · FRM Exam Part II · Credit Scoring and Rating

A risk analyst reviews a transition matrix built from an agency's historical data for BBB issuers. The one-year probability of staying BBB is 90%, and the matrix is assumed to be Markov. Observed data, however, show that issuers downgraded in the past year are more likely to be downgraded again than the matrix implies. Which limitation does this finding expose, and what is its likely effect on risk estimates?

The finding shows rating momentum, meaning migration depends on past changes, which violates the Markov assumption. Compounding the one-year matrix would therefore understate multi-year downgrade and default risk for recently downgraded issuers. It does not imply ratings are overly volatile or that matrices are unusable.

  1. ARatings momentum (serial dependence) violates the Markov assumption, so multi-year downgrade risk is underestimatedCorrect
  2. BRatings are too volatile, so one-year default risk is overestimated
  3. CThe matrix ignores the issuer-pays model, so default probabilities are biased to zero
  4. DTransition matrices cannot be used for investment-grade issuers at all

Explanation

A Markov matrix assumes next-period migration depends only on the current rating. If past downgrades predict further downgrades (momentum), the assumption fails. Multiplying the one-year matrix to get multi-year migration would then understate the chance of continued deterioration for recently downgraded names.

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