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Rating Through-the-Cycle: What does the Concept Imply for Rating Stability and Accuracy?

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  • Mr. John Kiff
  • Michael Kisser
  • Miss Liliana B Schumacher

Abstract

Credit rating agencies face a difficult trade-off between delivering both accurate and stable ratings. In particular, its users have consistently expressed a preference for rating stability, driven by the transactions costs induced by trading when ratings change frequently. Rating agencies generally assign ratings on a through-the-cycle basis whereas banks' internal valuations are often based on a point-in-time performance, that is they are related to the current value of the rated entity's or instrument's underlying assets. This paper compares the two approaches and assesses their impact on rating stability and accuracy. We find that while through-the-cycle ratings are initially more stable, they are prone to rating cliff effects and also suffer from inferior performance in predicting future defaults. This is because they are typically smooth and delay rating changes. Using a through-the-crisis methodology that uses a more stringent stress test goes halfway toward mitigating cliff effects, but is still prone to discretionary rating change delays.

Suggested Citation

  • Mr. John Kiff & Michael Kisser & Miss Liliana B Schumacher, 2013. "Rating Through-the-Cycle: What does the Concept Imply for Rating Stability and Accuracy?," IMF Working Papers 2013/064, International Monetary Fund.
  • Handle: RePEc:imf:imfwpa:2013/064
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    References listed on IDEAS

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    1. Fama, Eugene F & French, Kenneth R, 1988. "Permanent and Temporary Components of Stock Prices," Journal of Political Economy, University of Chicago Press, vol. 96(2), pages 246-273, April.
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    Cited by:

    1. Michael Jacobs, 2021. "Validation of Corporate Probability of Default Models Considering Alternative Use Cases," IJFS, MDPI, vol. 9(4), pages 1-22, November.
    2. Nguyen, Phuc Lam Thy & Alsakka, Rasha & Mantovan, Noemi, 2023. "The impact of sovereign credit ratings on voters’ preferences," Journal of Banking & Finance, Elsevier, vol. 154(C).
    3. Cesaroni, Tatiana, 2015. "Procyclicality of credit rating systems: How to manage it," Journal of Economics and Business, Elsevier, vol. 82(C), pages 62-83.
    4. Broto, Carmen & Molina, Luis, 2016. "Sovereign ratings and their asymmetric response to fundamentals," Journal of Economic Behavior & Organization, Elsevier, vol. 130(C), pages 206-224.
    5. Iván M. Rodríguez & Krishnan Dandapani & Edward R. Lawrence, 2019. "Measuring Sovereign Risk: Are CDS Spreads Better than Sovereign Credit Ratings?," Financial Management, Financial Management Association International, vol. 48(1), pages 229-256, March.
    6. Jurevičienė Daiva & Rauličkis Darius, 2016. "Identification of Indicators’ Applicability to Settle Borrowers’ Probability of Default," Economics and Culture, Sciendo, vol. 13(1), pages 53-64, June.
    7. Bannier, Christina E. & Bofinger, Yannik & Rock, Björn, 2019. "Doing safe by doing good: ESG investing and corporate social responsibility in the U.S. and Europe," CFS Working Paper Series 621, Center for Financial Studies (CFS).
    8. T. Gärtner & S. Kaniovski & Y. Kaniovski, 2021. "Numerical estimates of risk factors contingent on credit ratings," Computational Management Science, Springer, vol. 18(4), pages 563-589, October.
    9. Yukiko Konno & Yuki Itoh, 2016. "An alternative to the standardized approach for assessing credit risk under the Basel Accords," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1220119-122, December.
    10. Karminsky, A. & Dyachkova, N., 2020. "Empirical study of the relationship between credit cycles and changes in credit ratings," Journal of the New Economic Association, New Economic Association, vol. 48(4), pages 138-160.

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    Keywords

    WP; TTC approach; TTC rating;
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