IDEAS home Printed from https://ideas.repec.org/p/imf/imfwpa/2013-064.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.imf.org/external/pubs/cat/longres.aspx?sk=40378
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Zhizhen Chen & Guifen Shi & Boyang Sun, 2024. "Cross-border spillovers in G20 sovereign CDS markets: cluster analysis based on K-means machine learning algorithm and TVP–VAR models," Empirical Economics, Springer, vol. 67(6), pages 2463-2502, December.
    11. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Suleyman Basak & Georgy Chabakauri, 2012. "Dynamic Hedging in Incomplete Markets: A Simple Solution," The Review of Financial Studies, Society for Financial Studies, vol. 25(6), pages 1845-1896.
    2. Thomas Delcey, 2019. "Samuelson vs Fama on the Efficient Market Hypothesis: The Point of View of Expertise [Samuelson vs Fama sur l’efficience informationnelle des marchés financiers : le point de vue de l’expertise]," Post-Print hal-01618347, HAL.
    3. Neely, Christopher J. & Weller, Paul, 2000. "Predictability in International Asset Returns: A Reexamination," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(4), pages 601-620, December.
    4. Lo, Andrew W & MacKinlay, A Craig, 1990. "When Are Contrarian Profits Due to Stock Market Overreaction?," The Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 175-205.
    5. Dongweí Su, 2003. "Risk, Return and Regulation in Chinese Stock Markets," World Scientific Book Chapters, in: Chinese Stock Markets A Research Handbook, chapter 3, pages 75-122, World Scientific Publishing Co. Pte. Ltd..
    6. Cornelis A. Los, 2004. "Nonparametric Efficiency Testing of Asian Stock Markets Using Weekly Data," Finance 0409033, University Library of Munich, Germany.
    7. Dufour, Jean-Marie & García, René, 2008. "Measuring causality between volatility and returns with high-frequency data," UC3M Working papers. Economics we084422, Universidad Carlos III de Madrid. Departamento de Economía.
    8. Nicholas Apergis & Alexandros Gabrielsen & Lee Smales, 2016. "(Unusual) weather and stock returns—I am not in the mood for mood: further evidence from international markets," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 30(1), pages 63-94, February.
    9. Silva, A. Christian & Prange, Richard E., 2007. "Virtual volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 507-516.
    10. Edward L. Glaeser & Joseph Gyourko, 2006. "Housing Dynamics," NBER Working Papers 12787, National Bureau of Economic Research, Inc.
    11. Qingshuo Song & Qing Zhang, 2013. "An Optimal Pairs-Trading Rule," Papers 1302.6120, arXiv.org.
    12. Wouter J. Den Haan & Andrew T. Levin, 1995. "Inferences from parametric and non-parametric covariance matrix estimation procedures," International Finance Discussion Papers 504, Board of Governors of the Federal Reserve System (U.S.).
    13. Dmitry Kulikov, 2012. "Testing for Rational Speculative Bubbles on the Estonian Stock Market," Research in Economics and Business: Central and Eastern Europe, Tallinn School of Economics and Business Administration, Tallinn University of Technology, vol. 4(1).
    14. Yu Wang & Haicheng Shu, 2019. "Evaluating the Performance of Factor Pricing Models for Different Stock Market Trends: Evidence from China," Working Papers 2019-10-10, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    15. Balvers, Ronald J. & Mitchell, Douglas W., 2000. "Efficient gradualism in intertemporal portfolios," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 21-38, January.
    16. Emmanouil Mavrakis & Christos Alexakis, 2018. "Statistical Arbitrage Strategies under Different Market Conditions: The Case of the Greek Banking Sector," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 17(2), pages 159-185, August.
    17. Campbell, John Y., 2001. "Why long horizons? A study of power against persistent alternatives," Journal of Empirical Finance, Elsevier, vol. 8(5), pages 459-491, December.
    18. William A. Brock & Blake LeBaron, 1990. "Liquidity Constraints in Production-Based Asset-Pricing Models," NBER Chapters, in: Asymmetric Information, Corporate Finance, and Investment, pages 231-256, National Bureau of Economic Research, Inc.
    19. He, Xue-Zhong & Li, Kai & Santi, Caterina & Shi, Lei, 2022. "Social interaction, volatility clustering, and momentum," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 125-149.
    20. Xia, Yihong, 2000. "Learning About Predictability: The Effects of Parameter Uncertainty on Dynamic Asset Allocation," University of California at Los Angeles, Anderson Graduate School of Management qt3167f8mz, Anderson Graduate School of Management, UCLA.

    More about this item

    Keywords

    WP; TTC approach; TTC rating;
    All these keywords.

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:imf:imfwpa:2013/064. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Akshay Modi (email available below). General contact details of provider: https://edirc.repec.org/data/imfffus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.