Can rating agencies look through the cycle?

Can rating agencies look through the cycle? Rating agencies claim to look through the cycle when assigning corporate credit ratings, which entails that they are able to separate trend components of default risk from transitory ones. To test whether agencies possess this competence, I take market-based estimates of 1-year default probabilities of corporate bond issuers and estimate their long-run trend using the Hodrick-Prescott filter, local regression, or centered moving averages. I find that ratings help identify the current split into trend and cycle. In addition, rating stability is similar to the one of hypothetical ratings based on long-term trends. The results are robust to the use of different filter techniques. They are confirmed by a model-free analysis, which shows that ratings predict future changes in market-based default probability estimates. Since the examined trends are forward-looking in the sense that the trend filtering algorithms use future data, agency ratings exhibit important characteristics one would expect from ratings that see through the cycle. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Quantitative Finance and Accounting Springer Journals

Can rating agencies look through the cycle?

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Publisher
Springer US
Copyright
Copyright © 2012 by Springer Science+Business Media, LLC
Subject
Economics / Management Science; Finance/Investment/Banking; Accounting/Auditing; Econometrics; Operations Research/Decision Theory
ISSN
0924-865X
eISSN
1573-7179
D.O.I.
10.1007/s11156-012-0289-9
Publisher site
See Article on Publisher Site

Abstract

Rating agencies claim to look through the cycle when assigning corporate credit ratings, which entails that they are able to separate trend components of default risk from transitory ones. To test whether agencies possess this competence, I take market-based estimates of 1-year default probabilities of corporate bond issuers and estimate their long-run trend using the Hodrick-Prescott filter, local regression, or centered moving averages. I find that ratings help identify the current split into trend and cycle. In addition, rating stability is similar to the one of hypothetical ratings based on long-term trends. The results are robust to the use of different filter techniques. They are confirmed by a model-free analysis, which shows that ratings predict future changes in market-based default probability estimates. Since the examined trends are forward-looking in the sense that the trend filtering algorithms use future data, agency ratings exhibit important characteristics one would expect from ratings that see through the cycle.

Journal

Review of Quantitative Finance and AccountingSpringer Journals

Published: Apr 8, 2012

References

  • Assessing credit rating agencies by bond issuers and institutional investors
    Baker, HK; Mansi, SA
  • Forecasting default with the Merton distance to default model
    Bharath, ST; Shumway, T
  • An integrated multi-model credit rating system for private firms
    Butera, G; Faff, R

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