IDEAS home Printed from https://ideas.repec.org/p/fip/fednrp/9711.html
   My bibliography  Save this paper

Split ratings and the pricing of credit risk

Author

Listed:
  • Richard Cantor
  • Kevin Cole
  • Frank Packer

Abstract

Despite the fact that over 50 percent of all corporate bonds have different ratings from Moody's and Standard and Poor's at issuance, most bond pricing models ignore these differences of opinion. Our work compares a number of different methods of accounting for split ratings in estimating bond pricing models. We find that pricing rules that use only the Moody's or Standard and Poor's ratings produce unbiased but highly inefficient forecasts. If models rely instead on simply the higher or lower of the two ratings (but not both), greater bias is introduced with insignificant gains in efficiency. In general, the average rating is the best guide to predicting yields in terms of both bias and forecast prediction. However, the forecasting advantage from using the average rating rather than the lower rating derives almost entirely from the below-investment-grade subsample.

Suggested Citation

  • Richard Cantor & Kevin Cole & Frank Packer, 1997. "Split ratings and the pricing of credit risk," Research Paper 9711, Federal Reserve Bank of New York.
  • Handle: RePEc:fip:fednrp:9711
    as

    Download full text from publisher

    File URL: https://www.newyorkfed.org/medialibrary/media/research/staff_reports/research_papers/9711.pdf
    Download Restriction: no

    File URL: https://www.newyorkfed.org/medialibrary/media/research/staff_reports/research_papers/9711.html
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Leland Crabbe, 1991. "Callable corporate bonds: a vanishing breed," Finance and Economics Discussion Series 155, Board of Governors of the Federal Reserve System (U.S.).
    2. Louis H. Ederington & Jess B. Yawitz & Brian E. Roberts, 1987. "The Informational Content Of Bond Ratings," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 10(3), pages 211-226, September.
    3. Larry G. Perry, 1985. "The Effect Of Bond Rating Agencies On Bond Rating Models," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 8(4), pages 307-315, December.
    4. Allen, David S & Lamy, Robert E & Thompson, G Rodney, 1990. "The Shelf Registration of Debt and Self Selection Bias," Journal of Finance, American Finance Association, vol. 45(1), pages 275-287, March.
    5. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    6. Lamy, Robert E. & Thompson, G. Rodney, 1988. "Risk premia and the pricing of primary issue bonds," Journal of Banking & Finance, Elsevier, vol. 12(4), pages 585-601, December.
    7. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    8. Thompson, G Rodney & Vaz, Peter, 1990. "Dual Bond Ratings: A Test of the Certification Function of Rating Agencies," The Financial Review, Eastern Finance Association, vol. 25(3), pages 457-471, August.
    9. Crabbe, Leland, 1991. "Event Risk: An Analysis of Losses to Bondholders and "Super Poison Put" Bond Covenants," Journal of Finance, American Finance Association, vol. 46(2), pages 689-706, June.
    10. Fabozzi, Frank J. & West, Richard R., 1981. "Negotiated versus Competitive Underwritings of Public Utility Bonds: Just One More Time," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 16(3), pages 323-339, September.
    11. Liu, Pu & Moore, William T, 1987. "The Impact of Split Bond Ratings on Risk Premia," The Financial Review, Eastern Finance Association, vol. 22(1), pages 71-85, February.
    12. repec:bla:jfinan:v:44:y:1989:i:4:p:1085-97 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    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. Bae, Sung C. & Klein, Daniel P., 1997. "Further evidence on corporate bonds with event-risk covenants: Inferences from Standard and Poor's and Moody's bond ratings," The Quarterly Review of Economics and Finance, Elsevier, vol. 37(3), pages 709-724.
    2. Schaetzle, Dominik, 2011. "Ratingagenturen in der neoklassischen Finanzierungstheorie: Eine Auswertung empirischer Studien zum Informationsgehalt von Ratings," Arbeitspapiere 110, University of Münster, Institute for Cooperatives.
    3. Sucarrat, Genaro, 2009. "Forecast Evaluation of Explanatory Models of Financial Variability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 3, pages 1-33.
    4. Kish, Richard J. & Hogan, Karen M. & Olson, Gerard, 1999. "Does the market perceive a difference in rating agencies?," The Quarterly Review of Economics and Finance, Elsevier, vol. 39(3), pages 363-377.
    5. Heather L. R. Tierney, 2019. "Forecasting with the Nonparametric Exclusion-from-Core Inflation Persistence Model Using Real-Time Data," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 25(1), pages 39-63, February.
    6. Nii Ayi Armah & Norman Swanson, 2010. "Seeing Inside the Black Box: Using Diffusion Index Methodology to Construct Factor Proxies in Large Scale Macroeconomic Time Series Environments," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 476-510.
    7. Hang Luo & Linfeng Chen, 2019. "Bond yield and credit rating: evidence of Chinese local government financing vehicles," Review of Quantitative Finance and Accounting, Springer, vol. 52(3), pages 737-758, April.
    8. Fayez Elayan & Wei Hsu & Thomas Meyer, 2003. "The informational content of credit rating announcements for share prices in a small market," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 27(3), pages 337-356, September.
    9. Rapach, David E. & Wohar, Mark E., 2002. "Testing the monetary model of exchange rate determination: new evidence from a century of data," Journal of International Economics, Elsevier, vol. 58(2), pages 359-385, December.
    10. Massimiliano Marcellino, "undated". "Further Results on MSFE Encompassing," Working Papers 143, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    11. Tierney, Heather L.R., 2011. "Forecasting and tracking real-time data revisions in inflation persistence," MPRA Paper 34439, University Library of Munich, Germany.
    12. Leith, Campbell & Malley, Jim, 2005. "Estimated general equilibrium models for the evaluation of monetary policy in the US and Europe," European Economic Review, Elsevier, vol. 49(8), pages 2137-2159, November.
    13. Walter Krämer & André Güttler, 2008. "On comparing the accuracy of default predictions in the rating industry," Empirical Economics, Springer, vol. 34(2), pages 343-356, March.
    14. Melike Bildirici & Nilgun Guler Bayazit & Yasemen Ucan, 2020. "Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM," Energies, MDPI, vol. 13(11), pages 1-18, June.
    15. Michael T. Chng, 2010. "Comparing Different Economic Linkages Among Commodity Futures," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 37(9‐10), pages 1348-1389, November.
    16. Ludovic Moreau, 2009. "Regulatory versus Informational Value of Bond Ratings: Hints from History ..," Working Papers hal-04140847, HAL.
    17. Todd E. Clark & Michael W. McCracken, 2002. "Forecast-based model selection in the presence of structural breaks," Research Working Paper RWP 02-05, Federal Reserve Bank of Kansas City.
    18. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    19. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, November.
    20. Guillaume Chevillon, 2006. "Multi-step Forecasting in Unstable Economies: Robustness Issues in the Presence of Location Shifts," Economics Series Working Papers 257, University of Oxford, Department of Economics.

    More about this item

    Keywords

    Bonds; Credit; Risk;
    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:fip:fednrp:9711. 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: Gabriella Bucciarelli (email available below). General contact details of provider: https://edirc.repec.org/data/frbnyus.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.