IDEAS home Printed from https://ideas.repec.org/p/ces/ceswps/_5768.html
   My bibliography  Save this paper

Comparing Default Predictions in the Rating Industry for Different Sets of Obligors

Author

Listed:
  • Walter Kraemer
  • Simon Neumärker

Abstract

We generalize the refinement ordering for well calibrated probability forecasters to the case were the debtors under consideration are not necessarily identical. This ordering is consistent with many well known skill scores used in practice. We also add an illustration using default predictions made by the leading rating agencies Moody’s and S&P.

Suggested Citation

  • Walter Kraemer & Simon Neumärker, 2016. "Comparing Default Predictions in the Rating Industry for Different Sets of Obligors," CESifo Working Paper Series 5768, CESifo.
    • Handle: RePEc:ces:ceswps:_5768
    as

    Download full text from publisher

    File URL: https://www.cesifo.org/DocDL/cesifo1_wp5768.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    2. R. Winkler & Javier Muñoz & José Cervera & José Bernardo & Gail Blattenberger & Joseph Kadane & Dennis Lindley & Allan Murphy & Robert Oliver & David Ríos-Insua, 1996. "Scoring rules and the evaluation of probabilities," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 5(1), pages 1-60, June.
    3. Hauck, Achim & Neyer, Ulrike, 2014. "Disagreement between rating agencies and bond opacity: A theoretical perspective," Economics Letters, Elsevier, vol. 123(1), pages 82-85.
    4. Boumparis, Periklis & Milas, Costas & Panagiotidis, Theodore, 2015. "Has the crisis affected the behavior of the rating agencies? Panel evidence from the Eurozone," Economics Letters, Elsevier, vol. 136(C), pages 118-124.
    5. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    6. Czarnitzki, Dirk & Kraft, Kornelius, 2004. "Innovation indicators and corporate credit ratings: evidence from German firms," Economics Letters, Elsevier, vol. 82(3), pages 377-384, March.
    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. Krämer, Walter & Neumärker, Simon, 2019. "Skill Scores and modified Lorenz domination in default forecasts," Economics Letters, Elsevier, vol. 181(C), pages 61-64.

    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. Krämer, Walter & Neumärker, Simon, 2016. "Comparing the accuracy of default predictions in the rating industry for different sets of obligors," Economics Letters, Elsevier, vol. 145(C), pages 48-51.
    2. Krämer, Walter & Neumärker, Simon, 2019. "Skill Scores and modified Lorenz domination in default forecasts," Economics Letters, Elsevier, vol. 181(C), pages 61-64.
    3. Liu, Weiling & Moench, Emanuel, 2016. "What predicts US recessions?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1138-1150.
    4. Henri Nyberg, 2018. "Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(1), pages 1-15, January.
    5. S. Borağan Aruoba & Allan Drazen & Razvan Vlaicu, 2019. "A Structural Model Of Electoral Accountability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 60(2), pages 517-545, May.
    6. Lahiri, Kajal & Yang, Liu, 2016. "Asymptotic variance of Brier (skill) score in the presence of serial correlation," Economics Letters, Elsevier, vol. 141(C), pages 125-129.
    7. Lahiri, Kajal & Yang, Liu, 2015. "A further analysis of the conference board’s new Leading Economic Index," International Journal of Forecasting, Elsevier, vol. 31(2), pages 446-453.
    8. Kajal Lahiri & Liu Yang, 2018. "Confidence Bands for ROC Curves With Serially Dependent Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 115-130, January.
    9. Dendramis, Y. & Tzavalis, E. & Adraktas, G., 2018. "Credit risk modelling under recessionary and financially distressed conditions," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 160-175.
    10. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    11. repec:zbw:bofitp:2019_008 is not listed on IDEAS
    12. Vincent Brémond & Emmanuel Hache & Tovonony Razafindrabe, 2016. "The Oil Price and Exchange Rate Relationship Revisited: A time-varying VAR parameter approach," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 13(1), pages 97-131, June.
    13. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    14. Baumeister, Christiane & Kilian, Lutz & Lee, Thomas K., 2014. "Are there gains from pooling real-time oil price forecasts?," Energy Economics, Elsevier, vol. 46(S1), pages 33-43.
    15. Marco Del Negro & Michele Lenza & Giorgio E. Primiceri & Andrea Tambalotti, 2020. "What's Up with the Phillips Curve?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 51(1 (Spring), pages 301-373.
    16. Hajargasht, Gholamreza & Rao, D.S. Prasada, 2019. "Multilateral index number systems for international price comparisons: Properties, existence and uniqueness," Journal of Mathematical Economics, Elsevier, vol. 83(C), pages 36-47.
    17. Jacks, David S. & Stuermer, Martin, 2020. "What drives commodity price booms and busts?," Energy Economics, Elsevier, vol. 85(C).
    18. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    19. Kinateder, Harald & Wagner, Niklas, 2017. "Quantitative easing and the pricing of EMU sovereign debt," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 1-12.
    20. Janet L. Yellen, 2015. "Inflation Dynamics and Monetary Policy : A speech at the Philip Gamble Memorial Lecture, University of Massachusetts, Amherst, Amherst, Massachusetts, September 24, 2015," Speech 863, Board of Governors of the Federal Reserve System (U.S.).
    21. Ferrari, Davide & Ravazzolo, Francesco & Vespignani, Joaquin, 2021. "Forecasting energy commodity prices: A large global dataset sparse approach," Energy Economics, Elsevier, vol. 98(C).

    More about this item

    Keywords

    Moody's; S&P; probability forecasts;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

    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:ces:ceswps:_5768. 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: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/cesifde.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.