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Predicting issuer credit ratings using a semiparametric method

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  • Hwang, Ruey-Ching
  • Chung, Huimin
  • Chu, C.K.
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    Abstract

    This paper proposes a prediction method based on an ordered semiparametric probit model for credit risk forecast. The proposed prediction model is constructed by replacing the linear regression function in the usual ordered probit model with a semiparametric function, thus it allows for more flexible choice of regression function. The unknown parameters in the proposed prediction model are estimated by maximizing a local (weighted) log-likelihood function, and the resulting estimators are analyzed through their asymptotic biases and variances. A real data example for predicting issuer credit ratings is used to illustrate the proposed prediction method. The empirical result confirms that the new model compares favorably with the usual ordered probit model.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Empirical Finance.

    Volume (Year): 17 (2010)
    Issue (Month): 1 (January)
    Pages: 120-137

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    Handle: RePEc:eee:empfin:v:17:y:2010:i:1:p:120-137

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    Web page: http://www.elsevier.com/locate/jempfin

    Related research

    Keywords: Industry effect Issuer credit rating Market-driven variable Ordered linear probit model Ordered semiparametric probit model;

    References

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    1. Sreedhar T. Bharath & Tyler Shumway, 2008. "Forecasting Default with the Merton Distance to Default Model," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1339-1369, May.
    2. Marshall E. Blume & Felix Lim & A. Craig MacKinlay, . "The Declining Credit Quality of US Corporate Debt: Myth or Reality?," Rodney L. White Center for Financial Research Working Papers 3-98, Wharton School Rodney L. White Center for Financial Research.
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    7. Ruey-Ching Hwang & K. F. Cheng & Jack C. Lee, 2007. "A semiparametric method for predicting bankruptcy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(5), pages 317-342.
    8. Gentry, James A & Whitford, David T & Newbold, Paul, 1988. "Predicting Industrial Bond Ratings with a Probit Model and Funds Flow Components," The Financial Review, Eastern Finance Association, vol. 23(3), pages 269-86, August.
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    10. Ederington, Louis H, 1985. "Classification Models and Bond Ratings," The Financial Review, Eastern Finance Association, vol. 20(4), pages 237-62, November.
    11. Joan Jasiak & D. Feng & C. Gourieroux, 2006. "The Ordered Qualitative Model For Credit Rating Transitions," Working Papers 2006_2, York University, Department of Economics.
    12. Guttler, Andre & Wahrenburg, Mark, 2007. "The adjustment of credit ratings in advance of defaults," Journal of Banking & Finance, Elsevier, vol. 31(3), pages 751-767, March.
    13. Poon, Winnie P. H., 2003. "Are unsolicited credit ratings biased downward?," Journal of Banking & Finance, Elsevier, vol. 27(4), pages 593-614, April.
    14. Marshall E. Blume & Felix Lim & A. Craig MacKinlay, . "The Declining Credit Quality of US Corporate Debt: Myth or Reality?," Rodney L. White Center for Financial Research Working Papers 03-98, Wharton School Rodney L. White Center for Financial Research.
    15. Marshall E. Blume & Felix Lim & A. Craig Mackinlay, 1998. "The Declining Credit Quality of U.S. Corporate Debt: Myth or Reality?," Journal of Finance, American Finance Association, vol. 53(4), pages 1389-1413, 08.
    16. repec:fth:pennfi:67 is not listed on IDEAS
    17. Pogue, Thomas F. & Soldofsky, Robert M., 1969. "What's in a Bond Rating," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 4(02), pages 201-228, June.
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