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Corporate Credit Risk Modeling: Quantitative Rating System And Probability Of Default Estimation

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  • João Fernandes

    (Banco BPI)

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    Abstract

    The literature on corporate credit risk modeling for privately-held firms is scarce. Although firms with unlisted equity or debt represent a significant fraction of the corporate sector worldwide, research in this area has been hampered by the unavailability of public data. This study is an empirical application of credit scoring and rating techniques applied to the corporate historical database of one of the major Portuguese banks. Several alternative scoring methodologies are presented, thoroughly validated and statistically compared. In addition, two distinct strategies for grouping the individual scores into rating classes are developed. Finally, the regulatory capital requirements under the New Basel Capital Accord are calculated for a simulated portfolio, and compared to the capital requirements under the current capital accord.

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    File URL: http://128.118.178.162/eps/fin/papers/0505/0505013.pdf
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    Bibliographic Info

    Paper provided by EconWPA in its series Finance with number 0505013.

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    Length: 70 pages
    Date of creation: 13 May 2005
    Date of revision:
    Handle: RePEc:wpa:wuwpfi:0505013

    Note: Type of Document - pdf; pages: 70
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    Web page: http://128.118.178.162

    Related research

    Keywords: Credit Scoring; Credit Rating; Private Firms; Discriminatory Power; Basel Capital Accord; Capital Requirements;

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    References

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    1. Crook, Jonathan & Banasik, John, 2004. "Does reject inference really improve the performance of application scoring models?," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 857-874, April.
    2. Jarrow, Robert A & Turnbull, Stuart M, 1995. " Pricing Derivatives on Financial Securities Subject to Credit Risk," Journal of Finance, American Finance Association, vol. 50(1), pages 53-85, March.
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    6. Engelmann, Bernd & Hayden, Evelyn & Tasche, Dirk, 2003. "Measuring the Discriminative Power of Rating Systems," Discussion Paper Series 2: Banking and Financial Studies 2003,01, Deutsche Bundesbank, Research Centre.
    7. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    8. Merton, Robert C., 1973. "On the pricing of corporate debt: the risk structure of interest rates," Working papers 684-73., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    9. Duffie, Darrell & Singleton, Kenneth J, 1997. " An Econometric Model of the Term Structure of Interest-Rate Swap Yields," Journal of Finance, American Finance Association, vol. 52(4), pages 1287-1321, September.
    10. Jacobson, Tor & Roszbach, Kasper, 1998. "Bank Lending Policy, Credit Scoring and Value at Risk," Working Paper Series 68, Sveriges Riksbank (Central Bank of Sweden).
    11. Barniv, Ran & McDonald, James B, 1999. " Review of Categorical Models for Classification Issues in Accounting and Finance," Review of Quantitative Finance and Accounting, Springer, vol. 13(1), pages 39-62, July.
    12. Black, Fischer & Cox, John C, 1976. "Valuing Corporate Securities: Some Effects of Bond Indenture Provisions," Journal of Finance, American Finance Association, vol. 31(2), pages 351-67, May.
    13. Crouhy, Michel & Galai, Dan & Mark, Robert, 2001. "Prototype risk rating system," Journal of Banking & Finance, Elsevier, vol. 25(1), pages 47-95, January.
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    Cited by:
    1. Van Laere, Elisabeth & Baesens, Bart, 2010. "The development of a simple and intuitive rating system under Solvency II," Insurance: Mathematics and Economics, Elsevier, vol. 46(3), pages 500-510, June.

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