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A credit risk model for Italian SMEs

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Abstract

We use a multiple-factor credit risk model to provide new estimates of default probabilities in a sample of Italian Small and Medium-sized Enterprises. Results show that, on average, SMEs are riskier than large businesses within the retail segment. It is possible to distinguish different segments inside the SMEs population based on geographical location, sector of activity and juridical status.

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  • B. Luppi & M. Marzo & E. Scorcu, 2007. "A credit risk model for Italian SMEs," Working Papers 600, Dipartimento Scienze Economiche, Universita' di Bologna.
    • Handle: RePEc:bol:bodewp:600
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    1. Fabrizio Fabi & Sebastiano Laviola & Paolo Marullo Reedtz, 2004. "The treatment of SMEs loans in the New Basel Capital Accord: some evaluations," Banca Nazionale del Lavoro Quarterly Review, Banca Nazionale del Lavoro, vol. 57(228), pages 29-70.
    2. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    3. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    4. Jesús Saurina & Carlos Trucharte, 2004. "The Impact of Basel II on Lending to Small- and Medium-Sized Firms: A Regulatory Policy Assessment Based on Spanish Credit Register Data," Journal of Financial Services Research, Springer;Western Finance Association, vol. 26(2), pages 121-144, October.
    5. Fabrizio Fabi & Sebastiano Laviola & Paolo Marullo Reedtz, 2004. "The treatment of SMEs loans in the New Basel Capital Accord: some evaluations," BNL Quarterly Review, Banca Nazionale del Lavoro, vol. 57(228), pages 29-70.
    6. 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.
    7. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    8. 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.
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    Cited by:

    1. Nur Adiana Hiau Abdullah & Muhammad M. Ma'aji & Karren Lee-Hwei Khaw, 2016. "The Value of Governance Variables in Predicting Financial Distress Among Small and Medium-Sized Enterprises in Malaysia," Asian Academy of Management Journal of Accounting and Finance (AAMJAF), Penerbit Universiti Sains Malaysia, vol. 12(Suppl. 1), pages 1-77–91.
    2. Anna Burova & Henry Penikas & Svetlana Popova, 2021. "Probability of Default Model to Estimate Ex Ante Credit Risk," Russian Journal of Money and Finance, Bank of Russia, vol. 80(3), pages 49-72, September.
    3. Bakaykina, A., 2024. "The additionality of Russia's two-tier support system for small and medium-sized enterprises," Journal of the New Economic Association, New Economic Association, vol. 65(4), pages 68-94.

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