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Calibration of a credit rating scale for Polish companies

Author

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  • Aleksandra Wójcicka

Abstract

Increasing number of bankruptcy announcements means that even greater attention is being paid to the correct evaluation of the probability of default (PD) and decisions made on the basis of it. Reliable estimation of the likelihood of a company’s bankruptcy reduces risk, not only for the company itself but also for all co-operating companies and financial institutions. The financial crisis has led to a tightening up of the conditions for gaining finance from banks. However, it is not only the evaluation of PD itself that is so important but also the correct classification of companies according to their PD level (“good” or “bad” companies). There is very little consideration about possible adjustments of the credit risk scale, as usually the American scale is adopted with no changes which seems incorrect. This paper stresses the importance of correct calibration of the credit rating scale. It should not be assumed (as it was in the past) that once a scale is defined it remains fixed and independent of the country. Therefore, the research carried out on Polish companies shows that the credit rating scale should be changed and the default point (i.e. “cut-off” point) should be higher than in the past. The author uses a modified classification matrix based on the probability of default. The paper compares the classification of quoted Polish companies according to their credit risk level (PD) with the actual occurrence of default when various default “cut-off” points are used.

Suggested Citation

  • Aleksandra Wójcicka, 2012. "Calibration of a credit rating scale for Polish companies," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 22(3), pages 63-73.
  • Handle: RePEc:wut:journl:v:3:y:2012:p:63-73:id:1043
    DOI: 10.5277/ord120305
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    References listed on IDEAS

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    1. repec:bla:ecnote:v:33:y:2004:i:2:p:183-208 is not listed on IDEAS
    2. Bystrom, Hans & Kwon, Oh Kang, 2007. "A simple continuous measure of credit risk," International Review of Financial Analysis, Elsevier, vol. 16(5), pages 508-523.
    3. Nickell, Pamela & Perraudin, William & Varotto, Simone, 2007. "Ratings-based credit risk modelling: An empirical analysis," International Review of Financial Analysis, Elsevier, vol. 16(5), pages 434-451.
    4. Jon Frye, 2000. "Depressing recoveries," Emerging Issues, Federal Reserve Bank of Chicago, issue Oct.
    5. Nikola Tarashev, 2005. "Structural models of default: lessons from firm-level data," BIS Quarterly Review, Bank for International Settlements, September.
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