IDEAS home Printed from
   My bibliography  Save this article

Considering the dependence between the credit loss severity and the probability of default in the estimate of portfolio credit risk: an experimental analysis


  • Annalisa Di Clemente


In this paper a simple and innovative model for measuring more accurately the credit tail risk of a banking book is presented. This is a Monte Carlo simulation model in which the credit loss severity (LGD) is a stochastic variable and it is correlated to the default event. Specifically, LGD is assumed to be distributed as a conditional beta function and its two parameters a and b are estimated assuming a mean value of LGD linked to the value of the PD conditional to the value of the macro-economic risk factor generated in every Monte Carlo simulative scenario. The linkage between the average LGD and the conditional PD is obtained by a simple linear regression analysis calibrated by using the time series of easily available financial historical data (Moody’s, 2011; Standard & Poor’s, 2012).

Suggested Citation

  • Annalisa Di Clemente, 2013. "Considering the dependence between the credit loss severity and the probability of default in the estimate of portfolio credit risk: an experimental analysis," STUDI ECONOMICI, FrancoAngeli Editore, vol. 2013(109), pages 5-24.
  • Handle: RePEc:fan:steste:v:html10.3280/ste2013-109001

    Download full text from publisher

    File URL:
    Download Restriction: Single articles can be downloaded buying download credits, for info:

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Jones, E Philip & Mason, Scott P & Rosenfeld, Eric, 1984. " Contingent Claims Analysis of Corporate Capital Structures: An Empirical Investigation," Journal of Finance, American Finance Association, vol. 39(3), pages 611-625, July.
    2. 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 351-367, May.
    3. Acharya, Viral V. & Bharath, Sreedhar T. & Srinivasan, Anand, 2007. "Does industry-wide distress affect defaulted firms? Evidence from creditor recoveries," Journal of Financial Economics, Elsevier, vol. 85(3), pages 787-821, September.
    4. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    5. Geske, Robert, 1977. "The Valuation of Corporate Liabilities as Compound Options," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(04), pages 541-552, November.
    6. Gordy, Michael B., 2000. "A comparative anatomy of credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 119-149, January.
    7. Dirk Tasche, 2004. "The single risk factor approach to capital charges in case of correlated loss given default rates," Papers cond-mat/0402390,, revised Feb 2004.
    8. Simone Farinelli & Mykhaylo Shkolnikov, 2012. "Two Models of Stochastic Loss Given Default," Papers 1205.5369,, revised May 2012.
    9. Edward I. Altman & Brooks Brady & Andrea Resti & Andrea Sironi, 2005. "The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications," The Journal of Business, University of Chicago Press, vol. 78(6), pages 2203-2228, November.
    10. Crouhy, Michel & Galai, Dan & Mark, Robert, 2000. "A comparative analysis of current credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 59-117, January.
    11. Jon Frye, 2000. "Depressing recoveries," Emerging Issues, Federal Reserve Bank of Chicago, issue Oct.
    12. Acharya, Viral V & Bharath, Sreedhar T & Srinivasan, Anand, 2003. "Understanding the Recovery Rates on Defaulted Securities," CEPR Discussion Papers 4098, C.E.P.R. Discussion Papers.
    Full references (including those not matched with items on IDEAS)

    More about this item

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation


    Access and download statistics


    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:fan:steste:v:html10.3280/ste2013-109001. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Angelo Ventriglia). General contact details of provider: .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.