IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/860.html
   My bibliography  Save this paper

Loss Given Default Modelling under the Asymptotic Single Risk Factor Assumption

Author

Listed:
  • Kim, Joocheol
  • Kim, KiHyung

Abstract

The proposals of the Basel Committee on Banking Supervision for the revision of minimum requirements for bank's risk capital leave the quanti¯cation of loss-given-default (LGD) parameter used for capital calculation unspeci¯ed. This paper proposes a new methodology for incorporating LGD parameter explicitly into the Basel risk weight function. Numerical examples based on the new methodology are compared to the current proposals of the Basel committee on Banking Supervision.

Suggested Citation

  • Kim, Joocheol & Kim, KiHyung, 2006. "Loss Given Default Modelling under the Asymptotic Single Risk Factor Assumption," MPRA Paper 860, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:860
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/860/1/MPRA_paper_860.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gordy, Michael B., 2000. "A comparative anatomy of credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 119-149, January.
    2. Dirk Tasche, 2004. "The single risk factor approach to capital charges in case of correlated loss given default rates," Papers cond-mat/0402390, arXiv.org, revised Feb 2004.
    3. Gordy, Michael B., 2003. "A risk-factor model foundation for ratings-based bank capital rules," Journal of Financial Intermediation, Elsevier, vol. 12(3), pages 199-232, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Balogh Peter & BOLOCAN DRAGOS-MIHAIL, 2010. "The Management Of Credit Risk According To Internal Ratings- Based Approach," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(2), pages 665-671, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniel Rosch & Harald Scheule, 2008. "Credit Losses in Economic Downturns - Empirical Evidence for Hong Kong Mortgage Loans," Working Papers 152008, Hong Kong Institute for Monetary Research.
    2. Gürtler, Marc & Heithecker, Dirk, 2005. "Systematic credit cycle risk of financial collaterals: Modelling and evidence," Working Papers FW15V2, Technische Universität Braunschweig, Institute of Finance.
    3. Annalisa Di Clemente, 2020. "Modeling Portfolio Credit Risk Taking into Account the Default Correlations Using a Copula Approach: Implementation to an Italian Loan Portfolio," JRFM, MDPI, vol. 13(6), pages 1-24, June.
    4. Carole Bernard & Ludger Rüschendorf & Steven Vanduffel & Ruodu Wang, 2017. "Risk bounds for factor models," Finance and Stochastics, Springer, vol. 21(3), pages 631-659, July.
    5. Rösch, Daniel & Scheule, Harald, 2009. "The Empirical Relation between Credit Quality, Recovery and Correlation," Hannover Economic Papers (HEP) dp-418, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    6. Marc Gürtler & Dirk Heithecker, 2006. "Modellkonsistente Bestimmung des LGD im IRB-Ansatz von Basel II," Schmalenbach Journal of Business Research, Springer, vol. 58(5), pages 554-587, August.
    7. Arndt Claußen & Sebastian Löhr & Daniel Rösch, 2014. "An analytical approach for systematic risk sensitivity of structured finance products," Review of Derivatives Research, Springer, vol. 17(1), pages 1-37, April.
    8. Magdalena Pisa & Dennis Bams & Christian Wolff, 2012. "Modeling default correlation in a US retail loan portfolio," LSF Research Working Paper Series 12-19, Luxembourg School of Finance, University of Luxembourg.
    9. Barbagli, Matteo & Vrins, Frédéric, 2023. "Accounting for PD-LGD dependency: A tractable extension to the Basel ASRF framework," Economic Modelling, Elsevier, vol. 125(C).
    10. Ambrocio, Gene & Jokivuolle, Esa, 2017. "Should bank capital requirements be less risk-sensitive because of credit constraints?," Bank of Finland Research Discussion Papers 10/2017, Bank of Finland.
    11. Caballero, Diego & Lucas, André & Schwaab, Bernd & Zhang, Xin, 2020. "Risk endogeneity at the lender/investor-of-last-resort," Journal of Monetary Economics, Elsevier, vol. 116(C), pages 283-297.
    12. Anisa Caja & Frédéric Planchet, 2014. "Modeling Cycle Dependence in Credit Insurance," Risks, MDPI, vol. 2(1), pages 1-15, March.
    13. Düllmann, Klaus & Kunisch, Michael & Küll, Jonathan, 2008. "Estimating asset correlations from stock prices or default rates: which method is superior?," Discussion Paper Series 2: Banking and Financial Studies 2008,04, Deutsche Bundesbank.
    14. Maria Stefanova, 2012. "Recovery Risiko in der Kreditportfoliomodellierung," Springer Books, Springer, number 978-3-8349-4226-5, February.
    15. Carling, Kenneth & Rönnegård, Lars & Roszbach, Kasper, 2004. "Is Firm Interdependence within Industries Important for Portfolio Credit Risk?," Working Paper Series 168, Sveriges Riksbank (Central Bank of Sweden).
    16. Gürtler, Marc & Heithecker, Dirk, 2004. "Modellkonsistente Bestimmung des LGD im IRB-Ansatz von Basel II," Working Papers FW08V3, Technische Universität Braunschweig, Institute of Finance.
    17. Yagüe Gurucharri, Miguel & García-Hiernaux, Alfredo & Jerez, Miguel, 1974. "Rethinking Basel III and beyond: a theory model to understand credit allocation and real state bubbles," MPRA Paper 119559, University Library of Munich, Germany, revised 18 Dec 2023.
    18. Bernardi, Enrico & Falangi, Federico & Romagnoli, Silvia, 2015. "A hierarchical copula-based world-wide valuation of sovereign risk," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 155-169.
    19. Dietsch, Michel & Düllmann, Klaus & Fraisse, Henri & Koziol, Philipp & Ott, Christine, 2016. "Support for the SME supporting factor: Multi-country empirical evidence on systematic risk factor for SME loans," Discussion Papers 45/2016, Deutsche Bundesbank.
    20. Neumann, Tobias, 2018. "Mortgages: estimating default correlation and forecasting default risk," Bank of England working papers 708, Bank of England.

    More about this item

    Keywords

    LGD; Single Risk Factor; Basel;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    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:pra:mprapa:860. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.