IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/22929.html
   My bibliography  Save this paper

Severe Loss Probabilities in Portfolio Credit Risk Models

Author

Listed:
  • Babbs, Simon H
  • Johnson, Andrew E

Abstract

We derive explicit sharp bounds on the distribution of the number of defaults from a pool of obligors with common probability of default and default correlation. These bounds are extremely wide, implying that default probabilities and default correlations only very loosely determine probabilities of severe portfolio losses. Our results quantify and thereby reinforce Gordy’s (2002) statement that “Capital decisions ... depend on higher moments”.

Suggested Citation

  • Babbs, Simon H & Johnson, Andrew E, 1999. "Severe Loss Probabilities in Portfolio Credit Risk Models," MPRA Paper 22929, University Library of Munich, Germany, revised 14 Jan 2004.
  • Handle: RePEc:pra:mprapa:22929
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/22929/1/MPRA_paper_22929.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. Frey, Rudiger & McNeil, Alexander J., 2002. "VaR and expected shortfall in portfolios of dependent credit risks: Conceptual and practical insights," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1317-1334, July.
    Full references (including those not matched with items on IDEAS)

    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. Siem Jan Koopman & André Lucas & Pieter Klaassen, 2002. "Pro-Cyclicality, Empirical Credit Cycles, and Capital Buffer Formation," Tinbergen Institute Discussion Papers 02-107/2, Tinbergen Institute.
    2. Maria Stefanova, 2012. "Recovery Risiko in der Kreditportfoliomodellierung," Springer Books, Springer, number 978-3-8349-4226-5, September.
    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.
    4. Paolo Dai Pra & Wolfgang J. Runggaldier & Elena Sartori & Marco Tolotti, 2007. "Large portfolio losses: A dynamic contagion model," Papers 0704.1348, arXiv.org, revised Mar 2009.
    5. Szego, Giorgio, 2005. "Measures of risk," European Journal of Operational Research, Elsevier, vol. 163(1), pages 5-19, May.
    6. Alessandri, Piergiorgio & Drehmann, Mathias, 2010. "An economic capital model integrating credit and interest rate risk in the banking book," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 730-742, April.
    7. Maldonado, Diego & Pazmiño, Mariela, 2008. "Nuevas Herramientas para la Administración del Riesgo Crediticio: El caso de una Cartera Crediticia Ecuatoriana [New Management Tool for Credit Risk analysis: An aplication for Financial Institutio," MPRA Paper 17163, University Library of Munich, Germany, revised 30 Dec 2008.
    8. Gordy, Michael B. & Marrone, James, 2012. "Granularity adjustment for mark-to-market credit risk models," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 1896-1910.
    9. Cowan, Adrian M. & Cowan, Charles D., 2004. "Default correlation: An empirical investigation of a subprime lender," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 753-771, April.
    10. Shi, Xiaojun & Tang, Qihe & Yuan, Zhongyi, 2017. "A limit distribution of credit portfolio losses with low default probabilities," Insurance: Mathematics and Economics, Elsevier, vol. 73(C), pages 156-167.
    11. Kern, Markus & Rudolph, Bernd, 2001. "Comparative analysis of alternative credit risk models: An application on German middle market loan portfolios," CFS Working Paper Series 2001/03, Center for Financial Studies (CFS).
    12. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August.
    13. Arturo Cortés Aguilar, 2011. "Estimación del residual de un bono respaldado por hipotecas mediante un modelo de riesgo crédito: una comparación de resultados de la teoría de cópulas y el modelo IRB de Basilea II en datos del merca," Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), Tecnológico de Monterrey, Campus Ciudad de México, vol. 5(1), pages 50-64.
    14. Björn Häckel, 2010. "Risikoadjustierte Wertbeiträge zur ex ante Entscheidungsunterstützung: Ein axiomatischer Ansatz," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 21(1), pages 81-108, June.
    15. Eva Catarineu-Rabell & Patricia Jackson & Dimitrios Tsomocos, 2005. "Procyclicality and the new Basel Accord - banks’ choice of loan rating system," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 26(3), pages 537-557, October.
    16. Ebnother, Silvan & Vanini, Paolo, 2007. "Credit portfolios: What defines risk horizons and risk measurement?," Journal of Banking & Finance, Elsevier, vol. 31(12), pages 3663-3679, December.
    17. Koopman, Siem Jan & Lucas, André, 2008. "A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 510-525.
    18. Georges Dionne, 2003. "The Foundationsof Banks' Risk Regulation: A Review of Literature," THEMA Working Papers 2003-46, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    19. İsmail Başoğlu & Wolfgang Hörmann & Halis Sak, 2018. "Efficient simulations for a Bernoulli mixture model of portfolio credit risk," Annals of Operations Research, Springer, vol. 260(1), pages 113-128, January.
    20. 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.

    More about this item

    Keywords

    Portfolio Credit Risk Models;

    JEL classification:

    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G31 - Financial Economics - - Corporate Finance and Governance - - - Capital Budgeting; Fixed Investment and Inventory Studies

    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:22929. 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.