IDEAS home Printed from https://ideas.repec.org/a/aes/dbjour/v2y2011i1p13-22.html
   My bibliography  Save this article

Conceptual and Statistical Issues Regarding the Probability of Default and Modeling Default Risk

Author

Listed:
  • Emilia ?I?AN

    (Academy of Economic Studies, Bucharest)

  • Adela Ioana TUDOR

    (Academy of Economic Studies, Bucharest)

Abstract

In today’s rapidly evolving financial markets, risk management offers different techniques in order to implement an efficient system against market risk. Probability of default (PD) is an essential part of business intelligence and customer relation management systems in the financial institutions. Recent studies indicates that underestimating this important component, and also the loss given default (LGD), might threaten the stability and smooth running of the financial markets. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default is more valuable than the standard binary classification: credible or non credible clients. The Basle II Accord recognizes the methods of reducing credit risk and also PD and LGD as important components of advanced Internal Rating Based (IRB) approach.

Suggested Citation

  • Emilia ?I?AN & Adela Ioana TUDOR, 2011. "Conceptual and Statistical Issues Regarding the Probability of Default and Modeling Default Risk," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 2(1), pages 13-22, March.
    • Handle: RePEc:aes:dbjour:v:2:y:2011:i:1:p:13-22
    as

    Download full text from publisher

    File URL: http://dbjournal.ro/archive/3/2_Titan_Tudor.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Krink, Thiemo & Paterlini, Sandra & Resti, Andrea, 2008. "The optimal structure of PD buckets," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2275-2286, October.
    2. Dirk Tasche, 2003. "A traffic lights approach to PD validation," Papers cond-mat/0305038, arXiv.org.
    3. 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.
    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. Adela Ioana TUDOR & Adela BÂRA & Elena ANDREI (DRAGOMIR, 2012. "Clustering Analysis for Credit Default Probabilities in a Retail Bank Portfolio," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 3(2), pages 23-30, August.

    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. Kaposty, Florian & Kriebel, Johannes & Löderbusch, Matthias, 2020. "Predicting loss given default in leasing: A closer look at models and variable selection," International Journal of Forecasting, Elsevier, vol. 36(2), pages 248-266.
    2. Pascal François, 2019. "The Determinants of Market-Implied Recovery Rates," Risks, MDPI, vol. 7(2), pages 1-15, May.
    3. Paolo Gambetti & Francesco Roccazzella & Frédéric Vrins, 2022. "Meta-Learning Approaches for Recovery Rate Prediction," Risks, MDPI, vol. 10(6), pages 1-29, June.
    4. Cheng, Dan & Cirillo, Pasquale, 2018. "A reinforced urn process modeling of recovery rates and recovery times," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 1-17.
    5. Wang, Hong & Forbes, Catherine S. & Fenech, Jean-Pierre & Vaz, John, 2020. "The determinants of bank loan recovery rates in good times and bad – New evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 875-897.
    6. Do, Hung Xuan & Rösch, Daniel & Scheule, Harald, 2018. "Predicting loss severities for residential mortgage loans: A three-step selection approach," European Journal of Operational Research, Elsevier, vol. 270(1), pages 246-259.
    7. Bart Keijsers & Bart Diris & Erik Kole, 2018. "Cyclicality in losses on bank loans," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 533-552, June.
    8. Rafael Repullo & Javier Suarez, 2013. "The Procyclical Effects of Bank Capital Regulation," The Review of Financial Studies, Society for Financial Studies, vol. 26(2), pages 452-490.
    9. Thiemo Krink & Sandra Paterlini, 2011. "Multiobjective optimization using differential evolution for real-world portfolio optimization," Computational Management Science, Springer, vol. 8(1), pages 157-179, April.
    10. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.
    11. Andrea Cipollini & Giuseppe Missaglia, 2008. "Measuring bank capital requirements through Dynamic Factor analysis," Center for Economic Research (RECent) 010, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    12. Bank for International Settlements, 2005. "The role of ratings in structured finance: issues and implications," CGFS Papers, Bank for International Settlements, number 23, december.
    13. Santiago Forte & Lidija Lovreta, 2015. "Time†Varying Credit Risk Discovery in the Stock and CDS Markets: Evidence from Quiet and Crisis Times," European Financial Management, European Financial Management Association, vol. 21(3), pages 430-461, June.
    14. Balakrishna, B S, 2008. "Levy Density Based Intensity Modeling of the Correlation Smile," MPRA Paper 14922, University Library of Munich, Germany, revised 06 Apr 2009.
    15. Michael J. Fleming & Asani Sarkar, 2013. "The failure resolution of Lehman Brothers," Economic Policy Review, Federal Reserve Bank of New York, issue Dec, pages 175-206.
    16. Marianna Lyra & Akwum Onwunta & Peter Winker, 2015. "Threshold accepting for credit risk assessment and validation," Journal of Banking Regulation, Palgrave Macmillan, vol. 16(2), pages 130-145, April.
    17. Helmut Elsinger & Alfred Lehar & Martin Summer, 2006. "Risk Assessment for Banking Systems," Management Science, INFORMS, vol. 52(9), pages 1301-1314, September.
    18. repec:onb:oenbwp:y::i:152:b:1 is not listed on IDEAS
    19. Antje Berndt & Rohan Douglas & Darrell Duffie & Mark Ferguson, "undated". "Measuring Default Risk Premia from Default Swap Rates and EDFs," GSIA Working Papers 2006-E31, Carnegie Mellon University, Tepper School of Business.
    20. Fathi, Abid & Nader, Naifar, 2007. "Price Calibration of basket default swap: Evidence from Japanese market," MPRA Paper 6013, University Library of Munich, Germany.
    21. Hwang, Ruey-Ching & Chu, Chih-Kang & Yu, Kaizhi, 2020. "Predicting LGD distributions with mixed continuous and discrete ordinal outcomes," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1003-1022.

    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:aes:dbjour:v:2:y:2011:i:1:p:13-22. 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: Adela Bara (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.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.