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Survival Analysis in LGD Modeling

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Abstract

The paper proposes an application of the survival time analysis methodology to estimations of the Loss Given Default (LGD) parameter. The main advantage of the survival analysis approach compared to classical regression methods is that it allows exploiting partial recovery data. The model is also modified in order to improve performance of the appropriate goodness of fit measures. The empirical testing shows that the Cox proportional model applied to LGD modeling performs better than the linear and logistic regressions. In addition a significant improvement is achieved with the modified “pseudo” Cox LGD model.

Suggested Citation

  • Jiří Witzany & Michal Rychnovský & Pavel Charamza, 2010. "Survival Analysis in LGD Modeling," Working Papers IES 2010/02, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Feb 2010.
  • Handle: RePEc:fau:wpaper:wp2010_02
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    File URL: http://ies.fsv.cuni.cz/default/file/download/id/12703
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    References listed on IDEAS

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    1. Edward Altman & Andrea Resti & Andrea Sironi, 2004. "Default Recovery Rates in Credit Risk Modelling: A Review of the Literature and Empirical Evidence," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 33(2), pages 183-208, July.
    2. Jiří Witzany, 2009. "Unexpected Recovery Risk and LGD Discount Rate Determination," European Financial and Accounting Journal, University of Economics, Prague, vol. 2009(1), pages 61-84.
    3. Greg M. Gupton, 2005. "Advancing Loss Given Default Prediction Models: How the Quiet Have Quickened," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 34(2), pages 185-230, July.
    4. Sudheer Chava & Catalina Stefanescu & Stuart Turnbull, 2011. "Modeling the Loss Distribution," Management Science, INFORMS, vol. 57(7), pages 1267-1287, July.
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    Cited by:

    1. Matuszyk, Anna & So, Mee Chi & Mues, Christophe & Moore, Angela, 2016. "Modelling repayment patterns in the collections process for unsecured consumer debt: A case studyAuthor-Name: Thomas, Lyn C," European Journal of Operational Research, Elsevier, vol. 249(2), pages 476-486.
    2. repec:ris:apltrx:0329 is not listed on IDEAS
    3. Konstantin Belyaev & Aelita Belyaeva & Tomas Konecny & Jakub Seidler & Martin Vojtek, 2012. "Macroeconomic Factors as Drivers of LGD Prediction: Empirical Evidence from the Czech Republic," Working Papers 2012/12, Czech National Bank, Research Department.

    More about this item

    Keywords

    credit risk; recovery rate; loss given default; correlation; regulatory capital;

    JEL classification:

    • 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
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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