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Loss Given Default Modelling: Comparative Analysis

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  • Yashkir, Olga
  • Yashkir, Yuriy
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    Abstract

    In this study we investigated several most popular Loss Given Default (LGD) models (LSM, Tobit, Three-Tiered Tobit, Beta Regression, Inflated Beta Regression, Censored Gamma Regression) in order to compare their performance. We show that for a given input data set, the quality of the model calibration depends mainly on the proper choice (and availability) of explanatory variables (model factors), but not on the fitting model. Model factors were chosen based on the amplitude of their correlation with historical LGDs of the calibration data set. Numerical values of non-quantitative parameters (industry, ranking, type of collateral) were introduced as their LGD average. We show that different debt instruments depend on different sets of model factors (from three factors for Revolving Credit or for Subordinated Bonds to eight factors for Senior Secured Bonds). Calibration of LGD models using distressed business cycle periods provide better fit than data from total available time span. Calibration algorithms and details of their realization using the R statistical package are presented. We demonstrate how LGD models can be used for stress testing. The results of this study can be of use to risk managers concerned with the Basel accord compliance.

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    File URL: http://mpra.ub.uni-muenchen.de/46147/
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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 46147.

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    Date of creation: 27 Mar 2013
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    Handle: RePEc:pra:mprapa:46147

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    Keywords: LGD; Credit Risk; LGD model; Linear regression; Tobit model; Stress testing;

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    1. Bellotti, Tony & Crook, Jonathan, 2012. "Loss given default models incorporating macroeconomic variables for credit cards," International Journal of Forecasting, Elsevier, Elsevier, vol. 28(1), pages 171-182.
    2. McDonald, John F & Moffitt, Robert A, 1980. "The Uses of Tobit Analysis," The Review of Economics and Statistics, MIT Press, vol. 62(2), pages 318-21, May.
    3. J. Samuel Baixauli & Susana Alvarez, 2010. "The Role of Market-Implied Severity Modeling for Credit VaR," Annals of Economics and Finance, Society for AEF, vol. 11(2), pages 337-353, November.
    4. Radovan Chalupka & Juraj Kopecsni, 2009. "Modeling Bank Loan LGD of Corporate and SME Segments: A Case Study," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, Charles University Prague, Faculty of Social Sciences, vol. 59(4), pages 360-382, Oktober.
    5. 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, 07.
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