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Pitfalls in modeling loss given default of bank loans

Author

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  • Hibbeln, Martin
  • Gürtler, Marc

Abstract

The parameter loss given default (LGD) of loans plays a crucial role for risk-based decision making of banks including risk-adjusted pricing. Depending on the quality of the estimation of LGDs, banks can gain significant competitive advantage. For bank loans, the estimation is usually based on discounted recovery cash flows, leading to workout LGDs. In this paper, we reveal several problems that may occur when modeling workout LGDs, leading to LGD estimates which are biased or have low explanatory power. Based on a data set of 71,463 defaulted bank loans, we analyze these issues and derive recommendations for action in order to avoid these problems. Due to the restricted observation period of recovery cash flows the problem of length-biased sampling occurs, where long workout processes are underrepresented in the sample, leading to an underestimation of LGDs. Write-offs and recoveries are often driven by different influencing factors, which is ignored by the empirical literature on LGD modeling. We propose a two-step approach for modeling LGDs of non-defaulted loans which accounts for these differences leading to an improved explanatory power. For LGDs of defaulted loans, the type of default and the length of the default period have high explanatory power, but estimates relying on these variables can lead to a significant underestimation of LGDs. We propose a model for defaulted loans which makes use of these influence factors and leads to consistent LGD estimates.

Suggested Citation

  • Hibbeln, Martin & Gürtler, Marc, 2011. "Pitfalls in modeling loss given default of bank loans," Working Papers IF35V1, Technische Universität Braunschweig, Institute of Finance.
  • Handle: RePEc:zbw:tbsifw:if35v1
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    References listed on IDEAS

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    1. Bastos, João A., 2010. "Forecasting bank loans loss-given-default," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2510-2517, October.
    2. Calabrese, Raffaella & Zenga, Michele, 2010. "Bank loan recovery rates: Measuring and nonparametric density estimation," Journal of Banking & Finance, Elsevier, vol. 34(5), pages 903-911, May.
    3. Acharya, Viral V. & Bharath, Sreedhar T. & Srinivasan, Anand, 2007. "Does industry-wide distress affect defaulted firms? Evidence from creditor recoveries," Journal of Financial Economics, Elsevier, vol. 85(3), pages 787-821, September.
    4. Jankowitsch, Rainer & Pullirsch, Rainer & Veza, Tanja, 2008. "The delivery option in credit default swaps," Journal of Banking & Finance, Elsevier, vol. 32(7), pages 1269-1285, July.
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    6. 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.
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    8. Stefano Caselli & Stefano Gatti & Francesca Querci, 2008. "The Sensitivity of the Loss Given Default Rate to Systematic Risk: New Empirical Evidence on Bank Loans," Journal of Financial Services Research, Springer;Western Finance Association, vol. 34(1), pages 1-34, August.
    9. Dermine, J. & de Carvalho, C. Neto, 2006. "Bank loan losses-given-default: A case study," Journal of Banking & Finance, Elsevier, vol. 30(4), pages 1219-1243, April.
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    11. Grunert, Jens & Weber, Martin, 2009. "Recovery rates of commercial lending: Empirical evidence for German companies," Journal of Banking & Finance, Elsevier, vol. 33(3), pages 505-513, March.
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    Cited by:

    1. Tanoue, Yuta & Kawada, Akihiro & Yamashita, Satoshi, 2017. "Forecasting loss given default of bank loans with multi-stage model," International Journal of Forecasting, Elsevier, vol. 33(2), pages 513-522.

    More about this item

    Keywords

    Credit risk; Bank loans; Loss given default; Forecasting;

    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

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