Pitfalls in modeling loss given default of bank loans
AbstractThe 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. --
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Technische Universität Braunschweig, Institute of Finance in its series Working Papers with number IF35V1.
Date of creation: 2011
Date of revision:
Credit risk; Bank loans; Loss given default; Forecasting;
Find related papers by 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-02-20 (All new papers)
- NEP-BAN-2012-02-20 (Banking)
- NEP-CFN-2012-02-20 (Corporate Finance)
- NEP-RMG-2012-02-20 (Risk Management)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- David Citron & Mike Wright & Rod Ball & Fred Rippington, 2003. "Secured Creditor Recovery Rates from Management Buy-outs in Distress," European Financial Management, European Financial Management Association, European Financial Management Association, vol. 9(2), pages 141-161.
- 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, Elsevier, vol. 85(3), pages 787-821, September.
- Dermine, J. & de Carvalho, C. Neto, 2006. "Bank loan losses-given-default: A case study," Journal of Banking & Finance, Elsevier, Elsevier, vol. 30(4), pages 1219-1243, April.
- Grunert, Jens & Weber, Martin, 2009. "Recovery rates of commercial lending: Empirical evidence for German companies," Journal of Banking & Finance, Elsevier, Elsevier, vol. 33(3), pages 505-513, March.
- Gordy, Michael B., 2003.
"A risk-factor model foundation for ratings-based bank capital rules,"
Journal of Financial Intermediation, Elsevier,
Elsevier, vol. 12(3), pages 199-232, July.
- Michael B. Gordy, 2002. "A risk-factor model foundation for ratings-based bank capital rules," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 2002-55, Board of Governors of the Federal Reserve System (U.S.).
- Campbell, John & Thompson, Samuel P., 2008.
"Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?,"
2622619, Harvard University Department of Economics.
- John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Review of Financial Studies, Society for Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
- Olivier RENAULT & Olivier SCAILLET, 2003.
"On the Way to Recovery: A Nonparametric Bias Free Estimation of Recovery Rate Densities,"
FAME Research Paper Series, International Center for Financial Asset Management and Engineering
rp83, International Center for Financial Asset Management and Engineering.
- Renault, Olivier & Scaillet, Olivier, 2004. "On the way to recovery: A nonparametric bias free estimation of recovery rate densities," Journal of Banking & Finance, Elsevier, Elsevier, vol. 28(12), pages 2915-2931, December.
- Joao A. Bastos, 2009.
"Forecasting bank loans loss-given-default,"
CEMAPRE Working Papers, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon
0901, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
- Kiefer, Nicholas M, 1988. "Economic Duration Data and Hazard Functions," Journal of Economic Literature, American Economic Association, vol. 26(2), pages 646-79, June.
- 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.
- Calabrese, Raffaella & Zenga, Michele, 2010. "Bank loan recovery rates: Measuring and nonparametric density estimation," Journal of Banking & Finance, Elsevier, Elsevier, vol. 34(5), pages 903-911, May.
- 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, Springer, vol. 34(1), pages 1-34, August.
- Jankowitsch, Rainer & Pullirsch, Rainer & Veza, Tanja, 2008. "The delivery option in credit default swaps," Journal of Banking & Finance, Elsevier, Elsevier, vol. 32(7), pages 1269-1285, July.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (ZBW - German National Library of Economics).
If references are entirely missing, you can add them using this form.