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Modelling Bank Loan LGD of Corporate and SME Segments: A Case Study

The aim of this paper is to propose a methodology to estimate loss given default (LGD) and apply it to a set of micro-data of loans to SME and corporations of an anonymous commercial bank from Central Europe. LGD estimates are important inputs in the pricing of credit risk and the measurement of bank profitability and solvency. Basel II Advance IRB Approach requires internally estimates of LGD to calculate risk-weighted assets and to estimate expected loss. We analyse the recovery rate dynamically over time and identify the efficient recovery period of a workout department. Moreover, we focus on the appropriate choice of a discount factor by introducing risk premium based on a risk level of collaterals. We apply statistical methods to estimate LGD and test empirically its determinants. Particularly, we analyse generalised linear models using symmetric logit and asymmetric log-log link functions for ordinal responses as well as for fractional responses. For fractional responses we employ two alternatives, a beta inflated distribution and a quasi-maximum likelihood estimator. We find out that the main drivers of LGD are a relative value of collateral, a loan size as well as a year of the loan origination. Different models provided similar results. As for the different links in more complex models, log-log models in some cases perform better, implying an asymmetric response of the dependent variable.

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Paper provided by Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies in its series Working Papers IES with number 2008/27.

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Length: 33pages
Date of creation: Nov 2008
Date of revision: Nov 2008
Handle: RePEc:fau:wpaper:wp2008_27
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  1. Jon Frye, 2000. "Depressing recoveries," Emerging Issues, Federal Reserve Bank of Chicago, issue Oct.
  2. 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, vol. 59(4), pages 360-382, Oktober.
  3. 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.
  4. Mark Carey, 1998. "Credit Risk in Private Debt Portfolios," Journal of Finance, American Finance Association, vol. 53(4), pages 1363-1387, 08.
  5. Renault, Olivier & Scaillet, Olivier, 2004. "On the way to recovery: A nonparametric bias free estimation of recovery rate densities," Journal of Banking & Finance, Elsevier, vol. 28(12), pages 2915-2931, December.
  6. Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-32, Nov.-Dec..
  7. Thorburn, Karin S., 2000. "Bankruptcy auctions: costs, debt recovery, and firm survival," Journal of Financial Economics, Elsevier, vol. 58(3), pages 337-368, December.
  8. Jakub Seidler & Petr Jakubík, 2009. "Implied Market Loss Given Default in the Czech Republic: Structural-Model Approach," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 59(1), pages 20-40, January.
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