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Forecasting bank loans loss-given-default

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  • Joao A. Bastos

    (CEMAPRE, School of Economics and Management (ISEG), Technical University of Lisbon)

Abstract

With the advent of the new Basel Capital Accord, banking organizations are invited to estimate credit risk capital requirements using an internal ratings based approach. In order to be compliant with this approach, institutions must estimate the expected loss-given-default, the fraction of the credit exposure that is lost if the borrower defaults. This study evaluates the ability of a parametric fractional response regression and a nonparametric regression tree model to forecast bank loan credit losses. The out-of-sample predictive ability of these models is evaluated at several recovery horizons after the default event. The out-of-time predictive ability is also estimated for a recovery horizon of one year. The performance of the models is benchmarked against recovery estimates given by historical averages. The results suggest that regression trees are an interesting alternative to parametric models in modeling and forecasting loss-given-default.

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Bibliographic Info

Paper provided by Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon in its series CEMAPRE Working Papers with number 0901.

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Length: 16 pages
Date of creation: May 2009
Date of revision:
Handle: RePEc:cma:wpaper:0901

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Keywords: Loss-given-default; Forecasting; Bank loans; Fractional response regression; Regression trees;

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References

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  1. 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.
  2. Carlos González-Aguado & Max Bruche, 2006. "Recovery Rates, Default Probabilities and the Credit Cycle," FMG Discussion Papers dp572, Financial Markets Group.
  3. Qi, Min & Yang, Xiaolong, 2009. "Loss given default of high loan-to-value residential mortgages," Journal of Banking & Finance, Elsevier, vol. 33(5), pages 788-799, May.
  4. Leslie E. Papke & Jeffrey M. Wooldridge, 1993. "Econometric Methods for Fractional Response Variables with an Application to 401(k) Plan Participation Rates," NBER Technical Working Papers 0147, National Bureau of Economic Research, Inc.
  5. Diana Bonfim, 2007. "Credit Risk Drivers: Evaluating the Contribution of Firm Level Information and of Macroeconomic Dynamics," Working Papers w200707, Banco de Portugal, Economics and Research Department.
  6. 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.
  7. 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.
  8. Altman, Edward I, 1989. " Measuring Corporate Bond Mortality and Performance," Journal of Finance, American Finance Association, vol. 44(4), pages 909-22, September.
  9. 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.
  10. 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, vol. 34(1), pages 1-34, August.
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Cited by:
  1. 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.
  2. Stefan Hlawatsch & Sebastian Ostrowski, 2010. "Simulation and Estimation of Loss Given Default," FEMM Working Papers 100010, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
  3. Christoph Memmel & Angelika Sachs & Ingrid Stein, 2012. "Contagion in the Interbank Market with Stochastic Loss Given Default," International Journal of Central Banking, International Journal of Central Banking, vol. 8(3), pages 177-206, September.
  4. Hibbeln, Martin & Gürtler, Marc, 2011. "Pitfalls in modeling loss given default of bank loans," Working Papers IF35V1, Technische Universität Braunschweig, Institute of Finance.
  5. Gürtler, Marc & Hibbeln, Martin, 2013. "Improvements in loss given default forecasts for bank loans," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2354-2366.
  6. Stanhouse, Bryan & Schwarzkopf, Al & Ingram, Matt, 2011. "A computational approach to pricing a bank credit line," Journal of Banking & Finance, Elsevier, vol. 35(6), pages 1341-1351, June.
  7. Altman, Edward I. & Kalotay, Egon A., 2014. "Ultimate recovery mixtures," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 116-129.
  8. Diana Bonfim & Daniel Dias & Christine Richmond, 2011. "What Happens After Default? Stylized Facts on Access to Credit," Working Papers w201101, Banco de Portugal, Economics and Research Department.
  9. Pesola, Jarmo, 2011. "Joint effect of financial fragility and macroeconomic shocks on bank loan losses: Evidence from Europe," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 3134-3144, November.
  10. TOBBACK, Ellen & MARTENS, David & VAN GESTEL, Tony & BAESENS, Bart, 2012. "Forecasting loss given default models: Impact of account characteristics and the macroeconomic state," Working Papers 2012019, University of Antwerp, Faculty of Applied Economics.
  11. Guglielmo Maria Caporale & Alessandro Girardi, 2011. "Price Discovery and Trade Fragmentation in a Multi-Market Environment: Evidence from the MTS System," CESifo Working Paper Series 3525, CESifo Group Munich.
  12. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn, 2013. "A zero-adjusted gamma model for mortgage loan loss given default," International Journal of Forecasting, Elsevier, vol. 29(4), pages 548-562.
  13. Raffaella Calabrese, 2012. "Regression Model for Proportions with Probability Masses at Zero and One," Working Papers 201209, Geary Institute, University College Dublin.
  14. Qi, Min & Zhao, Xinlei, 2011. "Comparison of modeling methods for Loss Given Default," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2842-2855, November.
  15. Antão, Paula & Lacerda, Ana, 2011. "Capital requirements under the credit risk-based framework," Journal of Banking & Finance, Elsevier, vol. 35(6), pages 1380-1390, June.
  16. Bonfim, Diana & Dias, Daniel A. & Richmond, Christine, 2012. "What happens after corporate default? Stylized facts on access to credit," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2007-2025.
  17. Hartmann-Wendels, Thomas & Miller, Patrick & Töws, Eugen, 2014. "Loss given default for leasing: Parametric and nonparametric estimations," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 364-375.
  18. So Sohn & Yoon Kim, 2013. "Behavioral credit scoring model for technology-based firms that considers uncertain financial ratios obtained from relationship banking," Small Business Economics, Springer, vol. 41(4), pages 931-943, December.
  19. Joao A. Bastos, 2013. "Ensemble predictions of recovery rates," CEMAPRE Working Papers 1301, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
  20. Raffaella Calabrese, 2012. "Estimating bank loans loss given default by generalized additive models," Working Papers 201224, Geary Institute, University College Dublin.
  21. Han, Chulwoo & Jang, Youngmin, 2013. "Effects of debt collection practices on loss given default," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 21-31.

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