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Bank loan recovery rates: Measuring and nonparametric density estimation

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Author Info

  • Calabrese, Raffaella
  • Zenga, Michele

Abstract

In this paper we analyse a comprehensive database of 149,378 recovery rates on Italian bank loans. We investigate a new methodology to compute the recovery percentage that we suggest to consider as a mixed random variable. To estimate the probability density function of such a mixture, we propose the mixture of beta kernels estimator and we analyse its performance by Monte Carlo simulations. The application of these proposals to the Bank of Italy's data shows that, even if we remove the endpoints from the support of the recovery rate, the density function estimate is far from being a beta function.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Banking & Finance.

Volume (Year): 34 (2010)
Issue (Month): 5 (May)
Pages: 903-911

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Handle: RePEc:eee:jbfina:v:34:y:2010:i:5:p:903-911

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Web page: http://www.elsevier.com/locate/jbf

Related research

Keywords: Recovery rate Boundary problem Mixed random variable Mixture Beta kernel;

References

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  1. Jarrow, Robert A. & Turnbull, Stuart M., 2000. "The intersection of market and credit risk," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 271-299, January.
  2. Max Bruche & Carlos González Aguado, 2006. "Recovery Rates, Default Probabilities And The Credit Cycle," Working Papers wp2006_0612, CEMFI.
  3. 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.
  4. 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.
  5. Christian Gourieroux & Alain Monfort, 2006. "(Non) consistency of the Beta Kernel Estimator for Recovery Rate Distribution," Working Papers 2006-31, Centre de Recherche en Economie et Statistique.
  6. Koutsomanoli-Filippaki, Anastasia & Mamatzakis, Emmanuel, 2009. "Performance and Merton-type default risk of listed banks in the EU: A panel VAR approach," Journal of Banking & Finance, Elsevier, vol. 33(11), pages 2050-2061, November.
  7. 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.
  8. Anastasia Koutsomanoli-Filippaki & Emmanuel Mamatzakis, 2009. "Performance and Merton-Type Default Risk of Listed Banks in EU: a panel VAR approach," Discussion Paper Series 2009_09, Department of Economics, University of Macedonia, revised Apr 2009.
  9. Thomas C. Wilson, 1998. "Portfolio credit risk," Economic Policy Review, Federal Reserve Bank of New York, issue Oct, pages 71-82.
  10. Chen, Song Xi, 1999. "Beta kernel estimators for density functions," Computational Statistics & Data Analysis, Elsevier, vol. 31(2), pages 131-145, August.
  11. Altman, Edward I, 1989. " Measuring Corporate Bond Mortality and Performance," Journal of Finance, American Finance Association, vol. 44(4), pages 909-22, September.
  12. 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.
  13. 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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Citations

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Cited by:
  1. Bastos, João A., 2010. "Forecasting bank loans loss-given-default," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2510-2517, October.
  2. Raffaella Calabrese, 2012. "Regression Model for Proportions with Probability Masses at Zero and One," Working Papers 201209, Geary Institute, University College Dublin.
  3. Varotto, Simone, 2012. "Stress testing credit risk: The Great Depression scenario," Journal of Banking & Finance, Elsevier, vol. 36(12), pages 3133-3149.
  4. Raffaella Calabrese, 2012. "Estimating bank loans loss given default by generalized additive models," Working Papers 201224, Geary Institute, University College Dublin.
  5. Raffaella Calabrese, 2011. "Cost-sensitive classification for rare events: an application to the credit rating model validation for SMEs," Working Papers 201134, Geary Institute, University College Dublin.
  6. Song Li & Mervyn J. Silvapulle & Param Silvapulle & Xibin Zhang, 2012. "Bayesian Approaches to Non-parametric Estimation of Densities on the Unit Interval," Monash Econometrics and Business Statistics Working Papers 3/12, Monash University, Department of Econometrics and Business Statistics.
  7. Hibbeln, Martin & Gürtler, Marc, 2011. "Pitfalls in modeling loss given default of bank loans," Working Papers IF35V1, Technische Universität Braunschweig, Institute of Finance.
  8. 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.

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