Modelling LGD Using Survival Analysis

Document Type: Original Research

Author

Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Iran

Abstract

Loss Given Default (LGD) is one of the key parameters needed in order to estimate expected and unexpected credit losses necessary for credit pricing as well as for calculation of the regulatory Basel II requirement (BCBS, 2006). While the credit rating and probability of default (PD) techniques have been well developed in recent decades, LGD has attracted little attention before 2000s.In this paper, We compare linear regression and survival analysis models for modelling recovery rates and recovery amounts, in order to predict the  LGD for unsecured consumer loans or credit cards.

Keywords


Altman, E. I., Resti, A., & Sironi, A. (2005). Loss given default; a review ofthe literature in recovery risk. In E. I. Altman, A. Resti, & A. Sironi (Eds.), Recovery risk (pp. 41–59). London: Risk Books.
Andreeva, G. (2006): European Generic Scoring Models Using Survival Analysis. Journal of the Operational Research Society, 2006, vol. 57, no. 10, pp. 1180-1187.
Basel Committee on Banking Supervision (BCBS) (2004, updated 2005). International convergence of capital measurement and capital standards: a revised framework. Basel: Bank of International Settlement.
BCBS (2006): International Convergence of Capital Measurement and Capital Standards. A Revised Framework – Comprehensive Version. Basel, Basel Committee on Banking Supervision.
Bellotti, T., & Crook, J. (2009). Calculating LGD for credit cards. In Conference on risk management in the personal financial services sector. http://www3.imperial.ac.uk/ mathsinstitute/programmes/research/bankfin/qfrmc/events/past/jan09conference.
Benoit, D. F., & Van den Poel, D. (2009). Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: an application in financial services. Expert Systems with Applications, 36, 10475–10484.
Chava, S., Stefanescu, C. & Turnbull, S. (2008): Modeling the Loss Distribution. [on-line], London, London Business School, c2008, [cit. 25th May, 2012], <http://faculty.london.edu/cstefanescu/Chava_Stefanescu_Turnbull.pdf >.
Cox, D. R. (1972). Regression models and life tables (with discussion). Journal of the Royal Statistical Society, Series B, 34, 187–220.
Dermine, J., & de Carvalho, C. N. (2006). Bank loan losses given default: a case study. Journal of Banking and Finance, 30, 1219–1243.
Frye, J. (2003): A False Sense of Security, Risk, vol. 16, no. 8, pp. 63-67.
Gupton, G. (2005). Estimating recovery risk by means of a quantitative model: lossCalc. In E. I. Altman, A. Resti, & A. Sironi (Eds.), Recovery risk (pp. 61–86). London: Risk Books.
Huang, X. & Oosterlee, C. W. (2008): Generalized Beta Regression Models for Random Loss-Given-Default. Delft, Delft University of Technology Report 08-10, 2008.
Matuszyk, A., Mues, C., & Thomas, L. C. (2010). Modelling LGD for unsecured personal loans: decision tree approach. Journal of the Operational Research Society, 61, 393–398.
Narain, B. (1992): Survival Analysis and the Credit Granting Decision. In: Thomas, L. C. – Crook, J. N. – Edelman, D. B. (eds): Credit Scoring and Credit Control. Oxford, Oxford University Press, 1992, pp. 109-122.
Rychnovsky, M. (2009): Mathematical Models of LGD, Diploma Thesis. Praha, Charles University, Faculty of Mathematics and Physics, April 2009.
Schuermann, T. (2005). What do we know about loss given default? In E. I. Altman, A. Resti, & A. Sironi (Eds.), Recovery risk (pp. 3–24). London: Risk Books.
Somers, M., & Whittaker, J. (2007). Quantile regression for modelling distributions of profit and loss. European Journal of Operational Research, 183, 1477–1487.
Whittaker, J., Whitehead, C., & Somers, M. (2005). The neglog transformation and quantile regression for the analysis of a large credit scoring database. Journal of the Royal Statistical Society, Series C, 54, 863–878.