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Comparing debt characteristics and LGD models for different collections policies

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  • Thomas, L.C.
  • Matuszyk, A.
  • Moore, A.

Abstract

This paper discusses the similarities and differences in the collection process between in-house and 3rd party collection. The objective is to show that, although the same type of modelling approach to estimating the Loss Given Default (LGD) can be used in both cases, the details will be significantly different. In particular, the form of the LGD distribution suggests that one needs to split the distribution in different ways in the two cases, as well as using different variables. The comparisons are made using two data sets of the collection outcomes from two sets of unsecured consumer defaulters.

Suggested Citation

  • Thomas, L.C. & Matuszyk, A. & Moore, A., 2012. "Comparing debt characteristics and LGD models for different collections policies," International Journal of Forecasting, Elsevier, vol. 28(1), pages 196-203.
    • Handle: RePEc:eee:intfor:v:28:y:2012:i:1:p:196-203
    DOI: 10.1016/j.ijforecast.2010.11.004
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    References listed on IDEAS

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    1. William M. Makuch & Jeffrey L. Dodge & Joseph G. Ecker & Donna C. Granfors & Gerald J. Hahn, 1992. "Managing Consumer Credit Delinquency in the US Economy: A Multi-Billion Dollar Management Science Application," Interfaces, INFORMS, vol. 22(1), pages 90-109, February.
    2. A Matuszyk & C Mues & L C Thomas, 2010. "Modelling LGD for unsecured personal loans: decision tree approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 393-398, March.
    3. Somers, Mark & Whittaker, Joe, 2007. "Quantile regression for modelling distributions of profit and loss," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1477-1487, December.
    4. 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;Western Finance Association, vol. 34(1), pages 1-34, August.
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    Cited by:

    1. Nazemi, Abdolreza & Rezazadeh, Hani & Fabozzi, Frank J. & Höchstötter, Markus, 2022. "Deep learning for modeling the collection rate for third-party buyers," International Journal of Forecasting, Elsevier, vol. 38(1), pages 240-252.
    2. Viktar Fedaseyeu & Robert M. Hunt, 2014. "The economics of debt collection: enforcement of consumer credit contracts," Working Papers 14-7, Federal Reserve Bank of Philadelphia.
    3. Thamayanthi Chellathurai, 2017. "Probability Density Of Recovery Rate Given Default Of A Firm’S Debt And Its Constituent Tranches," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(04), pages 1-34, June.
    4. 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.
    5. Johannes Kriebel & Kevin Yam, 2020. "Forecasting recoveries in debt collection: Debt collectors and information production," European Financial Management, European Financial Management Association, vol. 26(3), pages 537-559, June.

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