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Forecasting loss given default models: Impact of account characteristics and the macroeconomic state

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  • TOBBACK, Ellen
  • MARTENS, David
  • VAN GESTEL, Tony
  • BAESENS, Bart

Abstract

Based on two datasets containing Loss Given Default (LGD) observations of home equity and corporate loans, we consider non-linear and non-parametric techniques to model and forecast LGD. These techniques include non-linear Support Vector Regression (SVR), a regression tree and a two-stage model combining a linear regression with SVR. We compare these models with an ordinary least squares linear regression. In addition, we incorporate several macroeconomic variables to estimate the influence of the economic state on loan losses. We investigate whether a Box-Cox transformation of the macroeconomic features improves the linear regression model. Due to the instable distributions, both out-of-time and out-of-sample setups are considered. The two-stage model outperforms the other techniques when forecasting out-of-time, while the non-parametric regression tree is the best performer when forecasting out-of-sample. The complete non-linear SVR reports poor prediction results, both in comprehensibility and accuracy. The incorporation of macroeconomic variables significantly improves the prediction performance of most of the models. These conclusions can help financial institutions when estimating LGD under the Internal Ratings Based Approach of the Basel Accords in order to estimate the downturn LGD needed to calculate the capital requirements.

Suggested Citation

  • 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 Business and Economics.
  • Handle: RePEc:ant:wpaper:2012019
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    File URL: https://repository.uantwerpen.be/docman/irua/e27748/dc44f673.pdf
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    References listed on IDEAS

    as
    1. Bastos, João A., 2010. "Forecasting bank loans loss-given-default," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2510-2517, October.
    2. Van Gestel, Tony & Martens, David & Baesens, Bart & Feremans, Daniel & Huysmans, Johan & Vanthienen, Jan, 2007. "Forecasting and analyzing insurance companies' ratings," International Journal of Forecasting, Elsevier, vol. 23(3), pages 513-529.
    3. Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, 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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