Estimating bank loans loss given default by generalized additive models
With the implementation of the Basel II accord, the development of accurate loss given default models is becoming increasingly important. The main objective of this paper is to propose a new model to estimate Loss Given Default (LGD) for bank loans by applying generalized additive models. Our proposal allows to represent the high concentration of LGDs at the boundaries. The model is useful in uncovering nonlinear covariate effects and in estimating the mean and the variance of LGDs. The suggested model is applied to a comprehensive survey on loan recovery process of Italian banks. To model LGD in downturn conditions, we include macroeconomic variables in the model. Out-of-time validation shows that our model outperforms popular models like Tobit, decision tree and linear regression models for different time horizons.
|Date of creation:||22 Oct 2012|
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- Radovan Chalupka & Juraj Kopecsni, 2009.
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Czech Journal of Economics and Finance (Finance a uver),
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CEMAPRE Working Papers
0901, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
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LSE Research Online Documents on Economics
24524, London School of Economics and Political Science, LSE Library.
- Bruche, Max & González-Aguado, Carlos, 2010. "Recovery rates, default probabilities, and the credit cycle," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 754-764, April.
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- 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.
- Bellotti, Tony & Crook, Jonathan, 2012. "Loss given default models incorporating macroeconomic variables for credit cards," International Journal of Forecasting, Elsevier, vol. 28(1), pages 171-182.
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