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Underperforming performance measures? A review of measures for Loss Given Default models

Underperforming performance measures? A review of measures for Loss Given Default models
Underperforming performance measures? A review of measures for Loss Given Default models
As far as Probability of Default (PD) prediction is concerned, the model performance is typically measured with the Gini coefficient and/or the Kolmogorov-Smirnov (KS) statistic. For Loss Given Default (LGD) models, there are no standard performance measures, though, and more than 15 different measures are used, including Mean Square Error (MSE), Mean Absolute Error (MAE), coefficient of determination (R-squared) as well as correlation coefficients between the observed and predicted LGD. However, some measures cannot be readily recommended for LGD models, even though they have been used for this purpose. It is argued that there are measures that should only be employed for specific types of models. It is also pointed out that some measures can be applied interchangeably to avoid information redundancy. Moreover, the Area Under the Receiver Operating Characteristic Curve (AUC) is critically discussed in the LGD context. Four new measures are then proposed: Mean Area Under the Receiver Operating Characteristic Curve (MAUROC), Mean Accuracy Ratio (MAR), Mean Enhanced Lin-Lin Error (MELLE) and a generalized lift. The review is illustrated using an empirical example.
Loss Given Default, model performance measures, error measures, goodness-of-fit measures, correlation measures, Area Under the Receiver Operating Characteristic Curve
1753-9579
1-28
Bijak, Katarzyna
5130b6b9-fbf1-44e8-9106-1dd69c6692a6
Thomas, Lyn C.
a3ce3068-328b-4bce-889f-965b0b9d2362
Bijak, Katarzyna
5130b6b9-fbf1-44e8-9106-1dd69c6692a6
Thomas, Lyn C.
a3ce3068-328b-4bce-889f-965b0b9d2362

Bijak, Katarzyna and Thomas, Lyn C. (2018) Underperforming performance measures? A review of measures for Loss Given Default models. Journal of Risk Model Validation, 12 (1), 1-28. (doi:10.21314/JRMV.2018.186).

Record type: Article

Abstract

As far as Probability of Default (PD) prediction is concerned, the model performance is typically measured with the Gini coefficient and/or the Kolmogorov-Smirnov (KS) statistic. For Loss Given Default (LGD) models, there are no standard performance measures, though, and more than 15 different measures are used, including Mean Square Error (MSE), Mean Absolute Error (MAE), coefficient of determination (R-squared) as well as correlation coefficients between the observed and predicted LGD. However, some measures cannot be readily recommended for LGD models, even though they have been used for this purpose. It is argued that there are measures that should only be employed for specific types of models. It is also pointed out that some measures can be applied interchangeably to avoid information redundancy. Moreover, the Area Under the Receiver Operating Characteristic Curve (AUC) is critically discussed in the LGD context. Four new measures are then proposed: Mean Area Under the Receiver Operating Characteristic Curve (MAUROC), Mean Accuracy Ratio (MAR), Mean Enhanced Lin-Lin Error (MELLE) and a generalized lift. The review is illustrated using an empirical example.

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A review of performance measures for LGD models_Paper - Accepted Manuscript
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Accepted/In Press date: 1 June 2017
e-pub ahead of print date: 26 March 2018
Published date: 26 March 2018
Keywords: Loss Given Default, model performance measures, error measures, goodness-of-fit measures, correlation measures, Area Under the Receiver Operating Characteristic Curve
Organisations: Decision Analytics & Risk

Identifiers

Local EPrints ID: 411181
URI: https://eprints.soton.ac.uk/id/eprint/411181
ISSN: 1753-9579
PURE UUID: 8ed6bac6-5a19-4c70-9d50-067809949e8f

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Date deposited: 15 Jun 2017 16:31
Last modified: 13 Mar 2019 19:47

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Contributors

Author: Katarzyna Bijak
Author: Lyn C. Thomas

University divisions

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