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Performance measures of LGD models

Performance measures of LGD models
Performance measures of LGD models
As far as Probability of Default (PD) assessment is concerned, the model performance (discriminatory power) 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 a number of different measures are used. In particular, they can be classified as either error measures or non-error measures. The former include Mean Square Error (MSE), Mean Absolute Error (MAE), relative errors etc. Among the latter are coefficient of determination (R-squared) as well as various correlation coefficients between the observed and predicted LGD. Obviously, the lower the error measures and the higher the non-error measures, the better the model performance. Whereas all performance measures have both advantages and disadvantages, some measures cannot be readily recommended for LGD models, even though they have been used for this purpose. It is argued that some measures should only be used for specific types of models (e.g. for linear regression). It is also pointed out that some measures can be used interchangeably to avoid information redundancy. Moreover, both the Area Over the Regression Error Characteristic Curve (AOC) and the Area Under the Receiver Operating Characteristic Curve (AUC) are critically discussed in the LGD context, and an alternative to the latter is proposed.
Loss Given Default, model performance measures
Bijak, Katarzyna
5130b6b9-fbf1-44e8-9106-1dd69c6692a6
Thomas, Lyn
a3ce3068-328b-4bce-889f-965b0b9d2362
Bijak, Katarzyna
5130b6b9-fbf1-44e8-9106-1dd69c6692a6
Thomas, Lyn
a3ce3068-328b-4bce-889f-965b0b9d2362

Bijak, Katarzyna and Thomas, Lyn (2015) Performance measures of LGD models. Credit Scoring and Credit Control XIV, United Kingdom. 26 - 28 Aug 2015.

Record type: Conference or Workshop Item (Other)

Abstract

As far as Probability of Default (PD) assessment is concerned, the model performance (discriminatory power) 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 a number of different measures are used. In particular, they can be classified as either error measures or non-error measures. The former include Mean Square Error (MSE), Mean Absolute Error (MAE), relative errors etc. Among the latter are coefficient of determination (R-squared) as well as various correlation coefficients between the observed and predicted LGD. Obviously, the lower the error measures and the higher the non-error measures, the better the model performance. Whereas all performance measures have both advantages and disadvantages, some measures cannot be readily recommended for LGD models, even though they have been used for this purpose. It is argued that some measures should only be used for specific types of models (e.g. for linear regression). It is also pointed out that some measures can be used interchangeably to avoid information redundancy. Moreover, both the Area Over the Regression Error Characteristic Curve (AOC) and the Area Under the Receiver Operating Characteristic Curve (AUC) are critically discussed in the LGD context, and an alternative to the latter is proposed.

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More information

Published date: 28 August 2015
Venue - Dates: Credit Scoring and Credit Control XIV, United Kingdom, 2015-08-26 - 2015-08-28
Keywords: Loss Given Default, model performance measures
Organisations: Centre of Excellence in Decision, Analytics & Risk Research

Identifiers

Local EPrints ID: 381477
URI: https://eprints.soton.ac.uk/id/eprint/381477
PURE UUID: 0b0dacba-5654-422b-a49a-f362cf7bb990

Catalogue record

Date deposited: 05 Oct 2015 14:42
Last modified: 17 Jul 2017 20:27

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