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A proposed framework for backtesting loss given default models

A proposed framework for backtesting loss given default models
A proposed framework for backtesting loss given default models
The Basel Accords require financial institutions to regularly validate their loss given default (LGD) models. This is crucial so banks are not misestimating the minimum required capital to protect them against the risks they are facing through their lending activities. The validation of an LGD model typically includes backtesting, which involves the process of evaluating to what degree the internal model estimates still correspond with the realized observations. Reported backtesting examples have typically been limited to simply measuring the similarity between model predictions and realized observations. It is however not straightforward to determine acceptable performance based on these measurements alone. Although recent research led to advanced backtesting methods for PD models, the literature on similar backtesting methods for LGD models is much scarcer. This study addresses this literature gap by proposing a backtesting framework using statistical hypothesis tests to support the validation of LGD models. The proposed statistical hypothesis tests implicitly define reliable reference values to determine acceptable performance and take into account the number of LGD observations, as a small sample may affect the quality of the backtesting procedure. This workbench of tests is applied to an LGD model fitted to real-life data and evaluated through a statistical power analysis.
basel committee on banking supervision (bcbs), loss given defualt (lgd), model validation, backtesting, probability of default (pd)
69-90
Loterman, G.
87bb0c85-8b43-49bf-92d7-4d894d4bb3aa
Debruyne, M.
e02f00f6-0e1c-449e-8a6c-10035f2b08eb
Vanden Branden, K.
a7bc4538-8b32-4917-9e26-613bc1e5b74e
Van Gestel, T.
ebd266da-f429-4493-a4e1-1f9a45c4c1c9
Mues, C.
07438e46-bad6-48ba-8f56-f945bc2ff934
Loterman, G.
87bb0c85-8b43-49bf-92d7-4d894d4bb3aa
Debruyne, M.
e02f00f6-0e1c-449e-8a6c-10035f2b08eb
Vanden Branden, K.
a7bc4538-8b32-4917-9e26-613bc1e5b74e
Van Gestel, T.
ebd266da-f429-4493-a4e1-1f9a45c4c1c9
Mues, C.
07438e46-bad6-48ba-8f56-f945bc2ff934

Loterman, G., Debruyne, M., Vanden Branden, K., Van Gestel, T. and Mues, C. (2014) A proposed framework for backtesting loss given default models. Journal of Risk Model Validation, 8 (1), 69-90. (doi:10.21314/JRMV.2014.117).

Record type: Article

Abstract

The Basel Accords require financial institutions to regularly validate their loss given default (LGD) models. This is crucial so banks are not misestimating the minimum required capital to protect them against the risks they are facing through their lending activities. The validation of an LGD model typically includes backtesting, which involves the process of evaluating to what degree the internal model estimates still correspond with the realized observations. Reported backtesting examples have typically been limited to simply measuring the similarity between model predictions and realized observations. It is however not straightforward to determine acceptable performance based on these measurements alone. Although recent research led to advanced backtesting methods for PD models, the literature on similar backtesting methods for LGD models is much scarcer. This study addresses this literature gap by proposing a backtesting framework using statistical hypothesis tests to support the validation of LGD models. The proposed statistical hypothesis tests implicitly define reliable reference values to determine acceptable performance and take into account the number of LGD observations, as a small sample may affect the quality of the backtesting procedure. This workbench of tests is applied to an LGD model fitted to real-life data and evaluated through a statistical power analysis.

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Accepted/In Press date: 17 February 2014
Published date: March 2014
Keywords: basel committee on banking supervision (bcbs), loss given defualt (lgd), model validation, backtesting, probability of default (pd)
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 386766
URI: https://eprints.soton.ac.uk/id/eprint/386766
PURE UUID: cfe73381-32b5-46c9-b721-36d7919e6825

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Date deposited: 04 Feb 2016 10:17
Last modified: 20 May 2019 16:31

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Contributors

Author: G. Loterman
Author: M. Debruyne
Author: K. Vanden Branden
Author: T. Van Gestel
Author: C. Mues

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