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A mixture model for credit card exposure at default using the GAMLSS framework

A mixture model for credit card exposure at default using the GAMLSS framework
A mixture model for credit card exposure at default using the GAMLSS framework
The Basel II and III Accords propose estimating the credit conversion factor (CCF) to model exposure at default (EAD) for credit cards and other forms of revolving credit. Alternatively, recent work has suggested it may be beneficial to predict the EAD directly, i.e.modelling the balance as a function of a series of risk drivers. In this paper, we propose a novel approach combining two ideas proposed in the literature and test its effectiveness using a large dataset of credit card defaults not previously used in the EAD literature. We predict EAD by fitting a regression model using the generalised additive model for location, scale, and shape (GAMLSS) framework. We conjecture that the EAD level and risk drivers of its mean and dispersion parameters could substantially differ between the debtors who hit the credit limit (i.e.“maxed out” their cards) prior to default and those who did not, and thus implement a mixture model conditioning on these two respective scenarios. In addition to identifying the most significant explanatory variables for each model component, our analysis suggests that predictive accuracy is improved, both by using GAMLSS (and its ability to incorporate non-linear effects) as well as by introducing the mixture component.
Basel Accords, Credit cards, Exposure at default, Generalised additive model, Risk analysis
0169-2070
Wattanawongwan, Suttisak
f2dac7d7-d4e6-461e-ab53-b585aa655acd
Mues, Christophe
07438e46-bad6-48ba-8f56-f945bc2ff934
Okhrati, Ramin
e8e0b289-be8c-4e73-aea5-c9835190a54a
Choudhry, Taufiq
6fc3ceb8-8103-4017-b3b5-2d38efa57728
So, Meko
c6922ccf-547b-485e-8b74-a9271e6225a2
Wattanawongwan, Suttisak
f2dac7d7-d4e6-461e-ab53-b585aa655acd
Mues, Christophe
07438e46-bad6-48ba-8f56-f945bc2ff934
Okhrati, Ramin
e8e0b289-be8c-4e73-aea5-c9835190a54a
Choudhry, Taufiq
6fc3ceb8-8103-4017-b3b5-2d38efa57728
So, Meko
c6922ccf-547b-485e-8b74-a9271e6225a2

Wattanawongwan, Suttisak, Mues, Christophe, Okhrati, Ramin, Choudhry, Taufiq and So, Meko (2022) A mixture model for credit card exposure at default using the GAMLSS framework. International Journal of Forecasting. (doi:10.1016/j.ijforecast.2021.12.014).

Record type: Article

Abstract

The Basel II and III Accords propose estimating the credit conversion factor (CCF) to model exposure at default (EAD) for credit cards and other forms of revolving credit. Alternatively, recent work has suggested it may be beneficial to predict the EAD directly, i.e.modelling the balance as a function of a series of risk drivers. In this paper, we propose a novel approach combining two ideas proposed in the literature and test its effectiveness using a large dataset of credit card defaults not previously used in the EAD literature. We predict EAD by fitting a regression model using the generalised additive model for location, scale, and shape (GAMLSS) framework. We conjecture that the EAD level and risk drivers of its mean and dispersion parameters could substantially differ between the debtors who hit the credit limit (i.e.“maxed out” their cards) prior to default and those who did not, and thus implement a mixture model conditioning on these two respective scenarios. In addition to identifying the most significant explanatory variables for each model component, our analysis suggests that predictive accuracy is improved, both by using GAMLSS (and its ability to incorporate non-linear effects) as well as by introducing the mixture component.

Text
IJF - Accepted Manuscript
Restricted to Repository staff only until 28 December 2023.
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More information

Accepted/In Press date: 28 December 2021
e-pub ahead of print date: 5 February 2022
Published date: 5 February 2022
Additional Information: Publisher Copyright: © 2022 International Institute of Forecasters
Keywords: Basel Accords, Credit cards, Exposure at default, Generalised additive model, Risk analysis

Identifiers

Local EPrints ID: 454482
URI: http://eprints.soton.ac.uk/id/eprint/454482
ISSN: 0169-2070
PURE UUID: 03727624-00c9-4dbc-a000-9ca308e42632
ORCID for Christophe Mues: ORCID iD orcid.org/0000-0002-6289-5490
ORCID for Ramin Okhrati: ORCID iD orcid.org/0000-0003-0103-7051
ORCID for Taufiq Choudhry: ORCID iD orcid.org/0000-0002-0463-0662
ORCID for Meko So: ORCID iD orcid.org/0000-0002-8507-4222

Catalogue record

Date deposited: 11 Feb 2022 17:32
Last modified: 18 Jun 2022 01:42

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Contributors

Author: Suttisak Wattanawongwan
Author: Christophe Mues ORCID iD
Author: Ramin Okhrati ORCID iD
Author: Taufiq Choudhry ORCID iD
Author: Meko So ORCID iD

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