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Quantile regression for modelling distribution of profit and loss.

Whittaker, Joseph and Somers, M. (2007) Quantile regression for modelling distribution of profit and loss. European Journal of Operational Research, 183 (3). pp. 1477-1487. ISSN 0377-2217

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Abstract

Quantile regression is applied in two retail credit risk assessment exercises exemplifying the power of the technique to account for the diverse distributions that arise in the financial service industry. The first application is to predict loss given default for secured loans, in particular retail mortgages. This is an asymmetric process since where the security (such as a property) value exceeds the loan balance the banks cannot retain the profit, whereas when the security does not cover the value of the defaulting loan then the bank realises a loss. In the light of this asymmetry it becomes apparent that estimating the low tail of the house value is much more relevant for estimating likely losses than estimates of the average value where in most cases no loss is realised. In our application quantile regression is used to estimate the distribution of property values realised on repossession that is then used to calculate loss given default estimates. An illustration is given for a mortgage portfolio from a European mortgage lender. A second application is to revenue modelling. While credit issuing organisations have access to large databases, they also build models to assess the likely effects of new strategies for which, by definition, there is no existing data. Certain strategies are aimed at increasing the revenue stream or decreasing the risk in specific market segments. Using a simple artificial revenue model, quantile regression is applied to elucidate the details of subsets of accounts, such as the least profitable, as predicted from their covariates. The application uses standard linear and kernel smoothed quantile regression.

Item Type: Article
Journal or Publication Title: European Journal of Operational Research
Additional Information: RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research
Uncontrolled Keywords: Regression ; Basel II ; Credit scoring ; Haircut distribution ; Kernel regression ; Loss given default ; Profit assessment ; Revenue modelling
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 2453
Deposited By: ep_importer
Deposited On: 29 Mar 2008 12:03
Refereed?: Yes
Published?: Published
Last Modified: 26 Jul 2012 16:25
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/2453

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