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**Quantile regression for modelling distribution of profit and loss.** / Whittaker, Joseph; Somers, M.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Whittaker, J & Somers, M 2007, 'Quantile regression for modelling distribution of profit and loss.', *European Journal of Operational Research*, vol. 183, no. 3, pp. 1477-1487. https://doi.org/10.1016/j.ejor.2006.08.063

Whittaker, J., & Somers, M. (2007). Quantile regression for modelling distribution of profit and loss. *European Journal of Operational Research*, *183*(3), 1477-1487. https://doi.org/10.1016/j.ejor.2006.08.063

Whittaker J, Somers M. Quantile regression for modelling distribution of profit and loss. European Journal of Operational Research. 2007 Dec 16;183(3):1477-1487. doi: 10.1016/j.ejor.2006.08.063

@article{ed8c1abb084b4c7685c4c67783e661bb,

title = "Quantile regression for modelling distribution of profit and loss.",

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.",

keywords = "Regression, Basel II, Credit scoring, Haircut distribution, Kernel regression, Loss given default, Profit assessment, Revenue modelling",

author = "Joseph Whittaker and M. Somers",

note = "RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research",

year = "2007",

month = dec,

day = "16",

doi = "10.1016/j.ejor.2006.08.063",

language = "English",

volume = "183",

pages = "1477--1487",

journal = "European Journal of Operational Research",

issn = "0377-2217",

publisher = "Elsevier Science B.V.",

number = "3",

}

TY - JOUR

T1 - Quantile regression for modelling distribution of profit and loss.

AU - Whittaker, Joseph

AU - Somers, M.

N1 - RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research

PY - 2007/12/16

Y1 - 2007/12/16

N2 - 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.

AB - 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.

KW - Regression

KW - Basel II

KW - Credit scoring

KW - Haircut distribution

KW - Kernel regression

KW - Loss given default

KW - Profit assessment

KW - Revenue modelling

U2 - 10.1016/j.ejor.2006.08.063

DO - 10.1016/j.ejor.2006.08.063

M3 - Journal article

VL - 183

SP - 1477

EP - 1487

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 3

ER -