A Bayesian hierarchical model for unsecured loan Loss Given Default
A Bayesian hierarchical model for unsecured loan Loss Given Default
Loss Given Default (LGD) is the lender’s loss on a loan due to the customer’s default, i.e. failure to meet the credit commitment. Modelling LGD for unsecured retail loans is often challenging. In the frequentist two-step approach, the first model (logistic regression) is used to separate positive values from zeroes and the second model (e.g. linear regression) is employed to estimate these values. The models are estimated independently, but they need to be combined to predict LGD, which may be found problematic. The frequentist approach produces a point estimate of LGD for each loan. Alternatively, LGD can be modelled using Bayesian methods. In the Bayesian framework, a single hierarchical model is developed. This makes the proposed approach more coherent. The Bayesian model generates an individual predictive distribution of LGD for each loan. Potential applications of such distributions include approximating the downturn LGD and stress testing LGD under the Basel Accords. As an illustration, the frequentist approach and Bayesian methods are applied to the data on personal loans provided by a large UK bank.
bayesian hierarchical model, loss given default, regression models
Bijak, Katarzyna
5130b6b9-fbf1-44e8-9106-1dd69c6692a6
10 August 2015
Bijak, Katarzyna
5130b6b9-fbf1-44e8-9106-1dd69c6692a6
Bijak, Katarzyna
(2015)
A Bayesian hierarchical model for unsecured loan Loss Given Default.
2015 Joint Statistical Meetings, Seattle, United States.
07 - 12 Aug 2015.
Record type:
Conference or Workshop Item
(Other)
Abstract
Loss Given Default (LGD) is the lender’s loss on a loan due to the customer’s default, i.e. failure to meet the credit commitment. Modelling LGD for unsecured retail loans is often challenging. In the frequentist two-step approach, the first model (logistic regression) is used to separate positive values from zeroes and the second model (e.g. linear regression) is employed to estimate these values. The models are estimated independently, but they need to be combined to predict LGD, which may be found problematic. The frequentist approach produces a point estimate of LGD for each loan. Alternatively, LGD can be modelled using Bayesian methods. In the Bayesian framework, a single hierarchical model is developed. This makes the proposed approach more coherent. The Bayesian model generates an individual predictive distribution of LGD for each loan. Potential applications of such distributions include approximating the downturn LGD and stress testing LGD under the Basel Accords. As an illustration, the frequentist approach and Bayesian methods are applied to the data on personal loans provided by a large UK bank.
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Published date: 10 August 2015
Venue - Dates:
2015 Joint Statistical Meetings, Seattle, United States, 2015-08-07 - 2015-08-12
Keywords:
bayesian hierarchical model, loss given default, regression models
Organisations:
Centre of Excellence in Decision, Analytics & Risk Research
Identifiers
Local EPrints ID: 381476
URI: http://eprints.soton.ac.uk/id/eprint/381476
PURE UUID: 94251e22-3b5f-4dc7-920e-bfe9695c2546
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Date deposited: 05 Oct 2015 14:34
Last modified: 12 Dec 2021 03:46
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