Modelling LGD for unsecured retail loans using Bayesian methods
Modelling LGD for unsecured retail loans using Bayesian methods
Loss Given Default (LGD) is the loss borne by the bank when a customer defaults on a loan. LGD for unsecured retail loans is often found difficult to model. In the frequentist (non-Bayesian) two-step approach, two separate regression models are estimated independently, which can be considered potentially problematic when trying to combine them to make predictions about LGD. The result is a point estimate of LGD for each loan. Alternatively, LGD can be modelled using Bayesian methods. In the Bayesian framework, one can build a single, hierarchical model instead of two separate ones, which makes this a more coherent approach. In this paper, Bayesian methods as well as the frequentist approach are applied to the data on personal loans provided by a large UK bank. As expected, the posterior means of parameters which have been produced using Bayesian methods are very similar to the frequentist estimates. The most important advantage of the Bayesian model is that it generates an individual predictive distribution of LGD for each loan. Potential applications of such distributions include the downturn LGD and the stressed LGD under Basel II.
loss given default, downturn LGD, bayesian, regression models
342-352
Bijak, Katarzyna
5130b6b9-fbf1-44e8-9106-1dd69c6692a6
Thomas, Lyn C.
a3ce3068-328b-4bce-889f-965b0b9d2362
12 February 2015
Bijak, Katarzyna
5130b6b9-fbf1-44e8-9106-1dd69c6692a6
Thomas, Lyn C.
a3ce3068-328b-4bce-889f-965b0b9d2362
Bijak, Katarzyna and Thomas, Lyn C.
(2015)
Modelling LGD for unsecured retail loans using Bayesian methods.
Journal of the Operational Research Society, 66 (2), .
(doi:10.1057/jors.2014.9).
Abstract
Loss Given Default (LGD) is the loss borne by the bank when a customer defaults on a loan. LGD for unsecured retail loans is often found difficult to model. In the frequentist (non-Bayesian) two-step approach, two separate regression models are estimated independently, which can be considered potentially problematic when trying to combine them to make predictions about LGD. The result is a point estimate of LGD for each loan. Alternatively, LGD can be modelled using Bayesian methods. In the Bayesian framework, one can build a single, hierarchical model instead of two separate ones, which makes this a more coherent approach. In this paper, Bayesian methods as well as the frequentist approach are applied to the data on personal loans provided by a large UK bank. As expected, the posterior means of parameters which have been produced using Bayesian methods are very similar to the frequentist estimates. The most important advantage of the Bayesian model is that it generates an individual predictive distribution of LGD for each loan. Potential applications of such distributions include the downturn LGD and the stressed LGD under Basel II.
Text
Modelling LGD for unsecured retail loans using Bayesian methods.pdf
- Accepted Manuscript
More information
Accepted/In Press date: 13 January 2014
e-pub ahead of print date: 12 February 2014
Published date: 12 February 2015
Keywords:
loss given default, downturn LGD, bayesian, regression models
Organisations:
Centre of Excellence for International Banking, Finance & Accounting
Identifiers
Local EPrints ID: 361321
URI: http://eprints.soton.ac.uk/id/eprint/361321
ISSN: 0160-5682
PURE UUID: e89a72cb-1589-4666-909c-cf4741463459
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Date deposited: 22 Jan 2014 11:34
Last modified: 15 Mar 2024 03:36
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Author:
Lyn C. Thomas
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