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Predicting loss given default (LGD) for residential mortgage loans: a two-stage model and empirical evidence for UK bank data

Predicting loss given default (LGD) for residential mortgage loans: a two-stage model and empirical evidence for UK bank data
Predicting loss given default (LGD) for residential mortgage loans: a two-stage model and empirical evidence for UK bank data
With the implementation of the Basel II regulatory framework, it became increasingly important for financial institutions to develop accurate loss models. This work investigates the loss given default (LGD) of mortgage loans using a large set of recovery data of residential mortgage defaults from a major UK bank. A Probability of Repossession Model and a Haircut Model are developed and then combined to give an expected loss percentage. We find that the Probability of Repossession Model should consist of more than just the commonly used loan-to-value ratio, and that the estimation of LGD benefits from the Haircut Model, which predicts the discount which the sale price of a repossessed property may undergo. This two-stage LGD model is shown to perform better than a single-stage LGD model (which models LGD directly from loan and collateral characteristics), as it achieves a better R2 value and matches the distribution of the observed LGD more accurately.
0169-2070
183-195
Leow, M.
c6736da0-476c-45b3-8f66-9a2269a34acb
Mues, C.
07438e46-bad6-48ba-8f56-f945bc2ff934
Leow, M.
c6736da0-476c-45b3-8f66-9a2269a34acb
Mues, C.
07438e46-bad6-48ba-8f56-f945bc2ff934

Leow, M. and Mues, C. (2012) Predicting loss given default (LGD) for residential mortgage loans: a two-stage model and empirical evidence for UK bank data. [in special issue: Special Section 1: The Predictability of Financial Markets. Special Section 2: Credit Risk Modelling and Forecasting] International Journal of Forecasting, 28 (1), 183-195. (doi:10.1016/j.ijforecast.2011.01.010).

Record type: Article

Abstract

With the implementation of the Basel II regulatory framework, it became increasingly important for financial institutions to develop accurate loss models. This work investigates the loss given default (LGD) of mortgage loans using a large set of recovery data of residential mortgage defaults from a major UK bank. A Probability of Repossession Model and a Haircut Model are developed and then combined to give an expected loss percentage. We find that the Probability of Repossession Model should consist of more than just the commonly used loan-to-value ratio, and that the estimation of LGD benefits from the Haircut Model, which predicts the discount which the sale price of a repossessed property may undergo. This two-stage LGD model is shown to perform better than a single-stage LGD model (which models LGD directly from loan and collateral characteristics), as it achieves a better R2 value and matches the distribution of the observed LGD more accurately.

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Published date: 2012
Organisations: Southampton Business School

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Local EPrints ID: 204745
URI: http://eprints.soton.ac.uk/id/eprint/204745
ISSN: 0169-2070
PURE UUID: 87e8ba8a-a5b2-4ce1-a1b5-0eb5b763fbfb

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Date deposited: 30 Nov 2011 09:57
Last modified: 16 Jul 2019 23:17

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Author: M. Leow
Author: C. Mues

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