Predicting loss given default (LGD) for residential mortgage loans: a two-stage model and empirical evidence for UK bank data
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. International Journal of Forecasting, 28, (1), 183-195. (doi:10.1016/j.ijforecast.2011.01.010).
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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.
|Subjects:||H Social Sciences > H Social Sciences (General)|
|Divisions:||Faculty of Business and Law > Southampton Management School
|Date Deposited:||30 Nov 2011 09:57|
|Last Modified:||29 Apr 2012 14:53|
|Contributors:||Leow, M. (Author)
Mues, C. (Author)
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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