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

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    Rights statement: © Leow, M., & 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. 10.1016/j.ijforecast.2011.01.010

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http://www.sciencedirect.com/science/article/pii/S0169207011000598
Original languageEnglish
Pages (from-to)183-195
JournalInternational Journal of Forecasting
Volume28
Issue number1
DOIs
StatePublished - 2012

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.

    Research areas

  • regression, finance, credit risk modelling, mortgage loans, loss given default, Basel II

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