Publication
Title
Forecasting loss given default models : impact of account characteristics and the macroeconomic state
Author
Abstract
Based on two datasets containing Loss Given Default (LGD) observations of home equity and corporate loans, we consider non-linear and non-parametric techniques to model and forecast LGD. These techniques include non-linear Support Vector Regression (SVR), a regression tree and a two-stage model combining a linear regression with SVR. We compare these models with an ordinary least squares linear regression. In addition, we incorporate several macroeconomic variables to estimate the in uence of the economic state on loan losses. We investigate whether a Box-Cox transformation of the macroeconomic features improves the linear regression model. Due to the instable distributions, both out-of-time and out-of-sample setups are considered. The two-stage model outperforms the other techniques when forecasting out-of-time, while the non-parametric regression tree is the best performer when forecasting out-of-sample. The complete non-linear SVR reports poor prediction results, both in comprehensibility and accuracy. The incorporation of macroeconomic variables signicantly improves the prediction performance of most of the models. These conclusions can help nancial institutions when estimating LGD under the Internal Ratings Based Approach of the Basel Accords in order to estimate the downturn LGD needed to calculate the capital requirements.
Language
English
Source (series)
Research paper / UA, Faculty of Applied Economics ; 2012:019
Publication
Antwerp : UA, 2012
Volume/pages
17 p.
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Record
Identification
Creation 16.01.2013
Last edited 04.09.2013
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