Forecasting loss given default models: Impact of account characteristics and the macroeconomic state

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 influence 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 significantly improves the prediction performance of most of the models. These conclusions can help financial 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.