Forecasting Loss Given Default models: impact of account characteristics and the macroeconomic state

Abstract

On the basis of two data sets 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, a transformed linear model 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 variants of 11 macroeconomic indicators to estimate the influence of the economic state on loan losses. The out-of-time set-up is complemented with an out-of-sample set-up to mitigate the limited number of credit crisis observations available in credit risk data sets. The two-stage/transformed model outperforms the other techniques when forecasting out-of-time for the home equity/corporate data set, while the non-parametric regression tree is the best performer when forecasting out-of-sample. The incorporation of macroeconomic variables significantly improves the prediction performance. The downturn impact ranges up to 5% depending on the data set and the macroeconomic conditions defining the downturn. 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. Banks are also required as part of stress test exercises to assess the impact of stressed macroeconomic scenarios on their Profit and Loss (P&L) and banking book, which favours the accurate identification of relevant macroeconomic variables driving LGD evolutions.

Keywords:

• Loss Given Default
• data mining
• prediction
• Basel III

Acknowledgements

Bart Baesens acknowledges the Flemish Research Council for financial support (Odysseus grant B.0915.09). We would like to thank the financial institutions who provided us with the LGD data. Ellen Tobback acknowledges the Research Foundation Flanders (FWO) for financial support.

Notes

1 For example, the S&P 500 price index was 164.93 on 1 January 1984. Twenty-eight years later, on 1 January 2012, the index stood at 1257.6.

2 Stratification is performed over Probability of Default, credit score and time.

3 Matlab’s financial toolbox.