Do macroeconomic variables improve credit loss forecasting?

Abstract: This thesis studies the relationship between the macroeconomic environment and banks' credit losses, examining whether macroeconomic variables can improve credit loss forecasting. By using quarterly data between 1993-2010 for the four largest Swedish banks, we have estimated models for the banks' credit loss levels (CLL) contingent on five selected macro economic factors. The estimated models have been used to produce out-of-sample forecasts, which have been evaluated against the forecasting ability of a simple AR(1) model. The obtained results suggest that adding macro variables to a simple AR(1) model in order to forecast the CLL does not improve the forecasting ability. The results show that the AR(1) models in most cases have a lower RMSE than the models including macro variables. It is therefore probable that other factors of today, disregarded in the forecasting models, might have higher explanatory power of tomorrow's CLL. These factors could be bank specific variables, such as credit portfolio characteristics and geographical exposures. The findings support the use of bank specific models and detailed calculations over simplified top-down methods to forecast CLL.