TY - JOUR
T1 - Loss given default models incorporating macroeconomic variables for credit cards
AU - Bellotti, Tony
AU - Crook, Jonathan
N1 - Funding Information:
We would like to thank our commercial partners for their assistance and comments in preparing this paper. This research was funded by UK EPSRC grant number EP/D505380/1 , working as part of the Quantitative Financial Risk Management Centre.
PY - 2012/1
Y1 - 2012/1
N2 - Based on UK data for major retail credit cards, we build several models of Loss Given Default based on account level data, including Tobit, a decision tree model, a Beta and fractional logit transformation. We find that Ordinary Least Squares models with macroeconomic variables perform best for forecasting Loss Given Default at the account and portfolio levels on independent hold-out data sets. The inclusion of macroeconomic conditions in the model is important, since it provides a means to model Loss Given Default in downturn conditions, as required by Basel II, and enables stress testing. We find that bank interest rates and the unemployment level significantly affect LGD.
AB - Based on UK data for major retail credit cards, we build several models of Loss Given Default based on account level data, including Tobit, a decision tree model, a Beta and fractional logit transformation. We find that Ordinary Least Squares models with macroeconomic variables perform best for forecasting Loss Given Default at the account and portfolio levels on independent hold-out data sets. The inclusion of macroeconomic conditions in the model is important, since it provides a means to model Loss Given Default in downturn conditions, as required by Basel II, and enables stress testing. We find that bank interest rates and the unemployment level significantly affect LGD.
KW - Basel II
KW - Credit cards
KW - Loss given default
UR - http://www.scopus.com/inward/record.url?scp=84155182788&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2010.08.005
DO - 10.1016/j.ijforecast.2010.08.005
M3 - Article
AN - SCOPUS:84155182788
SN - 0169-2070
VL - 28
SP - 171
EP - 182
JO - International Journal of Forecasting
JF - International Journal of Forecasting
IS - 1
ER -