It is common for banks to have liabilities attached to the Treasury's rate and assets attached to a corporate rate. A change in the difference between these rates (i.e., a change in the credit-spread) impacts the banks' balance sheet. In order to forecast this risk, I propose the use of (very) short estimation windows using the lasso estimation. The lasso shrinks some of the estimated
... [Show full abstract] coefficients to zero, improving their finite sample performance also allowing the use of smaller estimation windows. I compare the out-of-sample performance of several credit-spread forecasting models for each investment-grade credit-rating in the period between 2000 and 2011. Considering the 6 and 12 months forecasts of AAA-rated credit-spreads, the historical average outperforms, in terms of mean absolute prediction error, the Martingale and several other forecasting models. These models are based on the shape (level, slope and curvature) of the risky and risk-free yield curves, and based also on the spot, forward and average past yields. Considering all other credit-ratings, the forecasts given by the lasso tend to outperform those based on long estimation windows.