Dynamic Forward Intensities for Multiperiod Default

Using an adaptive Forward Intensities Approach (FIA) we investigate mutiperiod corporate defaults and other delisting schemes. The proposed approach is fully data-driven and is based on adaptive estimation and the selection of optimal estimation windows. Time-dependent model parameters are determined by a sequential testing procedure. This makes this approach adaptive at every time point. Applying the proposed method to monthly data on 2000 U.S. public firms over a sample period from 1991 to 2011, we estimate default probabilities over various prediction horizons. The prediction performance is evaluated against the global FIA that employs all past observations. For the six months prediction horizon, the local adaptive FIA performs with the same accuracy as the benchmark. The default prediction power is improved for the longer horizon (one to three years). Our method can be applied to any other specifications of forward intensities.