Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/103793
Authors: 
Dedy Dwi Prastyo
Härdle, Wolfgang Karl
Year of Publication: 
2014
Series/Report no.: 
SFB 649 Discussion Paper 2014-040
Abstract: 
Using a local adaptive Forward Intensities Approach (FIA) we investigate multiperiod corporate defaults and other delisting schemes. The proposed approach is fully datadriven and is based on local adaptive estimation and the selection of optimal estimation windows. Time-dependent model parameters are derived by a sequential testing procedure that yields adapted predictions at every time point. Applying the proposed method to monthly data on 2000 U.S. public rms 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 local adaptive method can be applied to any other speci cations of forward intensities.
Subjects: 
Accuracy ratio
Forward default intensity
Local adaptive
Mutiperiod prediction
JEL: 
C41
C53
C58
G33
Document Type: 
Working Paper

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