We discuss and analyze a neural network architecture, that enables learning a
model class for a set of different data samples rather than just learning a
single model for a specific data sample. In this sense, it may help to reduce
the overfitting problem, since, after learning the model class over a larger
data sample consisting of such different data sets, just a few parameters need
to be adjusted for modeling a new, specific problem. After analyzing the method
theoretically and by regression examples for different one-dimensional
problems, we finally apply the approach to one of the standard problems asset
managers and banks are facing: the calibration of spread curves. The presented
results clearly show the potential that lies within this method. Furthermore,
this application is of particular interest to financial practitioners, since
nearly all asset managers and banks which are having solutions in place may
need to adapt or even change their current methodologies when ESG ratings
additionally affect the bond spreads.Comment: 24 pages, 17 figure