Parameterized Neural Networks for Finance

TL;DR

This paper introduces a parameterized neural network architecture that learns a model class for diverse data samples, reducing overfitting and enabling efficient adaptation to new problems, demonstrated through financial spread curve calibration.

Contribution

The paper presents a novel neural network approach that learns a model class for multiple data sets, improving flexibility and reducing overfitting in financial applications.

Findings

The method reduces overfitting by learning a model class.

Application to spread curve calibration shows promising results.

Potential for adaptation to changing financial data environments.

Abstract

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.