Historical consistent neural networks: New perspectives on market modeling, forecasting and risk analysis

Abstract

From a mathematical point of view, neural networks allow the construction of models, which are able to handle high-dimensional problems along with a high degree of nonlinearity. In this chapter we deal with a special type of time-delay recurrent neural networks. In these models we understand a part of the world as a large recursive system which is only partially observable. We model and forecast all observables, avoiding the problem in open systems that we do not know the external drivers from present time on. This framework goes far beyond the paradigms of standard regression theory and allows us to forecast financial markets and perform a new way of risk analysis. © 2013 Springer-Verlag Berlin Heidelberg.