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Neural Networks in Finance: Gaining Predictive Edge in the Market Book

With neural network and other approximation methods, we approximate an unknown nonlinear process with less-restrictive semi-parametric models. With a polynomial or neural network model, the functional forms are given, but the degree of the polynomial or the number of neurons are not. Thus, the parameters are neither limited in number, nor do they have a straightforward interpretation, as the parameters do in linear or GARCH-M models. For this reason, we refer to these models as semiparametric. While GARCH and GARCH-M models are popular models for nonlinear financial econometrics, we show in Chapter 3 how well a rather simple neural network approximates a time series that is generated by a calibrated GARCH-M model.

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