Abstract
Neural networks have shown considerable success when used to model financial data series. However, a major weakness of this class of models is the lack of established procedures for misspecification testing and tests of statistical significance for the various estimated parameters. These issues are particularly important in the case of financial engineering where data generating processes are very complex and dominantly stochastic. After a brief review of neural network models, an input selection algorithm is proposed and discussed. It is based on a multistep multiple testing procedure calibrated by using subsampling. The simulation results show that the proposed testing procedure is an effective criterion for selecting a proper set of relevant inputs for the network. When applied to Euro exchange rates, the selected network models show that information contained in past percentage changes can be relevant to the prediction of future percentage changes of certain time series. The apparent predictability for some countries which we analysed does not seem to be an artifact of data snooping. Rather, it is the result of a testing procedure constructed to keep the family wise error rate under control. The results also remain stable while changing the subseries length.
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La Rocca, M., Perna, C. (2008). Neural Network Modelling with Applications to Euro Exchange Rates. In: Kontoghiorghes, E.J., Rustem, B., Winker, P. (eds) Computational Methods in Financial Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77958-2_9
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DOI: https://doi.org/10.1007/978-3-540-77958-2_9
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