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- W1995474051 abstract "Abstract We propose an extension to neural network time-series modelling methodology which is particularly appropriate for applications in the financial domain. We advocate the use of error feedback terms as a means of more parsimoniously modelling processes which are partly or wholly `moving average' in nature (i.e. where new terms are related to past shocks or unpredictable events) rather than purely auto-regressive (where new values are related to both the predictable and the unpredictable components of previous values). We demonstrate that although in principle a (sufficiently high-order) auto-regressive process can approximate the MA process, in practice the diminishing information content of the high-order AR terms will at some point be offset by the increase in model variance caused by the additional model parameters, leading to a degradation in generalisation performance. We report empirical results obtained both by Monte-Carlo simulations and by applying the methodology to the non-trivial problem of forecasting implied volatility for option pricing. In both cases we show a substantial and consistent improvement in results." @default.
- W1995474051 created "2016-06-24" @default.
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- W1995474051 date "1999-04-01" @default.
- W1995474051 modified "2023-10-07" @default.
- W1995474051 title "Modelling non-linear moving average processes using neural networks with error feedback: An application to implied volatility forecasting" @default.
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- W1995474051 doi "https://doi.org/10.1016/s0165-1684(98)00202-3" @default.
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