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In this paper we propose an alternative and modified Generalized Regression Neural Networks Autoregressive model (GRNN-AR) inÂ S&P 500 and FTSE 100 index returns, as also in Gross domestic product growth rate of Italy, USA and UK. We compare the forecasts withÂ Generalized Autoregressive conditional Heteroskedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models. TheÂ results indicate that GRNN outperform significant the conventional econometric models and can be an efficient alternative tool for forecasting.Â The MATLAB algorithm we propose is provided in appendix for further applications, suggestions, modifications and improvements.
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