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Xiao Yi1; Xiao Jin2; Liu John3; Wang Shouyang4
Source Publicationjournalofsystemsscienceandcomplexity
AbstractThe financial market volatility forecasting is regarded as a challenging task because of irregularity, high fluctuation, and noise. In this study, a multiscale ensemble forecasting model is proposed. The original financial series are decomposed firstly different scale components (i.e., approximation and details) using the maximum overlap discrete wavelet transform (MODWT). The approximation is predicted by a hybrid forecasting model that combines autoregressive integrated moving average (ARIMA) with feedforward neural network (FNN). ARIMA model is used to generate a linear forecast, and then FNN is developed as a tool for nonlinear pattern recognition to correct the estimation error in ARIMA forecast. Moreover, details are predicted by Elman neural networks. Three weekly exchange rates data are collected to establish and validate the forecasting model. Empirical results demonstrate consistent better performance of the proposed approach.
Document Type期刊论文
Recommended Citation
GB/T 7714
Xiao Yi,Xiao Jin,Liu John,et al. amultiscalemodelingapproachincorporatingarimaandannsforfinancialmarketvolatilityforecasting[J]. journalofsystemsscienceandcomplexity,2014,27(1):225.
APA Xiao Yi,Xiao Jin,Liu John,&Wang Shouyang.(2014).amultiscalemodelingapproachincorporatingarimaandannsforfinancialmarketvolatilityforecasting.journalofsystemsscienceandcomplexity,27(1),225.
MLA Xiao Yi,et al."amultiscalemodelingapproachincorporatingarimaandannsforfinancialmarketvolatilityforecasting".journalofsystemsscienceandcomplexity 27.1(2014):225.
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