amultiscalemodelingapproachincorporatingarimaandannsforfinancialmarketvolatilityforecasting

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	amultiscalemodelingapproachincorporatingarimaandannsforfinancialmarketvolatilityforecasting
	Xiao Yi 1; Xiao Jin 2; Liu John 3; Wang Shouyang4
	2014
发表期刊	journalofsystemsscienceandcomplexity
ISSN	1009-6124
卷号	27 期号:1 页码:225
摘要	The 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.
语种	英语
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/38267
专题	系统科学研究所
作者单位	1.华中师范大学 2.四川大学 3.香港城市大学 4.中国科学院数学与系统科学研究院
推荐引用方式 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.