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Conference Paper: Dynamic wavelet neural network model for forecasting returns of SHFE copper futures price

TitleDynamic wavelet neural network model for forecasting returns of SHFE copper futures price
Authors
KeywordsWavelet neural networks
SHFE copper futures
Forecasting
Financial time series
Fractal market
Issue Date2011
Citation
The 7th International Conference on Digital Enterprise Technology (DET 2011), Athens, Greece, 28-30 September 2011. In Proceedings of the 7th DET, 2011, p. 109-116 How to Cite?
AbstractAppropriate forecasting of commodity futures price returns is of crucial importance to achieve hedging effectiveness against the returns volatility risk. This paper presents a nonparametric dynamic recurrent wavelet neural network model for forecasting returns of Shanghai Futures Exchange (SHFE) copper futures price. The proposed model employs a wavelet basis function as the activation function for hidden-layer neurons of the neural network. The aim of this arrangement is to incorporate the fractal properties discovered in futures price return series. In the wavelet transform domain, fractal self-similarity information of the returns series over a certain time scale can be extracted. Input variables are analyzed and selected to facilitate effective forecasting. Statistical indices such as normal mean square error (NMSE) are adopted to evaluate forecasting performance of the proposed model. The forecasted result shows that dynamic wavelet neural network has good prediction properties compared with traditional linear statistical model such as ARIMA and other neural network forecasting models.
DescriptionSession C8: P13
Persistent Identifierhttp://hdl.handle.net/10722/143924
ISBN

 

DC FieldValueLanguage
dc.contributor.authorShi, Len_US
dc.contributor.authorChu, LKen_US
dc.contributor.authorChen, YHen_US
dc.date.accessioned2011-12-21T08:58:42Z-
dc.date.available2011-12-21T08:58:42Z-
dc.date.issued2011en_US
dc.identifier.citationThe 7th International Conference on Digital Enterprise Technology (DET 2011), Athens, Greece, 28-30 September 2011. In Proceedings of the 7th DET, 2011, p. 109-116en_US
dc.identifier.isbn978-960-88104-2-6en_US
dc.identifier.urihttp://hdl.handle.net/10722/143924-
dc.descriptionSession C8: P13-
dc.description.abstractAppropriate forecasting of commodity futures price returns is of crucial importance to achieve hedging effectiveness against the returns volatility risk. This paper presents a nonparametric dynamic recurrent wavelet neural network model for forecasting returns of Shanghai Futures Exchange (SHFE) copper futures price. The proposed model employs a wavelet basis function as the activation function for hidden-layer neurons of the neural network. The aim of this arrangement is to incorporate the fractal properties discovered in futures price return series. In the wavelet transform domain, fractal self-similarity information of the returns series over a certain time scale can be extracted. Input variables are analyzed and selected to facilitate effective forecasting. Statistical indices such as normal mean square error (NMSE) are adopted to evaluate forecasting performance of the proposed model. The forecasted result shows that dynamic wavelet neural network has good prediction properties compared with traditional linear statistical model such as ARIMA and other neural network forecasting models.-
dc.languageengen_US
dc.relation.ispartofProceedings of the 7th International Conference on Digital Enterprise Technology, DET 2011en_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectWavelet neural networks-
dc.subjectSHFE copper futures-
dc.subjectForecasting-
dc.subjectFinancial time series-
dc.subjectFractal market-
dc.titleDynamic wavelet neural network model for forecasting returns of SHFE copper futures priceen_US
dc.typeConference_Paperen_US
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=978-960-88104-2-6&volume=&spage=109&epage=116&date=2011&atitle=Dynamic+wavelet+neural+network+model+for+forecasting+returns+of+SHFE+copper+futures+priceen_US
dc.identifier.emailShi, L: amyshi0629@hku.hken_US
dc.identifier.emailChu, LK: lkchu@hkucc.hku.hk-
dc.identifier.emailChen, YH: jasonchen1227@gmail.com-
dc.identifier.authorityChu, LK=rp00113en_US
dc.description.naturepublished_or_final_version-
dc.identifier.hkuros197811en_US
dc.identifier.spage109en_US
dc.identifier.epage116en_US
dc.description.otherThe 7th International Conference on Digital Enterprise Technology (DET 2011), Athens, Greece, 28-30 September 2011. In Proceedings of the 7th DET, 2011, p. 109-116-

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