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dc.contributor.authorChen, Yi-Tingen_US
dc.contributor.authorSun, Edward W.en_US
dc.contributor.authorYu, Min-Tehen_US
dc.date.accessioned2015-12-02T02:59:34Z-
dc.date.available2015-12-02T02:59:34Z-
dc.date.issued2015-09-01en_US
dc.identifier.issn1081-1826en_US
dc.identifier.urihttp://dx.doi.org/10.1515/snde-2014-0057en_US
dc.identifier.urihttp://hdl.handle.net/11536/128354-
dc.description.abstractIntelligent pattern recognition imposes new challenges in high-frequency financial data mining due to its irregularities and roughness. Based on the wavelet transform for decomposing systematic patterns and noise, in this paper we propose a new integrated wavelet denoising method, named smoothness-oriented wavelet denoising algorithm (SOWDA), that optimally determines the wavelet function, maximal level of decomposition, and the threshold rule by using a smoothness score function that simultaneously detects the global and local extrema. We discuss the properties of our method and propose a new evaluation procedure to show its robustness. In addition, we apply this method both in simulation and empirical investigation. Both the simulation results based on three typical stylized features of financial data and the empirical results in analyzing high-frequency financial data from Frankfurt Stock Exchange confirm that SOWDA significantly (based on the RMSE comparison) improves the performance of classical econometric models after denoising the data with the discrete wavelet transform (DWT) and maximal overlap discrete wavelet transform (MODWT) methods.en_US
dc.language.isoen_USen_US
dc.subjectdata denoisingen_US
dc.subjectDWTen_US
dc.subjecthigh-frequency dataen_US
dc.subjectMODWTen_US
dc.subjectwaveleten_US
dc.titleImproving model performance with the integrated wavelet denoising methoden_US
dc.typeArticleen_US
dc.identifier.doi10.1515/snde-2014-0057en_US
dc.identifier.journalSTUDIES IN NONLINEAR DYNAMICS AND ECONOMETRICSen_US
dc.citation.volume19en_US
dc.citation.spage445en_US
dc.citation.epage467en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.identifier.wosnumberWOS:000361152700004en_US
dc.citation.woscount0en_US
Appears in Collections:Articles