標題: Improving model performance with the integrated wavelet denoising method
作者: Chen, Yi-Ting
Sun, Edward W.
Yu, Min-Teh
交大名義發表
National Chiao Tung University
關鍵字: data denoising;DWT;high-frequency data;MODWT;wavelet
公開日期: 1-Sep-2015
摘要: Intelligent 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.
URI: http://dx.doi.org/10.1515/snde-2014-0057
http://hdl.handle.net/11536/128354
ISSN: 1081-1826
DOI: 10.1515/snde-2014-0057
期刊: STUDIES IN NONLINEAR DYNAMICS AND ECONOMETRICS
Volume: 19
起始頁: 445
結束頁: 467
Appears in Collections:Articles