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A Nonlinear Filtering Algorithm based on Wavelet Transforms for High-Frequency Financial Data Analysis

Author

Listed:
  • Meinl Thomas

    (Karlsruhe Institute of Technology (KIT), Germany)

  • Sun Edward W.

    (BEM Bordeaux Management School, France)

Abstract

The increased availability of high-frequency financial data has imposed new challenges for its denoising analysis since the data exhibits heavy tails and long-memory effects that render the application of traditional methods difficult. In this paper, we introduce the local linear scaling approximation (in short, LLSA), which is a nonlinear filtering algorithm based on the linear maximal overlap discrete wavelet transform (MODWT). We show the unique properties of LLSA and compare its performance with MODWT. We empirically show the superior performance of LLSA in smoothing analysis (i.e., trend extraction) of high- frequency data from German equity market. Based on our results we conclude that LLSA is reliable and suitable for high-frequency data denoising analysis.

Suggested Citation

  • Meinl Thomas & Sun Edward W., 2012. "A Nonlinear Filtering Algorithm based on Wavelet Transforms for High-Frequency Financial Data Analysis," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(3), pages 1-24, September.
    • Handle: RePEc:bpj:sndecm:v:16:y:2012:i:3:n:5
    DOI: 10.1515/1558-3708.1920
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    References listed on IDEAS

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    5. Sun, Edward W. & Meinl, Thomas, 2012. "A new wavelet-based denoising algorithm for high-frequency financial data mining," European Journal of Operational Research, Elsevier, vol. 217(3), pages 589-599.
    6. Tkacz Greg, 2001. "Estimating the Fractional Order of Integration of Interest Rates Using a Wavelet OLS Estimator," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 5(1), pages 1-15, April.
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    8. Yongmiao Hong & Chihwa Kao, 2004. "Wavelet-Based Testing for Serial Correlation of Unknown Form in Panel Models," Econometrica, Econometric Society, vol. 72(5), pages 1519-1563, September.
    9. Sun, Wei & Rachev, Svetlozar & Fabozzi, Frank J., 2007. "Fractals or I.I.D.: Evidence of long-range dependence and heavy tailedness from modeling German equity market returns," Journal of Economics and Business, Elsevier, vol. 59(6), pages 575-595.
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    Cited by:

    1. Lin, Fu-Lai & Yang, Sheng-Yung & Marsh, Terry & Chen, Yu-Fen, 2018. "Stock and bond return relations and stock market uncertainty: Evidence from wavelet analysis," International Review of Economics & Finance, Elsevier, vol. 55(C), pages 285-294.
    2. Yi-Ting Chen & Wan-Ni Lai & Edward W. Sun, 2019. "Jump Detection and Noise Separation by a Singular Wavelet Method for Predictive Analytics of High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 809-844, August.
    3. Chen, Mei-Ping & Chen, Wen-Yi & Tseng, Tseng-Chan, 2017. "Co-movements of returns in the health care sectors from the US, UK, and Germany stock markets: Evidence from the continuous wavelet analyses," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 484-498.
    4. Chen Yi-Ting & Sun Edward W. & Yu Min-Teh, 2015. "Improving model performance with the integrated wavelet denoising method," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(4), pages 445-467, September.
    5. Yi-Ting Chen & Edward W. Sun & Min-Teh Yu, 2018. "Risk Assessment with Wavelet Feature Engineering for High-Frequency Portfolio Trading," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 653-684, August.
    6. Syed Jawad Hussain Shahzad & Jose Arreola‐Hernandez & Md Lutfur Rahman & Gazi Salah Uddin & Muhammad Yahya, 2021. "Asymmetric interdependence between currency markets' volatilities across frequencies and time scales," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2436-2457, April.
    7. Sun, Edward W. & Chen, Yi-Ting & Yu, Min-Teh, 2015. "Generalized optimal wavelet decomposing algorithm for big financial data," International Journal of Production Economics, Elsevier, vol. 165(C), pages 194-214.

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