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Analysis of stock market data by using Dynamic Fourier and Wavelets techniques

Author

Listed:
  • Mariani, Maria C.
  • Bhuiyan, Md Al Masum
  • Tweneboah, Osei K.
  • Beccar-Varela, Maria P.
  • Florescu, Ionut

Abstract

This work deals with the analysis of daily and minute sampled financial stock market data. We propose a Dynamic Fourier Transform (DFT) and a Wavelet Transform to estimate the power spectrum of returns. In order to estimate the power spectrum, we used the tapering process with the DFT technique and the scaling function with the wavelets methodology to avoid the spectral leakage or discontinuity in the sequence. Our result suggest that the power spectrum are effective in characterizing the minute and daily based data corresponding to different frequencies. This type of modeling techniques help to characterize some key variables of stationary time series that are very useful for making informed decisions in the stock market such as assessing financial risk in the market.

Suggested Citation

  • Mariani, Maria C. & Bhuiyan, Md Al Masum & Tweneboah, Osei K. & Beccar-Varela, Maria P. & Florescu, Ionut, 2020. "Analysis of stock market data by using Dynamic Fourier and Wavelets techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    • Handle: RePEc:eee:phsmap:v:537:y:2020:i:c:s0378437119315808
    DOI: 10.1016/j.physa.2019.122785
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    References listed on IDEAS

    as
    1. Mariani, Maria C. & Bhuiyan, Md Al Masum & Tweneboah, Osei K. & Gonzalez-Huizar, Hector & Florescu, Ionut, 2018. "Volatility models applied to geophysics and high frequency financial market data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 304-321.
    2. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    3. Beccar-Varela, Maria P. & Gonzalez-Huizar, Hector & Mariani, Maria C. & Tweneboah, Osei K., 2016. "Use of wavelets techniques to discriminate between explosions and natural earthquakes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 42-51.
    4. Beccar-Varela, Maria P. & Mariani, Maria C. & Tweneboah, Osei K. & Florescu, Ionut, 2017. "Analysis of the Lehman Brothers collapse and the Flash Crash event by applying wavelets methodologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 162-171.
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    1. Yao, Yinhong & Li, Jingyu & Chen, Wei, 2024. "Multiscale extreme risk spillovers among the Chinese mainland, Hong Kong, and London stock markets: Comparing the impacts of three Stock Connect programs," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1217-1233.

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