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Analysis of ultra-high-frequency financial data using advanced Fourier transforms

Author

Listed:
  • Giampaoli, Iacopo
  • Ng, Wing Lon
  • Constantinou, Nick

Abstract

This paper presents a novel application of advanced methods from Fourier analysis to the study of ultra-high-frequency financial data. The use of Lomb-Scargle Fourier transform, provides a robust framework to take into account the irregular spacing in time, minimising the computational effort. Likewise, it avoids complex model specifications (e.g. ACD or intensity models) or resorting to traditional methods, such as (linear or cubic) interpolation and regular resampling, which not only cause artifacts in the data and loss of information, but also lead to the generation and use of spurious information.

Suggested Citation

  • Giampaoli, Iacopo & Ng, Wing Lon & Constantinou, Nick, 2009. "Analysis of ultra-high-frequency financial data using advanced Fourier transforms," Finance Research Letters, Elsevier, vol. 6(1), pages 47-53, March.
    • Handle: RePEc:eee:finlet:v:6:y:2009:i:1:p:47-53
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    References listed on IDEAS

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    Cited by:

    1. Taufemback, Cleiton & Da Silva, Sergio, 2011. "Spectral analysis informs the proper frequency in the sampling of financial time series data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2067-2073.
    2. Iacopo Giampaoli & Wing Lon Ng & Nick Constantinou, 2013. "Periodicities Of Foreign Exchange Markets And The Directional Change Power Law," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(3), pages 189-206, July.

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