Analysis of ultra-high-frequency financial data using advanced Fourier transforms |
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| Authors: | Iacopo Giampaoli Wing Lon Ng Nick Constantinou |
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| Institution: | 1. School of Mathematical Sciences, Peking University, Beijing 100871, PR China;2. Key Laboratory of Mathematical Economics and Quantitative Finance, Peking University, Beijing 100871, PR China;1. School of Science, Beijing Jiaotong University, Beijing 100044, PR China;2. Beijing E-Hualu Information Technology Company, Beijing 100043, PR China;1. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong;2. Department of Mathematics and Physics, North China Electric Power University, 102206 Beijing, PR China;1. Department of Statistical Sciences, University of Toronto, Toronto, Ontario M5S 3G3, Canada;2. Quantitative Engineering and Development, TD Securities, Toronto, Ontario M5K 1A2, Canada;3. Department of Statistics and Actuarial Science, University of Iowa, Iowa City, IA 52242, USA |
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| 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. |
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