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Empirical mode decomposition analysis of two different financial time series and their comparison

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  • Guhathakurta, Kousik
  • Mukherjee, Indranil
  • Chowdhury, A. Roy

Abstract

Analysis of financial time series with,a view to understanding its underlying characteristic features has been the recent focus of scientists and practitioners studying the financial market. One of the key attributes of a time series is its periodicity. Because of their quasi-periodic nature, the financial time series do not reveal their periodicity clearly. One of the recent developments in time signal analysis is the Hilbert–Huang empirical mode decomposition (EMD) method, which elegantly brings out the underlying periodicity of any time-series. Not many efforts have been made to utilise this technique in qualitative analysis of financial time series. In the present study, we have used the EMD technique to analyse two different financial time series, viz., the daily movement of NIFTY index value of National Stock Exchange, India, and that of Hong Kong AOI, Hong Kong Stock Exchange from July 1990 to January 2006. The returns of the two indices are shown to have strikingly similar probability distribution. The IMF phase and amplitude probability distribution of the two indices also reveal striking similarity. This indicates a remarkable similarity of trading behaviour in the two markets. Considering the geographical and political separation of the two, this indeed is an important discovery.

Suggested Citation

  • Guhathakurta, Kousik & Mukherjee, Indranil & Chowdhury, A. Roy, 2008. "Empirical mode decomposition analysis of two different financial time series and their comparison," Chaos, Solitons & Fractals, Elsevier, vol. 37(4), pages 1214-1227.
    • Handle: RePEc:eee:chsofr:v:37:y:2008:i:4:p:1214-1227
    DOI: 10.1016/j.chaos.2006.10.065
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    Cited by:

    1. Nava, Noemi & Di Matteo, T. & Aste, Tomaso, 2016. "Anomalous volatility scaling in high frequency financial data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 434-445.
    2. Wang, Shujia & Yang, Zhuojin & Zheng, Xin & Ma, Zhuang, 2024. "Analysis of complex time series based on EEMD energy entropy plane," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    3. Wu, Jiaxin & Zhou, Xubing & Peng, Yi & Zhao, Xiaojun, 2022. "Recurrence analysis of urban traffic congestion index on multi-scale," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    4. Chatterjee, Soumya & Mukherjee, Indranil & Barat, P., 2018. "Analysis of the behaviour of the detrended BSE sensex data," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 186-196.
    5. Kousik Guhathakurtha, 2013. "Investigating The Nonlinear Dynamics Of Emerging And Developed Stock Markets," Working papers 142, Indian Institute of Management Kozhikode.
    6. Lin, Yu & Liao, Qidong & Lin, Zixiao & Tan, Bin & Yu, Yuanyuan, 2022. "A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction," Resources Policy, Elsevier, vol. 78(C).
    7. Xinchang Liu & Bolong Liu, 2023. "A Hybrid Time Series Model for Predicting the Displacement of High Slope in the Loess Plateau Region," Sustainability, MDPI, vol. 15(6), pages 1-26, March.
    8. Chen, Mu-Chen & Wei, Yu, 2011. "Exploring time variants for short-term passenger flow," Journal of Transport Geography, Elsevier, vol. 19(4), pages 488-498.
    9. Noemi Nava & T. Di Matteo & Tomaso Aste, 2015. "Anomalous volatility scaling in high frequency financial data," Papers 1503.08465, arXiv.org, revised Dec 2015.
    10. Ouyang, Fang-Yan & Zheng, Bo & Jiang, Xiong-Fei, 2019. "Dynamic fluctuations of cross-correlations in multi-time scale," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 515-521.
    11. Xu, Chao & Zhao, Xiaojun & Wang, Yanwen, 2022. "Causal decomposition on multiple time scales: Evidence from stock price-volume time series," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    12. Hongli Niu & Jun Wang, 2014. "Phase and multifractality analyses of random price time series by finite-range interacting biased voter system," Computational Statistics, Springer, vol. 29(5), pages 1045-1063, October.

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