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Multiresolution transmission of the correlation modes between bivariate time series based on complex network theory

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  • Huang, Xuan
  • An, Haizhong
  • Gao, Xiangyun
  • Hao, Xiaoqing
  • Liu, Pengpeng

Abstract

This study introduces an approach to study the multiscale transmission characteristics of the correlation modes between bivariate time series. The correlation between the bivariate time series fluctuates over time. The transmission among the correlation modes exhibits a multiscale phenomenon, which provides richer information. To investigate the multiscale transmission of the correlation modes, this paper describes a hybrid model integrating wavelet analysis and complex network theory to decompose and reconstruct the original bivariate time series into sequences in a joint time–frequency domain and defined the correlation modes at each time–frequency domain. We chose the crude oil spot and futures prices as the sample data. The empirical results indicate that the main duration of volatility (32–64 days) for the strongly positive correlation between the crude oil spot price and the futures price provides more useful information for investors. Moreover, the weighted degree, weighted indegree and weighted outdegree of the correlation modes follow power-law distributions. The correlation fluctuation strengthens the extent of persistence over the long term, whereas persistence weakens over the short and medium term. The primary correlation modes dominating the transmission process and the major intermediary modes in the transmission process are clustered both in the short and long term.

Suggested Citation

  • Huang, Xuan & An, Haizhong & Gao, Xiangyun & Hao, Xiaoqing & Liu, Pengpeng, 2015. "Multiresolution transmission of the correlation modes between bivariate time series based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 493-506.
  • Handle: RePEc:eee:phsmap:v:428:y:2015:i:c:p:493-506
    DOI: 10.1016/j.physa.2015.02.028
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    Cited by:

    1. Huang, Shupei & An, Haizhong & Gao, Xiangyun & Sun, Xiaoqi, 2017. "Do oil price asymmetric effects on the stock market persist in multiple time horizons?," Applied Energy, Elsevier, vol. 185(P2), pages 1799-1808.
    2. repec:eee:appene:v:207:y:2017:i:c:p:477-493 is not listed on IDEAS
    3. Huang, Shupei & An, Haizhong & Gao, Xiangyun & Huang, Xuan, 2016. "Time–frequency featured co-movement between the stock and prices of crude oil and gold," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 985-995.
    4. Shupei Huang & Haizhong An & Xiangyun Gao & Meihui Jiang, 2016. "The Multiscale Fluctuations of the Correlation between Oil Price and Wind Energy Stock," Sustainability, MDPI, Open Access Journal, vol. 8(6), pages 1-14, June.
    5. Jia, Xiaoliang & An, Haizhong & Sun, Xiaoqi & Huang, Xuan & Wang, Lijun, 2017. "Evolution of world crude oil market integration and diversification: A wavelet-based complex network perspective," Applied Energy, Elsevier, vol. 185(P2), pages 1788-1798.
    6. Shahzad, Syed Jawad Hussain & Kumar, Ronald Ravinesh & Ali, Sajid & Ameer, Saba, 2016. "Interdependence between Greece and other European stock markets: A comparison of wavelet and VMD copula, and the portfolio implications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 8-33.
    7. Berger, Theo, 2015. "A wavelet based approach to measure and manage contagion at different time scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 338-350.
    8. Raza, Naveed & Jawad Hussain Shahzad, Syed & Tiwari, Aviral Kumar & Shahbaz, Muhammad, 2016. "Asymmetric impact of gold, oil prices and their volatilities on stock prices of emerging markets," Resources Policy, Elsevier, vol. 49(C), pages 290-301.
    9. Li, Huajiao & An, Haizhong & Liu, Xueyong & Gao, Xiangyun & Fang, Wei & An, Feng, 2016. "Price fluctuation in the energy stock market based on fluctuation and co-fluctuation matrix transmission networks," Energy, Elsevier, vol. 117(P1), pages 73-83.
    10. Pal, Debdatta & Mitra, Subrata K., 2017. "Time-frequency contained co-movement of crude oil and world food prices: A wavelet-based analysis," Energy Economics, Elsevier, vol. 62(C), pages 230-239.
    11. Huang, Shupei & An, Haizhong & Gao, Xiangyun & Wen, Shaobo & Jia, Xiaoliang, 2016. "The global interdependence among oil-equity nexuses," Energy, Elsevier, vol. 107(C), pages 259-271.
    12. repec:eee:phsmap:v:490:y:2018:i:c:p:1501-1512 is not listed on IDEAS
    13. Jia, Xiaoliang & An, Haizhong & Sun, Xiaoqi & Huang, Xuan & Gao, Xiangyun, 2016. "Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 331-344.
    14. Wang, Minggang & Chen, Ying & Tian, Lixin & Jiang, Shumin & Tian, Zihao & Du, Ruijin, 2016. "Fluctuation behavior analysis of international crude oil and gasoline price based on complex network perspective," Applied Energy, Elsevier, vol. 175(C), pages 109-127.

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