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Mapping Coupled Time-series Onto Complex Network

Author

Listed:
  • Jamshid Ardalankia
  • Jafar Askari
  • Somaye Sheykhali
  • Emmanuel Haven
  • G. Reza Jafari

Abstract

In order to extract hidden joint information from two possibly uncorrelated time-series, we explored the measures of network science. Alongside common methods in time-series analysis of the economic markets, mapping the joint structure of two time-series onto a network provides insight into hidden aspects embedded in the couplings. We discretize the amplitude of two time-series and investigate relative simultaneous locations of those amplitudes. Each segment of a discretized amplitude is considered as a node. The simultaneity of the amplitudes of the two time-series is considered as the edges in the network. The frequency of occurrences forms the weighted edges. In order to extract information, we need to measure that to what extent the coupling deviates from the coupling of two uncoupled series. Also, we need to measure that to what extent the couplings inherit their characteristics from a Gaussian distribution or a non-Gaussian distribution. We mapped the network from two surrogate time-series. The results show that the couplings of markets possess some features which diverge from the same features of the network mapped from white noise, and from the network mapped from two surrogate time-series. These deviations prove that there exist joint information and cross-correlation therein. By applying the network's topological and statistical measures and the deformation ratio in the joint probability distribution, we distinguished basic structures of cross-correlation and coupling of cross-markets. It was discovered that even two possibly known uncorrelated markets may possess some joint patterns with each other. Thereby, those markets should be examined as coupled and \textit{weakly} coupled markets.

Suggested Citation

  • Jamshid Ardalankia & Jafar Askari & Somaye Sheykhali & Emmanuel Haven & G. Reza Jafari, 2020. "Mapping Coupled Time-series Onto Complex Network," Papers 2004.13536, arXiv.org, revised Aug 2020.
  • Handle: RePEc:arx:papers:2004.13536
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    References listed on IDEAS

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    1. Hadiseh Safdari & Milad Zare Kamali & Amirhossein Shirazi & Moein Khalighi & Gholamreza Jafari & Marcel Ausloos, 2016. "Fractional Dynamics of Network Growth Constrained by Aging Node Interactions," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-13, May.
    2. Hedayatifar, L. & Hassanibesheli, F. & Shirazi, A.H. & Vasheghani Farahani, S. & Jafari, G.R., 2017. "Pseudo paths towards minimum energy states in network dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 109-116.
    3. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    4. Andriana S L O Campanharo & M Irmak Sirer & R Dean Malmgren & Fernando M Ramos & Luís A Nunes Amaral, 2011. "Duality between Time Series and Networks," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-13, August.
    5. Westphal, Rebecca & Sornette, Didier, 2020. "Market impact and performance of arbitrageurs of financial bubbles in an agent-based model," Journal of Economic Behavior & Organization, Elsevier, vol. 171(C), pages 1-23.
    6. Xiong, Hui & Shang, Pengjian & He, Jiayi, 2019. "Nonuniversality of the horizontal visibility graph in inferring series periodicity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    7. Bahrami, Mohammad & Chinichian, Narges & Hosseiny, Ali & Jafari, Gholamreza & Ausloos, Marcel, 2020. "Optimization of the post-crisis recovery plans in scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    8. Rahul Kaushik & Stefano Battiston, 2013. "Credit Default Swaps Drawup Networks: Too Interconnected to Be Stable?," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-8, July.
    9. Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
    10. Namaki, A. & Shirazi, A.H. & Raei, R. & Jafari, G.R., 2011. "Network analysis of a financial market based on genuine correlation and threshold method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3835-3841.
    11. Zhao, Longfeng & Wang, Gang-Jin & Wang, Mingang & Bao, Weiqi & Li, Wei & Stanley, H. Eugene, 2018. "Stock market as temporal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 1104-1112.
    12. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    13. Caraiani, Petre & Haven, Emmanuel, 2015. "Evidence of multifractality from CEE exchange rates against Euro," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 395-407.
    14. Bekiros, Stelios & Nguyen, Duc Khuong & Sandoval Junior, Leonidas & Uddin, Gazi Salah, 2017. "Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets," European Journal of Operational Research, Elsevier, vol. 256(3), pages 945-961.
    15. Saeed Mehraban & Amirhossein Shirazi & Maryam Zamani & Gholamreza Jafari, 2013. "Coupling between time series: a network view," Papers 1301.1010, arXiv.org.
    16. Hosseiny, Ali, 2017. "A geometrical imaging of the real gap between economies of China and the United States," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 151-161.
    17. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang, 2009. "A network analysis of the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2956-2964.
    18. Eryiğit, Mehmet & Eryiğit, Resul, 2009. "Network structure of cross-correlations among the world market indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3551-3562.
    19. Baggio, Rodolfo & Sainaghi, Ruggero, 2016. "Mapping time series into networks as a tool to assess the complex dynamics of tourism systems," Tourism Management, Elsevier, vol. 54(C), pages 23-33.
    20. Mutua Stephen & Changgui Gu & Huijie Yang, 2015. "Visibility Graph Based Time Series Analysis," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-19, November.
    21. Etesami, Jalal & Habibnia, Ali & Kiyavash, Negar, 2017. "Econometric modeling of systemic risk: going beyond pairwise comparison and allowing for nonlinearity," LSE Research Online Documents on Economics 70769, London School of Economics and Political Science, LSE Library.
    22. Zunino, L. & Tabak, B.M. & Figliola, A. & Pérez, D.G. & Garavaglia, M. & Rosso, O.A., 2008. "A multifractal approach for stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(26), pages 6558-6566.
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    1. Liu, Jin-Long & Yu, Zu-Guo & Zhou, Yu, 2024. "A cross horizontal visibility graph algorithm to explore associations between two time series," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).

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