The structure and dynamics of granular complex networks deriving from financial time series

Li Tingting, Luo Chao and Shao Rui
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Li Tingting: School of Information Science and Engineering, Shandong Normal University, Jinan 250014, P. R. China
Luo Chao: School of Information Science and Engineering, Shandong Normal University, Jinan 250014, P. R. China†Shandong Provincial Key Laboratory for Novel, Distributed Computer Software Technology, Jinan 250014, P. R. China
Shao Rui: School of Information Science and Engineering, Shandong Normal University, Jinan 250014, P. R. China‡China Mobile Shandong Co., Ltd, Jinan 250014, P. R. China

International Journal of Modern Physics C (IJMPC), 2020, vol. 31, issue 06, 1-16

Abstract: High noise and strong volatility are the typical characteristics of financial time series. Combined with pseudo-randomness, nonsteady and self-similarity exhibiting in different time scales, it is a challenging issue for the pattern analysis of financial time series. Different from the existing works, in this paper, financial time series are converted into granular complex networks, based on which the structure and dynamics of network models are revealed. By using variable-length division, an extended polar fuzzy information granule (FIGs) method is used to construct granular complex networks from financial time series. Considering the temporal characteristics of sequential data, static networks and temporal networks are studied, respectively. As to the static network model, some features of topological structures of granular complex networks, such as distribution, clustering and betweenness centrality are discussed. Besides, by using the Markov chain model, the transfer processes among different granules are investigated, where the fluctuation pattern of data in the coming step can be evaluated from the transfer probability of two consecutive granules. Shanghai composite index and foreign exchange data as two examples in real life are applied to carry out the related discussion.

Keywords: Time series; fuzzy information granules; granular complex network; temporal network (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0129183120500874

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