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Complex Network Model of Global Financial Time Series Based on Different Distance Functions

Author

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  • Zhen Wang

    (School of Mathematics and Information Sciences, Yantai University, No. 30, Qingquan Road, Yantai 264005, China)

  • Jicai Ning

    (Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, No. 17, Chunhui Road, Yantai 264003, China)

  • Meng Gao

    (School of Mathematics and Information Sciences, Yantai University, No. 30, Qingquan Road, Yantai 264005, China)

Abstract

By constructing a complex network model grounded in time series analysis, this study delves into the intricate relationships between the stock markets of 18 countries worldwide. Utilizing 31 distinct time series distance functions to formulate the network, we employ Hamming distance to quantify the resemblance between networks derived from different distance functions. By modulating the network density through distance percentiles ( p = 0.1 , 0.3, 0.5), we demonstrate the similarity of various distance functions across multiple density levels. Our findings reveal that certain distance functions exhibit high degrees of similarity across varying network densities, suggesting their potential for mutual substitution in network construction. Furthermore, the centroid network identified via hierarchical cluster analysis highlights the similarities between the stock markets of different nations, mirroring the intricate interconnections within the global financial landscape. The insights gained from this study offer crucial perspectives for comprehending the intricate network structure of global financial time series data, paving the way for further analysis and prediction of global financial market dynamics.

Suggested Citation

  • Zhen Wang & Jicai Ning & Meng Gao, 2024. "Complex Network Model of Global Financial Time Series Based on Different Distance Functions," Mathematics, MDPI, vol. 12(14), pages 1-14, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:14:p:2210-:d:1435425
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    References listed on IDEAS

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    1. Caiado, Jorge & Crato, Nuno & Pena, Daniel, 2006. "A periodogram-based metric for time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2668-2684, June.
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