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A study on China’s systemically important financial institutions based on multi-time scale causality networks

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  • Hu, Yunchao
  • Lu, Guibin
  • Gao, Wenyu

Abstract

Studying systemically important financial institutions (SIFIs) becomes the research hotspots in risk management of financial markets. In this paper, we study China’s SIFIs based on the complex network modeling method on multi-time scales. First, we use complete empirical mode decomposition based on adaptive noise (CEEMDAN) and partial cross mapping (PCM) to construct multi-time scale causality networks based on the normalized daily returns of 30 institutions during 2011–2020.​ Then, the SIFIs is investigated from the perspective of connectedness (information transmission and node centrality) on multi-time scales. Finally, using the technique for order preference by similarity to an ideal solution with entropy weight method (EW-TOPSIS) to comprehensively evaluate SIFIs, and deliver a sensitivity analysis. We find that: (i) interconnectedness primarily concentrates on the inter-institution in the long-term; (ii) the evaluations of SIFIs measurement from perspective of connectedness on multi-time scale are different; (iii) the scores of SIFIs measured by multi-attribute EW-TOPSIS clarify that banks and insurers represent the SIFIs in each economic circle, and SIFIs can be evaluated according to preference for attributes.

Suggested Citation

  • Hu, Yunchao & Lu, Guibin & Gao, Wenyu, 2022. "A study on China’s systemically important financial institutions based on multi-time scale causality networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    • Handle: RePEc:eee:phsmap:v:607:y:2022:i:c:s0378437122007749
    DOI: 10.1016/j.physa.2022.128216
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