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Financial network linkages to predict economic output

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  • Huang, Wei-Qiang
  • Wang, Dan

Abstract

We investigate the impact of financial system on China's economic output from a financial institution tail-event driven networks (TENETs) perspective and forecast future economic growth by ARDL models. We assess five network topological measurements to reflect the change in the financial system, which detect tail risk spillover effects and reflect cross-sectional dimension of systemic risk. Through a study of relationship at different time lags, we find that except for total connectedness, all estimated network topological measurements have long-run positive impacts on economic growth and that test results present a highly accurate forecast for China's economic output using the network linkages.

Suggested Citation

  • Huang, Wei-Qiang & Wang, Dan, 2020. "Financial network linkages to predict economic output," Finance Research Letters, Elsevier, vol. 33(C).
    • Handle: RePEc:eee:finlet:v:33:y:2020:i:c:s1544612319301746
    DOI: 10.1016/j.frl.2019.06.004
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    Cited by:

    1. Tang, Pan & Xu, Wei & Wang, Haosen, 2024. "Network-Based prediction of financial cross-sector risk spillover in China: A deep learning approach," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    2. Chen, Yan & Mo, Dongxu & Xu, Zezhou, 2022. "A study of interconnections and contagion among Chinese financial institutions using a ΔCoV aR network," Finance Research Letters, Elsevier, vol. 45(C).
    3. Pacelli, Vincenzo & Miglietta, Federica & Foglia, Matteo, 2022. "The extreme risk connectedness of the new financial system: European evidence," International Review of Financial Analysis, Elsevier, vol. 84(C).

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