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Givers or Receivers? Return and volatility spillovers between Fintech and the Traditional Financial Industry

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  • Chen, Yuxuan
  • Chiu, Junmao
  • Chung, Huimin

Abstract

We investigate the return and volatility spillovers between a Fintech ETF and the ETFs of the traditional financial industry with an empirical network model. We find that the traditional financial ETFs are still the main givers, and the Fintech ETF is the net receiver. The Fintech ETF does not lead to greater volatility and financial instability in most of the traditional financial sectors. The information transmission between these ETFs is high, especially during the period of US-China trade friction. Our results provide a full understanding of the effect of changes in information transmission between Fintech and the traditional financial industry.

Suggested Citation

  • Chen, Yuxuan & Chiu, Junmao & Chung, Huimin, 2022. "Givers or Receivers? Return and volatility spillovers between Fintech and the Traditional Financial Industry," Finance Research Letters, Elsevier, vol. 46(PB).
    • Handle: RePEc:eee:finlet:v:46:y:2022:i:pb:s1544612321004220
    DOI: 10.1016/j.frl.2021.102458
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    3. Yousaf, Imran & Youssef, Manel & Goodell, John W., 2024. "Tail connectedness between artificial intelligence tokens, artificial intelligence ETFs, and traditional asset classes," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    4. Choudhary, Priya & Thenmozhi, M., 2024. "Fintech and financial sector: ADO analysis and future research agenda," International Review of Financial Analysis, Elsevier, vol. 93(C).
    5. Banerjee, Ameet Kumar & Pradhan, H.K. & Sensoy, Ahmet & Goodell, John W., 2024. "Assessing the US financial sector post three bank collapses: Signals from fintech and financial sector ETFs," International Review of Financial Analysis, Elsevier, vol. 91(C).
    6. Ziyao Wang & Yufei Xia & Yating Fu & Ying Liu, 2023. "Volatility Spillover Dynamics and Determinants between FinTech and Traditional Financial Industry: Evidence from China," Mathematics, MDPI, vol. 11(19), pages 1-23, September.
    7. Emmanuel Joel Aikins Abakah & Aviral Kumar Tiwari & Chi‐Chuan Lee & Matthew Ntow‐Gyamfi, 2023. "Quantile price convergence and spillover effects among Bitcoin, Fintech, and artificial intelligence stocks," International Review of Finance, International Review of Finance Ltd., vol. 23(1), pages 187-205, March.
    8. Perry Sadorsky, 2024. "Using Precious Metals to Reduce the Downside Risk of FinTech Stocks," FinTech, MDPI, vol. 3(4), pages 1-14, October.
    9. Chen, Yan & Wang, Gang-Jin & Zhu, You & Xie, Chi & Uddin, Gazi Salah, 2023. "Quantile connectedness and the determinants between FinTech and traditional financial institutions: Evidence from China," Global Finance Journal, Elsevier, vol. 58(C).
    10. 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).
    11. Guo, Junyan & Fang, Hanqing & Liu, Xuexin & Wang, Cizhi & Wang, Yuan, 2023. "FinTech and financing constraints of enterprises: Evidence from China," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).

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