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Spillover Effect of the Interaction between Fintech and the Real Economy Based on Tail Risk Dependent Structure Analysis

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  • Zhikai Peng

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Jinchuan Ke

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Fintech innovation has greatly improved the operation efficiency of the financial industry and promoted the sustainable development of the real economy. On the other hand, fintech also brings the problem of risk spillover. Through a time series analysis, vector auto-regression with the Granger causality test is conducted to analyze the interaction between fintech and the real economy. To deal with the nonlinear relationship and overcome the high-dimensional-dependent structure faced by Copula, this paper establishes a GARCH–Vine–Copula model to study the tail risk and dynamic dependency between fintech and industries of the real economy in China, and then analyzes the risk spillover by calculating the CoVaR. The results show that there is a positive dynamic correlation between fintech and the real economy, and this increases when facing risk impact; fintech is located in the leading position of R-vine-dependent structure, and has a high correlation coefficient with the upper and lower tail of various industries. The results of CoVaR show that the extreme risk events in fintech and various industries have different degrees of negative impact on each other; the risk events in fintech have an extreme impact on industry in a short time.

Suggested Citation

  • Zhikai Peng & Jinchuan Ke, 2022. "Spillover Effect of the Interaction between Fintech and the Real Economy Based on Tail Risk Dependent Structure Analysis," Sustainability, MDPI, vol. 14(13), pages 1-22, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7818-:d:848838
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    1. Kim, Daeyoung & Kim, Jong-Min & Liao, Shu-Min & Jung, Yoon-Sung, 2013. "Mixture of D-vine copulas for modeling dependence," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 1-19.
    2. Nikoloulopoulos, Aristidis K. & Joe, Harry & Li, Haijun, 2012. "Vine copulas with asymmetric tail dependence and applications to financial return data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3659-3673.
    3. Gennaioli, Nicola & Shleifer, Andrei & Vishny, Robert, 2012. "Neglected risks, financial innovation, and financial fragility," Journal of Financial Economics, Elsevier, vol. 104(3), pages 452-468.
    4. Joe, Harry & Li, Haijun & Nikoloulopoulos, Aristidis K., 2010. "Tail dependence functions and vine copulas," Journal of Multivariate Analysis, Elsevier, vol. 101(1), pages 252-270, January.
    5. Ulf Schepsmeier, 2019. "A goodness-of-fit test for regular vine copula models," Econometric Reviews, Taylor & Francis Journals, vol. 38(1), pages 25-46, January.
    6. Brechmann, Eike Christian & Schepsmeier, Ulf, 2013. "Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i03).
    7. Jose Arreola Hernandez & Shawkat Hammoudeh & Duc Khuong Nguyen & Mazin A. M. Al Janabi & Juan Carlos Reboredo, 2017. "Global financial crisis and dependence risk analysis of sector portfolios: a vine copula approach," Applied Economics, Taylor & Francis Journals, vol. 49(25), pages 2409-2427, May.
    8. Yong Jae Shin & Yongrok Choi, 2019. "Feasibility of the Fintech Industry as an Innovation Platform for Sustainable Economic Growth in Korea," Sustainability, MDPI, vol. 11(19), pages 1-21, September.
    9. Lee, In & Shin, Yong Jae, 2018. "Fintech: Ecosystem, business models, investment decisions, and challenges," Business Horizons, Elsevier, vol. 61(1), pages 35-46.
    10. Zhu, Kailun & Kurowicka, Dorota & Nane, Gabriela F., 2020. "Common sampling orders of regular vines with application to model selection," Computational Statistics & Data Analysis, Elsevier, vol. 142(C).
    11. Emmanouil N. Karimalis & Nikos K. Nomikos, 2018. "Measuring systemic risk in the European banking sector: a copula CoVaR approach," The European Journal of Finance, Taylor & Francis Journals, vol. 24(11), pages 944-975, July.
    12. Fadhah Amer Alanazi, 2021. "A Mixture of Regular Vines for Multiple Dependencies," Journal of Probability and Statistics, Hindawi, vol. 2021, pages 1-15, May.
    13. Dißmann, J. & Brechmann, E.C. & Czado, C. & Kurowicka, D., 2013. "Selecting and estimating regular vine copulae and application to financial returns," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 52-69.
    14. Reboredo, Juan C. & Ugolini, Andrea, 2015. "Downside/upside price spillovers between precious metals: A vine copula approach," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 84-102.
    15. Tim Bedford & Alireza Daneshkhah & Kevin J. Wilson, 2016. "Approximate Uncertainty Modeling in Risk Analysis with Vine Copulas," Risk Analysis, John Wiley & Sons, vol. 36(4), pages 792-815, April.
    16. Heston, Steven L & Nandi, Saikat, 2000. "A Closed-Form GARCH Option Valuation Model," The Review of Financial Studies, Society for Financial Studies, vol. 13(3), pages 585-625.
    17. Dalu Zhang & Meilan Yan & Andreas Tsopanakis, 2018. "Financial stress relationships among Euro area countries: an R-vine copula approach," The European Journal of Finance, Taylor & Francis Journals, vol. 24(17), pages 1587-1608, November.
    18. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    19. Chen, Xiaohui & Teng, Lei & Chen, Wen, 2022. "How does FinTech affect the development of the digital economy? Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    20. Hao Ji & Hao Wang & Brunero Liseo, 2018. "Portfolio Diversification Strategy Via Tail‐Dependence Clustering and ARMA‐GARCH Vine Copula Approach," Australian Economic Papers, Wiley Blackwell, vol. 57(3), pages 265-283, September.
    21. Rongda Chen & Huiwen Chen & Chenglu Jin & Bo Wei & Lean Yu, 2020. "Linkages and Spillovers between Internet Finance and Traditional Finance: Evidence from China," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(6), pages 1196-1210, May.
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    Cited by:

    1. Qian Liu & Yiheng You, 2023. "FinTech and Green Credit Development—Evidence from China," Sustainability, MDPI, vol. 15(7), pages 1-23, March.

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