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Dynamic connectedness between FinTech and energy markets: Evidence from fat tails, serial dependence, and Bayesian approach

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  • Thanh Ha, Le
  • Bouteska, Ahmed
  • Harasheh, Murad

Abstract

In a complex global environment, modeling the relationships among several markets and asset classes has become more challenging. Literature strives to provide conclusive evidence due to system complexities. Therefore, we investigate the dynamic relationship between Fintech and energy sectors using fat tails, serial dependence, and the Bayesian approach from May 1, 2019, to October 28, 2022. Results unveil significant temporal variations in network interconnections, with an evident increase in interlinkages across short, medium, and long durations due to transient market events. The COVID-19 pandemic and the onset of the Russia-Ukraine conflict have substantially altered the long-term relational dynamics within these networks. An analysis of net directional linkages reveals a pivotal shift in market roles—from being predominantly shock recipients to becoming shock transmitters—highlighted during the COVID-19 pandemic and the initial phase of the Russia-Ukraine War. Initial observations indicate that, amid the global impact of COVID-19, Fintech markets absorbed shocks through investments in green bonds and renewable energy sources like wind, solar, and clean energy. These findings suggest that while Fintech consistently acts as a shock absorber in long-term scenarios, it assumes the role of a shock transmitter during adverse economic states.

Suggested Citation

  • Thanh Ha, Le & Bouteska, Ahmed & Harasheh, Murad, 2024. "Dynamic connectedness between FinTech and energy markets: Evidence from fat tails, serial dependence, and Bayesian approach," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 574-586.
  • Handle: RePEc:eee:reveco:v:93:y:2024:i:pb:p:574-586
    DOI: 10.1016/j.iref.2024.04.034
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    1. Le, TN-Lan & Abakah, Emmanuel Joel Aikins & Tiwari, Aviral Kumar, 2021. "Time and frequency domain connectedness and spill-over among fintech, green bonds and cryptocurrencies in the age of the fourth industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    2. Prasenjit Chakrabarti & Mohammad Shameem Jawed & Manish Sarkhel, 2021. "COVID-19 pandemic and global financial market interlinkages: a dynamic temporal network analysis," Applied Economics, Taylor & Francis Journals, vol. 53(25), pages 2930-2945, May.
    3. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2022. "Energy Markets and Global Economic Conditions," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 828-844, October.
    4. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    5. Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Le, TN-Lan & Leyva-de la Hiz, Dante I., 2021. "Markov-switching dependence between artificial intelligence and carbon price: The role of policy uncertainty in the era of the 4th industrial revolution and the effect of COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    6. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    7. Huynh, Toan Luu Duc & Hille, Erik & Nasir, Muhammad Ali, 2020. "Diversification in the age of the 4th industrial revolution: The role of artificial intelligence, green bonds and cryptocurrencies," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    8. Elie Bouri & Luis A. Gil‐Alana & Rangan Gupta & David Roubaud, 2019. "Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(1), pages 412-426, January.
    9. Joshua C. C. Chan, 2020. "Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 68-79, January.
    10. Sangram Keshari Jena & Aviral Kumar Tiwari & Shawkat Hammoudeh & Muhammad Shahbaz, 2020. "Dynamics of FII flows and stock market returns in a major developing country: How does economic uncertainty matter?," The World Economy, Wiley Blackwell, vol. 43(8), pages 2263-2284, August.
    11. Long Chen & Lin William Cong & Yizhou Xiao, 2020. "A Brief Introduction to Blockchain Economics," World Scientific Book Chapters, in: Kashi R Balachandran (ed.), Information for Efficient Decision Making Big Data, Blockchain and Relevance, chapter 1, pages 1-40, World Scientific Publishing Co. Pte. Ltd..
    12. Goodell, John W. & Goutte, Stephane, 2021. "Co-movement of COVID-19 and Bitcoin: Evidence from wavelet coherence analysis," Finance Research Letters, Elsevier, vol. 38(C).
    13. Elsayed, Ahmed H. & Nasreen, Samia & Tiwari, Aviral Kumar, 2020. "Time-varying co-movements between energy market and global financial markets: Implication for portfolio diversification and hedging strategies," Energy Economics, Elsevier, vol. 90(C).
    14. Disli, Mustafa & Nagayev, Ruslan & Salim, Kinan & Rizkiah, Siti K. & Aysan, Ahmet F., 2021. "In search of safe haven assets during COVID-19 pandemic: An empirical analysis of different investor types," Research in International Business and Finance, Elsevier, vol. 58(C).
    15. Rainer Böhme & Nicolas Christin & Benjamin Edelman & Tyler Moore, 2015. "Bitcoin: Economics, Technology, and Governance," Journal of Economic Perspectives, American Economic Association, vol. 29(2), pages 213-238, Spring.
    16. Gronwald, Marc, 2019. "Is Bitcoin a Commodity? On price jumps, demand shocks, and certainty of supply," Journal of International Money and Finance, Elsevier, vol. 97(C), pages 86-92.
    17. Bruno Biais & Christophe Bisière & Matthieu Bouvard & Catherine Casamatta, 2019. "Blockchains, Coordination, and Forks," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 88-92, May.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Bayesian vector heterogeneous autoregressions; FinTech innovation development; Energy dynamics; COVID-19; Russia-Ukraine conflict;
    All these keywords.

    JEL classification:

    • F3 - International Economics - - International Finance
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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