IDEAS home Printed from https://ideas.repec.org/a/eee/ecanpo/v82y2024icp515-529.html
   My bibliography  Save this article

Dynamic spill-over influences of FinTech innovation development on renewable energy volatility during the time of war in pandemic: A novel insight from a wavelet model

Author

Listed:
  • Ha, Le Thanh

Abstract

The research investigates the correlation between FinTech innovation and the index of renewable energy volatility from May 1, 2019, to October 28, 2022, by utilizing cutting-edge multiple wavelet analysis (MWA) techniques, including coherency and gain of partial wavelets. FinTech innovation and the green energy sector exhibit multiple coherencies. These coherencies suggest that three cycles, spanning from January 2020 to June 2020, December 2020 to April 2021, and January 2022 to May 2022, were focused on the high-frequency spectrum (3–68 days). Meanwhile, the other cycles were focused on low frequencies (69–130 days), which coexisted between June 2019 and January 2021 and January 2022 and October 2022. Green bonds and FinTech innovation's partial coherency indicate that modifications to the green bond markets caused shifts in FinTech innovation between June and November of 2019. FinTech innovation is in sync with green bonds pushing from December 2021 to October 2022. The partial consistency of FinTech innovation and sustainable energy demonstrates that both metrics align with the current FinTech innovation cycle, pushing from May 2019 to August 2019 and from December 2020 to July 2021, and both indicators are in sync with ARKF pushing. We also demonstrate how unexpected events like the Ukraine-Russia conflict and the COVID-19 epidemic affect these dynamic connectednesses. Hence, our paper calls for policy designs that promote the role of FinTech development in stabilizing the renewable energy market.

Suggested Citation

  • Ha, Le Thanh, 2024. "Dynamic spill-over influences of FinTech innovation development on renewable energy volatility during the time of war in pandemic: A novel insight from a wavelet model," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 515-529.
    • Handle: RePEc:eee:ecanpo:v:82:y:2024:i:c:p:515-529
    DOI: 10.1016/j.eap.2024.03.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0313592624000730
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eap.2024.03.018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pata, Ugur Korkut & Alola, Andrew Adewale & Erdogan, Sinan & Kartal, Mustafa Tevfik, 2023. "The influence of income, economic policy uncertainty, geopolitical risk, and urbanization on renewable energy investments in G7 countries," Energy Economics, Elsevier, vol. 128(C).
    2. Mandler, Martin & Scharnagl, Michael, 2014. "Money growth and consumer price inflation in the euro area: A wavelet analysis," Discussion Papers 33/2014, Deutsche Bundesbank.
    3. Kow, Ken Weng & Wong, Yee Wan & Rajkumar, Rajparthiban Kumar & Rajkumar, Rajprasad Kumar, 2016. "A review on performance of artificial intelligence and conventional method in mitigating PV grid-tied related power quality events," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 334-346.
    4. Oumayma Gharbi & Yousra Trichilli & Mouna Boujelbéne, 2023. "Risk spillovers connectedness between the US Fintech industry VaR, behavioral biases and macroeconomic instability factors: COVID-19 implications," China Finance Review International, Emerald Group Publishing Limited, vol. 13(3), pages 410-443, June.
    5. Youssef, Ayman & El-Telbany, Mohammed & Zekry, Abdelhalim, 2017. "The role of artificial intelligence in photo-voltaic systems design and control: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 72-79.
    6. Ghoddusi, Hamed & Creamer, Germán G. & Rafizadeh, Nima, 2019. "Machine learning in energy economics and finance: A review," Energy Economics, Elsevier, vol. 81(C), pages 709-727.
    7. Zahraee, S.M. & Khalaji Assadi, M. & Saidur, R., 2016. "Application of Artificial Intelligence Methods for Hybrid Energy System Optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 617-630.
    8. 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).
    9. Luís Aguiar-Conraria & Maria Joana Soares, 2014. "The Continuous Wavelet Transform: Moving Beyond Uni- And Bivariate Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 344-375, April.
    10. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    11. 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).
    12. 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).
    13. 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.
    14. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    15. 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.
    16. Kartal, Mustafa Tevfik & Pata, Ugur Korkut & Kılıç Depren, Serpil & Depren, Özer, 2023. "Effects of possible changes in natural gas, nuclear, and coal energy consumption on CO2 emissions: Evidence from France under Russia’s gas supply cuts by dynamic ARDL simulations approach," Applied Energy, Elsevier, vol. 339(C).
    17. Conghui Chen & Lanlan Liu & Ningru Zhao, 2020. "Fear Sentiment, Uncertainty, and Bitcoin Price Dynamics: The Case of COVID-19," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(10), pages 2298-2309, August.
    18. 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.
    19. Le Thanh Ha, 2022. "Interlinkages of cryptocurrency and stock markets during COVID-19 pandemic by applying a TVP-VAR extended joint connected approach," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 50(3), pages 407-428, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Parthajit Kayal & Purnima Rohilla, 2021. "Bitcoin in the economics and finance literature: a survey," SN Business & Economics, Springer, vol. 1(7), pages 1-21, July.
    3. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    4. Apergis, Nicholas, 2022. "COVID-19 and cryptocurrency volatility: Evidence from asymmetric modelling," Finance Research Letters, Elsevier, vol. 47(PA).
    5. Xu, Yingying & Shao, Xuefeng & Tanasescu, Cristina, 2024. "How are artificial intelligence, carbon market, and energy sector connected? A systematic analysis of time-frequency spillovers," Energy Economics, Elsevier, vol. 132(C).
    6. Ştefan Cristian Gherghina & Liliana Nicoleta Simionescu, 2023. "Exploring the asymmetric effect of COVID-19 pandemic news on the cryptocurrency market: evidence from nonlinear autoregressive distributed lag approach and frequency domain causality," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-58, December.
    7. Tachibana, Minoru, 2022. "Safe haven assets for international stock markets: A regime-switching factor copula approach," Research in International Business and Finance, Elsevier, vol. 60(C).
    8. Yaqi Wu & Chen Zhang & Po Yun & Dandan Zhu & Wei Cao & Zulfiqar Ali Wagan, 2021. "Time–frequency analysis of the interaction mechanism between European carbon and crude oil markets," Energy & Environment, , vol. 32(7), pages 1331-1357, November.
    9. Diniz-Maganini, Natalia & Diniz, Eduardo H. & Rasheed, Abdul A., 2021. "Bitcoin’s price efficiency and safe haven properties during the COVID-19 pandemic: A comparison," Research in International Business and Finance, Elsevier, vol. 58(C).
    10. Bao, Hong & Li, Jianjun & Peng, Yuchao & Qu, Qiang, 2022. "Can Bitcoin help money cross the border: International evidence," Finance Research Letters, Elsevier, vol. 49(C).
    11. Aguiar-Conraria, Luís & Martins, Manuel M.F. & Soares, Maria Joana, 2020. "Okun’s Law across time and frequencies," Journal of Economic Dynamics and Control, Elsevier, vol. 116(C).
    12. Gaies, Brahim & Nakhli, Mohamed Sahbi & Sahut, Jean-Michel & Schweizer, Denis, 2023. "Interactions between investors’ fear and greed sentiment and Bitcoin prices," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    13. Mokni, Khaled & Youssef, Manel & Ajmi, Ahdi Noomen, 2022. "COVID-19 pandemic and economic policy uncertainty: The first test on the hedging and safe haven properties of cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 60(C).
    14. Aguiar-Conraria, Luis & Martins, Manuel M.F. & Soares, Maria Joana, 2018. "Estimating the Taylor rule in the time-frequency domain," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 122-137.
    15. Papadimitrakis, M. & Giamarelos, N. & Stogiannos, M. & Zois, E.N. & Livanos, N.A.-I. & Alexandridis, A., 2021. "Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    16. Aloui, Donia & Zouaoui, Riadh & Rachdi, Houssem & Guesmi, Khaled & Yarovaya, Larisa, 2024. "The impact of ECB’s Quantitative Easing on cryptocurrency markets during times of crisis," Research in International Business and Finance, Elsevier, vol. 69(C).
    17. Flor, Michael A. & Klarl, Torben, 2017. "On the cyclicity of regional house prices: New evidence for U.S. metropolitan statistical areas," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 134-156.
    18. Ghabri, Yosra & Ben Rhouma, Oussama & Gana, Marjène & Guesmi, Khaled & Benkraiem, Ramzi, 2022. "Information transmission among energy markets, cryptocurrencies, and stablecoins under pandemic conditions," International Review of Financial Analysis, Elsevier, vol. 82(C).
    19. Luís Aguiar-Conraria & Manuel M. F. Martins & Maria Joana Soares, 2019. "The Phillips Curve at 60: time for time and frequency," NIPE Working Papers 04/2019, NIPE - Universidade do Minho.
    20. Andrew Phiri, 2022. "Changing efficiency of BRICS currency markets during the COVID-19 pandemic," Economic Change and Restructuring, Springer, vol. 55(3), pages 1673-1699, August.

    More about this item

    Keywords

    Partial wavelet gain; FinTech innovation; Renewable energy volatility; Partial wavelet coherency; Ukraine-Russia conflict; COVID-19 pandemic;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • H1 - Public Economics - - Structure and Scope of Government

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecanpo:v:82:y:2024:i:c:p:515-529. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/economic-analysis-and-policy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.