IDEAS home Printed from https://ideas.repec.org/a/spr/futbus/v11y2025i1d10.1186_s43093-025-00451-8.html
   My bibliography  Save this article

Artificial intelligence and clean/dirty energy markets: tail-based pairwise connectedness and portfolio implications

Author

Listed:
  • Bechir Raggad

    (Majmaah University)

  • Elie Bouri

    (Lebanese American University)

Abstract

This study investigates the return and volatility connectedness between artificial intelligence (AI) stock ETF and each segment of the energy markets, namely clean energy, dirty energy, and WTI oil. Using a quantile-on-quantile connectedness approach on daily data from 14 September 2016 to 29 January 2024, the results reveal the following. Firstly, the degree of connectedness for the Clean-AI pair is more pronounced than that of the other pairs (AI-Dirty and AI-WTI), and Clean is mainly a receiver of return connectedness from AI stock ETF. Clean, Dirty, and WTI shift in roles to be primary transmitters of volatility shocks. Secondly, return and volatility shocks propagate more strongly at the tails of the conditional distribution than the middle of the distribution, and a dynamic analysis indicates that the average quantile-based total connectedness changes with time and strengthens during the COVID-19 outbreak. Thirdly, a portfolio and risk analysis with tail risk measures confirms the importance of considering a dynamic approach to tail-risk minimization.

Suggested Citation

  • Bechir Raggad & Elie Bouri, 2025. "Artificial intelligence and clean/dirty energy markets: tail-based pairwise connectedness and portfolio implications," Future Business Journal, Springer, vol. 11(1), pages 1-24, December.
    • Handle: RePEc:spr:futbus:v:11:y:2025:i:1:d:10.1186_s43093-025-00451-8
    DOI: 10.1186/s43093-025-00451-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s43093-025-00451-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1186/s43093-025-00451-8?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. Miklesh Prasad Yadav & Silky Vigg Kushwah & Farhad Taghizadeh-Hesary & Nandita Mishra, 2024. "Unveiling the dynamic linkages between energy, forex and financial markets amidst natural and man-made outbreaks," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 24(1), pages 129-151, November.
    2. Azhgaliyeva, Dina & Kapsalyamova, Zhanna & Mishra, Ranjeeta, 2022. "Oil price shocks and green bonds: An empirical evidence," Energy Economics, Elsevier, vol. 112(C).
    3. Kocaarslan, Baris & Soytas, Ugur, 2019. "Dynamic correlations between oil prices and the stock prices of clean energy and technology firms: The role of reserve currency (US dollar)," Energy Economics, Elsevier, vol. 84(C).
    4. Liu, Xiaoxing & Shehzad, Khurram & Kocak, Emrah & Zaman, Umer, 2022. "Dynamic correlations and portfolio implications across stock and commodity markets before and during the COVID-19 era: A key role of gold," Resources Policy, Elsevier, vol. 79(C).
    5. Managi, Shunsuke & Okimoto, Tatsuyoshi, 2013. "Does the price of oil interact with clean energy prices in the stock market?," Japan and the World Economy, Elsevier, vol. 27(C), pages 1-9.
    6. Dohyoung Kwon, 2025. "Oil Shocks, US Uncertainty, and Emerging Corporate Bond Markets," JRFM, MDPI, vol. 18(1), pages 1-15, January.
    7. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    8. Bonaparte, Yosef, 2024. "Artificial Intelligence in Finance: Valuations and Opportunities," Finance Research Letters, Elsevier, vol. 60(C).
    9. Fasanya, Ismail O. & Adekoya, Oluwasegun B. & Adetokunbo, Abiodun M., 2021. "On the connection between oil and global foreign exchange markets: The role of economic policy uncertainty," Resources Policy, Elsevier, vol. 72(C).
    10. Henriques, Irene & Sadorsky, Perry, 2008. "Oil prices and the stock prices of alternative energy companies," Energy Economics, Elsevier, vol. 30(3), pages 998-1010, May.
    11. Wu, Ran & Li, Ming & Liu, Feini & Zeng, Hongjun & Cong, Xiaoping, 2024. "Adjustment strategies and chaos in duopoly supply chains: The impacts of carbon trading markets and emission reduction policies," International Review of Economics & Finance, Elsevier, vol. 95(C).
    12. Acerbi, Carlo & Tasche, Dirk, 2002. "On the coherence of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1487-1503, July.
    13. Bouri, Elie & Saeed, Tareq & Vo, Xuan Vinh & Roubaud, David, 2021. "Quantile connectedness in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    14. Ahmad, Wasim, 2017. "On the dynamic dependence and investment performance of crude oil and clean energy stocks," Research in International Business and Finance, Elsevier, vol. 42(C), pages 376-389.
    15. Bouri, Elie, 2023. "Spillovers in the joint system of conditional higher-order moments: US evidence from green energy, brown energy, and technology stocks," Renewable Energy, Elsevier, vol. 210(C), pages 507-523.
    16. Sadorsky, Perry, 2012. "Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies," Energy Economics, Elsevier, vol. 34(1), pages 248-255.
    17. Zeng, Hongjun & Huang, Qingcheng & Abedin, Mohammad Zoynul & Ahmed, Abdullahi D. & Lucey, Brian, 2025. "Connectedness and frequency connection among green bond, cryptocurrency and green energy-related metals around the COVID-19 outbreak," Research in International Business and Finance, Elsevier, vol. 73(PA).
    18. Fredj Jawadi & Nabila Jawadi & Duc Khuong Nguyen & Hassan Obeid, 2013. "Information technology sector and equity markets: an empirical investigation," Applied Financial Economics, Taylor & Francis Journals, vol. 23(9), pages 729-737, May.
    19. Sim, Nicholas & Zhou, Hongtao, 2015. "Oil prices, US stock return, and the dependence between their quantiles," Journal of Banking & Finance, Elsevier, vol. 55(C), pages 1-8.
    20. Han, Heejoon & Linton, Oliver & Oka, Tatsushi & Whang, Yoon-Jae, 2016. "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series," Journal of Econometrics, Elsevier, vol. 193(1), pages 251-270.
    21. Ferrer, Román & Shahzad, Syed Jawad Hussain & López, Raquel & Jareño, Francisco, 2018. "Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices," Energy Economics, Elsevier, vol. 76(C), pages 1-20.
    22. Bondia, Ripsy & Ghosh, Sajal & Kanjilal, Kakali, 2016. "International crude oil prices and the stock prices of clean energy and technology companies: Evidence from non-linear cointegration tests with unknown structural breaks," Energy, Elsevier, vol. 101(C), pages 558-565.
    23. Zeng, Hongjun & Abedin, Mohammad Zoynul & Lucey, Brian & Ma, Shenglin, 2025. "Tail risk contagion and multiscale spillovers in the green finance index and large US technology stocks," International Review of Financial Analysis, Elsevier, vol. 97(C).
    24. Song, Yingjie & Ji, Qiang & Du, Ya-Juan & Geng, Jiang-Bo, 2019. "The dynamic dependence of fossil energy, investor sentiment and renewable energy stock markets," Energy Economics, Elsevier, vol. 84(C).
    25. Shiying Chen & Bisharat Hussain Chang & Hu Fu & ShiQi Xie, 2024. "Dynamic analysis of the relationship between exchange rates and oil prices: a comparison between oil exporting and oil importing countries," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    26. Qin, Meng & Hu, Wei & Qi, Xinzhou & Chang, Tsangyao, 2024. "Do the benefits outweigh the disadvantages? Exploring the role of artificial intelligence in renewable energy," Energy Economics, Elsevier, vol. 131(C).
    27. Tomohiro Ando & Matthew Greenwood-Nimmo & Yongcheol Shin, 2022. "Quantile Connectedness: Modeling Tail Behavior in the Topology of Financial Networks," Management Science, INFORMS, vol. 68(4), pages 2401-2431, April.
    28. Roger Koenker & Zhijie Xiao, 2004. "Unit Root Quantile Autoregression Inference," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 775-787, January.
    29. Niu, Hongli, 2021. "Correlations between crude oil and stocks prices of renewable energy and technology companies: A multiscale time-dependent analysis," Energy, Elsevier, vol. 221(C).
    30. Zhang, Hongwei & Fang, Beixin & He, Pengwei & Gao, Wang, 2024. "The asymmetric impacts of artificial intelligence and oil shocks on clean energy industries by considering COVID-19," Energy, Elsevier, vol. 291(C).
    31. Ishaya Tambari & Pierre Failler, 2020. "Determining If Oil Prices Significantly Affect Renewable Energy Investment in African Countries with Energy Security Concerns," Energies, MDPI, vol. 13(24), pages 1-21, December.
    32. Li, Xiafei & Li, Bo & Wei, Guiwu & Bai, Lan & Wei, Yu & Liang, Chao, 2021. "Return connectedness among commodity and financial assets during the COVID-19 pandemic: Evidence from China and the US," Resources Policy, Elsevier, vol. 73(C).
    33. Raggad, Bechir, 2023. "Can implied volatility predict returns on oil market? Evidence from Cross-Quantilogram Approach," Resources Policy, Elsevier, vol. 80(C).
    34. Ji, Qiang & Bouri, Elie & Roubaud, David & Kristoufek, Ladislav, 2019. "Information interdependence among energy, cryptocurrency and major commodity markets," Energy Economics, Elsevier, vol. 81(C), pages 1042-1055.
    35. Zhao, Qian & Wang, Lu & Stan, Sebastian-Emanuel & Mirza, Nawazish, 2024. "Can artificial intelligence help accelerate the transition to renewable energy?," Energy Economics, Elsevier, vol. 134(C).
    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. Ahmed, Walid M.A. & Sleem, Mohamed A.E., 2023. "Short- and long-run determinants of the price behavior of US clean energy stocks: A dynamic ARDL simulations approach," Energy Economics, Elsevier, vol. 124(C).
    2. Jiang, Yonghong & Wang, Jieru & Lie, Jiayi & Mo, Bin, 2021. "Dynamic dependence nexus and causality of the renewable energy stock markets on the fossil energy markets," Energy, Elsevier, vol. 233(C).
    3. Tan, Xueping & Geng, Yong & Vivian, Andrew & Wang, Xinyu, 2021. "Measuring risk spillovers between oil and clean energy stocks: Evidence from a systematic framework," Resources Policy, Elsevier, vol. 74(C).
    4. Benkraiem, Ramzi & Guesmi, Khaled & Ndubuisi, Gideon & Urom, Christian & Vigne, Samuel, 2024. "Dependence of green energy markets on big data and other fourth industrial revolution technologies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 96(C).
    5. Ozkan, Oktay & Olanipekun, Ifedolapo Olabisi & Olasehinde-Williams, Godwin, 2024. "Dynamic correlation among renewable energy, technology, and carbon markets: Evidence from a novel nonparametric time-frequency approach," Renewable Energy, Elsevier, vol. 237(PB).
    6. Farid, Saqib & Karim, Sitara & Naeem, Muhammad A. & Nepal, Rabindra & Jamasb, Tooraj, 2023. "Co-movement between dirty and clean energy: A time-frequency perspective," Energy Economics, Elsevier, vol. 119(C).
    7. Saeed, Tareq & Bouri, Elie & Alsulami, Hamed, 2021. "Extreme return connectedness and its determinants between clean/green and dirty energy investments," Energy Economics, Elsevier, vol. 96(C).
    8. Linh Pham, 2021. "How Integrated are Regional Green Equity Markets? Evidence from a Cross-Quantilogram Approach," JRFM, MDPI, vol. 14(1), pages 1-58, January.
    9. Urom, Christian & Mzoughi, Hela & Ndubuisi, Gideon & Guesmi, Khaled, 2022. "Directional predictability and time-frequency spillovers among clean energy sectors and oil price uncertainty," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 326-341.
    10. Alomari, Mohammed & Khoury, Rim El & Mensi, Walid & Vo, Xuan Vinh & Kang, Sang Hoon, 2024. "Extreme downside risk connectedness between green energy and stock markets," Energy, Elsevier, vol. 312(C).
    11. Tiwari, Aviral Kumar & Trabelsi, Nader & Abakah, Emmanuel Joel Aikins & Nasreen, Samia & Lee, Chien-Chiang, 2023. "An empirical analysis of the dynamic relationship between clean and dirty energy markets," Energy Economics, Elsevier, vol. 124(C).
    12. Su, Xianfang & Zhao, Yachao, 2023. "What has the strongest connectedness with clean energy? Technology, substitutes, or raw materials," Energy Economics, Elsevier, vol. 128(C).
    13. Su, Chi-Wei & Pang, Li-Dong & Qin, Meng & Lobonţ, Oana-Ramona & Umar, Muhammad, 2023. "The spillover effects among fossil fuel, renewables and carbon markets: Evidence under the dual dilemma of climate change and energy crises," Energy, Elsevier, vol. 274(C).
    14. Matteo Foglia & Eliana Angelini, 2020. "Volatility Connectedness between Clean Energy Firms and Crude Oil in the COVID-19 Era," Sustainability, MDPI, vol. 12(23), pages 1-22, November.
    15. Wei, Yu & Zhang, Jiahao & Chen, Yongfei & Wang, Yizhi, 2022. "The impacts of El Niño-southern oscillation on renewable energy stock markets: Evidence from quantile perspective," Energy, Elsevier, vol. 260(C).
    16. Yahya, Muhammad & Kanjilal, Kakali & Dutta, Anupam & Uddin, Gazi Salah & Ghosh, Sajal, 2021. "Can clean energy stock price rule oil price? New evidences from a regime-switching model at first and second moments," Energy Economics, Elsevier, vol. 95(C).
    17. Shahbaz, Muhammad & Trabelsi, Nader & Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Jiao, Zhilun, 2021. "Relationship between green investments, energy markets, and stock markets in the aftermath of the global financial crisis," Energy Economics, Elsevier, vol. 104(C).
    18. Hao, Wei & Pham, Linh, 2024. "Dynamic connectedness in the higher moments between clean energy and oil prices," Energy Economics, Elsevier, vol. 140(C).
    19. Li, Hailing & Li, Yuxin & Zhang, Hua, 2023. "The spillover effects among the traditional energy markets, metal markets and sub-sector clean energy markets," Energy, Elsevier, vol. 275(C).
    20. Christian Urom & Gideon Ndubuisi & Hela Mzoughi & Khaled Guesmi, 2024. "Exploring the coherency and predictability between the stocks of artificial intelligence and energy corporations," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-31, December.

    More about this item

    Keywords

    Artificial intelligence stock index ETF; Clean energy; Dirty energy; WTI oil; Return and volatility connectedness; Quantile on quantile;
    All these keywords.

    JEL classification:

    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other
    • Q29 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Other
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    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:spr:futbus:v:11:y:2025:i:1:d:10.1186_s43093-025-00451-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.