IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v183y2022ics0040162522003663.html
   My bibliography  Save this article

Quantile co-movement and dependence between energy-focused sectors and artificial intelligence

Author

Listed:
  • Urom, Christian
  • Ndubuisi, Gideon
  • Guesmi, Khaled
  • Benkraien, Ramzi

Abstract

This paper examines the dependence between Artificial Intelligence (AI) and eight energy-focused sectors (including renewable energy and coal) across different market conditions and investment horizons. This paper adopts both linear and non-linear models such as quantile regressions and quantile cross-spectral coherency models. Evidence from the linear model suggests that the performance of energy-focused corporations, especially those in the renewable energy sector depends strongly on the performance of AI. Results from the non-linear model indicate that dependence varies across both energy sectors, market conditions as well as investment horizons. By considering both negative and positive shocks on AI, we demonstrate that the dependence of energy corporations on AI also varies according to the direction of shocks on AI. Interestingly, negative and positive shocks on AI impact differently on the performance of energy corporations across different sectors and market conditions. Besides, we found that the dependence became stronger during the first wave of the COVID-19 pandemic. Our findings hold profound implications for portfolio managers and investors, who may be interested in holding the assets of AI and those of energy corporations.

Suggested Citation

  • Urom, Christian & Ndubuisi, Gideon & Guesmi, Khaled & Benkraien, Ramzi, 2022. "Quantile co-movement and dependence between energy-focused sectors and artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    • Handle: RePEc:eee:tefoso:v:183:y:2022:i:c:s0040162522003663
    DOI: 10.1016/j.techfore.2022.121842
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162522003663
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2022.121842?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. Tiwari, Aviral Kumar & Jena, Sangram Keshari & Mitra, Amarnath & Yoon, Seong-Min, 2018. "Impact of oil price risk on sectoral equity markets: Implications on portfolio management," Energy Economics, Elsevier, vol. 72(C), pages 120-134.
    2. 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.
    3. Demiralay, Sercan & Gencer, Hatice Gaye & Bayraci, Selcuk, 2021. "How do Artificial Intelligence and Robotics Stocks co-move with traditional and alternative assets in the age of the 4th industrial revolution? Implications and Insights for the COVID-19 period," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    4. 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).
    5. Corbet, Shaen & Goodell, John W. & Günay, Samet, 2020. "Co-movements and spillovers of oil and renewable firms under extreme conditions: New evidence from negative WTI prices during COVID-19," Energy Economics, Elsevier, vol. 92(C).
    6. 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.
    7. Baur, Dirk G., 2013. "The structure and degree of dependence: A quantile regression approach," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 786-798.
    8. 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.
    9. Maghyereh, Aktham & Abdoh, Hussein, 2021. "Tail dependence between gold and Islamic securities," Finance Research Letters, Elsevier, vol. 38(C).
    10. 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.
    11. 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).
    12. 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.
    13. Kumar, Surender & Managi, Shunsuke & Matsuda, Akimi, 2012. "Stock prices of clean energy firms, oil and carbon markets: A vector autoregressive analysis," Energy Economics, Elsevier, vol. 34(1), pages 215-226.
    14. 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.
    15. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    16. Jozef Baruník & Tobias Kley, 2019. "Quantile coherency: A general measure for dependence between cyclical economic variables," The Econometrics Journal, Royal Economic Society, vol. 22(2), pages 131-152.
    17. Mensi, Walid & Hammoudeh, Shawkat & Reboredo, Juan Carlos & Nguyen, Duc Khuong, 2014. "Do global factors impact BRICS stock markets? A quantile regression approach," Emerging Markets Review, Elsevier, vol. 19(C), pages 1-17.
    18. Kaza, Nikhil, 2010. "Understanding the spectrum of residential energy consumption: A quantile regression approach," Energy Policy, Elsevier, vol. 38(11), pages 6574-6585, November.
    19. 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.
    20. Conyon, Martin J. & He, Lerong, 2017. "Firm performance and boardroom gender diversity: A quantile regression approach," Journal of Business Research, Elsevier, vol. 79(C), pages 198-211.
    21. 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).
    22. Binder, Martin & Coad, Alex, 2011. "From Average Joe's happiness to Miserable Jane and Cheerful John: using quantile regressions to analyze the full subjective well-being distribution," Journal of Economic Behavior & Organization, Elsevier, vol. 79(3), pages 275-290, August.
    23. Maghyereh, Aktham I. & Awartani, Basel & Abdoh, Hussein, 2019. "The co-movement between oil and clean energy stocks: A wavelet-based analysis of horizon associations," Energy, Elsevier, vol. 169(C), pages 895-913.
    24. Inchauspe, Julian & Ripple, Ronald D. & Trück, Stefan, 2015. "The dynamics of returns on renewable energy companies: A state-space approach," Energy Economics, Elsevier, vol. 48(C), pages 325-335.
    25. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    26. Shin, Won & Han, Jeongyun & Rhee, Wonjong, 2021. "AI-assistance for predictive maintenance of renewable energy systems," Energy, Elsevier, vol. 221(C).
    27. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    28. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    29. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    30. Ahn, Jonghoon & Cho, Soolyeon, 2017. "Anti-logic or common sense that can hinder machine’s energy performance: Energy and comfort control models based on artificial intelligence responding to abnormal indoor environments," Applied Energy, Elsevier, vol. 204(C), pages 117-130.
    31. Nusair, Salah A. & Olson, Dennis, 2019. "The effects of oil price shocks on Asian exchange rates: Evidence from quantile regression analysis," Energy Economics, Elsevier, vol. 78(C), pages 44-63.
    32. Qin, Yun & Hong, Kairong & Chen, Jinyu & Zhang, Zitao, 2020. "Asymmetric effects of geopolitical risks on energy returns and volatility under different market conditions," Energy Economics, Elsevier, vol. 90(C).
    33. Gallego-Álvarez, Prof. Isabel & Ortas, Prof. Eduardo, 2017. "Corporate environmental sustainability reporting in the context of national cultures: A quantile regression approach," International Business Review, Elsevier, vol. 26(2), pages 337-353.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Urom, Christian & Ndubuisi, Gideon & Guesmi, Khaled, 2024. "Global macroeconomic factors and the connectedness among NFTs and (un)conventional assets," Research in International Business and Finance, Elsevier, vol. 71(C).
    2. Wang, Yong & Zhao, Wenhao & Ma, Xuejiao, 2024. "The spatial spillover impact of artificial intelligence on energy efficiency: Empirical evidence from 278 Chinese cities," Energy, Elsevier, vol. 312(C).
    3. 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).
    4. Yang, Shengyao & Zhu, Meng Nan & Yu, Haiyan, 2024. "Are artificial intelligence and blockchain the key to unlocking the box of clean energy?," Energy Economics, Elsevier, vol. 134(C).
    5. Zhang, Yan & Xu, Yushi & Zhu, Xintong & Huang, Jionghao, 2024. "Coal price shock propagation through sectoral financial interconnectedness in China's stock market: Quantile coherency network modelling and shock decomposition analysis," Journal of Commodity Markets, Elsevier, vol. 34(C).
    6. Boungou, Whelsy & Urom, Christian, 2023. "Climate change-related risks and bank stock returns," Economics Letters, Elsevier, vol. 224(C).

    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. 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).
    2. 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.
    3. Maghyereh, Aktham & Abdoh, Hussein, 2021. "The impact of extreme structural oil-price shocks on clean energy and oil stocks," Energy, Elsevier, vol. 225(C).
    4. Çelik, İsmail & Sak, Ahmet Furkan & Höl, Arife Özdemir & Vergili, Gizem, 2022. "The dynamic connectedness and hedging opportunities of implied and realized volatility: Evidence from clean energy ETFs," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    5. Asl, Mahdi Ghaemi & Canarella, Giorgio & Miller, Stephen M., 2021. "Dynamic asymmetric optimal portfolio allocation between energy stocks and energy commodities: Evidence from clean energy and oil and gas companies," Resources Policy, Elsevier, vol. 71(C).
    6. 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.
    7. Sohag, Kazi & Hassan, M. Kabir & Bakhteyev, Stepan & Mariev, Oleg, 2023. "Do green and dirty investments hedge each other?," Energy Economics, Elsevier, vol. 120(C).
    8. Umar, Muhammad & Farid, Saqib & Naeem, Muhammad Abubakr, 2022. "Time-frequency connectedness among clean-energy stocks and fossil fuel markets: Comparison between financial, oil and pandemic crisis," Energy, Elsevier, vol. 240(C).
    9. 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).
    10. 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).
    11. 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.
    12. Hemrit, Wael & Benlagha, Noureddine, 2021. "Does renewable energy index respond to the pandemic uncertainty?," Renewable Energy, Elsevier, vol. 177(C), pages 336-347.
    13. 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).
    14. 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).
    15. Syed Kumail Abbas Rizvi & Bushra Naqvi & Nawazish Mirza, 2022. "Is green investment different from grey? Return and volatility spillovers between green and grey energy ETFs," Annals of Operations Research, Springer, vol. 313(1), pages 495-524, June.
    16. Capucine Nobletz, 2021. "Return spillovers between green energy indexes and financial markets: a first sectoral approach," EconomiX Working Papers 2021-24, University of Paris Nanterre, EconomiX.
    17. 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).
    18. Emre Cevik & Emrah I Cevik & Sel Dibooglu & Raif Cergibozan & Mehmet Fatih Bugan & Mehmet Akif Destek, 2024. "Connectedness and risk spillovers between crude oil and clean energy stock markets," Energy & Environment, , vol. 35(7), pages 3319-3339, November.
    19. Fahmy, Hany, 2022. "The rise in investors’ awareness of climate risks after the Paris Agreement and the clean energy-oil-technology prices nexus," Energy Economics, Elsevier, vol. 106(C).
    20. Dan Nie & Yanbin Li & Xiyu Li & Xuejiao Zhou & Feng Zhang, 2022. "The Dynamic Spillover between Renewable Energy, Crude Oil and Carbon Market: New Evidence from Time and Frequency Domains," Energies, MDPI, vol. 15(11), pages 1-28, May.

    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:tefoso:v:183:y:2022:i:c:s0040162522003663. 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.sciencedirect.com/science/journal/00401625 .

    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.