Politická ekonomie X:X | DOI: 10.18267/j.polek.1561

Synergistic Dynamics: The Interplay Between Clean Power, Artificial Intelligence, and Global Assets Across Economic Quantiles

Seyi Saint Akadiri ORCID..., Oktay Özkan ORCID...
Seyi Saint Akadiri, Institute of Graduate Studies and Research, Cyprus International University, North Nicosia, Northern Cyprus, Turkey
Oktay Özkan, Department of Business Administration, Faculty of Economics and Administrative Sciences, Tokat Gaziosmanpaºa University, Tokat, Turkey; ARUCAD Research Centre, Arkin University of Creative Arts and Design, Kyrenia, Northern Cyprus, Turkey

This study examines nonlinear, quantile-dependent spillovers among clean power, artificial intelligence (AI), and global financial assets using the quantile-on-quantile VAR connectedness framework with generalised forecast-error generalised variance decomposition. Using daily data from June 1, 2018, to October 18, 2024, the analysis covers clean power, AI, commodities, equities, cryptocurrencies, green bonds, ESG, and alternative investment indices. The results reveal strong state-dependent interconnectedness. The total connectedness index (TCI) is lowest around the median quantiles (0.50-0.50), averaging 63-65, but rises sharply in extreme conditions, exceeding 95 at both lower (0.05-0.05) and upper (0.95-0.95) quantiles. Net connectedness estimates show that gold and green bonds are persistent net receivers of shocks, particularly in downside markets, confirming their safe-haven roles. Equities and cryptocurrencies serve as dominant net transmitters in the upper quantiles (0.75-0.95), amplifying systemic risk. Clean power and ESG assets exhibit increasing bidirectional spillovers, indicating deeper financial integration. Rolling-window results show that spillovers intensify during pandemic, geopolitical, and monetary-tightening episodes, with clear implications for tail-risk management and sustainable portfolio design.

Keywords: Financial interconnectedness, spillover effects, systemic risk, clean energy investments

Received: May 15, 2025; Revised: March 16, 2026; Accepted: April 7, 2026; Prepublished online: August 4, 2026 

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    References

    1. Arfaoui, N., Roubaud, D., Naeem, M. A. (2025). Energy transition metals, clean and dirty energy markets: A quantile-on-quantile risk transmission analysis of market dynamics: Energy Economics, 143, 108250. Go to original source...
    2. Akadiri, S. S., Adebayo, T. S. (2022). The criticality of financial risk to environmental sustainability in top carbon-emitting countries. Environmental Science and Pollution Research, 29(56), 84226-84242. Go to original source...
    3. Alam, M. R., Hoque, M. E., Naeem, M. A. (2024). Oil Shocks and Green Markets: Evidence from Cross-Spectral Quantile Coherency and Time-Varying Quantile Frequency Connectedness.
    4. Abbas, S., Saha, T., Sinha, A. (2024). Price response of top-five renewable energy firms to Russia-Ukraine conflict: An advanced quantile analysis to achieve net-zero in the United States of America. Journal of Cleaner Production, 442, 141153. Go to original source...
    5. Aloui, C., Mejri, S., Hamida, H. B., Yildirim, R. (2025). Green bonds and clean energy stocks: Safe havens against global uncertainties? A wavelet quantile-based examination. The North American Journal of Economics and Finance, 76, 102310. Go to original source...
    6. Abakah, E. J. A., Tiwari, A. K., Ghosh, S., Doğan, B. (2023). The dynamic effects of Bitcoin, fintech, and artificial intelligence stocks on eco-friendly assets, Islamic stocks, and conventional financial markets: Another look using quantile-based approaches: Technological Forecasting and Social Change, 192, 122566. Go to original source...
    7. Adebayo, T. S., Özkan, O. (2024). Evaluating the role of financial globalisation and oil consumption on ecological quality: A new perspective from quantile-on-quantile Granger causality. Heliyon, 10(2). https://doi.org/10.1016/j.heliyon.2024.e24636 Go to original source...
    8. Ambuli, T. V., Venkatesan, S., Sampath, K., Devi, K., Kumaran, S. (2024, August). AI-Driven Financial Management Optimising Investment Portfolios through Machine Learning. In 2024, the 7th International Conference on Circuit Power and Computing Technologies (ICCPCT) (Vol. 1, pp. 1822-1828). IEEE. Go to original source...
    9. Akadiri, S. S., Özkan, O. (2025). Financial turbulence and decarbonisation: evidence from energy transition materials. Mineral Economics, 1-28. Go to original source...
    10. Arfaoui, N., Roubaud, D., Naeem, M. A. (2025). Energy transition metals, clean and dirty energy markets: A quantile-on-quantile risk transmission analysis of market dynamics. Energy Economics, 143, 108250. https://doi.org/10.1016/j.eneco.2025.108250 Go to original source...
    11. Baker, M., Bergstresser, D., Serafeim, G., Wurgler, J. (2018). Financing the response to climate change: The pricing and ownership of US green bonds (No. w25194). National Bureau of Economic Research. Go to original source...
    12. Baker, M., Bergstresser, D., Serafeim, G., Wurgler, J. (2022). The pricing and ownership of US green bonds. Annual review of financial economics, 14(1), 415-437. Go to original source...
    13. Broock, W. A., Scheinkman, J. A., Dechert, W. D., LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric Reviews, 15(3), 197-235. https://doi.org/10.1080/07474939608800353 Go to original source...
    14. Bouri, E., Gabauer, D., Gupta, R., Tiwari, A. K. (2021). Volatility connectedness of major cryptocurrencies: The role of investor happiness. Journal of Behavioural and Experimental Finance, 30, 100463. Go to original source...
    15. Bouri, E., Shahzad, S. J. H., Roubaud, D., Kristoufek, L., Lucey, B. (2020). Bitcoin, gold, and commodities as safe havens for stocks: New insight through wavelet analysis. The Quarterly Review of Economics and Finance, 77, 156-164. Go to original source...
    16. Baruník, J., Koèenda, E. (2019). Total, asymmetric, and frequency-connectedness between oil and forex markets. The Energy Journal, 40(2_suppl), 157-174. Go to original source...
    17. Baur, D. G., Lucey, B. M. (2010). Is gold a hedge or a haven? An analysis of stocks, bonds, and gold. Financial Review, 45(2), 217-229. Go to original source...
    18. Baruník, J., Køehlík, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271-296. Go to original source...
    19. Cevik, E., Cevik, E. I., Dibooglu, S., Cergibozan, R., Bugan, M. F., Destek, M. A. (2024). Connectedness and risk spillovers between crude oil and clean energy stock markets. Energy & Environment, 35(7), 3319-3339. Go to original source...
    20. Chen, Y., Qi, H. (2024). Dynamic interplay between Chinese energy, renewable energy stocks, and commodity markets: Time-frequency causality study. Renewable Energy, 228, 120578. Go to original source...
    21. Diebold, F. X., Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119-134. Go to original source...
    22. Diebold, F. X., Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57-66. Go to original source...
    23. Gubareva, M., Shafiullah, M., Teplova, T. (2025). Cross-quantile risk assessment: The interplay of crude oil, artificial intelligence, clean tech, and other markets. Energy Economics, 141, 108085. Go to original source...
    24. Gabauer, D., Stenfors, A. (2024). Quantile-on-quantile connectedness measures: Evidence from the US treasury yield curve. Finance Research Letters, 60, 104852. https://doi.org/10.1016/j.frl.2023.104852 Go to original source...
    25. Ghaemi Asl, M., Adekoya, O. B., Oliyide, J. A., Shahzad, U., Tajmir Riahi, H. (2025). Multifractal Detrended Cross-Correlation Patterns in the Global Energy and Green Investment Markets Dynamics: Insights From Pre-COVID-19 and Pandemic Experiences. International Journal of Finance & Economics. Go to original source...
    26. Gubareva, M., Shafiullah, M., Teplova, T. (2025). Cross-quantile risk assessment: The interplay of crude oil, artificial intelligence, clean tech, and other markets. Energy Economics, 141, 108085. https://doi.org/10.1016/j.eneco.2024.108085 Go to original source...
    27. Hasan, M. B., Hassan, M. K., Karim, Z. A., Rashid, M. M. (2022). Exploring the hedge and haven properties of cryptocurrency in policy uncertainty. Finance Research Letters, 46, 102272. Go to original source...
    28. He, T. (2025). Asymmetric tail risk spillovers between carbon emission allowance and energy markets: evidence from China. Applied Economics, 1-18. Go to original source...
    29. Jarque, C. M., Bera, A. K. (1980). Efficient tests for normality, homoscedasticity, and serial independence of regression residuals. Economics Letters, 6(3), 255-259. https://doi.org/10.1016/0165-1765(80)90024-5 Go to original source...
    30. Jiang, W., Dong, L., Chen, Y. (2023). Time-frequency connectedness among traditional/new energy, green finance, and ESG in pre-and post-Russia-Ukraine war periods. Resources Policy, 83, 103618. Go to original source...
    31. Kumar, B. R., Paramaiah, C. (2024). Sustainable Finance and Investment: The Intersection of Profitability and Environmental Impact. Library of Progress-Library Science, Information Technology & Computer, 44(3).
    32. Koop, G., Pesaran, M. H., Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119-147. Go to original source...
    33. Liu, T., Zhang, Y., Zhang, W., Hamori, S. (2024). Quantile Connectedness of Uncertainty Indices, Carbon Emissions, Energy, and Green Assets: Insights from Extreme Market Conditions. Energies, 17(22), 5806. Go to original source...
    34. Madaleno, M., Taskin, D., Dogan, E., Tzeremes, P. (2023). A dynamic connectedness analysis between rare earth prices and renewable energy. Resources Policy, 85, 103888. Go to original source...
    35. Mhadhbi, M. (2024). The interconnected carbon, fossil fuels, and clean energy markets: Exploring Europe and China's perspectives on climate change. Finance Research Letters, 62, 105185. Go to original source...
    36. Markowitz, H. (1952). Modern portfolio theory. Journal of Finance, 7(11), 77-91. Go to original source...
    37. Niu, H., Cao, S. (2024). Spillover and dependence between Chinese carbon and new energy stock markets: A cross-quantile perspective. Journal of Cleaner Production, 479, 144027. Go to original source...
    38. Okhrin, Y., Uddin, G. S., Yahya, M. (2023). Nonlinear and asymmetric interconnectedness of crude oil with financial and commodity markets. Energy Economics, 125, 106853. Go to original source...
    39. Olasehinde-Williams, G., Özkan, O., Akadiri, S. S. (2023). Dynamic risk connectedness of crude oil price and sustainable investment in the United States: evidence from DCC-GARCH. Environmental Science and Pollution Research, 30(41), 94976-94987. Go to original source...
    40. Olasehinde-Williams, G., Akadiri, S. S. (2025). Sustainable Markets Dynamics Under Crude Oil Volatility in the United States. Energy Research Letters, 6(Early View). Go to original source...
    41. Özkan, O., Usman, O., Saint Akadiri, S. (2025). An asymmetric role of economic complexity, resource efficiency, and renewable energy on consumption-based CO2 emissions in China: energy, 138269. Go to original source...
    42. Pesaran, H. H., Shin, Y. (1998). Generalised impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17-29. Go to original source...
    43. Pattnaik, D., Ray, S., Raman, R. (2024). Artificial intelligence and machine learning applications in the financial services industry: A bibliometric review. Heliyon, 10(1). Go to original source...
    44. Rubbaniy, G., Khalid, A. A., Rizwan, M. F., Ali, S. (2022). Are ESG stocks a haven during COVID-19? Studies in Economics and Finance, 39(2), 239-255. Go to original source...
    45. SPG (2024). S&P Global. Retrieved October 20, 2024, from https://www.spglobal.com/en
    46. Sharma, A., Tiwari, A. K., Abakah, E. J. A., Owusu, F. B. (2024). A cross-quantile correlation and causality-in-quantile analysis on the relationship between green investments and energy commodities during the COVID-19 pandemic. Studies in Economics and Finance, 41(3), 478-501. Go to original source...
    47. Shi, F., Xiong, H., Ji, M. (2024). Quantile connectedness between China's new energy market and other key financial markets. Applied Economics, 1-18. Go to original source...
    48. Tiwari, A. K., Trabelsi, N., Abakah, E. J. A., Nasreen, S., Lee, C. C. (2023). An empirical analysis of the dynamic relationship between clean and dirty energy markets. Energy Economics, 124, 106766. Go to original source...
    49. Tiwari, S., Khan, S., Mohammed, K. S., Bilan, Y. (2024). Connectedness between artificial intelligence, clean energy, and conventional energy markets: Fresh findings from CQ and WLMC techniques. Gondwana Research, 136, 92-103. Go to original source...
    50. Zhang, J., Chen, X., Wei, Y., Bai, L. (2023). Does the connectedness among fossil energy returns matter for renewable energy stock returns? Fresh insights from the Cross-Quantilogram analysis. International Review of Financial Analysis, 88, 102659. Go to original source...
    51. Zeng, H., Abedin, M. Z., Ahmed, A. D. (2024). Quartile risk dependence between clean energy markets and the US travel and leisure index. Current Issues in Tourism, 1-25. Go to original source...

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