Artificial Intelligence and Volatility Connectedness in Energy Markets
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Abstract
This study analyses the role of artificial intelligence (AI) indices as sources of volatility transmission between clean energy and fossil fuel markets within a time–frequency framework. Using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) frequency connectedness approach, inter-market dynamics are decomposed into short- and medium-to-long-term horizons. The results indicate a high degree of connectedness across the markets under consideration, driven predominantly by short-term components. Directional connectedness findings show that AI indices generally act as net transmitters of volatility relative to clean energy and fossil fuel markets. The analysis further reveals that connectedness varies over time and intensifies markedly during periods of global uncertainty. Overall, the findings indicate that AI indices extend beyond a technological dimension and are meaningfully associated with risk dynamics in energy markets, with implications for financial stability and investment decision-making.
Jel Codes: C32, G15, O33, Q35, Q42
Keywords: Artificial intelligence; Clean energy; Fossil fuels; Volatility connectedness; Time frequency analysis
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Gökgöz, H. (2026) “Artificial Intelligence and Volatility Connectedness in Energy Markets”, World Journal of Applied Economics, 12(1), pp. 1-19. doi: 10.22440/wjae.12.1.1.
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References
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Kuang, W. (2021). Are clean energy assets a safe haven for international equity markets? Journal of Cleaner Production, 302 , 127006. doi:10.1016/j.jclepro.2021.127006
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Yousaf, I., Youssef, M., & Goodell, J. W. (2024). Tail connectedness between artificial intelligence tokens, artificial intelligence ETFs, and traditional asset classes. Journal of International Financial Markets, Institutions and Money, 91 , 101929. doi:10.1016/j.intfin.2023.101929
Zeng, H., Abedin, M. Z., Zhou, X., & Lu, R. (2024). Measuring the extreme linkages and time-frequency co-movements among artificial intelligence and clean energy indices. International Review of Financial Analysis, 92 , 103073. doi:10.1016/j.irfa.2024.103073
Zeng, H., Lu, R., & Ahmed, A. D. (2023). Return connectedness and multiscale spillovers across clean energy indices and grain commodity markets around COVID-19 crisis. Journal of Environmental Management, 340 , 117912. doi:10.1016/j.jenvman.2023.117912
Zhang, F., Wang, Q., & Li, R. (2025). How does clean energy reshape the nonlinear relationship between artificial intelligence and carbon emissions? Evidence from renewable and nuclear energy. Energy Economics, 149 , 108785. doi:10.1016/j.eneco.2025.108785
Zhang, H., Fang, B., He, P., & Gao, W. (2024). The asymmetric impacts of artificial intelligence and oil shocks on clean energy industries by considering COVID-19. Energy, 291 , 130197. doi:10.1016/j.energy.2023.130197
Zhao, Q., Wang, L., Stan, S.-E., & Mirza, N. (2024). Can artificial intelligence help accelerate the transition to renewable energy? Energy Economics, 134 , 107584. doi:10.1016/j.eneco.2024.107584
Zheng, J., Wen, B., Jiang, Y., Wang, X., & Shen, Y. (2023). Risk spillovers across geopolitical risk and global financial markets. Energy Economics, 127 , 107051. doi:10.1016/j.eneco.2023.107051
Adi, T. W. (2022). The International Gas and Crude Oil Price Variability Effect on Indonesian Coal Mining Companies Listed at IDX. International Journal of Energy Economics and Policy, 12 (5), 1-10. doi:10.32479/ijeep.13263
Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13 (4), 84. doi:10.3390/jrfm13040084
Barunik, J., & Krehlik, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16 (2), 271-296. doi:10.1093/jjfinec/nby001
Caglar, A. E., Rej, S., & Mert, M. (2025). Zero-emission vision: The roles of artificial intelligence and clean energy in environmental sustainability improvement. Journal of Environmental Management, 3950 , 127880. doi:10.1016/j.jenvman.2025.127880
Castrejon-Campos, O., Aye, L., & Hui, F. K. P. (2020). Making policy mixes more robust: An integrative and interdisciplinary approach for clean energy transitions. Energy Research & Social Science, 64 , 101425. doi:10.1016/j.erss.2020.101425
Chatziantoniou, I., Abakah, E. J. A., Gabauer, D., & Tiwari, A. K. (2022). Quantile time–frequency price connectedness between green bond, green equity, sustainable investments and clean energy markets. Journal of Cleaner Production, 361 , 132088. doi:10.1016/j.jclepro.2022.132088
Chatziantoniou, I., Gabauer, D., & Gupta, R. (2023). Integration and risk transmission in the market for crude oil: new evidence from a time-varying parameter frequency connectedness approach. Resources Policy, 103729. doi:10.1016/j.resourpol.2023.103729
Coskun, M., Khan, N., Saleem, A., & Hammoudeh, S. (2023). Spillover connectedness nexus geopolitical oil price risk, clean energy stocks, global stock, and commodity markets. Journal of Cleaner Production, 429 , 139583. doi:10.1016/j.jclepro.2023.139583
Dammak, W., Gokgoz, H., & Jeribi, A. (2025). Time-Frequency Connectedness in Global Banking: Volatility and Return Dynamics of BRICS and G7 Banks. Global Economy Journal, 25 (01n02), 2550004. doi:10.1142/S2194565925500046
Dawar, I., Dutta, A., Bouri, E., & Saeed, T. (2021). Crude oil prices and clean energy stock indices: Lagged and asymmetric effects with quantile regression. Renewable Energy, 163 , 288-299. doi:10.1016/j.renene.2020.08.162
Dias, R., Teixeira, N., Alexandre, P., & Chambino, M. (2023). Exploring the connection between clean and dirty energy: Implications for the transition to a carbon-resilient economy. Energies, 16 (13), 4982. doi:10.3390/en16134982
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. doi:10.1016/j.ijforecast.2011.02.006
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. doi:10.1016/j.jeconom.2014.04.012
Elie, B., Naji, J., Dutta, A., & Uddin, G. S. (2019). Gold and crude oil as safe-haven assets for clean energy stock indices: Blended copulas approach. Energy, 178 , 544-553. doi:10.1016/j.energy.2019.04.155
Gustafsson, R., Dutta, A., & Bouri, E. (2022). Are energy metals hedges or safe havens for clean energy stock returns? Energy, 244 , 122708. doi:10.1016/j.energy.2021.122708
Gokgoz, H., & Kandemir, T. (2023). Trampadan Kripto Paraya. Nobel, Ankara.
Hwang, Y. S., Um, J.-S., & Schluter, S. (2020). Evaluating the Mutual Relationship between IPAT/Kaya Identity Index and ODIAC-Based GOSAT Fossil-Fuel CO2 Flux: Potential and Constraints in Utilizing Decomposed Variables. International Journal of Environmental Research and Public Health, 17 (6), 5976. doi:10.3390/ijerph17165976
Khoury, R. E., Alshater, M. M., Li, Y., & Xiong, X. (2024). Quantile time-frequency connectedness among G7 stock markets and clean energy markets. The Quarterly Review of Economics and Finance, 93 , 71-90. doi:10.1016/j.qref.2023.11.004
Koop, G., & Korobilis, D. (2014). A new index of financial conditions. European Economic Review, 71 , 101–116. doi:10.1016/j.euroecorev.2014.07.002
Kuang, W. (2021). Are clean energy assets a safe haven for international equity markets? Journal of Cleaner Production, 302 , 127006. doi:10.1016/j.jclepro.2021.127006
Kyriakarakos, G. (2025). Artificial Intelligence and the Energy Transition. Sustainability, 17 (3), 1140. doi:10.3390/su17031140
Li, Z., Xing, Y., Shao, X., Zhong, Y., & Su, Y. H. (2025). Transitioning the energy landscape: AI’s role in shifting from fossil fuels to renewable energy. Energy Economics, 108729. doi:10.1016/j.eneco.2025.108729
Lin, N., Sun, Z., Li, Z., & Qin, C. (2025). The path to green Innovation: The impact of artificial intelligence applications on corporate continuous green innovation. Journal of Environmental Management, 395 , 127724. doi:10.1016/j.jenvman.2025.127724
Ma, C. Q., Liu, X., Klein, T., & Ren, Y. S. (2025). Decoding the nexus: How fintech and AI stocks drive the future of sustainable finance. International Review of Economics & Finance, 98 , 103877. doi:10.1016/j.iref.2025.103877
Maleki, R., Asadnia, M., & Razmjou, A. (2022). Artificial Intelligence-Based Material Discovery for Clean Energy Future. Advanced Intelligent Systems, 4 (10), 2200073. doi:10.1002/aisy.202200073
Memon, B. A., Aslam, F., Asadova, S., & Ferreira, P. (2023). Are clean energy markets efficient? A multifractal scaling and herding behavior analysis of clean and renewable energy markets before and during the COVID19 pandemic. Heliyon, 9 (12), e22694. doi:10.1016/j.heliyon.2023.e22694
Mustafa, A., Tariq, Z., Mahmoud, M., & Abdulraheem, A. (2022). Artificial Intelligence Approach for Predicting the Shale Brittleness Index - A Middle East Basin Case Study. In The SPE EuropEC - Europe Energy Conference featured at the 83rd EAGE Annual Conference & Exhibition, Madrid, Spain. doi:10.2118/209707-MS
Nasdaq OMX QGreen. (2025). https://indexes.nasdaqomx.com/Index/Overview/QGREEN.
Nepal, R., Zhao, X., Dong, K., Wang, J., & Sharif, A. (2025). Can artificial intelligence technology innovation boost energy resilience? The role of green finance. Energy Economics, 142 , 108159. doi:10.1016/j.eneco.2024.108159
Qi, S., Pang, L., Li, X., & Huang, L. (2025). The dynamic connectedness in the “carbonenergy-green finance” system: The role of climate policy uncertainty and artificial intelligence. Energy Economics, 143 , 108241. doi:10.1016/j.eneco.2025.108241
Raggad, B., & Bouri, E. (2025). Artificial intelligence and clean/dirty energy markets: tail-based pairwise connectedness and portfolio implications. Future Business Journal, 11 (1), 1-24. doi:10.1186/s43093-025-00451-8
Sanyel, S., & Wuables, D. J. (2022). The potential impact of a clean energy society on air quality. Earth’s Future, 10 , e2021EF002558. doi:10.1029/2021EF002558
S&P Kensho AI. (2025). https://www.spglobal.com/spdji/en/index-family/thematics/innovative-technology/ai-robotics-automation/#indices.
Tang, C., Aruga, K., & Hu, Y. (2023). The Dynamic Correlation and Volatility Spillover among Green Bonds, Clean Energy Stock, and Fossil Fuel Market. Sustainability, 15 (8), 6586. doi:10.3390/su15086586
Tiwari, A. K., Abakah, E. J. A., Le, T.-L., & Hiz, D. I. L. (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, 163 , 120434. doi:10.1016/j.techfore.2020.120434
Wang, B., Wang, J., Dong, K., & Nepal, R. (2024). How does artificial intelligence affect high-quality energy development? Achieving a clean energy transition society. Energy Policy, 186 , 114010. doi:10.1016/j.enpol.2024.114010
Wang, B., Yang, Z., Xuan, J., & Jiao, K. (2020). Crises and opportunities in terms of energy and AI technologies during the COVID-19 pandemic. Energy and AI , 1 , 100013. doi:10.1016/j.egyai.2020.100013
Wang, Q., Zhang, S., & Li, R. (2026). Artificial intelligence in the renewable energy transition: The critical role of financial development. Renewable and Sustainable Energy Reviews, 226 , 116280. doi:10.1016/j.rser.2025.116280
Wang, W., Zhou, Z., & Liu, C. (2019). Index systems for analysis and evaluation of clean energy development based on AHP. In IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), Changsha, China (p. 1353-1356). doi:10.1109/EI247390.2019.9061794
Xu, B. (2025). Inclusive green finance approach to assess energy resilience: Integrating artificial intelligence (AI) utilization in energy strategy perspective. Energy Strategy Reviews, 59 , 101696. doi:10.1016/j.esr.2025.101696
Yousaf, I., Youssef, M., & Goodell, J. W. (2024). Tail connectedness between artificial intelligence tokens, artificial intelligence ETFs, and traditional asset classes. Journal of International Financial Markets, Institutions and Money, 91 , 101929. doi:10.1016/j.intfin.2023.101929
Zeng, H., Abedin, M. Z., Zhou, X., & Lu, R. (2024). Measuring the extreme linkages and time-frequency co-movements among artificial intelligence and clean energy indices. International Review of Financial Analysis, 92 , 103073. doi:10.1016/j.irfa.2024.103073
Zeng, H., Lu, R., & Ahmed, A. D. (2023). Return connectedness and multiscale spillovers across clean energy indices and grain commodity markets around COVID-19 crisis. Journal of Environmental Management, 340 , 117912. doi:10.1016/j.jenvman.2023.117912
Zhang, F., Wang, Q., & Li, R. (2025). How does clean energy reshape the nonlinear relationship between artificial intelligence and carbon emissions? Evidence from renewable and nuclear energy. Energy Economics, 149 , 108785. doi:10.1016/j.eneco.2025.108785
Zhang, H., Fang, B., He, P., & Gao, W. (2024). The asymmetric impacts of artificial intelligence and oil shocks on clean energy industries by considering COVID-19. Energy, 291 , 130197. doi:10.1016/j.energy.2023.130197
Zhao, Q., Wang, L., Stan, S.-E., & Mirza, N. (2024). Can artificial intelligence help accelerate the transition to renewable energy? Energy Economics, 134 , 107584. doi:10.1016/j.eneco.2024.107584
Zheng, J., Wen, B., Jiang, Y., Wang, X., & Shen, Y. (2023). Risk spillovers across geopolitical risk and global financial markets. Energy Economics, 127 , 107051. doi:10.1016/j.eneco.2023.107051