Journal of Intelligent Strategic Management

Journal of Intelligent Strategic Management

Designing a Forecasting Model for Energy Crisis Contagion Across Economic Sectors Using the EGARCH Approach

Document Type : Original Article

Author
Mohammad Hossein Ardestani
Master of Quantitative Investment, University of New Brunswick, Canada.
10.87453/bumara.2026.373601.0877
Abstract
Energy crises, following the COVID-19 pandemic, geopolitical tensions, sharp increases in natural gas prices, supply chain disruptions, and policy uncertainty, have become one of the most important sources of volatility across economic sectors. The main objective of this study is to examine whether the magnitude and transmission path of volatility contagion from oil, gas, gasoline, and heating fuel markets to economic sectors can be predicted using an exponential generalized autoregressive conditional heteroskedasticity (EGARCH) framework combined with a composite energy crisis index.
To address this question, daily data covering the period from 2020 to June 9, 2026 were collected for five energy markets and twelve tradable sectoral indices. After constructing an energy crisis index, an augmented EGARCH model with a contagion component embedded in the conditional variance equation was specified and estimated.
The empirical findings indicate that energy crisis days significantly increase the conditional volatility of financial, industrial, real estate, basic materials, consumer staples, and communication sectors. Specifically, volatility increases during crisis periods were estimated at 44.2% for the financial sector, 43.2% for the industrial sector, and 39.2% for real estate. The contagion coefficient in the log-variance equation was higher for consumer staples, utilities, basic materials, real estate, and technology compared to other sectors. Moreover, the negative sign of the asymmetry parameter confirms that adverse news and negative shocks amplify future volatility more strongly than positive shocks.
The main contribution of this paper is the development of an early warning framework for intelligent strategic risk management. This framework can be applied at the firm, institutional investor, and policymaker levels for sectoral resilience prioritization, risk budgeting, and vulnerability assessment of value chains.
Keywords
Energy crisis
volatility contagion
exponential GARCH
economic sectors
intelligent strategic management
risk forecasting
Subjects
Adekoya, O. B., & Oliyide, J. A. (2021). How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques. *Resources Policy*. https://doi.org/10.1016/j.resourpol.2020.101898
Akyildirim, E., Cepni, O., Molnár, P., & Uddin, G. S. (2022). Connectedness of energy markets around the world during the COVID-19 pandemic. *Energy Economics*. https://doi.org/10.1016/j.eneco.2022.105900
Qi, H., Ma, L., Peng, P., Chen, H., et al. (2022). Dynamic connectedness between clean energy stock markets and energy commodity markets during times of COVID-19: Empirical evidence from China. *Resources Policy*. https://doi.org/10.1016/j.resourpol.2022.103094
Gong, X., & Xu, J. (2022). Geopolitical risk and dynamic connectedness between commodity markets. *Energy Economics*. https://doi.org/10.1016/j.eneco.2022.106028
Fang, Y., & Shao, Z. (2022). The Russia-Ukraine conflict and volatility risk of commodity markets. *Finance Research Letters*. https://doi.org/10.1016/j.frl.2022.103264
Karkowska, R., & Urjasz, S. (2023). How does the Russian-Ukrainian war change connectedness and hedging opportunities? Comparison between dirty and clean energy markets versus global stock indices. *Journal of International Financial Markets, Institutions and Money*. https://doi.org/10.1016/j.intfin.2023.101768
Al-Fayoumi, N., Bouri, E., & Abuzayed, B. (2023). Decomposed oil price shocks and GCC stock market sector returns and volatility. *Energy Economics*. https://doi.org/10.1016/j.eneco.2023.106930
Sevillano, M. C., Jareño, F., López, R., & Esparcia, C. (2024). Connectedness between oil price shocks and US sector returns: Evidence from TVP-VAR and wavelet decomposition. *Energy Economics*. https://doi.org/10.1016/j.eneco.2024.107398
Saif-Alyousfi, A. Y. H. (2025). Energy shocks and stock market returns under COVID-19: New insights from the United States. *Energy*. https://doi.org/10.1016/j.energy.2025.134546
Rahman, S. (2022). The asymmetric effects of oil price shocks on the U.S. stock market. *Energy Economics*. https://doi.org/10.1016/j.eneco.2021.105694
Zheng, T., Gong, L., & Ye, S. (2023). Global energy market connectedness and inflation at risk. *Energy Economics*. https://doi.org/10.1016/j.eneco.2023.106975
Ahmed, R., Chen, X. H., Kumpamool, C., & Nguyen, D. T. K. (2023). Inflation, oil prices, and economic activity in recent crisis: Evidence from the UK. *Energy Economics*. https://doi.org/10.1016/j.eneco.2023.106918
Ha, J., Kose, M. A., Ohnsorge, F., & Yilmazkuday, H. (2023). Understanding the global drivers of inflation: How important are oil prices? *Energy Economics*. https://doi.org/10.1016/j.eneco.2023.107096
Bigerna, S. (2023). Energy price shocks, exchange rates and inflation nexus. *Energy Economics*. https://doi.org/10.1016/j.eneco.2023.107156
Geng, J.-B., Chen, F.-R., Ji, Q., & Liu, B.-Y. (2021). Network connectedness between natural gas markets, uncertainty and stock markets. *Energy Economics*. https://doi.org/10.1016/j.eneco.2020.105001
Jin, Y., Zhao, H., Bu, L., & Zhang, D. (2023). Geopolitical risk, climate risk and energy markets: A dynamic spillover analysis. *International Review of Financial Analysis*. https://doi.org/10.1016/j.irfa.2023.102597
Zhu, Q., Ma, D., & Pan, Y. (2026). Dynamic spillover between geopolitical risk and energy markets: Portfolio hedging implication. *Energy Economics*. https://doi.org/10.1016/j.eneco.2026.109254
Liu, Z., Wang, Y., Yuan, X., Ding, Z., et al. (2025). Geopolitical risk and vulnerability of energy markets. *Energy Economics*. https://doi.org/10.1016/j.eneco.2024.108055
Zhao, Y., Chen, L., & Zhang, Y. (2024). Spillover effects of geopolitical risks on global energy markets: Evidence from CoVaR and CAViaR-EGARCH model. *Energy Exploration & Exploitation*. https://doi.org/10.1177/01445987231196617
Jiang, W., Zhang, Y., & Wang, K.-H. (2024). Analyzing the connectedness among geopolitical risk, traditional energy and carbon markets. *Energy*. https://doi.org/10.1016/j.energy.2024.131411
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*. https://doi.org/10.1016/j.jclepro.2023.139583
Qin, J., Cong, X., Ma, D., & Rong, X. (2024). Dynamic quantile connectedness between oil and stock markets: The impact of the interest rate. *Energy Economics*. https://doi.org/10.1016/j.eneco.2024.107741
Ziadat, S. A., Mensi, W., Al-Kharusi, S., Vo, X. V., et al. (2024). Are clean energy markets hedges for stock markets? A tail quantile connectedness regression. *Energy Economics*. https://doi.org/10.1016/j.eneco.2024.107757
Alomari, M., Khoury, R. E., Mensi, W., Vo, X. V., et al. (2024). Extreme downside risk connectedness between green energy and stock markets. *Energy*. https://doi.org/10.1016/j.energy.2024.133477
Xie, Q., Luo, C., Cong, X., & Wang, X. (2024). Volatility connectedness and its determinants of global energy stock markets. *Economic Systems*. https://doi.org/10.1016/j.ecosys.2024.101193
Li, A., & Zhong, B. (2025). Asymmetric spillover connectedness between clean energy markets and industrial stock markets: How uncertainties affect it. *PLOS ONE*. https://doi.org/10.1371/journal.pone.0316171
Tang, C., Aruga, K., & Hu, Y. (2023). The dynamic correlation and volatility spillover among green bonds, clean energy stock, and fossil fuel market. *Sustainability*. https://doi.org/10.3390/su15086586
Chen, J., Chen, Y., Gu, Q., & Zhou, W. (2023). Network evolution underneath the volatility spillover in traditional and clean energy markets. *Applied Economics*. https://doi.org/10.1080/00036846.2023.2166663
Ozkan, O., Abosedra, S., Sharif, A., & Alola, A. A. (2024). Dynamic volatility among fossil energy, clean energy and major assets: Evidence from the novel DCC-GARCH. *Economic Change and Restructuring*. https://doi.org/10.1007/s10644-024-09696-9
Dutta, A., Bouri, E., Saeed, T., & Vo, X. V. (2020). Impact of energy sector volatility on clean energy assets. *Energy*. https://doi.org/10.1016/j.energy.2020.118657
Zhang, L., Wang, L., Peng, L., & Luo, K. (2023). Measuring the response of clean energy stock price volatility to extreme shocks. *Renewable Energy*. https://doi.org/10.1016/j.renene.2023.02.066
Ghani, U., Zhu, B., Ghani, M., Khan, N., et al. (2023). Role of oil shocks in US stock market volatility: A new insight from GARCH-MIDAS perspective. *Resources Policy*. https://doi.org/10.1016/j.resourpol.2023.103933
Salisu, A. A., & Gupta, R. (2021). Oil shocks and stock market volatility of the BRICS: A GARCH-MIDAS approach. *Global Finance Journal*. https://doi.org/10.1016/j.gfj.2020.100546
Sreenu, N. (2022). Impact of crude oil price uncertainty on Indian stock market returns: Evidence from oil price volatility index. *Energy Strategy Reviews*. https://doi.org/10.1016/j.esr.2022.101002
Xiao, J., Xu, W., Liu, H., & Zhao, Y. (2025). Spillovers from oil price uncertainty to Chinese sectoral stock returns: New insights from effective transfer entropy. *International Review of Financial Analysis*. https://doi.org/10.1016/j.irfa.2025.104554
Liu, Z., Chen, S., Zhong, H., & Ding, Z. (2024). Coal price shocks, investor sentiment, and stock market returns. *Energy Economics*. https://doi.org/10.1016/j.eneco.2024.107619
Zeng, Q., Zhang, J., & Zhong, J. (2024). China’s futures market volatility and sectoral stock market volatility prediction. *Energy Economics*. https://doi.org/10.1016/j.eneco.2024.107429
Guo, X., Wang, Y., Hao, Y., & Zhang, W. (2023). Spillover effect among carbon bond market, carbon stock market and energy stock market: Evidence from China. *Finance Research Letters*. https://doi.org/10.1016/j.frl.2023.104521
Maneejuk, P., Huang, W., & Yamaka, W. (2025). Asymmetric volatility spillover effects from energy, agriculture, green bond, and financial market uncertainty on carbon market during major market crisis. *Energy Economics*. https://doi.org/10.1016/j.eneco.2025.108430
Ha, L. T. (2023). An application of QVAR dynamic connectedness between geopolitical risk and renewable energy volatility during the COVID-19 pandemic and Russia-Ukraine conflicts. *Journal of Environmental Management*. https://doi.org/10.1016/j.jenvman.2023.118290