The Effect of War Risks on the Petroleum and Petrochemical and Renewable Energy Industries: Evidence from Chinese Stock Market
DOI:
https://doi.org/10.54097/hbem.v5i.5092Keywords:
Russia-Ukrainian Conflict, Petroleum, Renewable Energy, Time Series Model.Abstract
This study explores the ramifications of war risks (Russo-Ukrainian Conflict specifically) on petroleum and renewable energy industries. Based on the stylized fact that war leads to spikes in oil prices, vector autoregressive (VAR) and autoregressive moving average-generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) model analyses were conducted to evaluate stock return and volatility, respectively, in both sectors resulting from the change in Brent crude oil prices. The data in this study primarily encompasses Brent crude oil prices and the stock index for renewable energy and petroleum products in China. While the result of the VAR model analysis suggests that the increasing oil price has led to a positive effect on stock return for renewables and a trifling impact on stock return for petroleum products, the ARMA-GARCH model indicates that the change in Brent crude oil prices has exerted a negligible effect on stock volatility in both sectors. The positive stock return in the renewable energy sector caused by the increasing oil prices may incentivize more investors and promote the development of renewables in China. The government will also likely promulgate relevant policies to avert the risk of energy supply uncertainties and expedite the energy transition.
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References
Song, Y., Chen, B., Wang, X. Y., Wang, P. P. Defending global oil price security: Based on the perspective of uncertainty risk. Energy Strategy Reviews, 2002, 41: 100858.
Estrada, M., Park, D., Tahir, M., Khan A. Simulations of US-Iran war and its impact on global oil price behavior. Borsa Istanbul Review, 2020, 20 (1): 1-12.
Bašta, M., Molnár, P. Oil market volatility and stock market volatility. Finance Research Letters, 2018, 26: 204-214.
Zhang, Y. J., Yan, X. X. The impact of U.S. economic policy uncertainty on WTI crude oil returns in different time and frequency domains. International Review of Economics & Finance, 2020, 69: 750-768.
Yuan, Y., Zhuang, X. T., Liu, Z. Y., Huang, W. Q. Analysis of the temporal properties of price shock sequences in crude oil markets. Physica: A: Statistical. Mechanics and its Applications, 2014, 394: 235-246.
Degiannakis, S., Filis G., Panagiotakopoulou, S. Oil price shocks and uncertainty: How stable is their relationship over time? Economic Modeling, 2018, 72: 42-53.
Cunado, J., Gupta, R., Lau, C. K. M., Sheng, X. Time-Varying Impact of Geopolitical Risks on Oil Prices. Defence and Peace Economics, 2020, 31(6): 692-706.
Qian, L. H., Zeng, Q., Li, T. Geopolitical risk and oil price volatility: Evidence from Markov-switching model. International Review of Economics & Finance, 2022, 81: 29-38.
Miller, J., Ratti, R. Crude oil and stock markets: Stability, instability, and bubbles. Energy Economics, 2009, 31(4): 559-568.
Moriartry, P., Honnery, D. What is the global potential for renewable energy? Renewable and Sustainable Energy Reviews, 2012, 16 (1): 244-252.
Coleman, L. Explaining crude oil prices using fundamental measure. Energy Policy, 2012, 40: 318-324.
Kesicki, F. The third oil price surge – What’s different this time? Energy Policy, 2010, 38(3): 1596-1606.
Araújo, K. The emerging field of energy transitions: Progress, challenges and opportunities. Energy Research & Social Science, 2014, 1: 112-121.
Zhang, B., Wang, P.J. Return and volatility spillovers between china and world oil markets. Economic Modeling, 2014, 42: 413-420.
Eastmoney homepage. [EB/OL]. https://choice.eastmoney.com/.
Sims, C. A. The Role of Approximate Prior Restrictions in Distributed Lag Estimation. Journal of the American Statistical Association, 1972, 67(337): 169-175.
Mann, H. B., Wald, A. On the Statistical Treatment of Linear Stochastic Difference Equations. Econometrica, 1943, 11: 173-220.
Franses, P. H., Ghijsels, H. Additive outliers, GARCH and forecasting volatility. International Journal of Forecasting, 1999, 15(1): 1-9.
Ghani, I. M., Rahim, H.A. Modeling and Forecasting of Volatility using ARMA-GARCH: Case Study on Malaysia Natural Rubber Prices. IOP Conference Series: Materials Science and Engineering, 2019, 548: 012023.
Engle, R. GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics. Journal of Economic Perspectives, 2001, 15 (4):157-168.
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