Examining the asymmetric effects of geopolitical risks on Iran's crude oil price: new evidence from the QARDL approach

Document Type : Original Article

Author

Assistant Professor in Economics, Department of Economics and Administrative Sciences, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

Abstract

In this study, using monthly data from 5/2005 to 5/2023 and the QARDL approach, the asymmetric effects of geopolitical risks on the price of heavy crude oil in Iran emphasizing the two characteristics of non-linearity and asymmetry in different quantiles are investigated. The findings show that the long-term relationship between geopolitical risks and the price of Iranian crude oil is significant and asymmetric in different quantiles. The effects of geopolitical risks on crude oil prices are negative when the crude oil market is decreasing and positive when it is increasing. The negative effects can be attributed to the existence of fear and panic among the investors of the global crude oil market, as well as the reduction of economic activities due to the existing risks and as a result, the reduction of crude oil demand. The positive effects can also be attributed to the concern about the future supply of crude oil and the disruption in supply and the increase in precautionary demand and finally the price increase.

Keywords

References

Abdel-Latif, H., & El-Gamal, M. (2018). Antecedents of war: the geopolitics of low oil prices and decelerating financial liquidity. Applied Economics Letter, 26(9), 765-769.
Apergis, N. Aslan, A. Aye, GC., & Gupta, R. (2015). the asymmetric effect of oil price on growth across US States. Energy Explor Exploit, 33(4), 575-590.
Attiaoui, I., & Boufateh, T. (2019). Impacts of climate change on cereal farming in Tunisia: a panel ARDL–PMG approach. Environ. Sci. Pollut. Res. Int, 26 (13), 13334–13345.
Bal, D.P., & Rath, B.N. (2015). Nonlinear causality between crude oil price and exchange rate: a comparative study of China and India. Energy Economics, 51, 149–156.
Bariviera, AF. Zunino, L., & Rosso, OA. (2017). Crude oil market and geopolitical events: an analysis based on information-theory-based quantifiers. Fuzzy Economics Review, 21(1), 41-51.
Bazzi, S., & Blattman, C. Economic shocks and conflict. (2014). evidence from commodity prices. Am Econ J Macroecon, 6(4), 1-38.
Caselli, F. Morelli, M., & Rohner, D. (2015). the geography of interstate resource wars. Quarterly Journal of Economics, 130(1), 267-316.
Chen, H. Liao, H. Tang, BJ., & Wei, YM. (2016). Impacts of OPEC's political risk on the international crude oil prices: an empirical analysis based on the SVAR models. Energy Economics, 57, 42-49.
Cho, J.S. Kim, T., & Shin, Y. (2015). Quantile cointegration in the autoregressive distributed-lag modeling framework. Journal of Economics, 188 (1), 281–300.
Cotet, AM., & Tsui, KK. (2013). Oil and conflict: what does the cross country evidence really show?. American Economic Journal: Macroeconomics, 5(1), 49-80.
Cunado, J. Gupta, R., & Lau, C.K.M. (2020).Time-varying impact of geopolitical risks on oil prices. Defense Peace Economics, 31 (6), 692–706.
Dario, C., & Iacoviello, M. (2022). Measuring Geopolitical Risk. American Economics Review. 112 (4), 1194–1225.
Guo, Y. Li, J., & Li, Y. (2021). The roles of political risk and crude oil in stock market based on quantile cointegration approach: a comparative study in China and US. Energy Economics, 97, 105-198.
Huang, J. Ding, Q., & Zhang, H. (2021). Nonlinear dynamic correlation between geopolitical risk and oil prices: a study based on high-frequency data. Research in International Bussiness and Finance, 56,  101-370.
Humphreys, M. (2005). Natural resources, conflict, and conflict resolution: uncovering the mechanisms. Journal of Conflict and Resolution, 49(4), 508-537.
Ivanovski, K., & Hailemariam, A. (2022). Time-varying geopolitical risk and oil prices. International Review Economics, 77, 206–221.
Ji, Q. Liu, BY. Nehler, H.m & Uddin, GS. (2018). Uncertainties and extreme risk spillover in the energy markets: a time-varying copula-based CoVaR approach. Energy Economics,76, 115-126
Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of American Statistics. Association, 101(475), 980–1006.
Kesicki, F. (2010). the third oil price surge: What’s different this time?. Energy Policy, 38(3), 1596-1606.
Kollias, C. Papadamou, S., & Arvanitis, V. (2013). Does terrorism affect the stock bond covariance? Evidence from European countries. South Economics Journal, 79(4), 832-848.
Leder, F., & Shapiro, JN. (2008). this time it's different: an inevitable decline in world petroleum production will keep oil product prices high, causing military conflicts and shifting wealth and power from democracies to authoritarian regimes. Energy Policy, 36(8), 2850-2852.
Li, Z. Shi, Q., & Bu, L. (2021). Is geopolitical risk an influence factor of international crude oil price volatility: an analysis based on GARCH-MIDAS model. World Economics Study, 11, 18-32.
Noguera-Santaella, J. (2016). Geopolitics and the oil price. Economic Modelling, 52, 301-309.
Monge, M Romero Rojo, M.F., & Gil-Alana, L.A. (2023). The impact of geopolitical risk on the behavior of oil prices and freight rates. Energy, 269, 126779.
Ormerod, P. Riordan, R. (2004). A new approach to the analysis of geo-political risk. Diplomacy & Statecraft, 15(4),1-12.
Pahlavan, S, Najafi Moghadam, A, Emamverdi, G, Darabi, R. (2022). Investigating the Impact of Financial, Economic, Political and International Risks on Tehran Stock Exchange Index Using Method ARDL. Investment Knowledge, 11(41). 303-332. (In Persian).
Pordel, P, Esfandiari, M, (2022). The Effect of Economic Policy Uncertainty on Oil Prices (Case Study: OPEC Countries). Quarterly Journal of Quantative Economics, Available Online from 13 June 2022. (In Persian).
Ren, X. Dou, Y., & Dong, K. (2022c). Information spillover and market connectedness: multi-scale quantile-on-quantile analysis of the crude oil and carbon markets. Appllied. Economicsc, 54 (38), 4465–4485.
Ren, X. Li, Y., & Wen, F. (2022b). The interrelationship between the carbon market and the green bonds market: evidence from wavelet quantile-on-quantile method. Technological. Forecasting and Social Change, 179, 121611.
Takroosta A, Mohajeri P, Mohammadi T, Shakeri A, Ghasemi A.(2019). An Analysis of Oil Prices Considering the Political Risk of OPEC. Journal of Economic Modeling Research Kharazmi University, 10 (37), 105-138. (In Persian).
Wang, Z.R. Fu, H.Q., & Ren, X.H, (2023). The impact of political connections on firm pollution: new evidence based on heterogeneous environmental regulation. Petroleum Science, 20(1), 636-647.
Xiaohang, R. Yaning, A., & Chenglu, J. (2023). The asymmetric effect of geopolitical risk on China’s crude oil prices: New evidence from a QARDL approach. Finance Research Letters, 53, 103637.
Yao, T. Zhang, Y.J., & Ma, C.Q. (2017). How does investor attention affect international crude oil prices?. Appllied Energy 205, 336–344.
You, W. Guo, Y., & Zhu, H. (2017). Oil price shocks, economic policy uncertainty and industry stock returns in China: asymmetric effects with quantile regression. Energy Economics, 68,1–18.
Zhang, D. Ji, Q., & Kutan, AM. (2019). Dynamic transmission mechanisms in global crude oil prices: Estimation and implications. Energy. 175,1181-1193.
Zhu, H. Peng, C., & You, W. (2016b). Quantile behaviour of cointegration between silver and gold prices. Finance Research. Letter. 19,119–125.
www.opec.org
www.policyuncertainty.com
 

Files

Share

How to cite

Statistics