Energy-agriculture market linkages: Asymmetric effects in the context of the Russia-Ukraine conflict

Authors

DOI:

https://doi.org/10.24136/oc.3313

Keywords:

agricultural commodity markets, energy price changes, connectedness, economic crisis

Abstract

Research background: The study examines the impact of geopolitical tensions, in particular the Russia-Ukraine conflict, on agricultural commodity markets at a global level. The research focuses on the period from February 2021 to February 2024, a period characterized by significant economic instability due to the ongoing conflict. The research covers global agricultural commodity markets, focusing on three main categories: soft commodities, grains and livestock.

Purpose of the article: The purpose of this paper is to examine the asymmetric interactions between crude oil and gas prices and agricultural commodity yields from February 2021 to February 2024. The study aims to analyze these interactions on a global scale, encompassing the world markets for soft commodities, grains and livestock. The research aims to provide insights into how geopolitical tensions, in particular the Russia-Ukraine conflict, are affecting these global agricultural markets and their linkages to energy prices.

Methods: This study uses the Nonlinear Autoregressive Distributed Lag (NARDL) model to analyze the asymmetric interactions between crude oil and gas prices and agricultural commodity yields, capturing both short-run and long-run asymmetries. The study divides the sample period into three distinct sub-periods of the Russia-Ukraine conflict, allowing for a detailed examination of how energy price fluctuations affect agricultural commodities under different economic conditions.

Findings & value added: Our main findings are the following: (1) positive correlations with oil and gas prices for soft commodities and grains; (2) weaker but significant relationship for livestock; (3) short-term asymmetries are particularly pronounced during periods of high economic turbulence (e.g. Russia-Ukraine conflict); (4) grain and livestock yields show stronger responses to negative oil price shocks; (5) no long-run equilibrium relationship found by cointegration tests. The present paper is unique in combining the Nonlinear Autoregressive Distributed Lag (NARDL) model with a detailed analysis of the Russia-Ukraine conflict, providing unprecedented insights into the asymmetric impact of geopolitical tensions on agricultural commodity markets, which is essential for understanding market dynamics during crises.

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References

Abubakr, M., Karim, S., Hasan, M., Lucey, B. M., & Hoon, S. (2022). Nexus between oil shocks and agriculture commodities: Evidence from time and frequency domain. Energy Economics, 112, 106148.
View in Google Scholar

Adekoya, O. B., Oliyide, J. A., Yaya, O. S., & Al-Faryan, M. A. S. (2022). Does oil connect differently with prominent assets during war? Analysis of intra-day data during the Russia-Ukraine saga. Resources Policy, 77, 102728. DOI: https://doi.org/10.1016/j.resourpol.2022.102728
View in Google Scholar

Aït-Youcef, C. (2019). How index investment impacts commodities: A story about the financialization of agricultural commodities. Economic Modelling, 80, 23–33. DOI: https://doi.org/10.1016/j.econmod.2018.04.007
View in Google Scholar

Akyildirim, E., Cepni, O., Molnár, P., & Uddin, G. S. (2022). Connectedness of energy markets around the world during the COVID-19 pandemic. Energy Economics, 109, 105900. DOI: https://doi.org/10.1016/j.eneco.2022.105900
View in Google Scholar

Antonakakis, N., & Gabauer, D. (2017). Refined measures of dynamic connectedness based on TVP-VAR. Mpra, 78282.
View in Google Scholar

Arize, A. C., Malindretos, J., & Igwe, E. U. (2017). Do exchange rate changes improve the trade balance: An asymmetric nonlinear cointegration approach. International Review of Economics & Finance, 49, 313–326. DOI: https://doi.org/10.1016/j.iref.2017.02.007
View in Google Scholar

Balcilar, M., Gabauer, D., & Umar, Z. (2021). Crude oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach. Resources Policy, 73, 102219. DOI: https://doi.org/10.1016/j.resourpol.2021.102219
View in Google Scholar

Basu, S., & Ishihara, K. N. (2023). Multivariate time–frequency interactions of renewable and non-renewable energy markets with macroeconomic factors in India. Energy Systems. DOI: https://doi.org/10.1007/s12667-023-00617-9
View in Google Scholar

Benlagha, N., & Abdelmalek, W. (2024). Dynamic connectedness between energy and agricultural commodities: Insights from the COVID-19 pandemic and Russia–Ukraine conflict. Eurasian Economic Review, 14(3), 781–825. DOI: https://doi.org/10.1007/s40822-024-00279-7
View in Google Scholar

Cabrera, B. L., & Schulz, F. (2016). Volatility linkages between energy and agricultural commodity prices. Energy Economics, 54, 190–203. DOI: https://doi.org/10.1016/j.eneco.2015.11.018
View in Google Scholar

Chevallier, J., & Ielpo, F. (2013). Volatility spillovers in commodity markets. Applied Economics Letters, 20(13), 1211–1227. DOI: https://doi.org/10.1080/13504851.2013.799748
View in Google Scholar

Dahl, R. E., Oglend, A., & Yahya, M. (2019). Dynamics of volatility spillover in commodity markets: Linking crude oil to agriculture. Journal of Commodity Markets, 20, 100111. DOI: https://doi.org/10.1016/j.jcomm.2019.100111
View in Google Scholar

Delatte, A. L., & Lopez, C. (2013). Commodity and equity markets: Some stylized facts from a copula approach. Journal of Banking & Finance, 37(12), 5346–5356. DOI: https://doi.org/10.1016/j.jbankfin.2013.06.012
View in Google Scholar

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: https://doi.org/10.1016/j.ijforecast.2011.02.006
View in Google Scholar

Diebold, F. X., & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. DOI: https://doi.org/10.1016/j.jeconom.2014.04.012
View in Google Scholar

Diebold, F. X., Liu, L., & Yilmaz, K. (2017). Commodity connectedness. NBER DOI: https://doi.org/10.3386/w23685
View in Google Scholar

Working Paper Series, 23685.
View in Google Scholar

Du, X., & McPhail, L. L. (2012). Inside the black box: The price linkage and transmission between energy and agricultural markets. Energy Journal, 33(2), 171–194. DOI: https://doi.org/10.5547/01956574.33.2.8
View in Google Scholar

Fang, Y., & Shao, Z. (2022). The Russia-Ukraine conflict and volatility risk of commodity markets. Finance Research Letters, 50, 103264. DOI: https://doi.org/10.1016/j.frl.2022.103264
View in Google Scholar

Farid, S., Kayani, G. M., Naeem, M. A., & Shahzad, S. J. H. (2021). Intraday volatility transmission among precious metals, energy and stocks during the COVID-19 pandemic. Resources Policy, 72, 102101. DOI: https://doi.org/10.1016/j.resourpol.2021.102101
View in Google Scholar

Fiszeder, P., & Małecka, M. (2022). Forecasting volatility during the outbreak of Russian invasion of Ukraine: Application to commodities, stock indices, currencies, and cryptocurrencies. Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(4), 939–967. DOI: https://doi.org/10.24136/eq.2022.032
View in Google Scholar

Fowowe, B. (2016). Do oil prices drive agricultural commodity prices? Evidence from South Africa. Energy, 104, 149–157. DOI: https://doi.org/10.1016/j.energy.2016.03.101
View in Google Scholar

Gardebroek, C., & Hernandez, M.A., 2013. Do energy prices stimulate food price volatility? Examining volatility transmission between US oil, ethanol and corn markets. Energy Economics, 40, 119–129. DOI: https://doi.org/10.1016/j.eneco.2013.06.013
View in Google Scholar

Harri, A., Nalley, L., & Hudson, D. (2009). The relationship between oil, exchange rates, and commodity prices. Journal of Agricultural and Applied Economics, 41(2), 501–510. DOI: https://doi.org/10.1017/S1074070800002959
View in Google Scholar

Hau, L., Zhu, H., Huang, R., & Ma, X. (2020). Heterogeneous dependence between crude oil price volatility and China’s agriculture commodity futures: Evidence from quantile-on-quantile regression. Energy, 213, 118781. DOI: https://doi.org/10.1016/j.energy.2020.118781
View in Google Scholar

Hung, N. T. (2021). Oil prices and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak. Resources Policy, 73, 102236. DOI: https://doi.org/10.1016/j.resourpol.2021.102236
View in Google Scholar

Inacio C.M.C., Kristoufek, L., & David, S.A. (2023). Assessing the impact of the Russia–Ukraine war on energy prices: A dynamic cross-correlation analysis. Physica A: Statistical Mechanics and its Applications, 626, 129084. DOI: https://doi.org/10.1016/j.physa.2023.129084
View in Google Scholar

Jareño, F., González, M., Tolentino, M., & Sierra, K. (2020). Bitcoin and gold price returns: A quantile regression and NARDL analysis. Resources Policy, 67, 101666. DOI: https://doi.org/10.1016/j.resourpol.2020.101666
View in Google Scholar

Jareño, F., Tolentino, M., González, M., & Oliver, A. (2019). Impact of changes in the level, slope and curvature of interest rates on U.S. sector returns: An asymmetric nonlinear cointegration approach. Economic Research, 32(1), 1275–1297. DOI: https://doi.org/10.1080/1331677X.2019.1632726
View in Google Scholar

Jebabli, I., Kouaissah, N., & Arouri, M. (2022). Volatility spillovers between stock and energy markets during crises: A comparative assessment between the 2008 global financial crisis and the COVID-19 pandemic crisis. Finance Research Letters, 46, 102363. DOI: https://doi.org/10.1016/j.frl.2021.102363
View in Google Scholar

Kaltalioglu, M., & Soytas, U. (2011). Volatility spillover from oil to food and agricultural raw material markets. Modern Economy, 2, 71–76. DOI: https://doi.org/10.4236/me.2011.22011
View in Google Scholar

Kang, S. H., McIver, R., & Yoon, S. M. (2017). Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets. Energy Economics, 62, 19–32. DOI: https://doi.org/10.1016/j.eneco.2016.12.011
View in Google Scholar

Koirala, K. H., Mishra, A. K., D'Antoni, J. M., & Mehlhorn, J. E. (2015). Energy prices and agricultural commodity prices: Testing correlation using copulas method. Energy, 81, 430–436. DOI: https://doi.org/10.1016/j.energy.2014.12.055
View in Google Scholar

Lombardi, M. J., Osbat, C., & Schnatz, B. (2012). Global commodity cycles and linkages: A FAVAR approach. Empirical Economics, 43, 651–670. DOI: https://doi.org/10.1007/s00181-011-0494-8
View in Google Scholar

Lovcha, Y., & Perez-Laborda, A. (2020). Dynamic frequency connectedness between oil and natural gas volatilities. Economic Modelling, 84, 181–189. DOI: https://doi.org/10.1016/j.econmod.2019.04.008
View in Google Scholar

Mensi, W., Hammoudeh, S., & Kang, S. H. (2015). Precious metals, cereal, oil and stock market linkages and portfolio risk management: Evidence from Saudi Arabia. Economic Modelling, 51, 340–358. DOI: https://doi.org/10.1016/j.econmod.2015.08.005
View in Google Scholar

Naeem, M. A., Karim, S., Hasan, M., Lucey, B. M., & Kang, S. H. (2022). Nexus between oil shocks and agriculture commodities: Evidence from time and frequency domain. Energy Economics, 112, 106148. DOI: https://doi.org/10.1016/j.eneco.2022.106148
View in Google Scholar

Nagayev, R., Disli, M., Inghelbrecht, K., & Ng, A. (2016). On the dynamic links between commodities and Islamic equity. Energy Economics, 58, 125–140. DOI: https://doi.org/10.1016/j.eneco.2016.06.011
View in Google Scholar

Natanelov, V., Alam, M. J., McKenzie, A. M., & Van Huylenbroeck, G. (2011). Is there co-movement of agricultural commodities futures prices and crude oil? Energy Policy, 39(9), 4971–4984. DOI: https://doi.org/10.1016/j.enpol.2011.06.016
View in Google Scholar

Nazlioglu, S., & Soytas, U. (2011). World oil prices and agricultural commodity prices: Evidence from an emerging market. Energy Economics, 33(3), 488–496. DOI: https://doi.org/10.1016/j.eneco.2010.11.012
View in Google Scholar

Nazlioglu, S., & Soytas, U. (2012). Oil price, agricultural commodity prices, and the dollar: A panel cointegration and causality analysis. Energy Economics, 34(4), 1098–1104. DOI: https://doi.org/10.1016/j.eneco.2011.09.008
View in Google Scholar

Pal, D., & Mitra, S.K. (2019). Correlation dynamics of crude oil with agricultural commodities: A comparison between energy and food crops. Economic Modelling, 82, 453–466. DOI: https://doi.org/10.1016/j.econmod.2019.05.017
View in Google Scholar

Panagiotidis, T., & Rutledge, E. (2007). Oil and gas markets in the UK: Evidence from a cointegrating approach. Energy Economics, 29(2), 329–347. DOI: https://doi.org/10.1016/j.eneco.2006.10.013
View in Google Scholar

Pesaran, M., & Shin, M. (1999). An Autoregressive Distributed Lag Modelling approach to cointegration analysis. Cambridge: Cambridge University Press.
View in Google Scholar

Pesaran, M., Shin, Y., & Smith, R. (2001). Bound testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. DOI: https://doi.org/10.1002/jae.616
View in Google Scholar

Qiu, C., Colson, G., Escalante, C., & Wetzstein, M. (2012). Considering macroeconomic indicators in the food before fuel nexus. Energy Economics, 34(6), 2021–2028. DOI: https://doi.org/10.1016/j.eneco.2012.08.018
View in Google Scholar

Reboredo, J. C. (2012). Do food and oil prices co-move? Energy policy, 49, 456–467. DOI: https://doi.org/10.1016/j.enpol.2012.06.035
View in Google Scholar

Roman, M., Górecka, A., & Domagała, J. (2020). The linkages between crude oil and food prices. Energies, 13(24), 6545. DOI: https://doi.org/10.3390/en13246545
View in Google Scholar

Saad, G. (2023). The impact of the Russia–Ukraine war on the United States natural gas futures prices. Kybernetes, 53(10), 3430–3443. DOI: https://doi.org/10.1108/K-01-2023-0138
View in Google Scholar

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, 131, 107398, DOI: https://doi.org/10.1016/j.eneco.2024.107398
View in Google Scholar

Shin, Y., Yu, B., & Greenwood-Nimmo, M. J., (2014). Modelling asymmetric cointegration and dynamic multipliers in a nonlinear framework. In W. C. Horrace & R. C. Sickles (Eds.). Festschrift in honor of Peter Schmidt: Econometric methods and applications (pp. 281–314). New York: Springer, Science & Business Media. DOI: https://doi.org/10.1007/978-1-4899-8008-3_9
View in Google Scholar

Si, D. K., Li, X. L., Xu, X., & Fang, Y. (2021). The risk spillover effect of the COVID-19 pandemic on energy sector: Evidence from China. Energy Economics, 102, 105498. DOI: https://doi.org/10.1016/j.eneco.2021.105498
View in Google Scholar

Silvennoinen, A., & Thorp, S. (2013). Financialization, crisis and commodity correlation dynamics. Journal of International Financial Markets, Institutions and Money, 24, 42–65. DOI: https://doi.org/10.1016/j.intfin.2012.11.007
View in Google Scholar

Tiwari, A. K., Abakah, E. J. A., Adewuyi, A. O., & Lee, C. C. (2022). Quantile risk spillovers between energy and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak. Energy Economics, 113, 106235. DOI: https://doi.org/10.1016/j.eneco.2022.106235
View in Google Scholar

Umar, Z., Aziz, S., & Tawil, D. (2021). The impact of COVID-19 induced panic on the return and volatility of precious metals. Journal of Behavioral and Experimental Finance, 31, 100525. DOI: https://doi.org/10.1016/j.jbef.2021.100525
View in Google Scholar

Uribe, J. M., Guillen, M., & Mosquera-López, S. (2018). Uncovering the nonlinear predictive causality between natural gas and electricity prices. Energy Economics, 74, 904–916. DOI: https://doi.org/10.1016/j.eneco.2018.07.025
View in Google Scholar

Wang, Y., Wu, C., & Yang, L. (2014). Oil price shocks and agricultural commodity prices. Energy Economics, 44, 22–35. DOI: https://doi.org/10.1016/j.eneco.2014.03.016
View in Google Scholar

Wu, Y., Ren, W., Wan, J., & Liu, X. (2023). Time-frequency volatility connectedness between fossil energy and agricultural commodities: Comparing the COVID-19 pandemic with the Russia-Ukraine conflict. Finance Research Letters, 55, 103866. DOI: https://doi.org/10.1016/j.frl.2023.103866
View in Google Scholar

Xing, X., Cong, Y., Wang, Y., & Wang, X. (2023). The impact of COVID-19 and war in Ukraine on energy prices of oil and natural gas. Sustainability, 15(19), 14208. DOI: https://doi.org/10.3390/su151914208
View in Google Scholar

Yang, Y., Zhao, L., Zhu, Y., Chen, L., Wang, G., & Wang, C. (2023). Spillovers from the Russia-Ukraine conflict. Research in International Business and Finance, 66, 102006. DOI: https://doi.org/10.1016/j.ribaf.2023.102006
View in Google Scholar

Yip, P. S., Brooks, R., Do, H. X., & Nguyen, D. K. (2020). Dynamic volatility spillover effects between oil and agricultural products. International Review of Financial Analysis, 69, 101465. DOI: https://doi.org/10.1016/j.irfa.2020.101465
View in Google Scholar

Yu, T. H. E., Bessler, D. A., & Fuller, S. W. (2006). Cointegration and causality analysis of world vegetable oil and crude oil prices. In Annual meeting, American Agricultural Economics Association. Long Beach: AgEcon Search.
View in Google Scholar

Zhang Z., Lohr, L., Escalante, C., & Wetzstein, M. (2009). Ethanol, corn, and soybean price relations in a volatile vehicle-fuels market. Energies, 2(2), 320–339. DOI: https://doi.org/10.3390/en20200320
View in Google Scholar

Zhang, H., Chen, J., & Shao, L. (2021). Dynamic spillovers between energy and stock markets and their implications in the context of COVID-19. International Review of Financial Analysis, 77, 101828. DOI: https://doi.org/10.1016/j.irfa.2021.101828
View in Google Scholar

Zhang, Q., & Reed, M. R. (2008). Examining the impact of the world. In Southern Agricultural Economics Association annual meetings. Dallas: AgEcon Search.
View in Google Scholar

Zhang, W., He, X., & Hamori, S. (2023). The impact of the COVID-19 pandemic and Russia-Ukraine war on multiscale spillovers in green finance markets: Evidence from lower and higher order moments. International Review of Financial Analysis, 89, 102735. DOI: https://doi.org/10.1016/j.irfa.2023.102735
View in Google Scholar

Zhou, X., Enilov, M., & Parhi, M. (2024). Does oil spin the commodity wheel? Quantile connectedness with a common factor error structure across energy and agricultural markets. Energy Economics, 132, 107468. DOI: https://doi.org/10.1016/j.eneco.2024.107468
View in Google Scholar

Živkov, D., Manić, S., & Durašković, J. (2020). Short and long-term volatility transmission from oil to agricultural commodities–The robust quantile regression approach. Borsa Istanbul Review, 20, 11–25. DOI: https://doi.org/10.1016/j.bir.2020.10.008
View in Google Scholar

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30-03-2025

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How to Cite

Jareño, F., Tolentino, M., & Fernández, M. del V. (2025). Energy-agriculture market linkages: Asymmetric effects in the context of the Russia-Ukraine conflict. Oeconomia Copernicana, 2025(16), 163-196. https://doi.org/10.24136/oc.3313

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