Time-frequency analysis of geopolitical risk and food commodity market: a wavelet based investigation
DOI:
https://doi.org/10.51599/are.2025.11.02.05Keywords:
geopolitical risk, food security, climate policy uncertainty, bi-wavelet analysis, Food Price Index.Abstract
Purpose. The most recent conflicts have demonstrated that geopolitical risk has evolved into a significant issue that has an impact on the global food markets. Through the use of bi-wavelet coherence analysis, the study aimed to establish the ways in which geopolitical risk and climate policy uncertainties influences the food commodity market using Geopolitical Risk Index (GPR index), Climate Policy Uncertainty Index (CPU index) and the five components that make up the FAO Food Price Index (FPI).
Methodology / approach. The study used monthly data spanning from January 1990 to March 2024. Geopolitical risk was measured using the GPR index developed through textual analysis of news articles. CPU index, developed using similar textual analysis, is used to represent the uncertainties related to climate change risk. The FAO’s FPI constituents were used to represent global food commodity market. The research applied advanced econometric methods including Johansen cointegration tests, Toda-Yamamoto causality analysis, Brock-Dechert-Scheinkman (BDS) nonlinearity tests, and bi-wavelet coherence analysis. Wavelet coherence analysis was particularly focused due to its capability to capture dynamic, time-frequency relationships among non-stationary data series.
Results. The study found two significant long-run cointegrating relationships among GPR, CPU and FPI constituents. Causality tests indicated that geopolitical risk significantly influenced climate policy uncertainty but not vice versa. Wavelet analysis revealed that GPR and vegetable oil has more strong co-movement, and it is also the same in the case of CPU. CPU has a leading influence on GPR, which means that policy uncertainties lead to increased geopolitical tensions. Uncertainties in climate policies have an effect on food commodity market in the short run. Whereas, GPR affects cereals during geopolitical tension periods. In the case of dairy products, time varying co-movements in the short run could be witnessed whereas in the long run medium co-movement could be seen. Volatilities occur in the prices of vegetable oils during periods of crisis which can exacerbate prices of other food commodities, which can lead to food security issues.
Originality / scientific novelty. The originality of the study lies in the fact that the main focus is on GPR, CPU and five constituents of FAO’s FPI. Moreover, the study uniquely incorporates CPU index as a proxy to climate change risk and its impact on food commodity market. Most of the studies focus on the spillover effect of geopolitical risk on different classes of asset. Significant number of literatures focus on the spillover effect on oil market, stock market and commodities market. However, there are only limited studies that focus on food commodity market. In addition, analysing these factors provides a deeper understanding of how they affect food security and market dynamics. This innovative approach offers valuable insights to policymakers, investors and stakeholders of food commodity market.
Practical value / implications. Creating a more economically sustainable environment is the goal of every country, which requires joint efforts by various sectors of the financial market, government officials and economic regulators. These findings are of great importance to policymakers and stakeholders in global food systems, highlighting the need to create adapted policy frameworks, focus on the vulnerability of individual commodities, and carefully implement climate policies to mitigate potential negative impacts on food security.
References
Antonakakis, N., Gupta, R., Kollias, C., & Papadamou, S. (2017). Geopolitical risks and the oil-stock nexus over 1899–2016. Finance Research Letters, 23, 165–173. https://doi.org/10.1016/j.frl.2017.07.017.
Armah, M., Amewu, G., & Bossman, A. (2022). Time-frequency analysis of financial stress and global commodities prices: insights from wavelet-based approaches. Cogent Economics & Finance, 10(1), 2114161. https://doi.org/10.1080/23322039.2022.2114161.
Asai, M., Gupta, R., & McAleer, M. (2020). Forecasting volatility and co-volatility of crude oil and gold futures: Effects of leverage, jumps, spillovers, and geopolitical risks. International Journal of Forecasting, 36(3), 933–948. https://doi.org/10.1016/j.ijforecast.2019.10.003.
Balcilar, M., Bonato, M., Demirer, R., & Gupta, R. (2018). Geopolitical risks and stock market dynamics of the BRICS. Economic Systems, 42(2), 295–306. https://doi.org/10.1016/j.ecosys.2017.05.008.
Bandara, J. S., & Cai, Y. (2014). The impact of climate change on food crop productivity, food prices and food security in South Asia. Economic Analysis and Policy, 44(4), 451–465. https://doi.org/10.1016/j.eap.2014.09.005.
Baruník, J., Vácha, L., & Krištoufek, L. (2011). Comovement of Central European stock markets using wavelet coherence: evidence from high-frequency data. IES Working Paper No. 22/2011. Charles University in Prague, Institute of Economic Studies (IES). Available at: https://hdl.handle.net/10419/83377.
Baur, D. G., & Smales, L. A. (2020). Hedging geopolitical risk with precious metals. Journal of Banking & Finance, 117, 105823. https://doi.org/10.1016/j.jbankfin.2020.105823.
Bohl, D., Hanna, T., Mapes, B. R., Moyer, J. D., Narayan, K., & Wasif, K. (2017). Understanding and forecasting geopolitical risk and benefits. https://doi.org/10.2139/ssrn.3941439.
Bouoiyour, J., Selmi, R., Hammoudeh, S., & Wohar, M. E. (2019). What are the categories of geopolitical risks that could drive oil prices higher? Acts or threats? Energy Economics, 84, 104523. https://doi.org/10.1016/j.eneco.2019.104523.
Broock, W. A., Scheinkman, J. A., Dechert, W. D., & LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric Reviews, 15(3), 197–235. https://doi.org/10.1080/07474939608800353.
Caldara, D., & Iacoviello, M. (2018). Measuring geopolitical risk. International Finance Discussion Papers, 2018(1222), 1–66. https://doi.org/10.17016/ifdp.2018.1222.
Carney, M. (2016). Resolving the climate paradox. In Arthur Burns Memorial Lecture. Available at: https://www.fsb.org/uploads/Resolving-the-climate-paradox.pdf.
Cazelles, B., Chavez, M., Berteaux, D., Ménard, F., Vik, J. O., Jenouvrier, S., & Stenseth, N. C. (2008). Wavelet analysis of ecological time series. Oecologia, 156(2), 287–304. https://doi.org/10.1007/s00442-008-0993-2.
Cevik, S., & Jalles, J. T. (2023). Eye of the Storm: the impact of climate shocks on inflation and growth. IMF Working Paper, 2023(087). https://doi.org/10.5089/9798400241307.001.
Chatzopoulos, T., Domínguez, I. P., Zampieri, M., & Toreti, A. (2020). Climate extremes and agricultural commodity markets: a global economic analysis of regionally simulated events. Weather and Climate Extremes, 27, 100193. https://doi.org/10.1016/j.wace.2019.100193.
Chowdhury, M. A. F., Meo, M. S., & Aloui, C. (2021). How world uncertainties and global pandemics destabilized food, energy and stock markets? Fresh evidence from quantile on quantile regressions. International Review of Financial Analysis, 76, 101759. https://doi.org/10.1016/j.irfa.2021.101759.
Cunado, J., Gupta, R., Lau, C. K. M., & Sheng, X. (2019). Time-varying impact of geopolitical risks on oil prices. Defence and Peace Economics, 31(6), 692–706. https://doi.org/10.1080/10242694.2018.1563854.
De Gorter, H., Drabik, D., & Just, D. R. (2013). How biofuels policies affect the level of grains and oilseed prices: theory, models and evidence. Global Food Security, 2(2), 82–88. https://doi.org/10.1016/j.gfs.2013.04.005.
Demiralay, S., Wang, Y., & Chen, C. (2024). Geopolitical risks and climate change stocks. Journal of Environmental Management, 352, 119995. https://doi.org/10.1016/j.jenvman.2023.119995.
Foglia, M., Palomba, G., & Tedeschi, M. (2023). Disentangling the geopolitical risk and its effects on commodities. Evidence from a panel of G8 countries. Resources Policy, 85, 104056. https://doi.org/10.1016/j.resourpol.2023.104056.
Frimpong, S., Gyamfi, E. N., Ishaq, Z., Agyei, S. K., Agyapong, D., & Adam, A. M. (2021). Can global economic policy uncertainty drive the interdependence of agricultural commodity prices? Evidence from Partial Wavelet Coherence Analysis. Complexity, 2021. https://doi.org/10.1155/2021/8848424.
Gong, X., & Xu, J. (2022). Geopolitical risk and dynamic connectedness between commodity markets. Energy Economics, 110, 106028. https://doi.org/10.1016/j.eneco.2022.106028.
Goyal, R., & Steinbach, S. (2023). Agricultural commodity markets in the wake of the black sea grain initiative. Economics Letters, 231, 111297. https://doi.org/10.1016/j.econlet.2023.111297.
Greene, W. H. (2000). Econometric analysis, 4th ed. New Jersey, Prentice Hall. Available at: https://www.ctanujit.org/uploads/2/5/3/9/25393293/_econometric_analysis_by_greence.pdf.
Grinsted, A., Moore, J. C., & Jevrejeva, S. (2004). Application of the cross wavelet transform and wavelet coherence to geophysical time series. Nonlinear Processes in Geophysics, 11(5/6), 561–566. https://doi.org/10.5194/npg-11-561-2004.
Han, L., Zhou, Y., & Yin, L. (2015). Exogenous impacts on the links between energy and agricultural commodity markets. Energy Economics, 49, 350–358. https://doi.org/10.1016/j.eneco.2015.02.021.
Harikumar, Y., & Muralikrishna, M. (2024). Pandemic’s ripple effect: Exploring dynamic connectedness of Indian equity and commodity markets. International Research Journal of Multidisciplinary Scope, 05(02), 399–412. https://doi.org/10.47857/irjms.2024.v05i02.0512.
Hudecová, K., & Rajčániová, M. (2023). The impact of geopolitical risk on agricultural commodity prices. Agricultural Economics – Czech, 69(4), 129–139. https://doi.org/10.17221/374/2022-agricecon.
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, 87, 102597. https://doi.org/10.1016/j.irfa.2023.102597.
Johansen, S. (1995). Likelihood-based inference in cointegrated vector autoregressive models. Oxford University Press. https://doi.org/10.1093/0198774508.001.0001.
Kannadhasan, M., & Das, D. (2020). Do Asian emerging stock markets react to international economic policy uncertainty and geopolitical risk alike? A quantile regression approach. Finance Research Letters, 34, 101276. https://doi.org/10.1016/j.frl.2019.08.024.
Khalfaoui, R., Goodell, J. W., Mefteh-Wali, S., Chishti, M. Z., & Gozgor, G. (2024). Impact of climate risk shocks on global food and agricultural markets: a multiscale and tail connectedness analysis. International Review of Financial Analysis, 93, 103206. https://doi.org/10.1016/j.irfa.2024.103206.
Le, T. H., Pham, L., & Do, X. H. (2023). Price risk transmissions in the water-energy-food nexus: Impacts of climate risks and portfolio implications. Energy Economics, 124, 106787. https://doi.org/10.1016/j.eneco.2023.106787.
Lee, C., Lee, C., & Li, Y. (2021). Oil price shocks, geopolitical risks, and green bond market dynamics. The North American Journal of Economics and Finance, 55, 101309. https://doi.org/10.1016/j.najef.2020.101309.
Li, Y., Huang, J., Gao, W., & Zhang, H. (2021). Analyzing the time-frequency connectedness among oil, gold prices and BRICS geopolitical risks. Resources Policy, 73, 102134. https://doi.org/10.1016/j.resourpol.2021.102134.
Liu, G., Luo, K., Xu, P., & Zhang, S. (2023). Climate policy uncertainty and its impact on major grain futures. Finance Research Letters, 58, 104412. https://doi.org/10.1016/j.frl.2023.104412.
Liu, J., Ma, F., Tang, Y., & Zhang, Y. (2019). Geopolitical risk and oil volatility: a new insight. Energy Economics, 84, 104548. https://doi.org/10.1016/j.eneco.2019.104548.
Liu, Y., Han, L., & Xu, Y. (2021). The impact of geopolitical uncertainty on energy volatility. International Review of Financial Analysis, 75, 101743. https://doi.org/10.1016/j.irfa.2021.101743.
Mastroeni, L., Mazzoccoli, A., Quaresima, G., & Vellucci, P. (2022). Wavelet analysis and energy-based measures for oil-food price relationship as a footprint of financialisation effect. Resources Policy, 77, 102692. https://doi.org/10.1016/j.resourpol.2022.102692.
Matošková, D. (2011). Volatility of agrarian markets aimed at the price development. Agricultural Economics – Czech, 57(1), 35–40. https://doi.org/10.17221/143/2010-agricecon.
Mawejje, J. (2016). Food prices, energy and climate shocks in Uganda. Agricultural and Food Economics, 4, 4. https://doi.org/10.1186/s40100-016-0049-6.
Mei, D., Ma, F., Liao, Y., & Wang, L. (2020). Geopolitical risk uncertainty and oil future volatility: evidence from MIDAS models. Energy Economics, 86, 104624. https://doi.org/10.1016/j.eneco.2019.104624.
Micallef, J., Grima, S., Spiteri, J., & Rupeika-Apoga, R. (2023). Assessing the causality relationship between the geopolitical risk index and the agricultural commodity markets. Risks, 11(5), 84. https://doi.org/10.3390/risks11050084.
Mo, B., Nie, H., & Zhao, R. (2023). Dynamic nonlinear effects of geopolitical risks on commodities: fresh evidence from quantile methods. Energy, 288, 129759. https://doi.org/10.1016/j.energy.2023.129759.
Pal, D., & Mitra, S. K. (2017). Time-frequency contained co-movement of crude oil and world food prices: a wavelet-based analysis. Energy Economics, 62, 230–239. https://doi.org/10.1016/j.eneco.2016.12.020.
Pindyck, R. S., & Rotemberg, J. J. (1990). The excess Co-Movement of commodity prices. The Economic Journal, 100(403), 1173. https://doi.org/10.2307/2233966.
Plakandaras, V., Gupta, R., & Wong, W. (2019). Point and density forecasts of oil returns: the role of geopolitical risks. Resources Policy, 62, 580–587. https://doi.org/10.1016/j.resourpol.2018.11.006.
S, A., & Muralikrishna, M. (2024). Exploratory bibliometric analysis on geopolitical risk. International Research Journal of Multidisciplinary Scope, 05(04), 1180–1197. https://doi.org/10.47857/irjms.2024.v05i04.01652.
Sarker, P. K., Bouri, E., & Marco, C. K. L. (2022). Asymmetric effects of climate policy uncertainty, geopolitical risk, and crude oil prices on clean energy prices. Environmental Science and Pollution Research, 30(6), 15797–15807. https://doi.org/10.1007/s11356-022-23020-w.
Sharif, A., Aloui, C., & Yarovaya, L. (2020). COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: fresh evidence from the wavelet-based approach. International Review of Financial Analysis, 70, 101496. https://doi.org/10.1016/j.irfa.2020.101496.
Sheenan, L. (2023). Green bonds, conventional bonds and geopolitical risk. Finance Research Letters, 58, 104587. https://doi.org/10.1016/j.frl.2023.104587.
Solarin, S. A., Gil‐Alana, L. A., & Gonzalez‐Blanch, M. J. (2021). Persistence and dependence in geopolitical risks in various developed and developing countries. International Journal of Finance & Economics, 28(2), 1488–1496. https://doi.org/10.1002/ijfe.2489.
Special feature: market developments and food price inflation drivers (2022). Available at: https://www.imf.org/en/Publications/SPROLLs/commodity-special-feature.
Subramaniam, Y., Masron, T. A., & Azman, N. H. N. (2020). Biofuels, environmental sustainability, and food security: a review of 51 countries. Energy Research & Social Science, 68, 101549. https://doi.org/10.1016/j.erss.2020.101549.
Sun, T.-T., & Su, C. W. (2024). How is geopolitical risk associated with food prices? International Journal of Emerging Markets. https://doi.org/10.1108/IJOEM-01-2023-0004.
Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66(1–2), 225–250. https://doi.org/10.1016/0304-4076(94)01616-8.
Vacha, L., Janda, K., Kristoufek, L., & Zilberman, D. (2013). Time-frequency dynamics of biofuel-fuel-food system. Energy Economics, 40, 233–241. https://doi.org/10.1016/j.eneco.2013.06.015.
Vo, D. H., & Dang, T. H. (2023). The geopolitical risk spillovers across BRICS countries: a quantile frequency connectedness approach. Scottish Journal of Political Economy, 71(1), 132–143. https://doi.org/10.1111/sjpe.12355.
Wang, Y., Bouri, E., Fareed, Z., & Dai, Y. (2022). Geopolitical risk and the systemic risk in the commodity markets under the war in Ukraine. Finance Research Letters, 49, 103066. https://doi.org/10.1016/j.frl.2022.103066.
Zhao, Z., Gozgor G., Lau, M. C. K., Mahalik, M. K., Patel, G., & Khalfaoui, R. (2023). The impact of geopolitical risks on renewable energy demand in OECD countries. Energy Economics, 122, 106700. https://doi.org/10.1016/j.eneco.2023.106700.