Belirsizliklerin, Risklerin ve Oynaklıkların Gölgesinde Küresel Gıda Fiyatları

Year 2025, Volume: 40 Issue: 4, 1001 - 1019, 01.10.2025

Veysel Karagöl

https://doi.org/10.24988/ije.1554013

Abstract

Bu çalışmanın amacı, küresel gıda fiyatlarının 1997:01-2024:06 aylık döneminde belirsizliklerden, risklerden ve oynaklıklardan nasıl etkilendiğini araştırmaktır. Rejim değişikliklerini tespit etmek ve her rejimde farklı ekonomik ilişkileri ortaya koymak için değişkenlerin doğrusal olmayan yapılarını dikkate alan Markov-Rejim Değişim regresyon yöntemi tercih edilmiştir. Rejim 0'ın temsil ettiği dönemler, genellikle 2007-2009 küresel finansal krizi, 2010-2011 Euro bölgesi borç krizi ve 2020-2022 COVID-19 pandemisi gibi yüksek belirsizlik ve kriz dönemleriyle örtüşmektedir. Bunun dışında kalan dönemleri kapsayan Rejim 1, normalleşme dönemlerini temsil etmektedir. Bulgular, gıda fiyatlarının artış eğilimindeyken (kriz dönemlerinde), bu artışın jeopolitik risklerden ve hisse senedi piyasası volatilitesinden önemli ölçüde etkilendiğine dair kanıtlar sunmaktadır. Gıda fiyatları azalma eğilimindeyken ise (normalleşme dönemlerinde) petrol fiyatları gibi maliyet unsurlarından güçlü bir biçimde, küresel ekonomik politika belirsizliğinden ise zayıf bir biçimde etkilenmektedir. Politika yapıcıların, küresel gıda fiyatlarındaki dalgalanmaları öngörebilmek için küresel belirsizliklere ve risklere odaklanmaları önerilmektedir.

Keywords

Küresel Gıda Fiyatları , Küresel Ekonomik Politika Belirsizliği , Jeopolitik Riskler , Hisse Senedi Piyasası Volatilitesi , Markov-Rejim Değişim Regresyon

Global Food Prices in the Shadow of Uncertainties, Risks and Volatilities

Abstract

This study aims to investigate how global food prices are affected by uncertainties, risks, and volatilities for the period from January 1997 to June 2024 on a monthly basis. To detect regime changes and reveal different economic relations in each regime, the Markov-switching regression method, which considers the non-linear structures of the variables, is preferred. The periods represented by Regime 0 often coincide with periods of high uncertainty and crisis, such as the 2007–2009 Global Financial Crisis, the 2010–2011 Eurozone Debt Crisis, and the 2020–2022 COVID-19 pandemic. Regime 1, which covers periods other than these, represents the normalization periods. The findings provide evidence that when food prices tend to increase (during crisis periods), this increase is significantly affected by geopolitical risks and equity market volatility. When food prices tend to decline (during normalization periods), they are strongly affected by cost factors such as oil prices and weakly affected by global economic policy uncertainty. Policymakers are advised to focus on global uncertainties and risks to anticipate fluctuations in global food prices.

Keywords

Global Food Prices , Global Economic Policy Uncertainty , Geopolitical Risks , Equity Market Volatility , Markov-Switching Regression.

References

• Akçayır, Ö. (2023). Ulusal riskler, jeopolitik riskler ve küresel belirsizliklerin Türk lirasının değeri üzerindeki etkileri. Alanya Akademik Bakış, 7(2), 649-669. https://doi.org/10.29023/alanyaakademik.1038217
• Alnour, M., Altintaş, H., & Rahman, M. N. (2023). Unveiling the asymmetric response of global food prices to the energy prices shocks and economic policy uncertainty. World Development Sustainability, 3, 100083. https://doi.org/10.1016/j.wds.2023.100083
• Ang, A., & Bekaert, G. (2002). Regime switches in interest rates. Journal of Business & Economic Statistics, 20(2), 163-182. https://doi.org/10.1198/073500102317351930
• Baker, S. B., Bloom, N., Davis, S. J., & Kost, K. (2019). Policy news and equity market volatility. NBER working paper, 25720.
• Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The quarterly journal of economics, 131(4), 1593-1636. https://doi.org/10.1093/qje/qjw024
• Bildirici, M. E., Alp, E. A., Ersin, Ö. Ö., & Bozoklu, Ü. (2010). İktisatta kullanılan doğrusal olmayan zaman serisi yöntemleri. İstanbul: Türkmen Kitabevi.
• Caldara, D., & Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review, 112(4), 1194-1225. https://doi.org/10.1257/aer.20191823
• Cao, Y., Cheng, S., & Li, X. (2023). How economic policy uncertainty affects asymmetric spillovers in food and oil prices: Evidence from wavelet analysis. Resources Policy, 86, 104086. https://doi.org/10.1016/j.resourpol.2023.104086
• Ceylan, F. (2024). Jeopolitik risklerin küresel enerji ve gıda fiyatları üzerinde zamanla değişen etkileri. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 25(3), 775-791. https://doi.org/10.17494/ogusbd.1548066
• Chen, S. T., Kuo, H. I., & Chen, C. C. (2010). Modeling the relationship between the oil price and global food prices. Applied Energy, 87(8), 2517-2525. https://doi.org/10.1016/j.apenergy.2010.02.020
• Cheng, S., & Cao, Y. (2019). On the relation between global food and crude oil prices: An empirical investigation in a nonlinear framework. Energy Economics, 81, 422-432. https://doi.org/10.1016/j.eneco.2019.04.007
• Çınar, G., & Uzmay, A. (2017). Does fear (VIX index) incite volatility in food prices?. International Journal of Food and Agricultural Economics, 5(2), 69-78. https://doi.org/10.22004/ag.econ.266472
• Davis, S. J. (2016). An index of global economic policy uncertainty (No. w22740). National Bureau of Economic Research.
• Derindag, O. F., Chang, B. H., Gohar, R., Wong, W. K., & Bhutto, N. A. (2023). Food prices response to global and national factors: Evidence beyond asymmetry. Cogent Economics & Finance, 11(1), 2187128. https://doi.org/10.1080/23322039.2023.2187128
• Diebold, F. X., & Rudebusch, G. D. (1994). Measuring business cycles: A modern perspective (Vol. 4643). Cambridge, Mass., USA: National Bureau of Economic Research.
• Dillon, B. M., & Barrett, C. B. (2016). Global oil prices and local food prices: Evidence from East Africa. American Journal of Agricultural Economics, 98(1), 154-171. https://doi.org/10.1093/ajae/aav040
• Ha, L. T. (2023). Fat tails, serial dependence, and interlinkages of the geopolitical risk and food market during the COVID-19 pandemic and war crisis: an application of Bayesian vector heterogeneous autoregressions. Environmental Science and Pollution Research, 1-18. https://doi.org/10.1007/s11356-023-29565-8
• Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica: Journal of the Econometric Society, 57(2) 357-384. https://doi.org/10.2307/1912559
• Hamilton, J. D., & Susmel, R. (1994). Autoregressive conditional heteroskedasticity and changes in regime. Journal of Econometrics, 64(1-2), 307-333. https://doi.org/10.1016/0304-4076(94)90067-1
• Han, X., Yuan, T., Wang, D., Zhao, Z., & Gong, B. (2023). How to understand high global food price? Using SHAP to interpret machine learning algorithm. Plos one, 18(8), e0290120. https://doi.org/10.1371/journal.pone.0290120
• Hua, J., Li, H., He, Z., Ding, J., & Jin, F. (2022). The microcosmic mechanism and empirical test of uncertainty on the non-linear fluctuation of Chinese grain prices-based on the perspective of global economic policy uncertainty. Agriculture, 12(10), 1526. https://doi.org/10.3390/agriculture12101526
• Hudecová, K., & Rajčániová, M. (2023). The impact of geopolitical risk on agricultural commodity prices. Agricultural Economics/Zemědělská Ekonomika, 69(4), 129-139. https://doi.org/10.17221/374/2022-AGRICECON
• Karagöl, V. (2023). Ekonomik politika belirsizliğinin gıda fiyatlarına etkisi: seçilmiş ülkeler için zamanla değişen nedensellik analizi. İktisat Politikası Araştırmaları Dergisi, 10(2), 409-433. https://doi.org/10.26650/JEPR1212094
• Karakotsios, A., Katrakilidis, C., & Kroupis, N. (2021). The dynamic linkages between food prices and oil prices. Does asymmetry matter?. The Journal of Economic Asymmetries, 23, e00203. https://doi.org/10.1016/j.jeca.2021.e00203
• Kartal, M. T., & Depren, Ö. (2023). Asymmetric relationship between global and national factors and domestic food prices: evidence from Turkey with novel nonlinear approaches. Financial Innovation, 9(1), 11. https://doi.org/10.1186/s40854-022-00407-9
• Krolzig, H. M. (2013). Markov-switching vector autoregressions: Modelling, statistical inference, and application to business cycle analysis (Vol. 454). Springer Science & Business Media.
• Kubatko, O., Merritt, R., Duane, S., & Piven, V. (2023). The impact of the COVID-19 pandemic on global food system resilience. Mechanism of an economic regulation, 1(99), 144-148. https://doi.org/10.32782/mer.2023.99.22
• Lin, F., Li, X., Jia, N., Feng, F., Huang, H., Huang, J., ... & Song, X. P. (2023). The impact of Russia-Ukraine conflict on global food security. Global Food Security, 36, 100661. https://doi.org/10.1016/j.gfs.2022.100661
• Long, S., Li, J., & Luo, T. (2023). The asymmetric impact of global economic policy uncertainty on international grain prices. Journal of Commodity Markets, 30, 100273. https://doi.org/10.1016/j.jcomm.2022.100273
• Ni, G., Teng, M., Chen, Z., Wu, Y., He, W., & Su, B. (2024). Exploring the impacts of major events on the global oil and food markets. Socio-Economic Planning Sciences, 95, 102019. https://doi.org/10.1016/j.seps.2024.102019
• Olayungbo, D. O. (2021). Global oil price and food prices in food importing and oil exporting developing countries: A panel ARDL analysis. Heliyon, 7(3), 1-10. https://doi.org/10.1016/j.heliyon.2021.e06357
• Onour, I. (2021). Dynamics of crude oil price change and global food commodity prices. Finance & Economics Review, 3(1), 38-50. https://doi.org/10.38157/finance-economics-review.v3i1.248
• Raza, S. A., Guesmi, K., Belaid, F., & Shah, N. (2022). Time-frequency causality and connectedness between oil price shocks and the world food prices. Research in International Business and Finance, 62, 101730. https://doi.org/10.1016/j.ribaf.2022.101730
• Saâdaoui, F., Jabeur, S. B., & Goodell, J. W. (2022). Causality of geopolitical risk on food prices: Considering the Russo–Ukrainian conflict. Finance Research Letters, 49, 103103. https://doi.org/10.1016/j.frl.2022.103103
• Sohag, K., Islam, M. M., Tomas Žiković, I., & Mansour, H. (2023). Food inflation and geopolitical risks: analyzing European regions amid the Russia-Ukraine war. British Food Journal, 125(7), 2368-2391. https://doi.org/10.1108/BFJ-09-2022-0793
• Sun, T. T. & Su, C. W. (2024). How is geopolitical risk associated with food prices? International Journal of Emerging Markets, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJOEM-01-2023-0004
• Sun, T. T., Su, C. W., Mirza, N., & Umar, M. (2021). How does trade policy uncertainty affect agriculture commodity prices?. Pacific-Basin Finance Journal, 66, 101514. https://doi.org/10.1016/j.pacfin.2021.101514
• Sun, Y., Gao, P., Raza, S. A., Shah, N., & Sharif, A. (2023). The asymmetric effects of oil price shocks on the world food prices: Fresh evidence from quantile-on-quantile regression approach. Energy, 270, 126812. https://doi.org/10.1016/j.energy.2023.126812
• Tesfamichael, S. G., & Shiferaw, Y. A. (2019). A Markov regime-switching regression approach to modelling NDVI from surface temperature and soil moisture. International Journal of Remote Sensing, 40(24), 9352-9379. https://doi.org/10.1080/01431161.2019.1630783
• Toromade, A. S., Soyombo, D. A., Kupa, E., & Ijomah, T. I. (2024). Reviewing the impact of climate change on global food security: Challenges and solutions. International Journal of Applied Research in Social Sciences, 6(7), 1403-1416. https://doi.org/10.51594/ijarss.v6i7.1300
• Wen, J., Khalid, S., Mahmood, H., & Zakaria, M. (2021). Symmetric and asymmetric impact of economic policy uncertainty on food prices in China: A new evidence. Resources Policy, 74, 102247. https://doi.org/10.1016/j.resourpol.2021.102247
• Yang, C., Niu, Z., & Gao, W. (2022). The time-varying effects of trade policy uncertainty and geopolitical risks shocks on the commodity market prices: Evidence from the TVP-VAR-SV approach. Resources Policy, 76, 102600. https://doi.org/10.1016/j.resourpol.2022.102600
• Yousfi, M., & Bouzgarrou, H. (2024). Geopolitical risk, economic policy uncertainty, and dynamic connectedness between clean energy, conventional energy, and food markets. Environmental Science and Pollution Research, 31(3), 4925-4945. https://doi.org/10.1007/s11356-023-31379-7