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Volatility Dynamics and Interdependencies in US Grain Commodity Futures: Evidence from CCC EGARCH Modeling

Savaş Tarkun0000-0002-2684-184X
Sayfa
385–398
Yayın Tarihi
2026-07-31
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

By elucidating the interconnectedness of grain futures markets, this study informs commodity market stakeholders, including investors, policymakers, and industry practitioners, enabling them to navigate complex market dynamics and make informed decisions to improve food security and economic stability.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This study employs the CCC EGARCH model with a t-distribution to analyze volatility dynamics and interdependencies among US grain commodity futures, focusing on wheat, corn, soybeans, and oats.
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Temel Bulgular & Veri:Significant volatility spillovers and conditional correlations are identified, with oats serving as a critical transmitter of volatility.
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Sonuç & Katkı:By elucidating the interconnectedness of grain futures markets, this study informs commodity market stakeholders, including investors, policymakers, and industry practitioners, enabling them to navigate complex market dynamics and make informed decisions to improve food security and economic stability.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Volatility Dynamics · CCC EGARCH · Grain Commodity Futures

Abstract

This study employs the CCC EGARCH model with a t-distribution to analyze volatility dynamics and interdependencies among US grain commodity futures, focusing on wheat, corn, soybeans, and oats. Significant volatility spillovers and conditional correlations are identified, with oats serving as a critical transmitter of volatility. The analysis reveals persistent shocks and leverage effects, particularly in wheat and oats, underscoring the necessity for robust risk management strategies. This research provides food policy-relevant insights, offering strategic recommendations for policymakers to enhance market stability and resilience. By elucidating the interconnectedness of grain futures markets, this study informs commodity market stakeholders, including investors, policymakers, and industry practitioners, enabling them to navigate complex market dynamics and make informed decisions to improve food security and economic stability.

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Bu Makaleye Atıf Yap

Tarkun, S. (2026). Volatility Dynamics and Interdependencies in US Grain Commodity Futures: Evidence from CCC EGARCH Modeling. Ege Akademik Bakış Dergisi, Cilt: 26 Sayı: 3, 385–398. https://doi.org/10.21121/eab.20260025

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Kaynakça (27)

  1. Ahumada, H., & Cornejo, M. (2016). Forecasting food prices: The case of corn, soybeans and wheat. International Journal of Forecasting, 32(3), 838–848. https://doi. org/10.1016/j.ijforecast.2016.01.002
  2. Attavanich, W., McCarl, B. A., Ahmedov, Z., Fuller, S. W., & Vedenov, D. V. (2013). Effects of climate change on US grain transport. Nature Climate Change, 3(7), 638–643. https://doi.org/10.1038/nclimate1892
  3. Bollerslev, T. (1990). Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized Arch Model. The Review of Economics and Statistics, 72(3), 498–505.
  4. Chang, C. L., McAleer, M., & Tansuchat, R. (2012). Modelling long memory volatility in agricultural commodity futures returns. Annals of Financial Economics, 7(2), 1–27. https://doi.org/10.1142/S2010495212500108
  5. Cheng, I. H., & Xiong, W. (2014). Financialization of commodity markets. Annual Review of Financial Economics, 6, 419–941. https://doi.org/10.1146/ annurev-financial-110613-034432
  6. Du, X., Yu, C. L., & Hayes, D. J. (2011). Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis. Energy Economics, 33(3), 497–503. https://doi.org/10.1016/j. eneco.2010.12.015
  7. Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339–350. https://doi.org/10.1198/073500102288618487
  8. 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
  9. Harri, A., & Hudos, D. (2009). Mean and Variance Dynamics between Agricultural Commodity Prices and Crude Oil Prices. Economics of Alternative Energy Sources and Globalization, November 15-17, 1–20.
  10. Hilliard, J. E., Hilliard, J., & Wu, Y. (2023). Cointegration Between the Black Sea and Kansas City Wheat Futures: the Impact of Russian Invasion of Ukraine. Journal of Eastern European and Central Asian Research, 10(3), 413–424. https://doi.org/10.15549/jeecar.v10i3.1177.
  11. Iorgulescu, E. A. M., Pütz, A., & Siklos, P. L. (2022). Grain Futures Trading During the Interwar Period: Introducing a New Dataset and Evidence. SSRN Electronic Journal. https://doi.org/10.2139/ ssrn.4000689
  12. Koutmos, G. (1996). Modeling the dynamic interdependence of major European stock markets. Journal of Business Finance and Accounting, 23(7), 975–988. https://doi.org/10.1111/j.1468-5957.1996. tb01035.x
  13. Lawson, J., Alam, R., & Etienne, X. (2021). Speculation and food-grain prices. Applied Economics, 53(20), 2305– 2321. https://doi.org/10.1080/00036846.2020.18594 51
  14. Li, K. X., Xiao, Y., Chen, S. L., Zhang, W., Du, Y., & Shi, W. (2018). Dynamics and Interdependencies Among Different Shipping Freight Markets. Maritime Policy and Management, 45(7), 837–849. https://doi.org/10. 1080/03088839.2018.1488187.
  15. Martins, A. M. (2024). Short-term market impact of Black Sea Grain Initiative on four grain markets. Journal of Futures Markets, 44(4), 619–630. https://doi. org/10.1002/fut.22481
  16. McPhail, L. L., Du, X., & Muhammad, A. (2012). Disentangling Corn Price Volatility: The Role of Global Demand, Speculation, and Energy. Journal of Agricultural and Applied Economics, 44(3), 401–410. https://doi.org/10.1017/s107407080000050x.
  17. Mei, D., & Xie, Y. (2022). U.S. grain commodity futures price volatility: Does trade policy uncertainty matter? Finance Research Letters, 48(May), 103028. https://doi. org/10.1016/j.frl.2022.103028
  18. Mensi, W., Rehman, M. U., Maitra, D., Al-Yahyaee, K. H., & Vo, X. V. (2021). Oil, natural gas and BRICS stock markets: Evidence of systemic risks and co-movements in the time-frequency domain. Resources Policy, 72(January), 102062. https://doi.org/10.1016/j.resourpol.2021.102062
  19. Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347. https://doi.org/10.2307/2938260
  20. Özer, H., & Yarbaşı, İ. Y. (2023). Tahıl Emtia Fiyat Oynaklığının Markov Değişim Asimetrik Garch Modelleriyle İncelenmesi (Investigation of Grain Commodity Price Volatility Using Markov Switching Asymmetric Garch Models). Journal of Business Research - Turk, 15(1), 500–513. https://doi.org/10.20491/isarder.2023.1600
  21. Polat, O., Doğan Başar, B., Torun, E., & Ekşi, İ. H. (2023). Dynamic interlinkages between geopolitical stress and agricultural commodity market: Novel findings in the wake of the Russian Ukrainian conflict. Borsa Istanbul Review, 23, S74–S83. https://doi.org/10.1016/j.bir.2023.05.007
  22. Sanders, D. R., & Irwin, S. H. (2011). New Evidence on the Impact of Index Funds in U.S. Grain Futures Markets. Canadian Journal of Agricultural Economics, 59(4), 519–532. https://doi.org/10.1111/j.1744- 7976.2011.01226.x
  23. Sanders, D. R., Irwin, S. H., & Merrin, R. P. (2010). The adequacy of speculation in agricultural futures markets: Too much of a good thing? Applied Economic Perspectives and Policy, 32(1), 77–94. https://doi. org/10.1093/aepp/ppp006
  24. Staugaitis, A. J., & Vaznonis, B. (2022). Financial Speculation Impact on Agricultural and Other Commodity Return Volatility: Implications for Sustainable Development and Food Security. Agriculture, 12(11), 1892. https:// doi.org/10.3390/agriculture12111892
  25. Tadesse, G., Algieri, B., Kalkuhl, M., & von Braun, J. (2014). Drivers and triggers of international food price spikes and volatility. Food Policy, 47, 117–128. https://doi.org/10.1016/j.foodpol.2013.08.014
  26. Triantafyllou, A., Dotsis, G., & Sarris, A. H. (2015). Volatility Forecasting and Time-varying Variance Risk Premiums in Grains Commodity Markets. Journal of Agricultural Economics, 66(2), 329–357. https://doi. org/10.1111/1477-9552.12101
  27. Xiarchos, I. M., & Burnett, J. W. (2018). Dynamic volatility spillovers between agricultural and energy commodities. Journal of Agricultural and Applied Economics, 50(3), 291–318. https://doi.org/10.1017/aae.2017.34