IDEAS home Printed from https://ideas.repec.org/p/ags/n13419/309629.html

Driving Black Sea Grain Prices: Evidence on CBoT Futures and Exchange Rates

Author

Listed:
  • Heigermoser, Maximilian
  • Gotz, Linde
  • Jaghdani, Tinoush Jamali

Abstract

Over the last two decades, the Black Sea region developed to be a key global exporting region for corn and wheat. However, many market participants grapple with insufficient knowledge of factors that drive Black Sea spot prices, while effective futures markets that could facilitate price discovery and risk management are still missing. In our study, we identify marketspecific drivers of volatility of Ukrainian corn and Russian wheat prices. We use daily Black Sea spot price indices for both grains to estimate non-parametric realized volatility measures. These are regressed on several potential drivers, namely, respective futures prices, exchange rates, oil prices and freight rates that serve as a proxy for demand shifts. Estimation results suggest that Ukrainian corn price volatility is well explained by futures price movements and demand shifts, while Russian wheat markets are rather isolated from futures price movements and mostly depend on own lagged volatility and exchange rate movements. Additionally, we find asymmetric responses to price movements at the CBoT: both Black Sea markets react significantly stronger to price increases at the CBoT than to price decreases.

Suggested Citation

  • Heigermoser, Maximilian & Gotz, Linde & Jaghdani, Tinoush Jamali, 2019. "Driving Black Sea Grain Prices: Evidence on CBoT Futures and Exchange Rates," 2019 Conference, April 15-16, 2019, Minneapolis, Minnesota 309629, NCR-134/ NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
  • Handle: RePEc:ags:n13419:309629
    DOI: 10.22004/ag.econ.309629
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/309629/files/Heigermoser_Goetz_Jaghdani_NCCC-134_2019.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.309629?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Gafarova, Gulmira & Perekhozhuk, Oleksandr & Glauben, Thomas, 2015. "Price Discrimination And Pricing-To-Market Behavior Of Black Sea Region Wheat Exporters," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 47(3), pages 287-316, August.
    2. Manuel A. Hernandez & Raul Ibarra & Danilo R. Trupkin, 2014. "How far do shocks move across borders? Examining volatility transmission in major agricultural futures markets," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 41(2), pages 301-325.
    3. Joseph P. Janzen & Michael K. Adjemian, 2017. "Estimating the Location of World Wheat Price Discovery," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 99(5), pages 1188-1207.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Kevin D. Hoover & Stephen J. Perez, 1999. "Data mining reconsidered: encompassing and the general-to-specific approach to specification search," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 167-191.
    6. Óscar Carchano & Ángel Pardo, 2009. "Rolling over stock index futures contracts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 29(7), pages 684-694, July.
    7. McPhail, Lihong Lu & Du, Xiaodong & Muhammad, Andrew, 2012. "Disentangling Corn Price Volatility: The Role of Global Demand, Speculation, and Energy," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 44(3), pages 401-410, August.
    8. Kerstin Uhl & Oleksandr Perekhozhuk & Thomas Glauben, 2016. "Price Discrimination in Russian Wheat Exports: Evidence from Firm-level Data," Journal of Agricultural Economics, Wiley Blackwell, vol. 67(3), pages 722-740, September.
    9. Serra, Teresa, 2011. "Volatility spillovers between food and energy markets: A semiparametric approach," Energy Economics, Elsevier, vol. 33(6), pages 1155-1164.
    10. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    11. Nazlioglu, Saban & Erdem, Cumhur & Soytas, Ugur, 2013. "Volatility spillover between oil and agricultural commodity markets," Energy Economics, Elsevier, vol. 36(C), pages 658-665.
    12. Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.
    13. Berna Karali & Gabriel J. Power, 2013. "Short- and Long-Run Determinants of Commodity Price Volatility," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(3), pages 724-738.
    14. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Mingtao & Ma, Yong, 2025. "Climate risk and predictability of global stock market volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 101(C).
    2. Serra, Teresa & Zilberman, David, 2013. "Biofuel-related price transmission literature: A review," Energy Economics, Elsevier, vol. 37(C), pages 141-151.
    3. Pal, Debdatta & Mitra, Subrata K., 2019. "Correlation dynamics of crude oil with agricultural commodities: A comparison between energy and food crops," Economic Modelling, Elsevier, vol. 82(C), pages 453-466.
    4. Rehim Kılıç, 2025. "Linear and nonlinear econometric models against machine learning models: realized volatility prediction," Finance and Economics Discussion Series 2025-061, Board of Governors of the Federal Reserve System (U.S.).
    5. Barbara Będowska-Sójka, 2018. "Is intraday data useful for forecasting VaR? The evidence from EUR/PLN exchange rate," Risk Management, Palgrave Macmillan, vol. 20(4), pages 326-346, November.
    6. Lyócsa, Štefan & Molnár, Peter & Todorova, Neda, 2017. "Volatility forecasting of non-ferrous metal futures: Covariances, covariates or combinations?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 228-247.
    7. Yusui Tang & Feng Ma & Yaojie Zhang & Yu Wei, 2022. "Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4770-4783, October.
    8. Sami Mestiri & Sabrine Abdelghani, 2021. "La modélisation de la dynamique des volatilités et des corrélations entre les prix des matières premières et les rendements boursiers," Working Papers hal-03432761, HAL.
    9. Gardebroek, Cornelis & Hernandez, Manuel A., 2013. "Do energy prices stimulate food price volatility? Examining volatility transmission between US oil, ethanol and corn markets," Energy Economics, Elsevier, vol. 40(C), pages 119-129.
    10. Ahmadi, Maryam & Bashiri Behmiri, Niaz & Manera, Matteo, 2016. "How is volatility in commodity markets linked to oil price shocks?," Energy Economics, Elsevier, vol. 59(C), pages 11-23.
    11. Fernandez-Diaz, Jose M. & Morley, Bruce, 2019. "Interdependence among agricultural commodity markets, macroeconomic factors, crude oil and commodity index," Research in International Business and Finance, Elsevier, vol. 47(C), pages 174-194.
    12. repec:fpr:export:1344 is not listed on IDEAS
    13. Papantonis, Ioannis & Rompolis, Leonidas & Tzavalis, Elias, 2023. "Improving variance forecasts: The role of Realized Variance features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1221-1237.
    14. Dalheimer, Bernhard & Herwartz, Helmut & Lange, Alexander, 2021. "The threat of oil market turmoils to food price stability in Sub-Saharan Africa," Energy Economics, Elsevier, vol. 93(C).
    15. Santos, Douglas G. & Candido, Osvaldo & Tófoli, Paula V., 2022. "Forecasting risk measures using intraday and overnight information," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    16. Bee, Marco & Dupuis, Debbie J. & Trapin, Luca, 2016. "Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 86-99.
    17. Bekierman, Jeremias & Manner, Hans, 2018. "Forecasting realized variance measures using time-varying coefficient models," International Journal of Forecasting, Elsevier, vol. 34(2), pages 276-287.
    18. Shen, Yifan & Shi, Xunpeng & Variam, Hari Malamakkavu Padinjare, 2018. "Risk transmission mechanism between energy markets: A VAR for VaR approach," Energy Economics, Elsevier, vol. 75(C), pages 377-388.
    19. Algirdas Justinas Staugaitis & Bernardas Vaznonis, 2022. "Short-Term Speculation Effects on Agricultural Commodity Returns and Volatility in the European Market Prior to and during the Pandemic," Agriculture, MDPI, vol. 12(5), pages 1-26, April.
    20. Stewart, Shamar L. & Isengildina Massa, Olga, 2024. "Food & Oil Price Volatility Dynamics: Insights from a TVP-SVAR-DCC-MIDAS Model," 2024 Annual Meeting, July 28-30, New Orleans, LA 343936, Agricultural and Applied Economics Association.
    21. repec:uts:finphd:39 is not listed on IDEAS
    22. Fei Su & Lei Wang, 2020. "Conditional Volatility Persistence and Realized Volatility Asymmetry: Evidence from the Chinese Stock Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(14), pages 3252-3269, November.

    More about this item

    Keywords

    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:n13419:309629. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: http://www.farmdoc.illinois.edu/nccc134/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.