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Agricultural commodities price dependence on Brazilian financial market

Author

Listed:
  • Casagranda, Yasmin Gomes
  • Casarotto, Eduardo Luis
  • Pereira, Gênesis Miguel
  • Amorin, Anderson Luís Walker
  • Schollkopf, Joana Cechele
  • Mores, Giana de Vargas

Abstract

This study aims to identify whether there is dependence between agricultural commodities traded on the Brazilian market. We used the bivariate copula method over a ten-year period to assess the extreme effects on the returns of the following commodities: soybean, wheat, Arabica coffee, and Robusta coffee. The relationship directly affects the dependence between Arabica and Robusta coffees commodities. While the relationship between wheat, Arabica and Robusta coffees, and soybean is positively dependent. Economic growth, market dynamics, and the prices of an agricultural commodity tend to increase the price of other commodities.

Suggested Citation

  • Casagranda, Yasmin Gomes & Casarotto, Eduardo Luis & Pereira, Gênesis Miguel & Amorin, Anderson Luís Walker & Schollkopf, Joana Cechele & Mores, Giana de Vargas, 2023. "Agricultural commodities price dependence on Brazilian financial market," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 14(01), January.
    • Handle: RePEc:ags:ijofsd:346691
    DOI: 10.22004/ag.econ.346691
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    as
    1. Yahya, Muhammad & Oglend, Atle & Dahl, Roy Endré, 2019. "Temporal and spectral dependence between crude oil and agricultural commodities: A wavelet-based copula approach," Energy Economics, Elsevier, vol. 80(C), pages 277-296.
    2. Bohl, Martin T. & Gross, Christian & Souza, Waldemar, 2019. "The role of emerging economies in the global price formation process of commodities: Evidence from Brazilian and U.S. coffee markets," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 203-215.
    3. de Araujo, Fernando Henrique Antunes & Bejan, Lucian & Stosic, Borko & Stosic, Tatijana, 2020. "An analysis of Brazilian agricultural commodities using permutation – information theory quantifiers: The influence of food crisis," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. Huang, Jianbai & Li, Yingli & Zhang, Hongwei & Chen, Jinyu, 2021. "The effects of uncertainty measures on commodity prices from a time-varying perspective," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 100-114.
    5. López Cabrera, Brenda & Schulz, Franziska, 2016. "Volatility linkages between energy and agricultural commodity prices," Energy Economics, Elsevier, vol. 54(C), pages 190-203.
    6. Coletti, Donald & Lalonde, René & Masson, Paul & Muir, Dirk & Snudden, Stephen, 2021. "Commodities and monetary policy: Implications for inflation and price level targeting," Journal of Policy Modeling, Elsevier, vol. 43(5), pages 982-999.
    7. Shah, Adil Ahmad & Dar, Arif Billah, 2021. "Exploring diversification opportunities across commodities and financial markets: Evidence from time-frequency based spillovers," Resources Policy, Elsevier, vol. 74(C).
    8. Shahzad, Syed Jawad Hussain & Hernandez, Jose Arreola & Al-Yahyaee, Khamis Hamed & Jammazi, Rania, 2018. "Asymmetric risk spillovers between oil and agricultural commodities," Energy Policy, Elsevier, vol. 118(C), pages 182-198.
    9. Nguyen, Duc Binh Benno & Prokopczuk, Marcel, 2019. "Jumps in commodity markets," Journal of Commodity Markets, Elsevier, vol. 13(C), pages 55-70.
    10. Brechmann, Eike Christian & Schepsmeier, Ulf, 2013. "Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i03).
    11. Harding, Torfinn & Herzberg, Julika & Kuralbayeva, Karlygash, 2021. "Commodity prices and robust environmental regulation: Evidence from deforestation in Brazil," Journal of Environmental Economics and Management, Elsevier, vol. 108(C).
    12. Ji, Qiang & Bouri, Elie & Roubaud, David & Shahzad, Syed Jawad Hussain, 2018. "Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model," Energy Economics, Elsevier, vol. 75(C), pages 14-27.
    13. Edward Frees & Emiliano Valdez, 1998. "Understanding Relationships Using Copulas," North American Actuarial Journal, Taylor & Francis Journals, vol. 2(1), pages 1-25.
    14. Hamadi, Hassan & Bassil, Charbel & Nehme, Tamara, 2017. "News surprises and volatility spillover among agricultural commodities: The case of corn, wheat, soybean and soybean oil," Research in International Business and Finance, Elsevier, vol. 41(C), pages 148-157.
    15. Kilian, Lutz & Zhou, Xiaoqing, 2018. "Modeling fluctuations in the global demand for commodities," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 54-78.
    16. Nekhili, Ramzi & Mensi, Walid & Vo, Xuan Vinh, 2021. "Multiscale spillovers and connectedness between gold, copper, oil, wheat and currency markets," Resources Policy, Elsevier, vol. 74(C).
    17. El Hedi Arouri, Mohamed & Huong Dinh, Thanh & Khuong Nguyen, Duc, 2010. "Time-varying predictability in crude-oil markets: the case of GCC countries," Energy Policy, Elsevier, vol. 38(8), pages 4371-4380, August.
    18. repec:ipg:wpaper:2014-561 is not listed on IDEAS
    19. Reboredo, Juan Carlos & Ugolini, Andrea & Hernandez, Jose Arreola, 2021. "Dynamic spillovers and network structure among commodity, currency, and stock markets," Resources Policy, Elsevier, vol. 74(C).
    20. Chkili, Walid & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2014. "Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory," Energy Economics, Elsevier, vol. 41(C), pages 1-18.
    21. Ye, Wuyi & Guo, Ranran & Deschamps, Bruno & Jiang, Ying & Liu, Xiaoquan, 2021. "Macroeconomic forecasts and commodity futures volatility," Economic Modelling, Elsevier, vol. 94(C), pages 981-994.
    22. He, Chaohua & Jiang, Cheng & Molyboga, Marat, 2019. "Risk premia in Chinese commodity markets," Journal of Commodity Markets, Elsevier, vol. 15(C), pages 1-1.
    23. Zhang, Yongmin & Ding, Shusheng, 2021. "Liquidity effects on price and return co-movements in commodity futures markets," International Review of Financial Analysis, Elsevier, vol. 76(C).
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    Keywords

    Agribusiness; Demand and Price Analysis;

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