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Market interdependence and volatility transmission among major crops

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  • Cornelis Gardebroek
  • Manuel A. Hernandez
  • Miguel Robles

Abstract

This article provides a comprehensive analysis of the dynamics of volatility across major agricultural commodities in the United States. Volatility interactions across markets may lower the effectiveness of diversification strategies to mitigate price risks and should be taken into account when analyzing the pricing behavior of different agricultural commodities. We follow a multivariate GARCH approach to evaluate the time evolution of conditional correlations and volatility transmission across corn, wheat, and soybeans price returns on a daily, weekly, and monthly basis. The period of analysis is from 1998 to 2012. The estimation results indicate a lack of lead-lag relationships between corn, wheat, and soybeans price returns at the mean level. We find, however, important volatility spillovers across commodities, particularly at the weekly and monthly level. Wheat and corn seem to play a major role in terms of volatility transmission. Despite the supposed higher financial market integration of agricultural commodities, we do not observe that agricultural markets have become more interdependent in recent years.

Suggested Citation

  • Cornelis Gardebroek & Manuel A. Hernandez & Miguel Robles, 2016. "Market interdependence and volatility transmission among major crops," Agricultural Economics, International Association of Agricultural Economists, vol. 47(2), pages 141-155, March.
    • Handle: RePEc:bla:agecon:v:47:y:2016:i:2:p:141-155
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    File URL: http://hdl.handle.net/10.1111/agec.12184
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    Cited by:

    1. Saghaian, Sayed & Nemati, Mehdi & Walters, Cory & Chen, Bo, 2018. "Asymmetric Price Volatility Transmission between U.S. Biofuel, Corn, and Oil Markets," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 43(1), January.
    2. de Nicola, Francesca & De Pace, Pierangelo & Hernandez, Manuel A., 2014. "Co-movement of major commodity price returns: A time-series assessment:," IFPRI discussion papers 1354, International Food Policy Research Institute (IFPRI).
    3. Matthias Kalkuhl & Lukas Kornher & Marta Kozicka & Pierre Boulanger & Maximo Torero, 2013. "Conceptual framework on price volatility and its impact on food and nutrition security in the short term," FOODSECURE Working papers 15, LEI Wageningen UR.
    4. Julyerme M. Tonin & Carlos M. R. Vieira & Rui M. de Sousa Fragoso & João G. Martines Filho, 2020. "Conditional correlation and volatility between spot and futures markets for soybean and corn," Agribusiness, John Wiley & Sons, Ltd., vol. 36(4), pages 707-724, October.
    5. Śmiech, Sławomir & Papież, Monika & Fijorek, Kamil & Dąbrowski, Marek A., 2019. "What drives food price volatility? Evidence based on a generalized VAR approach applied to the food, financial and energy markets," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 13, pages 1-32.
    6. Refk Selmi & Jamal Bouoiyour, 2020. "Global Market's Diagnosis on Coronavirus : A Tug of War between Hope and Fear," Working Papers hal-02514428, HAL.
    7. Bonato, Matteo, 2019. "Realized correlations, betas and volatility spillover in the agricultural commodity market: What has changed?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 184-202.
    8. 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.
    9. Ayesha Sayed & Christo Auret, 2020. "Volatility transmission in the South African white maize futures market," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 10(1), pages 71-88, March.
    10. Ceballos, Francisco & Hernandez, Manuel A. & Minot, Nicholas & Robles, Miguel, 2017. "Grain Price and Volatility Transmission from International to Domestic Markets in Developing Countries," World Development, Elsevier, vol. 94(C), pages 305-320.
    11. Santiago Guerrero & Gerardo Hernández†del†Valle & Miriam Juárez†Torres, 2017. "Using a functional approach to test trending volatility in the price of Mexican and international agricultural products," Agricultural Economics, International Association of Agricultural Economists, vol. 48(1), pages 3-13, January.
    12. Dohnal, Mirko & Doubravsky, Karel, 2016. "Equationless and equation-based trend models of prohibitively complex technological and related forecasts," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 297-304.
    13. Czudaj, Robert L., 2019. "Dynamics between trading volume, volatility and open interest in agricultural futures markets: A Bayesian time-varying coefficient approach," Econometrics and Statistics, Elsevier, vol. 12(C), pages 78-145.
    14. Christian Gross, 2017. "Examining the Common Dynamics of Commodity Futures Prices," CQE Working Papers 6317, Center for Quantitative Economics (CQE), University of Muenster.

    More about this item

    JEL classification:

    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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