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The effects of dollar-sterling exchange rate volatility on futures markets for coffee and cocoa

Author

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  • Adusei Jumah

Abstract

The paper uses multivariate autoregressive conditional heteroscedasticity models to investigate the effect of dollar-sterling exchange rate fluctuations on coffee and cocoa futures prices on the London LIFFE and the New York CSCE. For both commodities and in both markets, the exchange rate emerges as a main source of risk for the commodity futures price. We find that the commodities show similarities not only in their long-run features and first-order shock propagation, but also in their characteristics of volatility propagation. Copyright 2001, Oxford University Press.

Suggested Citation

  • Adusei Jumah, 2001. "The effects of dollar-sterling exchange rate volatility on futures markets for coffee and cocoa," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 28(3), pages 307-328, October.
    • Handle: RePEc:oup:erevae:v:28:y:2001:i:3:p:307-328
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    Cited by:

    1. Roche, M. & McQuinn, K., 2003. "Efficient allocation of land in a decoupled world," Economics Department Working Paper Series n1190103, Department of Economics, National University of Ireland - Maynooth.
    2. Stanislav Yugay & Linde Götz & Miranda Svanidze, 2024. "Impact of the Ruble exchange rate regime and Russia's war in Ukraine on wheat prices in Russia," Agricultural Economics, International Association of Agricultural Economists, vol. 55(2), pages 384-411, March.
    3. Oyinbo, O. & Rekwot, G. Z., 2014. "Econometric Analysis of the Nexus of Exchange Rate Deregulation and Agricultural Share of Gross Domestic Product in Nigeria," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 6(01), pages 1-7.
    4. Kepulaje Abhaya Kumar & Cristi Spulbar & Prakash Pinto & Iqbal Thonse Hawaldar & Ramona Birau & Jyeshtaraja Joisa, 2022. "Using Econometric Models to Manage the Price Risk of Cocoa Beans: A Case from India," Risks, MDPI, vol. 10(6), pages 1-18, June.
    5. Bingzi Jin & Xiaojie Xu, 2025. "Steel price index forecasts through machine learning for northwest China," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(4), pages 811-833, December.
    6. Maurice J. Roche & Kieran McQuinn, 2003. "Grain price volatility in a small open economy," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 30(1), pages 77-98, March.
    7. Jesus Crespo Cuaresma & Adusei Jumah & Sohbet Karbuz, 2009. "Modelling and Forecasting Oil Prices: The Role of Asymmetric Cycles," The Energy Journal, , vol. 30(3), pages 81-90, July.
    8. Carcano, G. & Falbo, P. & Stefani, S., 2005. "Speculative trading in mean reverting markets," European Journal of Operational Research, Elsevier, vol. 163(1), pages 132-144, May.
    9. Guillermo Benavides Peralesv & Francisco Venegas Martínez, 2022. "Impact of Exchange Rate Volatility on Agricultural Trade between the U.S. and Mexico (1990-2017)," Economía: teoría y práctica, Universidad Autónoma Metropolitana, México, vol. 56(1), pages 131-154, Enero-Jun.

    More about this item

    JEL classification:

    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance

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