IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/34079.html
   My bibliography  Save this paper

Global food and energy markets: volatility transmission and impulse response effects

Author

Listed:
  • Onour, Ibrahim
  • Sergi, Bruno

Abstract

This paper investigates volatility spillover across crude oil market and wheat and corn markets. The corn commodity is taken here to assess the impact of change in demand for biofuel on wheat market. Results of multivariate GARCH model show evidence of corn price volatility transmission to wheat market . Our results indicate that while shocks (unexpected news) in crude oil market have significant impact on volatility in wheat and corn markets, the effect of crude oil price changes on corn and wheat markets is insignificant. The impulse response analysis indicate shocks in oil markets have permanent effect on food commodity price changes. Also indicated that fertilizers markets influenced by own-shocks and shocks in oil markets.

Suggested Citation

  • Onour, Ibrahim & Sergi, Bruno, 2011. "Global food and energy markets: volatility transmission and impulse response effects," MPRA Paper 34079, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:34079
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/34079/1/MPRA_paper_34079.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    2. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    3. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
    4. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
    5. Ibrahim Onour, "undated". "Forecasting Volatility in Global Food Commodity Prices," API-Working Paper Series 1101, Arab Planning Institute - Kuwait, Information Center.
    6. Du, Xiaodong & Yu, Cindy L. & Hayes, Dermot J., 2011. "Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis," Energy Economics, Elsevier, vol. 33(3), pages 497-503, May.
    7. Ibrahim A. Onour, 2010. "Global food crisis and crude oil price changes: Do they share common cyclical features?," International Journal of Economic Policy in Emerging Economies, Inderscience Enterprises Ltd, vol. 3(1), pages 61-70.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lee , Hyun-Hoon & Park, Cyn-Young, 2013. "International Transmission of Food Prices and Volatilities: A Panel Analysis," ADB Economics Working Paper Series 373, Asian Development Bank.
    2. Serra, Teresa & Zilberman, David, 2013. "Biofuel-related price transmission literature: A review," Energy Economics, Elsevier, vol. 37(C), pages 141-151.
    3. Lochhead, Kyle & Ghafghazi, Saeed & Havlik, Petr & Forsell, Nicklas & Obersteiner, Michael & Bull, Gary & Mabee, Warren, 2016. "Price trends and volatility scenarios for designing forest sector transformation," Energy Economics, Elsevier, vol. 57(C), pages 184-191.
    4. Agie Wandala Putra & Jatna Supriatna & Raldi Hendro Koestoer & Tri Edhi Budhi Soesilo, 2021. "Differences in Local Rice Price Volatility, Climate, and Macroeconomic Determinants in the Indonesian Market," Sustainability, MDPI, vol. 13(8), pages 1-21, April.
    5. Nicholas Apergis & Sofia Eleftheriou & Dimitrios Voliotis, 2017. "Asymmetric Spillover Effects between Agricultural Commodity Prices and Biofuel Energy Prices," International Journal of Energy Economics and Policy, Econjournals, vol. 7(1), pages 166-177.

    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. Algieri, Bernardina, 2014. "The influence of biofuels, economic and financial factors on daily returns of commodity futures prices," Energy Policy, Elsevier, vol. 69(C), pages 227-247.
    2. Christensen, Bent Jesper & Nielsen, Morten Ørregaard & Zhu, Jie, 2010. "Long memory in stock market volatility and the volatility-in-mean effect: The FIEGARCH-M Model," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 460-470, June.
    3. Ender Su & John Bilson, 2011. "Trading asymmetric trend and volatility by leverage trend GARCH in Taiwan stock index," Applied Economics, Taylor & Francis Journals, vol. 43(26), pages 3891-3905.
    4. Ibrahim A. ONOUR & Bruno S. SERGI, 2011. "Modeling and forecasting volatility in global food commodity prices," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 57(3), pages 132-139.
    5. Klein, Tony & Pham Thu, Hien & Walther, Thomas, 2018. "Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performance," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 105-116.
    6. Audrone Virbickaite & M. Concepción Ausín & Pedro Galeano, 2015. "Bayesian Inference Methods For Univariate And Multivariate Garch Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 76-96, February.
    7. Halkos, George & Tzirivis, Apostolos, 2018. "Effective energy commodities’ risk management: Econometric modeling of price volatility," MPRA Paper 90781, University Library of Munich, Germany.
    8. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    9. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
    10. Harry Vander Elst, 2015. "FloGARCH : Realizing long memory and asymmetries in returns volatility," Working Paper Research 280, National Bank of Belgium.
    11. Catherine Doz & Eric Renault, 2004. "Conditionaly Heteroskedastic Factor Models : Identificationand Instrumental variables Estmation," THEMA Working Papers 2004-13, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    12. Zexuan Yin & Paolo Barucca, 2022. "Variational Heteroscedastic Volatility Model," Papers 2204.05806, arXiv.org.
    13. Mauro Bernardi & Leopoldo Catania, 2015. "Switching-GAS Copula Models With Application to Systemic Risk," Papers 1504.03733, arXiv.org, revised Jan 2016.
    14. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
    15. Kirt C. Butler & Katsushi Okada, 2008. "Higher-Order Terms in Bivariate Returns to International Stock Market Indices," Multinational Finance Journal, Multinational Finance Journal, vol. 12(1-2), pages 127-155, March-Jun.
    16. Hassan, Syed Aun & Malik, Farooq, 2007. "Multivariate GARCH modeling of sector volatility transmission," The Quarterly Review of Economics and Finance, Elsevier, vol. 47(3), pages 470-480, July.
    17. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024.
    18. Sucarrat, Genaro & Escribano, Álvaro, 2010. "The power log-GARCH model," UC3M Working papers. Economics we1013, Universidad Carlos III de Madrid. Departamento de Economía.
    19. Klein, Tony & Hien, Pham Thu & Walther, Thomas, 2018. "Bitcoin Is Not the New Gold: A Comparison of Volatility, Correlation, and Portfolio Performance," QBS Working Paper Series 2018/01, Queen's University Belfast, Queen's Business School.
    20. Jie Zhu, 2008. "FIEGARCH-M and and International Crises: A Cross-Country Analysis," CREATES Research Papers 2008-16, Department of Economics and Business Economics, Aarhus University.

    More about this item

    Keywords

    Volatility; global food; impulse response;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy; Animal Welfare Policy

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:34079. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.