IDEAS home Printed from https://ideas.repec.org/a/ags/aolpei/276061.html
   My bibliography  Save this article

Price Volatility Modelling – Wheat: GARCH Model Application

Author

Listed:
  • Čermák, M.
  • Malec, K.
  • Maitah, M.

Abstract

This paper is focused on the modelling of volatility in the agricultural commodity market, specifically on wheat. The aim of this study is to develop an applicable and relevant model of conditional heteroscedasticity from the GARCH family for wheat futures prices. The GARCH (1,1) model has the ability to capture the main characteristics of the commodity market, specifically leptokurtic distribution and volatility clustering. The results show that the forecasted volatility of wheat has a tendency towards standard error reversion in the long-run and the position of price distribution is closed to the normal distribution. The wheat production can be hedged against the price variability with long-term contracts. The price of wheat was influenced during the years of 2005 to 2015 by different events, in particular; financial crisis, increasing grain demand and cross-sectional price variability. The results suggest that agricultural producers should focus on short-term structural events the wheat market, rather than long-term variability.

Suggested Citation

  • Čermák, M. & Malec, K. & Maitah, M., 2017. "Price Volatility Modelling – Wheat: GARCH Model Application," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 9(4).
  • Handle: RePEc:ags:aolpei:276061
    DOI: 10.22004/ag.econ.276061
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/276061/files/352_agris-on-line-2017-4-cermak-malec-maitah.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.276061?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. David Zilberman & Gal Hochman & Deepak Rajagopal & Steve Sexton & Govinda Timilsina, 2013. "The Impact of Biofuels on Commodity Food Prices: Assessment of Findings," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(2), pages 275-281.
    2. Mohammad Najand, 2002. "Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models," The Financial Review, Eastern Finance Association, vol. 37(1), pages 93-104, February.
    3. Jian Yang & Michael Haigh & David Leatham, 2001. "Agricultural liberalization policy and commodity price volatility: a GARCH application," Applied Economics Letters, Taylor & Francis Journals, vol. 8(9), pages 593-598.
    4. Tully, Edel & Lucey, Brian M., 2007. "A power GARCH examination of the gold market," Research in International Business and Finance, Elsevier, vol. 21(2), pages 316-325, June.
    5. WenShwo Fang & Stephen M. Miller & ChunShen Lee, 2008. "Cross‐Country Evidence On Output Growth Volatility: Nonstationary Variance And Garch Models," Scottish Journal of Political Economy, Scottish Economic Society, vol. 55(4), pages 509-541, September.
    6. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    7. Darren D. Lee & Robert W. Faff, 2009. "Corporate Sustainability Performance and Idiosyncratic Risk: A Global Perspective," The Financial Review, Eastern Finance Association, vol. 44(2), pages 213-237, May.
    8. Dima Alberg & Haim Shalit & Rami Yosef, 2008. "Estimating stock market volatility using asymmetric GARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 18(15), pages 1201-1208.
    9. Klotz, Philipp & Lin, Tsoyu Calvin & Hsu, Shih-Hsun, 2014. "Global commodity prices, economic activity and monetary policy: The relevance of China," Resources Policy, Elsevier, vol. 42(C), pages 1-9.
    10. Wei, Yu & Wang, Yudong & Huang, Dengshi, 2010. "Forecasting crude oil market volatility: Further evidence using GARCH-class models," Energy Economics, Elsevier, vol. 32(6), pages 1477-1484, November.
    11. Bruce A. Babcock, 2011. "The Impact of Ethanol and Ethanol Subsidies on Corn Prices: Revisiting History," Food and Agricultural Policy Research Institute (FAPRI) Publications (archive only) 11-pb5, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    12. Baur, Dirk G., 2011. "Explanatory mining for gold: Contrasting evidence from simple and multiple regressions," Resources Policy, Elsevier, vol. 36(3), pages 265-275, September.
    13. Ibrahim Onour, "undated". "Forecasting Volatility in Global Food Commodity Prices," API-Working Paper Series 1101, Arab Planning Institute - Kuwait, Information Center.
    14. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    15. Ole Worapree Maneesoonthorn, 2015. "High-Frequency Financial Econometrics , by Yacine Aït-Sahalia and Jean Jacod ( Princeton University Press , Princeton, NJ , 2014 ), pp. xxiv + 659 ," The Economic Record, The Economic Society of Australia, vol. 91(295), pages 542-544, December.
    16. Marco Zuppiroli & Cesar Revoredo-Giha, 2016. "Hedging effectiveness of European wheat futures markets: an application of multivariate GARCH models," International Journal of Applied Management Science, Inderscience Enterprises Ltd, vol. 8(2), pages 132-148.
    17. Krane, Jim, 2015. "A refined approach: Saudi Arabia moves beyond crude," Energy Policy, Elsevier, vol. 82(C), pages 99-104.
    18. Engle, Robert F. & Yoo, Byung Sam, 1987. "Forecasting and testing in co-integrated systems," Journal of Econometrics, Elsevier, vol. 35(1), pages 143-159, May.
    19. Satchell, Stephen & Knight, John, 2007. "Forecasting Volatility in the Financial Markets," Elsevier Monographs, Elsevier, edition 3, number 9780750669429.
    20. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    21. 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.
    22. Bai, Xuezheng & Russell, Jeffrey R. & Tiao, George C., 2003. "Kurtosis of GARCH and stochastic volatility models with non-normal innovations," Journal of Econometrics, Elsevier, vol. 114(2), pages 349-360, June.
    23. Yue-Jun Zhang & Ting Yao & Ling-Yun He, 2015. "Forecasting crude oil market volatility: can the Regime Switching GARCH model beat the single-regime GARCH models?," Papers 1512.01676, arXiv.org.
    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. Burakov, D. & Freidin, M., 2018. "Is the Halloween Effect Present on the Markets for Agricultural Commodities?," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 10(2).
    2. Andrzej Hornowski & Andrzej Parzonko & Pavel Kotyza & Tomasz Kondraszuk & Piotr Bórawski & Luboš Smutka, 2020. "Factors Determining the Development of Small Farms in Central and Eastern Poland," Sustainability, MDPI, vol. 12(12), pages 1-21, June.

    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. Halkos, George & Tzirivis, Apostolos, 2018. "Effective energy commodities’ risk management: Econometric modeling of price volatility," MPRA Paper 90781, University Library of Munich, Germany.
    2. Chen, Hongtao & Liu, Li & Li, Xiaolei, 2018. "The predictive content of CBOE crude oil volatility index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 837-850.
    3. Halkos, George E. & Tsirivis, Apostolos S., 2019. "Effective energy commodity risk management: Econometric modeling of price volatility," Economic Analysis and Policy, Elsevier, vol. 63(C), pages 234-250.
    4. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    5. Hasanov, Akram Shavkatovich & Poon, Wai Ching & Al-Freedi, Ajab & Heng, Zin Yau, 2018. "Forecasting volatility in the biofuel feedstock markets in the presence of structural breaks: A comparison of alternative distribution functions," Energy Economics, Elsevier, vol. 70(C), pages 307-333.
    6. Francesco Guidi, 2009. "Volatility and Long-Term Relations in Equity Markets: Empirical Evidence from Germany, Switzerland, and the UK," The IUP Journal of Financial Economics, IUP Publications, vol. 0(2), pages 7-39, June.
    7. Wang, Yudong & Liu, Li & Ma, Feng & Wu, Chongfeng, 2016. "What the investors need to know about forecasting oil futures return volatility," Energy Economics, Elsevier, vol. 57(C), pages 128-139.
    8. Alexander Subbotin & Thierry Chauveau & Kateryna Shapovalova, 2009. "Volatility Models: from GARCH to Multi-Horizon Cascades," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00390636, HAL.
    9. Subbotin, Alexandre, 2009. "Volatility Models: from Conditional Heteroscedasticity to Cascades at Multiple Horizons," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 15(3), pages 94-138.
    10. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    11. Harry-Paul Vander Elst, 2015. "FloGARCH: Realizing Long Memory and Asymmetries in Returns Valitility," Working Papers ECARES ECARES 2015-12, ULB -- Universite Libre de Bruxelles.
    12. repec:ipg:wpaper:2013-009 is not listed on IDEAS
    13. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
    14. David Daewhan Cho, 2004. "Uncertainty in Second Moments: Implications for Portfolio Allocation," Econometric Society 2004 Far Eastern Meetings 433, Econometric Society.
    15. S. M. Abdullah & Salina Siddiqua & Muhammad Shahadat Hossain Siddiquee & Nazmul Hossain, 2017. "Modeling and forecasting exchange rate volatility in Bangladesh using GARCH models: a comparison based on normal and Student’s t-error distribution," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-19, December.
    16. 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.
    17. Long H. Vo, 2017. "Estimating Financial Volatility with High-Frequency Returns," Journal of Finance and Economics Research, Geist Science, Iqra University, Faculty of Business Administration, vol. 2(2), pages 84-114, October.
    18. Li, Gang & Li, Yong, 2015. "Forecasting copper futures volatility under model uncertainty," Resources Policy, Elsevier, vol. 46(P2), pages 167-176.
    19. Zhang, Yue-Jun & Yao, Ting & He, Ling-Yun & Ripple, Ronald, 2019. "Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 302-317.
    20. Dupoyet, B. & Fiebig, H.R. & Musgrove, D.P., 2012. "Arbitrage-free self-organizing markets with GARCH properties: Generating them in the lab with a lattice model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4350-4363.
    21. Alessandra Amendola & Vincenzo Candila & Antonio Scognamillo, 2017. "On the influence of US monetary policy on crude oil price volatility," Empirical Economics, Springer, vol. 52(1), pages 155-178, February.

    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:aolpei:276061. 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: https://edirc.repec.org/data/fevszcz.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.