[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
IDEAS home Printed from https://ideas.repec.org/p/ipt/iptwpa/jrc84138.html
   My bibliography  Save this paper

Agricultural Commodity Price Volatility and Its Macroeconomic Determinants: A GARCH-MIDAS Approach

Author

Abstract
This paper investigates the main drivers of the agricultural commodity price volatility using the GARCH-MIDAS model of Engel et al. (2013), a new class of component models that allows for isolating the low-frequency component of volatility and taking into consideration macroeconomic factors via mixed data sampling. We show that modelling the agricultural price volatility as the product of high and low frequency components is more efficient than filtering it through a standard GARCH(1,1) model. After combing wheat, corn and soybean daily prices with monthly market-specific and common macroeconomic drivers over the period 1986-2012, it appears that supply-demand indicators and conventional speculation proxies are crucial in explaining the low-frequency component of volatility while monetary factors and energy markets play significant but less important role. Nevertheless, when we consider only the period following the recent price spikes (2006-2012), the monetary factors especially interest rate become essential to describe agricultural price fluctuations, suggesting also that the heterogeneity in the effects of the drivers on different crops is decreasing.

Suggested Citation

  • Emiliano Magrini & Ayca Donmez, 2013. "Agricultural Commodity Price Volatility and Its Macroeconomic Determinants: A GARCH-MIDAS Approach," JRC Research Reports JRC84138, Joint Research Centre.
    • Handle: RePEc:ipt:iptwpa:jrc84138
    as

    Download full text from publisher

    File URL: https://publications.jrc.ec.europa.eu/repository/handle/JRC84138
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Robles, Miguel & Torero, Maximo & von Braun, Joachim, 2009. "When speculation matters:," Issue briefs 57, International Food Policy Research Institute (IFPRI).
    2. Christopher F. Baum & Mustafa Caglayan & Neslihan Ozkan, 2004. "Nonlinear effects of exchange rate volatility on the volume of bilateral exports," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(1), pages 1-23.
    3. Yuqing Zheng & Henry Kinnucan & Henry Thompson, 2008. "News and volatility of food prices," Applied Economics, Taylor & Francis Journals, vol. 40(13), pages 1629-1635.
    4. Williams,Jeffrey C. & Wright,Brian D., 2005. "Storage and Commodity Markets," Cambridge Books, Cambridge University Press, number 9780521023399, September.
    5. Algieri, Bernardina, 2013. "A Roller Coaster Ride: an empirical investigation of the main drivers of wheat price," Discussion Papers 145556, University of Bonn, Center for Development Research (ZEF).
    6. Christopher L. Gilbert, 2010. "Speculative Influences On Commodity Futures Prices 2006-2008," UNCTAD Discussion Papers 197, United Nations Conference on Trade and Development.
    7. Hayo, Bernd & Kutan, Ali M. & Neuenkirch, Matthias, 2012. "Communication matters: US monetary policy and commodity price volatility," Economics Letters, Elsevier, vol. 117(1), pages 247-249.
    8. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    9. Balcombe, Kelvin, 2009. "The Nature and Determinants of Volatility in Agricultural Prices," MPRA Paper 24819, University Library of Munich, Germany.
    10. repec:bla:jfinan:v:44:y:1989:i:5:p:1115-53 is not listed on IDEAS
    11. Anderson, Kym & Nelgen, Signe, 2012. "Trade Barrier Volatility and Agricultural Price Stabilization," World Development, Elsevier, vol. 40(1), pages 36-48.
    12. Berna Karali & Gabriel J. Power, 2013. "Short- and Long-Run Determinants of Commodity Price Volatility," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(3), pages 724-738.
    13. Jennifer Clapp, 2009. "Food Price Volatility and Vulnerability in the Global South: considering the global economic context," Third World Quarterly, Taylor & Francis Journals, vol. 30(6), pages 1183-1196.
    14. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    15. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2005. "There is a risk-return trade-off after all," Journal of Financial Economics, Elsevier, vol. 76(3), pages 509-548, June.
    16. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    17. Dwight R. Sanders & Scott H. Irwin & Robert P. Merrin, 2010. "The Adequacy of Speculation in Agricultural Futures Markets: Too Much of a Good Thing?," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 32(1), pages 77-94.
    18. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    19. Karali, Berna & Thurman, Walter N., 2010. "Components of Grain Futures Price Volatility," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 35(2), pages 1-16, August.
    20. Engle, Robert F. & White (the late), Halbert (ed.), 1999. "Cointegration, Causality, and Forecasting: Festschrift in Honour of Clive W. J. Granger," OUP Catalogue, Oxford University Press, number 9780198296836.
    21. Frankel, Jeffrey A., 2010. "The Natural Resource Curse: A Survey," Scholarly Articles 4454156, Harvard Kennedy School of Government.
    22. Hernandez, Manuel & Torero, Maximo, 2010. "Examining the dynamic relationship between spot and future prices of agricultural commodities," IFPRI discussion papers 988, International Food Policy Research Institute (IFPRI).
    23. Merton, Robert C., 1980. "On estimating the expected return on the market : An exploratory investigation," Journal of Financial Economics, Elsevier, vol. 8(4), pages 323-361, December.
    24. Timmer, C. Peter, 2010. "Reflections on food crises past," Food Policy, Elsevier, vol. 35(1), pages 1-11, February.
    25. Machiko Nissanke, 2012. "Commodity Market Linkages in the Global Financial Crisis: Excess Volatility and Development Impacts," Journal of Development Studies, Taylor & Francis Journals, vol. 48(6), pages 732-750, June.
    26. Bruce A. Babcock, 2012. "The impact of US biofuel policies on agricultural price levels and volatility," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 4(4), pages 407-426, November.
    27. Asgharian, Hossein & Hou, Ai Jun & Javed, Farrukh, 2013. "Importance of the macroeconomic variables for variance prediction A GARCH-MIDAS approach," Knut Wicksell Working Paper Series 2013/4, Lund University, Knut Wicksell Centre for Financial Studies.
    28. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    29. Christopher Gilbert & Wyn Morgan, 2010. "Has food price volatility risen?," Department of Economics Working Papers 1002, Department of Economics, University of Trento, Italia.
    30. Allan D. Brunner, 2002. "El Niño and World Primary Commodity Prices: Warm Water or Hot Air?," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 176-183, February.
    31. Pietola, Kyösti & Liu, Xing & Robles, Miguel, 2010. "Price, inventories, and volatility in the global wheat market," IFPRI discussion papers 996, International Food Policy Research Institute (IFPRI).
    32. Girardin, Eric & Joyeux, Roselyne, 2013. "Macro fundamentals as a source of stock market volatility in China: A GARCH-MIDAS approach," Economic Modelling, Elsevier, vol. 34(C), pages 59-68.
    33. C.P. Chandrasekhar, 2012. "Food Price Levels and Volatility: Sources, Impact and Implications," IDS Bulletin, Blackwell Publishing, vol. 43, pages 74-83, July.
    34. 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.
    35. Seung‐Ryong Yang & B. Wade Brorsen, 1993. "Nonlinear dynamics of daily futures prices: Conditional heteroskedasticity or chaos?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 13(2), pages 175-191, April.
    36. Irwin, Scott H. & Sanders, Dwight R., 2012. "Testing the Masters Hypothesis in commodity futures markets," Energy Economics, Elsevier, vol. 34(1), pages 256-269.
    37. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    38. Mr. Shaun K. Roache, 2010. "What Explains the Rise in Food Price Volatility?," IMF Working Papers 2010/129, International Monetary Fund.
    39. Brian D. Wright, 2011. "The Economics of Grain Price Volatility," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 33(1), pages 32-58.
    40. Miffre, Joelle & Rallis, Georgios, 2007. "Momentum strategies in commodity futures markets," Journal of Banking & Finance, Elsevier, vol. 31(6), pages 1863-1886, June.
    41. Conrad, Christian & Loch, Karin & Rittler, Daniel, 2012. "On the Macroeconomic Determinants of the Long-Term Oil-Stock Correlation," Working Papers 0525, University of Heidelberg, Department of Economics.
    42. Scott H. Irwin & Dwight R. Sanders, 2011. "Index Funds, Financialization, and Commodity Futures Markets," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 33(1), pages 1-31.
    43. Gustafson, Robert L., 1958. "Carryover levels for grains: A method for determining amounts that are optimal under specified conditions," Technical Bulletins 157231, United States Department of Agriculture, Economic Research Service.
    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. Ofentse, Goetswamang Phankie, 2022. "Evaluation of the prospects of hedging Botswana's maize prices against the Johannesburg Stock Exchange Commodity Market Derivative," Research Theses 334751, Collaborative Masters Program in Agricultural and Applied Economics.
    2. Fang, Libing & Yu, Honghai & Xiao, Wen, 2018. "Forecasting gold futures market volatility using macroeconomic variables in the United States," Economic Modelling, Elsevier, vol. 72(C), pages 249-259.
    3. Ruobing Liu & Jianhui Yang & Chuan-Yang Ruan, 2019. "The Impact of Macroeconomic News on Chinese Futures," IJFS, MDPI, vol. 7(4), pages 1-14, October.
    4. Duc Khuong Nguyen & Thomas Walther, 2020. "Modeling and forecasting commodity market volatility with long‐term economic and financial variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 126-142, March.
    5. Chimaliro, Aubrey Victor, 2018. "Analysis of main determinants of soya bean price volatility in Malawi," Research Theses 334743, Collaborative Masters Program in Agricultural and Applied Economics.
    6. Mo, Di & Gupta, Rakesh & Li, Bin & Singh, Tarlok, 2018. "The macroeconomic determinants of commodity futures volatility: Evidence from Chinese and Indian markets," Economic Modelling, Elsevier, vol. 70(C), pages 543-560.
    7. Ayca Donmez & Gloria Solano-Hermosilla & Vladimir Bougay & Balaji Subbaraman & Robert M'barek & Abdoulaye Adam & Stephen Bahemuka & Oliver J. M. Chinganya & Vladimir Eskin & Koua Louis Kouakou & Charl, 2017. "Using web and mobile phone technologies to collect food market prices in Africa. Approaching real-time data and use of crowdsourcing, 2013 - 2016," JRC Research Reports JRC104311, Joint Research Centre.
    8. Aktham Maghyereh & Hussein Abdoh, 2022. "Can news-based economic sentiment predict bubbles in precious metal markets?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-29, December.
    9. Suranjana Joarder, 2018. "The Commodity Futures Volatility and Macroeconomic Fundamentals - The Case of Oil and Oilseed Commodities in India," International Econometric Review (IER), Econometric Research Association, vol. 10(2), pages 33-50, September.

    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. Ojogho, Osaihiomwan & Egware, Robert Awotu, 2015. "Price Generating Process And Volatility In Nigerian Agricultural Commodities Market," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 3(4), pages 1-10, October.
    2. Mo, Di & Gupta, Rakesh & Li, Bin & Singh, Tarlok, 2018. "The macroeconomic determinants of commodity futures volatility: Evidence from Chinese and Indian markets," Economic Modelling, Elsevier, vol. 70(C), pages 543-560.
    3. Chimaliro, Aubrey Victor, 2018. "Analysis of main determinants of soya bean price volatility in Malawi," Research Theses 334743, Collaborative Masters Program in Agricultural and Applied Economics.
    4. Kornher, Lukas & Kalkuhl, Matthias, 2013. "Food Price Volatility in Developing Countries and its Determinants," Quarterly Journal of International Agriculture, Humboldt-Universitaat zu Berlin, vol. 52(4), pages 1-32, November.
    5. Haase, Marco & Huss, Matthias, 2018. "Guilty speculators? Range-based conditional volatility in a cross-section of wheat futures," Journal of Commodity Markets, Elsevier, vol. 10(C), pages 29-46.
    6. Haase, Marco & Seiler Zimmermann, Yvonne & Zimmermann, Heinz, 2016. "The impact of speculation on commodity futures markets – A review of the findings of 100 empirical studies," Journal of Commodity Markets, Elsevier, vol. 3(1), pages 1-15.
    7. Duc Khuong Nguyen & Thomas Walther, 2020. "Modeling and forecasting commodity market volatility with long‐term economic and financial variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 126-142, March.
    8. Lu Wang & Feng Ma & Guoshan Liu & Qiaoqi Lang, 2023. "Do extreme shocks help forecast oil price volatility? The augmented GARCH‐MIDAS approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 2056-2073, April.
    9. 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.
    10. Hervé Ott, 2014. "Extent and possible causes of intrayear agricultural commodity price volatility," Agricultural Economics, International Association of Agricultural Economists, vol. 45(2), pages 225-252, March.
    11. Manera, Matteo & Nicolini, Marcella & Vignati, Ilaria, 2016. "Modelling futures price volatility in energy markets: Is there a role for financial speculation?," Energy Economics, Elsevier, vol. 53(C), pages 220-229.
    12. Ott, Herve, 2012. "Which factors drive which volatility in the grain sector?," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122486, European Association of Agricultural Economists.
    13. Amendola, Alessandra & Candila, Vincenzo & Gallo, Giampiero M., 2019. "On the asymmetric impact of macro–variables on volatility," Economic Modelling, Elsevier, vol. 76(C), pages 135-152.
    14. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
    15. Ruobing Liu & Jianhui Yang & Chuan-Yang Ruan, 2019. "The Impact of Macroeconomic News on Chinese Futures," IJFS, MDPI, vol. 7(4), pages 1-14, October.
    16. Abdelradi, Fadi & Serra, Teresa, 2015. "Food–energy nexus in Europe: Price volatility approach," Energy Economics, Elsevier, vol. 48(C), pages 157-167.
    17. Julien Chevallier & Bilel Sanhaji, 2023. "Jump-Robust Realized-GARCH-MIDAS-X Estimators for Bitcoin and Ethereum Volatility Indices," Stats, MDPI, vol. 6(4), pages 1-32, December.
    18. Yin, Libo & Zhou, Yimin, 2016. "What drives long-term oil market volatility? Fundamentals versus speculation," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 10, pages 1-26.
    19. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    20. González, Mariano & Nave, Juan & Rubio, Gonzalo, 2018. "Macroeconomic determinants of stock market betas," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 26-44.

    More about this item

    Keywords

    Price Volatility; Agricultural Commodities; GARCH-MIDAS; macroeconomic indicators; speculation;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance

    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:ipt:iptwpa:jrc84138. 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: Publication Officer (email available below). General contact details of provider: https://edirc.repec.org/data/ipjrces.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.