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U.S. grain commodity futures price volatility: Does trade policy uncertainty matter?

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  • Mei, Dexiang
  • Xie, Yutang

Abstract

The outbreak and continuation of the COVID-19 pandemic have affected the trade policies of various countries and influenced global food security. This paper aims to use U.S. major grain commodity futures price and trade policy uncertainty (TPU) index data to examine the impact of TPU on the volatility of U.S. grain futures prices under the GARCH-MIDAS framework. The in-sample estimates confirm the impact of TPU on the volatility of US grain commodity futures prices. Out-of-sample testing further reveals that considering TPU could improve predictions of future price fluctuations for different grain commodities. Finally, we also consider other uncertainty indices. Since the grain market is often used as a tool to hedge financial risks, this article can provide some advice for investors in times of policy instability and especially trade policy uncertainty.

Suggested Citation

  • Mei, Dexiang & Xie, Yutang, 2022. "U.S. grain commodity futures price volatility: Does trade policy uncertainty matter?," Finance Research Letters, Elsevier, vol. 48(C).
    • Handle: RePEc:eee:finlet:v:48:y:2022:i:c:s1544612322002689
    DOI: 10.1016/j.frl.2022.103028
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    1. Witsanu Attavanich & Bruce A. McCarl & Zafarbek Ahmedov & Stephen W. Fuller & Dmitry V. Vedenov, 2013. "Effects of climate change on US grain transport," Nature Climate Change, Nature, vol. 3(7), pages 638-643, July.
    2. Giot, Pierre & Laurent, Sebastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 379-398, June.
    3. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    4. Christopher L. Gilbert, 2010. "How to Understand High Food Prices," Journal of Agricultural Economics, Wiley Blackwell, vol. 61(2), pages 398-425, June.
    5. Ali, Sajid & Bouri, Elie & Czudaj, Robert Lukas & Shahzad, Syed Jawad Hussain, 2020. "Revisiting the valuable roles of commodities for international stock markets," Resources Policy, Elsevier, vol. 66(C).
    6. Mei, Dexiang & Zeng, Qing & Cao, Xiang & Diao, Xiaohua, 2019. "Uncertainty and oil volatility: New evidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 155-163.
    7. Dwight R. Sanders & Scott H. Irwin, 2011. "New Evidence on the Impact of Index Funds in U.S. Grain Futures Markets," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 59(4), pages 519-532, December.
    8. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    9. Ahumada, H. & Cornejo, M., 2016. "Forecasting food prices: The case of corn, soybeans and wheat," International Journal of Forecasting, Elsevier, vol. 32(3), pages 838-848.
    10. Hong, Yanran & Wang, Lu & Liang, Chao & Umar, Muhammad, 2022. "Impact of financial instability on international crude oil volatility: New sight from a regime-switching framework," Resources Policy, Elsevier, vol. 77(C).
    11. Badshah, Ihsan & Demirer, Riza & Suleman, Muhammad Tahir, 2019. "The effect of economic policy uncertainty on stock-commodity correlations and its implications on optimal hedging," Energy Economics, Elsevier, vol. 84(C).
    12. Athanasios Triantafyllou & George Dotsis & Alexandros H. Sarris, 2015. "Volatility Forecasting and Time-varying Variance Risk Premiums in Grains Commodity Markets," Journal of Agricultural Economics, Wiley Blackwell, vol. 66(2), pages 329-357, June.
    13. Morris Goldstein & Mohsin S. Khan, 2017. "The Supply and Demand for Exports: A Simultaneous Approach," World Scientific Book Chapters, in: TRADE CURRENCIES AND FINANCE, chapter 2, pages 83-104, World Scientific Publishing Co. Pte. Ltd..
    14. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    15. Barbara Rossi & Atsushi Inoue, 2012. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
    16. 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.
    17. Headey, Derek, 2011. "Rethinking the global food crisis: The role of trade shocks," Food Policy, Elsevier, vol. 36(2), pages 136-146, April.
    18. Chao Liang & Feng Ma & Lu Wang & Qing Zeng, 2021. "The information content of uncertainty indices for natural gas futures volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1310-1324, November.
    19. 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.
    20. Joshua Lawson & Rafayet Alam & Xiaoli Etienne, 2021. "Speculation and food-grain prices," Applied Economics, Taylor & Francis Journals, vol. 53(20), pages 2305-2321, April.
    21. 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.
    22. Nonejad, Nima, 2021. "The price of crude oil and (conditional) out-of-sample predictability of world industrial production," Journal of Commodity Markets, Elsevier, vol. 23(C).
    23. Hossein Asgharian & Ai Jun Hou & Farrukh Javed, 2013. "The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(7), pages 600-612, November.
    24. Liang, Chao & Li, Yan & Ma, Feng & Wei, Yu, 2021. "Global equity market volatilities forecasting: A comparison of leverage effects, jumps, and overnight information," International Review of Financial Analysis, Elsevier, vol. 75(C).
    25. Lu Wang & Feng Ma & Guoshan Liu, 2020. "Forecasting stock volatility in the presence of extreme shocks: Short‐term and long‐term effects," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 797-810, August.
    26. Rapach, David E. & Wohar, Mark E., 2006. "In-sample vs. out-of-sample tests of stock return predictability in the context of data mining," Journal of Empirical Finance, Elsevier, vol. 13(2), pages 231-247, March.
    27. Chao Liang & Yu Wei & Xiafei Li & Xuhui Zhang & Yifeng Zhang, 2020. "Uncertainty and crude oil market volatility: new evidence," Applied Economics, Taylor & Francis Journals, vol. 52(27), pages 2945-2959, May.
    28. Li, Xiafei & Liang, Chao & Chen, Zhonglu & Umar, Muhammad, 2022. "Forecasting crude oil volatility with uncertainty indicators: New evidence," Energy Economics, Elsevier, vol. 108(C).
    29. Chen, Xiaoyu & Chiang, Thomas C., 2020. "Empirical investigation of changes in policy uncertainty on stock returns—Evidence from China’s market," Research in International Business and Finance, Elsevier, vol. 53(C).
    30. Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
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    Cited by:

    1. Oscar Melo-Vega-Angeles & Bryan Chuquillanqui-Lichardo, 2023. "The Impact of COVID-19 on the Volatility of Copper Futures," Economies, MDPI, vol. 11(7), pages 1-15, July.
    2. Jiang, Wei & Dong, Lingfei & Chen, Yunfei, 2023. "Time-frequency connectedness among traditional/new energy, green finance, and ESG in pre- and post-Russia-Ukraine war periods," Resources Policy, Elsevier, vol. 83(C).
    3. Junguo Hua & Hui Li & Zejun He & Jing Ding & Futong Jin, 2022. "The Microcosmic Mechanism and Empirical Test of Uncertainty on the Non-Linear Fluctuation of Chinese Grain Prices-Based on the Perspective of Global Economic Policy Uncertainty," Agriculture, MDPI, vol. 12(10), pages 1-17, September.

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    More about this item

    Keywords

    Trade policy uncertainty; Grain markets; Volatility forecast; GARCH-MIDAS;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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