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The Trilogy of the Chinese Apple Futures Market: Price Discovery, Risk-Hedging and Cointegration

Author

Listed:
  • Xiaokang Hou

    (Department of Economics and Management, Northwest A&F University, Xianyang 710021, China)

  • Shah Fahad

    (School of Management, Hainan University, Haikou 570228, China
    School of Economics and Management, Leshan Normal University, Leshan 641000, China)

  • Peipei Zhao

    (Department of Economics and Management, Northwest A&F University, Xianyang 710021, China)

  • Beibei Yan

    (Department of Economics and Management, Northwest A&F University, Xianyang 710021, China)

  • Tianjun Liu

    (Department of Economics and Management, Northwest A&F University, Xianyang 710021, China)

Abstract

The agricultural futures market plays an extremely important role in price discovery, hedging risks, integrating agricultural markets and promoting agricultural economic growth. China is the largest apple producer and consumer in the world. In 2017, Chinese apple futures were listed on the Zhengzhou Commodity Exchange (CZCE) as the first fruit futures contract globally. This paper aims to study the efficiency of the apple futures market by using the Wild Bootstrapping Variance Ratio model to estimate the price discovery function, the ARIMA-GARCH model to estimate the risk-hedging function, and the ARDL-ECM model to estimate the cointegration relationship of the futures and spot market. Experimental results firstly demonstrate that the apple futures market conforms to the weak-form efficiency, which indicates that it is efficient in price discovery. Secondly, the apple futures market is not of semi-strong efficiency because it generated abnormal profit margins amid China–US trade friction, climate disaster, and COVID-19; in terms of the degree of impact, the COVID-19 pandemic had the greatest impact, followed by the rainstorm disaster and trade friction. Thirdly, the results of this study indicate that the cointegration relationships exist between the futures market and the spot markets of the main producing areas. This paper is not only conducive to sustainable development of the global fresh or fruit futures market, but also has potential and practical importance for China in developing the agricultural futures market, strengthening market risk management and promoting market circulation.

Suggested Citation

  • Xiaokang Hou & Shah Fahad & Peipei Zhao & Beibei Yan & Tianjun Liu, 2022. "The Trilogy of the Chinese Apple Futures Market: Price Discovery, Risk-Hedging and Cointegration," Sustainability, MDPI, vol. 14(19), pages 1-16, October.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12864-:d:936935
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    as
    1. Qingfu Liu & Qian Luo & Yiuman Tse & Yuchi Xie, 2020. "The market quality of commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(11), pages 1751-1766, November.
    2. Jia, Rui-Lin & Wang, Dong-Hua & Tu, Jing-Qing & Li, Sai-Ping, 2016. "Correlation between agricultural markets in dynamic perspective—Evidence from China and the US futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 83-92.
    3. Kristoufek, Ladislav & Vosvrda, Miloslav, 2014. "Commodity futures and market efficiency," Energy Economics, Elsevier, vol. 42(C), pages 50-57.
    4. Qianqian Mao & Yanjun Ren & Jens-Peter Loy, 2020. "Price bubbles in agricultural commodity markets and contributing factors: evidence for corn and soybeans in China," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 13(1), pages 22-53, September.
    5. H. Holly Wang & Bingfan Ke, 2005. "Efficiency tests of agricultural commodity futures markets in China," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 49(2), pages 125-141, June.
    6. Arnade, Carlos & Cooke, Bryce & Gale, Fred, 2017. "Agricultural price transmission: China relationships with world commodity markets," Journal of Commodity Markets, Elsevier, vol. 7(C), pages 28-40.
    7. M. Hashem Pesaran & Yongcheol Shin & Richard J. Smith, 2001. "Bounds testing approaches to the analysis of level relationships," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 289-326.
    8. P. J. Dawson & Ana I. Sanjuán, 2006. "Structural Breaks, the Export Enhancement Program and the Relationship between Canadian and US Hard Wheat Prices," Journal of Agricultural Economics, Wiley Blackwell, vol. 57(1), pages 101-116, March.
    9. Zhang, Xiaoyu & Liu, Yongfu, 2020. "The dynamic impact of international agricultural commodity price fluctuation on Chinese agricultural commodity prices," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 23(3), August.
    10. Garbade, Kenneth D & Silber, William L, 1983. "Price Movements and Price Discovery in Futures and Cash Markets," The Review of Economics and Statistics, MIT Press, vol. 65(2), pages 289-297, May.
    11. Jian Li & Chongguang Li & Jean-Paul Chavas, 2017. "Food Price Bubbles and Government Intervention: Is China Different?," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 65(1), pages 135-157, March.
    12. Ma, Wanglin & Renwick, Alan & Yuan, Peng & Ratna, Nazmun, 2018. "Agricultural cooperative membership and technical efficiency of apple farmers in China: An analysis accounting for selectivity bias," Food Policy, Elsevier, vol. 81(C), pages 122-132.
    13. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    14. Menzie D. Chinn & Olivier Coibion, 2014. "The Predictive Content of Commodity Futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 34(7), pages 607-636, July.
    15. Holbrook Working, 1948. "Theory of the Inverse Carrying Charge in Futures Markets," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 30(1), pages 1-28.
    16. Cashin, Paul & Mohaddes, Kamiar & Raissi, Mehdi, 2017. "Fair weather or foul? The macroeconomic effects of El Niño," Journal of International Economics, Elsevier, vol. 106(C), pages 37-54.
    17. Ayadi, Ahmed & Gana, Marjène & Goutte, Stéphane & Guesmi, Khaled, 2021. "Equity-commodity contagion during four recent crises: Evidence from the USA, Europe and the BRICS," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 376-423.
    18. Xiaoyong Xiao & Qingsong Tian & Shuxia Hou & Chongguang Li, 2019. "Economic policy uncertainty and grain futures price volatility: evidence from China," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 11(4), pages 642-654, August.
    19. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    20. Dou, Yue & Li, Yiying & Dong, Kangyin & Ren, Xiaohang, 2022. "Dynamic linkages between economic policy uncertainty and the carbon futures market: Does Covid-19 pandemic matter?," Resources Policy, Elsevier, vol. 75(C).
    21. Kim, Jae H., 2009. "Automatic variance ratio test under conditional heteroskedasticity," Finance Research Letters, Elsevier, vol. 6(3), pages 179-185, September.
    22. Acharya, Viral V. & Lochstoer, Lars A. & Ramadorai, Tarun, 2013. "Limits to arbitrage and hedging: Evidence from commodity markets," Journal of Financial Economics, Elsevier, vol. 109(2), pages 441-465.
    23. Algieri, Bernardina & Kalkuhl, Matthias & Koch, Nicolas, 2017. "A tale of two tails: Explaining extreme events in financialized agricultural markets," Food Policy, Elsevier, vol. 69(C), pages 256-269.
    24. Makkonen, Adam & Vallström, Daniel & Uddin, Gazi Salah & Rahman, Md Lutfur & Haddad, Michel Ferreira Cardia, 2021. "The effect of temperature anomaly and macroeconomic fundamentals on agricultural commodity futures returns," Energy Economics, Elsevier, vol. 100(C).
    25. Indriawan, Ivan & Martinez, Valeria & Tse, Yiuman, 2021. "The impact of the change in USDA announcement release procedures on agricultural commodity futures," Journal of Commodity Markets, Elsevier, vol. 23(C).
    26. Yuanlong Ge & Holly H. Wang & Sung K. Ahn, 2010. "Cotton market integration and the impact of China's new exchange rate regime," Agricultural Economics, International Association of Agricultural Economists, vol. 41(5), pages 443-451, September.
    27. A. J. Aulton & C. T. Ennew & A. J. Rayner, 1997. "Efficiency Tests Of Futures Markets For Uk Agricultural Commodities," Journal of Agricultural Economics, Wiley Blackwell, vol. 48(1‐3), pages 408-424, January.
    28. Zhang, Chuanguo & Qu, Xuqin, 2015. "The effect of global oil price shocks on China's agricultural commodities," Energy Economics, Elsevier, vol. 51(C), pages 354-364.
    29. Kim, Jae H., 2006. "Wild bootstrapping variance ratio tests," Economics Letters, Elsevier, vol. 92(1), pages 38-43, July.
    30. Joseph Santos, 2002. "Did Futures Markets Stabilise US Grain Prices?," Journal of Agricultural Economics, Wiley Blackwell, vol. 53(1), pages 25-36, March.
    31. Md Rafayet Alam & Scott Gilbert, 2017. "Monetary policy shocks and the dynamics of agricultural commodity prices: evidence from structural and factor†augmented VAR analyses," Agricultural Economics, International Association of Agricultural Economists, vol. 48(1), pages 15-27, January.
    32. Linwood A. Hoffman & Xiaoli L. Etienne & Scott H. Irwin & Evelyn V. Colino & Jose I. Toasa, 2015. "Forecast performance of WASDE price projections for U.S. corn," Agricultural Economics, International Association of Agricultural Economists, vol. 46(S1), pages 157-171, November.
    33. Gozgor, Giray & Lau, Chi Keung Marco & Bilgin, Mehmet Huseyin, 2016. "Commodity markets volatility transmission: Roles of risk perceptions and uncertainty in financial markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 35-45.
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