IDEAS home Printed from https://ideas.repec.org/a/wly/jfutmk/v39y2019i4p522-534.html
   My bibliography  Save this article

The trilogy of China cotton markets: The lead–lag relationship among spot, forward, and futures markets

Author

Listed:
  • Mert Demir
  • Terrence F. Martell
  • Jun Wang

Abstract

China is a leading participant in the world cotton market. China’s distinctive regulatory structure and procedures and business environment provide an opportunity to explore some unique market dynamics. This study investigates the interrelationship among the spot, futures, and forward cotton markets in China over a period of a major policy change: A temporary State reserve program for cotton that was established in 2011 and ended in 2014. This government intervention significantly distorted the way farmers, manufacturers, and speculators interacted and was not sustainable. Overall, our results support futures market’s dominant role in the price discovery process.

Suggested Citation

  • Mert Demir & Terrence F. Martell & Jun Wang, 2019. "The trilogy of China cotton markets: The lead–lag relationship among spot, forward, and futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(4), pages 522-534, April.
    • Handle: RePEc:wly:jfutmk:v:39:y:2019:i:4:p:522-534
    DOI: 10.1002/fut.21981
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/fut.21981
    Download Restriction: no
    File URL: https://libkey.io/10.1002/fut.21981?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. Sokbae Lee & Myung Hwan Seo & Youngki Shin, 2017. "Correction," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 883-883, April.
    2. Yongmin Zhang & Shusheng Ding & Eric Scheffel, 2018. "Policy impact on volatility dynamics in commodity futures markets: Evidence from China," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(10), pages 1227-1245, October.
    3. Hammoudeh, Shawkat & Li, Huimin, 2004. "The impact of the Asian crisis on the behavior of US and international petroleum prices," Energy Economics, Elsevier, vol. 26(1), pages 135-160, January.
    4. Bohl, Martin T. & Siklos, Pierre L. & Wellenreuther, Claudia, 2018. "Speculative activity and returns volatility of Chinese agricultural commodity futures," Journal of Asian Economics, Elsevier, vol. 54(C), pages 69-91.
    5. Toda, Hiro Y. & Yamamoto, Taku, 1995. "Statistical inference in vector autoregressions with possibly integrated processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 225-250.
    6. Cheung, Yin-Wong & Ng, Lilian K., 1996. "A causality-in-variance test and its application to financial market prices," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 33-48.
    7. Chen, Pei-Fen & Lee, Chien-Chiang & Zeng, Jhih-Hong, 2014. "The relationship between spot and futures oil prices: Do structural breaks matter?," Energy Economics, Elsevier, vol. 43(C), pages 206-217.
    8. MacDonald, Stephen & Gale, Fred & Hansen, James, 2015. "Cotton Policy in China," MPRA Paper 70863, University Library of Munich, Germany.
    9. 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).
    10. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    11. Huang, Bwo-Nung & Yang, C.W. & Hwang, M.J., 2009. "The dynamics of a nonlinear relationship between crude oil spot and futures prices: A multivariate threshold regression approach," Energy Economics, Elsevier, vol. 31(1), pages 91-98, January.
    12. Brorsen, B. Wade & Bailey, DeeVon & Richardson, James W., 1984. "Investigation Of Price Discovery And Efficiency For Cash And Futures Cotton Prices," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 9(1), pages 1-7, July.
    13. Rausser, Gordon C. & James, Chalfant A. & Love, H. Alan & Stamoulis, Kostas G., 1986. "Macroeconomic linkages, taxes, and subsidies in the U.S. agricultural sector," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt1nj635tk, Department of Agricultural & Resource Economics, UC Berkeley.
    14. Martin T. Bohl & Pierre L. Siklos & Claudia Wellenreuther, 2018. "Speculative activity and returns volatility of Chinese major agricultural commodity futures," CAMA Working Papers 2018-06, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    15. Gordon C. Rausser & James A. Chalfant & H. Alan Love & Kostas G. Stamoulis, 1986. "Macroeconomic Linkages, Taxes, and Subsidies in the U.S. Agricultural Sector," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 68(2), pages 399-412.
    16. 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.
    17. Bekiros, Stelios D. & Diks, Cees G.H., 2008. "The relationship between crude oil spot and futures prices: Cointegration, linear and nonlinear causality," Energy Economics, Elsevier, vol. 30(5), pages 2673-2685, September.
    18. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    19. Param Silvapulle & Imad A. Moosa, 1999. "The relationship between spot and futures prices: Evidence from the crude oil market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 19(2), pages 175-193, April.
    20. Lee, Chien-Chiang & Chiu, Yi-Bin, 2013. "Modeling OECD energy demand: An international panel smooth transition error-correction model," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 372-383.
    21. Kapetanios, George & Shin, Yongcheol & Snell, Andy, 2006. "Testing For Cointegration In Nonlinear Smooth Transition Error Correction Models," Econometric Theory, Cambridge University Press, vol. 22(2), pages 279-303, April.
    22. Granger, Clive W J, 1986. "Developments in the Study of Cointegrated Economic Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 48(3), pages 213-228, August.
    23. Foster, Andrew J., 1996. "Price discovery in oil markets: a time varying analysis of the 1990-1991 Gulf conflict," Energy Economics, Elsevier, vol. 18(3), pages 231-246, July.
    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. Li, Miao & Xiong, Tao, 2023. "Is China's new live hog futures market efficient? Evidence from an analysis of market quality, price discovery and hedging effectiveness," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 68(01), September.
    2. Chen, Peng & He, Limin & Yang, Xuan, 2021. "On interdependence structure of China's commodity market," Resources Policy, Elsevier, vol. 74(C).
    3. Li, Miao & Xiong, Tao, 2021. "Dynamic price discovery in Chinese agricultural futures markets," Journal of Asian Economics, Elsevier, vol. 76(C).
    4. Pradhan, Rudra P. & Hall, John H. & du Toit, Elda, 2021. "The lead–lag relationship between spot and futures prices: Empirical evidence from the Indian commodity market," Resources Policy, Elsevier, vol. 70(C).

    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. Balcilar, Mehmet & Gungor, Hasan & Hammoudeh, Shawkat, 2015. "The time-varying causality between spot and futures crude oil prices: A regime switching approach," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 51-71.
    2. Xianfang Su & Huiming Zhu & Xinxia Yang, 2019. "Heterogeneous Causal Relationships between Spot and Futures Oil Prices: Evidence from Quantile Causality Analysis," Sustainability, MDPI, vol. 11(5), pages 1-17, March.
    3. Shao, Ying-Hui & Yang, Yan-Hong & Shao, Hao-Lin & Stanley, H. Eugene, 2019. "Time-varying lead–lag structure between the crude oil spot and futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 723-733.
    4. Chen, Pei-Fen & Lee, Chien-Chiang & Zeng, Jhih-Hong, 2014. "The relationship between spot and futures oil prices: Do structural breaks matter?," Energy Economics, Elsevier, vol. 43(C), pages 206-217.
    5. Cagli, Efe Caglar & Taskin, Dilvin & Evrim Mandaci, Pınar, 2019. "The short- and long-run efficiency of energy, precious metals, and base metals markets: Evidence from the exponential smooth transition autoregressive models," Energy Economics, Elsevier, vol. 84(C).
    6. Chang, Kuang-Liang & Lee, Chingnun, 2020. "The asymmetric spillover effect of the Markov switching mechanism from the futures market to the spot market," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 374-388.
    7. Xu Xiaojie, 2018. "Linear and Nonlinear Causality between Corn Cash and Futures Prices," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 16(2), pages 1-16, November.
    8. Caporin, Massimiliano & Costola, Michele, 2022. "Time-varying Granger causality tests in the energy markets: A study on the DCC-MGARCH Hong test," Energy Economics, Elsevier, vol. 111(C).
    9. Troster, Victor & Shahbaz, Muhammad & Uddin, Gazi Salah, 2018. "Renewable energy, oil prices, and economic activity: A Granger-causality in quantiles analysis," Energy Economics, Elsevier, vol. 70(C), pages 440-452.
    10. Bampinas Georgios & Panagiotidis Theodore, 2015. "On the relationship between oil and gold before and after financial crisis: linear, nonlinear and time-varying causality testing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(5), pages 657-668, December.
    11. Miroslava Zavadska & Lucía Morales & Joseph Coughlan, 2018. "The Lead–Lag Relationship between Oil Futures and Spot Prices—A Literature Review," IJFS, MDPI, vol. 6(4), pages 1-22, October.
    12. Mehmet Balcilar & İsmail H. Genç & Rangan Gupta, 2016. "The Links between Crude Oil Prices and GCC Stock Markets: Evidence from Time-Varying Granger Causality Tests," Working Papers 201644, University of Pretoria, Department of Economics.
    13. Carlotta Penone & Elisa Giampietri & Samuele Trestini, 2022. "Futures–spot price transmission in EU corn markets," Agribusiness, John Wiley & Sons, Ltd., vol. 38(3), pages 679-709, July.
    14. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871, January.
    15. Alexander Zeitlberger & Alexander Brauneis, 2016. "Modeling carbon spot and futures price returns with GARCH and Markov switching GARCH models," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 24(1), pages 149-176, March.
    16. Magkonis, Georgios & Tsouknidis, Dimitris A., 2017. "Dynamic spillover effects across petroleum spot and futures volatilities, trading volume and open interest," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 104-118.
    17. Chen, Fan & Linn, Scott C., 2017. "Investment and operating choice: Oil and natural gas futures prices and drilling activity," Energy Economics, Elsevier, vol. 66(C), pages 54-68.
    18. Sebastian Nick, 2016. "The Informational Efficiency of European Natural Gas Hubs: Price Formation and Intertemporal Arbitrage," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    19. Caporina, Massimiliano & Costola, Michele, 2021. "Time-varying granger causality tests for applications in global crude oil markets: A study on the DCC-MGARCH Hong test," SAFE Working Paper Series 324, Leibniz Institute for Financial Research SAFE.
    20. Beckmann, Joscha & Belke, Ansgar & Czudaj, Robert, 2014. "Regime-dependent adjustment in energy spot and futures markets," Economic Modelling, Elsevier, vol. 40(C), pages 400-409.

    More about this item

    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:wly:jfutmk:v:39:y:2019:i:4:p:522-534. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0270-7314/ .

    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.