IDEAS home Printed from https://ideas.repec.org/a/col/000107/016035.html
   My bibliography  Save this article

Study on spillover effect between international soybean market and China's domestic soybean market

Author

Listed:
  • Kun Ma
  • Gang Diao

Abstract

Due to high import dependency, China's domestic soybean market became unstable and soybean production was lingering and declining. It would be better to know the correlation between international and China's domestic soybean market for policy-making and production decision. This study used data of CBOT soybean futures price, imported soybean distribution price at Qingdao port and soybean spot price in China from September 10, 2011 to November 19, 2016 and chose multivariate GARCH model to check the spillover effect and correlation between them. The results showed that price volatilities of three markets had significant clustering effect while GARCH effect was stronger than ARCH effect. The spillover effect and correlations between markets were remarkable. It demonstrated the imported soybean market was significantly affected by the international soybean future market volatility, and such instability then resulted in violent fluctuations of China's domestic soybean spot market. Policies should be made to keep China's soybean industry safe and developed.

Suggested Citation

  • Kun Ma & Gang Diao, 2017. "Study on spillover effect between international soybean market and China's domestic soybean market," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 35(84), pages 260-266, December.
  • Handle: RePEc:col:000107:016035
    DOI: 10.1016/j.espe.2017.11.003
    as

    Download full text from publisher

    File URL: https://doi.org/10.1016/j.espe.2017.11.003
    Download Restriction: no

    File URL: https://libkey.io/10.1016/j.espe.2017.11.003?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. Y.K. Tse & Albert K.C. Tsui, 2000. "A Multivariate GARCH Model with Time-Varying Correlations," Econometrics 0004007, University Library of Munich, Germany.
    2. Chang, Chia-Lin & González-Serrano, Lydia & Jimenez-Martin, Juan-Angel, 2013. "Currency hedging strategies using dynamic multivariate GARCH," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 164-182.
    3. 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.
    4. Beckmann, Joscha & Czudaj, Robert, 2014. "Volatility transmission in agricultural futures markets," Economic Modelling, Elsevier, vol. 36(C), pages 541-546.
    5. Teterin, Pavel & Brooks, Robert & Enders, Walter, 2016. "Smooth volatility shifts and spillovers in U.S. crude oil and corn futures markets," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 22-36.
    6. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    9. Kroner, Kenneth F & Ng, Victor K, 1998. "Modeling Asymmetric Comovements of Asset Returns," The Review of Financial Studies, Society for Financial Studies, vol. 11(4), pages 817-844.
    10. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    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. Hasan Murat Ertuğrul & Ünal Seven, 2023. "Dynamic spillover analysis of international and Turkish food prices," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1918-1928, April.

    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. Kun Ma & Gang Diao, 2017. "Study on spillover effect between international soybean market and China's domestic soybean market," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 35(84), pages 260-266, December.
    2. Kun Ma & Gang Diao, 2017. "Study on spillover effect between international soybean market and China's domestic soybean market," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 35(84), pages 260-266, December.
    3. M. Hashem Pesaran & Paolo Zaffaroni, 2004. "Model Averaging and Value-at-Risk Based Evaluation of Large Multi Asset Volatility Models for Risk Management," CESifo Working Paper Series 1358, CESifo.
    4. Andrea Silvestrini & David Veredas, 2008. "Temporal Aggregation Of Univariate And Multivariate Time Series Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 22(3), pages 458-497, July.
    5. Maghyereh, Aktham I. & Awartani, Basel & Tziogkidis, Panagiotis, 2017. "Volatility spillovers and cross-hedging between gold, oil and equities: Evidence from the Gulf Cooperation Council countries," Energy Economics, Elsevier, vol. 68(C), pages 440-453.
    6. de Oliveira, Felipe A. & Maia, Sinézio F. & de Jesus, Diego P. & Besarria, Cássio da N., 2018. "Which information matters to market risk spreading in Brazil? Volatility transmission modelling using MGARCH-BEKK, DCC, t-Copulas," The North American Journal of Economics and Finance, Elsevier, vol. 45(C), pages 83-100.
    7. Abdul Hakim & Michael McAleer, 2010. "Modelling the interactions across international stock, bond and foreign exchange markets," Applied Economics, Taylor & Francis Journals, vol. 42(7), pages 825-850.
    8. David Gabauer, 2020. "Volatility impulse response analysis for DCC‐GARCH models: The role of volatility transmission mechanisms," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 788-796, August.
    9. Hasanov, Akram Shavkatovich & Do, Hung Xuan & Shaiban, Mohammed Sharaf, 2016. "Fossil fuel price uncertainty and feedstock edible oil prices: Evidence from MGARCH-M and VIRF analysis," Energy Economics, Elsevier, vol. 57(C), pages 16-27.
    10. Lebotsa Daniel Metsileng & Ntebogang Dinah Moroke & Johannes Tshepiso Tsoku, 2020. "The Application of the Multivariate GARCH Models on the BRICS Exchange Rates," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 9, July.
    11. Morema, Kgotso & Bonga-Bonga, Lumengo, 2018. "The impact of oil and gold price fluctuations on the South African equity market: volatility spillovers and implications for portfolio management," MPRA Paper 87637, University Library of Munich, Germany.
    12. Duchesne, Pierre, 2006. "Testing for multivariate autoregressive conditional heteroskedasticity using wavelets," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2142-2163, December.
    13. Felipe de Oliveira & Sinézio Fernandes Maia & Diego Pita de Jesus, 2017. "Which information matters to Market risk spreading in Brazil? Volatility transmission modeling using MGARH-BEKK, DCC, t-COPULAS," EcoMod2017 10378, EcoMod.
    14. Jiang, Yonghong & Jiang, Cheng & Nie, He & Mo, Bin, 2019. "The time-varying linkages between global oil market and China's commodity sectors: Evidence from DCC-GJR-GARCH analyses," Energy, Elsevier, vol. 166(C), pages 577-586.
    15. Chrétien, Stéphane & Ortega, Juan-Pablo, 2014. "Multivariate GARCH estimation via a Bregman-proximal trust-region method," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 210-236.
    16. Feng, Yuanhua, 2006. "A local dynamic conditional correlation model," MPRA Paper 1592, University Library of Munich, Germany.
    17. Li, Chenxing, 2022. "A multivariate GARCH model with an infinite hidden Markov mixture," MPRA Paper 112792, University Library of Munich, Germany.
    18. Bollerslev, Tim & Patton, Andrew J. & Quaedvlieg, Rogier, 2020. "Multivariate leverage effects and realized semicovariance GARCH models," Journal of Econometrics, Elsevier, vol. 217(2), pages 411-430.
    19. Rosenow, Bernd, 2008. "Determining the optimal dimensionality of multivariate volatility models with tools from random matrix theory," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 279-302, January.
    20. Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2015. "Testing for structural breaks in correlations: Does it improve Value-at-Risk forecasting?," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 135-152.

    More about this item

    Keywords

    Soybean price volatility; Multivariate GARCH model; Clustering effect; Spillover effect;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • F16 - International Economics - - Trade - - - Trade and Labor Market Interactions
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness

    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:col:000107:016035. 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: Espe (email available below). General contact details of provider: https://edirc.repec.org/data/brcgvco.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.