IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v58y2016icp22-33.html
   My bibliography  Save this article

How is China's coke price related with the world oil price? The role of extreme movements

Author

Listed:
  • Guo, Yanfeng
  • Wen, Xiaoqian
  • Wu, Yanrui
  • Guo, Xiumei

Abstract

This paper focuses on the relationship between the world oil price and China's coke price, particularly with respect to extreme movements in the world oil price. Based on a daily sample from 2009 to 2015 and the ARJI-GARCH models and copulas, our empirical results show that China's coke price and the world oil price are characterized by GARCH volatility and jump behaviors. Specifically, negative oil price shocks lead to falls in China's coke returns on the following day while positive oil prices have no significant effects. In addition, current coke returns positively respond to the very recent oil price jump intensity, and a time-varying and volatile lower tail dependence is found between the world oil price and China's coke price. Our results are expected to have implications for coke producers and users and policy makers.

Suggested Citation

  • Guo, Yanfeng & Wen, Xiaoqian & Wu, Yanrui & Guo, Xiumei, 2016. "How is China's coke price related with the world oil price? The role of extreme movements," Economic Modelling, Elsevier, vol. 58(C), pages 22-33.
  • Handle: RePEc:eee:ecmode:v:58:y:2016:i:c:p:22-33
    DOI: 10.1016/j.econmod.2016.05.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264999316301419
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Reboredo, Juan C., 2011. "How do crude oil prices co-move?: A copula approach," Energy Economics, Elsevier, vol. 33(5), pages 948-955, September.
    2. Apostolos Serletis & Ricardo Rangel-Ruiz, 2007. "Testing for Common Features in North American Energy Markets," World Scientific Book Chapters,in: Quantitative And Empirical Analysis Of Energy Markets, chapter 14, pages 172-187 World Scientific Publishing Co. Pte. Ltd..
    3. Ghorbel, Ahmed & Trabelsi, Abdelwahed, 2014. "Energy portfolio risk management using time-varying extreme value copula methods," Economic Modelling, Elsevier, vol. 38(C), pages 470-485.
    4. Beine, Michel & Laurent, Sebastien, 2003. "Central bank interventions and jumps in double long memory models of daily exchange rates," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 641-660, December.
    5. Chiou, Jer-Shiou & Lee, Yen-Hsien, 2009. "Jump dynamics and volatility: Oil and the stock markets," Energy, Elsevier, vol. 34(6), pages 788-796.
    6. Daal, Elton & Naka, Atsuyuki & Yu, Jung-Suk, 2007. "Volatility clustering, leverage effects, and jump dynamics in the US and emerging Asian equity markets," Journal of Banking & Finance, Elsevier, vol. 31(9), pages 2751-2769, September.
    7. Zaklan, Aleksandar & Cullmann, Astrid & Neumann, Anne & von Hirschhausen, Christian, 2012. "The globalization of steam coal markets and the role of logistics: An empirical analysis," Energy Economics, Elsevier, vol. 34(1), pages 105-116.
    8. Chen, Shyh-Wei & Shen, Chung-Hua, 2004. "GARCH, jumps and permanent and transitory components of volatility: the case of the Taiwan exchange rate," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 67(3), pages 201-216.
    9. Zavaleta, Armando & Walls, W.D. & Rusco, Frank W., 2015. "Refining for export and the convergence of petroleum product prices," Energy Economics, Elsevier, vol. 47(C), pages 206-214.
    10. John M. Maheu & Thomas H. McCurdy, 2004. "News Arrival, Jump Dynamics, and Volatility Components for Individual Stock Returns," Journal of Finance, American Finance Association, vol. 59(2), pages 755-793, April.
    11. Aloui, Riadh & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2013. "A time-varying copula approach to oil and stock market dependence: The case of transition economies," Energy Economics, Elsevier, vol. 39(C), pages 208-221.
    12. Honarvar, Afshin, 2009. "Asymmetry in retail gasoline and crude oil price movements in the United States: An application of hidden cointegration technique," Energy Economics, Elsevier, vol. 31(3), pages 395-402, May.
    13. Serletis, Apostolos, 1994. "A cointegration analysis of petroleum futures prices," Energy Economics, Elsevier, vol. 16(2), pages 93-97, April.
    14. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    15. Delphine Lautier and Franck Raynaud, 2012. "Systemic Risk in Energy Derivative Markets: A Graph-Theory Analysis," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    16. Yang, Chi-Jen & Xuan, Xiaowei & Jackson, Robert B., 2012. "China's coal price disturbances: Observations, explanations, and implications for global energy economies," Energy Policy, Elsevier, vol. 51(C), pages 720-727.
    17. Gronwald, Marc, 2012. "A characterization of oil price behavior — Evidence from jump models," Energy Economics, Elsevier, vol. 34(5), pages 1310-1317.
    18. Koch, Nicolas, 2014. "Tail events: A new approach to understanding extreme energy commodity prices," Energy Economics, Elsevier, vol. 43(C), pages 195-205.
    19. Reboredo, Juan C., 2015. "Is there dependence and systemic risk between oil and renewable energy stock prices?," Energy Economics, Elsevier, vol. 48(C), pages 32-45.
    20. Wang, Xiao & Zhang, Chuanguo, 2014. "The impacts of global oil price shocks on China׳s fundamental industries," Energy Policy, Elsevier, vol. 68(C), pages 394-402.
    21. Aloui, Riadh & Aïssa, Mohamed Safouane Ben & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2014. "Dependence and extreme dependence of crude oil and natural gas prices with applications to risk management," Energy Economics, Elsevier, vol. 42(C), pages 332-342.
    22. Zhang, Chuanguo & Chen, Xiaoqing, 2014. "The impact of global oil price shocks on China’s bulk commodity markets and fundamental industries," Energy Policy, Elsevier, vol. 66(C), pages 32-41.
    23. Delphine Lautier & Franck Raynaud, 2012. "Systemic risk in energy derivative markets: a graph theory analysis," Post-Print halshs-00738201, HAL.
    24. Apostolos Serletis & Todd Kemp, 2007. "The Cyclical Behavior of Monthly NYMEX Energy Prices," World Scientific Book Chapters,in: Quantitative And Empirical Analysis Of Energy Markets, chapter 12, pages 149-155 World Scientific Publishing Co. Pte. Ltd..
    25. Wen, Xiaoqian & Wei, Yu & Huang, Dengshi, 2012. "Measuring contagion between energy market and stock market during financial crisis: A copula approach," Energy Economics, Elsevier, vol. 34(5), pages 1435-1446.
    26. Regnier, Eva, 2007. "Oil and energy price volatility," Energy Economics, Elsevier, vol. 29(3), pages 405-427, May.
    27. Chan, Wing H & Maheu, John M, 2002. "Conditional Jump Dynamics in Stock Market Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 377-389, July.
    28. Lescaroux, François, 2009. "On the excess co-movement of commodity prices--A note about the role of fundamental factors in short-run dynamics," Energy Policy, Elsevier, vol. 37(10), pages 3906-3913, October.
    29. Moutinho, Victor & Vieira, Joel & Carrizo Moreira, António, 2011. "The crucial relationship among energy commodity prices: Evidence from the Spanish electricity market," Energy Policy, Elsevier, vol. 39(10), pages 5898-5908, October.
    30. Sensoy, Ahmet & Hacihasanoglu, Erk & Nguyen, Duc Khuong, 2015. "Dynamic convergence of commodity futures: Not all types of commodities are alike," Resources Policy, Elsevier, vol. 44(C), pages 150-160.
    31. Lee, Yen-Hsien & Hu, Hsu-Ning & Chiou, Jer-Shiou, 2010. "Jump dynamics with structural breaks for crude oil prices," Energy Economics, Elsevier, vol. 32(2), pages 343-350, March.
    32. Tong, Bin & Wu, Chongfeng & Zhou, Chunyang, 2013. "Modeling the co-movements between crude oil and refined petroleum markets," Energy Economics, Elsevier, vol. 40(C), pages 882-897.
    33. Reboredo, Juan C., 2013. "Modeling EU allowances and oil market interdependence. Implications for portfolio management," Energy Economics, Elsevier, vol. 36(C), pages 471-480.
    34. Huo, Hong & Lei, Yu & Zhang, Qiang & Zhao, Lijian & He, Kebin, 2012. "China's coke industry: Recent policies, technology shift, and implication for energy and the environment," Energy Policy, Elsevier, vol. 51(C), pages 397-404.
    35. Aloui, Riadh & Ben Aïssa, Mohamed Safouane & Nguyen, Duc Khuong, 2013. "Conditional dependence structure between oil prices and exchange rates: A copula-GARCH approach," Journal of International Money and Finance, Elsevier, vol. 32(C), pages 719-738.
    36. Neil A. Wilmot and Charles F. Mason, 2013. "Jump Processes in the Market for Crude Oil," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    37. Chang, Kuang-Liang, 2012. "The time-varying and asymmetric dependence between crude oil spot and futures markets: Evidence from the Mixture copula-based ARJI–GARCH model," Economic Modelling, Elsevier, vol. 29(6), pages 2298-2309.
    38. repec:dau:papers:123456789/9709 is not listed on IDEAS
    39. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    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. repec:eee:quaeco:v:68:y:2018:i:c:p:23-30 is not listed on IDEAS
    2. Wen, Xiaoqian & Bouri, Elie & Roubaud, David, 2017. "Can energy commodity futures add to the value of carbon assets?," Economic Modelling, Elsevier, vol. 62(C), pages 194-206.
    3. repec:eee:eneeco:v:70:y:2018:i:c:p:297-306 is not listed on IDEAS

    More about this item

    Keywords

    World oil price; China's coke price; ARJI-GARCH; Copulas;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • Q38 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Government Policy (includes OPEC Policy)
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

    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:eee:ecmode:v:58:y:2016:i:c:p:22-33. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/inca/30411 .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.