IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v61y2019icp564-571.html
   My bibliography  Save this article

Dynamic impact of China's stock market on the international commodity market

Author

Listed:
  • Wen, Shaobo
  • An, Haizhong
  • Huang, Shupei
  • Liu, Xueyong

Abstract

It is generally believed that the international commodity markets’ downturn is attributed to China's stock markets collapse. To verify this, this paper assesses the influence of Shanghai (securities) stock exchange composite (SSEC) index on S&P GSCI total returns, a leading commodity composite index from August 31, 2006, to September 19, 2016. We implement wavelet transform to extract the approximations of SSEC and GSCI. Furthermore, we use the state space model to determine the margin between SSEC and GSCI as well as display the margin dynamic evolution over time. The margin denotes the units of GSCI change when SSEC increases a unit, which can display China's stock (equity) influence (CEI) on the international commodity market. We found that CEI reached its peak in 2007, then experienced two steep declines and an immediate mild rebound, and eventually hovered at a historically low level. Moreover, each inflection point of CEI could correspond to a specific international economic event, which indicates that the change of CEI is influenced by exogenous variables, especially the absolute increment of GDP and the U.S. dollar index. The influence of China's stock market on the international commodity market is not as great as believed and the diversification benefit still exists for the construction of portfolio. In addition, the change of China's stock market influence is closely related to exogenous factors, which deserves more attention for policy making to avoid unnecessary trade and monetary friction owing to misjudgment.

Suggested Citation

  • Wen, Shaobo & An, Haizhong & Huang, Shupei & Liu, Xueyong, 2019. "Dynamic impact of China's stock market on the international commodity market," Resources Policy, Elsevier, vol. 61(C), pages 564-571.
  • Handle: RePEc:eee:jrpoli:v:61:y:2019:i:c:p:564-571
    DOI: 10.1016/j.resourpol.2018.06.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420717305858
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.resourpol.2018.06.009?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
    ---><---

    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. Richard Clarida & Jordi Galí & Mark Gertler, 2000. "Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(1), pages 147-180.
    2. Jammazi, Rania, 2012. "Cross dynamics of oil-stock interactions: A redundant wavelet analysis," Energy, Elsevier, vol. 44(1), pages 750-777.
    3. repec:dau:papers:123456789/14980 is not listed on IDEAS
    4. Power, Gabriel J. & Eaves, James & Turvey, Calum & Vedenov, Dmitry, 2017. "Catching the curl: Wavelet thresholding improves forward curve modelling," Economic Modelling, Elsevier, vol. 64(C), pages 312-321.
    5. Ali M. Kutan, 2007. "Contagion or Real Linkages? Some Evidence from China's Emerging Parallel Markets," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 15(4), pages 52-65, July.
    6. Creti, Anna & Joëts, Marc & Mignon, Valérie, 2013. "On the links between stock and commodity markets' volatility," Energy Economics, Elsevier, vol. 37(C), pages 16-28.
    7. Vassilios Babalos & Stavros Stavroyiannis & Rangan Gupta, 2015. "Do Commodities Herd? Evidence from a Time-Varying Stochastic Volatility Model," Working Papers 201554, University of Pretoria, Department of Economics.
    8. Tian, Shuairu & Hamori, Shigeyuki, 2016. "Time-varying price shock transmission and volatility spillover in foreign exchange, bond, equity, and commodity markets: Evidence from the United States," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 163-171.
    9. Kae-Yih Tzeng & Joseph Chang Pying Shieh, 2016. "The transmission from equity markets to commodity markets in crises periods," Applied Economics, Taylor & Francis Journals, vol. 48(48), pages 4666-4689, October.
    10. Jammazi, Rania & Lahiani, Amine & Nguyen, Duc Khuong, 2015. "A wavelet-based nonlinear ARDL model for assessing the exchange rate pass-through to crude oil prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 34(C), pages 173-187.
    11. Cifter, Atilla, 2011. "Value-at-risk estimation with wavelet-based extreme value theory: Evidence from emerging markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(12), pages 2356-2367.
    12. Batten, Jonathan A. & Ciner, Cetin & Lucey, Brian M., 2010. "The macroeconomic determinants of volatility in precious metals markets," Resources Policy, Elsevier, vol. 35(2), pages 65-71, June.
    13. Silvennoinen, Annastiina & Thorp, Susan, 2013. "Financialization, crisis and commodity correlation dynamics," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 24(C), pages 42-65.
    14. Guglielmo Maria Caporale & Fabio Spagnolo & Nicola Spagnolo, 2017. "Macro News and Commodity Returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 22(1), pages 68-80, January.
    15. Lutz Kilian & Bruce Hicks, 2013. "Did Unexpectedly Strong Economic Growth Cause the Oil Price Shock of 2003–2008?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 385-394, August.
    16. Le, Thai-Ha & Chang, Youngho, 2016. "Dynamics between strategic commodities and financial variables: Evidence from Japan," Resources Policy, Elsevier, vol. 50(C), pages 1-9.
    17. Sadorsky, Perry, 2014. "Modeling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat," Energy Economics, Elsevier, vol. 43(C), pages 72-81.
    18. Haven, Emmanuel & Liu, Xiaoquan & Shen, Liya, 2012. "De-noising option prices with the wavelet method," European Journal of Operational Research, Elsevier, vol. 222(1), pages 104-112.
    19. Babalos, Vassilios & Stavroyiannis, Stavros & Gupta, Rangan, 2015. "Do commodity investors herd? Evidence from a time-varying stochastic volatility model," Resources Policy, Elsevier, vol. 46(P2), pages 281-287.
    20. Mensi, Walid & Hammoudeh, Shawkat & Reboredo, Juan Carlos & Nguyen, Duc Khuong, 2014. "Do global factors impact BRICS stock markets? A quantile regression approach," Emerging Markets Review, Elsevier, vol. 19(C), pages 1-17.
    21. Michael Graham & Jarno Kiviaho & Jussi Nikkinen, 2013. "Short-term and long-term dependencies of the S&P 500 index and commodity prices," Quantitative Finance, Taylor & Francis Journals, vol. 13(4), pages 583-592, March.
    22. Jain, Anshul & Biswal, P.C., 2016. "Dynamic linkages among oil price, gold price, exchange rate, and stock market in India," Resources Policy, Elsevier, vol. 49(C), pages 179-185.
    23. Gordon, Robert J, 1990. "What Is New-Keynesian Economics?," Journal of Economic Literature, American Economic Association, vol. 28(3), pages 1115-1171, September.
    24. Athina Georgopoulou & Jiaguo (George) Wang, 2017. "The Trend Is Your Friend: Time-Series Momentum Strategies across Equity and Commodity Markets," Review of Finance, European Finance Association, vol. 21(4), pages 1557-1592.
    25. Elder, John & Miao, Hong & Ramchander, Sanjay, 2012. "Impact of macroeconomic news on metal futures," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 51-65.
    26. Enrico Capobianco, 2001. "Wavelet Transforms For The Statistical Analysis Of Returns Generating Stochastic Processes," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 4(03), pages 511-534.
    27. Ziyao Luo & Christophe Schinckus, 2015. "The influence of the US market on herding behaviour in China," Applied Economics Letters, Taylor & Francis Journals, vol. 22(13), pages 1055-1058, September.
    28. Labys, W. C. & Achouch, A. & Terraza, M., 1999. "Metal prices and the business cycle," Resources Policy, Elsevier, vol. 25(4), pages 229-238, December.
    29. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    30. Bailey, Warren & Chang, K C, 1993. "Macroeconomic Influences and the Variability of the Commodity Futures Basis," Journal of Finance, American Finance Association, vol. 48(2), pages 555-573, June.
    31. Scott H. Irwin & Dwight R. Sanders, 2011. "Index Funds, Financialization, and Commodity Futures Markets," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 33(1), pages 1-31.
    32. repec:oup:rfinst:v:21:y:2017:i:4:p:1557-1592. is not listed on IDEAS
    33. Chng, Michael T., 2009. "Economic linkages across commodity futures: Hedging and trading implications," Journal of Banking & Finance, Elsevier, vol. 33(5), pages 958-970, 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. Wen, Danyan & Wang, Yudong, 2021. "Volatility linkages between stock and commodity markets revisited: Industry perspective and portfolio implications," Resources Policy, Elsevier, vol. 74(C).
    2. Niu, Hongli & Hu, Ziang, 2021. "Information transmission and entropy-based network between Chinese stock market and commodity futures market," Resources Policy, Elsevier, vol. 74(C).
    3. Qi, Yajie & Li, Huajiao & Liu, Yanxin & Feng, Sida & Li, Yang & Guo, Sui, 2020. "Granger causality transmission mechanism of steel product prices under multiple scales—The industrial chain perspective," Resources Policy, Elsevier, vol. 67(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. Antonakakis, Nikolaos & Kizys, Renatas, 2015. "Dynamic spillovers between commodity and currency markets," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 303-319.
    2. Liu, Pan & Power, Gabriel J. & Vedenov, Dmitry, 2021. "Fair-weather Friends? Sector-specific volatility connectedness and transmission," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 712-736.
    3. Nini Johana Marín-Rodríguez & Juan David González-Ruiz & Sergio Botero Botero, 2022. "Dynamic Co-Movements among Oil Prices and Financial Assets: A Scientometric Analysis," Sustainability, MDPI, vol. 14(19), pages 1-26, October.
    4. Mensi, Walid & Al-Yahyaee, Khamis Hamed & Hoon Kang, Sang, 2017. "Time-varying volatility spillovers between stock and precious metal markets with portfolio implications," Resources Policy, Elsevier, vol. 53(C), pages 88-102.
    5. Raza, Naveed & Ali, Sajid & Shahzad, Syed Jawad Hussain & Raza, Syed Ali, 2018. "Do commodities effectively hedge real estate risk? A multi-scale asymmetric DCC approach," Resources Policy, Elsevier, vol. 57(C), pages 10-29.
    6. Drachal, Krzysztof, 2016. "Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?," Energy Economics, Elsevier, vol. 60(C), pages 35-46.
    7. Kuruppuarachchi, Duminda & Premachandra, I.M., 2016. "Information spillover dynamics of the energy futures market sector: A novel common factor approach," Energy Economics, Elsevier, vol. 57(C), pages 277-294.
    8. Amar, Amine Ben & Goutte, Stéphane & Isleimeyyeh, Mohammad & Benkraiem, Ramzi, 2022. "Commodity markets dynamics: What do cross-commodities over different nearest-to-maturities tell us?," International Review of Financial Analysis, Elsevier, vol. 82(C).
    9. Ma, Richie Ruchuan & Xiong, Tao, 2021. "Price explosiveness in nonferrous metal futures markets," Economic Modelling, Elsevier, vol. 94(C), pages 75-90.
    10. Akkoc, Ugur & Civcir, Irfan, 2019. "Dynamic linkages between strategic commodities and stock market in Turkey: Evidence from SVAR-DCC-GARCH model," Resources Policy, Elsevier, vol. 62(C), pages 231-239.
    11. Reboredo, Juan C. & Uddin, Gazi Salah, 2016. "Do financial stress and policy uncertainty have an impact on the energy and metals markets? A quantile regression approach," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 284-298.
    12. de Boyrie Maria E. & Pavlova Ivelina, 2018. "Equities and Commodities Comovements: Evidence from Emerging Markets," Global Economy Journal, De Gruyter, vol. 18(3), pages 1-14, September.
    13. Cunado, Juncal & Gil-Alana, Luis A. & Gupta, Rangan, 2019. "Persistence in trends and cycles of gold and silver prices: Evidence from historical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 345-354.
    14. Semeyutin, Artur & Downing, Gareth, 2022. "Co-jumps in the U.S. interest rates and precious metals markets and their implications for investors," International Review of Financial Analysis, Elsevier, vol. 81(C).
    15. Mensi, Walid & Hammoudeh, Shawkat & Kang, Sang Hoon, 2015. "Precious metals, cereal, oil and stock market linkages and portfolio risk management: Evidence from Saudi Arabia," Economic Modelling, Elsevier, vol. 51(C), pages 340-358.
    16. Ipsita Saishree & Puja Padhi, 2022. "Exploring the dynamics of the equity–commodity nexus: A study of base metal futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(8), pages 1573-1596, August.
    17. 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).
    18. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.
    19. Sang Hoon Kang & Ron McIver & Seong-Min Yoon, 2016. "Modeling Time-Varying Correlations in Volatility Between BRICS and Commodity Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 52(7), pages 1698-1723, July.
    20. Farid, Saqib & Kayani, Ghulam Mujtaba & Naeem, Muhammad Abubakr & Shahzad, Syed Jawad Hussain, 2021. "Intraday volatility transmission among precious metals, energy and stocks during the COVID-19 pandemic," Resources Policy, Elsevier, vol. 72(C).

    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:jrpoli:v:61:y:2019:i:c:p:564-571. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .

    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.