Prague Economic Papers 2023, 32(6):659-698 | DOI: 10.18267/j.pep.847

Analysis of Comovement Between China's Commodity Futures and World Crude Oil Prices

Tianding Zhang ORCID...a, Song Zenga, b, Jie Li ORCID...c
a Economics and Management School, Wuhan University, Wuhan, China,
b The HSBC Financial Research Institute, Peking University, Shenzhen, China,
c International Education School, Wuhan University, Wuhan, China

We Examine the Comovement between China's Commodity Futures and World Crude Oil Prices Based on Their Daily Return Series. Using a Dynamic Time-Varying Approach, We Combine the Generalized Autoregressive Score (Gas) Model with the Copula Approach, Allowing for Asymmetry and Tail Dependence. Our Results Demonstrate a Significant Nonlinear Causal Impact of World Crude Oil Prices on Each of China's Commodities. The Comovement between China's Commodity Futures and Crude Oil Prices Is Positive, with Varying Degrees of Significance across Different Commodity Types. Notably, Non-Ferrous Metal and Chemical Commodity Futures Are More Vulnerable to Rising Crude Oil Prices. From a Dynamic Perspective, We Observe Continued Volatility in the Comovement between China's Commodity Futures and World Crude Oil Prices in Recent Years. Moreover, the Time-Varying Dependence between the Three Non-Ferrous Metals and Crude Oil Prices Is Higher than That of Other Commodities. These Findings Hold Significant Implications for Global Investors, Risk Managers and Policymakers.

Keywords: comovement, commodity futures, world crude oil prices, copula
JEL classification: C58, Q02, Q37

Received: January 1, 2023; Revised: July 1, 2023; Accepted: July 31, 2023; Published: December 19, 2023  Show citation

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Zhang, T., Zeng, S., & Li, J. (2023). Analysis of Comovement Between China's Commodity Futures and World Crude Oil Prices. Prague Economic Papers32(6), 659-698. doi: 10.18267/j.pep.847
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References

  1. Adams, Z., Collot, S., Kartsakli, M. (2020). Have Commodities Become a Financial Asset? Evidence from Ten Years of Financialization. Energy Economics, 89, 104769. https://doi.org/10.1016/j.eneco.2020.104769 Go to original source...
  2. Adams, Z., Glück, T. (2015). Financialization in Commodity Markets: A Passing Trend or the New Normal? Journal of Banking & Finance, 60, 93-111. https://doi.org/10.1016/j.jbankfin.2015.07.008 Go to original source...
  3. Adhikari, R., Putnam, K. J. (2020). Comovement in the Commodity Futures Markets: An Analysis of the Energy, Grains, and Livestock Sectors. Journal of Commodity Markets, 18, 100090. https://doi.org/10.1016/j.jcomm.2019.04.002 Go to original source...
  4. Ahmadi, M., Bashiri Behmiri, N., Manera, M. (2016). How is Volatility in Commodity Markets Linked to Oil Price Shocks? Energy Economics, 59, 11-23. https://doi.org/10.1016/j.eneco.2016.07.006 Go to original source...
  5. Alquist, R., Bhattarai, S., Coibion, O. (2020). Commodity-price Comovement and Global Economic Activity. Journal of Monetary Economics, 112, 41-56. https://doi.org/10.1016/j.jmoneco.2019.02.004 Go to original source...
  6. An, Y., Sun, M., Gao, C., Han, D., Li, X. (2018). Analysis of the Impact of Crude Oil Price Fluctuations on China's Stock Market in Different Periods - Based on Time Series Network Model. Physica A: Statistical Mechanics and its Applications, 492, 1016-1031. https://doi.org/10.1016/j.physa.2017.11.032 Go to original source...
  7. Balcilar, M., Gabauer, D., Umar, Z. (2021). Crude Oil Futures Contracts and Commodity Markets: New Evidence from a tvp-var Extended Joint Connectedness Approach. Resources Policy, 73, 102219. https://doi.org/10.1016/j.resourpol.2021.102219 Go to original source...
  8. Basak, S., Pavlova, A. (2016). A Model of Financialization of Commodities. The Journal of Finance, 71, 1511-1556. https://doi.org/10.1111/jofi.12408 Go to original source...
  9. Cai, G., Zhang, H., Chen, Z. (2019). Comovement between Commodity Sectors. Physica A: Statistical Mechanics and its Applications, 525, 1247-1258. https://doi.org/10.1016/j.physa.2019.04.116 Go to original source...
  10. Charfeddine, L., Benlagha, N. (2016). A Time-varying Copula Approach for Modelling Dependency: New Evidence from Commodity and Stock Markets. Journal of Multinational Financial Management, 37-38, 168-189. https://doi.org/10.1016/j.mulfin.2016.10.003 Go to original source...
  11. Creal, D., Koopman, S. J., Lucas, A. (2013). Generalized Autoregressive Score Models with Applications. Journal of Applied Econometrics, 28, 777-795. https://doi.org/10.1002/jae.1279 Go to original source...
  12. Csörgő, S., Faraway, J. J. (1996). The Exact and Asymptotic Distributions of Cramér-von Mises Statistics. Journal of the Royal Statistical Society: Series B (Methodological), 58, 221-234. https://doi.org/10.1111/j.2517-6161.1996.tb02077.x Go to original source...
  13. Dahl, R. E., Oglend, A., Yahya, M. (2020). Dynamics of Volatility Spillover in Commodity Markets: Linking Crude Oil to Agriculture. Journal of Commodity Markets, 20, 100111. https://doi.org/10.1016/j.jcomm.2019.100111 Go to original source...
  14. Du, X., Yu, C. L., Hayes, D. J. (2011). Speculation and Volatility Spillover in the Crude Oil and Agricultural Commodity Markets: A Bayesian Analysis. Energy Economics, 33, 497-503. https://doi.org/10.1016/j.eneco.2010.12.015 Go to original source...
  15. Engle, R. (2009) Anticipating Correlations: A New Paradigm for Risk Management. Princeton University Press. Go to original source...
  16. Fernandez, V. (2015). Commodity Price Excess Co-movement From a Historical Perspective: 1900-2010. Energy Economics, 49, 698-710. https://doi.org/10.1016/j.eneco.2015.04.003 Go to original source...
  17. Fowowe, B. (2016). Do Oil Prices Drive Agricultural Commodity Prices? Evidence from South Africa. Energy, 104, 149-157. https://doi.org/10.1016/j.energy.2016.03.101 Go to original source...
  18. Fretheim, T. (2019). An Empirical Analysis of the Correlation between Large Daily Changes in Grain and Oil Futures Prices. Journal of Commodity Markets, 14, 66-75. https://doi.org/10.1016/j.jcomm.2018.07.002 Go to original source...
  19. Ghorbel, A., Hamma, W., Jarboui, A. (2016). Dependence between Oil and Commodities Markets using Time-varying Archimedean Copulas and Effectiveness of Hedging Strategies. Journal of Applied Statistics, 44, 1509-1542. https://doi.org/10.1080/02664763.2016.1155107 Go to original source...
  20. Gilbert, C. L. (2010). How to Understand High Food Prices. Journal of Agricultural Economics, 61, 398-425. https://doi.org/10.1111/j.1477-9552.2010.00248.x Go to original source...
  21. Gu, F., Wang, J. Q., Guo, J. F., Fan, Y. (2020). Dynamic Linkages between International Oil Price, Plastic Stock Index and Recycle Plastic Markets in China. International Review of Economics & Finance, 68, 167-179. https://doi.org/10.1016/j.iref.2020.03.015 Go to original source...
  22. Guhathakurta, K., Dash, S. R., Maitra, D. (2020). Period Specific Volatility Spillover Based Connectedness between Oil and Other Commodity Prices and Their Portfolio Implications. Energy Economics, 85, 104566. https://doi.org/10.1016/j.eneco.2019.104566 Go to original source...
  23. Hamilton, J. D. (1996). This Is What Happened to the Oil Price Macroeconomy Relationship. Journal of Monetary Economics, 38, 215-220. https://doi.org/10.1016/S0304-3932(96)01282-2 Go to original source...
  24. Hammoudeh, S., Yuan, Y. (2008). Metal Volatility in Presence of Oil and Interest Rate Shocks. Energy Economics, 30, 606-620. https://doi.org/10.1016/j.eneco.2007.09.004 Go to original source...
  25. Hau, L., Zhu, H., Huang, R., Ma, X. (2020). Heterogeneous Dependence between Crude Oil Price Volatility and China's Agriculture Commodity Futures: Evidence from Quantile-on-Quantile Regression. Energy, 213, 118781. https://doi.org/10.1016/j.energy.2020.118781 Go to original source...
  26. Huang, X., Huang, S. (2020). Identifying the Comovement of Price between China's and International Crude Oil Futures: A Time-frequency Perspective. International Review of Financial Analysis, 72, 101562. https://doi.org/10.1016/j.irfa.2020.101562 Go to original source...
  27. Joe, H., Xu, J. J. (1996) The Estimation Method of Inference Functions for Margins for Multivariate Models. http://dx.doi.org/10.14288/1.0225985. Go to original source...
  28. Kang, S. H., McIver, R., Yoon, S.-M. (2017). Dynamic Spillover Effects among Crude Oil, Precious Metal, and Agricultural Commodity Futures Markets. Energy Economics, 62, 19-32. https://doi.org/10.1016/j.eneco.2016.12.011 Go to original source...
  29. Kilian, L. (2009). Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market. American Economic Review, 99, 1053-1069. https://doi.org/10.1257/aer.99.3.1053 Go to original source...
  30. Koirala, K. H., Mishra, A. K., D'Antoni, J. M., Mehlhorn, J. E. (2015). Energy Prices and Agricultural Commodity Prices: Testing Correlation Using Copulas Method. Energy, 81, 430-436. https://doi.org/10.1016/j.energy.2014.12.055 Go to original source...
  31. Li, M., Yang, L. (2013). Modeling the Volatility of Futures Return in Rubber and Oil - a Copula-Based GARCH Model Approach. Economic Modelling, 35, 576-581. https://doi.org/10.1016/j.econmod.2013.07.016 Go to original source...
  32. Liu, F., Zhang, C., Tang, M. (2021). The Impacts of Oil Price Shocks and Jumps on China's Nonferrous Metal Markets. Resources Policy, 73, 102228. https://doi.org/10.1016/j.resourpol.2021.102228 Go to original source...
  33. Liu, S., Fang, W., Gao, X., An, F., Jiang, M., Li, Y. (2019). Long-term Memory Dynamics of Crude Oil Price Spread in Non-dollar Countries under the Influence of Exchange Rates. Energy, 182, 753-764. https://doi.org/10.1016/j.energy.2019.06.072 Go to original source...
  34. López Cabrera, B., Schulz, F. (2016). Volatility Linkages between Energy and Agricultural Commodity Prices. Energy Economics, 54, 190-203. https://doi.org/10.1016/j.eneco.2015.11.018 Go to original source...
  35. Lucotte, Y. (2016). Co-Movements between Crude Oil and Food Prices: a Post-Commodity Boom Perspective. Economics Letters, 147, 142-147. https://doi.org/10.1016/j.econlet.2016.08.032 Go to original source...
  36. Ma, Z., Xu, R., Dong, X. (2015). World Oil Prices and Agricultural Commodity Prices: the Evidence from China. Agricultural Economics (Zemědělská ekonomika), 61, 564-576. https://doi.org/10.17221/6/2015-AGRICECON Go to original source...
  37. Mo, K., Suvankulov, F., Griffiths, S. (2021). Financial Distress and Commodity Hedging: Evidence from Canadian Oil Firms. Energy Economics, 97, 105162. https://doi.org/10.1016/j.eneco.2021.105162 Go to original source...
  38. Mohammadi, H., Su, L. (2010). International Evidence on Crude Oil Price Dynamics: Applications of ARIMA-GARCH Models. Energy Economics, 32, 1001-1008. https://doi.org/10.1016/j.eneco.2010.04.009 Go to original source...
  39. Mokni, K. (2020). A Dynamic Quantile Regression Model for the Relationship Between Oil Price and Stock Markets in Oil-Importing and Oil-Exporting Countries. Energy, 213, 118-639. https://doi.org/10.1016/j.energy.2020.118639 Go to original source...
  40. Nazlioglu, S. (2011). World Oil and Agricultural Commodity Prices: Evidence from Nonlinear Causality. Energy Policy, 39, 2935-2943. https://doi.org/10.1016/j.enpol.2011.03.001 Go to original source...
  41. Nazlioglu, S., Soytas, U. (2011). World Oil Prices and Agricultural Commodity Prices: Evidence from an Emerging Market. Energy Economics, 33, 488-496. https://doi.org/10.1016/j.eneco.2010.11.012 Go to original source...
  42. Nicola, F. d., De Pace, P., Hernandez, M. A. (2016). Co-Movement of Major Energy, Agricultural, and Food Commodity Price Returns: a Time-Series Assessment. Energy Economics, 57, 28-41. https://doi.org/10.1016/j.eneco.2016.04.012 Go to original source...
  43. Paris, A. (2018). On the Link Between Oil and Agricultural Commodity Prices: Do Biofuels Matter? International Economics, 155, 48-60. https://doi.org/10.1016/j.inteco.2017.12.003 Go to original source...
  44. Patton, A. (2013) Chapter 16 - Copula Methods for Forecasting Multivariate Time Series. In: Elliott, G. and Timmermann, A., (eds.) Handbook of economic forecasting, Elsevier. Go to original source...
  45. Patton, A. J. (2006). Modelling Asymmetric Exchange Rate Dependence. International Economic Review, 47, 527-556. https://doi.org/10.1111/j.1468-2354.2006.00387.x Go to original source...
  46. Pindyck, R. S., Rotemberg, J. J. (1990). The Excess Co-Movement of Commodity Prices. The Economic Journal, 100, 1173-1189. https://doi.org/10.2307/2233966 Go to original source...
  47. Qian, C., Zhang, T., Li, J. (2023). The Impact of International Commodity Price Shocks on Macroeconomic Fundamentals: Evidence From the US and China. Resources Policy, 85, 103904. https://doi.org/10.1016/j.resourpol.2023.103904 Go to original source...
  48. Rafiq, S., Bloch, H. (2016). Explaining Commodity Prices Through Asymmetric Oil Shocks: Evidence from Nonlinear Models. Resources Policy, 50, 34-48. https://doi.org/10.1016/j.resourpol.2016.08.005 Go to original source...
  49. Reboredo, J. C. (2012). Do Food and Oil Prices Co-Move? Energy Policy, 49, 456-467. https://doi.org/10.1016/j.enpol.2012.06.035 Go to original source...
  50. Reboredo, J. C., Rivera-Castro, M. A. (2014). Wavelet-Based Evidence of the Impact of Oil Prices on Stock Returns. International Review of Economics & Finance, 29, 145-176. https://doi.org/10.1016/j.iref.2013.05.014 Go to original source...
  51. Sklar, M. (1959). Fonctions de Répartition À n Dimensions et Leurs Marges. Publications de l'Institut Statistique de l'Université de Paris, 8, 3.
  52. Song, M. L., Fang, K. N., Zhang, J., Wu, J. B. (2019). The Co-Movement between Chinese Oil Market and Other Main International Oil Markets: a DCC-MGARCH Approach. Computational Economics, 54, 1303-1318. https://doi.org/10.1007/s10614-016-9564-5 Go to original source...
  53. Tomanová, P., Holý, V. (2021). Clustering of Arrivals in Queueing Systems: Autoregressive Conditional Duration Approach. Central European Journal of Operations Research, 29, 859-874. https://doi.org/10.1007/s10100-021-00744-7 Go to original source...
  54. Uebele, M. (2013). What Drives Commodity Market Integration? Evidence from the 1800s. CESifo Economic Studies, 59, 412-442. https://doi.org/.10.1093/cesifo/ifs009 Go to original source...
  55. Umar, M., Mirza, N., Rizvi, S. K. A., Furqan, M. (2023). Asymmetric Volatility Structure of Equity Returns: Evidence from an Emerging Market. The Quarterly Review of Economics and Finance, 87, 330-336. https://doi.org/10.1016/j.qref.2021.04.016 Go to original source...
  56. Umar, Z., Jareño, F., Escribano, A. (2021). Oil Price Shocks and the Return and Volatility Spillover between Industrial and Precious Metals. Energy Economics, 99, 105-291. https://doi.org/10.1016/j.eneco.2021.105291 Go to original source...
  57. Wang, L., Ahmad, F., Luo, G.-l., Umar, M., Kirikkaleli, D. (2022). Portfolio Optimization of Financial Commodities with Energy Futures. Annals of Operations Research, 313, 401-439. https://doi.org/10.1007/s10479-021-04283-x Go to original source...
  58. Wright, B. D. (2011). The Economics of Grain Price Volatility. Applied Economic Perspectives and Policy, 33, 32-58. https://doi.org/10.1093/aepp/ppq033 Go to original source...
  59. Wu, F., Zhao, W. L., Ji, Q., Zhang, D. Y. (2020). Dependency, Centrality and Dynamic Networks for International Commodity Futures Prices. International Review of Economics & Finance, 67, 118-132. https://doi.org/10.1016/j.iref.2020.01.004 Go to original source...
  60. Yahya, M., Oglend, A., Dahl, R. E. (2019). Temporal and Spectral Dependence between Crude Oil and Agricultural Commodities: a Wavelet-Based Copula Approach. Energy Economics, 80, 277-296. https://doi.org/10.1016/j.eneco.2019.01.011 Go to original source...
  61. Yang, L., Cai, X. J., Hamori, S. (2017). Does the Crude Oil Price Influence the Exchange Rates of Oil-Importing and Oil-Exporting Countries Differently? A Wavelet Coherence Analysis. International Review of Economics & Finance, 49, 536-547. https://doi.org/10.1016/j.iref.2017.03.015 Go to original source...
  62. Yin, L., Han, L. (2016). Macroeconomic Impacts on Commodity Prices: China Vs. The United States. Quantitative Finance, 16, 489-500. https://doi.org/10.1080/14697688.2015.1018308 Go to original source...
  63. Zavadska, M., Morales, L., Coughlan, J. (2020). Brent Crude Oil Prices Volatility During Major Crises. Finance Research Letters, 32, 101078. https://doi.org/10.1016/j.frl.2018.12.026 Go to original source...
  64. Zhang, C., Chen, X. (2014). The Impact of Global Oil Price Shocks on China's Bulk Commodity Markets and Fundamental Industries. Energy Policy, 66, 32-41. https://doi.org/10.1016/j.enpol.2013.09.067 Go to original source...
  65. Zhang, C., Qu, X. (2015). The Effect of Global Oil Price Shocks on China's Agricultural Commodities. Energy Economics, 51, 354-364. https://doi.org/10.1016/j.eneco.2015.07.012 Go to original source...
  66. Zhang, C., Shi, X., Yu, D. (2018). The Effect of Global Oil Price Shocks on China's Precious Metals Market: a Comparative Analysis of Gold and Platinum. Journal of Cleaner Production, 186, 652-661. https://doi.org/10.1016/j.jclepro.2018.03.154 Go to original source...
  67. Zhang, D., Broadstock, D. C. (2020). Global Financial Crisis and Rising Connectedness in the International Commodity Markets. International Review of Financial Analysis, 68, 101239. https://doi.org/10.1016/j.irfa.2018.08.003 Go to original source...
  68. Zhang, K.-S., Zhao, Y.-Y. (2021). Modeling Dynamic Dependence Between Crude Oil and Natural Gas Return Rates: a Time-Varying Geometric Copula Approach. Journal of Computational and Applied Mathematics, 386, 113243. https://doi.org/10.1016/j.cam.2020.113243 Go to original source...
  69. Zhang, X., Xiao, J., Zhang, Z. (2020). An Anatomy of Commodity Futures Returns in China. Pacific-Basin Finance Journal, 62, 101366. https://doi.org/10.1016/j.pacfin.2020.101366 Go to original source...

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