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Modern Management 2024
国际原油期货市场与中国原油市场的波动溢出研究
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Abstract:
研究国际原油期货与中国原油市场之间的关联具有重要意义,本文聚焦于其间的波动溢出效应。本文采用时间序列数据建模的方法,选取WTI和BRENT表征国际原油期货市场,INE原油期货表征中国原油市场,对数据进行预处理后采用VAR-BEKK/DCC-GARCH模型验证波动溢出效应,并采用DY、BK溢出指数分别进行时域和频域的研究,最后采用大庆原油现货和胜利原油现货替代INE的SC作稳健性检验。本文的研究结果表明,国际原油期货市场与中国原油市场之间存在波动溢出效应,且中国原油市场为风险净接收方。
It is of great significance to study the association between international crude oil futures and China’s crude oil market, and this paper focuses on the volatility spillover effect between them. This paper adopts the method of time series data modeling, selecting WTI and BRENT to characterize the international crude oil futures market and INE crude oil futures to characterize the Chinese crude oil market, and adopting the VAR-BEKK/DCC-GARCH model to validate the volatility spillover effect after preprocessing the data. In this paper, DY and BK spillover indices are used to study in time and frequency domains respectively, and finally, Daqing crude oil spot and Shengli crude oil spot are used to replace the SC of INE for robustness test. The results of this paper show that there is a volatility spillover effect between the international crude oil futures market and the Chinese crude oil market, and the Chinese crude oil market is a net risk receiver.
[1] | Lin, S.X. and Tamvakis, M.N. (2001) Spillover Effects in Energy Futures Markets. Energy Economics, 23, 43-56. https://doi.org/10.1016/s0140-9883(00)00051-7 |
[2] | Thömmes, M. and Winker, P. (2013) Multivariate Modelling of Cross-Commodity Price Relations along the Petrochemical Value Chain. In: Lausen, B., Van den Poel, D. and Ultsch, A., Eds., Algorithms from and for Nature and Life: Classification and Data Analysis, Springer International Publishing, 427-435. https://doi.org/10.1007/978-3-319-00035-0_43 |
[3] | Yang, J. and Zhou, Y. (2020) Return and Volatility Transmission between China’s and International Crude Oil Futures Markets: A First Look. Journal of Futures Markets, 40, 860-884. https://doi.org/10.1002/fut.22103 |
[4] | Liu, M. and Lee, C. (2021) Capturing the Dynamics of the China Crude Oil Futures: Markov Switching, Co-Movement, and Volatility Forecasting. Energy Economics, 103, Article ID: 105622. https://doi.org/10.1016/j.eneco.2021.105622 |
[5] | Silvapulle, P. and Moosa, I.A. (1999) The Relationship between Spot and Futures Prices: Evidence from the Crude Oil Market. Journal of Futures Markets, 19, 175-193. https://doi.org/10.1002/(sici)1096-9934(199904)19:2<175::aid-fut3>3.3.co;2-8 |
[6] | Miroslava, J. (2020) Volatility Patterns of Brent Crude Oil Spot and Futures Prices during Major Economic Crises. Journal of Energy Economics, 45, 123-145. |
[7] | 李英良. 国内外原油期货市场的动态相关性研究——基于DCC-GARCH模型[J]. 中国商论, 2021(7): 19-23. |
[8] | 田洪志, 姚峰, 李慧. 中国是否拥有原油的国际定价权?——基于油价间独立性与传导性视角[J]. 中国管理科学, 2020, 28(11): 90-99. |
[9] | 王良, 李璧肖, 马续涛, 郑炜. 中国原油期货与国际原油期货的价格波动溢出效应及其持续性——基于BEKK-MGARCH模型的研究[J]. 系统工程, 2021, 39(3): 102-120. |
[10] | Zavadska, M., Morales, L. and Coughlan, J. (2020) Brent Crude Oil Prices Volatility during Major Crises. Finance Research Letters, 32, Article ID: 101078. https://doi.org/10.1016/j.frl.2018.12.026 |
[11] | Mcaleer, M. and da Veiga, B. (2008) Forecasting Value‐at‐Risk with a Parsimonious Portfolio Spillover GARCH (PS‐GARCH) Model. Journal of Forecasting, 27, 1-19. https://doi.org/10.1002/for.1049 |
[12] | Wang, Y. and Guo, Z. (2018) The Dynamic Spillover between Carbon and Energy Markets: New Evidence. Energy, 149, 24-33. https://doi.org/10.1016/j.energy.2018.01.145 |
[13] | Antonakakis, N., Chatziantoniou, I. and Gabauer, D. (2020) The Impact of Euro through Time: Exchange Rate Dynamics under Different Regimes. International Journal of Finance & Economics, 26, 1375-1408. https://doi.org/10.1002/ijfe.1854 |
[14] | Tan, X., Geng, Y., Vivian, A. and Wang, X. (2021) Measuring Risk Spillovers between Oil and Clean Energy Stocks: Evidence from a Systematic Framework. Resources Policy, 74, Article ID: 102406. https://doi.org/10.1016/j.resourpol.2021.102406 |
[15] | Nasreen, S., Tiwari, A.K., Eizaguirre, J.C. and Wohar, M.E. (2020) Dynamic Connectedness between Oil Prices and Stock Returns of Clean Energy and Technology Companies. Journal of Cleaner Production, 260, Article ID: 121015. https://doi.org/10.1016/j.jclepro.2020.121015 |
[16] | Janda, K., Kristoufek, L. and Zhang, B. (2022) Return and Volatility Spillovers between Chinese and U.S. Clean Energy Related Stocks. Energy Economics, 108, Article ID: 105911. https://doi.org/10.1016/j.eneco.2022.105911 |
[17] | Diebold, F.X. and Yilmaz, K. (2012) Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting, 28, 57-66. https://doi.org/10.1016/j.ijforecast.2011.02.006 |
[18] | Baruník, J. and Křehlík, T. (2018) Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk. Journal of Financial Econometrics, 16, 271-296. https://doi.org/10.1093/jjfinec/nby001 |