Toward energy finance market transition: Does China’s oil futures shake up global spots market?

Xingyu DAI, Ling XIAO, Matthew C. LI, Qunwei WANG

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PDF(7496 KB)
Front. Eng ›› 2022, Vol. 9 ›› Issue (3) : 409-424. DOI: 10.1007/s42524-022-0207-3
RESEARCH ARTICLE
RESEARCH ARTICLE

Toward energy finance market transition: Does China’s oil futures shake up global spots market?

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Abstract

China is breaking through the petrodollar system, establishing RMB-dominating crude oil futures market. The country is achieving a milestone in its transition to energy finance market internationalization. This study explores the price leadership of China’s crude oil futures and identifies its price co-movement to uncover whether it truly shakes up the global oil spots market. First, we find that for oil spots under different gravities, China’s oil futures is only a net price information receiver from light-, medium-, and heavy-gravity oil spots, but it has a relatively stronger price co-movement with these three spots. Second, for oil spots under different sulfur contents, China’s oil futures still has weak price leadership in sweet, neutral, and sour oil spots, but it has strong co-movement with them. Third, for oil spots under different geographical origins, China’s oil futures shows price leadership in East Asian and Australian oil spots at the medium- and long-run time scales and strong price co-movement with East Asian, Middle Eastern, Latin American and Australian oil spots. China’s oil futures may not have good price leadership in global spots market, but it features favorable price co-movement.

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Keywords

China’s oil futures / price information spillover / price co-movement / BK spillover index / BDECO model

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Xingyu DAI, Ling XIAO, Matthew C. LI, Qunwei WANG. Toward energy finance market transition: Does China’s oil futures shake up global spots market?. Front. Eng, 2022, 9(3): 409‒424 https://doi.org/10.1007/s42524-022-0207-3

References

[1]
An, Y Zhou, D Yu, J Shi, X Wang, Q (2021). Carbon emission reduction characteristics for China’s manufacturing firms: Implications for formulating carbon policies. Journal of Environmental Management, 284: 112055
CrossRef Pubmed Google scholar
[2]
Awadh, S M Al-Mimar, H (2015). Statistical analysis of the relations between API, specific gravity and sulfur content in the universal crude oil. International Journal of Science and Research, 4( 5): 1279–1284
[3]
Baruník, J Křehlík, T (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16( 2): 271–296
CrossRef Google scholar
[4]
Basher, S A Sadorsky, P (2016). Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH. Energy Economics, 54: 235–247
CrossRef Google scholar
[5]
BritishPetroleum (2021). Statistical Review of World Energy 2021
[6]
Buyuksahin, B Harris, J H (2011). Do speculators drive crude oil futures prices?. Energy Journal, 32( 2): 75–95
CrossRef Google scholar
[7]
Chang, C P Lee, C C (2015). Do oil spots and futures prices move together?. Energy Economics, 50: 379–390
CrossRef Google scholar
[8]
Chang, K L (2012). The time-varying and asymmetric dependence between crude oil spots and futures markets: Evidence from the mixture copula-based ARJI–GARCH model. Economic Modelling, 29( 6): 2298–2309
CrossRef Google scholar
[9]
Charfeddine, L Barkat, K (2020). Short- and long-run asymmetric effect of oil prices and oil and gas revenues on the real GDP and economic diversification in oil-dependent economy. Energy Economics, 86: 104680
CrossRef Google scholar
[10]
Chen, K C Chen, S Wu, L (2009). Price causal relations between China and the world oil markets. Global Finance Journal, 20( 2): 107–118
CrossRef Google scholar
[11]
Chen, P F Lee, C C Zeng, J H (2014). The relationship between spots and futures oil prices: Do structural breaks matter?. Energy Economics, 43: 206–217
CrossRef Google scholar
[12]
Dai, X Wang, Q Zha, D Zhou, D (2020). Multi-scale dependence structure and risk contagion between oil, gold, and US exchange rate: A wavelet-based vine-copula approach. Energy Economics, 88: 104774
CrossRef Google scholar
[13]
Dai, X Xiao, L Wang, Q Dhesi, G (2021). Multiscale interplay of higher-order moments between the carbon and energy markets during Phase III of the EU ETS. Energy Policy, 156: 112428
CrossRef Google scholar
[14]
Diebold, F X Yilmaz, K (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28( 1): 57–66
CrossRef Google scholar
[15]
Elder, J Miao, H Ramchander, S (2014). Price discovery in crude oil futures. Energy Economics, 46: S18–S27
CrossRef Google scholar
[16]
Engle, R (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20( 3): 339–350
CrossRef Google scholar
[17]
Engle, R Kelly, B (2012). Dynamic equicorrelation. Journal of Business & Economic Statistics, 30( 2): 212–228
CrossRef Google scholar
[18]
Ferrer, R Shahzad, S J H López, R Jareño, F (2018). Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices. Energy Economics, 76: 1–20
CrossRef Google scholar
[19]
Garbade, K D Silber, W L (1983). Price movements and price discovery in futures and cash markets. Review of Economics and Statistics, 65( 2): 289–297
CrossRef Google scholar
[20]
Gülen, S G (1998). Efficiency in the crude oil futures market. Journal of Energy Finance & Development, 3( 1): 13–21
CrossRef Google scholar
[21]
Gülen, S G (1999). Regionalization in the world crude oil market: Further evidence. Energy Journal, 20( 1): 125–139
CrossRef Google scholar
[22]
Huang, B N Yang, C W Hwang, M J (2009). The dynamics of a nonlinear relationship between crude oil spots and futures prices: A multivariate threshold regression approach. Energy Economics, 31( 1): 91–98
CrossRef Google scholar
[23]
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
CrossRef Google scholar
[24]
Ji, Q Fan, Y (2015). Dynamic integration of world oil prices: A reinvestigation of globalisation vs. regionalisation. Applied Energy, 155: 171–180
CrossRef Google scholar
[25]
Ji, Q Fan, Y (2016). Evolution of the world crude oil market integration: A graph theory analysis. Energy Economics, 53: 90–100
CrossRef Google scholar
[26]
Ji, Q Zhang, D (2019). China’s crude oil futures: Introduction and some stylized facts. Finance Research Letters, 28: 376–380
CrossRef Google scholar
[27]
Kang, S H Tiwari, A K Albulescu, C T Yoon, S M (2019). Exploring the time-frequency connectedness and network among crude oil and agriculture commodities V1. Energy Economics, 84: 104543
CrossRef Google scholar
[28]
Kaufmann, R K (2016). Price differences among crude oils: The private costs of supply disruptions. Energy Economics, 56: 1–8
CrossRef Google scholar
[29]
Lee, C C Zeng, J H (2011). Revisiting the relationship between spots and futures oil prices: Evidence from quantile cointegrating regression. Energy Economics, 33( 5): 924–935
CrossRef Google scholar
[30]
Li, J Huang, L Li, P (2021). Are Chinese crude oil futures good hedging tools?. Finance Research Letters, 38: 101514
CrossRef Google scholar
[31]
Lu, X Ma, F Wang, J Wang, J (2020). Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models. Energy, 212: 118743
CrossRef Pubmed Google scholar
[32]
Mensi, W Hammoudeh, S Al-Jarrah, I M W Sensoy, A Kang, S H (2017). Dynamic risk spillovers between gold, oil prices and conventional, sustainability and Islamic equity aggregates and sectors with portfolio implications. Energy Economics, 67: 454–475
CrossRef Google scholar
[33]
Miao, X Wang, Q Dai, X (2022). Is oil-gas price decoupling happening in China? A multi-scale quantile-on-quantile approach. International Review of Economics & Finance, 77: 450–470
CrossRef Google scholar
[34]
Mohammadi, H (2009). Electricity prices and fuel costs: Long-run relations and short-run dynamics. Energy Economics, 31( 3): 503–509
CrossRef Google scholar
[35]
Motomura, M (2014). Japan’s need for Russian oil and gas: A shift in energy flows to the Far East. Energy Policy, 74: 68–79
CrossRef Google scholar
[36]
Ouyang, Z Y Qin, Z Cao, H Xie, T Y Dai, X Y Wang, Q W (2021). A spillover network analysis of the global crude oil market: Evidence from the post-financial crisis era. Petroleum Science, 18( 4): 1256–1269
CrossRef Google scholar
[37]
Pan, Z Wang, Y Liu, L (2016). The relationships between petroleum and stock returns: An asymmetric dynamic equi-correlation approach. Energy Economics, 56: 453–463
CrossRef Google scholar
[38]
Pan, Z Wang, Y Yang, L (2014). Hedging crude oil using refined product: A regime switching asymmetric DCC approach. Energy Economics, 46: 472–484
CrossRef Google scholar
[39]
Peng, Q Wen, F Gong, X (2021). Time-dependent intrinsic correlation analysis of crude oil and the US dollar based on CEEMDAN. International Journal of Finance & Economics, 26( 1): 834–848
CrossRef Google scholar
[40]
Sari, R Soytas, U Hacihasanoglu, E (2011). Do global risk perceptions influence world oil prices?. Energy Economics, 33( 3): 515–524
CrossRef Google scholar
[41]
Switzer, L N El-Khoury, M (2007). Extreme volatility, speculative efficiency, and the hedging effectiveness of the oil futures markets. The Journal of Futures Markets, 27( 1): 61–84
CrossRef Google scholar
[42]
Tong, Y Wan, N Dai, X Bi, X Wang, Q (2022). China’s energy stock market jumps: To what extent does the COVID-19 pandemic play a part?. Energy Economics, 109: 105937
CrossRef Google scholar
[43]
Tsvetanov, D Coakley, J Kellard, N (2016). Bubbling over! The behaviour of oil futures along the yield curve. Journal of Empirical Finance, 38: 516–533
CrossRef Google scholar
[44]
Wang, Q Dai, X Zhou, D (2020). Dynamic correlation and risk contagion between “black” futures in China: A multi-scale variational mode decomposition approach. Computational Economics, 55( 4): 1117–1150
CrossRef Google scholar
[45]
Wang, X Wang, Y (2019). Volatility spillovers between crude oil and Chinese sectoral equity markets: Evidence from a frequency dynamics perspective. Energy Economics, 80: 995–1009
CrossRef Google scholar
[46]
Weiner, R J (1991). Is the world oil market “one great pool”?. Energy Journal, 12( 3): 95–107
CrossRef Google scholar
[47]
Yang, J Zhou, Y (2020). Return and volatility transmission between China’s and international crude oil futures markets: A first look. The Journal of Futures Markets, 40( 6): 860–884
CrossRef Google scholar
[48]
Zhai, X An, Y (2020). Analyzing influencing factors of green transformation in China’s manufacturing industry under environmental regulation: A structural equation model. Journal of Cleaner Production, 251: 119760
CrossRef Google scholar
[49]
Zhai, X An, Y (2021). The relationship between technological innovation and green transformation efficiency in China: An empirical analysis using spatial panel data. Technology in Society, 64: 101498
CrossRef Google scholar
[50]
Zhang, C Zhou, B Tian, X (2022a). Political connections and green innovation: The role of a corporate entrepreneurship strategy in state-owned enterprises. Journal of Business Research, 146: 375–384
CrossRef Google scholar
[51]
Zhang, C Zhou, X Zhou, B Zhao, Z (2022b). Impacts of a mega sporting event on local carbon emissions: A case of the 2014 Nanjing Youth Olympics. China Economic Review, 73: 101782
CrossRef Google scholar
[52]
Zhang, D Ji, Q Kutan, A M (2019). Dynamic transmission mechanisms in global crude oil prices: Estimation and implications. Energy, 175: 1181–1193
CrossRef Google scholar
[53]
Zhang, Y J Li, Z C (2021). Forecasting the stock returns of Chinese oil companies: Can investor attention help?. International Review of Economics & Finance, 76: 531–555
CrossRef Google scholar
[54]
Zhang, Y J Ma, S J (2021). Exploring the dynamic price discovery, risk transfer and spillover among INE, WTI and Brent crude oil futures markets: Evidence from the high-frequency data. International Journal of Finance & Economics, 26( 2): 2414–2435
CrossRef Google scholar
[55]
Zhang, Y J Pan, X (2021). Does the risk aversion of crude oil market investors have directional predictability for the precious metal and agricultural markets?. China Agricultural Economic Review, 13( 4): 894–911
CrossRef Google scholar
[56]
Zhang, Y J Wei, Y M (2010). The crude oil market and the gold market: Evidence for cointegration, causality and price discovery. Resources Policy, 35( 3): 168–177
CrossRef Google scholar
[57]
Zhang, Y J Yan, X X (2020). The impact of US economic policy uncertainty on WTI crude oil returns in different time and frequency domains. International Review of Economics & Finance, 69: 750–768
CrossRef Google scholar

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