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

Please wait a minute...

Shopping Cart Email List Chinese

Advanced Search Fig/Tab

Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

CN 10-1205/N

Postal Subscription Code 80-905

Featured Articles | Most Downloaded | Most Reading

Online Submission Free E-mail Alert Subscribe to Our RSS Content Feed

Front. Eng

2022, Vol. 9

Issue (3) : 409-424 https://doi.org/10.1007/s42524-022-0207-3

RESEARCH ARTICLE

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

Xingyu DAI¹, Ling XIAO², Matthew C. LI², Qunwei WANG¹(

)

¹. College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; Research Center for Soft Energy Science, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
². Royal Holloway University of London, Egham TW20 0EX, United Kingdom

Download: PDF(7496 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

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.

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

Corresponding Author(s): Qunwei WANG

About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.

Just Accepted Date: 08 June 2022 Online First Date: 11 August 2022 Issue Date: 07 September 2022

Cite this article:

Xingyu DAI,Ling XIAO,Matthew C. LI, et al. Toward energy finance market transition: Does China’s oil futures shake up global spots market?[J]. Front. Eng, 2022, 9(3): 409-424.

URL:

https://academic.hep.com.cn/fem/EN/10.1007/s42524-022-0207-3
https://academic.hep.com.cn/fem/EN/Y2022/V9/I3/409

Tab.1 Information of chosen crude oil spots

Fig.1 Net price information spillover network in oils spots under different gravities.

Fig.2 Net price information spillover network in oil spots under different sulfur contents.

Fig.3 Net price information spillover network in oil spots under different geographical origins.

Fig.4 Price co-movement between oil futures and all oil spots markets.

Fig.5 Price co-movement between oil futures and oil spots markets under different gravities.

Fig.6 Price co-movement between oil futures and oil spots markets under different sulfur contents.

Fig.7 Price co-movement between oil futures and oil spots markets under different geographical origins.

1	Y An, D Zhou, J Yu, X Shi, Q Wang, (2021). Carbon emission reduction characteristics for China’s manufacturing firms: Implications for formulating carbon policies. Journal of Environmental Management, 284: 112055 https://doi.org/10.1016/j.jenvman.2021.112055 pmid: 33540202
2	S M Awadh, H Al-Mimar, (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	J Baruník, T Křehlík, (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16( 2): 271–296 https://doi.org/10.1093/jjfinec/nby001
4	S A Basher, P Sadorsky, (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 https://doi.org/10.1016/j.eneco.2015.11.022
5	Petroleum British (2021). Statistical Review of World Energy 2021
6	B Buyuksahin, J H Harris, (2011). Do speculators drive crude oil futures prices?. Energy Journal, 32( 2): 75–95 https://doi.org/10.5547/ISSN0195-6574-EJ-Vol32-No2-7
7	C P Chang, C C Lee, (2015). Do oil spots and futures prices move together?. Energy Economics, 50: 379–390 https://doi.org/10.1016/j.eneco.2015.02.014
8	K L Chang, (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 https://doi.org/10.1016/j.econmod.2012.06.016
9	L Charfeddine, K Barkat, (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 https://doi.org/10.1016/j.eneco.2020.104680
10	K C Chen, S Chen, L Wu, (2009). Price causal relations between China and the world oil markets. Global Finance Journal, 20( 2): 107–118 https://doi.org/10.1016/j.gfj.2008.11.001
11	P F Chen, C C Lee, J H Zeng, (2014). The relationship between spots and futures oil prices: Do structural breaks matter?. Energy Economics, 43: 206–217 https://doi.org/10.1016/j.eneco.2014.03.006
12	X Dai, Q Wang, D Zha, D Zhou, (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 https://doi.org/10.1016/j.eneco.2020.104774
13	X Dai, L Xiao, Q Wang, G Dhesi, (2021). Multiscale interplay of higher-order moments between the carbon and energy markets during Phase III of the EU ETS. Energy Policy, 156: 112428 https://doi.org/10.1016/j.enpol.2021.112428
14	F X Diebold, K Yilmaz, (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28( 1): 57–66 https://doi.org/10.1016/j.ijforecast.2011.02.006
15	J Elder, H Miao, S Ramchander, (2014). Price discovery in crude oil futures. Energy Economics, 46: S18–S27 https://doi.org/10.1016/j.eneco.2014.09.012
16	R Engle, (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20( 3): 339–350 https://doi.org/10.1198/073500102288618487
17	R Engle, B Kelly, (2012). Dynamic equicorrelation. Journal of Business & Economic Statistics, 30( 2): 212–228 https://doi.org/10.1080/07350015.2011.652048
18	R Ferrer, S J H Shahzad, R López, F Jareño, (2018). Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices. Energy Economics, 76: 1–20 https://doi.org/10.1016/j.eneco.2018.09.022
19	K D Garbade, W L Silber, (1983). Price movements and price discovery in futures and cash markets. Review of Economics and Statistics, 65( 2): 289–297 https://doi.org/10.2307/1924495
20	S G Gülen, (1998). Efficiency in the crude oil futures market. Journal of Energy Finance & Development, 3( 1): 13–21 https://doi.org/10.1016/S1085-7443(99)80065-9
21	S G Gülen, (1999). Regionalization in the world crude oil market: Further evidence. Energy Journal, 20( 1): 125–139 https://doi.org/10.5547/ISSN0195-6574-EJ-Vol20-No1-7
22	B N Huang, C W Yang, M J Hwang, (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 https://doi.org/10.1016/j.eneco.2008.08.002
23	X Huang, S Huang, (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
24	Q Ji, Y Fan, (2015). Dynamic integration of world oil prices: A reinvestigation of globalisation vs. regionalisation. Applied Energy, 155: 171–180 https://doi.org/10.1016/j.apenergy.2015.05.117
25	Q Ji, Y Fan, (2016). Evolution of the world crude oil market integration: A graph theory analysis. Energy Economics, 53: 90–100 https://doi.org/10.1016/j.eneco.2014.12.003
26	Q Ji, D Zhang, (2019). China’s crude oil futures: Introduction and some stylized facts. Finance Research Letters, 28: 376–380 https://doi.org/10.1016/j.frl.2018.06.005
27	S H Kang, A K Tiwari, C T Albulescu, S M Yoon, (2019). Exploring the time-frequency connectedness and network among crude oil and agriculture commodities V1. Energy Economics, 84: 104543 https://doi.org/10.1016/j.eneco.2019.104543
28	R K Kaufmann, (2016). Price differences among crude oils: The private costs of supply disruptions. Energy Economics, 56: 1–8 https://doi.org/10.1016/j.eneco.2016.02.005
29	C C Lee, J H Zeng, (2011). Revisiting the relationship between spots and futures oil prices: Evidence from quantile cointegrating regression. Energy Economics, 33( 5): 924–935 https://doi.org/10.1016/j.eneco.2011.02.012
30	J Li, L Huang, P Li, (2021). Are Chinese crude oil futures good hedging tools?. Finance Research Letters, 38: 101514 https://doi.org/10.1016/j.frl.2020.101514
31	X Lu, F Ma, J Wang, J Wang, (2020). Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models. Energy, 212: 118743 https://doi.org/10.1016/j.energy.2020.118743 pmid: 32904908
32	W Mensi, S Hammoudeh, I M W Al-Jarrah, A Sensoy, S H Kang, (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 https://doi.org/10.1016/j.eneco.2017.08.031
33	X Miao, Q Wang, X Dai, (2022). Is oil-gas price decoupling happening in China? A multi-scale quantile-on-quantile approach. International Review of Economics & Finance, 77: 450–470 https://doi.org/10.1016/j.iref.2021.10.016
34	H Mohammadi, (2009). Electricity prices and fuel costs: Long-run relations and short-run dynamics. Energy Economics, 31( 3): 503–509 https://doi.org/10.1016/j.eneco.2009.02.001
35	M Motomura, (2014). Japan’s need for Russian oil and gas: A shift in energy flows to the Far East. Energy Policy, 74: 68–79 https://doi.org/10.1016/j.enpol.2014.08.024
36	Z Y Ouyang, Z Qin, H Cao, T Y Xie, X Y Dai, Q W Wang, (2021). A spillover network analysis of the global crude oil market: Evidence from the post-financial crisis era. Petroleum Science, 18( 4): 1256–1269 https://doi.org/10.1016/j.petsci.2021.05.003
37	Z Pan, Y Wang, L Liu, (2016). The relationships between petroleum and stock returns: An asymmetric dynamic equi-correlation approach. Energy Economics, 56: 453–463 https://doi.org/10.1016/j.eneco.2016.04.008
38	Z Pan, Y Wang, L Yang, (2014). Hedging crude oil using refined product: A regime switching asymmetric DCC approach. Energy Economics, 46: 472–484 https://doi.org/10.1016/j.eneco.2014.05.014
39	Q Peng, F Wen, X Gong, (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 https://doi.org/10.1002/ijfe.1823
40	R Sari, U Soytas, E Hacihasanoglu, (2011). Do global risk perceptions influence world oil prices?. Energy Economics, 33( 3): 515–524 https://doi.org/10.1016/j.eneco.2010.12.006
41	L N Switzer, M El-Khoury, (2007). Extreme volatility, speculative efficiency, and the hedging effectiveness of the oil futures markets. The Journal of Futures Markets, 27( 1): 61–84 https://doi.org/10.1002/fut.20235
42	Y Tong, N Wan, X Dai, X Bi, Q Wang, (2022). China’s energy stock market jumps: To what extent does the COVID-19 pandemic play a part?. Energy Economics, 109: 105937 https://doi.org/10.1016/j.eneco.2022.105937
43	D Tsvetanov, J Coakley, N Kellard, (2016). Bubbling over! The behaviour of oil futures along the yield curve. Journal of Empirical Finance, 38: 516–533 https://doi.org/10.1016/j.jempfin.2015.08.009
44	Q Wang, X Dai, D Zhou, (2020). Dynamic correlation and risk contagion between “black” futures in China: A multi-scale variational mode decomposition approach. Computational Economics, 55( 4): 1117–1150 https://doi.org/10.1007/s10614-018-9857-y
45	X Wang, Y Wang, (2019). Volatility spillovers between crude oil and Chinese sectoral equity markets: Evidence from a frequency dynamics perspective. Energy Economics, 80: 995–1009 https://doi.org/10.1016/j.eneco.2019.02.019
46	R J Weiner, (1991). Is the world oil market “one great pool”?. Energy Journal, 12( 3): 95–107 https://doi.org/10.5547/ISSN0195-6574-EJ-Vol12-No3-7
47	J Yang, Y Zhou, (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 https://doi.org/10.1002/fut.22103
48	X Zhai, Y An, (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 https://doi.org/10.1016/j.jclepro.2019.119760
49	X Zhai, Y An, (2021). The relationship between technological innovation and green transformation efficiency in China: An empirical analysis using spatial panel data. Technology in Society, 64: 101498 https://doi.org/10.1016/j.techsoc.2020.101498
50	C Zhang, B Zhou, X Tian, (2022a). Political connections and green innovation: The role of a corporate entrepreneurship strategy in state-owned enterprises. Journal of Business Research, 146: 375–384 https://doi.org/10.1016/j.jbusres.2022.03.084
51	C Zhang, X Zhou, B Zhou, Z Zhao, (2022b). Impacts of a mega sporting event on local carbon emissions: A case of the 2014 Nanjing Youth Olympics. China Economic Review, 73: 101782 https://doi.org/10.1016/j.chieco.2022.101782
52	D Zhang, Q Ji, A M Kutan, (2019). Dynamic transmission mechanisms in global crude oil prices: Estimation and implications. Energy, 175: 1181–1193 https://doi.org/10.1016/j.energy.2019.03.162
53	Y J Zhang, Z C Li, (2021). Forecasting the stock returns of Chinese oil companies: Can investor attention help?. International Review of Economics & Finance, 76: 531–555 https://doi.org/10.1016/j.iref.2021.07.006
54	Y J Zhang, S J Ma, (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 https://doi.org/10.1002/ijfe.1914
55	Y J Zhang, X Pan, (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 https://doi.org/10.1108/CAER-05-2020-0099
56	Y J Zhang, Y M Wei, (2010). The crude oil market and the gold market: Evidence for cointegration, causality and price discovery. Resources Policy, 35( 3): 168–177 https://doi.org/10.1016/j.resourpol.2010.05.003
57	Y J Zhang, X X Yan, (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 https://doi.org/10.1016/j.iref.2020.04.001

Viewed

Full text

Abstract

Cited

Shared

Discussed