Please wait a minute...
Frontiers in Energy

ISSN 2095-1701

ISSN 2095-1698(Online)

CN 11-6017/TK

邮发代号 80-972

2019 Impact Factor: 2.657

Frontiers in Energy  2020, Vol. 14 Issue (4): 726-739   https://doi.org/10.1007/s11708-020-0701-4
  研究论文 本期目录
上海LNG价格指数能否反映中国市场? ——基于价格发现理论的计量经济学研究
曾叶丽1, 董聪2(), HÖÖK Mikael3, 孙金华4, 时丹阳1
1. 中国石油大学经济管理学院,北京102249
2. 对外经济贸易大学国际经济贸易学院,北京100029
3. 乌普萨拉大学地球科学系,乌普萨拉75105,瑞典
4. 中国石油国际事业有限公司原油部,北京100033
Can the Shanghai LNG Price Index indicate Chinese market? An econometric investigation using price discovery theory
Yeli ZENG1, Cong DONG2(), Mikael HÖÖK3, Jinhua SUN4, Danyang SHI1
1. School of Economics and Management, China University of Petroleum, Beijing 102249, China
2. School of International Trade and Economics, University of International Business and Economics, Beijing 100029, China
3. Department of Earth Sciences, Uppsala University, Uppsala 75105, Sweden
4. Crude Oil Department, Petro China International Co., Ltd., Beijing 100033, China
 全文: PDF(1342 KB)   HTML
摘要:

2018年,中国成为世界第二大液化天然气(LNG)进口国,但由于缺乏议价能力,进口成本居高不下。对2015年首次发布的上海LNG价格指数进行评估,对于加深对这些成本动态的理解至关重要。本文收集整理了自2015年7月1日上海石油天然气交易中心成立至2018年12月31日的LNG价格信息及其他国内外价格播报机构的LNG价格指数数据,采用计量经济学模型,评估了上海LNG价格指数的长期均衡和短期调节机制、对市场冲击的反应以及其对市场信息传递产生的先导-滞后效应。结果表明,上海LNG价格指数已表现出反映平均价格水平和市场变动的长期均衡和短期调整机制,但该指数的市场信息透明度和价格发现能力仍显不足。中国的LNG市场相对独立于其他天然气市场,并且中国的市场化改革正在进行中。预计SHPGX的LNG价格指数对市场参与者交易决策的影响,将随着中国LNG改革的进一步发展、天然气进入-退出机制的形成和天然气交易枢纽流动性的提高而增大。

Abstract

China became the world’s second largest liquefied natural gas (LNG) importer in 2018 but has faced extremely high import costs due to a lack of bargaining power. Assessments of the Shanghai LNG Price Index, first released in 2015, are vital for improving the understanding of these cost dynamics. This paper, using the LNG price index data from the Shanghai Petroleum and Gas Exchange (SHPGX) coupled with domestic and international LNG prices from July 1, 2015 to December 31, 2018, estimates several econometric models to evaluate the long-term and short-term equilibriums of the Shanghai LNG Price Index, the responses to market information shocks and the leading or lagging relationships with LNG and alternative energy prices from other agencies. The results show that the LNG price index of the SHPGX has already exhibited a long-term equilibrium and short-term adjustment mechanisms to reflect the average price level and market movements, but the market information transparency and price discovery efficiency of the index are still inadequate. China’s LNG market is still relatively independent of other natural gas markets, and marketization reforms are under way in China. The influence of the SHPGX LNG price index on the trading decisions of market participants is expected to improve with further development of China’s LNG reforms, the formation of a natural gas entry-exit system, and the increasing liquidity of the hub.

Key wordsliquefied natural gas    price index    Shanghai Petroleum and Gas Exchange    price discovery    market reforms
收稿日期: 2020-01-20      出版日期: 2020-12-21
通讯作者: 董聪     E-mail: cdong@uibe.edu.cn
Corresponding Author(s): Cong DONG   
 引用本文:   
曾叶丽, 董聪, HÖÖK Mikael, 孙金华, 时丹阳. 上海LNG价格指数能否反映中国市场? ——基于价格发现理论的计量经济学研究[J]. Frontiers in Energy, 2020, 14(4): 726-739.
Yeli ZENG, Cong DONG, Mikael HÖÖK, Jinhua SUN, Danyang SHI. Can the Shanghai LNG Price Index indicate Chinese market? An econometric investigation using price discovery theory. Front. Energy, 2020, 14(4): 726-739.
 链接本文:  
https://academic.hep.com.cn/fie/CN/10.1007/s11708-020-0701-4
https://academic.hep.com.cn/fie/CN/Y2020/V14/I4/726
Variables Sample period N Range Mean Standard deviation Skewness Kurtosis J.B.
SHLNGT 2015.7.1–2018.12.31 784 3001.45–5617.19 4031.42 631.02 0.433 1.949 31.847***
OGLNGT 2015.7.1–2018.12.31 1014 3054.50–5571.82 3981.89 646.88 0.658 2.214 40.354***
PLJKMSD 2015.7.1–2018.12.31 875 1345.76–4299.54 2875.41 737.24 0.106 1.545 37.132***
HHNGS 2015.7.1–2018.12.31 900 505.82–2111.68 1082.14 178.39 2.167 9.951 1151.858***
SHLNGWS 2016.11.1–2018.12.31 513 2979.00–7472.00 3802.38 937.97 1.580 5.335 264.980***
SHHNSE 2016.6.1–2018.12.31 622 3045.00–4650.00 3965.47 371.80 -0.120 1.762 27.282***
OGLNGWS 2015.7.1–2018.12.31 996 2660.74–8031.48 3798.73 1101.19 1.751 5.669 332.813***
OGCBMWS 2015.7.1–2018.12.31 983 2522.50–8646.43 3907.35 1194.37 2.038 7.116 576.112***
Tab.1  
Fig.1  
Fig.2  
Variables ADF PP KPSS
t-statistic Probability t-statistic Probability LM statistic Probability
Level SHLNGT -3.1525 0.0234 -3.0329 0.0325 1.3962 <1%
OGLNGT -1.1063 0.7150 -1.3131 0.6253 1.7278 <1%
PLJKMSD -0.6634 0.9877 -0.9922 0.7576 2.8885 <1%
HHNGS -5.1954 0.0000 -5.1849 0.0000 2.2509 <1%
SHLNGWS -0.1570 0.9408 -0.8129 0.8140 1.4323 <1%
SHHNSE -1.7215 0.4198 -1.4895 0.5384 3.6429 <1%
OGLNGWS -1.1181 0.7104 -1.2708 0.6448 1.7596 <1%
OGCBMWS -1.6912 0.4353 -2.5286 0.1090 1.7831 <1%
First difference DSHLNGT -36.4199 0.0000 -39.2146 0.0001 0.1796 >10%
DOGLNGT -11.6324 0.0000 -23.7752 0.0000 0.1672 >10%
DPLJKMSD -16.3254 0.0000 -16.2896 0.0000 0.0868 >10%
DHHNGS -19.2335 0.0000 -19.2093 0.0000 0.0620 >10%
DSHLNGWS -6.9507 0.0000 -6.7557 0.0000 0.0398 >10%
DSHHNSE -26.7850 0.0000 -28.3663 0.0000 0.1625 >10%
DOGLNGWS -14.6233 0.0000 -14.0206 0.0000 0.1055 >10%
DOGCBMWS -24.7127 0.0000 -24.7031 0.0000 0.0290 >10%
R(0) RSHLNGT(0) -34.9778 0.0000 -37.8854 0.0001 0.1332 >10%
ROGLNGT(0) -23.9507 0.0000 -24.8559 0.0000 0.1913 >10%
RPLJKMSD(0) -17.2898 0.0000 -17.3153 0.0000 0.0863 >10%
RHHNGS(0) -19.1769 0.0000 -19.1707 0.0000 0.0504 >10%
RSHLNGWS(0) -7.0765 0.0000 -6.9891 0.0000 0.0615 >10%
RSHHNSE(0) -26.8948 0.0000 -28.9369 0.0000 0.1339 >10%
ROGLNGWS(0) -16.1440 0.0000 -15.7754 0.0000 0.1662 >10%
ROGCBMWS(0) -23.8153 0.0000 -23.8153 0.0000 0.0728 >10%
Tab.2  
Hypothesized No. of CE(s) Eigenvalue Trace Max-Eigen
Statistic Prob.** Statistic Prob.**
None* 0.3827 422.3842 0.0000 145.7028 0.0000
At most 1* 0.3138 276.6813 0.0000 113.7179 0.0000
At most 2* 0.2604 162.9635 0.0000 91.1153 0.0000
At most 3* 0.1372 71.8481 0.0341 44.5730 0.0019
At most 4 0.0508 27.2752 0.8436 15.7423 0.6875
At most 5 0.0314 11.5328 0.9469 9.6239 0.7791
At most 6 0.0042 1.9089 0.9961 1.2841 0.9992
At most 7 0.0021 0.6249 0.4292 0.6249 0.4292
Tab.3  
Cointegration Eq. (1) SHLNGT Cointegration Eq. (2) SHLNGWS Cointegration Eq. (3) SHHNSE
Variables Coefficients Variables Coefficients Variables Coefficients
SHLNGT(-1) 1.000 SHLNGWS(-1) 1.000 SHHNSE(-1) 1.000
OGLNGT(-1) -1.511*** SHLNGT(-1) 0.600*** SHLNGT(-1) -2.029***
PLJKMSD(-1) -0.026 OGLNGT(-1) -0.906*** OGLNGT(-1) 3.065***
HHNGS(-1) -1.235*** PLJKMSD(-1) -0.015 PLJKMSD(-1) 0.052
SHLNGWS(-1) 1.667*** HHNGS(-1) -0.741*** HHNGS(-1) 2.505***
SHHNSE(-1) -0.493*** SHHNSE(-1) -0.296*** SHLNGWS(-1) -3.382***
OGLNGWS(-1) 0.258 OGLNGWS(-1) 0.155 OGLNGWS(-1) -0.523
OGCBMWS(-1) -1.176*** OGCBMWS(-1) -0.705*** OGCBMWS(-1) 2.386***
C 2621.086 C 1572.150 C -5317.222
Tab.4  
VECM estimates VAR estimates
Error correction D(SHLNGT) D(SHLNGWS) D(SHHNSE) Variables R(SHLNGT)) R(SHLNGWS R(SHHNSE)
Cointegration Eq. (1) -0.149*** -0.062*** 0.036
D(SHLNGT(-1)) -0.521*** 0.106** -0.125** R(SHLNGWS(-1)) 0.120 0.440*** 0.117
D(SHLNGT(-2)) -0.129 -0.038 -0.085 R(SHLNGWS(-2)) 0.240 0.796*** 0.120
D(OGLNGT(-1)) -0.097 -0.715*** 0.165 R(SHLNGT(-1)) -0.620*** 0.049 -0.133***
D(OGLNGT(-2)) -0.430 -0.795*** 0.164 R(SHLNGT(-2)) -0.285*** -0.047 -0.076
D(PLJKMSD(-1)) -0.027 -0.073 0.016 R(SHHNSE(-1)) 0.196 -0.019 -0.285***
D(PLJKMSD(-2)) -0.091 0.096 0.050 R(SHHNSE(-2)) -0.081 0.019 -0.224***
D(HHNGS(-1)) -0.067 -0.446*** 0.188* R(PLJKMSD(-1)) 0.107 -0.007 0.003
D(HHNGS(-2)) 0.094 -0.036 -0.077 R(PLJKMSD(-2)) -0.156 0.025 0.027
D(SHLNGWS(-1)) 0.144 0.541*** 0.046 R(OGLNGWS(-1)) -0.221 0.402*** -0.142
D(SHLNGWS(-2)) 0.532 1.313*** 0.038 R(OGLNGWS(-2)) -0.369 -0.410*** -0.122
D(SHHNSE(-1)) 0.215 -0.009 -0.285*** R(OGLNGT(-1)) 0.073 -0.365 0.091
D(SHHNSE(-2)) -0.107 0.004 -0.196*** R(OGLNGT(-2)) 0.131 -0.362** 0.087
D(OGLNGWS(-1)) -0.143 0.536*** -0.121 R(OGCBMWS(-1)) 0.234 -0.017 0.010
D(OGLNGWS(-2)) -0.530* -0.577*** -0.075 R(OGCBMWS(-2)) 0.003 -0.134** 0.025
D(OGCBMWS(-1)) 0.034 -0.172** 0.036 R(HHNGS(-1)) 0.038 -0.069*** 0.049
D(OGCBMWS(-2)) 0.093 -0.157** 0.012 R(HHNGS(-2)) -0.013 -0.027 -0.019
C -0.313 8.854** -1.913 C -0.002 0.002** 0.000
R-squared 0.4157 0.7312 0.2649 R-squared 0.369085 0.6387 0.2792
Adj. R-squared 0.3220 0.6880 0.1470 Adj. R-squared 0.2747 0.5847 0.1714
Sum square resids 685864.50 148761.70 177208.90 Sum square resids 0.0485 0.0064 0.0136
S.E. equation 80.4389 37.4621 40.8874 S.E. equation 0.0213 0.0077 0.0112
F-statistic 4.4361*** 16.9574*** 2.24706*** F-statistic 3.9121*** 11.8234*** 2.5907***
Tab.5  
Fig.3  
Dependent variable: SHLNGWS Dependent variable: SHLNGT Dependent variable: SHHNSE
Eliminated variables Chi-sq Probability Eliminated variables Chi-sq Probability Eliminated variables Chi-sq Probability
SHLNGT 5.9780 0.0503 SHLNGWS 0.3821 0.8261 SHLNGWS 0.6329 0.7287
SHHNSE 0.4716 0.7899 SHHNSE 3.0873 0.2136 SHLNGT 7.1465 0.0281
PLJKMSD 0.1754 0.9160 PLJKMSD 1.1463 0.5637 PLJKMSD 0.1179 0.9427
OGLNGWS 22.0784 0.0000 OGLNGWS 2.3258 0.3126 OGLNGWS 1.6662 0.4347
OGLNGT 10.9545 0.0042 OGLNGT 0.1221 0.9408 OGLNGT 0.3067 0.8578
OGCBMWS 4.6891 0.0959 OGCBMWS 1.8999 0.3868 OGCBMWS 0.0786 0.9615
HHNGS 10.1526 0.0062 HHNGS 0.3791 0.8273 HHNGS 2.3557 0.3079
All 52.8808 0.0000 All 9.6163 0.7897 All 10.3529 0.7360
Tab.6  
Fig.4  
Fig.5  
Fig.6  
LNG Liquified natural gas
SHPGX Shanghai Petroleum and Gas Exchange
PRAs Price reporting agencies
PNG Pipeline natural gas
VECM Vector error correction model
ADF Augmented Dickey-Fuller (test)
VAR Vector autoregression (model)
SHLNGT LNG terminal quotes released by the SHPGX
OGLNGT LNG terminal quotes reported by a Chinese PRA
PLJKMSD Platts DES (Delivered Ex Ship) LNG spot price in Japan/South Korea
HHNGS Henry Hub natural gas spot price
EIA (American) Energy Information Administration
SHLNGWS Chinese national ex-factory price index for LNG released by the SHPGX
SHHNSE LNG alternative energy price index released by the SHPGX
OGLNGWS Chinese national ex-factory price index for LNG released by a Chinese PRA
OGCBMWS Ex-factory price of coalbed methane reported by a Chinese PRA
USD United States dollar
MBtu Million British thermal unit
AIC Akaike information criterion
LR Likelihood ratio
CQPGX Chongqing Petroleum and Gas Exchange
  
1 C Dong, X Dong, Q Jiang, K Dong, G Liu. What is the probability of achieving the carbon dioxide emission targets of the Paris Agreement? Evidence from the top ten emitters. Science of the Total Environment, 2018, 622–623: 1294–1303
https://doi.org/10.1016/j.scitotenv.2017.12.093
2 T Toichi. Development of the natural gas market in the Asia-Pacific region. ASEAN Economic Bulletin, 1989, 6(2): 149–156
https://doi.org/10.1355/AE6-2B
3 C Dong, X Dong, J Gehman, L Lefsrud. Using BP neural networks to prioritize risk management approaches for China’s unconventional shale gas industry. Sustainability, 2017, 9(6): 979
https://doi.org/10.3390/su9060979
4 K Dong, R Sun, C Dong, H Li, X Zeng, G Ni. Environmental Kuznets curve for PM2.5 emissions in Beijing, China: what role can natural gas consumption play? Ecological Indicators, 2018, 93: 591–601
https://doi.org/10.1016/j.ecolind.2018.05.045
5 NAJI Samah Zaki, ABD Ammar Ali. Sensitivity analysis of using diethanolamine instead of methyldiethanolamine solution for GASCO’s Habshan acid gases removal plant. Frontiers in Energy, 2019, 13(2): 317–324
https://doi.org/10.1016/j.ecolind.2018.05.045
6 China’s National Development and Reform Commission. 2019–11–29, available at website of ndrc.gov.cn
7 BP. BP statistical review of world energy. 2019–6-20, available at website of bp.com
8 Z Wang, Q Jiang, X Dong, et al. China’s Oil and Gas Industry Development Analysis and Outlook Report Blue Book (2018–2019). Beijing: China Petrochemical Press, 2019
9 China’s General Administration of Customs. Statistical bulletin (6). 2019–1-14, available at website of customs.gov.cn
10 X Tong, J Zheng, B Fang. Strategic analysis on establishing a natural gas trading hub in China. Natural Gas Industry B, 2014, 1(2): 210–220
https://doi.org/10.1016/j.ngib.2014.11.014
11 X Shi, H M Padinjare Variam. Gas and LNG trading hubs, hub indexation and destination flexibility in East Asia. Energy Policy, 2016, 96: 587–596
https://doi.org/10.1016/j.enpol.2016.06.032
12 V Vivoda. LNG import diversification and energy security in Asia. Energy Policy, 2019, 129: 967–974
https://doi.org/10.1016/j.enpol.2019.01.073
13 O Johnson. The Price Reporters: a Guide to PRAS and Commodity Benchmarks. New York: Routledge, 2017
14 K D Garbade, W L Silber. Price movements and price discovery in futures and cash markets. Review of Economics and Statistics, 1983, 65(2): 289–297
https://doi.org/10.2307/1924495
15 C M Oellermann, B W Brorsen, P L Farris. Price discovery for feeder cattle. Journal of Futures Markets, 1989, 9(2): 113–121
https://doi.org/10.1002/fut.3990090204
16 A E Bopp, G M Lady. A comparison of petroleum futures versus spot prices as predictors of prices in the future. Energy Economics, 1991, 13(4): 274–282
https://doi.org/10.1016/0140-9883(91)90007-M
17 E Schultz, J Swieringa. Price discovery in European natural gas markets. Energy Policy, 2013, 61: 628–634
https://doi.org/10.1016/j.enpol.2013.06.080
18 S D Bekiros, C G Diks. The relationship between crude oil spot and futures prices: cointegration, linear and nonlinear causality. Energy Economics, 2008, 30(5): 2673–2685
https://doi.org/10.1016/j.eneco.2008.03.006
19 S Yousefi, I Weinreich, D Reinarz. Wavelet-based prediction of oil prices. Chaos, Solitons, and Fractals, 2005, 25(2): 265–275
https://doi.org/10.1016/j.chaos.2004.11.015
20 C Chang, C Lee. Do oil spot and futures prices move together? Energy Economics, 2015, 50: 379–390
https://doi.org/10.1016/j.eneco.2015.02.014
21 E Schultz, J Swieringa. Catalysts for price discovery in the European Union emissions trading system. Journal of Banking & Finance, 2014, 42(5): 112–122
https://doi.org/10.1016/j.jbankfin.2014.01.012
22 Y Tse, G G Booth. Information shares in international oil futures markets. International Review of Economics & Finance, 1997, 6(1): 49–56
https://doi.org/10.1016/S1059-0560(97)90013-7
23 D Lien, K Shrestha. Hedging effectiveness comparisons: a note. International Review of Economics & Finance, 2008, 17(3): 391–396
https://doi.org/10.1016/j.iref.2006.12.002
24 L T Zhao, J L Yan, L Cheng, Y Wang. Empirical study of the functional changes in price discovery in the Brent crude oil market. Energy Procedia, 2017, 142: 2917–2922
https://doi.org/10.1016/j.egypro.2017.12.417
25 I A Moosa, P Silvapulle. The price-volume relationship in the crude oil futures market some results based on linear and nonlinear causality testing. International Review of Economics & Finance, 2000, 9(1): 11–30
https://doi.org/10.1016/S1059-0560(99)00044-1
26 R K Kaufmann, B Ullman. Oil prices, speculation, and fundamentals: interpreting causal relations among spot and futures prices. Energy Economics, 2009, 31(4): 550–558
https://doi.org/10.1016/j.eneco.2009.01.013
27 M Alzahrani, M Masih, O Altiti. Linear and nonlinear Granger causality between oil spot and futures prices: a wavelet based test. Journal of International Money and Finance, 2014, 48: 175–201
https://doi.org/10.1016/j.jimonfin.2014.07.001
28 H Park, J W Mjelde, D A Bessler. Price interactions and discovery among natural gas spot markets in North America. Energy Policy, 2008, 36(1): 290–302
https://doi.org/10.1016/j.enpol.2007.09.012
29 F Asche, B Misund, M Sikveland. The relationship between spot and contract gas prices in Europe. Energy Economics, 2013, 38: 212–217
https://doi.org/10.1016/j.eneco.2013.02.010
30 H Ghoddusi. Integration of physical and futures prices in the US natural gas market. Energy Economics, 2016, 56: 229–238
https://doi.org/10.1016/j.eneco.2016.03.011
31 K Shrestha. Price discovery in energy markets. Energy Economics, 2014, 45: 229–233
https://doi.org/10.1016/j.eneco.2014.06.007
32 M Caporin, F Fontini. The long-run oil-natural gas price relationship and the shale gas revolution. Energy Economics, 2017, 64: 511–519
https://doi.org/10.1016/j.eneco.2016.07.024
33 J Stern, H V Rogers. The Dynamics of a Liberalised European Gas Market. Oxford: Oxford Institute for Energy Studies, 2014
34 J Stern, H V Rogers. The Transition to Hub-Based Gas Pricing in Continental Europe. Oxford: Oxford Institute for Energy Studies, 2011
35 P Heather. European Traded Gas Hubs: a Decade of Change. Oxford: Oxford Institute for Energy Studies, 2019
36 P Heather. “A Hub for Europe” The Iberian promise? Oxford: Oxford Institute for Energy Studies, 2019
37 P Heather. The Evolution of European Traded Gas Hubs. Oxford: Oxford Institute for Energy Studies, 2015
38 P Heather. Continental European Gas Hubs: Are they fit for purpose? Oxford: Oxford Institute for Energy Studies, 2012
39 P Heather, B Petrovich. European Traded Gas Hubs: an Updated Analysis on Liquidity, Maturity and Barriers to Market Integration. Oxford: Oxford Institute for Energy Studies, 2017
40 B Petrovich. European Gas Hubs Price Correlation: Barriers to Convergence? Oxford: Oxford Institute for Energy Studies, 2014
41 J Stern. International gas pricing in Europe and Asia: a crisis of fundamentals. Energy Policy, 2014, 64: 43–48
https://doi.org/10.1016/j.enpol.2013.05.127
42 D Y Zhang, M Shi, X P Shi. Oil indexation, market fundamentals, and natural gas prices: an investigation of the Asian premium in natural gas trade. Energy Economics, 2018, 69: 33–41
https://doi.org/10.1016/j.eneco.2017.11.001
43 T Wang, D Zhang, Q Ji, X Shi. Market reforms and determinants of import natural gas prices in China. Energy, 2020, 196: 117105
https://doi.org/10.1016/j.energy.2020.117105
44 Y Kim. Obstacles to the creation of gas trading hubs and a price index in Northeast Asia. Geosystem Engineering, 2019, 22(2): 59–71
https://doi.org/10.1080/12269328.2018.1452637
45 T Gao. Econometric Analysis Methods and Modeling. Beijing: Tsinghua University Press, 2006
46 Z Li, W Pan. Econometrics. 4th ed. Beijing: Higher Education Press, 2015
47 P M Michael. Econometrics: A Modern Introduction. Beijing: Machinery Industry Press, 2009
48 C Miriello, M Polo. The development of gas hubs in Europe. Energy Policy, 2015, 84: 177–190
https://doi.org/10.1016/j.enpol.2015.05.003
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed