Is China's natural gas market globally connected?

Energy Policy 132 (2019) 940–949

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Energy Policy journal homepage: www.elsevier.com/locate/enpol

Is China's natural gas market globally connected? Jian Chai

a,b,d

a,∗

c,∗∗

, Zhaohao Wei , Yi Hu

d

, Siping Su (Sue) , Zhe George Zhang

T d

a

International Business School, Shaanxi Normal University, Xi'an, 710119, China School of Economics & Management, Xidian University, Xi'an, 710126, China School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China d Department of Decision Sciences, College of Business and Economics, Western Washington.University, Bellingham, WA, 98225, USA b c

A R T I C LE I N FO

A B S T R A C T

Keywords: Natural gas market LNG Price linkage DCC-GARCH NARDL

The rise of the shale gas industry and the need for clean, low-carbon energy transformation in China has meant that the relationship between domestic and foreign natural gas markets has become closer. Therefore, identifying the changing relationships between the major global natural gas markets and China's natural gas market has important practical value for the current Chinese domestic natural gas pricing system. This paper used the DCC-GARCH-NARDL-ARDL-ECM as the analytical framework to study these relationships, from which it was found that there was no unified global natural gas market and that China's natural gas market was not yet aligned with this market. It was shown that there was no significant asymmetry in the impact of international gas prices on China's gas prices as no flexible adjustment measures were found in China's imported natural gas pricing mechanism. Moreover, while international natural gas prices were found to have a significant impact on China's natural gas prices over the long term, there were obvious regional differences over the short term. Accordingly, new pricing policies should be designed to promote market-oriented natural gas pricing reforms in acknowledging features such as asymmetry, the difference between imported gas sources and the periodicity of price adjustment.

1. Introduction The 2019 BP Energy Outlook (BP, 2019) and numerous other studies have claimed that China is expected to have a rapidly increasing gas consumer demand in the next few years (Zeng and Li, 2016; Zhang and Yang, 2015; Zou et al., 2018). However, because of China's limited natural gas resources and independent mining capacity restrictions, China will need to import large quantities of natural gas in the future (Lin and Wang, 2012; Wang et al., 2013, 2016), with the dependence on foreign sourced natural gas expected to rise from 38% in 2017 to 43% in 2040 (BP- China Special Topic, 2019). Although China has established two natural gas trading centers in recent years and gradually opened up its natural gas market pricing system, no complete market mechanism driven by the relationship between supply and demand has yet been established (Paltsev and Zhang, 2015). Therefore, because there is no mature Chinese natural gas pricing system, the large imported natural gas quantities will have a significant impact on China's natural gas market prices, and may even threaten the safety of China's natural gas supply. At present, because of restricted transportation channels, the natural gas pipeline market has distinct regional

∗

characteristics. On the contrary, due to the maturity of LNG transportation technologies, such as shipping, tanker transportation and lowtemperature railway transportation, efficient and low-cost transportation mode has accelerated the globalization and financialization of LNG. Compared with pipeline natural gas, LNG has more significant advantages in solving the imbalance between supply and demand in the international natural gas market. Based on this, because of its stronger liquidity, this paper takes the LNG market price as an example to measure the risks in the dynamic fluctuation spillover effects in the major global natural gas markets to assess the impact these effects could have on price volatility in China's natural gas market. By studying the pricing relationships between China and the global natural gas market, this paper seeks to provide an important reference for China to gradually connect with the global natural gas market and reform its natural gas pricing system. Previous studies have found that because there was low integration in the global natural gas market before 2011, the global natural gas market trade had a relatively limited impact (Siliverstovs et al., 2005; Geng et al., 2014), primarily because of the different natural gas market pricing mechanisms and the price discrimination and arbitrage

Corresponding author. Corresponding author. E-mail addresses: [email protected] (Z. Wei), [email protected] (Y. Hu).

∗∗

https://doi.org/10.1016/j.enpol.2019.06.042 Received 11 March 2019; Received in revised form 27 May 2019; Accepted 22 June 2019 0301-4215/ © 2019 Elsevier Ltd. All rights reserved.

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J. Chai, et al.

The main contributions of this research are as follows:

restrictions between different regions. When there are unreasonable spreads in different natural gas markets, price discrimination and arbitrage restrictions prevent global gas dealers from participating in different natural gas markets to conduct corresponding arbitrage activities (Ritz, 2014). However in the past 5–8 years, the situation has begun to change; for example, due to the rapid growth in shale gas production, the United States has gradually transformed from a natural gas importing country into an important natural gas exporter, and, using its cost and price advantages, the US has demonstrated the potential replacement capabilities of shale gas for other higher-priced natural gas exporters in the natural gas markets in Europe and Asia (Moryadee et al., 2014; Kumar et al., 2011). Further, Japan has been attempting to decouple the market price for LNG from the price of imported oil with the aim of establishing a more reasonable reference price for the Asian natural gas market (Vivoda, 2014). The possible further deterioration of the Russian-Ukrainian dispute, the massive supply of global LNG, and other events have also caused major changes in supply and demand patterns and the regional distribution in the global natural gas market (Egging and Holz, 2016). Therefore, as a result of this rapid integration of the LNG market, the competition between LNG trade exporters has shifted from regional only markets to global markets, with the links between the LNG markets around the world becoming closer (Chen et al., 2016). Due to the high liquidity of LNG and price signal characteristics, it is expected that the expanding global LNG market will lead to an integration of the global natural gas market (Barnes and Bosworth, 2015). With this gradual increase in the degree of correlation risk between the world's natural gas markets, China needs to establish a price mechanism that fully reflects market supply and demand and links with the changing international natural gas market to protect the interests of natural gas importers, suppliers, and consumers (Shi et al., 2017). Therefore, it is of great practical value to identify the changes in the international natural gas market and their effects on China's natural gas market to provide a scientific reference for China's natural gas market reforms. To date, there have been several studies on the interaction between markets and the price reform problems in China's natural gas market. For example, Shi and Variam (2016) used the Nexant World Gas Model to study the impacts of the changes in the East Asian pricing benchmarks and natural gas contract flexibility on East Asian regional and global gas markets, Zhang et al. (2018) used ARDL and other research methods to investigate the natural gas demand price elasticity in different industries in China from a multi-sectoral perspective and found that except for the residential sector, the long term natural gas demand price elasticity in other Chinese sectors was opposite to that in developed countries, and Shi (2017) reviewed the successful development of the European natural gas trading hub and discussed its implications on China's natural gas market development. In other recent studies, Dong et al. (2017) analyzed the main problems and challenges faced by China's natural gas industry from the perspective of historical development, and proposed that a shareholding system reform should be the focus of China's future natural gas market reform, and Chai et al. (2018) used a Bayesian structural equation model to compare domestic and international natural gas markets, and found that compared with the international market, China's natural gas was expensive and lacked price elasticity. Therefore, in recent years, there has been significant research focused on comparing the different natural gas pricing mechanisms in China and developed countries and analyzing the possible price reforms needed in China's natural gas market. However, as some studies were based on only qualitative research, the conclusions lacked a quantitative assessment of the representative areas and the price selections in the international natural gas market. Further, as only static or linear relationships have been examined when assessing the influences between markets, the complex market interactions have not been systematically considered. Therefore, to rectify these research gaps, this paper studied the above problems from dynamic, asymmetric, and nonlinear perspectives.

(1). This paper examined the recent changes in the links between the world's natural gas markets and characterized the time-varying characteristics of these relationship changes. Different from other similar studies, this paper provides a quantitative basis for dividing the regional distribution of the world's natural gas market, and confirms the current low degree of global integration. (2). The representative price for the international natural gas market was selected based on the risk linkage intensities between the world's natural gas market regions, and with a view to the liberalization of China's unconventional natural gas market pricing. The specific impact of the price fluctuations in the world's natural gas market on China's natural gas market prices are measured, thereby confirming the regional and periodic effects from multiple perspectives. Based on these results, policy recommendations are proposed for the long-term sustainable development of China's natural gas industry. This paper's analytical framework is based on the DCC-GARCHNARDL-ARDL- ECM, under which the dynamic links between the natural gas markets in the major regions of the world and the specific impact of international natural gas market price fluctuations on China's natural gas market prices are examined and the possible reasons for these changes and the implications for China's future natural gas market analyzed. The remainder of this paper is organized as follows. Section 2 employs the DCC-GARCH model to measure and analyze the dynamic changes in the price linkage relationships between the different world natural gas markets, identifies the causes, and then determines a representative international natural gas price based on the results from the international natural gas market divisions. Section 3 combines the analysis results from Section 2 and then uses an ARDL series model to measure the specific effects of international natural gas price fluctuations on the asymmetry and symmetry of the Chinese natural gas market over the long and short-term. Section 4 gives the conclusion, summarizes the reference significance and important inspirations derived from the research, and provides guidance on China's future natural gas market pricing system reforms. 2. The price linkage effect between major international natural gas markets 2.1. DCC-GARCH theoretical model Before studying the impact of price volatility in the international natural gas market on China's natural gas market, this paper first measured the risk volatility spillover relationship between the major international natural gas markets, analyzed the changes in the degree of integration between the major international natural gas markets in recent years, and selected a representative price for the international natural gas market. As the price linkage relationships between China and the international major natural gas markets were surmised to have dynamic time-varying characteristics, this study employed a DCCGARCH model to correlate the dynamic change process. The DCC-GARCH model proposed by Engle (2002) was chosen, which is expressed as follows:

rt |φt − 1 ∼ N (0, Ht )

(1)

Ht = Dt ρt Dt

(2)

ρt = Jt Qt Jt

(3)

Qt = (1 − θ1 − θ2) Q + θ1 Qt − 1 + θ2 ηt − 1 ηt′− 1

(4)

where rt is the sequence of yields for k different assets, φt − 1 is the set of information that rt can collect at time t, Ht is the conditional covariance 941

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J. Chai, et al. 1/2 1/2 matrix, Dt = diag {σ11, t , ⋯, σkk , t } is the diagonal matrix for k-dimensional volatility at time t obtained from the GARCH model, and ρt is the dynamic conditional correlation matrix obtained after obtaining the standardized residual vectors. In formula (3) and formula (4), −1/2 −1/2 Jt = diag {q11, t , ⋯, qkk , t } , where qii, t is the (i,i) element for Qt , Qt = (qij , t ) is the conditional covariance matrix for the standardized residual sequence, Q is the unconditional covariance matrix for the standardized residual ηt , and θ1 and θ2 are non-negative scale parameters that satisfy 0 < θ1 + θ2 < 1.

Table 2 Autocorrelation test of the square value and the absolute value for the Japanese yield series.

Q(5) Q(10)

Japan

38.820 (0.000) 85.726 (0.000)

70.560 (0.000) 141.98 (0.000)

one to five order lag and a one to ten order lag at a significance level of 5% was chosen, the autocorrelation tests for the above yield sequences for all countries and regions except Japan were significantly autocorrelated, indicating that the yield sequences in each country or region (excluding Japan) had obvious volatility agglomeration; that is, there were often large fluctuations followed by large fluctuations, with the smaller fluctuations following the characteristics of smaller fluctuations. However, as the test results for Japan's natural gas market yield sequence found no autocorrelation at the 5% significance level, a series of autocorrelation tests were conducted on the square value and absolute value of Japanese natural gas yield sequences; therefore, Japan's natural gas market yield sequence was expressed as Japan^2 and |Japan| for the square value and the absolute value, the specific test results for which are shown in Table 2. As can be seen in Table 2, the Japanese natural gas price yield was not sequence independent. Although there was no autocorrelation found in the original sequence, there was a certain dependence, which explained the volatility aggregation in the sequence itself. In summary, the logarithmic LNG price yield sequence in the major countries and regions selected in this paper were observed to have obvious “spikes and thick tails” and volatility aggregation. Therefore, the GARCH models were used to fit these sample characteristics.

2.2. Data In the study of the price linkages between the natural gas markets in the different regions, this paper screened the world's major natural gas consumption regions, export regions, and those regions that have introduced market competitive pricing mechanisms and have developed natural gas trading centers. Specifically, the countries and regions selected were the Far East, the Middle East, the United States, Europe, China, and Japan. For the natural gas price index, the United States and the Middle East use the FOB price for spot propane as the representative price of their respective natural gas markets, Japan, Europe and the Far East use the CIF spot propane price as the representative price, and China uses the domestic LNG spot benchmark price. As China fully liberalized its unconventional gas market pricing in 2014, it is important to fully assess the reform of China's natural gas market price system and re-examine the domestic and international natural gas market relationships. Therefore, the data interval selected from the Wind database was from April 24, 2014 to March 5, 2018, for which there were 855 sets of observations in the selected samples after excluding the missing data from individual days. 2.3. DCC-GARCH model estimation

2.3.2. Univariate GARCH model construction and estimation To establish the univariate GARCH model, the SC criterion and the AIC criterion were employed to determine the optimal lag order. In addition to Japan, the ARMA model was used to establish the univariate GARCH mean equation for the yield sequences for the other countries and regions. However, as the Japanese yield series were not autocorrelated, it was not possible to establish a mean equation that directly eliminated the mean; therefore, through a comparison and analysis of the estimation results, the optimal mean value equations for each country were determined as follows.

2.3.1. Data and unit root test The yield sequences for each market were obtained by taking the logarithm of the original data and determining the differences, as shown below:

ri, t = ln(Pi, t ) − ln(Pi, t − 1)

Japan^2

(5)

where Pi,t represents the natural gas market price of the i-th market on the t-th day, and Pi,t−1 represents the natural gas market price of the i-th market on the t-1st day. The descriptive statistical results for the sample yield sequences are shown in Table 1. As can be seen in Table 1, all yield sequences passed the ADF test, indicating that the natural gas market yield sequences in all regions or countries were stationary sequences, and therefore the pseudo regression problem that can be generated from direct modeling was able to be avoided. Further, the kurtosis of these sequences was found to be much larger than 3, which indicated that these variables were generally characterized by "spikes and thick tails". As can be seen in the results of the Q(5) and Q(10) statistics when a

1).The Far East: Rt = −0.092Rt − 2 + at

(6)

2).Japan: Rt = −0.074 + at

(7)

3).The United States: Rt = 0.093Rt − 8 − 0.089at − 5 + at

(8)

4).China: Rt = 0.338Rt − 1 + 0.084Rt − 2 + at

(9)

5).North − western Europe: Rt = −0.134Rt − 3 + 0.125at − 1 + at

(10)

6).The Middle East: Rt = −0.092Rt − 2 + 0.096Rt − 4 + at

(11)

Table 1 Statistical table for the natural gas market yield rate in the various countries/regions.

Mean Std.Dev Skewness Kurtosis Jarque-Bera Probability Q(5) Q(10) ADF test Observations

China

US

Far East

Japan

Middle East

Europe

−0.063597 1.335580 −0.917528 20.91364 11551.97 0.000000 162.41 (0.000) 180.46 (0.000) −19.840*** 855

−0.045039 3.089999 −0.061816 8.305286 1003.248 0.000000 12.714 (0.026) 26.123 (0.004) −27.522*** 855

−0.074820 2.615283 0.318262 6.021945 339.7669 0.000000 16.690 (0.005) 20.357 (0.026) −22.127*** 855

−0.074356 2.696051 0.319692 6.344967 413.1649 0.000000 9.7274 (0.083) 12.662 (0.243) −28.097*** 855

−0.064216 2.800151 −0.261413 9.675510 1597.275 0.000000 18.657 (0.002) 24.190 (0.007) −22.847*** 855

−0.064302 3.110642 0.610948 13.33672 3859.644 0.000000 34.908 (0.000) 39.979 (0.000) −19.432*** 855

Note: *** indicates statistically significant at a 1% level. 942

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The autocorrelation tests were conducted on the residual sequence of the above mean value equations, from which it was found that the P value corresponding to the Q statistic was larger, which indicated that the null hypothesis (there was no autocorrelation for the residual sequence) could not be rejected. Then, an ARCH effect test was conducted for the residual sequence obtained from the above equation. For all other countries and regions except Japan, the residual sequence of the equation was observed to have a significant ARCH effect, indicating that the GARCH model could be established for the analysis. For Japan, however, because there was no autocorrelation found in its yield sequence, the ARMA model was not established; therefore, an autocorrelation test was performed on the square value of the residual sequence to test whether the residual sequence had an ARCH effect, from which it was found that there was significant autocorrelation in Japan's de-mean residual squared sequence, which indicated that as there was also a significant ARCH effect in Japan's residual square sequence, the GARCH model could be established for the analysis. Based on the autoregressive mean equation determined above, the GARCH(1,1) model combined with the maximum likelihood method was used to re-estimate the specific yield fluctuations, the results for which are shown in Table 3. As can be seen from the estimation results, the residual sequences for the above GARCH model established for the various countries and regions were found to have no autocorrelation or ARCH effects; that is, the established GARCH model was considered reasonable for the setting of the mean and variance. The parameter estimates in the table were all passed through the Z test, from which it was found that the Middle East had a small α + β value and the α + β estimates for the other countries and regions were larger, which indicated that most countries/regions had significant sustained LNG price fluctuation characteristics. It is worth noting that as the β for Japan and the Far East were somewhat larger than in the other countries/regions, the Japanese and Far East natural gas market price fluctuations were relatively long; that is, when faced with the same shock, the natural gas market in these two regions tended to absorb and reflect the shock for a longer period.

Table 4 DCC-GARCH model estimation results. θ1 China - Japan China - US China - Far East China - Eur China - Middle East Japan - Far East Japan - US Japan - Eur Japan - Middle East Far East - US Far East - Eur Far East - Middle East US - Eur US - Middle East Eur - Middle East

Β(z-Statistic)

α+β

0.2198 (11.43) 0.1202 (8.00) 0.0422 (5.41)

0.5355 (20.16)

0.7553

US Japan

0.4069 (14.68) 0.2956 (4.96) 0.0717 (4.66)

0.9811 0.9922

Middle East Far East

3.9974 (8.32) 0.0794 (4.51)

0.2248 (7.1) 0.0440 (5.17)

North-western Europe

0.1233 (3.58)

0.0699 (9.88)

0.8609 (49.40) 0.9500 (113.88) 0.2803 (3.45) 0.9464 (103.28) 0.9206 (111.26)

China

Max

0.1031 0.1342 0.1378 0.1543 0.1245 0.8593 0.3989 0.4464 0.0589 0.4212 0.4626 0.0996 0.2701 −0.0524 0.4066

0.1031 0.1342 0.1378 0.1543 0.1245 0.9913 0.3989 0.4464 0.9621 0.4212 0.4626 0.9672 0.5056 0.6831 0.4066

changes in the previous period, and the correlation changes between the regions had strong sustainable characteristics. Second, the changes in the dynamic correlation coefficient indicated that the correlation coefficients between the natural gas markets in the United States and northwest Europe, the United States and the Middle East, Japan and the Far East, Japan and the Middle East, and the Far East and the Middle East had obvious time-varying characteristics. However, the correlation coefficient for the natural gas market in the other countries/regions had few changes and the time-varying characteristics were almost non-existent. Finally, the dynamic correlation coefficient value indicated that there was a positive correlation coefficient between the different natural gas markets; that is, the yields between the different natural gas markets generally had a positive correlation. However, there were significant differences found between the different natural gas markets for the correlation sizes, as shown in Figs. 1-3, from which it can be seen that within the whole sample interval, there was a large dynamic condition correlation coefficient between the natural gas markets in the Far East and the Middle East, with the coefficient interval being mainly concentrated between 0.75 and 0.90. As an important member of the Far East, Japan was also observed to maintain a strong price linkage relationship with the natural gas markets in the Far East and the Middle East. The generally higher correlation coefficient indicated that the yields between the natural gas markets in these countries/regions had a strong positive correlation with fluctuations; that is, there was a high degree of convergence between the price movements in these markets, with the volatility in one market being transmitted to another market and with the degree of integration between these markets being higher. The estimation results also showed that the dynamic correlation coefficient fluctuation range between these natural gas markets was relatively small and was generally concentrated between 0.75 and 0.9, which indicated that the long-term relationship between these markets was relatively stable. The dynamic correlation coefficient was mainly was from 0.35 to 0.45 between the United States and European markets, and between the United States, European and Japanese, and the Far Eastern and the Middle Eastern markets, which indicated that there was a certain degree of risk transfer between the markets, but that the overall market integration was still relatively weak. China's dynamic correlation coefficient with Japan, the United States, the Far East, Europe and the Middle East was relatively small at around 0.1, which indicated that there was significant segmentation between China's natural gas market and the natural gas markets of the other countries/ regions, and that there was a large price trend deviation; that is, the degree of integration between China's natural gas market and other natural gas markets was relatively low. In summary, first, the deepest integration was found in the Far East

Table 3 GARCH (1,1) model estimation results. Α(z-Statistic)

8.84*10^(-8) 7.24*10^(-10) 1.22*10^(-8) 7.81*10^(-10) 5.64*10^(-9) 0.0529 3.76*10^(-8) 4.58*10^(-9) 0.1086 2.12*10^(-9) 3.67*10^(-9) 0.1126 0.0062 0.0529 2.65*10^(-8)

Min

Note: Min and Max in the table respectively indicate the minimum and maximum value of the estimated volatility in the different countries and regions.

2.3.3. DCC-GARCH model parameter estimation Based on the above estimation results, fifteen different DCC-GARCH models were constructed to estimate the size and dynamic changes in the fluctuation relationships between the gas markets in the different regions of the world, the specific results for which are shown in Table 4. First, the estimation results indicated that the θ2 coefficient values were generally small between the natural gas markets in the countries/ regions, and that the standardized residual product variation in the lag phase between the different natural gas markets had a small influence on the change in the dynamic correlation coefficient. However, the estimate value of the θ1 coefficient was generally large, indicating that the variations in the dynamic correlation coefficient between the different natural gas markets was largely affected by previous market changes; that is, the correlation coefficient between the natural gas markets between the countries/regions was mainly affected by the

W(z-Statistic)

0.9902 0.9816 0.9918 0.9871 0.9866 0.8694 0.9839 0.9807 0.8054 0.9832 0.9813 0.8119 0.9866 0.6492 0.9774

θ2

0.5051 0.9904 0.9905

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Fig. 1. Dynamic conditional correlation coefficients.

the most. On the other hand, limited by the different natural gas market pricing mechanism, the risk transfer relationship between European and the United States markets is further weakened. During the sample period, the United States uses natural gas spot and futures markets to price natural gas. The price of natural gas reflects its own supply-demand relationship to a large extent. However, Europe still mainly adopts the method of linking the price of natural gas with the price of international crude oil. The gas price reflects more about the relationship between natural gas and its alternative energy. Therefore, under different pricing models, there is no strong linkage between the two markets. Finally, China's natural gas market maintained a relatively independent and stable relationship with the other countries/regions, primarily because of its dual natural gas market pricing system. And in the pricing of natural gas, China still adopts the method of linking with fuel oil and liquefied petroleum gas (weights are 60% and 40% respectively), and has not formed a pattern of pricing competition between “gas and gas”. Although China has gradually liberalized its market pricing for LNG and other unconventional natural gas in recent years, the domestic LNG retail price growth rate has remained slow and continues to be lower than the import price, which has had some serious consequences. The price of residential gas in China is strictly controlled, which means that the price of imported LNG gas into the country's natural gas pipeline network has been restricted to some extent. Further as the price adjustment scope is still limited, the price of domestic gas is unable to truly reflect the changes in the natural gas market supply and demand. In addition, because Japan is an important member of the Far East,

and the Middle East natural gas markets because as the Far East had imported large natural gas quantities from the Middle East in recent years, the two regions had a close relationship in terms of the supply and consumption of natural gas, which had led to a strong natural gas market convergence effect. Second, the United States and Europe were found to respectively have a relatively strong price linkage relationship with the Middle East and the Far East, which was mainly because the United States had gradually increased its LNG exports to the Middle East and the Far East in recent years, which had strengthened its links with the Far East and the Middle East natural gas markets, and because Europe had been importing natural gas from the Middle East in recent years, which has a similar natural gas supply structure to the Far East; therefore, there was a certain positive correlation observed between Europe and the natural gas markets in both the Far East and the Middle East over the long term. However, the price linkage relationship between the United States and European natural gas markets had gradually declined in recent years. As shown in Fig. 4, the price linkage relationship between the two countries decreased significantly during the period from the end of 2015 to the first half of 2016. On the one hand, this is mainly because the United States has gradually changed from a natural gas importing country to a natural gas exporting country in recent years, which has led to a weakening of the ability of LNG to regulate gas prices across the region between the United States and Europe. From 2015 to 2016, it was the year in which the United States exported the largest increase in natural gas (a year-on-year increase of 450%), and it was also the year in which the US natural gas dependence on foreign countries has fallen

Fig. 2. Dynamic condition correlation coefficients. for the Middle East and the Far East between Japan and the Far East. 944

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Fig. 3. Dynamic conditional correlation coefficients.

there is a clear convergence between its natural gas price changes and the prices changes in the Far East and the Middle East. Therefore, this paper proposes that Japan's natural gas prices could be representative of natural gas prices in both the Middle East and the Far East to some extent. In the following study, considering the availability of data and in order to minimize the redundancy of the model, this paper will select Japan's natural gas market price as a representative price of the natural gas market in the Middle East and the Far East for further research. The above analysis results clearly indicated that there was not yet a globally integrated natural gas market. The natural gas market in the Far East and the Middle East was found to have the highest integration degree, and their correlation coefficient had obvious time-varying characteristics and long-term stability. However, as the integration degree between the natural gas markets in the United States, Europe and the other regions was found to be relatively low, these cannot be considered as a unified whole. It was also apparent that China continued to have a separate relationship with the other regions' natural gas markets. Because of the price link relationships between the different natural gas markets and the further modeling requirements, the natural gas market prices in Japan, Europe and the United States were selected as being representative of the international natural gas market prices for the next study.

Table 5 Granger causality test results. The null hypothesis

F-statistics

Prob

US does not Granger Cause CHINA CHINA does not Granger Cause US Japan does not Granger Cause CHINA CHINA does not Granger Cause Japan EUR does not Granger Cause CHINA CHINA does not Granger Cause EUR

9.0070 0.0356 7.8225 0.2684 10.4839 0.2302

0.0028 0.8504 0.0004 0.7646 0.0013 0.6315

Granger causality relationship were first examined between the Chinese natural gas market yield sequences and the natural gas market yield sequences in Japan, the US and Europe, the specific test results for which are shown in Table 5. As can be seen, there was a one-way Granger causality found between the natural gas market yield series in China and Japan, the United States, and Europe. At a 1% significance level, as the natural gas price yield series in Japan, the US and Europe were shown to be the Granger reasons for the Chinese natural gas market yield series, the introduction of these variables better explained the price fluctuations in China's natural gas market. However, as the fluctuations in China's natural gas market price did not explain the fluctuations in the price of natural gas in the other regions in the Granger analysis, there was a weak risk volatility spillover effect between China and Japan, the United States and Europe, indicating that the price changes in the

2.3.4. Granger causality test Before the analysis of the impact of price volatility in the international natural gas market on China's natural gas market price, the

Fig. 4. Dynamic condition correlation coefficients, between Japan and the Middle East, between the United States and Europe. 945

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natural gas markets in Japan, the United States and Europe affected the price changes in China's natural gas market. Therefore, it was necessary to further analyze the specific impact of the price changes in the world's natural gas markets on the price changes in China's natural gas market.

Table 6 Unit root test results. Test sequence

t-Statistic

Original sequence: CHN JPN EUR US First order difference: △CHN △JPN △EUR △US

3. Impact of international natural gas price shocks on China's natural gas prices 3.1. NARDL model Because economic activities are complex, the interactions between economic variables are usually presented in an asymmetric form. Therefore, based on the general to the special principle, it was assumed that the impacts of international natural gas price fluctuations on China's natural gas price were asymmetric, with the following research conducted based on the nonlinear asymmetric cointegration method proposed by Shin (Shin et al., 2014). The general form for the NARDL model is as follows:

Prob

−1.7176 −0.7555 −1.1835 −1.6556

0.4151 0.8209 0.6725 0.4457

−7.0884*** −4.8877*** −7.0347*** −7.8489***

0.0000 0.0003 0.0000 0.0000

Note: *** indicates statistically significant at 1% significance level. p−1

Δyt = c + ρyt − 1 + θx t − 1 +

q−1

∑ ri Δyt−i + ∑ (πi+Δxt+−i + πi−Δxt−−i) + εt i=1

i=0

(16)

p−1

Δyt = c + ρyt − 1 + θ+x t+− 1 + θ−x t−− 1 +

∑ ri Δyt−i

3.2. Empirical analysis

i=1 q−1

∑ (πi+Δxt+−i + πi−Δxt−−i) + εt

+

In reference to the frequency requirements for the ARDL series model for the sample data, in this part of the study, the LNG monthly price data for CHN①, JPN, US and EUR from January 2014 to June 2017 were selected from the Wind database as the research samples.

(12)

i=0

where Δyt is the change in the price of China's LNG market in the t-th period, p and q are the maximum order of the distribution lag of the dependent variable and the independent variable, and x t = x 0 + x t+ + x t−, x t indicates the explanatory variables that may affect China's natural gas market price fluctuations; that is, the Japanese natural gas price, the US natural gas price, and the European natural gas price, in which x 0 is the initial value of the explanatory variable, and x t+ and x t− are the positive and negative impacts that x t is subjected to, which can be specifically expressed as follows. t

t

t

t

3.3. Unit root test To avoid a pseudo-regression problem in the modeling, the ADF test was used to test the stability of the original sequence and its first-order difference sequence for the selected data, the results for which are shown in Table 6. The unit root test results showed that the original sequences for CHN, JPN, EUR and US did not pass the stationarity test; however, all the first-order difference sequences were stable, which was consistent with the modeling requirements of the NARDL and ARDL models.

x t+ = ∑ j = 1 Δx+j = ∑ j = 1 max(Δx j , 0) x t− = ∑ j = 1 Δx −j = ∑ j = 1 min(Δx j , 0)

(13)

where Δx j = x j − x j − 1.The long-term coefficients β + = −θ+/ ρ and β − = −θ−/ ρ calculated using modeling respectively describe the longterm asymmetric effects of the positive and negative changes of external shocks on China's natural gas prices, and πi+ and πi− respectively represent the specific impact of one unit of positive and negative impacts on natural gas prices in the short term. The standard Wald test was used in the modeling process to test the long-term symmetry hypothesis of the model: β + = β −, and the shortq−1 q−1 term symmetry hypothesis: ∑i = 0 πi+ = ∑i = 0 πi−. Then, the specific model form was selected based on the test results for the asymmetric hypothesis over the long and short term. The specific form of the model is shown in equation (12) if the model simultaneously rejects the symmetry hypothesis in the long and short term. If the model can neither reject the symmetry hypothesis inThe long term nor reject the symmetry hypothesis in the short term, the model is simplified to the symmetric ARDL(p,q) form as shown in equation (14). If the model merely rejects the assumption of long-term symmetry, the model is constructed as a long-term asymmetric and short-term symmetric NARDL, as shown in formula (15). If the short-term symmetry hypothesis is rejected, the model is constructed as a long-term symmetric and short-term asymmetric NARDL form, as shown in equation (16). p−1

Δyt = c + ρyt − 1 + θx t − 1 +

Table 7 Co-integration test results.

q−1

∑ ri Δyt−i + ∑ πi Δxt−i + εt i=1

i=0 p−1

Δyt = c + ρyt − 1 + θ+x t+− 1 + θ−x t−− 1 +

3.3.1. Cointegration and symmetry test Before the cointegration and symmetry tests, the optimal lag order of the NARDL model was first determined. Therefore, in reference to the AIC criterion, the lag order of the model was selected by the maximum lag order p = q = 5. Finally, the optimal lag orders between the natural gas market price series in China and Japan were determined as p = 2 and q = 3; that is, the NARDL (2,3) model should be used to measure the natural gas price fluctuation relationship between China and Japan. Similarly, the optimal lag order between China and the United States was determined as p = 2 and q = 1, and the optimal lag order between China and Europe was p = 2 and q = 1. Therefore, based on the general-to-special modeling principle, an unconstrained NARDL model② was established between China and Japan, the United States, and Europe in accordance with formula (12), and the corresponding cointegration test and symmetry relationship tests were then conducted, the specific test results for which are shown in Tables 7 and 8. From the results in Table 7, it can be seen that in the NARDL model of price transfer in Japan, the United States, and Europe, both the t

tBDM FPss

q−1

∑ ri Δyt−i + ∑ πi Δxt−i + εt i=1

(14)

i=0

JPN

US

EUR

−4.8487*** 13.0354***

−7.4234*** 23.9919***

−7.3290*** 25.7565***

Note: ***, **, * indicate statistically significant at 1%, 5%, and 10% significance levels, respectively.

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natural gas market had no significant effect on the changes in CHN. However, the respective changes in EUR and JPN were shown to have a statistically significant influence on the changes in CHN at a significance level of 5%, primarily because both Japan and Europe had imported a large LNG quantities from the Middle East and other regions in recent years and therefore had a similar natural gas import structure to China, with all three markets having linked their natural gas prices to crude oil prices, which meant that the Chinese, Japanese and European markets had a close risk transmission relationship over the short term. Because of its significant recent increase in shale gas exploitation, the United States has become a natural gas exporting country rather than a natural gas importing country. Its average annual growth rate of LNG export volume remained at around 99.71% between 2014 and 2017 (BP Statistical Review, 2015; BP Statistical Review, 2016; BP Statistical Review, 2017; BP Statistical Review 2018). Therefore, against the background of increasing LNG export volume and decreasing LNG import volume, the United States natural gas market has little risk of suffering from any short-term price volatility in the world's main gas export markets, which means that there is no significant influencing relationship between the US and Chinese natural gas markets over the short term. Specifically, the estimation results showed that the influence coefficients for the changes in the EUR, US and JPN natural gas market prices on CHN changes in the same period were respectively 0.2587, −0.1025 and −0.0229. However, the US and JPN influence coefficients were not significant at the 5% significance level, with only the EUR influence coefficient passing the significance test. In particular, if all other conditions remain unchanged, every 1% increase in the natural gas market price in Europe was observed to lead to an average increase of 0.2587% in the natural gas market price in China over the same period. The lag effect of the price change results, however, showed that there was no obvious hysteresis between CHN and EUR, and the US. In the explanatory variables, only JPN's early changes were observed to have a significant lag effect on current changes in CHN, with the JPN lag one phase and lag two phase price change influence coefficients being 0.2170 and −0.2373, which meant that if all other conditions remained unchanged, every 1% increase in the JPN lag one phase and lag two phase natural gas prices respectively resulted in an average increase of 0.2170% and an average decrease of 0.2373% in China's current natural gas market prices. The reason for this hysteresis was that the JPN natural gas market price was observed to be an important benchmark for the overall Far East natural gas pricing, and therefore had a certain reference precursor role to the pricing process in China's short-term natural gas imports. Over the long term, natural gas price changes in the EUR, US, and JPN were found to have a significant positive impact on CHN changes, with respective impact factors of 0.3361, 0.1627, and 0.0926. The comparison of a one unit change on the explanatory variable on the variation in CHN found that Europe had the largest degree of influence, followed by the United States, and Japan. This was because the large number of new LNG projects in the United States and other regions led to a rapid development in the global natural gas market, which led to a greater globalization of the natural gas markets by deepening the longterm linked relationships between the world's natural gas markets. Further, as China has actively sought to integrate with the world natural gas market, the natural gas prices in countries/regions with more developed natural gas trading centers have greater reference significance for China's long-term natural gas contract pricing. The LM test and ARCH LM test results respectively indicated that there was no sequence correlation or ARCH effect in the residual sequence for the obtained regression equation. CUSUM and CUSUMSQ stability test methods were then employed to confirm the short-term and long-term coefficient stability of the explanatory variables in the equation, from which it was found that the residuals in the established equations did not significantly deviate from the critical interval range at a 5% significance level, which proved that the estimation results in the

Table 8 Wald test results for long and short-term asymmetry.

WLR WSR

JPN

US

EUR

0.0705(0.7926) 0.0008(0.9774)

1.0579(0.3112) 0.0575(0.8119)

1.3733(0.2496) 0.2075(0.6517)

Note: The numbers in parentheses are the P values corresponding to the test results, WLR represents the Wald test for long-term symmetry, WSR represents the Wald test for short-term symmetry.

statistic and the F statistic reject the null hypothesis at a significance level of 1%. Therefore, a significant cointegration relationship was shown between the natural gas market price in China and the natural gas price in Japan, the United States, and Europe. Then, the long and short-term asymmetric model relationships were examined, the specific results for which are shown in Table 8. Table 8 shows that the null hypothesis for long and short-term symmetry could not be rejected at either the 5% or 10% significance level, indicating that any rise of fall in the natural gas prices in Japan, Europe and the United States had no short-term or long-term asymmetric effects on China's natural gas prices. The reason is that, on the one hand, limited by the strict control of residential gas prices, the price adjustment space of LNG in the field of living consumption has been greatly limited. The phenomenon of gas price control is particularly obvious in the natural gas consumption scenarios such as residential gas consumption, gas consumption in pension welfare institutions and gas consumption for students. The lack of flexible terminal gas pricing mechanism limits China's price adjustment ability and price adjustment range in the context of large fluctuations in international gas prices. Therefore, to a large extent, it “covers up” the possible asymmetric influence relationship between China and international natural gas prices. On the other hand, from the perspective of the consumption structure of the natural gas market, domestic onshore gas, imported pipeline natural gas and imported liquefied natural gas are the main sources of natural gas consumption in China, and there is a significant competitive substitution and pricing reference relationship among the three. When the international gas price fluctuates greatly, the bargaining space of imported liquefied natural gas (LNG) is greatly reduced between the supplier and the demander due to the restriction of domestic onshore gas and imported pipeline gas strictly controlled gate price and its symmetrical price adjustment mode. Therefore, the practical application of flexible and asymmetric pricing schemes for LNG retailers is limited to a large extent. Reflected in the natural gas market, it often shows that there is no significant asymmetric relationship between domestic and foreign gas prices. Therefore, the ARDL (p, q) model, which assumes that the effects between variables exist in a symmetrical form in both the short and long term, was chosen for the following analyses. 3.3.2. ARDL model estimation results Before the ARDL model estimation, equation (14) was converted into an equivalent error correction model form with reference to the ARDL model proposed by Pesaran et al. (2001), the specific form for which is shown in equation (17). p−1

Δyt = ρξt − 1 +

q−1

∑ ri Δyt−i + ∑ πi Δxt−i + εt i=1

i=0

(17)

in which the correction term ξt − 1 = yt − 1 − βx t − 1 and the adjustment factor ρ reflect the adjustment speed of yt − 1 with respect to the deviation of x t − 1 during the t-1 period, where β = −θ / ρ describes the longterm relationship between the independent variable and the dependent variable. Using equation (17), Table 9 gives the results for estimating and testing the natural gas prices between China and Japan, the United States, and Europe using the ARDL model. As shown in Table 9, over the short-term, the changes in the US 947

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Table 9 ARDL-ECM model estimation and test results. Japanese natural gas price (JPN) Long and short-term symmetric ARDL (2,3) model

ρ △CHN(-1) △JPN △JPN(-1) △JPN(-2) C

R2 WCI LM test-S ARCH LM test-S LM test-L ARCH LM test-L CUSUM CUSUMSQ L

−0.2652** (0.0228) 0.1984 (0.2248) −0.0229 (0.7408) 0.2170*** (0.0052) −0.2373*** (0.0040) −0.1202* (0.0536) 0.4362 17.8273*** 0.2922 (0.5926) 0.8437 (0.3646) 0.6436 (0.4278) 3.4912 (0.0696) Stable Stable 0.0926*

US natural gas price (US) Long and short-term symmetric ARDL (2,1) model

European natural gas price (EUR) Long and short-term symmetric ARDL (2,1) model

ρ △CHN(-1) △US C

−0.2869*** (0.0027) −0.0209 (0.9070) −0.1025 (0.5163) −0.1294* (0.0781)

ρ △CHN(-1) △EUR C

−0.7428*** (0.0000) −0.0492 (0.7179) 0.2587** (0.0138) −0.0785 (0.1438)

R2 WCI LM test-S ARCH LM test-S LM test-L ARCH LM test-L CUSUM CUSUMSQ L

0.1912

R2 WCI LM test-S ARCH LM test-S LM test-L ARCH LM test-L CUSUM CUSUMSQ L

0.4458

25.3332*** 1.1019 (0.3012) 2.7184 (0.1079) 1.6379 (0.2090) 0.5918 (0.4466) Stable Stable 0.1627*

26.1329*** 0.0068 (0.9348) 0.0115 (0.9152) 0.9740 (0.3304) 0.0064 (0.9368) Stable Stable 0.3361***

Note: Table 9 shows the results of the ARDL model established between China and Japan, the United States and Europe. (1). The estimated P value is indicated in parentheses. (2). ***, **, and * indicate statistically significant at the 1%, 5%, and 10% significance levels, respectively. (3). Indicates the test results from using the boundary test method to test whether the equation had a cointegration relationship. (4). The LM test-S and LM test-L respectively indicate the short and long-term LM test results. (5). The ARCH LM test-S and ARCH LM test-L respectively indicate short and long-term ARCH LM test results. (6). CUSUM and CUSUMSQ are two methods used to respectively test the stability of the explanatory variable coefficients in the short and long-term. (7). L is the long term coefficient estimate for the explanatory variables.

affected by price changes in the natural gas markets in Japan, the US and Europe. All in all, the impact of changes in international natural gas prices on China's natural gas prices is characterized by significant regional differences and period differences, and this effect is often accompanied by a symmetrical method of gas price regulation. A scientific analysis of the changes in world natural gas market patterns and its impact on China indicated the premise and basis for the rational formulation of a sustainable development strategy for China's natural gas industry. From a risk spillover perspective, China's natural gas market was not yet in line with the world natural gas markets, primarily because the division between the domestic and foreign natural gas markets has hindered any timely transmission of gas prices. This time lag risk, therefore, has created opportunities for potential speculation, which in turn brings huge risks to the long-term development of China's major gas industry enterprises and their downstream industrial chains. Therefore, the following policy recommendations are proposed: 1. Implement differential pricing policies for the different import gas sources. The unified gas price has further aggravated the phenomenon of "natural gas price inversion”. The results showed that there were significant differences in the impact of the natural gas being imported from different sources on China's gas price fluctuations. Therefore, it is necessary to refine and implement differential pricing policies for different gas sources based on the different import volumes and import periods, which could eliminate the "natural gas price inversion” and protect the interests of China's natural gas importers. 2. Implement monthly or weekly high-frequency dynamic price adjustment policies. The analysis of the monthly frequency data indicated that the price linkage effect between the world natural gas markets had significant time-varying characteristics, and that the dynamic price adjustment mechanism dispersed the risk between the natural gas markets in the different regions. As China's domestic natural gas consumption relies heavily on imports, the Chinese natural gas price fluctuations have not yet established a dynamic relationship with the world natural gas markets. Therefore, to clarify the influence of international gas prices on China's gas prices, introducing a monthly or weekly high-frequency dynamic price adjustment policy would allow China to adjust gas prices in time and ensure a long-term stable natural gas supply. 3. Introduce an asymmetric price adjustment mechanism. Previous

equations were reliable and stable. 4. Conclusions and implications In recent years, as China has focused on energy transformation, its natural gas market has become increasingly dependent on foreign imports and therefore, the frequent fluctuations in the international natural gas market prices have resulted in risks and challenges to China's immature natural gas pricing system. In this context, this paper first examined the risk volatility spillover effects between the world's major natural gas markets and re-divided the various international natural gas market regions based on their degree of integration, after which the specific impact of international natural gas price fluctuations on China's natural gas price fluctuations in different periods were calculated. This analysis provides an important reference for future reforms in China's natural gas market price mechanism. The results show that the degree of integration between the world natural gas markets is relatively low, and the regional characteristics are obvious. At present, the natural gas market between different geographical regions can not be fully regarded as the same whole. According to the result of the division of the world natural gas market, the natural gas markets in Japan, the United States and Europe are highly representative in the international natural gas market. Restricted by the strict control of the price of residential gas, the current natural gas market in China has not yet been effectively integrated with the international natural gas market. In terms of price relationship, that is to say, there is still a large deviation between the natural gas prices in China and internationally, and there is no obvious time-varying feature in its correlation. However, in the context of China's natural gas consumption relying heavily on external imports, the fluctuation of international gas prices is still an important inducement for the fluctuation of China's gas prices. The calculation results show that due to similar natural gas import structures and pricing methods, in the short-term, the price changes in China's natural gas market were somewhat affected by price changes in the European natural gas market in the same period, were affected by two-period lag price changes in the Japanese natural gas market, but were not affected by price fluctuations in the US natural gas market. However, because of the guiding significance of international gas prices on China's natural gas contract pricing, in the long term, the price changes in China's natural gas market were significantly 948

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research has identified a significant asymmetry in the impact of the rise and fall of energy prices on the economy (Wang and Wu, 2018; Cheng et al., 2018; Guo et al., 2016). The calculation results in this paper indicated that China has not yet established an asymmetric mechanism for natural gas price adjustments. As it is obviously unreasonable to adopt the same price adjustment mechanism to deal with extreme situations such as sharp rises and falls in natural gas prices, to better cope with the risks of large changes in international natural gas prices and because the price change law for natural gas alternative energy is closely related to natural gas price changes and the characteristics of China's natural gas import structure, China should establish an asymmetric price adjustment mechanism for the natural gas industry.

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