The dynamic spillover between carbon and energy markets: New evidence

Accepted Manuscript The dynamic spillover between carbon and energy markets: New evidence

Yudong Wang, Zhuangyue Guo PII:

S0360-5442(18)30173-7

DOI:

10.1016/j.energy.2018.01.145

Reference:

EGY 12257

To appear in:

Energy

Received Date:

19 April 2017

Revised Date:

10 January 2018

Accepted Date:

27 January 2018

Please cite this article as: Yudong Wang, Zhuangyue Guo, The dynamic spillover between carbon and energy markets: New evidence, Energy (2018), doi: 10.1016/j.energy.2018.01.145

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ACCEPTED MANUSCRIPT

The dynamic spillover between carbon and energy markets: New evidence

Yudong Wang*, Zhuangyue Guo School of Economics and Management, Nanjing University of Science and Technology, China

*Corresponding author Email: [email protected] Tel: 86 13681663442 Address: 200 Xiaolingwei street, Xuanwu District, Nanjing 210094, China

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ACCEPTED MANUSCRIPT

Abstract The relations between carbon and energy market is a hot topic but little research has focused on the time-varying spillover in a quantitative way. This paper employs the method introduced by Diebold and Yilmaz (2012) which constructs the spillover index by variance decomposition of the prediction error. The results reveal the asymmetric spillover effect between two types of markets in return and volatility series. Among three energy markets including WTI oil, Brent oil and natural gas markets, WTI oil market transmits the strongest spillover effect to the system, and the spillover effect of natural gas to carbon market is also prominent. Then, we adopt the rolling window technique and detect the time-variation property in the spillover effect. It turns out that some major policy changes and events can cause great changes in spillover index. Furthermore, we study the spillover under extreme conditions and our results show the significant mean spillover (volatility spillover) from oil (natural gas) market to the carbon market.

Keywords: Market integration; Carbon market; Energy market; Spillover index

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ACCEPTED MANUSCRIPT 1. Introduction During the last decade, climate warming has been claimed to be the most serious environment problem. From the international perspective, policy makers have implemented an agreement leading to a reduction in carbon emissions. Up to now, the enacted agreement (the Kyoto protocol) has been able to ease the pressure of rising CO2 equivalent emissions, and carbon markets have been growing at a rapid rate since the Kyoto Protocol came into force on February 16, 2005. EU ETS (European Emission Trading Scheme), an emerging compulsory carbon market, is an important part of global financial and commodity markets. Greenhouse gas policy and regulation are expected to increase the costs of burning fossil fuels, and thus slow the pace of climate change. Therefore, the relationship between energy consumption and greenhouse gas emissions has important implications for economic growth and the environment conservation. On the one hand, industrial sectors need sufficient information between carbon and energy prices to balance their energy consumption structure and carbon emission reduction. On the other hand, with the rapid development of advanced financial tools and the process of economic globalization, various assets are correlated more heavily with each other. Information shocks may lead to fluctuations in one market, or even transmit its volatility to other markets. Carbon price volatility plays a significant role in understanding the carbon market dynamics and in making decisions about carbon dioxide emissions reduction. Since the impact of emission control policies on prices of EUA is linked to EUA 3

ACCEPTED MANUSCRIPT volatility (see, e.g., Daskalakis and Markellos, 2009), policy makers and investors are especially concerned about carbon price uncertainty, as long-term investments related to CO2 abatement technologies may be discouraged by high carbon price volatility. Since oil price changes are closely linked to global macroeconomic activities, oil prices are channels through which macroeconomic uncertainties could spread to the emissions market. Therefore, it is important for academics to focus on the volatility spillover between carbon and energy markets. Volatility is related to the rate of information flow to a market, and changes reflect the arrival of new information. So exploring the extent to which energy price volatility shocks are transmitted to carbon markets and vice versa is essential for measuring information transmission between these two markets. Information transmission between the energy and carbon markets is of great importance in asset pricing, particularly for derivatives. It is also essential for risk management, as it determines the benefits of diversification, the optimal hedge ratio against risk and the value-at-risk measure. As the impact of emission control policies on prices of EUA is linked to EUA volatility (see, e.g., Daskalakis and Markellos, 2009), the consideration of volatility transmission is essential to making efficient econometric inferences and accurate forecasts of volatility. In addition, with more comprehensive understanding of the information transmission and risk transmission mechanisms between two markets, the regulators can prevent the spread of risk between markets to the greatest degree, improving the efficiency of joint supervision. In addition, when some extreme events such as financial crisis occur the 4

ACCEPTED MANUSCRIPT financial market volatility generally increases sharply and spills over across markets, which reduces the benefits of portfolio diversification. Investors would like to be able to measure and monitor such spillovers to improve the performance of portfolio allocation. Our study makes two major contributions to the empirical literature on modeling energy and carbon emission linkages. On the one hand, we provide new evidence that there exist asymmetrical spillover effects and volatility interdependence between EUA and energy markets. Compared with Zhang and Sun (2016), which concludes that there is no significant spillover between carbon market and WTI oil market, we capture not only the magnitude but also the direction of spillovers between these two markets, including cases under extreme events. It proves that carbon market plays the role of a receiver in return spillover and a transmitter in volatility spillover. Different from the result adopted by Hammoudeh et al. (2015), which states that the natural gas prices have a symmetric negative effect on the carbon prices. In our study, the natural gas market, which is a minor receiver and producer of spillover effect under normal circumstances, becomes the most influential factor on carbon market receiving volatility spillover under extreme conditions. This connection could even be strengthened with extreme shocks increasing. On the other hand, we are the first to use the DY framework to shed light on the volatility spillover between the carbon and energy markets, taking into account the extreme cases. This method has some advantages over the traditional approaches. First of all, the spillover index can reveal the effects of innovations in one market on others quantitatively, while the traditional 5

ACCEPTED MANUSCRIPT VAR and GARCH can only measure the return and volatility spillover through the significance of parameters in estimation. Next, adopting the new DY method allows us to evaluate the volatility correlations among carbon markets and energy markets independent of the Cholesky factor identification of VAR model. Thirdly, BEKKGARCH model with more variables is difficult to detect the time-varying characteristics of spillover effect, and it will cause the result of non-convergence in estimating parameters, while the DY method can overcome this drawback and can be used to derive the dynamic spillover index through rolling sample approach. This study can provide better information for the global or domestic investors, industrial firms and policy makers. With the WTI market being the main spillover transmitter, hedging strategies against the risk of carbon price changes can be designed using oil commodities. Investors can pay more attention to the interaction between carbon and natural gas market in extreme cases. The turning points in return spillover index correspond to carbon market highs in most cases, which can be taken as the selling signals, and the turning points in volatility spillover index are always corresponding to the market lows, which can be considered as the buying signals. When economic shocks occur, industrial emission enterprises should also put more emphasis on the greater influence of natural gas market on the carbon price. On monitoring the carbon market, regulators should be concerned about the potential sources of carbon price risk like oil price uncertainty, natural gas volatility and the some internal innovations in carbon market, especially the process of the Post Kyoto Phase. As Marko Sencar (2014) introduced a conceptual view of three pillars of 6

ACCEPTED MANUSCRIPT energy policy: competitiveness, a functioning and integrated internal market and a stable regulation. Policymakers and traders should be aware that energy futures are not always highly effective as hedge instruments because of occasional breaks. The remainder of this paper is organized as follows: Section 2 gives a brief literature review on the related studies. Section 3 shows data description. Section 4 reports the empirical results and Section 5 performs further investigation under the extreme conditions. The last section concludes the paper. 2. A brief literature review Carbon pricing, energy pricing and their linkages have been examined in several papers (Arieke and Bert (2014); Wang and Sun (2017)). On the one hand, Erik and Hans (2007) came up with the concept of “switching point”, which means the allowance cost necessary to switch a certain coal fired plant with a certain gas fired plant in the merit order, and this finding leads to the use of switching points in establishing allowance cost profiles. On the other hand, Fabio (2016) concluded that the oil price evolution is dependent on the consumption rate of the oil, the ratio of mass extraction to mass consumption rates, and some usual economic parameters. Huang and An (2017) put forward that political instability and oil stocks affected the oil price over the short and medium time horizons, and the world economy and oil production only exerted their influence on oil price over medium and long time horizons. Nazifi and Milunovich (2010) find no long-run relationship between the prices of carbon allowance, coal, oil, natural gas and electricity, but instead some short-run linkages. Aatola et al (2013) investigate the price determination of the European Union 7

ACCEPTED MANUSCRIPT emission allowance (EUA), and find the EUA forward price depends on fundamentals, especially on the price of electricity as well as on the gas–coal difference, in a statistically significant way. Considering the relationship between carbon and oil market, Zhang and Wei (2010) find significant time-varying cointegration exists between fossil fuel prices and carbon prices. Among three energy sources, oil price appears the most significant factor for carbon price change, followed by natural gas price and coal price, with the natural gas price’s effects lasting for the longest time. Using the nonlinear autoregressive distributed lag (NARDL) model, Hammoudeh et al. (2015) conclude that the crude oil prices have a long-run negative and asymmetric effect on the CO2 allowance prices. Crude oil is not only the main supplier of energy that closely related to the development of modern transportation industry and plants, carbon market also has complex relationships with coal and natural gas markets. Creti et al. (2012) investigate the determinants of carbon prices during the two phases of EU ETS. The results show that the oil price is a significant determinant of carbon prices in both phases, and the switching price between natural gas and coal is also important in the second phase. Furthermore, Marimoutou and Soury (2015) examined the dependence between the volatility of the prices of the carbon emissions with the volatility of energy prices, results suggest that the dynamics of the dependence between the volatility of the two markets do vary over time, although not much in stable periods but rise noticeably during the period of crisis and turmoil. 8

ACCEPTED MANUSCRIPT Mansanet et al. (2007) show how energy sources affect carbon price. The coefficients for lagged Brent oil and lagged natural gas price changes are positive, meaning that CO2 price changes increase after Brent and gas price changes. However, coal price changes and the quotient between the gas price changes do not influence carbon price changes significantly. The potential reason is studied by Bertrand (2014), who found that the consumption switch degree between coal and natural gas will become deeper in power enterprises when uncontrolled carbon emission increases, and at this moment, natural gas prices may affect carbon prices more significantly. As the natural gas is becoming an important clean energy in power generating, the volatilities in natural gas market can influence enterprise carbon emission demand and eventually carbon prices. Just as Carlo and Derek (2009) hold that, carbon prices can affect electricity prices through natural gas prices. Similarly, Wook and Deb (2010) proposed that carbon costs, either in the form of a carbon tax or through permit prices in an emissions trading scheme, would ultimately be reflected in higher electricity prices. Access to abundant and cheap natural gas has already gradually swayed energy production away from coal. The most important and enduring policy that can help speed up the transition to renewable energy involves the phasing out of coal subsidies (Jay Squalli, 2017). Multivariate GARCH models are considered to have the advantage of describing dynamic correlation for mass data (Efimova and Serletis (2014)), and GARCH models have been frequently employed in previous studies to research the volatility in energy and carbon markets. For example, Byun and Cho (2013) investigate the 9

ACCEPTED MANUSCRIPT volatility forecasting abilities of Brent oil futures, UK natural gas, European coal futures and European electricity markets on carbon futures volatility using GARCH models, but they just point out the direction of volatility spillover but do not present its magnitude. Hsiang-Hsi Liu and Yi-Chun Chen (2013) study in the interactions, volatility spillovers and long memory effects for carbon, oil, natural gas and coal markets. They find that futures returns of carbon and energy have long memory and own-mean spillover effects, the conditional variances also have volatility spillovers, long memory effects and amplitudes. It also extends that extreme weather has certain impacts on carbon, oil, natural gas and coal markets. Zhang and Sun (2016) measure the dynamic conditional correlation and volatility spillover between carbon and fossil energy markets, they find that there is significant unidirectional volatility spillover from coal market to carbon market and from carbon market to natural gas market, whereas there exists no significant volatility spillover between carbon market and Brent oil market. Mehmet Balcılar and Rıza Demirer (2016) used a Markov regimeswitching dynamic correlation, MS-DCC-GARCH model to examine the volatility spillover between four primary energy futures prices and carbon futures contracts in the EUA and CER markets, results show that the carbon emission market is linked to changes in the electricity, natural gas and coal futures markets, and more significantly so in the case of the EUA market. There are also many studies focus on the carbon market spillover effect in China. Sun and Wang (2014) found that the carbon emission transfer between Chinese provinces is highly spatially aggregated on a regional scale. A single carbon emission 10

ACCEPTED MANUSCRIPT import or single carbon emission export in most provinces can exert a positive economic spillover effect. Zhou and Feng (2016) analyzed the impact of environmental regulation on fossil energy consumption in China with non-spatial and spatial models. The results show that, in the direct path, due to the “Green Paradox” and “compliance cost” phenomena, there exists an inverted “U” shaped, non-linear relationship between environmental regulation and fossil energy consumption in practice. In the indirect path, environmental regulation has promoted production technological progress to induce energy-saving practices. In this paper, we investigate in the integration of carbon market and energy market from a new perspective, using a fresh method of Diebold and Yilmaz (2012) (here after, DY). This is a simpler and more intuitive measure of interdependence of asset returns and volatilities, namely spillover index. It produces continuously-varying indexes (unlike, for example, the “high state/low state’’ indicator of Edwards & Susmel (2001)), and is econometrically tractable even for very large numbers of assets. It may provide the value of the directional spillover between any two markets, between one market and any set of (regional) markets, or between one market and global (all) markets. As Diebold and Yilmaz pointed out, the DY 2011 avoids the controversial issues associated with the definition and existence of episodes of contagion (see the debate in Forbes and Rigobon (2002)). The rolling-window analysis of spillover index can clearly exhibit that, as time goes by, whether the volatility spillover can be positively enhanced or negatively weakened between carbon and energy markets. 11

ACCEPTED MANUSCRIPT The remainder of this paper is organized as follows: the next section (Section 3) introduces the methodology. Section 4 describes the data used in this paper with some preliminary analysis. Section 5 reports empirical findings and discusses the results. The last section concludes the paper. 3. Methodology We use the spillover index introduced by Diebold and Yilmaz (2009) to construct the spillover index by variance decomposition of the prediction error. The methodology is described below: Considering an n-dimensional random vector 𝑥𝑡 = (𝑥1𝑡 ,𝑥2𝑡 ⋯𝑥𝑛𝑡) that satisfies the formula: 𝑥 𝑡 = 𝐶 𝑥 𝑡 ‒ 1 + 𝜀𝑡

(1)

𝑖

This is a autoregressive model of structural vector, where 𝐶𝑖 (i=1, 2, …p) is coefficient matrix,𝜀𝑡 = [𝜀1𝑡 𝜀2𝑡 …𝜀𝑛𝑡] is a white noise vector that is assumed to be individually and mutually independent. Then we convert equation (1) to the moving average form: 𝑥𝑡 = 𝛩(𝐿)𝜀𝑡 Where𝛩(𝐿) = (𝐼 ‒ 𝐶𝑖𝐿) to E(𝑢𝑡,𝑢𝑡') = 𝐼, and 𝑄

‒1

‒1 is 𝑡

(2)

, we make A(L) = 𝛩(𝐿)𝑄

‒1

, 𝑢𝑡 = 𝑄𝑡𝜀𝑡 , then leading

the only lower-triangular Cholesky factor of the

covariance matrix of 𝜀𝑡, that leads to: 𝑥𝑡 = 𝐴(𝐿)𝑢𝑡

(3)

Now we can make an one-step forecast for example: 𝑥𝑡 + 1,𝑡 = 𝐶𝑖𝑥𝑡 12

(4)

ACCEPTED MANUSCRIPT From equation (4), we can get the error matrix of one step prediction:

[

𝑎0,11 𝑒𝑡 = 𝑥𝑡 + 1 ‒ 𝑥𝑡 + 1,𝑡 = 𝐴0𝑢𝑡 + 1 = 𝑎 0,21

] [

]

𝑎0,12 𝑢1,𝑡 + 1 = 𝑎0,22 𝑢2,𝑡 + 1

(5)

From equation (5), we can obtain the error covariance matrix: E

(

)

' 𝑒𝑡 + 1,𝑡 , 𝑒𝑡 + 1,𝑡

' = 𝐴0𝐴0

=

[

2

2

𝑎0,11 + 𝑎0,12

𝑎0,11𝑎0,21 + 𝑎0,12𝑎0,22

𝑎0,21𝑎0,11 + 𝑎0,22𝑎0,12

𝑎0,21 + 𝑎0,22

2

2

]

(6)

We use this equation (6) to obtain the one step prediction error variance: 2

2

2

2

2

2

𝛿𝑥1,𝑡 = 𝑎0,11 + 𝑎0,12 𝛿𝑥2,𝑡 = 𝑎0,21 + 𝑎0,22 Each variable prediction error variance can be separated into parts through variance decomposition. However, the results of the variance decomposition depend on the ordering of the variables. For this consideration, Diebold and Yilmaz (2012) work with the generalized VAR framework proposed by Koop et al. (1996) and Pesaran and Shin (1998) (hereafter, KPPS) and developed a variance decomposition that is independent of the order of variables. Diebold and Yilmaz defined “own variance shares” to be the fractions of the 1step-ahead error variances in forecasting xi due to shocks to xi, for i = 1,2, and “cross variance shares” to be the fractions of the 1-step-ahead error variances in forecasting xi due to shocks to xj, for i,j =1,2, i≠j. We can generalize it to richer dynamic environments. a pth-order N-variable VAR , using H-step-ahead forecasts. The KPPS H-step-ahead forecast error variance decomposition is 13

ACCEPTED MANUSCRIPT

σ

∑

‒1 𝑖𝑖

𝑔

𝛽𝑖𝑗 =

ℎ‒1

∑

(𝑒𝑖'𝐵ℎ

𝑑=0

2

𝑒𝑗 )

(7)

ℎ‒1

∑ (𝑒 '𝐵 ∑𝐵 '𝑒 ) 𝑖

ℎ 𝑗

ℎ

𝑑=0

where  is the covariance matrix for the error vector  , ii is the standard deviation of the error term for the ith equation, and ei is the selection vector with 1 as 𝑁

𝑔

the ith element, and 0 otherwise. The generalized VAR has a property that ∑𝑗 = 1𝛽𝑖𝑗(ℎ ) ≠ 1. We normalize each element of the variance decomposition matrix by the row sum as: 𝑔

𝑔 𝛽𝑖𝑗(h) =

Where

𝛽𝑖𝑗(ℎ)

∑

𝑔 ∑𝑁 𝛽𝑖𝑗(ℎ) = 1 𝑗=1

𝑁

(8)

𝑔

𝛽𝑖𝑗(ℎ)

𝑗=1

𝑔

𝑁

and ∑𝑖,𝑗 = 1𝛽𝑖𝑗(h) =N.

The total volatility spillover index can be conducted as follows: 𝑁

∑ 𝛽 (ℎ)

𝑁

𝑔 𝑖𝑗

𝑠=

𝑖,𝑗 = 1 𝑖≠𝑗 𝑁

∑

𝑔 𝛽𝑖𝑗(ℎ) 𝑖,𝑗 = 1

∑ 𝛽 (ℎ) 𝑔 𝑖𝑗

∗ 100 =

𝑖,𝑗 = 1

(9) ∗ 100

N

This index is used to measure the fraction of the error variances caused by various markets’ shocks in total forecasting error variance. As the KPPS method solves the traditional VAR’s ordering problem, the directional spillover index can be computed using the normalized elements of the variance decomposition matrix. We have the directional spillover to market i from all other markets j :

14

ACCEPTED MANUSCRIPT

∑

𝑁

∑

𝑁

𝑔

𝑆 𝑖. (h) =

𝑔 𝛽 (ℎ) 𝑗 = 1 𝑖𝑗 𝑗≠𝑖

∗ 100

(10)

𝑔 𝛽𝑖𝑗(ℎ) 𝑗=1

In the same way, we can calculate the directional spillovers from market i to all other markets j as:

∑

𝑁

∑

𝑁

𝑔

𝑆 .𝑖 (h) =

𝑔 𝛽 (ℎ) 𝑗 = 1 𝑗𝑖 𝑗≠𝑖

∗ 100

(11)

𝑔 𝛽𝑗𝑖(ℎ) 𝑗=1

Thus, the net spillover from market i to all other markets can be obtained by the difference between equation（10）and equation（11）: 𝑔

𝑔

𝑔

(12)

𝑆 𝑖 (ℎ) = 𝑆 .𝑖 (ℎ) ‒ 𝑆 𝑖. (ℎ)

Then, we can compute the net spillover between two specific markets as follows: 𝑔 𝑆𝑖𝑗(ℎ) =

{

𝑔

𝛽𝑖𝑗(ℎ) 𝑁

𝑔

+

𝛽𝑗𝑖(ℎ) 𝑁

∑𝛽

∑𝛽

𝑘=1

𝑘=1

𝑔 𝑖𝑘(ℎ)

𝑔 𝑗𝑘(ℎ)

}

(13) ∗ 100

We use these “row normalized” indices to calculate the return and volatility spillover across carbon market and energy markets in this paper. 3. Data and descriptions We use the daily futures prices of EUA from the compulsory market, EU ETS, as the carbon prices. To generate a continuous time series, we choose the daily settlement prices of one-year contracts in the year before they are expired. Respectively, the EUA future contracts Dec07, Dec08, Dec09, Dec10, Dec11, Dec12, Dec13, Dec14, Dec15, Dec16 are picked up from International continental Exchange (ICE). We use the daily prices of Dec07 in 2006 for the continuous carbon price series 15

ACCEPTED MANUSCRIPT in 2006. In the same way, other contracts’ daily prices are used in corresponding year to create the whole time series. For energy market, we use the daily price of Brent oil futures contracts launched by London International Petroleum Exchange (IPE) from WIND database. Since the continuous price data of IPE natural gas futures in 2014 are lacked in records, we take the daily price of natural gas and WTI oil futures from the Energy Information Administration (EIA) (www.eig.gov). For that the US has the worldwide largest and the most mature natural gas market, and WTI futures is the futures of the largest trading volume in the world, it has great impact on global oil prices. Our sample data cover the period from January 10, 2006, to May 31, 2017. We remove the non-common business days, resulting in a total of 2814 observations for each series. The first order differences of prices are computed as the returns, and volatilities are conditional variance series extracted using fitted GARCH models. Figure 1 shows a graphical illustration of carbon price and energy price data. There are some consistent patterns in the evolution of prices. During the period of 2007-2009, they all have experienced great increases and sharp decreases, which is resulted from the economic bubbles and global financial crisis in 2008. Then, with the recovery of the economy, the differences between the carbon and energy prices became greater since 2010. Insert Figure 1 here We firstly detect the relationship in general by computing the correlation coefficients of daily returns of carbon and energy markets. Table1 displays the results. It can be found that EUA market is significantly positive correlated with three energy 16

ACCEPTED MANUSCRIPT markets, showing trends of carbon and energy returns are relatively consistent on the whole. Insert Table 1 here The descriptive statistics of the four return and volatility series are reported in Table 2 and Table 3, respectively. The ADF(Augmented Dickey–Fuller) test and PP(Phillips and Perron) test are used to detect the stationarity of time series. The results show the rejection of null hypothesis of unit root for all series at the 1% significance level. The standard deviations values of four return series are close. All the skewness are not equal to zero, and all the kurtosis are greater than three, suggesting the fat-tailed distribution. The three energy returns and carbon returns are all right skewed. The Jarque-Bera statistics consistently show the rejections of the null hypothesis of normal distribution at the 1% significance level, confirming that returns and volatilities are fat-tail distributed. The Ljung-Box Q statistics show that both returns and volatilities have significant serial auto-correlations. The results of an ARCH test reveal the significant ARCH effect for returns and volatilities of energy, while for carbon returns the ARCH effect is not significant. Insert Table 2 here Insert Table 3 here 4. Empirical results 4.1. Full-sample spillover analysis 𝑔

We calculate the spillover index 𝛽𝑖𝑗(h) in equation (8) for the full sample. We use a four-variable VAR including EUA and other three energy return series. And a 17

ACCEPTED MANUSCRIPT five-period ahead forecasting horizon (H) for variance decomposition is used to construct the spillover table. In Table4 and Table5, the ijth entry is the estimated contribution to the forecast error variance of market i resulting from innovations to market j. The last but one row exhibits the magnitude of the volatility spillover influence to other three markets. The last row denotes the total influence generated by market j, including the contribution on its own. And the number in the last column shows the net spillovers from other three markets. The total spillover index is presented in the lower right-hand corner of the table. For example, in Table4, the figure in row 1, column 2 denotes the return variance contribution from Brent oil to EUA market (3.390%).The figure in row 5, column 1 denotes the total contribution from EUA market to Brent oil, WTI oil and natural gas markets (3.236%). The figure in row 1, column 5 represents the total return variance contribution from three energy markets to EUA market (8.764%). As Table 4 and Figure 2(a) reports the spillover index for return series based on four-variable VAR. We can generally find that Brent oil and WTI oil are two markets that produce most return spillover to carbon market. Brent oil is the market receiving most spillover, with totally 50.841% contribution from another three, and WTI oil is market affecting others most, with 54.409% variance contribution to others. Meanwhile, natural gas market seems to be a minor receiver (with only 4.189% from others) and carbon market is a minor producer (only 3.236% to others) of spillover effect. It may be explained that North American natural gas market is almost selfsufficient, the main driving force comes from the mainland, while the oil market is a 18

ACCEPTED MANUSCRIPT globalized market, where price is driven by supply and demand, oil refining capacity and geopolitical factors. Carbon market is not developed, its breadth and depth is not yet enough to have a significant impact on the energy markets. The total return spillover index is 26.721%, which is calculated as the average of the spillovers from all other markets. This implies that in the full sample, approximately 26.721% of the forecast error variances are due to return spillovers among different markets. As Table 5 and Figure 2(b) show the spillover index for volatility series, we can find several consistent patterns with the results of return spillover. For example, WTI oil market volatility also contribute (59.626%) to more than they receive (28.930%) from the system. Brent oil market receives most volatility spillover. The total spillover index is 26.671%, close to that computed in Table 4, but there are some differences. Firstly, compared with results of return spillover, carbon market delivers much more volatility spillover (30.720%) to other three markets and receives less spillover (1.459%) from others. By contrast, Brent oil market contributes less (14.634%) and receives more (73.863%). Secondly, natural gas market produces more than 70% of volatility spillover that received by carbon market, becoming the most influential market on carbon’s volatility. Natural gas is considered relatively cheap, is getting more abundant and competes directly with other energy sources in producing activities. Natural gas market volatility can pose great influence on the marginal cost of fuel switching, and thus on the carbon price. The total spillover index indicates that nearly a quarter of the forecast error variances are due to volatility spillovers among different markets. 19

ACCEPTED MANUSCRIPT Insert Figure 2 here Insert Table 4 here Insert Table 5 here 4.2. Dynamic rolling-window analysis The full-sample analysis alone is insufficient in revealing the dynamic evolution of spillover overtime. Due to this consideration, we use the rolling window test which allows us to check the effects of structural breaks on spillover relationships across various markets. As the full sample comprises 2814 observations, we get 2813 return observations. A quarter of the number of observations is used as the window size to allow for enough samples in each VAR estimation and able to reflect the time-varying characteristics (Zhou, 2012), so the window length is set to be 500 business days in forecasting future spillovers with a 5-day horizon (H) based on the VAR (4) model. More specifically, we initially use the first 500 observations to calculate the spillover index, covering the period from the beginning date of the sample to January 11, 2008. Then, we roll the window forward by adding a new observation and dropping the most distant observation. In this way, the number of observations in each window is fixed. As the window rolls forward, we compute the spillover index every time. Figure 3 shows the total spillover index for return and volatility series among the four markets over the sample period. We highlight some characteristics in these plots: First, the total return spillover index varies between 18% and 40%, while the total volatility spillover index evolves more fiercely than that for returns, varying between 20% and 75%. It can present some co-movement between carbon and energy markets. 20

ACCEPTED MANUSCRIPT Second, some major climate policies can cause drastic change of spillover index. For example, both return and volatility spillover index had a cliff fall at the end of 2008, which seems to happen right after Phase I (the post Kyoto phase) of EUETS (European emission trading system) ended in 2008. Thirdly, during the period from 2009 to 2011, the return spillover index and the volatility spillover index share a similar evolution pattern: experiencing a rise in early 2009 and then declining sharply. This is possibly due to the influence of European debt crisis. Lastly, there are sharp downs and ups on the volatility spillover in 2012, 2014 and early 2015. It is possibly related to some events in the market: the PhaseⅡof EUETS ended in 2012. The Organization of Petroleum Exporting Countries (OPEC) announced no reduction in oil production repeatedly, resulting in international oil price’s violent shock in 2014, and prices struggled at low level continuously in 2015. Insert Figure 3 here Figure 4 presents directional spillover index and net spillover index for return and volatility series. There are some findings from the two figures: For the return spillover in Figure4 (a), firstly, spillover from carbon markets to the energy market are relatively stable over time, while the spillover from energy always evolves more fiercely, with much greater value in most of the sample period. The “net affect others” curve staying below the zero value line indicates that carbon market has always been a return spillover receiver. Secondly, we also can note some significant structural breaks in the connectedness between EUA and other energy. They appeared in 2008 and the period from 2013 to 2015, where the spillover affected 21

ACCEPTED MANUSCRIPT by energy stays near zero. It seems to happen because of the uncertainty in the carbon market after the phase (the post Kyoto phase) changed. For the volatility spillover in Figure4 (b), its movement path is quite different. In the most of the time, the red and green curve respectively representing net spillover and total spillover from carbon almost coincide, which indicates that the carbon market played the role of volatility spillover transmitter. This may be resulted from its characteristic of relatively new and immature market. The volatility in carbon prices can have an impact on the cost of production sectors, which may change their energy consumption structure in response. This cause fluctuations in energy supply and demand, directly resulting in the energy market volatility. During the year of 2011, the spillover received and generated by carbon market both experienced drastic up and down, with carbon market’s net spillover reducing to negative value. It is because that EUETS was suffering from low price of carbon, resulting from the economic crisis and excessive distribution of carbon quotas. In 2011, it was exposed that there exit violations of reusing CERs and stealing carbon quotas. The regulatory rules and frameworks of the carbon market are therefore subject to controversy, and the carbon market experienced constant turmoil. Insert Figure 4 here 4.3. Robustness test We now perform some simple variations on our basic analysis, with an eye toward checking robustness with respect to the rolling window width and the forecast horizon. 22

ACCEPTED MANUSCRIPT In Figure 5 we present Spillover Plots using a longer 1000-day rolling window width, and two different variance decomposition forecast horizons,(10-step forecast horizon in blue line and a 1-step horizon in red line)with return variance index in panel (a) and volatility variance index in panel (b). The presenting results appear largely robust to variation in window width and forecast horizon, the spillover index for the 1000-day window is more stable over time because it uses more observations but it generally has a similar path to the spillover index for the 500-day window. It is worth noting that the variance spillover index matrix may change if forecast horizon (H) is set to be too small .The matrix, however, converges quickly to a stable value when H goes higher, which is consistent with findings of Diebold and Yilmaz (2009). Insert Figure 5 here 5. Spillover in extreme cases We will proceed with the spillover of returns and volatility in some extreme cases to see the interaction between carbon and energy markets when economic shocks occur. An efficient approach to modeling extreme events is to attempt to focus not only the largest (maximum) events, but on all events greater than one large preset threshold. So we employed the method used by Hong et al. (2007) to generate a standardized return as following:

{

𝑅𝑡 = 𝑟𝑡 , 𝑖𝑓

𝑟𝑡

||

> T, otherwise 𝜎 𝑅𝑡 = 0

(14)

Where 𝑟𝑡 is the original price return,  is the standard deviation, and T is the threshold 23

ACCEPTED MANUSCRIPT value. We pre-specified three threshold value, T=1 and 1.5 to investigate the extreme conditions, the results are exhibited in Table6. Insert Table 6 here In research of extreme events, we find that carbon market continued to receive more spillover from energy market than it contributed in return when threshold is set to be 1 and 1.5, and for the greater threshold value, the total spillover index is almost stable, but the directional spillover from energy (Brent oil, WTI oil) to carbon market becomes weaker. It indicates that the carbon market is not so efficient that can always absorb the innovations from oil market in time. Under greater threshold value, the return spillover transmitted from Natural gas market is nearly unchanged, at relatively low level. It can be interpreted that the price drivers are different. As for volatility spillover in Panel B, compared to oil markets, natural gas market contributes more spillover to carbon market in extreme conditions. The directional spillover from natural gas to carbon even increases when the threshold value goes higher, while the total spillover index is still stable. It indicates that natural gas market volatility can intensify carbon market volatility on the whole in extreme cases. 6. Conclusions and implications Exploring the extent to which energy price volatility shocks are transmitted to carbon markets and vice versa is essential for measuring information transmission between these two markets. The co-movement and potential volatility spillover effect between carbon and energy markets have been studied in current literature, but little 24

ACCEPTED MANUSCRIPT literature focused on the time-varying, directional spillover in a quantitative way. To promote more comprehensive understanding of the market integration, we carry out this research. In this paper, we proposed measures of total volatility spillover and directional volatility spillover between the EUA and Brent oil, WTI oil and natural gas markets from January 10, 2006 to May 31, 2017, using the generalized vector autoregressive (VAR) framework of Diebold and Yilmaz (2012), where forecast-error variance decompositions are invariant to variable ordering. We find that part of variations in carbon return and volatility are from the innovations to energy markets, especially the oil markets. The WTI oil market transmits strongest spillover effect to the system. This result implies that WTI oil returns (volatilities) can provide useful information for forecasting carbon returns (volatilities). Carbon market plays the role of a receiver in return spillover and a transmitter in volatility spillover. Although natural gas market seems to be a minor receiver and producer of spillover effect in return series, it proves to be the most influential factor on carbon market receiving volatility spillover. For that natural gas is considered relatively cheap, is getting more abundant and competes directly with other energy sources in producing activities. Natural gas market volatility can pose great influence on the marginal cost of fuel switching, and thus on the carbon market. Moreover, a rolling-windows analysis is also used to allow for the time-varying natures of this system. We find that some major climate policies and market events can cause drastic change of the spillover index, indicating that large changes in carbon 25

ACCEPTED MANUSCRIPT price are always from internal innovations, rather than the common fundamentals in world market. As the EUETS is compulsory market reliable on policy and regulation, policy factors should be taken into account when forecasting carbon returns, especially the process of the Post Kyoto Phase. Spillover is also investigated under extreme conditions, we find that the directional return spillover from energy (Brent oil, WTI oil) to carbon market becomes weaker under higher threshold, indicating that the carbon market is not so efficient that can always absorb the innovations from oil market in time. Natural gas market volatility can intensify carbon market volatility on the whole in extreme cases. The results can have good applicability in practice. From the spillover index trend, the market response to information can be extracted to help investors predict the trend, facilitating their reasonable asset allocation. Since there are strong connections among carbon and energy markets in Europe, volatility mechanisms can help investors to consider the dynamic of each market to diversify their investment portfolio, and construct the advantageous investment and arbitrage portfolios related to the carbon or energy (oil, natural gas) futures. Specifically, with the WTI market being the main spillover transmitter, hedging strategies against the risk of emission price changes can be designed using oil commodities. When economic shocks lead to large fluctuations in market, the hedging performance and forecasting ability of WTI oil market may be weakened. In this condition, investors can employ natural gas commodities or futures instead, as we find that natural gas market volatility can intensify carbon market volatility on the whole 26

ACCEPTED MANUSCRIPT in extreme cases. In market’s turmoil, the number of sudden changes in spillover index indicates the frequency of market rotation. It can be adopted as transaction indication signal: The turning points in return spillover index correspond to carbon market highs in most cases, which can be used as selling signals in carbon market, and the turning points in volatility spillover index is always corresponding to the market lows, which can be used as buying signals. The dynamic spillover effect between carbon and energy market may have important implications for power producers and other industrials firms involved in energy consumption and emission trading. The dependence does vary over time and is not constant. It rises considerably when facing a period of turmoil and instability, since the rise of energy prices encourage and push firms to substitute it by other type of energy and resources which imply a decrease in its demand and so the same goes for the CO2 allowances. Firms can use past prices and spillover trend to forecast the future prices, thus adopting the energy structure that can maximize manufacturing efficiency, or even make appropriate strategies, such as to buy or sell the allowances of CO2 emissions. Under extreme conditions, industrial emission enterprises should put more emphasis on natural gas price. It can poses great impact on the marginal cost of fuel switching, thus influencing energy consuming cost. The government plays a crucial role in providing related information of risk and establishing appropriate policies. We do find a significant impact of energy prices on the CO2 emission prices, so the policy measures aimed at reducing the fluctuations in the CO2 allowance emission prices and dampening the effects of changes in energy 27

ACCEPTED MANUSCRIPT prices can prove fruitful. For instance, by imposing limits on firms’ banking emissions allowances during periods when the allowance price is too low, and easing their borrowing allowances when the price is too high, thus the costs of carbon emissions can be controlled substantially. The government can also set a safety valve based on dynamic spillover effect, when the allowance price hits the ceiling, additional allowances will be released to the market. It may help to stabilize both the carbon market and energy market. In monitoring the carbon market, the market regulators have to concern about not only the potential sources of carbon price risk like oil price uncertainty, natural gas volatility, but also some internal innovations in carbon market, especially the process of the Post Kyoto Phase. Policymakers and traders should be aware that energy futures are not always highly effective as hedge instruments because of occasional breaks. Carbon prices volatility can be affected by R&D in clean energy technologies and renewable energy sources. Some other measures could be introduced to reduce CO2 emissions, like encouraging the development and use of alternative clean activities.

Acknowledgements This work is supported by the National Natural Science Foundation of China (Nos.71501095 and 71722015) and the Fundamental Research Funds for the Central Universities (Nos. 30916013106 and 30917011203).

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Figure 1 Carbon and energy prices

33

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Figure 2(a) System’s contribution to each return variable

34

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Figure 2(a) System’s contribution to each volatility variable

35

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Figure 3 Total spillover indexes over time

Figure 4(a) Directional spillover indexes to the carbon returns over time

36

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Figure 4(b) Directional spillover indexes to the carbon volatilities over time

37

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Figure 5 Rolling spillover index with alternative window length

Tables

Table 1 Correlations among daily returns of carbon and energy prices

EUA return Brent oil return

EUA

Brent oil

WTI oil

Natural gas

1

0.100***

0.124***

0.049***

1

0.849***

0.213***

1

0.203***

WTI oil return Natural gas return

1 38

ACCEPTED MANUSCRIPT Note: *** denotes significance at 1% level.

Table 2 Descriptive statistics of returns EUA

Brent oil

mean

-5.366e-04

-7.400e-05

-9.64e-05

- 3.950e-4

Std.dev

0.036

0.021

0.024

0.032

min

-0.346

-0.109

-0.131

-0.174

max

0.922

0.147

0.164

0.269

skewness

5.292

0.027

0.168

0.612

kurtosis

168.221

6.258

7.461

7.907

JB

3.213e+6***

1244.465***

2345.282***

2997.852***

LBQ(1)

29.040***

8.014***

13.751***

16.734***

LBQ(10)

69.290 ***

23.579***

24.471***

48.683***

ARCH(1)

2.939

68.258***

169.182***

12.590***

ARCH(10)

4.698

423.923***

548.969***

96.600***

ADF

--38.481***

-55.900***

-56.843***

-57.263***

39

WTI oil

Natural gas

ACCEPTED MANUSCRIPT PP

-47.870***

-55.909***

-56.852***

-57.264***

Notes: JB is the Jarque and Bera (1980) statistic testing for the null hypothesis of Gaussian distribution. ADF and PP denote the statistics of Augmented Dickey and Fuller (1979) and Phillips and Perron (1988) unit root tests, respectively. The optimal lag length of the ADF test is chosen based on the Schwarz information criterion (SIC) (Schwarz, 1978), and the optimal bandwidths of the PP unit root test are determined based on the Newey-West criterion (Newey and West, 1994). Q(l) is the Ljung and Box(1978) statistics for up to the lth order serial correlation. ARCH(l) is the statistic of the Engle (1980) test for the ARCH effect for up to the lth order. *, ** and *** denote rejection of the null hypothesis at 10%, 5% and 1% significance levels, respectively.

Table 3 Descriptive statistics of volatilities EUA

Brent oil

mean

1.876e-3

4.49e-4

6.030e-4

1.069e-3

Std.dev

0.005

4.130e-4

7.000e-4

7.31e-4

min

5.430e-5

7.260e-05

9.110e-05

3.530e-4

max

0.099

0.003

0.006

0.008

skewness

10.058

2.848

3.446

3.044

kurtosis

133.669

13.371

17.036

18.546

JB

2.049e+6***

1.641e+4***

2.866e+4***

3.267e+4***

LBQ(1)

2470.2***

2779.9***

2765.7***

2657.9***

LBQ(10)

1.454e+4***

2.608e+4***

2.531e+4***

2.127e+4***

ARCH(1)

2.063e+03***

2.742e+03***

2.696e+03***

2.452e+03***

ARCH(10)

2.057e+03***

2.741e+03***

2.701e+03***

2.449e+03***

ADF

-9.591***

-3.218**

-4.013***

-6.344***

40

WTI oil

Natural gas

ACCEPTED MANUSCRIPT PP

-9.050***

-2.010**

-2.670***

-3.602***

Notes: JB is the Jarque and Bera (1980) statistic testing for the null hypothesis of Gaussian distribution. ADF and PP denote the statistics of Augmented Dickey and Fuller (1979) and Phillips and Perron (1988) unit root tests, respectively. The optimal lag length of the ADF test is chosen based on the Schwarz information criterion (SIC) (Schwarz, 1978), and the optimal bandwidths of the PP unit root test are determined based on the Newey-West criterion (Newey and West, 1994). Q(l) is the Ljung and Box(1978) statistics for up to the lth order serial correlation. ARCH(l) is the statistic of the Engle (1980) test for the ARCH effect for up to the lth order. *, ** and *** denote rejection of the null hypothesis at 10%, 5% and 1% significance levels, respectively.

Table 4 Return spillover index based on four-variable VAR EUA

Brent oil

WTI oil

Natural gas

From others

EUA

91.236

3.390

4.617

0.758

8.764

Brent oil

1.468

49.159

47.437

1.937

50.841

WTI oil

1.702

39.302

56.911

2.085

43.089

Natural gas

0.066

1.767

2.356

95.811

4.189

To others

3.236

44.458

54.409

4.779

Spillover index

Including

94.472

93.617

111.321

100.591

26.721

own

41

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Table 5 Volatility spillover index based on four-variable VAR EUA

Brent oil

WTI oil

Natural gas

From others

EUA

98.541

0.045

0.385

1.029

1.459

Brent oil

14.147

26.137

59.173

0.543

73.863

WTI oil

14.236

14.564

71.070

0.130

28.930

Natural gas

2.336

0.025

0.068

97.571

2.429

To others

30.720

14.634

59.626

1.702

Spillover index

Including own

129.261

40.771

130.695

99.273

26.671

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Table 6 Spillover index for large fluctuations based on four-variable VAR EUA

Brent oil

WTI oil

Natural gas

From others

Return spillover, Threshold=1 EUA

85.198

5.646

7.895

1.261

14.802

Brent oil

0.355

60.501

38.686

0.458

39.499

WTI oil

0.509

43.502

55.355

0.635

44.645

Natural gas

0.342

2.971

3.509

93.178

6.822

To others

1.207

52.118

50.090

2.353

Spillover index

Including own

86.405

112.619

105.445

95.531

26.442

Return spillover, threshold=1.5 EUA

92.161

3.876

2.446

1.517

7.839

Brent oil

0.330

63.785

35.457

0.427

36.215

WTI oil

0.274

43.676

55.701

0.349

44.299

Natural gas

0.306

2.454

2.417

94.823

5.177

To others

0.910

50.006

40.320

2.293

Spillover index

43

ACCEPTED MANUSCRIPT Including own

93.071

113.792

96.021

97.116

23.382

Volatility spillover, threshold=1 EUA

97.809

0.075

0.665

1.451

2.191

Brent oil

3.784

47.980

48.199

0.037

52.020

WTI oil

5.067

31.988

62.864

0.081

37.136

Natural gas

1.259

0.057

0.113

98.571

1.429

To others

10.110

32.120

48.977

1.569

Spillover index

Including own

107.920

80.099

111.841

100.140

23.194

Volatility spillover, threshold=1.5 EUA

96.468

0.094

0.398

3.040

3.532

Brent oil

4.870

50.065

45.034

0.031

49.935

WTI oil

3.829

33.194

62.846

0.131

37.154

Natural gas

1.511

0.083

0.048

98.358

1.642

To others

10.209

33.371

45.481

3.202

Spillover index

Including own

106.678

83.436

108.326

101.560

23.066

44

ACCEPTED MANUSCRIPT Highlights 

A spillover index is employed to analyze the spillover effect.



The model can capture time-varying, directional and quantitative spillover.



We find the asymmetric spillover effect in return and volatility series.



We investigate the spillover effect in extreme cases.