Economic Modelling 42(2014)413-420 Contents lists available at ScienceDirect Economic Modelling ELSEVIER journal homepage:www.elsevier.com/locate/ecmod Return and volatility spillovers between china and world oil markets CrossMark Bing Zhang,Peijie Wang b.1 Department of Finance and Insurance,Nanjing University,PR China School of Management,University of Plymouth.UK ARTICLE INFO ABSTRACT Article history: We examine return and volatility spillovers between China and world oil markets.This topic is of great Accepted 3 July 2014 importance because China is the world's second-largest oil importer and has exhibited substantial growth in Available online 8 August 2014 oil consumption.Extending Diebold and Yilmaz's(2012)method of catching spillover dynamics,it is found that return and volatility spillovers between China and world oil markets are bi-directional and asymmetric. Keyword达： The Chinese oil market is highly affected by world oil markets and exerts an influence on world oil markets. China World oil market although to a lesser extent.Moreover,the volatility spillover index has increased significantly since the peak of Spillover index the last financial crisis in September 2008.Although the US oil market impacts China's market most in terms Financial crisis of spillover,the influence of China's oil market on the world oil market has intensified in recent years. Volatility 2014 Elsevier B.V.All rights reserved 1.Introduction at levels referenced to those of international markets to maintain the incentive for the exploration and production of crude oil within China. A deep understanding of return and volatility spillovers between Consequently,the sharp fluctuations in world oil prices may have a China and world oil markets is of great importance.First,China is the great impact on China's institutions,individuals and even entire econo- world's second-largest oil consumer behind the US.The increasing my.Second,the 2008 subprime crisis in the US heavily influenced the demand for and greater dependence on imported crude oil in China world economy,including the Chinese economy.After the financial could lead to closer connections between Chinese and international oil crisis,an enormous drop in world oil prices made the co-movement markets.China's oil import reached 305.9 million tons in 2011 and between China and world oil prices even more tightly linked.This was ranked second in the world.However,China uses only one tenth development has reignited the researcher's interest in the return and of the per capita oil consumption of the US,so there is plenty of room volatility spillover effect between China and world oil markets.It is of for the country's consumption to (attempt to)grow.This phenomenon importance to know whether international market linkages have promises to maintain strong pressure on energy prices for the foresee- strengthened after the financial crisis.Third,understanding return and able future(BP.2011).According to the International Energy Agency. volatility spillovers between China and world oil prices is helpful for in- China's oil consumption growth represented over a third of the world's stitutional and individual investors to engage in effective risk manage- oil consumption growth in 2010.IEA forecasts that China's oil consump- ment and superior asset allocation.For policy makers and regulators, tion will continue to grow during 2012 and 2013 at a moderate pace. more information about spillover characteristics of China and the Even so,the anticipated oil growth of over 0.8 million barrels per day world oil markets they obtain will enable better energy policies.During between 2011 and 2013 would represent 64%of the projected growth the crisis,the world witnessed increasing volatility spillovers across in world oil demand during the 2-year forecast period.2 Since 1998. markets.There exists great practical value in measuring and monitoring the market mechanism of oil pricing has gradually been established in such spillovers-both to provide "early warning systems"for emergent China,and crude oil in the Chinese domestic market has been priced crisis and to track the progress of potential crises.Finally,the impact of China's increased oil demand on oil prices has become a hot topic of debate in recent financial press,these debates prompt us to conduct a further investigation. The spillover effect among different markets is a hot topic that has Corresponding author at:Department of Finance and Insurance,Business School, recently attracted a large volume of empirical research.Lanza et al. Nanjing University.210093.PR China.Tel:+86 25 83621102. E-mail address:zhangbing@njueducn(B.Zhang). (2005)examine heavy crude oil prices from 1994-2001 for Europe 1Te:+441752585705. and the US,using an error correction model.Lin and Tamvakis(2001) 2 http://www.eia.gov/countries/cab.cfm?fips-CH. investigate volatility spillover effects between NYMEX and International http://dx.doi.org/10.1016/j.econmod.201407.013 0264-99930 2014 Elsevier B.V.All rights reserved
Return and volatility spillovers between china and world oil markets Bing Zhang a, ⁎, Peijie Wang b,1 a Department of Finance and Insurance, Nanjing University, PR China b School of Management, University of Plymouth, UK article info abstract Article history: Accepted 3 July 2014 Available online 8 August 2014 Keywords: China World oil market Spillover index Financial crisis Volatility We examine return and volatility spillovers between China and world oil markets. This topic is of great importance because China is the world's second-largest oil importer and has exhibited substantial growth in oil consumption. Extending Diebold and Yilmaz's (2012) method of catching spillover dynamics, it is found that return and volatility spillovers between China and world oil markets are bi-directional and asymmetric. The Chinese oil market is highly affected by world oil markets and exerts an influence on world oil markets, although to a lesser extent. Moreover, the volatility spillover index has increased significantly since the peak of the last financial crisis in September 2008. Although the US oil market impacts China's market most in terms of spillover, the influence of China's oil market on the world oil market has intensified in recent years. © 2014 Elsevier B.V. All rights reserved. 1. Introduction A deep understanding of return and volatility spillovers between China and world oil markets is of great importance. First, China is the world's second-largest oil consumer behind the US. The increasing demand for and greater dependence on imported crude oil in China could lead to closer connections between Chinese and international oil markets. China's oil import reached 305.9 million tons in 2011 and was ranked second in the world. However, China uses only one tenth of the per capita oil consumption of the US, so there is plenty of room for the country's consumption to (attempt to) grow. This phenomenon promises to maintain strong pressure on energy prices for the foreseeable future (BP, 2011). According to the International Energy Agency, China's oil consumption growth represented over a third of the world's oil consumption growth in 2010. IEA forecasts that China's oil consumption will continue to grow during 2012 and 2013 at a moderate pace. Even so, the anticipated oil growth of over 0.8 million barrels per day between 2011 and 2013 would represent 64% of the projected growth in world oil demand during the 2-year forecast period.2 Since 1998, the market mechanism of oil pricing has gradually been established in China, and crude oil in the Chinese domestic market has been priced at levels referenced to those of international markets to maintain the incentive for the exploration and production of crude oil within China. Consequently, the sharp fluctuations in world oil prices may have a great impact on China's institutions, individuals and even entire economy. Second, the 2008 subprime crisis in the US heavily influenced the world economy, including the Chinese economy. After the financial crisis, an enormous drop in world oil prices made the co-movement between China and world oil prices even more tightly linked. This development has reignited the researcher's interest in the return and volatility spillover effect between China and world oil markets. It is of importance to know whether international market linkages have strengthened after the financial crisis. Third, understanding return and volatility spillovers between China and world oil prices is helpful for institutional and individual investors to engage in effective risk management and superior asset allocation. For policy makers and regulators, more information about spillover characteristics of China and the world oil markets they obtain will enable better energy policies. During the crisis, the world witnessed increasing volatility spillovers across markets. There exists great practical value in measuring and monitoring such spillovers—both to provide “early warning systems” for emergent crisis and to track the progress of potential crises. Finally, the impact of China's increased oil demand on oil prices has become a hot topic of debate in recent financial press, these debates prompt us to conduct a further investigation. The spillover effect among different markets is a hot topic that has recently attracted a large volume of empirical research. Lanza et al. (2005) examine heavy crude oil prices from 1994–2001 for Europe and the US, using an error correction model. Lin and Tamvakis (2001) investigate volatility spillover effects between NYMEX and International Economic Modelling 42 (2014) 413–420 ⁎ Corresponding author at: Department of Finance and Insurance, Business School, Nanjing University, 210093, PR China. Tel.: +86 25 83621102. E-mail address: zhangbing@nju.edu.cn (B. Zhang). 1 Tel.: +44 1752 585705. 2 http://www.eia.gov/countries/cab.cfm?fips=CH. http://dx.doi.org/10.1016/j.econmod.2014.07.013 0264-9993/© 2014 Elsevier B.V. All rights reserved. Contents lists available at ScienceDirect Economic Modelling journal homepage: www.elsevier.com/locate/ecmod
414 B.Zhang.P.Wang Economic Modelling 42 (2014)413-420 Petroleum Exchange(IPE)crude oil contracts in both non-overlapping effects between China and the world oil markets.Jiao et al.(2007) and simultaneous trading hours.The authors find that substantial spill- find that oil prices in China have been increasingly affected by interna- over effects do exist when both markets are trading simultaneously. tional oil prices.Chen et al.(2009)examine China's influence on the vol- Sadorsky(2012)tests the volatility dynamics between oil prices and atilities of crude oil prices in international markets in the 11-year period the stock prices of clean energy and technology companies and finds between 1997 and 2007.The authors find that fluctuations in oil prices that the dynamic conditional correlation MGARCH model fits the data in China have little impact on the volatilities of the world crude oil mar- the best.Chang et al.(2010)present evidence of volatility spillovers kets,whereas the reverse impact is relatively slow and weak.However and asymmetric effects on conditional variances for most international these papers use the earlier data,setting the applicability of the results oil markets.Liu and Tu(2012)investigate jump spillover effects of five to the new era covering the recent financial crisis in doubt.It is impera- energy (petroleum)futures and their implications for diversification tive to understand what types of connections exist between China and benefits.Furio and Chulia(2012)observe that Brent crude oil and world oil markets in recent years.In addition,the present paper covers Zeebrugge natural gas forward prices play a prominent role in the both return and volatility spillover effects between China and interna- Spanish electricity price formation process.Furthermore,the authors tional oil markets. find that causations run from Brent crude oil and natural gas forward The rest of the paper is organized as follows:Section 2 briefly intro markets to the Spanish electricity forward market in both price and duces the spillover index method.Section 3 provides the empirical anal- volatility spillovers. ysis of return spillovers.Section 4 provides the empirical analysis of Studies covering the relationship between China and world oil volatility spillovers.Section 5 concludes this study,advancing a number markets,there generally hold one of two views.On the one hand. of suggestions and recommendations. Li and Leung(2011)find that China is now an active participant in the world oil market.On the other hand,Du et al.(2010)find that China's 2.Methodology economic activity fails to affect the world oil prices,indicating that the world oil price remains exogenous with respect to China's macro- The original measure of return and volatility spillover index by economy in a time series sense,and China has not yet possessed oil Diebold and Yilmaz(2009)is based on forecast-error variance de- pricing power in the world oil markets. compositions in a vector autoregressive framework.Consider first To investigate return and volatility spillovers in international stock the simple example of a covariance stationary first-order two-variable markets more effectively,Diebold and Yilmaz(2009)propose a novel VAR measure based on forecast error variance decompositions in a vector autoregressive framework,which is abbreviated as DY2009 in the X:DX -1+8 (1) paper.Diebold and Yilmaz(2011)apply this new method to analyze re- turn and volatility spillovers among four South American countries. where Xr =(XitX2t)',which represents either a vector of asset returns Diebold and Yilmaz(2012)have recently developed a new method, or a vector of asset return volatilities.is a 2 x 2 parameter matrix,and designated DY2012 in the paper.The authors employ this upgraded Er is a residual vector.By covariance stationary,the moving average model,DY2012,to explore the spillovers among major US financial as- representation of the VAR exists and is given by: sets including stocks,bonds,foreign currencies,and commodities from 1999 to 2009,focusing on volatility interaction during the subprime X:(L)Et (2) mortgage crisis.Zhou et al.(2012)adopt this method to study volatility spillovers between China and world equity markets and finds that the where (L)=(1 -L).We may rewrite the moving average US market exerted dominant volatility impacts on other markets during representation as: the subprime mortgage crisis.Whereas the other markets were also very volatile and driven by bad news,their massive volatilities were X,=A(L)ut (3) transmitted back to the US market as well. The purpose of the current paper is to examine return and volatility with ur =Q E.Q,is the unique lower-triangular Cholesky factor of spillovers between China and the world oil markets.Our study contrib- the covariance matrix of Er.The one-step-ahead forecasting error is then: utes to the existing literature in two aspects.First,our paper differs from those in the previous literature in that we are the first to adopt and then extend the DY2012 method to examine return and volatility spillovers e+1=X:+1-Xt+14 Aout+1= C0,11 00.12 山1+1 between the Chinese and world oil markets.We extend the DY2012 0C0.21 00.22 u2.r+1 (4) method to catch the dynamic patterns in spillovers and make the E(et+1e+1)=AoAo extended method more pertinent to the present study.The DY2012 method has several advantages over the other models.This method does not depend on the Cholesky factor identification of VAR.Therefore, Therefore,in particular,the variance of the one-step-ahead error in the results of variance decomposition do not hinge on the sequence of forecasting Xit is ao.n1+a6.12.and in forecasting X2t is a621+a622. the variables.In addition,DY2012 may be used to indicate the direction The spillover index may be expressed as: of the spillover as well.That is,it may provide the value of directional spillovers between any two markets.DY2012 avoids the controversial issues associated with the definition and existence of episodes of conta- S= 62+a62.100 (5 gion.To our best knowledge,this is the first study to apply this method trace(AoA) to address the spillover effect in world oil markets.In particular,we introduce rolling window techniques to further enhance the power of the DY2012 method.This augmentation is particularly helpful for ana- Furthermore,Diebold and Yilmaz (2012)work with the generalized lyzing the dynamic linkages between the world and China oil markets, VAR framework and produce a variance decomposition invariant to which may provide a more vivid and insightful picture of the position ordering,thus overcoming pitfalls generally found in identification and power of the Chinese oil market in the world arena. schemes of variance decompositions.Diebold and Yilmaz(2012)define Second,the paper explores the role of China in world oil markets "own variance shares"as the fraction of the H-step ahead error vari- using recent crude oil data.Due to the relatively late development of ances in forecasting X due to shocks to X for i 1,2....N and "cross the Chinese oil market,little is known about the volatility spillover variance shares"as the fractions of the H-step-ahead error variances
Petroleum Exchange (IPE) crude oil contracts in both non-overlapping and simultaneous trading hours. The authors find that substantial spillover effects do exist when both markets are trading simultaneously. Sadorsky (2012) tests the volatility dynamics between oil prices and the stock prices of clean energy and technology companies and finds that the dynamic conditional correlation MGARCH model fits the data the best. Chang et al. (2010) present evidence of volatility spillovers and asymmetric effects on conditional variances for most international oil markets. Liu and Tu (2012) investigate jump spillover effects of five energy (petroleum) futures and their implications for diversification benefits. Furió and Chuliá (2012) observe that Brent crude oil and Zeebrugge natural gas forward prices play a prominent role in the Spanish electricity price formation process. Furthermore, the authors find that causations run from Brent crude oil and natural gas forward markets to the Spanish electricity forward market in both price and volatility spillovers. Studies covering the relationship between China and world oil markets, there generally hold one of two views. On the one hand, Li and Leung (2011) find that China is now an active participant in the world oil market. On the other hand, Du et al. (2010) find that China's economic activity fails to affect the world oil prices, indicating that the world oil price remains exogenous with respect to China's macroeconomy in a time series sense, and China has not yet possessed oil pricing power in the world oil markets. To investigate return and volatility spillovers in international stock markets more effectively, Diebold and Yilmaz (2009) propose a novel measure based on forecast error variance decompositions in a vector autoregressive framework, which is abbreviated as DY2009 in the paper. Diebold and Yilmaz (2011) apply this new method to analyze return and volatility spillovers among four South American countries. Diebold and Yilmaz (2012) have recently developed a new method, designated DY2012 in the paper. The authors employ this upgraded model, DY2012, to explore the spillovers among major US financial assets including stocks, bonds, foreign currencies, and commodities from 1999 to 2009, focusing on volatility interaction during the subprime mortgage crisis. Zhou et al. (2012) adopt this method to study volatility spillovers between China and world equity markets and finds that the US market exerted dominant volatility impacts on other markets during the subprime mortgage crisis. Whereas the other markets were also very volatile and driven by bad news, their massive volatilities were transmitted back to the US market as well. The purpose of the current paper is to examine return and volatility spillovers between China and the world oil markets. Our study contributes to the existing literature in two aspects. First, our paper differs from those in the previous literature in that we are the first to adopt and then extend the DY2012 method to examine return and volatility spillovers between the Chinese and world oil markets. We extend the DY2012 method to catch the dynamic patterns in spillovers and make the extended method more pertinent to the present study. The DY2012 method has several advantages over the other models. This method does not depend on the Cholesky factor identification of VAR. Therefore, the results of variance decomposition do not hinge on the sequence of the variables. In addition, DY2012 may be used to indicate the direction of the spillover as well. That is, it may provide the value of directional spillovers between any two markets. DY2012 avoids the controversial issues associated with the definition and existence of episodes of contagion. To our best knowledge, this is the first study to apply this method to address the spillover effect in world oil markets. In particular, we introduce rolling window techniques to further enhance the power of the DY2012 method. This augmentation is particularly helpful for analyzing the dynamic linkages between the world and China oil markets, which may provide a more vivid and insightful picture of the position and power of the Chinese oil market in the world arena. Second, the paper explores the role of China in world oil markets using recent crude oil data. Due to the relatively late development of the Chinese oil market, little is known about the volatility spillover effects between China and the world oil markets. Jiao et al. (2007) find that oil prices in China have been increasingly affected by international oil prices. Chen et al. (2009) examine China's influence on the volatilities of crude oil prices in international markets in the 11-year period between 1997 and 2007. The authors find that fluctuations in oil prices in China have little impact on the volatilities of the world crude oil markets, whereas the reverse impact is relatively slow and weak. However, these papers use the earlier data, setting the applicability of the results to the new era covering the recent financial crisis in doubt. It is imperative to understand what types of connections exist between China and world oil markets in recent years. In addition, the present paper covers both return and volatility spillover effects between China and international oil markets. The rest of the paper is organized as follows: Section 2 briefly introduces the spillover index method. Section 3 provides the empirical analysis of return spillovers. Section 4 provides the empirical analysis of volatility spillovers. Section 5 concludes this study, advancing a number of suggestions and recommendations. 2. Methodology The original measure of return and volatility spillover index by Diebold and Yilmaz (2009) is based on forecast-error variance decompositions in a vector autoregressive framework. Consider first the simple example of a covariance stationary first-order two-variable VAR: Xt ¼ ΦXt−1 þ εt ð1Þ where Xt = (X1t, X2t)′, which represents either a vector of asset returns or a vector of asset return volatilities. Φ is a 2 × 2 parameter matrix, and εt is a residual vector. By covariance stationary, the moving average representation of the VAR exists and is given by: Xt ¼ Φð ÞL εt ð2Þ where Φ(L) = (1 − ΦL)−1 . We may rewrite the moving average representation as: Xt ¼ A Lð Þut ð3Þ with ut = Qtεt. Qt −1 is the unique lower-triangular Cholesky factor of the covariance matrix of εt. The one-step-ahead forecasting error is then: etþ1;t ¼ Xtþ1−Xtþ1;t ¼ A0utþ1 ¼ α0;11 α0;12 α0;21 α0;22 u1;tþ1 u2;tþ1 E etþ1;te 0 tþ1;t ¼ A0A0 0 ð4Þ Therefore, in particular, the variance of the one-step-ahead error in forecasting X1t is α0,11 2 + α0,12 2 , and in forecasting X2t is α0,21 2 + α0,22 2 . The spillover index may be expressed as: S ¼ α2 0;12 þ α2 0;21 trace A0A0 0 100 ð5Þ Furthermore, Diebold and Yilmaz (2012) work with the generalized VAR framework and produce a variance decomposition invariant to ordering, thus overcoming pitfalls generally found in identification schemes of variance decompositions. Diebold and Yilmaz (2012) define “own variance shares” as the fraction of the H-step ahead error variances in forecasting Xi due to shocks to Xi for i = 1,2,..,N and “cross variance shares” as the fractions of the H-step-ahead error variances 414 B. Zhang, P. Wang / Economic Modelling 42 (2014) 413–420
B.Zhang,P.Wang Economic Modelling 42 (2014)413-420 415 160 Table 2 Return spillover index of three oil markets. 120 US CH UK From others US 49.5 26.6 23.9 CH 27.1 49.6 23.2 50 UK 247 24.7 50.7 49 w Contnbution to others 52 51 47 Contribution including own 101 101 98 50.1% Brent 40 we may calculate the directional spillover index using the normalized elements of the variance decomposition matrix. We present the directional spillovers received by market i from all 2002-12-26 001-12-26 2004-12-26 200312-26 200512-26 207-12-26 2006-12-26 209-12-26 2008-12-26 202-12-26 2011-12-26 2010-12-26 2013-1226 other markets j as follows: N N (H) Fig.1.The prices of three oil markets H i=1 in forecast X due to shocks to Xj.for i.j=1.2.....N,such that i j.The (H) j≠i 100 ≠i 100 (9) KPPS H-step ahead forecast error variance decomposition is: H- 听(eg)2 Likewise,we may calculate the directional volatility spillovers (H= =0 i,j=1,,n (6) transmitted by market i to all other markets j as: h=0 (H) 疏(H) where E is the variance matrix for the error vector st.ou is the standard i= deviation of the error term for the ith equation,and e,is the selection j≠i j≠i S(H) 100= ,100 (10) vector with 1 as the ith element,and 0 otherwise.According to the char- N (H) acteristics of generalized VAR,we have:(H)1.We normalize each entry of the variance decomposition matrix by the row sum as: We may also obtain the net volatility spillover from market i to all other markets j by calculating the difference between Eqs.(10)and 醋(H= H (7 (9)as: ∑H S(H)=S(H)-S (H). (11) Based on KPSS variance decomposition,we may construct a total The net directional spillover (11)provides summary information spillover index as follows: about the net magnitude of each market's contribution to volatility in other markets.It is also of interest for us to examine the net pairwise volatility spillovers,which we define as: H (H） i,j=1 i.j=1 S(H= 100= i≠j ,100 (8) 丽 N (H)= 100 (12) H) Ok(H) （ i.j-1 =1 The index is used to measure the contributions from the spillovers of Given the background of the world oil market evolution and return and volatility shocks across various markets to the total forecast turbulence,especially after the collapse of Lehman Brothers,it seems error variance.Because the KPSS framework solves the ordering prob- impossible that any single parameter model would apply over the entire lem (the generalized VAR procedure accounts for contemporaneous in- sample.Although the full sample spillover table and spillover index novations with the observed historical distribution of errors,which is provide a useful summary of "average"return and volatility spillover different from the former method used to orthogonalize innovations) behavior,they might miss potentially important secular and cyclical Table 1 Summary statistics of crude oil retums () Table 3 wn DAQING BRENT Return spillover index of three oil markets before Dec 24,2008. Mean 0.0232 0.0257 0.0271 Median 0.0541 0.0344 0.0431 US CH UK From others Maximum 13.1611 53979 7.8736 US 44.3 21.2 34.5 56 Minimum -6.5970 -5.7400 -7.3100 CH 252 51.9 22.9 Std.Dev. 1.0721 0.9258 0.9554 UK 35.3 19.9 44.8 Skewness 0.4311 -02672 -0.1270 Contribution to others 61 41 57 159 Kurtosis 11.8589 42083 5.0643 Contribution including own 105 93 102 53.0%
in forecast Xi due to shocks to Xj, for i, j = 1, 2,…, N, such that i ≠ j. The KPPS H-step ahead forecast error variance decomposition is: θ g ijð Þ¼ H σ−1 jj H X−1 h¼0 e 0 iAhej 2 H X−1 h¼0 e 0 iAh XA0 hej ; i; j ¼ 1; …; n ð6Þ where Σ is the variance matrix for the error vector εt, σii is the standard deviation of the error term for the ith equation, and ei is the selection vector with 1 as the ith element, and 0 otherwise. According to the characteristics of generalized VAR, we have: ∑ N j¼1 θ g ijð Þ H ≠1. We normalize each entry of the variance decomposition matrix by the row sum as: eθ g ijð Þ¼ H θ g ijð Þ H XN j¼1 θ g ijð Þ H ð7Þ Based on KPSS variance decomposition, we may construct a total spillover index as follows: S g ð Þ¼ H XN i; j ¼ 1 i≠j eθ g ijð Þ H XN i; j¼1 eθ g ijð Þ H 100 ¼ XN i; j ¼ 1 i≠j eθ g ijð Þ H N 100: ð8Þ The index is used to measure the contributions from the spillovers of return and volatility shocks across various markets to the total forecast error variance. Because the KPSS framework solves the ordering problem (the generalized VAR procedure accounts for contemporaneous innovations with the observed historical distribution of errors, which is different from the former method used to orthogonalize innovations), we may calculate the directional spillover index using the normalized elements of the variance decomposition matrix. We present the directional spillovers received by market i from all other markets j as follows: S g i ð Þ¼ H XN j ¼ 1 j≠i eθ g ijð Þ H XN j¼1 eθ g ijð Þ H 100 ¼ XN j ¼ 1 j≠i eθ g ijð Þ H N 100: ð9Þ Likewise, we may calculate the directional volatility spillovers transmitted by market i to all other markets j as: S g i ð Þ H ¼ XN j ¼ 1 j≠i eθ g jið Þ H XN j¼1 eθ g jið Þ H 100 ¼ XN j ¼ 1 j≠i eθ g jið Þ H N 100: ð10Þ We may also obtain the net volatility spillover from market i to all other markets j by calculating the difference between Eqs. (10) and (9) as: S g i ð Þ¼ H S g i ð Þ H −S g i ð Þ H : ð11Þ The net directional spillover (11) provides summary information about the net magnitude of each market's contribution to volatility in other markets. It is also of interest for us to examine the net pairwise volatility spillovers, which we define as: S g ijð Þ¼ H eθ g ijð Þ H XN k¼1 eθ g ikð Þ H − eθ g jið Þ H XN k¼1 eθ g jkð Þ H 0 BBBB@ 1 CCCCA 100: ð12Þ Given the background of the world oil market evolution and turbulence, especially after the collapse of Lehman Brothers, it seems impossible that any single parameter model would apply over the entire sample. Although the full sample spillover table and spillover index provide a useful summary of “average” return and volatility spillover behavior, they might miss potentially important secular and cyclical 0 20 40 60 80 100 120 140 160 WTI Daqing Brent Fig. 1. The prices of three oil markets. Table 1 Summary statistics of crude oil returns (%). WTI DAQING BRENT Mean 0.0232 0.0257 0.0271 Median 0.0541 0.0344 0.0431 Maximum 13.1611 5.3979 7.8736 Minimum −6.5970 −5.7400 −7.3100 Std. Dev. 1.0721 0.9258 0.9554 Skewness 0.4311 −0.2672 −0.1270 Kurtosis 11.8589 4.2083 5.0643 Table 2 Return spillover index of three oil markets. US CH UK From others US 49.5 26.6 23.9 51 CH 27.1 49.6 23.2 50 UK 24.7 24.7 50.7 49 Contribution to others 52 51 47 150 Contribution including own 101 101 98 50.1% Table 3 Return spillover index of three oil markets before Dec 24, 2008. US CH UK From others US 44.3 21.2 34.5 56 CH 25.2 51.9 22.9 48 UK 35.3 19.9 44.8 55 Contribution to others 61 41 57 159 Contribution including own 105 93 102 53.0% B. Zhang, P. Wang / Economic Modelling 42 (2014) 413–420 415
416 B.Zhang.P.Wang Economic Modelling 42 (2014)413-420 Table4 yield meaningful estimation results without a serious small sample Return spillover index of three oil markets after Dec 24.2008. bias issue.We focus on this recent 10-year history to make our study 你 CH UK From others topical.We collect all the data from Wind database US 51.7 23.2 25.1 48 Fig.1 presents a graphical illustration of our data.As shown,there CH 19.9 45.9 34.2 54 are three distinct characteristics:1)the oil prices of WTI,Brent and UK 21.6 33.5 448 55 Daqing are highly correlated:2)there is a general upward trend Contribution to others 41 57 59 158 throughout the sample period until August 2008.Since then,the Contribution including own 93 103 104 52.5% financial crisis had a heavy influence on the entire world,with oil prices suddenly dropping and then gradually rising again;and 3)the price dif- ference between various oil markets has broadened in recent months. movements in spillovers.In addition,according to Du et al.(2010),the Table 1 provides summary statistics of crude oil returns in three structural stability tests indicate a structural break in China's oil VAR countries.As shown,the US has the lowest average oil returns and the model because of the reforms of China's oil pricing mechanism;there- largest standard deviations over the sample period.The US oil market fore,it is more appropriate to break the entire sample into different has positive skewness,indicating that it has undergone huge price sub-samples for the estimation of the model. hikes.The fluctuation of the Chinese Daqing price is less than that of To address this issue,we will estimate return spillovers using rolling international crude oil price. samples.Rolling window techniques are effective robustness test It is obvious that trading hours of various oil markets are different. methods (Swanson,1998).The paper employs a fixed rolling window We must consider this non-synchronous trading problem prior to technique,which indicates that after determining a sample size,the examining any further results.Such being the case,we use a simple start and end dates move forward at the same time.Let (x1....xT)be a method that is similar to the method of Cai et al.(2009)to solve this sequence of the stock returns.When using the Moving Window non-synchronous trading problem.The daily returns of the US and UK method,one makes T-w 1 sub samples (x -w+1.....x)for oil markets are lagged one day in estimation of the VAR model together =w.....T with sub sample size w.called a window width,and with the current volatilities of the Chinese Daging oil markets.In our then derives the spillover index value for each sub sample.A 250-day paper,we mainly present the empirical results with this adjustment (50-week)window is chosen for it is one calendar year,and we find and discuss the economic implications based on them that the fluctuations of return spillovers in the 100-day rolling sample are too strong for us to detect any meaningful patterns.We assess the extent and nature of spillover variations over time via the correspond- 3.2.The full-sample return spillover table ing time series of spillover indices. We calculate (H)in Eq.(6)with the full sample and obtain the results reported in Table 2.Its ijth entry is the estimated contributions 3.Data and empirical analysis of return spillovers to the forecast error variance of market i coming from innovations to market j.Therefore,the off-diagonal column sums(labeled contributions 3.1.Data to others)or row sums(labeled contributions from others),are the "to" and "from"directional spillovers,and the "from minus to"differences are The Daging oil field is the largest in China,and its crude oil output ac- the net directional volatility spillovers.Moreover,owing to row normal- counts for approximately 25%of national production.Because its price ization,the sum of the variances in a row is 100%.The last row in the basically represents the level of crude oil prices in China,the paper table represents the contribution to the volatilities of all markets from chooses Daqing crude oil spot prices as representative of the Chinese this particular market.The total return spillover index,which appears crude oil price.There currently are two major benchmarks for world in the lower right-hand corner of the table,is computed as the sum of oil prices:West Texas Intermediate (WTl)crude oil and Brent crude all variances in the 3 x 3 matrix minus the sum of the diagonal variances. oil.The sample period covers 12 years from December 27,2001 through The return spillover table may be viewed as the "input-output"decom- December 24,2013,and,after deleting non-match data,we have a total position of the total return spillover index. of 2871 observations.From the econometric point of view,12 years of In the ijth entry in the 3 x 3 matrix is the estimated contribution to daily data (a total of 2871 return observations)are long enough to the forecast error variance of market i resulting from innovations to 65 SPILLRETURNS 60 55 50 45 40 35 30 T1111111111111111111111111111 2003 2004”200520062007200820092010201120122013 time Spillover of all the market plot.Retums.50 week window.10 step horizon Fig.2.Total retumn spillovers of three oil markets(50-week windows)
movements in spillovers. In addition, according to Du et al. (2010), the structural stability tests indicate a structural break in China's oil VAR model because of the reforms of China's oil pricing mechanism; therefore, it is more appropriate to break the entire sample into different sub-samples for the estimation of the model. To address this issue, we will estimate return spillovers using rolling samples. Rolling window techniques are effective robustness test methods (Swanson, 1998). The paper employs a fixed rolling window technique, which indicates that after determining a sample size, the start and end dates move forward at the same time. Let (x1,…,xτ) be a sequence of the stock returns. When using the Moving Window method, one makes τ − w + 1 sub samples (xτ − w+1,…, xτ) for τ = w,…, τ with sub sample size w, called a window width, and then derives the spillover index value for each sub sample. A 250-day (50-week) window is chosen for it is one calendar year, and we find that the fluctuations of return spillovers in the 100-day rolling sample are too strong for us to detect any meaningful patterns. We assess the extent and nature of spillover variations over time via the corresponding time series of spillover indices. 3. Data and empirical analysis of return spillovers 3.1. Data The Daqing oil field is the largest in China, and its crude oil output accounts for approximately 25% of national production. Because its price basically represents the level of crude oil prices in China, the paper chooses Daqing crude oil spot prices as representative of the Chinese crude oil price. There currently are two major benchmarks for world oil prices: West Texas Intermediate (WTI) crude oil and Brent crude oil. The sample period covers 12 years from December 27, 2001 through December 24, 2013, and, after deleting non-match data, we have a total of 2871 observations. From the econometric point of view, 12 years of daily data (a total of 2871 return observations) are long enough to yield meaningful estimation results without a serious small sample bias issue. We focus on this recent 10-year history to make our study topical. We collect all the data from Wind database. Fig. 1 presents a graphical illustration of our data. As shown, there are three distinct characteristics: 1) the oil prices of WTI, Brent and Daqing are highly correlated; 2) there is a general upward trend throughout the sample period until August 2008. Since then, the financial crisis had a heavy influence on the entire world, with oil prices suddenly dropping and then gradually rising again; and 3) the price difference between various oil markets has broadened in recent months. Table 1 provides summary statistics of crude oil returns in three countries. As shown, the US has the lowest average oil returns and the largest standard deviations over the sample period. The US oil market has positive skewness, indicating that it has undergone huge price hikes. The fluctuation of the Chinese Daqing price is less than that of international crude oil price. It is obvious that trading hours of various oil markets are different. We must consider this non-synchronous trading problem prior to examining any further results. Such being the case, we use a simple method that is similar to the method of Cai et al. (2009) to solve this non-synchronous trading problem. The daily returns of the US and UK oil markets are lagged one day in estimation of the VAR model together with the current volatilities of the Chinese Daqing oil markets. In our paper, we mainly present the empirical results with this adjustment and discuss the economic implications based on them. 3.2. The full-sample return spillover table We calculate θij g (H) in Eq. (6) with the full sample and obtain the results reported in Table 2. Its ijth entry is the estimated contributions to the forecast error variance of market i coming from innovations to market j. Therefore, the off-diagonal column sums (labeled contributions to others) or row sums (labeled contributions from others), are the “to” and “from” directional spillovers, and the “from minus to” differences are the net directional volatility spillovers. Moreover, owing to row normalization, the sum of the variances in a row is 100%. The last row in the table represents the contribution to the volatilities of all markets from this particular market. The total return spillover index, which appears in the lower right-hand corner of the table, is computed as the sum of all variances in the 3 × 3 matrix minus the sum of the diagonal variances. The return spillover table may be viewed as the “input–output” decomposition of the total return spillover index. In the ijth entry in the 3 × 3 matrix is the estimated contribution to the forecast error variance of market i resulting from innovations to Table 4 Return spillover index of three oil markets after Dec 24, 2008. US CH UK From others US 51.7 23.2 25.1 48 CH 19.9 45.9 34.2 54 UK 21.6 33.5 44.8 55 Contribution to others 41 57 59 158 Contribution including own 93 103 104 52.5% Spillover of all the market plot. Returns. 50 week window. 10 step horizon time The spillover index (%) 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 30 35 40 45 50 55 60 65 SPILLRETURNS Fig. 2. Total return spillovers of three oil markets (50-week windows). 416 B. Zhang, P. Wang / Economic Modelling 42 (2014) 413–420
B.Zhang,P.Wang Economic Modelling 42 (2014)413-420 417 62.5 SPILLRETURNS 60.0 8 575 apu 55.0 52.5 JaAolllds 50.0 47.5 45.0 425 11111111111111111111111111111111111 2004 2005 2006 2007 2008 200920102011 2012 2013 time Spillover of all the market plot.Retums.100 week window.10 step horizon Fig.3.Total return spillovers of three oil markets (100-week windows). market j.For example,the element in row 2,column 3 is the volatility contribution of the Chinese oil market to other markets has increased contribution from the UK market to the variance of the China market from-7%to 3%. (23.2%).The sum of the variances in column 3 is the total contribution We inspect spillover variations over time via the corresponding time from the UK market to all other markets in terms of return spillover series of spillover indices,which are exhibited graphically in the total (47%).The total return spillover index is 50.1%.This finding indicates spillover plots of Figs.2 and 3. that in the full sample,approximately 50.1%of the forecast error variance is due to return spillovers among different markets. As Table 2 shows,we find that the oil market that affects others most 3.3.Rolling test results strongly is the US oil market;it is the most affected oil market as the same.The contribution of the Chinese oil market to other markets is 3.3.1.Spillover index The total return spillover among the three oil markets during the 51%,and the effect of the other markets on the Chinese market is 50% the net spillover index of Chinese Daging oil market is 1%,which sample period is presented in Fig.2(50-week window)and Fig.3 demonstrated that the Daging oil market is a weak influencer of (100-week window). international oil market returns.As mentioned before,the end of the We highlight some major events in these plots as follows: 2008 global financial crisis may strongly influence the link between 1.From 2003 to 2013,the spillover index changes from 31%to 63%in China and the world oil market,which should be considered carefully. the 250-day chart and from 42%to 61%in the 500-day chart,reveal- Therefore,we use December 24,2008 as a break point to separate the ing an overall upward trend in the spillover index among the three entire samples into two parts,the two sub-samples'return spillover markets. tables are shown in Tables 3 and 4,respectively.It may be observed 2.The second spike of the spillover index in October 2008 indicates the that the Chinese oil market's influence on the world has become more strong influence of the global financial crisis on the world oil markets. pronounced following the world financial crisis.The total spillover The period featuring the highest spillover index of the global oil 75 SPRLLRETURNS_CHO SPLLRETURNS_CH 50 25 olllds 25 1111111厂111111厂111111厂11 2003200420052006200720082009201020112012 2013 time net Spillover of CH plot.Retums.50 week window.10 step horizon Fig.4.Return spillovers between the Chinese and other oil markets
market j. For example, the element in row 2, column 3 is the volatility contribution from the UK market to the variance of the China market (23.2%). The sum of the variances in column 3 is the total contribution from the UK market to all other markets in terms of return spillover (47%). The total return spillover index is 50.1%. This finding indicates that in the full sample, approximately 50.1% of the forecast error variance is due to return spillovers among different markets. As Table 2 shows, we find that the oil market that affects others most strongly is the US oil market; it is the most affected oil market as the same. The contribution of the Chinese oil market to other markets is 51%, and the effect of the other markets on the Chinese market is 50%, the net spillover index of Chinese Daqing oil market is 1%, which demonstrated that the Daqing oil market is a weak influencer of international oil market returns. As mentioned before, the end of the 2008 global financial crisis may strongly influence the link between China and the world oil market, which should be considered carefully. Therefore, we use December 24, 2008 as a break point to separate the entire samples into two parts, the two sub-samples' return spillover tables are shown in Tables 3 and 4, respectively. It may be observed that the Chinese oil market's influence on the world has become more pronounced following the world financial crisis. The total spillover contribution of the Chinese oil market to other markets has increased from −7% to 3%. We inspect spillover variations over time via the corresponding time series of spillover indices, which are exhibited graphically in the total spillover plots of Figs. 2 and 3. 3.3. Rolling test results 3.3.1. Spillover index The total return spillover among the three oil markets during the sample period is presented in Fig. 2 (50-week window) and Fig. 3 (100-week window). We highlight some major events in these plots as follows: 1. From 2003 to 2013, the spillover index changes from 31% to 63% in the 250-day chart and from 42% to 61% in the 500-day chart, revealing an overall upward trend in the spillover index among the three markets. 2. The second spike of the spillover index in October 2008 indicates the strong influence of the global financial crisis on the world oil markets. The period featuring the highest spillover index of the global oil time The spillover index (%) 200 4 200 5 200 6 200 7 200 8 200 9 201 0 201 1 201 2 201 3 42.5 45.0 47.5 50.0 52.5 55.0 57.5 60.0 62.5 SPILLRETURNS Spillover of all the market plot. Returns. 100 week window. 10 step horizon Fig. 3. Total return spillovers of three oil markets (100-week windows). time The spillover index (%) 200 3 200 4 200 5 200 6 200 7 200 8 2009 201 0 2011 201 2 201 3 -25 0 25 50 75 net Spillover of CH plot. Returns. 50 week window. 10 step horizon SPILLRETURNS_CHO SPILLRETURNS_OTH SPILLRETURNS_CH Fig. 4. Return spillovers between the Chinese and other oil markets. B. Zhang, P. Wang / Economic Modelling 42 (2014) 413–420 417
418 B.Zhang.P.Wang Economic Modelling 42 (2014)413-420 40 930 20 10 2003 20042005 2006 20072008 20092010 201120122013 time net Spillover of CH to US plot.Retums.50 week window.10 step horizon Fig.5.Return spillovers between the Chinese and US oil markets. markets was from July 2007 through December 2008,with the spill- index jumped to positive and then slowly reached the highest level over index reaching its climax in September and October 2008,when approximately 12%,in the end of 2009 with the continuing rising oil Lehman Brothers'bankruptcy was announced.From November 2008 price.Then the index fluctuated around the zero line and had another through the end of 2009,the spillover decreased gradually. positive region between the 3rdquarter of 2011 to the middle of the 3.As Fig.3 shows,since 2010,the index had a sharp rise and then 2012.In sum,the Chinese oil market has always been a return spillover reached the highest level in 2011.Then,the index had gradually receiver before the crisis and has had some effect on international oil dropped back to the average level. markets after the crisis;it has not become an oil pricing power in the 4.The spillover index curve is similar between the 50-week and 100- world although.The previous literature is consistent with the findings week windows.The 100-week return curve appears to be more stable, above.For example,Chen et al.(2009)find that China has little impact as it contains more data on the volatility of the world crude oil markets,whereas the reverse impact is relatively slow and weak. 3.3.2.Retum spillovers between the Chinese market and other markets Fig.4illustrates the return spillover between the Chinese oil market and the other two major markets:WTI and Brent.The black line denotes 3.3.3.Return spillovers between the Chinese market and the US WTI the spillover from the Chinese market to the other markets.The blue oil market Fig.5 illustrates the return spillover between the Chinese market line denotes the spillover from the other markets to the Chinese market. The green line denotes the net directional spillover from the Chinese and the US WTI market.The black line denotes the spillover from the Chinese market to the US markets.The blue line denotes the spillover market to the other markets. from the US markets to the Chinese market.The green line is the net The contribution of the Chinese oil market to the world oil markets is spillover from the Chinese market to the US markets. concentrated between 17%and 71%(250-day window).whereas the index fluctuated and continued to increase in the long run.The highest The net spillover index of China to the US showed a positive trend value occurred at the end of 2009.These findings indicate that the and reached its highest level in the beginning of 2010.Since then,it be- Chinese oil market has become increasingly important in the world oil comes quite stable.It suggests that the Chinese oil market is beginning markets.The return spillover from the world markets to the Chinese to play a more important role in world oil markets. oil market is between 24%and 63%and is higher than the spillover index from China to the world most of the time,especially before the 3.3.4.Return spillovers between the Chinese market and the UK Brent oil crisis that China was a net return spillover receiver.The net return spill- market over from the Chinese to the other markets is between-13%and 12% Fig.6 illustrates the return spillover between the Chinese oil market (250-day window),it's obvious that the 2008 financial crisis could be and UK Brent oil market.The return spillover from the UK to the Chinese a break point to divide the whole period into two sections.The crisis oil market is always higher than that of China to UK except for heavily influenced the net return spillover index,after the crisis the 2009-2010 financial crisis periods,which shows UK's influence to 0 30 20 10- 10 2003T'20041200s'2061200712008T'20091'20101201'1'201212013 time net Spillover of CH to UK plot.Returns.50 week window.10 step horizon Fig.6.Return spillovers between the Chinese market and UK markets
markets was from July 2007 through December 2008, with the spillover index reaching its climax in September and October 2008, when Lehman Brothers' bankruptcy was announced. From November 2008 through the end of 2009, the spillover decreased gradually. 3. As Fig. 3 shows, since 2010, the index had a sharp rise and then reached the highest level in 2011. Then, the index had gradually dropped back to the average level. 4. The spillover index curve is similar between the 50-week and 100- week windows. The 100-week return curve appears to be more stable, as it contains more data. 3.3.2. Return spillovers between the Chinese market and other markets Fig. 4 illustrates the return spillover between the Chinese oil market and the other two major markets:WTI and Brent. The black line denotes the spillover from the Chinese market to the other markets. The blue line denotes the spillover from the other markets to the Chinese market. The green line denotes the net directional spillover from the Chinese market to the other markets. The contribution of the Chinese oil market to the world oil markets is concentrated between 17% and 71% (250-day window), whereas the index fluctuated and continued to increase in the long run. The highest value occurred at the end of 2009. These findings indicate that the Chinese oil market has become increasingly important in the world oil markets. The return spillover from the world markets to the Chinese oil market is between 24% and 63% and is higher than the spillover index from China to the world most of the time, especially before the crisis that China was a net return spillover receiver. The net return spillover from the Chinese to the other markets is between −13% and 12% (250-day window), it's obvious that the 2008 financial crisis could be a break point to divide the whole period into two sections. The crisis heavily influenced the net return spillover index, after the crisis the index jumped to positive and then slowly reached the highest level, approximately 12%, in the end of 2009 with the continuing rising oil price. Then the index fluctuated around the zero line and had another positive region between the 3rdquarter of 2011 to the middle of the 2012. In sum, the Chinese oil market has always been a return spillover receiver before the crisis and has had some effect on international oil markets after the crisis; it has not become an oil pricing power in the world although. The previous literature is consistent with the findings above. For example, Chen et al. (2009) find that China has little impact on the volatility of the world crude oil markets, whereas the reverse impact is relatively slow and weak. 3.3.3. Return spillovers between the Chinese market and the US WTI oil market Fig. 5 illustrates the return spillover between the Chinese market and the US WTI market. The black line denotes the spillover from the Chinese market to the US markets. The blue line denotes the spillover from the US markets to the Chinese market. The green line is the net spillover from the Chinese market to the US markets. The net spillover index of China to the US showed a positive trend and reached its highest level in the beginning of 2010. Since then, it becomes quite stable. It suggests that the Chinese oil market is beginning to play a more important role in world oil markets. 3.3.4. Return spillovers between the Chinese market and the UK Brent oil market Fig. 6 illustrates the return spillover between the Chinese oil market and UK Brent oil market. The return spillover from the UK to the Chinese oil market is always higher than that of China to UK except for 2009–2010 financial crisis periods, which shows UK's influence to time The spillover index (%) 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 -10 0 10 20 30 40 net Spillover of CH to US plot. Returns. 50 week window. 10 step horizon SPILLRETURNS_CHU SPILLRETURNS_USC SPILLRETURNS_CH_ Fig. 5. Return spillovers between the Chinese and US oil markets. time The spillover index (%) 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 -10 0 10 20 30 40 SPILLRETURNS_CHU SPILLRETURNS_UKC SPILLRETURNS_CH_ net Spillover of CH to UK plot. Returns. 50 week window. 10 step horizon Fig. 6. Return spillovers between the Chinese market and UK markets. 418 B. Zhang, P. Wang / Economic Modelling 42 (2014) 413–420
B.Zhang.P.Wang Economic Modelling 42 (2014)413-420 419 Table 5 oil markets.2)Chinese oil market is not always a volatility receiver. Volatility spillover index of three oil markets. Since the end of 2004,partly due to huge demand of oil import from US UK From others world market,Chinese oil market has become the net transmitter of US 56.6 253 18.1 43 volatility,and even reached 30%at the beginning of 2011.3)Chinese oil CH 25.2 57.2 17.6 43 market volatility's influence to the world markets is positive at the UK 23.2 20.6 562 44 current time. Contributon to others 48 46 36 130 This result is somewhat different with that of Ji and Fan(2012).They Contribution including own 105 103 92 43.3% find that the Daqing crude oil spot market is the price taker and is signif- icantly influenced by international oil markets volatilities.Our rolling window spillover index method may be able to find the exact periods China is greater than that of China to UK.In the beginning of 2012. China's return spillover was positive. when Chinese oil market strongly influences the world oil markets. 5.Conclusion 4.Volatility spillover Using the generalized vector autoregressive framework proposed by In the empirical studies,we use the absolute value of returns to Diebold and Yilmaz(2012)wherein forecast-error variance decomposi- define volatility:it is one of the most common academic definitions of tions are invariant to variable ordering and produce continuously vary- volatility and provides the most stable results given varying sample ing indexes,we propose measures of total spillover and directional sizes.Forsberg and Ghysels(2007)find that absolute returns predict vol- spillover between the Chinese and world oil markets between December atility quite well.The authors show that regressors involving volatility 2001 and December 2013.Specifically,the paper uses a rolling window measures based on absolute returns are better at predicting future vol- method,providing a more vivid and insightful picture of the position atility for the following reasons:(i)desirable population predictability and power of the Chinese oil market in the world arena as well as a features,(ii)better sampling error behavior,and (iii)immunity to thorough analysis of how shocks and volatility are transmitted from jumps. the world oil market to the Chinese oil market and vice versa.In this We obtain similar to that of the return spillover index in Table 5.The study,we find that the Chinese oil market is heavily affected by world UKoil market is a net volatility receiver(8%).China oil market is a net oil markets in terms of both return and volatility spillovers.Although volatility contributor. the net effect remains limited,the influence of the Chinese oil market Fig.7 illustrates that since the beginning of 2007,the spillover index on the world market has enhanced substantially during this period. continued to rise and reached its peak in the end of 2008,which was the Table 6 provides information about oil imports in China and the period marked by severe financial crisis.The index remained at a high world.This table also presents the proportion of Chinese oil imports level in 2009 and has gradually decreased.The 2008 financial crisis led within world oil imports.All the three indexes tend to increase over to a pronounced increase in the volatility spillover index.The index time.It shows that since 2005,the proportion of Chinese oil imports reached other apexes in the middle of 2011 and 2013 and followed among world oil imports has consistently grown.This trend suggests rapid decline in the next half years. that China is playing a more important role in the global oil import In Fig.8,the black line denotes the spillover from the Chinese market and the world's oil market.The impact of China on the global oil trade to the other markets within 50 week rolling windows.The blue line is becoming more significant.We could rationally deduce that this denotes the spillover from the other markets to the Chinese market. phenomenon is caused by China's strong GDP growth and economic The green line is the net directional spillover from the Chinese market condition,especially compared with the rest of the world.Note that to the other markets.Compared with the return spillovers between the proportion has been more than 10%,proving that China's influence the Chinese market and other markets in Fig.4,there are several notable on the oil market has enhanced in recent periods. differences:1)the magnitudes have enlarged,especially when global Considering that the Chinese oil market's net spillover index is neg- financial crisis happened in the end of 2008,the volatility index reached ative in most periods,we may suppose that China's larger share of global its peak.This shows that global financial crisis has changed the return oil imports contributes to its larger oil market net spillover index.In spillover,and changed more strongly the volatility pattern of the world addition,China's larger share of global oil imports may be caused by 70 SPILLRETURNS 60 10 1111111111111111111111111111111111111111 2003 2004200520062007200820092010201120122013 time Spillover of all the market plot.Retums.50 week window.10 step horizon Fig.7.Total volatility spillovers,three oil markets
China is greater than that of China to UK. In the beginning of 2012, China's return spillover was positive. 4. Volatility spillover In the empirical studies, we use the absolute value of returns to define volatility; it is one of the most common academic definitions of volatility and provides the most stable results given varying sample sizes. Forsberg and Ghysels(2007) find that absolute returns predict volatility quite well. The authors show that regressors involving volatility measures based on absolute returns are better at predicting future volatility for the following reasons: (i) desirable population predictability features, (ii) better sampling error behavior, and (iii) immunity to jumps. We obtain similar to that of the return spillover index in Table 5. The UK oil market is a net volatility receiver (−8%). China oil market is a net volatility contributor. Fig. 7 illustrates that since the beginning of 2007, the spillover index continued to rise and reached its peak in the end of 2008, which was the period marked by severe financial crisis. The index remained at a high level in 2009 and has gradually decreased. The 2008 financial crisis led to a pronounced increase in the volatility spillover index. The index reached other apexes in the middle of 2011 and 2013 and followed rapid decline in the next half years. In Fig. 8, the black line denotes the spillover from the Chinese market to the other markets within 50 week rolling windows. The blue line denotes the spillover from the other markets to the Chinese market. The green line is the net directional spillover from the Chinese market to the other markets. Compared with the return spillovers between the Chinese market and other markets in Fig. 4, there are several notable differences: 1) the magnitudes have enlarged, especially when global financial crisis happened in the end of 2008, the volatility index reached its peak. This shows that global financial crisis has changed the return spillover, and changed more strongly the volatility pattern of the world oil markets. 2) Chinese oil market is not always a volatility receiver. Since the end of 2004, partly due to huge demand of oil import from world market, Chinese oil market has become the net transmitter of volatility, and even reached 30% at the beginning of 2011. 3) Chinese oil market volatility's influence to the world markets is positive at the current time. This result is somewhat different with that of Ji and Fan (2012). They find that the Daqing crude oil spot market is the price taker and is significantly influenced by international oil markets volatilities. Our rolling window spillover index method may be able to find the exact periods when Chinese oil market strongly influences the world oil markets. 5. Conclusion Using the generalized vector autoregressive framework proposed by Diebold and Yilmaz (2012) wherein forecast-error variance decompositions are invariant to variable ordering and produce continuously varying indexes, we propose measures of total spillover and directional spillover between the Chinese and world oil markets between December 2001 and December 2013. Specifically, the paper uses a rolling window method, providing a more vivid and insightful picture of the position and power of the Chinese oil market in the world arena as well as a thorough analysis of how shocks and volatility are transmitted from the world oil market to the Chinese oil market and vice versa. In this study, we find that the Chinese oil market is heavily affected by world oil markets in terms of both return and volatility spillovers. Although the net effect remains limited, the influence of the Chinese oil market on the world market has enhanced substantially during this period. Table 6 provides information about oil imports in China and the world. This table also presents the proportion of Chinese oil imports within world oil imports. All the three indexes tend to increase over time. It shows that since 2005, the proportion of Chinese oil imports among world oil imports has consistently grown. This trend suggests that China is playing a more important role in the global oil import and the world's oil market. The impact of China on the global oil trade is becoming more significant. We could rationally deduce that this phenomenon is caused by China's strong GDP growth and economic condition, especially compared with the rest of the world. Note that the proportion has been more than 10%, proving that China's influence on the oil market has enhanced in recent periods. Considering that the Chinese oil market's net spillover index is negative in most periods, we may suppose that China's larger share of global oil imports contributes to its larger oil market net spillover index. In addition, China's larger share of global oil imports may be caused by Table 5 Volatility spillover index of three oil markets. US CH UK From others US 56.6 25.3 18.1 43 CH 25.2 57.2 17.6 43 UK 23.2 20.6 56.2 44 Contribution to others 48 46 36 130 Contribution including own 105 103 92 43.3% time The spillover index (%) 200 3 200 4 200 5 200 6 200 7 200 8 200 9 201 0 201 1 201 2 201 3 10 20 30 40 50 60 70 SPILLRETURNS Spillover of all the market plot. Returns. 50 week window. 10 step horizon Fig. 7. Total volatility spillovers, three oil markets. B. Zhang, P. Wang / Economic Modelling 42 (2014) 413–420 419
420 B.Zhang.P.Wang Economic Modelling 42 (2014)413-420 80 2003 2004 2005 2006 .2007 2008 200920102011 20122013 time net Spillover of CH plot.Retums.50 week window.10 step horizon Fig 8.Volatility spillovers between the Chinese and other oil markets. Table6 Cai,Yijie,Chou.Ray Yeutien,Li.Dan,2009.Explaining international stock correlations with Oil import table of China and the world (from 2002 to 2011). CPI fluctuations and market volatility.L Bank.Financ.33.2026-2035. Chang.Chia-Lin,McAleer.Michael,Tansuchat,Roengchai,2010.Analyzing and forecasting Year Chinese oil World oil Proportion of Chinese volatility spillovers,asymmetries and hedging in major oil markets.Energy Econ.32. Imports/ imports/ Oil imports amongworld 1445-1455. million tons million tons oil imports ( Chen,K.C.Chen,Shaoling.Wu,Lifan,2009.Price causal relations between China and the world oil markets.Glob.Financ.I.20,107-118. 2002 q96 2152 4.68 Diebold.Francis X..Yilmaz,Kamil,2009.Measuring financial asset return and volatility 2003 129.6 2259 5.71 spillovers with application to global equity markets.Econ.I.119,158-171. 2004 170.6 2373 7.19 Diebold.Francis X.Yilmaz,Kamil,2011.Equity market spillovers in the Americas.In: 2005 168.5 2462 6.84 Alfaro.R.(Ed.).Financial Stability.Monetary Policy.and Central Banking.vol.15. 2006 191.7 2741 6.99 Bank of Chile Central Banking Series,Santiago.pp.199-214. 2007 203.1 2897 7.01 Diebold,Francis X..Yilmaz.Kamil,2012.Better to give than to receive:predictive 2008 217.8 2848 7.64 directional measurement of volatility spillovers.Int.J.Forecast.28,57-66. 2009 2533 2729 9.28 Du,Limin,He.Yanan,Wei,Chu,2010.The relationship between oil price shocks and 2010 294.5 2790 10.55 China's macro-economy:an empirical analysis.Energy Policy 38,4142-4151. 305.9 10.75 Forsberg.Lars.Ghysels,Eric,2007.Why do absolute retums predict volatility so well?J. 2011 2846 Financ.Econ.1.31-6/. Furio.Dolores.Chulia.Helena,2012.Price and volatility dynamics between electricity and fuel costs:some evidence for Spain.Energy Econ.34.2058-2065. China's rapid economic growth and expanding energy demand amid a Ji.Qiang.Fan,Ying.2012.Analysis of the information transmission between Chinese and slowdown in the rest of the world due to financial crisis. nternational oil markets.Working Paper. Jiao,Jian-Lin,Fan,Ying.Wei,Yi-Ming.Han,Zhi-Yong Zhang.Jiu-Tian,2007.Analysis of the Because of China's increasing impact on the world,we could 6 vement between Chinese and international crude oil price.Int.J.Glob.Energy reasonably expect that China will no longer be just a price-taker. China represents a large share of the world's aggregate oil demand,as Lanza,Alessandro.Manera,Matteo,Giovaannini,Massimo,2005.Modeling and forecast- ing cointegrated relationships among heavy oil and product prices.Energy Econ it is growing so rapidly.With the help of its own oil futures market. 27.831-848. China could be able to strengthen its oil pricing power.Because the Li,Raymond.Leung Guy C.K..2011.The integration of China into the world crude oil impact of China's oil market on the world oil market has intensified in market since 1998.Energy Policy 39.5159-5166. Lin.Sharon Xiaowen,Tamvakis,Michael N.2001.Spillover effects in energy futures recent years,increasing oil pricing power has been a reasonable and markets.Energy Econ.23,43-56. necessary decision for Chinese policy makers to consider. Liu,Qingfu,Tu,Anthony H..2012.Jump spillovers in energy futures markets:implications for diversification benefits.Energy Econ.34,1447-1464. Acknowledgments Sadorsky.Perry,2012.Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies.Energy Econ,Elsevier,34. 248-255. The authors acknowledge the financial support from the China Swanson,Norman R.,1998.Money and output viewed through a rolling window. National Science Fund 71171108 and 71371096. J.Monet.Econ.41,455-474. Zhou.Xiangyi.Zhang.Weijin,Zhang.Jie,2012.Volatility spillovers between the Chinese and world equity markets.Pac.Basin Financ J.10.247-270. References BP Statistical Review of World Energy.2011.BP Statistical Review of World Energy www.bp.com/centres/energy/index.asp
China's rapid economic growth and expanding energy demand amid a slowdown in the rest of the world due to financial crisis. Because of China's increasing impact on the world, we could reasonably expect that China will no longer be just a price-taker. China represents a large share of the world's aggregate oil demand, as it is growing so rapidly. With the help of its own oil futures market, China could be able to strengthen its oil pricing power. Because the impact of China's oil market on the world oil market has intensified in recent years, increasing oil pricing power has been a reasonable and necessary decision for Chinese policy makers to consider. Acknowledgments The authors acknowledge the financial support from the China National Science Fund 71171108 and 71371096. References BP Statistical Review of World Energy, 2011. BP Statistical Review of World Energy. www.bp.com/centres/energy/index.asp. Cai, Yijie,Chou, Ray Yeutien,Li, Dan, 2009. Explaining international stock correlations with CPI fluctuations and market volatility. J. Bank. Financ. 33, 2026–2035. Chang, Chia-Lin,McAleer, Michael,Tansuchat, Roengchai, 2010. Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets. Energy Econ. 32, 1445–1455. Chen, K.C., Chen, Shaoling,Wu, Lifan, 2009. Price causal relations between China and the world oil markets. Glob. Financ. J. 20, 107–118. Diebold, Francis X., Yilmaz, Kamil, 2009. Measuring financial asset return and volatility spillovers with application to global equity markets. Econ. J. 119, 158–171. Diebold, Francis X., Yilmaz, Kamil, 2011. Equity market spillovers in the Americas. In: Alfaro, R. (Ed.), Financial Stability, Monetary Policy, and Central Banking, vol. 15. Bank of Chile Central Banking Series, Santiago, pp. 199–214. Diebold, Francis X., Yilmaz, Kamil, 2012. Better to give than to receive: predictive directional measurement of volatility spillovers. Int. J. Forecast. 28, 57–66. Du, Limin, He, Yanan, Wei, Chu, 2010. The relationship between oil price shocks and China's macro-economy: an empirical analysis. Energy Policy 38, 4142–4151. Forsberg, Lars,Ghysels, Eric, 2007. Why do absolute returns predict volatility so well? J. Financ. Econ. 1, 31–67. Furió, Dolores,Chuliá, Helena, 2012. Price and volatility dynamics between electricity and fuel costs: some evidence for Spain. Energy Econ. 34, 2058–2065. Ji, Qiang, Fan, Ying, 2012. Analysis of the information transmission between Chinese and international oil markets. Working Paper. Jiao, Jian-Lin, Fan, Ying,Wei, Yi-Ming,Han, Zhi-Yong,Zhang, Jiu-Tian, 2007. Analysis of the co-movement between Chinese and international crude oil price. Int. J. Glob. Energy 27, 61–76. Lanza, Alessandro,Manera, Matteo,Giovaannini, Massimo, 2005. Modeling and forecasting cointegrated relationships among heavy oil and product prices. Energy Econ. 27, 831–848. Li, Raymond, Leung, Guy C.K., 2011. The integration of China into the world crude oil market since 1998. Energy Policy 39, 5159–5166. Lin, Sharon Xiaowen, Tamvakis, Michael N., 2001. Spillover effects in energy futures markets. Energy Econ. 23, 43–56. Liu, Qingfu,Tu, Anthony H., 2012. Jump spillovers in energy futures markets: implications for diversification benefits. Energy Econ. 34, 1447–1464. Sadorsky, Perry, 2012. Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies. Energy Econ., Elsevier, 34, 248–255. Swanson, Norman R., 1998. Money and output viewed through a rolling window. J. Monet. Econ. 41, 455–474. Zhou, Xiangyi, Zhang, Weijin, Zhang, Jie, 2012. Volatility spillovers between the Chinese and world equity markets. Pac. Basin Financ. J. 10, 247–270. time The spillover index (%) 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 -40 -20 0 20 40 60 80 SPILLRETURNS_CHO SPILLRETURNS_OTH SPILLRETURNS_CH net Spillover of CH plot. Returns. 50 week window. 10 step horizon Fig. 8. Volatility spillovers between the Chinese and other oil markets. Table 6 Oil import table of China and the world (from 2002 to 2011). Year Chinese oil Imports/ million tons World oil imports/ million tons Proportion of Chinese Oil imports amongworld oil imports (%) 2002 99.6 2152 4.68 2003 129.6 2259 5.71 2004 170.6 2373 7.19 2005 168.5 2462 6.84 2006 191.7 2741 6.99 2007 203.1 2897 7.01 2008 217.8 2848 7.64 2009 253.3 2729 9.28 2010 294.5 2790 10.55 2011 305.9 2846 10.75 420 B. Zhang, P. Wang / Economic Modelling 42 (2014) 413–420