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Identifying bubbles and the contagion effect between oil and stock markets: New evidence from China
Journal Pre-proof Identifying bubbles and the contagion effect between oil and stock markets: New evidence from China Zhao Zhao, Huwei Wen, Ke Li PII:
S0264-9993(19)31819-X
DOI:
https://doi.org/10.1016/j.econmod.2020.02.018
Reference:
ECMODE 5160
To appear in:
Economic Modelling
Received Date: 14 November 2019 Revised Date:
4 January 2020
Accepted Date: 9 February 2020
Please cite this article as: Zhao, Z., Wen, H., Li, K., Identifying bubbles and the contagion effect between oil and stock markets: New evidence from China, Economic Modelling (2020), doi: https:// doi.org/10.1016/j.econmod.2020.02.018. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.
Identifying bubbles and the contagion effect between oil and stock markets: New evidence from China Zhao Zhaoa, Huwei Wenb*, Ke Lic, a
School of Economics, Huazhong University of Science and Technology, Wuhan
430074, Hubei, China b
Research Center for Central China Economic and Social Development, School of
Economics & Management, Nanchang University, Nanchang 330031, Jiangxi, China c
Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education of
China), School of Mathematics and Statistics, Hunan Normal University, Changsha 410081, Hunan, China * Corresponding author: Research Center for Central China Economic and Social Development, School of Economics& Management, Nanchang University, Nanchang 330031, Jiangxi, China E-mail: [email protected] (H. Wen)
Acknowledgements We gratefully acknowledge and thank for the helpful comments and suggestions from the Editor, Professor Paresh Narayan and three anonymous reviewers on the previous version of this paper. We acknowledge the financial support from the Humanities and Social Science Fund of Ministry of Education of China (No. 18YJC790232) and the National Natural Science Foundation of China (Grant No. 71803055, 71773028). 1
Identifying bubbles and the contagion effect between oil and stock markets: New evidence from China
Abstract This study employs six price series from international and Chinese crude oil markets and Chinese stock market to test for bubbles. Based on the efficient market hypothesis and the Generalized Supremum Augmented Dickey–Fuller test, we identify two bubble episodes in each series, namely, the 2007–2008 global financial crisis and 2014–2015 oil excess capacity bubbles. Furthermore, using Granger causality test, we find empirical evidence for the bilateral contagion effect of bubbles between oil markets and Chinese stock market. The direction of contagion is from stock to oil market for the first bubble and from oil to stock for the second. We also find that Chinese oil market is becoming increasingly sensitive to the fluctuation of the international oil prices. These findings provide important enlightenments for regulators to prevent systematic financial risks and for investors to diversify their portfolios.
Keywords: Oil price; Stock market; Bubbles; Generalized SADF; Contagion effect
1. Introduction With the rapid growth of economy and the upgrade of energy consumption structure, China’s oil consumption has increased dramatically over the last decades. 1
According to BP Statistical Review of World Energy, China’s oil consumption increased from 110.3 million tonnes oil equivalent (Mtoe) to 608.4 Mtoe over the period 1990–2017. In 2017, China’s oil consumption accounted for 13.2% of the global consumption, ranked closely behind the United States (19.8%). Moreover, China’s oil imports are growing quickly because of the insufficient oil reserve. In 2017, China’s oil importation reached 10,241 thousand barrels per day, which surpassed the United States, and resulted in a degree of dependence on import as high as over 68%. The strict control on refined oil prices in China used to be considered as a protecting wall from the international oil price fluctuation. However, China has been working hard on the market-oriented reform of refined oil pricing mechanism. On March 26, 2013, China carried out a reform that shortens the refined oil adjustment cycle from 22 workdays to 10 workdays and removes the limit of 4% on oil price change1. This reform has greatly relaxed the control on refined oil prices. Further, on March 26, 2018, the crude oil future has been listed on Shanghai Future Exchange. The price discovery function of crude oil future has further improved the oil pricing mechanism. With the increase of dependence on oil import and the revolutions of oil pricing system in China, the domestic oil prices are becoming increasingly related to international oil prices. Given this background, fluctuations of international oil prices have noticeable impacts on Chinese oil market (Jia et al., 2015), real economy (Tang et al., 2010), and
1
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stock market (Xiao et al., 2018). For instance, if international oil prices increase, on the one hand, the increase in cost will result in investment and output decrease, which will affect the stock market adversely; however, on the other hand, the stock market could also be positively affected through investor sentiments and spillover effects (Li and Wei, 2018). Besides the ambiguous impacts from international oil price to Chinese stock market, China’s economic growth also affects international oil prices (Beirne et al., 2013; Wang and Zhang, 2014) as China’s oil import takes an important proportion of global oil trade. Considering the complicated impact mechanisms and the important practical enlightenments for investors and policymakers, we study the dependence between oil and stock markets from a new perspective, i.e., the contagion effect of bubbles. Existing studies support the importance of bubbles from multiple perspectives. Narayan and Narayan (2017) conclude bubbles are important in explaining the energy price adjustment. Narayan et al. (2013) find positive effects of trading volume on stock price bubbles. Narayan, Sharma, and Phan (2016) show stock price bubbles are welfare enhancing. Narayan, Phan, Thuraisamy, et al. (2016) find stock portfolios constructed based on asset price bubbles have the minimum volatility. Motived by these findings, we attempt to find answers for the following questions. Do bubble episodes exist in oil and Chinese stock markets? If yes, then when do the bubbles begin and collapse? What is the relationship between the bubbles in oil and Chinese stock markets? Is there a contagion effect between the bubbles? What is the direction of the contagion effect? 3
To address these questions, we use the most recently proposed Generalized Supremum Augmented Dickey–Fuller (GSADF) method to test for bubbles in Chinese and international crude oil prices, and Chinese energy sector and market indices from September 1, 2004 to July 9, 2018. Based on the backward Supremum Augmented Dickey–Fuller (BSADF) statistics, we identify two bubble episodes in each of the time series during our sample period: the 2007–2008 global financial crisis and the 2014–2015 oil excess capacity. Analyzing bubbles in each of the six time series, we find the following results that offer important clues to the relationship between extreme price volatilities in oil and stock markets. First, China’s crude oil prices are more sensitive to international crude oil prices during the 2014–2015 oil excess capacity than during the 2007–2008 global financial crisis. Second, the 2014– 2015 bubble starts in crude oil prices and then spreads to the Chinese stock market, whereas the transmission of the 2007–2008 bubble is the other way around. We further explain the changing contagion effect between oil and stock price bubbles and enlighten regulators on preventing systematic financial risks. Our research is of great policy significance given the key role oil plays in Chinese economy and the strong government regulations in refined oil prices and in stock markets. Different from previous studies, we define a price bubble based on the efficient market hypothesis rather than the conventionally-used rational bubble theory. According to the rational bubble theory, a bubble is defined as the difference between actual price and fundamental price. However, scholars such as Pavlidis et al. (2018), argue that the measurement of fundamental price is too subjective and always contain 4
serious specification errors. Thus, they believe using the market expectation of the price is a better way to detect asset price bubbles. Given the young age of Chinese crude oil futures market and the low dividend ratio of Chinese stocks, it is inappropriate to calculate the corresponding fundamental prices based on oil future prices and stock dividends. In addition, it is well-known that fundamental prices for both oil and stock should be nonexplosive to reflect the general law of economic growth, thus we define a price bubble directly on the actual price: a bubble exists if an explosive process occurs in the actual price. This definition is consistent with Pavlidis et al. (2018), except that we use the actual price of the past day as the market expectation of the price. Moreover, to the best of our knowledge, we are the first to investigate the contagion effect of oil and stock price bubbles identified by the BSADF method based on the Granger causality test. Literature provides substantial evidence on the spillover effects between oil and stock markets, but none of them focuses on whether the price bubble is contagious across the markets. During bubble periods, prices are no longer simply determined by supply and demand, so the contagion effect should be different from the usual spillover effect. Most of the early articles define a contagion effect as the increasingly-close correlations among assets in turbulence periods. However, it has been criticized for ignoring the irrational effects caused by extreme events. Researchers then employ extreme value theory (EVT) (Chen and Lv, 2015), copula approach (Wen et al., 2012), quantile regression (Baur and Schulze, 2005) and multinomial logistic regression (Bae et al., 2003) to measure the “co-exceedance” 5
contagion effect characterized by the joint occurrence of two exceedances. Consistent with these studies, our research takes the extreme event into account. But different from them, we define an extreme event by the occurrence of a bubble, which we believe is more intuitive than using the EVT to calculate the cut-offs. Gürkaynak (2008) surveys the methods theretofore for testing bubbles and concludes that econometric detection of asset price bubbles cannot be achieved with a satisfactory degree of certainty. A breakthrough is made by Phillps et al. (2011), who propose the SADF test method based on the supremum of a set of forward recursive right-tailed ADF tests. The method is well-known for detecting periodically collapsing bubble with discriminatory power. To improve the power for testing multiple bubbles, Phillps et al. (2015) extend SADF to GSADF, making the starting points of the examination windows also recursive. They also propose a consistent real-time date-stamping strategy based on BSADF statistics. This method has turned out to be superior to its rivals (Pavlidis et al., 2018) and thus has been widely used in previous studies, such as Bohl et al. (2013), Caspi et al. (2018), Su et al. (2017), and Sharm and Escobari (2018). Employing GSADF test and BSADF statistics, we can identify multiple bubbles in prices with high power and precise dates, which enables us to explore the contagion effect intuitively. We find empirical evidence supporting the contagion effect of bubbles in oil markets and Chinese stock market that when a bubble occurs in one of the oil or stock markets, it is very likely that it will spread to the other market. Our research not only provides knowledge on the bubble periods and contagion 6
effects in international oil, Chinese oil and stock markets, but also contributes to the literature on a wide range of research issues, including the increasingly-close relationship between international and Chinese oil markets, abnormal flunctuations or bubbles in oil and stock markets, dependence and spillover effects of oil and stock markets, and the bubble contagion effects under extreme market conditions. A detailed analysis is provided in Section 2. The rest of the paper is presented as follows: Section 2 provides the literature review; Section 3 describes the dataset; Section 4 introduces the empirical model; Section 5 provides the main results; Section 6 checks for robustness and Section 7 concludes the paper. 2. Literature Oil and stock price fluctuations have been extensively investigated by the academia. First, lots of studies regard the dramatic changes in oil prices as structural breaks or bubbles. For instance, Lee et al. (2010) find multiple structural breaks in the WTI and WTIF markets by establishing a reconstituted component-ARJI model. Chen et al. (2015) conduct various unit root tests for the WTI–Brent crude oil price spreads and verify the hypothesis of persistence change. In July 2008, the international oil prices reached more than $140/barrel, causing hot discussions. Phillps and Yu (2011), Shi and Arora (2012), and Gronwald (2016) all detect bubbles in oil prices in 2008. Tokic (2010) claims the bubble is caused by the anti-deflationary strategies of the Federal Reserve after the 2008 financial crisis. In the beginning of 2015, the international oil prices fell to less than $50/barrel. Fantazzini (2016) identifies a 7
negative bubble in oil prices in 2014/2015. Tokic (2015) explains the plummet by the devaluation of Euro versus US dollars and Domanski et al. (2015) suggest the increased leverage of oil firms could be the reason. Sharm and Escobari (2018) attribute the oil price collapse to the increase in non-OPEC nations’ oil exports, especially the continued growth in US shale production. Second, some studies detect bubbles in Chinese stock market. For instance, using the Log Periodic Power Law (LPPL) method, Jiang et al. (2010) detect the 2005-2007 and 2008-2009 bubbles in Chinese stock market. Shu and Zhu (2019) find three positive bubbles and four negative ones from March 1, 2005 to April 2, 2018 in Chinese stock market. Gong et al. (2019) analyze bubbles in Chinese stock market based on the rational asset price bubble theory. While Yu and Ma (2019) consider the breaks in time trend following the core idea of Narayan and Popp (2010) and find a rather short bubble period, they do not provide an effective method to distinguish breaks in time trend from explosive processes. The above studies focus on structural changes or bubbles in oil market and in Chinese stock market separately, but they do not link up the abnormal behaviors in the two markets. We attempt to fill this gap by dealing with their linkage through analyzing the exact time of bubble periods in Chinese stock market, Chinese crude oil market and international crude oil market. There is a large body of literature investigating the impact of oil price fluctuations on macro economy. Most scholars believe an adverse impact of oil-price increase on real economy, which can be explained by several channels, including 8
supply-side shocks, wealth transfer, inflation, real balance, unexpected effects, and adjustment costs (Brown and Yücel, 2002; El Anshasy and Bradley, 2012; Kim et al., 2017). However, Wei and Guo (2016) find evidence for the positive effect of oil-price increase on China’s output and explain it by the increase in exports. Jarrett et al. (2019) study the mixed relationship among oil price, financial stability, and economic growth, and infer that better financial institutions mitigate the influence of oil volatility on economic growth. Recent studies focus on risk spillover effects between oil and stock markets. Smyth and Narayan (2018) provide a nice survey on how oil prices influence stock returns. In general, positive risk spillover effects from oil markets to financial markets are identified (Li and Wei, 2018; Wang and Wu, 2018), and the effects are found to be time-varying (Xiao et al., 2018) and asymmetric (Xu et al., 2019). For instance, based on a dataset of 2178 Islamic stocks, Narayan et al. (2019) find oil prices influence about 32% of the stocks significantly. You et al. (2017) and Wang and Wu (2018) conclude that the change of oil-price has asymmetric impacts on financial markets owing to market conditions, investor sentiments, and uninformed traders in markets. Balcilar et al. (2018) and Tsuji (2018) find bilateral risk spillover effects between equity markets and oil markets. In addition, Chen and Lv (2015), Ding et al. (2016), Mensi et al. (2014), Sukcharoen et al. (2014), and Wen et al. (2012) focus on the relation under extreme market conditions and provide evidence for the positive extreme dependence. It is clear to see that the majority of literature pays attention to the general 9
linkage between oil and stock markets and the rest focuses on the extreme-value dependence, but none of them analyzes the particular contagion relationship between speculative bubbles in these markets. Thus, we first test for bubbles based on GSADF method, then determine the origination and termination of bubbles based on BSADF statistics, and finally identify the contagion effects of the bubbles using the Granger causality test. In sum, existing literature investigates the fluctuation characteristics of oil and stock prices, and the impacts of oil-price fluctuations on macro economy and on stock markets. Relying on different theoretic frameworks, they find ample evidence for the close relationships among oil prices, real economy, and stock markets. However, the two strands of literature are separate from each other. Thus, we integrate both strands by studying the contagion effect between bubbles in Chinese stock market and oil markets, where both international and Chinese crude oil prices are considered. 3. Data To test for bubbles and identify the contagion effect between oil and stock markets, we adopt two indices reflecting the whole stock market of China (CSI300) and the stock market related to energy in China (Oil & Gas Exploration Index, denoted as OGEI), respectively. We also consider two prices representing the crude oil market of China (Daqing and Shengli) and two international crude oil prices (WTI and Brent). Daqing Oilfield is well-known as China’s largest oilfield and one of the world’s top 10 oilfields, and Shengli Oilfield is the second largest oilfield in China. Both oilfields produced a total of 57.4 million tons of oil in 2017, accounting for 10
approximately 30% of the total output in China. Thus, their prices can represent the oil price in China. The data are from the database of the knowledge service system for energy (http://energy.ckcest.cn/home). Besides, WTI and Brent prices are the benchmark prices for the international oil market. These two series are from EIA website (www.eia.gov). All the six time series are daily data. Limited by the availability of data of Chinese crude oil prices, we select a sample period from September 1, 2004 to July 9, 2018. For simplicity and good comparison, we remove dates with missing values. Finally, we obtain 3,138 observations for each variable. Note that all the four oil prices are measured in US$ per barrel, where WTI and Brent oil prices are free on board. 4. Methodology According to the efficient market hypothesis, the market fundamental price following a random walk well reflects the usual impact of shocks from expectation, noise, and variety of market rules, as well as the relationship between supply and demand that determines the price (Fama, 1970). Therefore, we define that a price bubble exists if a significant departure from random walk occurs in the actual price. Denote the index or price series to be tested by , = 1, … , . Assume the regression model has a weak (local to zero) intercept item, as follows: = + + ,
(1)
where is the sample size, is a localizing coefficient, and the disturbance term
∼ 0, . Following Phillps et al. (2015) , we set > 0.5 to ensure that the intercept is asymptotically negligible. is a random walk with drift when = 1, 11
and an explosive process when > 1, implying a departure from the efficient market. We regard the departure as the signal of the existence of a bubble. Naturally, we build the null hypothesis : = 1 and the corresponding alternative hypothesis : > 1
to test for bubbles. Only if is rejected in the right-side test, can we conclude that a bubble exists. To use the recursive right-side ADF test, we complement Model (1) with transient dynamics to deal with the autocorrelation in disturbance terms as in the standard ADF test. Suppose the regression subsample begins at the !th fraction of the total sample and ends at the ! th fraction. The recursive regression is as follows: * " = #$% $& + '$% $& + ∑+ * - )$% $& " * + ,
(2)
where #$% $& is the constant intercept, '$% $& = 1 − $% $& and / is the lag order selected by minimizing the Akaike information criterion (AIC) or Bayesian information criterion (BIC), ∼ 00, $% $& 1. The ADF statistic from Model (2) can be denoted by 23$%& . To test whether bubbles exist during the sample period, we use the GSADF $
statistic, which is defined as the supremum of the ADF statistics sequence over all the feasible ranges of ! and ! . Suppose the minimum window width of regression
sample is 4! 5, where 4. 5 denotes the integer part of the argument. The GSADF
statistic, denoted by 6723! , is as follows: 6723! = sup
$& $& ∈<$= ,> ?23$% @ $% ∈< ,$& $= >
= sup
CD D B % & $& ∈<$= ,> AEF0B CD D 1G. % & $% ∈< ,$& $= >
(3)
The expression indicates that the GSADF test not only allows the subsample to change end point fraction ! from ! to 1 but also allows it to change the starting point
fraction ! from 0 to ! − ! . Compared with the SADF test that only allows the 12
change of the end point fraction, this setting ensures that the GSADF test has discriminatory power even when more than one speculative bubble exists during the sample period. Although Eq. (3) can examine the presence of bubbles, it cannot reveal when the bubbles are formed/burst. To locate the specific starting and end points of bubbles, we compare the BSADF statistics with the 95% SADF finite sample critical value sequence. Suppose that the end point of a sample to be tested is fixed at 4! 5 and that
the minimum window size is set to 4! 5. BSADF statistics are constructed following two steps. First, continuously change the starting point of the sample backwards from 4! − ! 5 to 0. For every change, one observation is increased, and one ADF statistic is obtained. Second, calculate the supremum of the ADF statistics sequence, which is, H723$& ! = sup$% ∈< ,$& $=> ?23$%& @. $
(4)
The starting and end point fractions of a bubble are respectively determined by the following equations: !̂F =
!̂R =
inf ?! : H723$& ! > M$NOPQ @, & ! ∈
inf ?! : H723$& ! < M$NOPQ @, & ! ∈
(5) (6)
where M$NOPQ is the critical value of SADF when the sample size equals to 4! 5, and &
4S TUV 5 is the minimum duration2 of a bubble. The date-stamping strategy suggests
that the starting point (denoted by 4!̂F 5) is defined as the first observation whose 2
Phillips et al. (2015) highlight that frequency dependent parameter S can be selected according to the setting and the sample size. Here, we set S = 0.4. 13
H723$& ! surpasses the critical value of the SADF statistic, and the end point
(denoted by Z!̂R [) is defined as the first observation after 4!̂F + S TUV / 5 whose H723$& ! becomes smaller than the critical value of the SADF statistic.
Phillps et al. (2015) prove under the null hypothesis that is a random walk
with an asymptotically negligible drift, the limit distributions of SADF and GSADF test statistics are respectively as follows: % % \]& ^ ] _= ]E`E & & %⁄& % % A_= ]E& `E a_= ]E`Eb G
,
(7)
and sup
D % $ \]$& & ]$% & $e ^ _D & ]$`$ <]$& ]$% > & e % & %⁄& D& D %⁄& & $e A$e _D ]$ `$ a_D & ]$`$ b G % %
$& ∈<$= ,> d $% ∈< ,$& $= >
f,
(8)
where !g = ! − ! . H represents a standard Brownian motion. Both limit distributions are non-standard, and consequently the critical values can only be obtained by numerical simulation (asymptotic critical values) or Monte Carlo simulation (finite sample critical values). Specifically, we use finite sample critical values in the empirical study which are obtained from the 2,000 replications of Monte Carlo simulations. 5. Empirical Results [Insert Table 1 here] As a result of price control from Chinese government, the average prices of Daqing and Shengli crude oil are lower than the international prices represented by WTI and Brent crude oil. Table 1 presents the descriptive statistics for all six series. Fig. 1 exhibits data graphs for the six time series, each of which indicates two periods 14
of abnormity. Moreover, the figure shows that each of the six series increases dramatically from 2006 to 2007 and reaches the peak around 2007. After that peak, the trends of the two stock indices reveal an apparent difference from those of the four oil prices. These features motivate us to explore the questions raised in the introduction: whether and when bubbles occur in each of the six series, and whether any relevance exists between the presence of bubbles in oil and stock markets. [Insert Figure 1 here] On the basis of Eq. (3), we compute the GSADF statistics for the two indices and four price series and report them in the second column of Table 2. As all series share the same sample size, which is = 3,138, they share the sets of critical values. Following the recommendation for the window width fraction of the smallest sample proposed by Phillps et al. (2015), we set ! = 0.01 + 1.8⁄√ = 0.042, and thus has a
minimum window size 4! 5 = 132. In addition, Phillps et al. (2015) recommend
using a fixed lag length in both the GSADF testing and the BSADF date-stamping. Therefore, we set the lag order / = 0 to consider a transient dynamic relation. We also consider / = 3 in Section 6 for robustness check. The corresponding 90%, 95%
and 99% finite sample critical values obtained from the 2,000 replications of Monte Carlo simulations for / = 0 are 2.281, 2.508 and 2.908, respectively. Comparing the six GSADF statistics with the critical values, we find strong evidence supporting the existence of bubbles in all tested series. The null hypothesis of random walk with drift is rejected at the 1% significance level for each of the series, except the WTI crude oil price, which rejects the null at the 5% significance level. In short, bubbles exist in the 15
two stock indices and the four crude oil prices. To locate the time of bubbles in the two indices and four price series, we compute the BSADF statistics sequences by using Eq. (4) and plot them in Figs. 2–4 (dotted lines). Apart from the BSADF statistics sequences, we also plot the 95% finite sample critical values (dashed lines), both corresponding to the left vertical axis. For good comparison, we plot the series of data in the same figures (solid lines), but these series correspond to the right vertical axis. Table 2 summarizes the results. [Insert Table 2 here] [Insert Figures 2-4 here] First, two bubble episodes exist in each of the six series we tested. The collapse of the first stock bubble matches the time of the well-known 2007–2008 global financial crisis. The second one begins at the end of 2014 and terminates in the middle of 2015 in the stock market, but the bubble does not collapse until the beginning of 2016 in each of the four oil prices. The first bubble in Chinese stock market, beginning in April 2006, is mainly attributed to the exuberance in the US credit market, which attracts a large amount of speculative funds. The second bubble in oil markets is likely to be caused by both the positive oil supply shock and the negative demand shock. From the supply side, the expansion of shale gas production in the US, commonly referred to as “shale gas revolution”, has gradually increasing and persistent effects on oil markets. Shale gas production is also mainly manifested in a substantial increase in oil exports in non-OPEC nations, which has a large negative effect on oil prices. From the demand side, the gradual slowdown of 16
economic growth in China reduces oil demand. As China is the largest oil importer, and its demand accounts for half of the global oil imports, the slowdown leads to a sharp reduction in oil demand. Both facts lead to the 2014–2015 excess capacity in oil. Second, bubble episodes overlap in the two indices and in the four oil prices. For the two indices, the first bubble episode is from April 2006 to January 2008, whereas the second one begins at the end of 2014 and collapses in June 2015. For the four oil prices, the first bubble in WTI and Brent crude oil prices begins earlier than that in Daqing and Shengli crude oil prices, whereas the episodes of the second bubble for the four oil prices almost perfectly overlap. In particular, the first bubble in WTI and Brent crude oil prices starts at the end of 2007 while the global financial crisis bubble in stock markets is spreading. However, the first bubble in Daqing and Shengli crude oil prices does not occur until the middle of 2008, when the bubble in stock market has already collapsed. The second bubble in Chinese crude oil prices is from October 2014 to February 2016, which is the same as that of the Brent crude oil price. It implies that the rigid control in refined oil prices in China fails to prevent the oil market from international shocks. This can be explained by the enhanced association between Chinese and international oil prices since March 2013, when the Chinese government performed a new round of oil price reform. Third, we find bubble contagion effects by comparing the time, duration, and size of bubbles in the two stock indices with the four oil prices. In terms of the time of the bubbles, the first bubble begins in the two stock indices in April 2006, which is 17
one to two years earlier than that in the four oil prices. In contrast, the second bubble starts in the oil prices at the beginning of October 2014 and spreads to Chinese stock market quickly in two months. In terms of the duration and size of bubbles, the 2007– 2008 global financial crisis bubble is both longer and larger than the 2014–2015 oil excess capacity bubble for the stock market, but the opposite is true for the oil market. In other words, the 2014–2015 oil excess capacity bubble is a more serious problem for oil markets, which even stimulates the bubble in Chinese stock market. To verify our conjectures on bubble contagion effects, we split our sample into two subsamples, each with 1569 observations, and use the time series data to test whether the 2007-2008 bubble in Chinese stock market is the Granger cause3 of the 2007-2008 bubble in four crude oil prices, and whether the 2014-2015 bubble in four crude oil prices is the Granger cause of the 2014-2015 bubble in Chinese stock market (see last Column in Table 2). Results show that at the 10% significance level, 20072008 bubble in stock market is the Granger cause of the bubble in four oil prices and at the 5% significance level, the 2014-2015 bubble in oil prices is the Granger cause of the bubble in Chinese stock market. Unlike most of the previous findings, which emphasize the unilateral volatility spillover effect from oil market to Chinese stock market (e.g., Xiao et al., 2018) or find the contagion effect from oil market to Chinese stock market (e.g., Fang and Egan, 2018), our study reveals the close relationship involving bilateral contagion effects of bubbles between the two markets. We verify that the 2007–2008 global 3
Using the unit root test with two structural breaks in level and slope at unknown time proposed by Narayan and Popp (2010), we find all the six time series data are I(1) processes, so we carry out the Granger casualty test for I(1) variables proposed by Toda and Yamamoto (1995). 18
financial crisis bubble first starts in the stock market and then spreads to oil markets in one to two years. Moreover, the strong government regulation in refined oil prices successfully protects the Chinese oil market and postpones the oil price bubble. In contrast, the 2014–2015 oil excess capacity bubble starts in four crude oil prices and then quickly spreads to the Chinese stock market in two months. These findings imply the following contagion mechanisms. In 2007, the excessive exuberance in stock markets spreads to the oil markets through heightened investor sentiments, thereby causing the excessive oil demand and consequently the sharp increase in oil prices. In the beginning of 2008, bubbles in stock markets collapse, driving the speculative funds to oil markets and resulting in irrational increase in oil prices. Besides, the after-crisis anti-deflationary policies also promote the increase of oil prices. In 2014, the declining oil demand caused by the slowdown of economic growth and the excessive supply jointly lead to the sharp decline in oil prices. The negative bubble in oil prices stimulates the expansion of firms’ investment and leverage, and thus causes the irrational exuberance in Chinese stock market. Despite being able to stimulate investments, negative bubbles in oil prices do more harm than good as they could lead to misallocation of resources and inefficiency of market pricing mechanisms, and finally cause irrational exuberances and systematic financial risks. Our results have important policy implications and are valuable for regulators and investors. First, we can use the BSADF bubble date-stamping procedure to develop a scientific warning system for the unusual fluctuations of oil prices and 19
stock markets. Since the method is a real-time testing procedure for bubbles rather than an ex-post analysis, it can be effectively used to prevent a bubble from inflating at the very beginning of abnormal periods and maintain the stability of financial markets. Given the close relationship between oil and stock markets, whenever a bubble is identified in one of the six series, regulators should prevent the bubble from spreading and urge the corresponding price to return to the efficient level. Investors should also take the contagion effect of bubbles into consideration when diversifying their portfolios. Second, it is important to exploit a mechanism for adequate information disclosure to stabilize expectations and to reduce speculative or irrational behaviors in both oil market and stock market. With the integration of the Chinese and international oil markets, the risk effects from international oil price fluctuations to Chinese oil and stock markets are nonnegligible. It is therefore important for Chinese government to better control risks from international oil price fluctuations through diversified ways, including the national oil strategic reserve system and Chinese crude oil futures market. Third, China's oil pricing reform inhibits speculations and promotes the orderly competition in oil markets, but it also increases China’s risk exposure to the international oil markets. Thus, it is necessary for Chinese government to build a risk firewall based on institutions and regulations, while at the same time improving the market-oriented mechanism for determining oil prices. For instance, Chinese
20
government sets ceilings and floors 4 for refined oil products in the current pricing mechanism and also establishes reserves to control oil price risks. Besides, we suggest Chinese government build a more efficient marketing system by administrative means, such as increasing penalties for illegal trading and speculative activities. 6. Robustness check To add reliability of our empirical results, we conduct two robustness checks. First, we change the lag order of GSADF test from K=0 to K=3. The results in Table 3 show that the change of lag order does not affect our results. Second, we consider an alternative bubble testing method proposed by Harvey et al. (2015), which is a union strategy5 (we denote it as UR) based on SADF test and the backward recursive Chow test (denote as SDFC) of Homm and Breitung (2012). Suppose is a random walk for = 1, … , 4! 5 and changes to an explosive
process for = 4! 5 + 1, … , . Let the subsample start from the beginning of the total
sample and end at the ! th fraction of the total sample (denote by 4! 5 ). The following regression model is constructed: " = #$ + '$ 1l4$m5 +
(9)
where #$ is the constant intercept, 1∙ is an indicator function, '$ > 0 , ∼ 0, $ . The SDFC statistic, denoted by 73o! , is
4
Domestic refined oil prices increase less if the corresponding international crude oil price increases exceeding $130 per barrel, while domestic refined oil prices stop to decline if the corresponding international crude oil price falls below $40 per barrel. 5 Harvey et al. (2015) find that SADF test performs better when the explosive behaviors occur early or towards the middle of the sample, while SDFC test is preferred in other cases, and thus recommend a union strategy (we denote it as UR) which turns out to attain power close to the better of the two tests in any case. 21
73o! = sup$∈< , $= > 3o$ = sup$∈< , $= >
where z$ =
m
coefficient '$ .
∑t ru4Dt5v% pqr qrs%
& xD y∑t w ru4Dt5v% qrs%
(10)
∑m- z ，z = " − '{$ 1l4$m5 , '{$ is the OLS estimator of
UR test statistic can be written as: |}! = max 723! ,
73o!
(11)
where 723! = sup$∈<$= ,> 23 $ , 23 $ is the ADF statistic based on regression
(2) where ! = 0, ! = !. 73o! is the statistic constructed by the supremum of a
set of DF-type Chow tests. M NOPQ and M NPQ are critical values of SADF and SDFC, respectively.
The results of UR test are basically consistent with our results from GSADF test, except that the significance levels for oil price bubbles reduce from 1%-5% to 5%10%. Besides, we find UR test is dominated by SADF test in our sample. Actually, if SDFC method alone is used, the results would be rather misleading: no bubble is detected in any time series. This can be explained by the characteristic of our data that a significant explosive episode occurs early in the sample period which invalidates SDFC method. 7. Conclusion Given the large oil consumption and its high dependence on foreign oil for China, relationships among international crude oil, Chinese oil and stock markets attract a great deal of attention from academic researchers, investors, and policymakers. This study investigates the contagion effect of bubbles among these 22
three markets from September 1, 2004 to July 9, 2018. Six daily series, namely, two international crude oil prices (WTI and Brent crude oil prices), two Chinese oil prices (Daqing and Shengli crude oil prices), and two Chinese stock market indices (CSI300 and OGEI) are used as materials. Employing GSADF test, we detect bubbles in each of the six series. The finding is robust if we set an alternative lag order in GSADF test or use a different bubble testing method. Also, we identify the time, duration, and size of the bubbles in the six series based on BSADF statistics. Moreover, our empirical results indicate that the 2007–2008 bubble begins in the stock market and then spreads to the international and Chinese oil markets in one to two years; while the 2014–2015 bubble begins simultaneously in the international and Chinese oil markets (October 2014) and then spreads to the Chinese stock market in two months. In other words, we find the bilateral contagion effects of bubbles between the oil and Chinese stock markets. The contagion effect is further verified by the Granger causality test. In addition, we find that with the reform of China’s oil pricing mechanism, Chinese oil market is becoming increasingly sensitive to the fluctuation of the international oil prices. These findings have important implications for regulators and investors to prevent systematic risk and to diversify portfolios. Finally, identifying the contagion effect of bubbles in oil and stock markets is crucial to prevent financial risk and maintain financial stability. We analyze the contagion effect based on the accurate time of bubbles estimated by BSADF method and support our finding with evidence from Granger causality test, but our analysis is 23
far from adequate. A more solid examination of the bubble contagion effect and a deep analysis of the underlying reasons (such as, whether the contagion effect is driven by common factors) will be the topic for future research. Besides, while the high power of detecting multiple bubbles is widely accepted, GSADF method does not allow breaks in levels or time trends. Also, we leave this extension on method for future research. References Bae, K.H., Karolyi, G.A., Stulz, R.M., 2003. A new approach to measuring financial contagion. Review of Financial Studies 16(3), 717–763. Balcilar, M., Hammoudeh, S., Toparli, E.A., 2018. On the risk spillover across the oil market, stock market, and the oil related CDS sectors: A volatility impulse response approach. Energy Economics 74, 813–827. Baur, D., Schulze, N., 2005. Co-exceedances in financial markets—A quantile regression analysis of contagion. Emerging Markets Review 6(1), 21–43. Beirne, J., Beulen, C., Liu, G., Mirzaei, A., 2013. Global oil prices and the impact of China. China Economic Review 27, 37-51. Bohl, M.T., Kaufmann, P., Stephan, P.M., 2013. From hero to zero: Evidence of performance reversal and speculative bubbles in German renewable energy stocks. Energy Economics 37, 40–51. Brown, S.P.A., Yücel, M.K., 2002. Energy prices and aggregate economic activity: an interpretative survey. The Quarterly Review of Economics and Finance 42, 193–208. 24
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Tables and Figures Table 1 Descriptive statistics. CSI300
OGEI
Daqing
Shengli
WTI
Brent
Obs.
3,138
3,138
3,138
3,138
3,138
3,138
Mean
2233.1585
3087.9639
60.0208
56.7332
62.4460
65.2540
SD
811.3187
1248.0074
21.7251
21.2489
19.2378
21.8030
Min
818.2444
983.2094
14.3170
14.1023
21.3854
20.3562
Max
5217.5923
10861.3799
121.9220
108.5262
125.4495
129.3949
Notes: CSI300 and OGEI are from WIND, Daqing and Shengli oil prices are from database of KEST, and WTI and Brent oil prices are from EIA. All four oil prices are measured in US$ per barrel, where WTI and Brent are free on board. The sample period is Sep 01, 2004-July 09, 2018.
Table 2 Results of bubble tests, date-stamping and granger causality tests of bubbles. GSADF
Exuberance
Collapse 30
Duration
Maximum
Granger Causality
CSI300
OGEI
Daqing
Shengli
WTI
Brent
6.620***
6.024***
3.382***
3.461***
2.625**
4.622***
Date
Date
BSADF
Test
2006/04/04
2008/01/25
661D
6.620
–
2014/11/25
2015/06/25
212D
5.601
3.705** (0.011)
2006/04/19
2008/01/18
639D
6.024
–
2014/12/05
2015/06/11
188D
2.856
7.895***(0.000)
2008/05/09
2008/12/31
236D
1.403
3.589** (0.013)
2014/10/08
2016/02/24
504D
3.382
–
2008/04/16
2008/12/31
259D
2.144
2.118* (0.096)
2014/10/08
2016/03/04
513D
3.461
–
2007/10/18
2008/12/24
433D
1.362
3.746** (0.011)
2014/11/04
2016/01/21
443D
2.625
–
2007/11/12
2008/12/31
425D
1.751
2.796** (0.039)
2014/10/08
2016/02/25
505D
4.622
–
Notes: The GSADF statistics are obtained from Eq. (3) with lag order K=0. The beginning and end dates of bubbles are obtained following the date-stamping strategies of Phillps et al. (2015) (Eqs. (5) and (6)). For GSADF statistics, the 90%, 95%, 99% critical values are 2.281, 2.508 and 2.908, respectively. For BSADF statistics, critical values are varying with the sub-sample size (see the dashed lines in Figs. 2-4). We compare the BSADF statistics with the 95% finite sample critical values, which are obtained from 2,000 replications of Monte Carlo simulations. Column 5 shows the duration of the corresponding bubble period. Column 6 displays the maximum BSADF statistic obtained from each sample and for each series, which is a proxy for the size of the bubble. Column 7 shows the statistics and p values (in parentheses) for Granger causality test. The null hypotheses are: the first bubble in CSI300 is the granger causality of bubbles in four oil prices, and the second bubble in Brent Crude Oil price is the granger causality of bubbles in CSI300 and OGEI. Sample periods for the two granger causality tests are September 1, 2004 to August 3, 2011, and August 4, 2011 to July 9, 2018, respectively, each containing 1569 observations. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.
Table 3 Results of bubble tests based on GSADF method with lag order K=3 and UR method. 31
GSADF
UR
K=3
UR
SADF
SDFC
CSI300
6.801***
6.600***
6.600***
-0.252
OGEI
5.694***
6.024***
6.024***
-0.432
Daqing
3.411***
0.948*
0.948*
-0.240
Shengli
3.990***
1.258**
1.258**
-0.243
WTI
2.904***
0.919*
0.919*
-0.353
Brent
3.978***
1.353**
1.353***
-0.328
Panel A: Test Statistics
Panel B: Finite Sample Critical Values 90%
2.651
0.916
0.912
1.532
95%
2.709
1.249
1.235
1.860
99%
2.766
1.378
1.318
2.079
Notes: The GSADF statistics are obtained from Eq. (3) with lag order K=3, and the UR statistics are obtained from Eq. (11) following Harvey et al. (2015). SADF statistics and SDFC statistics are intermediate variables used in UR, which are from Phillips et al. (2011) and Homm and Breitung (2012), respectively. In calculating UR statistics, 95% critical values of SADF and SDFC statistics are used. All our critical values are obtained from Monte Carlo simulations with 2000 replications (sample size 3138, smallest window size 132). ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.
32
Fig. 1. Six time series from Chinese stock, crude oil, and international crude oil markets Notes: The first graph shows time series of CSI300 and OGEI, representing Chinese stock market. The other graphs show four crude oil prices with Daqing and Shengli Crude Oil Prices in the second, representing Chinese crude oil market; while WTI and Brent Crude Oil Prices in the third, representing the international crude oil market.
33
Fig. 2. BSADF sequence: bubble periods in CSI300 and OGEI Notes: The sample spans from Sep 01, 2004 to July 09, 2018. There are 3,138 observations for each series. The solid lines corresponding to the right vertical axis show the values of the time series data, while the dotted lines and dashed lines corresponding to the left vertical axis are BSADF sequences obtained from Eq. (4) and the 95% finite sample critical values obtained from 2,000 replications of Monte Carlo simulations, respectively. A bubble period is verified when the BSADF sequence exceeds the corresponding critical value.
34
Fig. 3. BSADF sequence: bubble periods in Daqing and Shengli crude oil prices Notes: The sample spans from Sep 01, 2004 to July 09, 2018. There are 3,138 observations for each series. The solid lines corresponding to the right vertical axis show the values of the time series data, while the dotted lines and dashed lines corresponding to the left vertical axis are BSADF sequences obtained from Eq. (4) and the 95% finite sample critical values obtained from 2,000 replications of Monte Carlo simulations, respectively. A bubble period is verified when the BSADF sequence exceeds the corresponding critical value.
35
Fig. 4. BSADF sequence: bubble periods in WTI and Brent crude oil prices Notes: The sample spans from Sep 01, 2004 to July 09, 2018. There are 3,138 observations for each series. The solid lines corresponding to the right vertical axis show the values of the time series data, while the dotted lines and dashed lines corresponding to the left vertical axis are BSADF sequences obtained from Eq. (4) and the 95% finite sample critical values obtained from 2,000 replications of Monte Carlo simulations, respectively. A bubble period is verified when the BSADF sequence exceeds the corresponding critical value.
36
Highlights
We identify bubbles in international oil, Chinese oil and Chinese stock markets.
The analysis is based on GSADF test and the efficient market hypothesis.
There are two bubble episodes in each of the markets: 2007-2008 and 2014-2015.
We find bilateral contagion effects of bubbles in oil and stock markets.
The contagion effects are verified by Granger causality test.
S0264-9993(19)31819-X
DOI:
https://doi.org/10.1016/j.econmod.2020.02.018
Reference:
ECMODE 5160
To appear in:
Economic Modelling
Received Date: 14 November 2019 Revised Date:
4 January 2020
Accepted Date: 9 February 2020
Please cite this article as: Zhao, Z., Wen, H., Li, K., Identifying bubbles and the contagion effect between oil and stock markets: New evidence from China, Economic Modelling (2020), doi: https:// doi.org/10.1016/j.econmod.2020.02.018. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.
Identifying bubbles and the contagion effect between oil and stock markets: New evidence from China Zhao Zhaoa, Huwei Wenb*, Ke Lic, a
School of Economics, Huazhong University of Science and Technology, Wuhan
430074, Hubei, China b
Research Center for Central China Economic and Social Development, School of
Economics & Management, Nanchang University, Nanchang 330031, Jiangxi, China c
Key Laboratory of Computing and Stochastic Mathematics (Ministry of Education of
China), School of Mathematics and Statistics, Hunan Normal University, Changsha 410081, Hunan, China * Corresponding author: Research Center for Central China Economic and Social Development, School of Economics& Management, Nanchang University, Nanchang 330031, Jiangxi, China E-mail: [email protected] (H. Wen)
Acknowledgements We gratefully acknowledge and thank for the helpful comments and suggestions from the Editor, Professor Paresh Narayan and three anonymous reviewers on the previous version of this paper. We acknowledge the financial support from the Humanities and Social Science Fund of Ministry of Education of China (No. 18YJC790232) and the National Natural Science Foundation of China (Grant No. 71803055, 71773028). 1
Identifying bubbles and the contagion effect between oil and stock markets: New evidence from China
Abstract This study employs six price series from international and Chinese crude oil markets and Chinese stock market to test for bubbles. Based on the efficient market hypothesis and the Generalized Supremum Augmented Dickey–Fuller test, we identify two bubble episodes in each series, namely, the 2007–2008 global financial crisis and 2014–2015 oil excess capacity bubbles. Furthermore, using Granger causality test, we find empirical evidence for the bilateral contagion effect of bubbles between oil markets and Chinese stock market. The direction of contagion is from stock to oil market for the first bubble and from oil to stock for the second. We also find that Chinese oil market is becoming increasingly sensitive to the fluctuation of the international oil prices. These findings provide important enlightenments for regulators to prevent systematic financial risks and for investors to diversify their portfolios.
Keywords: Oil price; Stock market; Bubbles; Generalized SADF; Contagion effect
1. Introduction With the rapid growth of economy and the upgrade of energy consumption structure, China’s oil consumption has increased dramatically over the last decades. 1
According to BP Statistical Review of World Energy, China’s oil consumption increased from 110.3 million tonnes oil equivalent (Mtoe) to 608.4 Mtoe over the period 1990–2017. In 2017, China’s oil consumption accounted for 13.2% of the global consumption, ranked closely behind the United States (19.8%). Moreover, China’s oil imports are growing quickly because of the insufficient oil reserve. In 2017, China’s oil importation reached 10,241 thousand barrels per day, which surpassed the United States, and resulted in a degree of dependence on import as high as over 68%. The strict control on refined oil prices in China used to be considered as a protecting wall from the international oil price fluctuation. However, China has been working hard on the market-oriented reform of refined oil pricing mechanism. On March 26, 2013, China carried out a reform that shortens the refined oil adjustment cycle from 22 workdays to 10 workdays and removes the limit of 4% on oil price change1. This reform has greatly relaxed the control on refined oil prices. Further, on March 26, 2018, the crude oil future has been listed on Shanghai Future Exchange. The price discovery function of crude oil future has further improved the oil pricing mechanism. With the increase of dependence on oil import and the revolutions of oil pricing system in China, the domestic oil prices are becoming increasingly related to international oil prices. Given this background, fluctuations of international oil prices have noticeable impacts on Chinese oil market (Jia et al., 2015), real economy (Tang et al., 2010), and
1
http://www.gov.cn/zwgk/2013-03/27/content_2363601.htm 2
stock market (Xiao et al., 2018). For instance, if international oil prices increase, on the one hand, the increase in cost will result in investment and output decrease, which will affect the stock market adversely; however, on the other hand, the stock market could also be positively affected through investor sentiments and spillover effects (Li and Wei, 2018). Besides the ambiguous impacts from international oil price to Chinese stock market, China’s economic growth also affects international oil prices (Beirne et al., 2013; Wang and Zhang, 2014) as China’s oil import takes an important proportion of global oil trade. Considering the complicated impact mechanisms and the important practical enlightenments for investors and policymakers, we study the dependence between oil and stock markets from a new perspective, i.e., the contagion effect of bubbles. Existing studies support the importance of bubbles from multiple perspectives. Narayan and Narayan (2017) conclude bubbles are important in explaining the energy price adjustment. Narayan et al. (2013) find positive effects of trading volume on stock price bubbles. Narayan, Sharma, and Phan (2016) show stock price bubbles are welfare enhancing. Narayan, Phan, Thuraisamy, et al. (2016) find stock portfolios constructed based on asset price bubbles have the minimum volatility. Motived by these findings, we attempt to find answers for the following questions. Do bubble episodes exist in oil and Chinese stock markets? If yes, then when do the bubbles begin and collapse? What is the relationship between the bubbles in oil and Chinese stock markets? Is there a contagion effect between the bubbles? What is the direction of the contagion effect? 3
To address these questions, we use the most recently proposed Generalized Supremum Augmented Dickey–Fuller (GSADF) method to test for bubbles in Chinese and international crude oil prices, and Chinese energy sector and market indices from September 1, 2004 to July 9, 2018. Based on the backward Supremum Augmented Dickey–Fuller (BSADF) statistics, we identify two bubble episodes in each of the time series during our sample period: the 2007–2008 global financial crisis and the 2014–2015 oil excess capacity. Analyzing bubbles in each of the six time series, we find the following results that offer important clues to the relationship between extreme price volatilities in oil and stock markets. First, China’s crude oil prices are more sensitive to international crude oil prices during the 2014–2015 oil excess capacity than during the 2007–2008 global financial crisis. Second, the 2014– 2015 bubble starts in crude oil prices and then spreads to the Chinese stock market, whereas the transmission of the 2007–2008 bubble is the other way around. We further explain the changing contagion effect between oil and stock price bubbles and enlighten regulators on preventing systematic financial risks. Our research is of great policy significance given the key role oil plays in Chinese economy and the strong government regulations in refined oil prices and in stock markets. Different from previous studies, we define a price bubble based on the efficient market hypothesis rather than the conventionally-used rational bubble theory. According to the rational bubble theory, a bubble is defined as the difference between actual price and fundamental price. However, scholars such as Pavlidis et al. (2018), argue that the measurement of fundamental price is too subjective and always contain 4
serious specification errors. Thus, they believe using the market expectation of the price is a better way to detect asset price bubbles. Given the young age of Chinese crude oil futures market and the low dividend ratio of Chinese stocks, it is inappropriate to calculate the corresponding fundamental prices based on oil future prices and stock dividends. In addition, it is well-known that fundamental prices for both oil and stock should be nonexplosive to reflect the general law of economic growth, thus we define a price bubble directly on the actual price: a bubble exists if an explosive process occurs in the actual price. This definition is consistent with Pavlidis et al. (2018), except that we use the actual price of the past day as the market expectation of the price. Moreover, to the best of our knowledge, we are the first to investigate the contagion effect of oil and stock price bubbles identified by the BSADF method based on the Granger causality test. Literature provides substantial evidence on the spillover effects between oil and stock markets, but none of them focuses on whether the price bubble is contagious across the markets. During bubble periods, prices are no longer simply determined by supply and demand, so the contagion effect should be different from the usual spillover effect. Most of the early articles define a contagion effect as the increasingly-close correlations among assets in turbulence periods. However, it has been criticized for ignoring the irrational effects caused by extreme events. Researchers then employ extreme value theory (EVT) (Chen and Lv, 2015), copula approach (Wen et al., 2012), quantile regression (Baur and Schulze, 2005) and multinomial logistic regression (Bae et al., 2003) to measure the “co-exceedance” 5
contagion effect characterized by the joint occurrence of two exceedances. Consistent with these studies, our research takes the extreme event into account. But different from them, we define an extreme event by the occurrence of a bubble, which we believe is more intuitive than using the EVT to calculate the cut-offs. Gürkaynak (2008) surveys the methods theretofore for testing bubbles and concludes that econometric detection of asset price bubbles cannot be achieved with a satisfactory degree of certainty. A breakthrough is made by Phillps et al. (2011), who propose the SADF test method based on the supremum of a set of forward recursive right-tailed ADF tests. The method is well-known for detecting periodically collapsing bubble with discriminatory power. To improve the power for testing multiple bubbles, Phillps et al. (2015) extend SADF to GSADF, making the starting points of the examination windows also recursive. They also propose a consistent real-time date-stamping strategy based on BSADF statistics. This method has turned out to be superior to its rivals (Pavlidis et al., 2018) and thus has been widely used in previous studies, such as Bohl et al. (2013), Caspi et al. (2018), Su et al. (2017), and Sharm and Escobari (2018). Employing GSADF test and BSADF statistics, we can identify multiple bubbles in prices with high power and precise dates, which enables us to explore the contagion effect intuitively. We find empirical evidence supporting the contagion effect of bubbles in oil markets and Chinese stock market that when a bubble occurs in one of the oil or stock markets, it is very likely that it will spread to the other market. Our research not only provides knowledge on the bubble periods and contagion 6
effects in international oil, Chinese oil and stock markets, but also contributes to the literature on a wide range of research issues, including the increasingly-close relationship between international and Chinese oil markets, abnormal flunctuations or bubbles in oil and stock markets, dependence and spillover effects of oil and stock markets, and the bubble contagion effects under extreme market conditions. A detailed analysis is provided in Section 2. The rest of the paper is presented as follows: Section 2 provides the literature review; Section 3 describes the dataset; Section 4 introduces the empirical model; Section 5 provides the main results; Section 6 checks for robustness and Section 7 concludes the paper. 2. Literature Oil and stock price fluctuations have been extensively investigated by the academia. First, lots of studies regard the dramatic changes in oil prices as structural breaks or bubbles. For instance, Lee et al. (2010) find multiple structural breaks in the WTI and WTIF markets by establishing a reconstituted component-ARJI model. Chen et al. (2015) conduct various unit root tests for the WTI–Brent crude oil price spreads and verify the hypothesis of persistence change. In July 2008, the international oil prices reached more than $140/barrel, causing hot discussions. Phillps and Yu (2011), Shi and Arora (2012), and Gronwald (2016) all detect bubbles in oil prices in 2008. Tokic (2010) claims the bubble is caused by the anti-deflationary strategies of the Federal Reserve after the 2008 financial crisis. In the beginning of 2015, the international oil prices fell to less than $50/barrel. Fantazzini (2016) identifies a 7
negative bubble in oil prices in 2014/2015. Tokic (2015) explains the plummet by the devaluation of Euro versus US dollars and Domanski et al. (2015) suggest the increased leverage of oil firms could be the reason. Sharm and Escobari (2018) attribute the oil price collapse to the increase in non-OPEC nations’ oil exports, especially the continued growth in US shale production. Second, some studies detect bubbles in Chinese stock market. For instance, using the Log Periodic Power Law (LPPL) method, Jiang et al. (2010) detect the 2005-2007 and 2008-2009 bubbles in Chinese stock market. Shu and Zhu (2019) find three positive bubbles and four negative ones from March 1, 2005 to April 2, 2018 in Chinese stock market. Gong et al. (2019) analyze bubbles in Chinese stock market based on the rational asset price bubble theory. While Yu and Ma (2019) consider the breaks in time trend following the core idea of Narayan and Popp (2010) and find a rather short bubble period, they do not provide an effective method to distinguish breaks in time trend from explosive processes. The above studies focus on structural changes or bubbles in oil market and in Chinese stock market separately, but they do not link up the abnormal behaviors in the two markets. We attempt to fill this gap by dealing with their linkage through analyzing the exact time of bubble periods in Chinese stock market, Chinese crude oil market and international crude oil market. There is a large body of literature investigating the impact of oil price fluctuations on macro economy. Most scholars believe an adverse impact of oil-price increase on real economy, which can be explained by several channels, including 8
supply-side shocks, wealth transfer, inflation, real balance, unexpected effects, and adjustment costs (Brown and Yücel, 2002; El Anshasy and Bradley, 2012; Kim et al., 2017). However, Wei and Guo (2016) find evidence for the positive effect of oil-price increase on China’s output and explain it by the increase in exports. Jarrett et al. (2019) study the mixed relationship among oil price, financial stability, and economic growth, and infer that better financial institutions mitigate the influence of oil volatility on economic growth. Recent studies focus on risk spillover effects between oil and stock markets. Smyth and Narayan (2018) provide a nice survey on how oil prices influence stock returns. In general, positive risk spillover effects from oil markets to financial markets are identified (Li and Wei, 2018; Wang and Wu, 2018), and the effects are found to be time-varying (Xiao et al., 2018) and asymmetric (Xu et al., 2019). For instance, based on a dataset of 2178 Islamic stocks, Narayan et al. (2019) find oil prices influence about 32% of the stocks significantly. You et al. (2017) and Wang and Wu (2018) conclude that the change of oil-price has asymmetric impacts on financial markets owing to market conditions, investor sentiments, and uninformed traders in markets. Balcilar et al. (2018) and Tsuji (2018) find bilateral risk spillover effects between equity markets and oil markets. In addition, Chen and Lv (2015), Ding et al. (2016), Mensi et al. (2014), Sukcharoen et al. (2014), and Wen et al. (2012) focus on the relation under extreme market conditions and provide evidence for the positive extreme dependence. It is clear to see that the majority of literature pays attention to the general 9
linkage between oil and stock markets and the rest focuses on the extreme-value dependence, but none of them analyzes the particular contagion relationship between speculative bubbles in these markets. Thus, we first test for bubbles based on GSADF method, then determine the origination and termination of bubbles based on BSADF statistics, and finally identify the contagion effects of the bubbles using the Granger causality test. In sum, existing literature investigates the fluctuation characteristics of oil and stock prices, and the impacts of oil-price fluctuations on macro economy and on stock markets. Relying on different theoretic frameworks, they find ample evidence for the close relationships among oil prices, real economy, and stock markets. However, the two strands of literature are separate from each other. Thus, we integrate both strands by studying the contagion effect between bubbles in Chinese stock market and oil markets, where both international and Chinese crude oil prices are considered. 3. Data To test for bubbles and identify the contagion effect between oil and stock markets, we adopt two indices reflecting the whole stock market of China (CSI300) and the stock market related to energy in China (Oil & Gas Exploration Index, denoted as OGEI), respectively. We also consider two prices representing the crude oil market of China (Daqing and Shengli) and two international crude oil prices (WTI and Brent). Daqing Oilfield is well-known as China’s largest oilfield and one of the world’s top 10 oilfields, and Shengli Oilfield is the second largest oilfield in China. Both oilfields produced a total of 57.4 million tons of oil in 2017, accounting for 10
approximately 30% of the total output in China. Thus, their prices can represent the oil price in China. The data are from the database of the knowledge service system for energy (http://energy.ckcest.cn/home). Besides, WTI and Brent prices are the benchmark prices for the international oil market. These two series are from EIA website (www.eia.gov). All the six time series are daily data. Limited by the availability of data of Chinese crude oil prices, we select a sample period from September 1, 2004 to July 9, 2018. For simplicity and good comparison, we remove dates with missing values. Finally, we obtain 3,138 observations for each variable. Note that all the four oil prices are measured in US$ per barrel, where WTI and Brent oil prices are free on board. 4. Methodology According to the efficient market hypothesis, the market fundamental price following a random walk well reflects the usual impact of shocks from expectation, noise, and variety of market rules, as well as the relationship between supply and demand that determines the price (Fama, 1970). Therefore, we define that a price bubble exists if a significant departure from random walk occurs in the actual price. Denote the index or price series to be tested by , = 1, … , . Assume the regression model has a weak (local to zero) intercept item, as follows: = + + ,
(1)
where is the sample size, is a localizing coefficient, and the disturbance term
∼ 0, . Following Phillps et al. (2015) , we set > 0.5 to ensure that the intercept is asymptotically negligible. is a random walk with drift when = 1, 11
and an explosive process when > 1, implying a departure from the efficient market. We regard the departure as the signal of the existence of a bubble. Naturally, we build the null hypothesis : = 1 and the corresponding alternative hypothesis : > 1
to test for bubbles. Only if is rejected in the right-side test, can we conclude that a bubble exists. To use the recursive right-side ADF test, we complement Model (1) with transient dynamics to deal with the autocorrelation in disturbance terms as in the standard ADF test. Suppose the regression subsample begins at the !th fraction of the total sample and ends at the ! th fraction. The recursive regression is as follows: * " = #$% $& + '$% $& + ∑+ * - )$% $& " * + ,
(2)
where #$% $& is the constant intercept, '$% $& = 1 − $% $& and / is the lag order selected by minimizing the Akaike information criterion (AIC) or Bayesian information criterion (BIC), ∼ 00, $% $& 1. The ADF statistic from Model (2) can be denoted by 23$%& . To test whether bubbles exist during the sample period, we use the GSADF $
statistic, which is defined as the supremum of the ADF statistics sequence over all the feasible ranges of ! and ! . Suppose the minimum window width of regression
sample is 4! 5, where 4. 5 denotes the integer part of the argument. The GSADF
statistic, denoted by 6723! , is as follows: 6723! = sup
$& $& ∈<$= ,> ?23$% @ $% ∈< ,$& $= >
= sup
CD D B % & $& ∈<$= ,> AEF0B CD D 1G. % & $% ∈< ,$& $= >
(3)
The expression indicates that the GSADF test not only allows the subsample to change end point fraction ! from ! to 1 but also allows it to change the starting point
fraction ! from 0 to ! − ! . Compared with the SADF test that only allows the 12
change of the end point fraction, this setting ensures that the GSADF test has discriminatory power even when more than one speculative bubble exists during the sample period. Although Eq. (3) can examine the presence of bubbles, it cannot reveal when the bubbles are formed/burst. To locate the specific starting and end points of bubbles, we compare the BSADF statistics with the 95% SADF finite sample critical value sequence. Suppose that the end point of a sample to be tested is fixed at 4! 5 and that
the minimum window size is set to 4! 5. BSADF statistics are constructed following two steps. First, continuously change the starting point of the sample backwards from 4! − ! 5 to 0. For every change, one observation is increased, and one ADF statistic is obtained. Second, calculate the supremum of the ADF statistics sequence, which is, H723$& ! = sup$% ∈< ,$& $=> ?23$%& @. $
(4)
The starting and end point fractions of a bubble are respectively determined by the following equations: !̂F =
!̂R =
inf ?! : H723$& ! > M$NOPQ @, & ! ∈
inf ?! : H723$& ! < M$NOPQ @, & ! ∈
(5) (6)
where M$NOPQ is the critical value of SADF when the sample size equals to 4! 5, and &
4S TUV 5 is the minimum duration2 of a bubble. The date-stamping strategy suggests
that the starting point (denoted by 4!̂F 5) is defined as the first observation whose 2
Phillips et al. (2015) highlight that frequency dependent parameter S can be selected according to the setting and the sample size. Here, we set S = 0.4. 13
H723$& ! surpasses the critical value of the SADF statistic, and the end point
(denoted by Z!̂R [) is defined as the first observation after 4!̂F + S TUV / 5 whose H723$& ! becomes smaller than the critical value of the SADF statistic.
Phillps et al. (2015) prove under the null hypothesis that is a random walk
with an asymptotically negligible drift, the limit distributions of SADF and GSADF test statistics are respectively as follows: % % \]& ^ ] _= ]E`E & & %⁄& % % A_= ]E& `E a_= ]E`Eb G
,
(7)
and sup
D % $ \]$& & ]$% & $e ^ _D & ]$`$ <]$& ]$% > & e % & %⁄& D& D %⁄& & $e A$e _D ]$ `$ a_D & ]$`$ b G % %
$& ∈<$= ,> d $% ∈< ,$& $= >
f,
(8)
where !g = ! − ! . H represents a standard Brownian motion. Both limit distributions are non-standard, and consequently the critical values can only be obtained by numerical simulation (asymptotic critical values) or Monte Carlo simulation (finite sample critical values). Specifically, we use finite sample critical values in the empirical study which are obtained from the 2,000 replications of Monte Carlo simulations. 5. Empirical Results [Insert Table 1 here] As a result of price control from Chinese government, the average prices of Daqing and Shengli crude oil are lower than the international prices represented by WTI and Brent crude oil. Table 1 presents the descriptive statistics for all six series. Fig. 1 exhibits data graphs for the six time series, each of which indicates two periods 14
of abnormity. Moreover, the figure shows that each of the six series increases dramatically from 2006 to 2007 and reaches the peak around 2007. After that peak, the trends of the two stock indices reveal an apparent difference from those of the four oil prices. These features motivate us to explore the questions raised in the introduction: whether and when bubbles occur in each of the six series, and whether any relevance exists between the presence of bubbles in oil and stock markets. [Insert Figure 1 here] On the basis of Eq. (3), we compute the GSADF statistics for the two indices and four price series and report them in the second column of Table 2. As all series share the same sample size, which is = 3,138, they share the sets of critical values. Following the recommendation for the window width fraction of the smallest sample proposed by Phillps et al. (2015), we set ! = 0.01 + 1.8⁄√ = 0.042, and thus has a
minimum window size 4! 5 = 132. In addition, Phillps et al. (2015) recommend
using a fixed lag length in both the GSADF testing and the BSADF date-stamping. Therefore, we set the lag order / = 0 to consider a transient dynamic relation. We also consider / = 3 in Section 6 for robustness check. The corresponding 90%, 95%
and 99% finite sample critical values obtained from the 2,000 replications of Monte Carlo simulations for / = 0 are 2.281, 2.508 and 2.908, respectively. Comparing the six GSADF statistics with the critical values, we find strong evidence supporting the existence of bubbles in all tested series. The null hypothesis of random walk with drift is rejected at the 1% significance level for each of the series, except the WTI crude oil price, which rejects the null at the 5% significance level. In short, bubbles exist in the 15
two stock indices and the four crude oil prices. To locate the time of bubbles in the two indices and four price series, we compute the BSADF statistics sequences by using Eq. (4) and plot them in Figs. 2–4 (dotted lines). Apart from the BSADF statistics sequences, we also plot the 95% finite sample critical values (dashed lines), both corresponding to the left vertical axis. For good comparison, we plot the series of data in the same figures (solid lines), but these series correspond to the right vertical axis. Table 2 summarizes the results. [Insert Table 2 here] [Insert Figures 2-4 here] First, two bubble episodes exist in each of the six series we tested. The collapse of the first stock bubble matches the time of the well-known 2007–2008 global financial crisis. The second one begins at the end of 2014 and terminates in the middle of 2015 in the stock market, but the bubble does not collapse until the beginning of 2016 in each of the four oil prices. The first bubble in Chinese stock market, beginning in April 2006, is mainly attributed to the exuberance in the US credit market, which attracts a large amount of speculative funds. The second bubble in oil markets is likely to be caused by both the positive oil supply shock and the negative demand shock. From the supply side, the expansion of shale gas production in the US, commonly referred to as “shale gas revolution”, has gradually increasing and persistent effects on oil markets. Shale gas production is also mainly manifested in a substantial increase in oil exports in non-OPEC nations, which has a large negative effect on oil prices. From the demand side, the gradual slowdown of 16
economic growth in China reduces oil demand. As China is the largest oil importer, and its demand accounts for half of the global oil imports, the slowdown leads to a sharp reduction in oil demand. Both facts lead to the 2014–2015 excess capacity in oil. Second, bubble episodes overlap in the two indices and in the four oil prices. For the two indices, the first bubble episode is from April 2006 to January 2008, whereas the second one begins at the end of 2014 and collapses in June 2015. For the four oil prices, the first bubble in WTI and Brent crude oil prices begins earlier than that in Daqing and Shengli crude oil prices, whereas the episodes of the second bubble for the four oil prices almost perfectly overlap. In particular, the first bubble in WTI and Brent crude oil prices starts at the end of 2007 while the global financial crisis bubble in stock markets is spreading. However, the first bubble in Daqing and Shengli crude oil prices does not occur until the middle of 2008, when the bubble in stock market has already collapsed. The second bubble in Chinese crude oil prices is from October 2014 to February 2016, which is the same as that of the Brent crude oil price. It implies that the rigid control in refined oil prices in China fails to prevent the oil market from international shocks. This can be explained by the enhanced association between Chinese and international oil prices since March 2013, when the Chinese government performed a new round of oil price reform. Third, we find bubble contagion effects by comparing the time, duration, and size of bubbles in the two stock indices with the four oil prices. In terms of the time of the bubbles, the first bubble begins in the two stock indices in April 2006, which is 17
one to two years earlier than that in the four oil prices. In contrast, the second bubble starts in the oil prices at the beginning of October 2014 and spreads to Chinese stock market quickly in two months. In terms of the duration and size of bubbles, the 2007– 2008 global financial crisis bubble is both longer and larger than the 2014–2015 oil excess capacity bubble for the stock market, but the opposite is true for the oil market. In other words, the 2014–2015 oil excess capacity bubble is a more serious problem for oil markets, which even stimulates the bubble in Chinese stock market. To verify our conjectures on bubble contagion effects, we split our sample into two subsamples, each with 1569 observations, and use the time series data to test whether the 2007-2008 bubble in Chinese stock market is the Granger cause3 of the 2007-2008 bubble in four crude oil prices, and whether the 2014-2015 bubble in four crude oil prices is the Granger cause of the 2014-2015 bubble in Chinese stock market (see last Column in Table 2). Results show that at the 10% significance level, 20072008 bubble in stock market is the Granger cause of the bubble in four oil prices and at the 5% significance level, the 2014-2015 bubble in oil prices is the Granger cause of the bubble in Chinese stock market. Unlike most of the previous findings, which emphasize the unilateral volatility spillover effect from oil market to Chinese stock market (e.g., Xiao et al., 2018) or find the contagion effect from oil market to Chinese stock market (e.g., Fang and Egan, 2018), our study reveals the close relationship involving bilateral contagion effects of bubbles between the two markets. We verify that the 2007–2008 global 3
Using the unit root test with two structural breaks in level and slope at unknown time proposed by Narayan and Popp (2010), we find all the six time series data are I(1) processes, so we carry out the Granger casualty test for I(1) variables proposed by Toda and Yamamoto (1995). 18
financial crisis bubble first starts in the stock market and then spreads to oil markets in one to two years. Moreover, the strong government regulation in refined oil prices successfully protects the Chinese oil market and postpones the oil price bubble. In contrast, the 2014–2015 oil excess capacity bubble starts in four crude oil prices and then quickly spreads to the Chinese stock market in two months. These findings imply the following contagion mechanisms. In 2007, the excessive exuberance in stock markets spreads to the oil markets through heightened investor sentiments, thereby causing the excessive oil demand and consequently the sharp increase in oil prices. In the beginning of 2008, bubbles in stock markets collapse, driving the speculative funds to oil markets and resulting in irrational increase in oil prices. Besides, the after-crisis anti-deflationary policies also promote the increase of oil prices. In 2014, the declining oil demand caused by the slowdown of economic growth and the excessive supply jointly lead to the sharp decline in oil prices. The negative bubble in oil prices stimulates the expansion of firms’ investment and leverage, and thus causes the irrational exuberance in Chinese stock market. Despite being able to stimulate investments, negative bubbles in oil prices do more harm than good as they could lead to misallocation of resources and inefficiency of market pricing mechanisms, and finally cause irrational exuberances and systematic financial risks. Our results have important policy implications and are valuable for regulators and investors. First, we can use the BSADF bubble date-stamping procedure to develop a scientific warning system for the unusual fluctuations of oil prices and 19
stock markets. Since the method is a real-time testing procedure for bubbles rather than an ex-post analysis, it can be effectively used to prevent a bubble from inflating at the very beginning of abnormal periods and maintain the stability of financial markets. Given the close relationship between oil and stock markets, whenever a bubble is identified in one of the six series, regulators should prevent the bubble from spreading and urge the corresponding price to return to the efficient level. Investors should also take the contagion effect of bubbles into consideration when diversifying their portfolios. Second, it is important to exploit a mechanism for adequate information disclosure to stabilize expectations and to reduce speculative or irrational behaviors in both oil market and stock market. With the integration of the Chinese and international oil markets, the risk effects from international oil price fluctuations to Chinese oil and stock markets are nonnegligible. It is therefore important for Chinese government to better control risks from international oil price fluctuations through diversified ways, including the national oil strategic reserve system and Chinese crude oil futures market. Third, China's oil pricing reform inhibits speculations and promotes the orderly competition in oil markets, but it also increases China’s risk exposure to the international oil markets. Thus, it is necessary for Chinese government to build a risk firewall based on institutions and regulations, while at the same time improving the market-oriented mechanism for determining oil prices. For instance, Chinese
20
government sets ceilings and floors 4 for refined oil products in the current pricing mechanism and also establishes reserves to control oil price risks. Besides, we suggest Chinese government build a more efficient marketing system by administrative means, such as increasing penalties for illegal trading and speculative activities. 6. Robustness check To add reliability of our empirical results, we conduct two robustness checks. First, we change the lag order of GSADF test from K=0 to K=3. The results in Table 3 show that the change of lag order does not affect our results. Second, we consider an alternative bubble testing method proposed by Harvey et al. (2015), which is a union strategy5 (we denote it as UR) based on SADF test and the backward recursive Chow test (denote as SDFC) of Homm and Breitung (2012). Suppose is a random walk for = 1, … , 4! 5 and changes to an explosive
process for = 4! 5 + 1, … , . Let the subsample start from the beginning of the total
sample and end at the ! th fraction of the total sample (denote by 4! 5 ). The following regression model is constructed: " = #$ + '$ 1l4$m5 +
(9)
where #$ is the constant intercept, 1∙ is an indicator function, '$ > 0 , ∼ 0, $ . The SDFC statistic, denoted by 73o! , is
4
Domestic refined oil prices increase less if the corresponding international crude oil price increases exceeding $130 per barrel, while domestic refined oil prices stop to decline if the corresponding international crude oil price falls below $40 per barrel. 5 Harvey et al. (2015) find that SADF test performs better when the explosive behaviors occur early or towards the middle of the sample, while SDFC test is preferred in other cases, and thus recommend a union strategy (we denote it as UR) which turns out to attain power close to the better of the two tests in any case. 21
73o! = sup$∈< , $= > 3o$ = sup$∈< , $= >
where z$ =
m
coefficient '$ .
∑t ru4Dt5v% pqr qrs%
& xD y∑t w ru4Dt5v% qrs%
(10)
∑m- z ，z = " − '{$ 1l4$m5 , '{$ is the OLS estimator of
UR test statistic can be written as: |}! = max 723! ,
73o!
(11)
where 723! = sup$∈<$= ,> 23 $ , 23 $ is the ADF statistic based on regression
(2) where ! = 0, ! = !. 73o! is the statistic constructed by the supremum of a
set of DF-type Chow tests. M NOPQ and M NPQ are critical values of SADF and SDFC, respectively.
The results of UR test are basically consistent with our results from GSADF test, except that the significance levels for oil price bubbles reduce from 1%-5% to 5%10%. Besides, we find UR test is dominated by SADF test in our sample. Actually, if SDFC method alone is used, the results would be rather misleading: no bubble is detected in any time series. This can be explained by the characteristic of our data that a significant explosive episode occurs early in the sample period which invalidates SDFC method. 7. Conclusion Given the large oil consumption and its high dependence on foreign oil for China, relationships among international crude oil, Chinese oil and stock markets attract a great deal of attention from academic researchers, investors, and policymakers. This study investigates the contagion effect of bubbles among these 22
three markets from September 1, 2004 to July 9, 2018. Six daily series, namely, two international crude oil prices (WTI and Brent crude oil prices), two Chinese oil prices (Daqing and Shengli crude oil prices), and two Chinese stock market indices (CSI300 and OGEI) are used as materials. Employing GSADF test, we detect bubbles in each of the six series. The finding is robust if we set an alternative lag order in GSADF test or use a different bubble testing method. Also, we identify the time, duration, and size of the bubbles in the six series based on BSADF statistics. Moreover, our empirical results indicate that the 2007–2008 bubble begins in the stock market and then spreads to the international and Chinese oil markets in one to two years; while the 2014–2015 bubble begins simultaneously in the international and Chinese oil markets (October 2014) and then spreads to the Chinese stock market in two months. In other words, we find the bilateral contagion effects of bubbles between the oil and Chinese stock markets. The contagion effect is further verified by the Granger causality test. In addition, we find that with the reform of China’s oil pricing mechanism, Chinese oil market is becoming increasingly sensitive to the fluctuation of the international oil prices. These findings have important implications for regulators and investors to prevent systematic risk and to diversify portfolios. Finally, identifying the contagion effect of bubbles in oil and stock markets is crucial to prevent financial risk and maintain financial stability. We analyze the contagion effect based on the accurate time of bubbles estimated by BSADF method and support our finding with evidence from Granger causality test, but our analysis is 23
far from adequate. A more solid examination of the bubble contagion effect and a deep analysis of the underlying reasons (such as, whether the contagion effect is driven by common factors) will be the topic for future research. Besides, while the high power of detecting multiple bubbles is widely accepted, GSADF method does not allow breaks in levels or time trends. Also, we leave this extension on method for future research. References Bae, K.H., Karolyi, G.A., Stulz, R.M., 2003. A new approach to measuring financial contagion. Review of Financial Studies 16(3), 717–763. Balcilar, M., Hammoudeh, S., Toparli, E.A., 2018. On the risk spillover across the oil market, stock market, and the oil related CDS sectors: A volatility impulse response approach. Energy Economics 74, 813–827. Baur, D., Schulze, N., 2005. Co-exceedances in financial markets—A quantile regression analysis of contagion. Emerging Markets Review 6(1), 21–43. Beirne, J., Beulen, C., Liu, G., Mirzaei, A., 2013. Global oil prices and the impact of China. China Economic Review 27, 37-51. Bohl, M.T., Kaufmann, P., Stephan, P.M., 2013. From hero to zero: Evidence of performance reversal and speculative bubbles in German renewable energy stocks. Energy Economics 37, 40–51. Brown, S.P.A., Yücel, M.K., 2002. Energy prices and aggregate economic activity: an interpretative survey. The Quarterly Review of Economics and Finance 42, 193–208. 24
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Tables and Figures Table 1 Descriptive statistics. CSI300
OGEI
Daqing
Shengli
WTI
Brent
Obs.
3,138
3,138
3,138
3,138
3,138
3,138
Mean
2233.1585
3087.9639
60.0208
56.7332
62.4460
65.2540
SD
811.3187
1248.0074
21.7251
21.2489
19.2378
21.8030
Min
818.2444
983.2094
14.3170
14.1023
21.3854
20.3562
Max
5217.5923
10861.3799
121.9220
108.5262
125.4495
129.3949
Notes: CSI300 and OGEI are from WIND, Daqing and Shengli oil prices are from database of KEST, and WTI and Brent oil prices are from EIA. All four oil prices are measured in US$ per barrel, where WTI and Brent are free on board. The sample period is Sep 01, 2004-July 09, 2018.
Table 2 Results of bubble tests, date-stamping and granger causality tests of bubbles. GSADF
Exuberance
Collapse 30
Duration
Maximum
Granger Causality
CSI300
OGEI
Daqing
Shengli
WTI
Brent
6.620***
6.024***
3.382***
3.461***
2.625**
4.622***
Date
Date
BSADF
Test
2006/04/04
2008/01/25
661D
6.620
–
2014/11/25
2015/06/25
212D
5.601
3.705** (0.011)
2006/04/19
2008/01/18
639D
6.024
–
2014/12/05
2015/06/11
188D
2.856
7.895***(0.000)
2008/05/09
2008/12/31
236D
1.403
3.589** (0.013)
2014/10/08
2016/02/24
504D
3.382
–
2008/04/16
2008/12/31
259D
2.144
2.118* (0.096)
2014/10/08
2016/03/04
513D
3.461
–
2007/10/18
2008/12/24
433D
1.362
3.746** (0.011)
2014/11/04
2016/01/21
443D
2.625
–
2007/11/12
2008/12/31
425D
1.751
2.796** (0.039)
2014/10/08
2016/02/25
505D
4.622
–
Notes: The GSADF statistics are obtained from Eq. (3) with lag order K=0. The beginning and end dates of bubbles are obtained following the date-stamping strategies of Phillps et al. (2015) (Eqs. (5) and (6)). For GSADF statistics, the 90%, 95%, 99% critical values are 2.281, 2.508 and 2.908, respectively. For BSADF statistics, critical values are varying with the sub-sample size (see the dashed lines in Figs. 2-4). We compare the BSADF statistics with the 95% finite sample critical values, which are obtained from 2,000 replications of Monte Carlo simulations. Column 5 shows the duration of the corresponding bubble period. Column 6 displays the maximum BSADF statistic obtained from each sample and for each series, which is a proxy for the size of the bubble. Column 7 shows the statistics and p values (in parentheses) for Granger causality test. The null hypotheses are: the first bubble in CSI300 is the granger causality of bubbles in four oil prices, and the second bubble in Brent Crude Oil price is the granger causality of bubbles in CSI300 and OGEI. Sample periods for the two granger causality tests are September 1, 2004 to August 3, 2011, and August 4, 2011 to July 9, 2018, respectively, each containing 1569 observations. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.
Table 3 Results of bubble tests based on GSADF method with lag order K=3 and UR method. 31
GSADF
UR
K=3
UR
SADF
SDFC
CSI300
6.801***
6.600***
6.600***
-0.252
OGEI
5.694***
6.024***
6.024***
-0.432
Daqing
3.411***
0.948*
0.948*
-0.240
Shengli
3.990***
1.258**
1.258**
-0.243
WTI
2.904***
0.919*
0.919*
-0.353
Brent
3.978***
1.353**
1.353***
-0.328
Panel A: Test Statistics
Panel B: Finite Sample Critical Values 90%
2.651
0.916
0.912
1.532
95%
2.709
1.249
1.235
1.860
99%
2.766
1.378
1.318
2.079
Notes: The GSADF statistics are obtained from Eq. (3) with lag order K=3, and the UR statistics are obtained from Eq. (11) following Harvey et al. (2015). SADF statistics and SDFC statistics are intermediate variables used in UR, which are from Phillips et al. (2011) and Homm and Breitung (2012), respectively. In calculating UR statistics, 95% critical values of SADF and SDFC statistics are used. All our critical values are obtained from Monte Carlo simulations with 2000 replications (sample size 3138, smallest window size 132). ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.
32
Fig. 1. Six time series from Chinese stock, crude oil, and international crude oil markets Notes: The first graph shows time series of CSI300 and OGEI, representing Chinese stock market. The other graphs show four crude oil prices with Daqing and Shengli Crude Oil Prices in the second, representing Chinese crude oil market; while WTI and Brent Crude Oil Prices in the third, representing the international crude oil market.
33
Fig. 2. BSADF sequence: bubble periods in CSI300 and OGEI Notes: The sample spans from Sep 01, 2004 to July 09, 2018. There are 3,138 observations for each series. The solid lines corresponding to the right vertical axis show the values of the time series data, while the dotted lines and dashed lines corresponding to the left vertical axis are BSADF sequences obtained from Eq. (4) and the 95% finite sample critical values obtained from 2,000 replications of Monte Carlo simulations, respectively. A bubble period is verified when the BSADF sequence exceeds the corresponding critical value.
34
Fig. 3. BSADF sequence: bubble periods in Daqing and Shengli crude oil prices Notes: The sample spans from Sep 01, 2004 to July 09, 2018. There are 3,138 observations for each series. The solid lines corresponding to the right vertical axis show the values of the time series data, while the dotted lines and dashed lines corresponding to the left vertical axis are BSADF sequences obtained from Eq. (4) and the 95% finite sample critical values obtained from 2,000 replications of Monte Carlo simulations, respectively. A bubble period is verified when the BSADF sequence exceeds the corresponding critical value.
35
Fig. 4. BSADF sequence: bubble periods in WTI and Brent crude oil prices Notes: The sample spans from Sep 01, 2004 to July 09, 2018. There are 3,138 observations for each series. The solid lines corresponding to the right vertical axis show the values of the time series data, while the dotted lines and dashed lines corresponding to the left vertical axis are BSADF sequences obtained from Eq. (4) and the 95% finite sample critical values obtained from 2,000 replications of Monte Carlo simulations, respectively. A bubble period is verified when the BSADF sequence exceeds the corresponding critical value.
36
Highlights
We identify bubbles in international oil, Chinese oil and Chinese stock markets.
The analysis is based on GSADF test and the efficient market hypothesis.
There are two bubble episodes in each of the markets: 2007-2008 and 2014-2015.
We find bilateral contagion effects of bubbles in oil and stock markets.
The contagion effects are verified by Granger causality test.