Table 4 shows Cumulative response of Chinese sector indexes volatilities to three different oil market uncertainty indicators before and after the reform. Before the reform, OVX shocks show significant 1-day and 5-day cumulative impacts on three sector indexes (Ene, Uti, Ind), while the impacts of other two oil market uncertainty indicators seem insignificant for all Chinese sector indexes volatilities. Moreover, the girfs of OVX are the largest one among the three indicators in most cases. After the reform, the results consolidate that of Table 3, the cumulative responses of all Chinese sector volatilities to OVX shocks are significant and are more economically and statistically significant than other two oil market uncertainty indicators. In summary, the Chinese refined oil pricing reform of March 27, 2013 does not change our conclusions and OVX show the most economically and statistically significant impacts on all Chinese sector indexes volatilities among the three indicators after the reform.
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This study contributes to the choice of OVX from the perspective of volatility spillover from oil market to Chinese stock market. The conclusion highlights the importance of OVX and can provide some meaningful implications. Moreover, [25] identify the most influential energy stocks in Chinese stock market and we identify the most influential oil market uncertainty indicators (at least for the response of Chinese stock market volatilities). These two studies seem to be complementary and a further step could be identifying which indicators has the strongest impact on the influential energy stocks. Last but not least, for some researches document the insignificant spillover effects between oil market and stock market, they could revisit by utilizing OVX rather than WTI and/or Brent to embody the oil market uncertainty. Our sample covers the Chinese refined oil pricing reform of March 27, 2013 and that reform are thought as a milestone in the history of oil pricing mechanism of china [13,21].
- We do not present the girf results of VIX and VXFXI here (control variables), the estimations of them indicate the impact of VIX and VXFXI on Chinese sector indexes volatilities are statistically significant in most cases.
- The work of [33] investigate the volatility spillover among crude oil, natural gas, coal, stock, and currency markets in the US and China, conditional on the volatility spillover index of [34].
- The results of section 3.3 cannot overturn our conclusion that the impacts of OVX on Chinese sector volatilities are more significant than that of WTI and Brent.
- A plausible explanation is OVX contains future information which is not incorporated in WTI and Brent prices, which further makes OVX is relatively more informative.
- Firstly, at first glance, for all sector indexes we can observe that the black lines (i.e., the response of sectors volatility to oil market uncertainty shocks) lay above zero line in most cases.
- Specifically, for WTI and Brent volatility and Chinese stock sectors volatilities, we replace the square of log returns with the volatilities estimated from GARCH models.
Oil (WTI) Snapshot
Oil prices were edging higher ahead of key U.S. inflation data that will offer more cues on the path of interest rates. The most oversold stocks in the energy sector presents an opportunity to buy into undervalued companies. WTI is not the most commonly used benchmark globally, that honor goes to Brent, where two-thirds of oil contracts globally use Brent as a benchmark. Both, however, are considered high-quality oils and are therefore the two most important oil benchmarks in the world. As mentioned, WTI has a sulfur content between 0.24% and 0.34%, whereas Brent has a sulfur content between 0.35% to 0.40%. The lower the sulfur content of an oil, the easier it is to refine, making it more attractive.
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These are standardized products used to determine the prices for all other types. The reference oil traded most frequently and of major significance for the USA is West is etoro safe Texas Intermediate (WTI), while the most important in Asia is Dubai Fateh. Other reference oil types include Leona, Tijuana, Alaska North Slope, Zueitina or Urals.
Hence, combine the above economic and statistic aspects, we conclude that the cumulative responses of Chinese sector volatilities to OVX shocks are more economically and statistically significant than that to the other two oil market uncertainty indicators shocks. A plausible explanation is OVX contains future information which is not incorporated in WTI and Brent prices, which further makes OVX is relatively more informative. In this study, OVX, raw WTI and Brent oil prices realized volatility, CSI 300 index (Csi) along with ten Chinese sector indexes are selected to investigate the impact of OVX on Chinese stock markets at volatility level.
On the one hand, the cumulative girfs in OVX column are larger than the corresponding numbers in WTI and Brent column. With the increase of lags, according to Fig 1, the girfs of the Chinese stock volatility to shocks tend to zero which further means the cumulative girfs of Chinese stock volatility to oil market uncertainty shocks tend to be gradually stable. In this sense, for 20 days cumulative responses of the sectors, we can find the responses to OVX shocks are about twice as much as that to WTI and Brent shocks. In another word, for the future Chinese stock sector volatilities, the impacts of one standard deviation OVX changes are about twice as much as that of one standard deviation WTI/Brent changes.
Fig 1 shows the generalized impulse response of Chinese sector indexes volatilities to oil market uncertainty shocks. Firstly, at first glance, for all sector indexes we can observe that the black lines (i.e., the response of sectors volatility to oil market uncertainty shocks) lay above zero line in most cases. That means the responses are positive which imply higher oil price uncertainty tends to trigger higher Chinese sector volatilities. This is not surprising and is consistent with previous literatures’ conclusions that there exists volatility spillover from oil market to stock markets. A reason for that is as oil is an important energy source, volatile oil price means volatile cost of goods which further imply fluctuation in corporate earnings, that causes stock price to be more volatile.
VXFXI is an implied volatility index reflects the market anticipation of Chinese stock market fluctuation, VXFXI is to Chinese stock market as VIX is to American stock market. In their study, OVX change show significant positive impact on VXFXI change and the impact seems stronger in the bear market condition. Another related research is [24], through copulas and CoVaR methods, they investigate the extreme risk spillover between OVX, VXFXI and USD/CNY exchange rate https://forex-reviews.org/limefx/ market uncertainty (i.e., USDCNYV1M). The empirical evidences show the risk spillover effects from OVX to VXFXI is stronger than that from OVX to USDCNYV1M and that from USDCNYV1M to VXFXI. As VAR model require stationarity of variables, we first test whether the variables of interests are stationary. The results based on popular Phillips and Perron (PP) test are shown in Table 1 and the null hypotheses that the variable is not stationary are rejected in all cases.
The results of section 3.3 cannot overturn our conclusion that the impacts of OVX on Chinese sector volatilities are more significant than that of WTI and Brent. At first, we obtain the volatilities of WTI, Brent and 11 Chinese sectors from ARMA (1,1)-GARCH (1,1) model and repeat the analysis process of section 3.1 and 3.2. Similarly, we check whether these volatility series are stationary based on PP test and the results are presented in Table 5. Accordingly, we take first difference for all series (including OVX and controlling variables) and implement our analysis based on the new series. The p values of PP test of new series are presented in Table 6 and we can find all of them become stationary.
By employing similar method, [35] examines the volatility transmission between WTI and stock markets of G7 countries plus India and China. They find the significant volatility spillover amid stock markets, while no significant evidences of volatility transmission between WTI and stock markets are found. The aforementioned literatures base their study on WTI crude oil prices and opine volatility of oil market do not affect Chinese stock volatility. In this https://forex-review.net/ study, we can find the impacts of OVX on Chinese stock volatilities are highly significant, especially after the reform, and the OVX’s impacts are more significant than other two crude oil prices’ impacts. That imply using different oil market volatility indictors may lead to different conclusions. Moreover, from the perspective of stock sectors, some researches show the volatility spillover between oil market and stock sectors are sector-specific.
That may imply the cumulative responses of Chinese sector volatilities to OVX shocks are more economically significant than that to the other two oil market uncertainty indicators. On the other hand, from the perspective of statistical significance, it is obvious the p-values in OVX column are smaller than that in WTI and Brent column. Moreover, according to the p-values, the cumulative responses of some non-energy related sectors (e.g., Consumer Staples Index (Con); Telecommunication Services Index (Tel)) to WTI and Brent shocks seems to be insignificant while that to OVX shocks are significant. That imply the cumulative girfs of Chinese sector volatilities to OVX shocks are more statistically significant than that to WTI and Brent shocks.
The most important type of crude oil used in Europe is Brent Crude, named after the North Sea oilfield where it is extracted. Brent Crude is a particularly light crude oil which is carried from the North Sea to the Sullom Voe Terminal on Mainland, Shetland by an underwater pipeline. On an international level there are a number of different types of crude oil, each of which have different properties and prices. The different types of crude oil come from regions as diverse as Alaska North Lope, Arab Light or Zueitina in Libya. For the purposes of trading on futures exchanges in London or New York, however, reference oils are used.
Crude oil is by far the world’s most important energy source and the price of oil therefore plays an important role in industrial and economic development. The types of crude oil come from regions as diverse as Alaska North Lope, Arab Light or Zueitina in Libya. These are standardised products used to determine the prices for all other types. We further calculate the cumulative impulse responses to make the comparation more comprehensive and intuitive. Table 8 depict the detailed results and we can find the cumulative responses of sector volatilities to OVX are larger than that to WTI and Brent for almost all cases (except for “tel” (5 days and 20 days)). The red, green and blue line indicate the response of Chinese sector volatilities to OVX, WTI and Brent shocks, respectively.
Secondly, we can find the black lines in the first/fourth row is higher than that in the second and third/fifth and sixth rows. That means the level of responses of indexes volatilities to one standard deviation of OVX shocks seem higher than to one standard deviation of other two commonly used oil market uncertainty indicators (WTI and Brent) shocks. Moreover, the red dots imply OVX shocks effects are more statistically significant than the other two. We do not present the girf results of VIX and VXFXI here (control variables), the estimations of them indicate the impact of VIX and VXFXI on Chinese sector indexes volatilities are statistically significant in most cases.
Taken together, they show the impact of OVX on Chinese stock market volatility is more economically and statistically significant than that of realized volatility of both WTI and Brent oil prices. This is important, it means when we focus on oil-Chinese stock volatility nexus, OVX seems more informative than WTI and Brent oil price realized volatility. A plausible explanation is OVX contains both historical and future information of oil market while the other two indicators based on raw oil prices contain only historical information, which makes OVX seems more informative than the other two.
We conduct a robustness check by using the conditional volatility estimated from the GARCH-family model. Specifically, for WTI and Brent volatility and Chinese stock sectors volatilities, we replace the square of log returns with the volatilities estimated from GARCH models. Note that all unit root modules of VAR model should be smaller than one to ensure the impulse responses converge to zero with the increase of the lag order. Table 2 has shown the maximum unit root module of each VAR model and we can find all of them are smaller than one, which imply our models are rational and we can further calculate the impulse response. In December 2005 the global demand for crude oil was 83.3 million barrels per day according to the International Energy Agency (IEA) and will rise further. Since the shale boom in the U.S., which resulted in a production increase of WTI, the price of WTI has gone down and usually trades at a discount to Brent.
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With the growing oil demand of China, it is interesting and beneficial for scholars and investors to investigate the oil-stock nexus between oil market and Chinese stock market. Some studies have paid attention to study how oil market affect the returns of Chinese stock market ([7–12]; among others). Another strand of literatures pay attention to the impact of oil market on the volatility of Chinese stock market [13–16]. Although the topic of this study is the impact of oil market on the other (Chinese stock market), oil market as a receiver rather than a giver attracts attentions either.
Similar to Tables 2 and 7 presents the maximum unit root module for each estimated VAR models and we can find all of them are smaller than one. That imply our models are rational and we can further analyze the generalized impulse responses of Chinese sector volatilities to three different oil market uncertainty proxy shocks. Given the strong impact of OVX on Chinese stock returns and that market volatility investigation is also important, it is surprised that little attention was paid for the impact of OVX on the volatility of Chinese stock market. The one exception which seems related with this topic is [23], they investigate the impact of OVX change on the change of VXFXI.