Global Oil Shocks and China’s Commodity Markets: The Role of OVX

ABSTRACT

This paper investigates the effects of global oil shocks on the returns and volatilities of Chinese commodities from 1997 to 2016. We identify the different causes of oil shocks by using a structural vector autoregressive (SVAR) model. Particularly, we employ the crude oil volatility index (OVX) issued by the Chicago Board Options Exchange (CBOE) to proxy for the oil volatility shock and differentiate it from oil price shocks. Results indicate that both the responses of returns and volatilities of China’s commodities differ depending on the underlying causes of global oil shocks. Furthermore, the OVX shock has significant negative effects on the returns and positive effects on the realized volatilities of Chinese commodities, while the impacts of oil shocks caused by changes in oil supply and global economic activity are insignificant and negligible, especially after the 2008 financial crisis.

KEY WORDS:

• China’s commodity markets
• global oil shocks
• oil volatility index
• SVAR model

JEL:

• G15, Q4

Acknowledgments

We are grateful to Ali Kutan for his encouragement. We also acknowledge helpful comments from two anonymous referees, Qiang Ji, Dayong Zhang and other participants at the 2017 International Conference on Energy Finance. We gratefully acknowledge the financial support from the National Science Foundation of China (Project No. 71673249) and the Fundamental Research Funds for the Central Universities.

Supplementary Material

Supplemental data for this article can be accessed on thepublisher’s website.

Correction Statement

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This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

1. Bouri et al. (2018a) and Bouri et al. (2018b) also highlight the important role of the oil implied volatility index in BRICS and emerging economies.

2. The ordering of OVX and WTI returns in the identification of structural oil shocks may be controversial. Although some researchers have investigated the relation between OVX and returns of WTI returns, but no consistent conclusion on the direction of relation has been achieved (see, Aboura and Chevallier 2013; Agbeyegbe 2015; Ji and Fan 2016; Liu, Ji, and Fan 2017). To check the influence of ordering of OVX and WTI returns in the structural shocks, we use an unrestricted VAR model with ordering-free generalized impulse response in the robustness checks and obtain similar results (See the Supplementary Material, available online). In addition, we also try to reverse the ordering of OVX and WTI returns while keeping other variables the same order. The results show that the responses of all oil shocks are consistent with the responses reported in this paper, but the contribution of oil price shock becomes larger than the OVX shock, which slightly differs from the contributions in this paper. These results are available upon request.

3. Since the 2008 Financial crisis first broke in Financial markets July 2007 and then spread to commodity markets, we use 31 December 2007 as the date to divide the data and the results of using 31 July 2008 (Ji and Fan 2012) as the dividing date is similar.

4. To match the frequency of other monthly variables, we use the OVX at the end day of a month as the implied volatility of the month and convert the annualized daily volatility into monthly volatility.

5. The VIX Index is a calculation designed to produce a measure of 30-day expect the volatility of the U.S. stock market, derived from real-time prices of S&P 500 Index call and put options and the methodology of VIX calculation is available on the website of CBOE, http://www.cboe.com/micro/vix/vix-index-rules-and-methodology.pdf.

6. To save space, we do not report the impacts of the oil shocks on the realized volatilities of the commodity indices because we obtain similar results on this issue. These findings are available upon request.

Additional information

Funding

The National Natural Science Foundation of China (Project No. 71673249), the Natural Science Foundation of Zhejiang Province (LQ20G030027) and financial support from Academy of Financial Research of Zhejiang University.