Exploring the dynamic price discovery, risk transfer and spillover amongINE,WTIand Brent crude oil futures markets: Evidence from the high-frequency data

被引:23
作者
Zhang, Yue-Jun [1 ,2 ]
Ma, Shu-Jiao [1 ,2 ]
机构
[1] Hunan Univ, Sch Business, Changsha 410082, Peoples R China
[2] Hunan Univ, Ctr Resource & Environm Management, Changsha, Peoples R China
基金
中国国家自然科学基金;
关键词
INE crude oil market; price discovery; return spillover; risk transfer; volatility spillover; VOLATILITY SPILLOVERS; ENERGY MARKETS; CARBON; SPOT; WTI; COINTEGRATION; CONNECTEDNESS; INDEXATION; RETURN;
D O I
10.1002/ijfe.1914
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
In order to test whether Chinese crude oil futures (INE) has already played the role of futures market and whether it has had a significant impact on international benchmark market, we construct the permanent temporary model and Information Share model based on 15 min of high-frequency trading data from March 26, 2018 to October 30, 2018 to inspect the proportions of new information in INE and Brent markets, and use the Garbade-Silber model to measure the risk transfer effect. Furthermore, the generalised spillover index is proposed to examine the effects of return and volatility spillovers among INE, WTI and Brent futures markets. The results reveal that: firstly, during the sample period, INE is not yet a promoter of international benchmark crude oil prices, but more obvious followers. Secondly, although INE has begun to display the price discovery function, it is weaker than that of Brent, and the risk transfer function between them does not appear strong. Finally, INE market mainly acts as a net transmitter of return spillover before August 2018, but it has almost always been the net transmitter of volatility spillover during the full sample period. These findings are of interest to policy makers as well as investors for risk hedging and asset allocation of crude oil portfolios.
引用
收藏
页码:2414 / 2435
页数:22
相关论文
共 44 条
[1]   Price discovery and common factor models [J].
Baillie, RT ;
Booth, GG ;
Tse, Y ;
Zabotina, T .
JOURNAL OF FINANCIAL MARKETS, 2002, 5 (03) :309-321
[2]   Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk [J].
Barunik, Jozef ;
Krehlik, Tomas .
JOURNAL OF FINANCIAL ECONOMETRICS, 2018, 16 (02) :271-296
[3]   Trading co-integrated assets with price impact [J].
Cartea, Alvaro ;
Gan, Luhui ;
Jaimungal, Sebastian .
MATHEMATICAL FINANCE, 2019, 29 (02) :542-567
[4]   Forecasting the WTI crude oil price by a hybrid-refined method [J].
Chai, Jian ;
Xing, Li-Min ;
Zhou, Xiao-Yang ;
Zhang, Zhe George ;
Li, Jie-Xun .
ENERGY ECONOMICS, 2018, 71 :114-127
[5]   On the network topology of variance decompositions: Measuring the connectedness of financial firms [J].
Diebold, Francis X. ;
Yilmaz, Kamil .
JOURNAL OF ECONOMETRICS, 2014, 182 (01) :119-134
[6]   Better to give than to receive: Predictive directional measurement of volatility spillovers [J].
Diebold, Francis X. ;
Yilmaz, Kamil .
INTERNATIONAL JOURNAL OF FORECASTING, 2012, 28 (01) :57-66
[7]   MEASURING FINANCIAL ASSET RETURN AND VOLATILITY SPILLOVERS, WITH APPLICATION TO GLOBAL EQUITY MARKETS [J].
Diebold, Francis X. ;
Yilmaz, Kamil .
ECONOMIC JOURNAL, 2009, 119 (534) :158-171
[8]   Price discovery in crude oil futures [J].
Elder, John ;
Miao, Hong ;
Ramchander, Sanjay .
ENERGY ECONOMICS, 2014, 46 :S18-S27
[9]   COINTEGRATION AND ERROR CORRECTION - REPRESENTATION, ESTIMATION, AND TESTING [J].
ENGLE, RF ;
GRANGER, CWJ .
ECONOMETRICA, 1987, 55 (02) :251-276
[10]   Modelling and measuring price discovery in commodity markets [J].
Figuerola-Ferretti, Isabel ;
Gonzalo, Jesus .
JOURNAL OF ECONOMETRICS, 2010, 158 (01) :95-107