Published online: 25 October 2020 Article Views: 30
Abstract
This paper explores the dynamic volatility spillovers among five major futures in China, including rebar, hot-rolled coils, iron ore, cooking coal and coke. We employ the Dynamic Conditional Correlation (DCC) GARCH model to examine the volatility spillover effects among the markets considering structural breaks invariance. We used a modified Iterated Cumulative Sum of Squares (ICSS) algorithm to detect the structural breaks. The empirical results show there are strong correlations across the black series futures market. Especially, the relation between rebar and hot-rolled coils, coke and cooking coal is more closely than other pairs. This research provides insight into the information transmission in black series futures market, which is meaningful to market participants making hedging and trading strategies. This study extends the literature by investigating the dynamic linkage among five kinds of black series futures.
References
Behmiri, N. B., Manera, M., & Nicolini, M. (2019). Understanding dynamic conditional correlations between oil, natural gas and non-energy commodity futures markets. The Energy Journal, 40(2), 1-32.
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427–431. doi:https://doi.org/10.1080/01621459.1979.10482531
Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339–350.
Fałdzinski, M., & Pietrzak, M. B. (2015). The multivariate DCC-GARCH model with interdependence among markets ́ in conditional variances’ equations. Przegl ̨ad Statystyczny. Statistical Review, 62(4), 397-413.
Güloglu, B., Kaya, P., & Aydemir, R. (2016). Volatility transmission among latin american stock markets under ̆structural breaks. Physica A: Statistical Mechanics and its Applications, 462, 330–340. doi:https://doi.org/10.1016/j.physa.2016.06.093
Huang, Y. Q. (2017). An empirical study on the price discovery of China’s coke, coal and steel futures market (Master dissertation). Zhejiang Gongshang University, Zhejiang, China.
Inclan, C., & Tiao, G. C. (1994). Use of cumulative sums of squares for retrospective detection of changes of variance. Journal of the American Statistical Association, 89(427), 913–923.
Kim, K., & Lim, S. (2019). Price discovery and volatility spillover in spot and futures markets: Evidences from steel-related commodities in China. Applied Economics Letters, 26(5), 351–357. doi:https://doi.org/10.1080/13504851.2018.1478385
Kinata, E. J. (2016). The effect of market volatility and firm size towards the difference of market reaction around stock-split announcement in Indonesia stock exchange. Journal of Administrative and Business Studies, 2(6), 304–313. doi:https://doi.org/10.20474/jabs-2.6.5
Kwiatkowski, D., Phillips, P. C., Schmidt, P., Shin, Y., et al. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1-3), 159–178. doi:https://doi.org/10.1016/0304-4076(92)90104-Y
Li, Q. Y. (2014). The research of cooking coal, coke, iron ore and rebar futures arbitrage strategy (Master dissertation). Shanghai Jiao Tong University, Shanghai, China.
Mensi, W., Hammoudeh, S., & Yoon, S.-M. (2015). Structural breaks, dynamic correlations, asymmetric volatility transmission, and hedging strategies for petroleum prices and USD exchange rate. Energy Economics, 48, 46–60. doi:https://doi.org/10.1016/j.eneco.2014.12.004
Mikosch, T., & Staric ̆ a, C. (2004). Nonstationarities in financial time series, the long-range dependence, and ̆the IGARCH effects. Review of Economics and Statistics, 86(1), 378–390. doi:https://doi.org/10.1162/003465304323023886
Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. doi:https://doi.org/10.1093/biomet/75.2.335
Rapach, D. E., & Strauss, J. K. (2008). Structural breaks and GARCH models of exchange rate volatility. Journal of Applied Econometrics, 23(1), 65–90. doi:https://doi.org/10.1002/jae.976
Sansó, A., Carrion, J., & Aragó, V. (2020). Testing for changes in the unconditional variance of financial time series. Revista de Economía Financiera, 4, 32-52.
Su, P. (2017). The research on hedging of futures black industry Chain: Analysis of price linkage based on combination variety (Master dissertation). Wuhan University, Wuhan, China.
Wang, J., & Zheng, Z. Z. (2019). Research on linkage between the futures price of iron ore and coke. Price Monthly, 5(11), 1-8.
Wang, Q., Dai, X., & Zhou, D. (2020). Dynamic correlation and risk contagion between “black” futures in China: A multi-scale variational mode decomposition approach. Computational Economics, 55(4), 1117–1150. doi:https://doi.org/10.1007/s10614-018-9857-y
Wang, Q., Hang, Y., Su, B., & Zhou, P. (2018). Contributions to sector-level carbon intensity change: An integrated decomposition analysis. Energy Economics, 70, 12–25. doi:https://doi.org/10.1016/j.eneco.2017.12.014
Xiao, X. X. (2014). Research on the relationship between coke futures and spot market (Master dissertation). Harbin Institute of Technology, Harbin, China.
Xie, C., & Zhao, D. (2012). Do structural breaks influence the characteristics of RMB exchange rate volatility? South China Journal of Economics, 9, 102-115.
Zhou, J. (2010). Research on the relationship between steel futures and spot market (Master dissertation). Fudan University, Shanghai, China.
Zhou, L. (2017). Research on arbitrage of cross – variety based on cointegration: Take black futures as an example price. Theory & Practice, 4, 112-115.
To Cite this article
Yang, R., Pastpipatkul, P., & Nimanussornkul, C. (2020). Dynamic volatility spillover among Chinese black series futures under structural breaks. International Journal of Business and Administrative Studies, 6(5), 236-246. doi: https://dx.doi.org/10.20469/ijbas.6.10002-5
