Does China’s iron ore futures market have price discovery function? Analysis based on VECM and State-space perspective
As the world’s largest importer, trading of iron ore occupies a pivotal position in China’s international trade. In order to seek the decision power of deciding the price for iron ore, China’s Dalian Commodity Exchange (DCE) listed iron ore futures in October 2013,which has become the world’s largest iron ore financial derivatives trading market now. Based on VECM and state-space perspective, this paper aims to explore the price discovery function of iron ore futures on the DCE. Comprehensive analysis from the views of long-term equilibrium relationship, short-term information shocks and dynamic contribution share are made in this paper. The empirical results show that: firstly, from the perspective of cointegration test, there is a long-term equilibrium relationship between the futures prices in DCE and the spot prices; secondly, when facing with short-term information shocks, iron ore futures in DCE have an obviously price discovery function by the analysis of impulse response and variance decomposition; finally, by the way of state-space and Kalman filter algorithm, the long-term equilibrium relationship dynamic contribution for price discovery function of DCE's iron ore futures remains stable between 60% and 70% now.
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Baillie, R. T., et al. (2002). Price discovery and common factor models. Journal of Financial Markets, 5(3), 309-321. https://doi.org/10.1016/S1386-4181(02)00027-7
Bernardo, L., Leonardo, P., Jorge, M., & Guillermo, F. (2019). A Kalman filter method for estimation and prediction of space-time data with an autoregressive structure. Journal of Statistical Planning and Inference, 203, 117-130. https://doi.org/10.1016/j.jspi.2019.03.005
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
Booth, G., So, R., & Tse, Y. (1999). Price discovery in the German equity index derivatives markets. Journal of Futures Markets, 6, 619-643. https://doi.org/10.1002/(SICI)1096-9934(199909)19:6<619::AID-FUT1>3.0.CO;2-M
Burcu, K., & Jose, O. (2019). An analysis of price discovery between Bitcoin futures and spot markets. Economics Letters, 1, 62-64. https://doi.org/10.1016/j.econlet.2018.10.031
Caporale, G. M., Ciferri, D., & Girardi, A. (2010). Time-varying spot and futures oil price dynamics. Scottish Journal of Political Economy, 3(61). https://doi.org/10.2139/ssrn.1633862
Dang, H. Q. (2018). Research on the dynamic efficiency of price discovery in the rebar futures market. Dissertation for the Master Degree in Harbin Institute of Technology, 6.
Ding, C. Z., & Xiao, H. F. (2018). On spillover effects and correlation between domestic and international cotton futures market: A comparative analysis under different policy backgrounds. Journal of Central South University (Social Science), 9, 117-128.
Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: Representation estimation and testing. Econometrica, 2, 251-276. https://doi.org/10.2307/1913236
Fang, W., Feng, G. Z., Lu, F. B., & Wang, S. Y. (2019). Research on dynamic evolution of price discovery in China’s steel trading market. Systems Engineering Theory & Practice, 1, 51-61.
Fu, Q., Ji, J. W., & Zhong, H. Y. (2017). Continuous trading system and price discovery ability-based on China’s gold futures market. Journal of Applied Statistics and Management, 6, 1119-1130.
Garbade, K. D., & Silber, W. L. (1983). Price movements and price discovery in futures and cash markets. The Review of Economics and Statistics, 65(2), 289-297. https://doi.org/10.2307/1924495
Ghosh, A. (1993). Cointegration and error correction models: Intertemporal causality between index and futures prices. Journal of Futures Markets, 13(2), 193-198. https://doi.org/10.1002/fut.3990130206
Gonzalo, J., & Granger, C. (1995). Estimation of common long-memory components in cointegrated systems. Journal of Business & Economic Statistics, 13(1), 27-35. https://doi.org/10.1080/07350015.1995.10524576
Hamilton, J. D. (1994). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometric Reviews, 57, 357-384. https://doi.org/10.2307/1912559
Harvey, A. (1989). Forecasting structural time series models and the Kalman filter. Cambridge University Press. https://doi.org/10.1017/CBO9781107049994
Hasbrouck, J. (1995). One security, many markets: Determining the contributions to price discovery. Journal of Finance, 50(4), 1175-1199. https://doi.org/10.1111/j.1540-6261.1995.tb04054.x
Huang, J. B., Liu, K., & Guo, Y. Q. (2014). An empirical study on dynamic contribution of price discovery in Shanghai copper futures market-based on the state-space model. Journal of Technical Economics & Management, 2, 67-72.
Johansen, S. (1988). Statistical analysis of cointegrating vectors. Journal of Economic Dynamics and Control, 12, 213-254. https://doi.org/10.1016/0165-1889(88)90041-3
Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration: with application to the demand for money. Oxford Bulletin of Economics and Statistics, 52, 169-210. https://doi.org/10.1111/j.1468-0084.1990.mp52002003.x
Lehmann, B. N. (2002). Some desiderata for the measurement of price discovery across markets. Finance Market, 5, 259-276. https://doi.org/10.1016/S1386-4181(02)00025-3
Liu, H. Z., & Chen, Y. (2017). Research on the price discovery ability of rebar futures market in China-Based on different regional spot market. Price: Theory & Practice, 10, 115-118.
Schreiber, P. S., & Schwartz, R. A. (1986). Price discovery in securities markets. Journal of Portfolio Management, 12(4), 43-48. https://doi.org/10.3905/jpm.1986.409071
Shi, B. F., Li, A. W., & Wang, J. (2018). Price discovery on Chinese rebar futures market. Operations Research and Management Science, 6, 162-171.
Song, B., & Xing T. C. (2018). Comparative study on the influence of China’s Shanghai copper, LME copper and COMEX copper futures markets-analysis of dynamic relationship based on price discovery and spill over effect. Price: Theory & Practice, 3, 127-130.
Sun, M., & Shi, B. J. (2019). Research on Shenzhen-Hong Kong stock market linkage and risk spill over effect before and after the implementation of Shenzhen-Hong Kong stock connect. Journal of North China University of Science and Technology (Social Science Edition), 2, 55-61.
Tse, Y. K. (1995). Nonlinear dynamics of the Nikkei Stock Average Futures. Financial Engineering and the Japanese Markets, 2(3), 181-195. https://doi.org/10.1007/BF02425195
Wang, S. S., Yu, Y. R., Liu, H. M., & Kang, Y. B. (2017). Research into price discovery of Chinese treasury bond futures based on high frequencies data. Operations Research and Management Science, 6, 117-123.
Wang, B. J., & Li, A. W. (2016). Night trading and linkage between China and USA futures markets: from the perspective of volatility spillover effect and dynamic correlation. Financial Economics Research, 5, 65-74.
Wu, L., & Ma, J. L. (2013). A systematic analysis of price discovery measures. China Economic Quarterly, 13(1), 399-424.
Xie, S. Q., & Yang, W. T. (2018). Empirical test of spot price discovery function in high frequency stock index futures. Statistics & Decision, 4, 148-152.
Xu, C. S., & Rao, S. S. (2018). Discovery efficiency and dynamic changes of prices in China’s iron ore futures market. Journal of Jiangxi University of Finance and Economics, 1, 20-29.
Zheng, Y., & Ma, J. (2018). Study on the spillover effect and dynamic relationship between egg futures market and spot market in China. Journal of China Agricultural University, 23, 222-231.
Zou, S. H., & Zhang, T. (2018). International relationship between international carbon future price and domestic carbon price. Journal of Shandong University (Natural Science), 5, 70-79.