Further discussion about the price discovery function of Chinese stock index futures: evidence from three listed products
(2.School of Economics, Nankai University 300071)
【Abstract】This paper comprehensively studied the price discovery function of Chinese stock index futures and index spot, explored the time-varying features from the perspective of both statistical and economic significance by long-term weakly exogenous test, generalized variance decomposition, PT model and IS model. We adopted five-minute high-frequency data of capitalization-weighted stock market index (CSI) 300, Shanghai Stock Exchange (SSE) 50, and CSI 500 stock index futures for the first time in this research field. The empirical results show a relatively stable long-run equilibrium relationship between the stock index futures and index spot of the three products. For both CSI 300 and CSI 500, the frequency of the futures price as a weakly exogenous variable is over 90%, in which case the futures price leads the spot price or the two lead each other in the remaining recursive samples. In addition, the futures price contributes more to the price discovery than the spot price in all recursive samples. For SSE 50, the frequency of future price leading spot price is above 70%, and the frequency of the two leading each other is about 10%, in which case the price discovery contribution of futures market is higher than that of spot market. However, during the stock market crash in 2015, a special case occurred that SSE 50 index spot led stock index futures, and the price discovery contribution of spot market surpassed futures market. All in all, the Chinese stock index futures market is becoming mature with sound performance in price discovery.
【Keywords】 stock index futures; lead-lag relationship; price discovery; contribution measurement;
. ① Stock index futures underlying indices of the three products represent comprehensive market, large-cap blue chips and mid/small-cap stocks, where CSI 300 stock index futures is mainly used to meet the risk management demand of the investors for systematic risk of the market. However, there is an alternative “seesaw” effect between large-cap blue chips and mid/small-cap stocks, which lowers the hedging efficiency of CSI 300 stock index futures. Stock index futures of a single product is hard to meet the market demand, while the stock index futures of SSE 50 and CSI 500 can meet the hedging demand of large-cap blue chips and mid/small-cap stocks respectively. Multiple stock index futures products provide investors with finer risk management instruments. [^Back]
. ① Direct estimation of equation (3) is the parameter of unconstrained VAR model. Based on this, the variance decomposition and impulse response results are inconsistent in the long forecast period. [^Back]
. ② For a specific introduction of generalized variance decomposition, refer to Yang et al. (2006) and Liang et al. (2015). [^Back]
. ③ The common factor means the common implicit effective price of futures and spot. [^Back]
. ① Horizontal series are all price series taken natural logarithm. [^Back]
. ① Basis means the difference between spot price and futures price with an implicit unbiased hypothesis. [^Back]
. ① It refers to the trace statistics testing the null hypothesis H(0) of “the number of cointegration vectors is 0.” [^Back]
. ① The net spillover of a market is equal to the outbound information spillover of the market minus that accepted by the market from other markets. For the futures market, its net spillover means the information spillover of futures towards spot minus the spillover accepted by futures from spot, and so does the spot market. [^Back]
. ② Yang et al. (2012), after studying the trading data of CSI 300 stock index futures at the beginning of its listing, found that CSI 300 index spot led stock index futures with spot market at the center of price discovery. [^Back]
. ① For details about the development status of stock index futures markets in other countries and regions, check the website of CFFEX (http://www.cffex.com.cn/tzzfw/jryspxy/gzqh/zxyd/sz50jzz500wd/). [^Back]
1. Fang, K. & Cai, Z. Statistical Research (统计研究), (5) (2012).
2. He, C., Zhang, L. & Chen, W. The Journal of Quantitative & Technical Economics (数量经济技术经济研究), (5) (2011).
3. Hua, R. & Liu, Q. The Journal of Quantitative & Technical Economics (数量经济技术经济研究), (10) (2010).
4. Liang, Q., Li, Z. & Hao, X. Economic Research Journal (经济研究), (4) (2015).
5. Liu, Q. & Hua, R. Statistical Research (统计研究), (11) (2011).
6. Liu, X. & Zhang, Y. Business Review (管理评论), (2) (2012).
7. Project Team of Tsinghua University National Institute of Financial Research. research report, No.13, 2015.
8. Wei, Z., Yang, C. & Liu, X. Finance & Trade Economics (财贸经济), (8) (2012).
9. Yan, M., Ba, S. & Wu, B. Systems Engineering (系统工程), (10) (2009).
10. Baillie, R. T., Booth, G. G., Tse, Y., & Zabotina, T., Price Discovery and Common Factor Models. Journal of Financial Markets, Vol. 5, No. 3, 2002, pp. 309–321.
11. Diebold, F. X., & Yilmaz, K., Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets. Economic Journal, Vol. 119, No. 534, 2009, pp. 158–171.
12. Diebold, F. X., & Yilmaz, K., Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting, Vol. 28, No. 1, 2012, pp. 57–66.
13. Gonzalo, J., & Granger, C., Estimation of Common Long-Memory Components in Cointegrated Systems. Journal of Business & Economic Statistics, Vol. 13, No. 1, 1995, pp. 27–35.
14. Hansen, H., & Johansen, S., Some Tests for Parameter Constancy in Cointegrated VAR‐Models. Econometrics Journal, Vol. 2, No. 2, 1999, pp. 306–333.
15. Hasbrouck, J., One Security, Many Markets: Determining the Contributions to Price Discovery. Journal of Finance, Vol. 50, No. 4, 1995, pp. 1175–1199.
16. Johansen, S., Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, Vol. 59, No. 6, 1991, pp. 1551–1580.
17. Johansen, S., Determination of Cointegration Rank in the Presence of a Linear Trend. Oxford Bulletin of Economics and Statistics, Vol. 54, No. 3, 1992, pp. 383–397.
18. Judge, A., & Reancharoen, T., An Empirical Examination of the Lead-Lag Relationship between Spot and Futures Markets: Evidence from Thailand. Pacific-Basin Finance Journal, Vol. 29, 2014, pp. 335–358.
19. Kawaller, I. G., Koch, P. D., & Koch, T. W., The Temporal Price Relationship between S&P 500 Futures and the S&P 500 Index. Journal of Finance, Vol. 42, No. 5, 1987, pp. 1309–1329.
20. Phillips, P. C., Impulse Response and Forecast Error Variance Asymptotics in Nonstationary VARs. Journal of Econometrics, Vol. 83, No. 1, 1998, pp. 21–56.
21. Yang, J., Bessler, D. A., & Leatham, D. J., Asset Storability and Price Discovery in Commodity Futures Markets: A New Look. Journal of Futures Markets, Vol. 21, No. 3, 2001, pp. 279–300.
22. Yang, J., Hsiao, C., Li, Q., & Wang, Z., The Emerging Market Crisis and Stock Market Linkages: Further Evidence. Journal of Applied Econometrics, Vol. 21, No. 6, 2006, pp. 727–744.
23 Yang, J., Yang, Z., & Zhou, Y., Intraday Price Discovery and Volatility Transmission in Stock Index and Stock Index Futures Markets: Evidence from China. Journal of Futures Markets, Vol. 32, No. 2, 2012, pp. 99–121.