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Intraday dynamic relationships between CSI 300 index futures and spot markets: a high-frequency analysis

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Abstract

Based on intraday 5-min high-frequency dataset, this paper empirically analyzes the intraday dynamic relationships between China’s CSI 300 index futures and spot markets with vector autoregression (VAR) and multivariate GARCH (MGARCH) models. By comparing four VAR–MGARCH models (dynamic conditional correlation, constant conditional correlation, diagonal and BEKK), the VAR–DCC–MGARCH model is found to fit the data the best and be preferred over the other models. The results of this model show that although there are bidirectional price causal relationships between the CSI 300 index futures and spot markets, the index futures return shock affects the spot market more severely than the spot return shock affects the futures market, indicating that the index futures market dominates the price discovery process between the two markets. There are bidirectional volatility spillovers effects between the CSI 300 index futures and spot markets, and the spillovers effects from index futures to spot almost equal to that from index spot to futures. The time-varying conditional correlations between the CSI 300 index futures and spot markets change from 0.4787 to 0.9594 across time, showing there is a strong positive correlation and linkage effect between the two markets. These results indicate that after a period of time of development, the price discovery performance of the CSI 300 index futures market has begun to function well, and the impact of the CSI 300 index futures market on its underlying spot market has strengthened.

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References

  1. Pizzi MA, Ecnomopoulos AJ, O’ Neill HM (1998) An examination of the relationship between stock index cash and futures markets: a cointegration approach. J Futures Mark 18(3):297–305

    Article  Google Scholar 

  2. Kim M, Szakmary AC, Schwarz TV (1999) Trading costs and price discovery across stock index futures and cash market. J Futures Mark 19(4):475–498

    Article  Google Scholar 

  3. Brooks C, Rew AG, Ritson SA (2001) A trading strategy based on the lead-lag relationship between the spot index and futures contract for the FTSE 100. Int J Forecast 17(1):31–44

    Article  Google Scholar 

  4. Antoniou A, Pescetto G, Violaris A (2003) Modeling international price relationships and interdependencies between the stock index and stock index futures markets of three EU countries: a multivariate analysis. J Bus Finance Account 30(5–6):645–667

    Article  Google Scholar 

  5. Chatrath A, Charistie-David R, Dhanda KWK, Koch TW (2002) Index futures leadership, basis behavior, and trader selectivity. J Futures Mark 22(7):649–677

    Article  Google Scholar 

  6. Zhong M, Darrat AF, Otero R (2004) Price discovery and volatility spillovers in index futures markets: some evidence from Mexico. J Bank Financ 28(12):3037–3054

    Article  Google Scholar 

  7. Kavussanos MG, Visvikis ID, Alexakis PD (2008) The lead-lag relationship between cash and stock index futures in a new market. Eur Financ Manag 14(5):1007–1025

    Article  Google Scholar 

  8. Kim JS, Ryu D (2014) Intraday price dynamics in spot and derivatives markets. Phys A 394:247–253

    Article  Google Scholar 

  9. Jackline S, Deo M (2011) Lead-lag relationship between the futures and spot prices. J Econ Int Finance 3(7):424–427

    Google Scholar 

  10. Han Q, Guo B, Ryu D, Webb RI (2012) Asymmetric and negative return-volatility relationship: the case of the VKOSPI. Invest Anal J 76:69–78

    Google Scholar 

  11. Ryu D (2013) Price impact asymmetry of futures trades: trade direction and trade size. Emerg Markets Rev 14:110–130

    Article  Google Scholar 

  12. Amrit J, Tipprapa R (2014) An empirical examination of the lead-lag relationship between spot and futures markets: evidence from Thailand. Pacific-Basin Finance J 29:335–358

    Article  Google Scholar 

  13. Wahab M, Lashgari M (1993) Price dynamics and error correction in stock index and stock index futures markets: a cointegration approach. J Futures Mark 13(7):711–742

    Article  Google Scholar 

  14. Yang J, Yang Z, Zhou Y (2012) Intraday price discovery and volatility transmission in stock index and stock index futures markets: evidence from China. J Futures Mark 32(2):99–121

    Article  Google Scholar 

  15. Ross SA (1989) Information and volatility: the no-arbitrage martingale approach to timing and resolution irrelevancy. J Finance 44(1):1–17

    Article  Google Scholar 

  16. Koutmos G, Tucker M (1996) Temporal relationship and dynamic interactions between spot and futures stock markets. J Futures Mark 16(1):55–69

    Article  Google Scholar 

  17. Kuo WH, Hsu H, Chiang MH (2008) Foreign investment, regulation, volatility spillovers between the futures and spot markets: evidence from Taiwan. Appl Financ Econ 18(5):421–430

    Article  Google Scholar 

  18. Chiang TC, Yu HC, Wu MC (2009) Statistical properties, dynamic conditional correlation and scaling analysis: evidence from Dow Jones and Nasdaq high-frequency data. Phys A 388(8):1555–1570

    Article  Google Scholar 

  19. Tao J, Green CJ (2012) Asymmetries, causality and correlation between FTSE100 spot and futures: a DCC-TGARCH-M analysis. Int Rev Financ Anal 24:26–37

    Article  Google Scholar 

  20. Kang SH, Cheong C, Yoon SM (2013) Intraday volatility spillovers between spot and futures indices: evidence from the Korean stock market. Phys A 392(8):1795–1802

    Article  Google Scholar 

  21. Liu L, Wang Y (2014) Cross-correlations between spot and futures markets of nonferrous metals. Phys A 400:20–30

    Article  Google Scholar 

  22. Shi J, Xu T (2013) Price and volatility dynamics between securitized real estate spot and futures markets. Econ Model 35:582–592

    Article  Google Scholar 

  23. Rittler D (2012) Price discovery and volatility spillovers in the European Union emissions trading scheme: a high-frequency analysis. J Bank Finance 36(3):774–785

    Article  Google Scholar 

  24. Engle RF (1982) Autoregressive conditional heterskedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50(4):987–1007

    Article  MathSciNet  MATH  Google Scholar 

  25. Bollerslev T (1986) Generalized autoregressive conditional heterskedasticity. Econometrics 31:307–327

    Article  MathSciNet  MATH  Google Scholar 

  26. Zhao H (2010) Dynamic relationship between exchange rate and stock price: evidence from China. Res Int Bus Finance 24(2):103–112

    Article  Google Scholar 

  27. Bauwens L, Laurent S, Rombouts JVK (2006) Multivariate GARCH models: a survey. J Appl Econom 21:79–109

    Article  MathSciNet  Google Scholar 

  28. Kuttu S (2014) Returns and volatility dynamics among four African equity markets: a multivariate VAR-EGARCH analysis. Glob Finance J 25(2):56–69

    Article  Google Scholar 

  29. Ling S, McAleer M (2003) Asymptotic theory for a vector ARMA-GARCH model. Econom Theory 19(2):280–310

    Article  MathSciNet  Google Scholar 

  30. Chang CL, McAleer M, Tansuchat R (2010) Analyzing and forecasting volatility spillovers, asymmetries and hedging in major oil markets. Energy Econ 32:1445–1455

    Article  Google Scholar 

  31. Sadorsky P (2012) Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies. Energy Econ 34(1):248–255

    Article  Google Scholar 

  32. Mensi W, Beljid M, Boubaker A, Managi S (2013) Correlations and volatility spillovers across commodity and stock markets: linking energies, food, and gold. Econ Model 32(C):15–22

    Article  Google Scholar 

  33. Engle RF, Kroner KF (1995) Multivariate simultaneous generalized ARCH. Econom Theory 11(1):122–150

    Article  MathSciNet  Google Scholar 

  34. Engle RF (2002) Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J Bus Econ Stat 20(3):339–350

    Article  MathSciNet  Google Scholar 

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grants 91024028 and 71271070.

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Authors and Affiliations

  1. School of Economics, Harbin University of Science and Technology, Harbin, 150080, Heilongjiang, China

    Bei Zhou

  2. School of Management, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China

    Chong Wu

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  1. Bei Zhou
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  2. Chong Wu
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Correspondence to Bei Zhou.

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Zhou, B., Wu, C. Intraday dynamic relationships between CSI 300 index futures and spot markets: a high-frequency analysis. Neural Comput & Applic 27, 1007–1017 (2016). https://doi.org/10.1007/s00521-015-1915-y

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  • DOI: https://doi.org/10.1007/s00521-015-1915-y

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