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TESTING LINEAR AND NONLINEAR GRANGER CAUSALITY IN CSI300 FUTURES AND SPOT MARKETS BASED ON NEW CONCEPTS OF NONLINEAR POSITIVE/NEGATIVE SPILLOVER

ZHOU Pu1 , LU Fengbin2 , WANG Shouyang2   

  1. 1.Risk Management Unit, Bank of China Head Office Beijing 100032, China.; 2.Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
  • Received:2012-12-10 Online:2014-08-25 Published:2014-08-19
  • Supported by:

    This research was supported by the National Natural Science Foundation of China under Grant Nos. 71001096, 70933003, and 71071170.

ZHOU Pu , LU Fengbin , WANG Shouyang. TESTING LINEAR AND NONLINEAR GRANGER CAUSALITY IN CSI300 FUTURES AND SPOT MARKETS BASED ON NEW CONCEPTS OF NONLINEAR POSITIVE/NEGATIVE SPILLOVER[J]. Journal of Systems Science and Complexity, 2014, 27(4): 729-742.

Hiemstra and Jones (1994) argued that a significant negative value of their nonlinear Granger causality test (H-J test) means there is a confounding effect in the prediction. However, from the theoretical analysis and Monte Carlo simulations, the authors find that H-J test is significantly negative under the circumstance of negative volatility spillover. Furthermore, the authors put forward the conceptions of positive/negative nonlinear spillover, and apply H-J test to examine positive/negative nonlinear spillover effect. The empirical study on China stock futures and spot markets shows that: 1) There is significant positive nonlinear spillover from futures to spot market; 2) There is significant negative nonlinear spillover from spot to futures market. The authors argue that there is “risk absorption” mechanism in information spillover from the spot market to the futures market, which is due to the temporal transfer of speculative trading from the analysis.
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