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The Behavior and Impact of Heterogeneous Investors in China’s Stock Index Futures Market: An Agent‐Based Model on Cross‐Market Trades

Author

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  • Zhuoyi Yang
  • Xiong Xiong
  • Lijian Wei
  • Yian Cui
  • Li Wan

Abstract

Since the period of unusual volatility in China’s A‐share market in 2015, there has been an ongoing discussion about the role of stock index futures in the A‐share market. There is no unified consensus among academics and industry insiders on whether stock index futures affect spot market volatility. Using agent‐based modeling, we construct a theoretical model of the order book of the stock index futures market to assess the microbehavior of speculators, arbitrageurs, and hedgers in this market. We then calibrate the link between the futures and spot models to explore the respective influences of heterogeneous investors in the two markets. We find that speculators, arbitrageurs, and hedgers all play different roles and have varying effects on the two markets. While speculators serve as the foundation for other investors to participate in trading activities, both arbitrageurs and hedgers affect the spot market by significantly reducing volatility, enhancing price efficiency, and playing a positive role in the operation of this market. We develop our model from the perspective of investor behavior and explain why the stock index futures market can reduce spot market volatility. In addition, our conclusion may help regulators understand the roles played by different types of investors in the Chinese stock index futures market.

Suggested Citation

  • Zhuoyi Yang & Xiong Xiong & Lijian Wei & Yian Cui & Li Wan, 2022. "The Behavior and Impact of Heterogeneous Investors in China’s Stock Index Futures Market: An Agent‐Based Model on Cross‐Market Trades," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:9439957
    DOI: 10.1155/2022/9439957
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    References listed on IDEAS

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    1. Miao, Hong & Ramchander, Sanjay & Wang, Tianyang & Yang, Dongxiao, 2017. "Role of index futures on China's stock markets: Evidence from price discovery and volatility spillover," Pacific-Basin Finance Journal, Elsevier, vol. 44(C), pages 13-26.
    2. Wei, Lijian & Zhang, Wei & Xiong, Xiong & Shi, Lei, 2015. "Position limit for the CSI 300 stock index futures market," Economic Systems, Elsevier, vol. 39(3), pages 369-389.
    3. Song Cao & Ziran Li & Kees G. Koedijk & Xiang Gao, 2022. "The emotional cost-of-carry: Chinese investor sentiment and equity index futures basis," China Finance Review International, Emerald Group Publishing Limited, vol. 12(3), pages 451-476, January.
    4. Vince Darley & Alexander V Outkin, 2007. "A NASDAQ Market Simulation:Insights on a Major Market from the Science of Complex Adaptive Systems," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 6217, September.
    5. Chiarella, Carl & Iori, Giulia, 2009. "The impact of heterogeneous trading rules on the limit order book and order flows," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 525-537.
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