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An Agent-Based Computational Model for China’s Stock Market and Stock Index Futures Market

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  • Hai-Chuan Xu
  • Wei Zhang
  • Xiong Xiong
  • Wei-Xing Zhou

Abstract

This study presents an agent-based computational cross market model for Chinese equity market structure, which includes both stocks and CSI 300 index futures. In this model, we design several stocks and one index future to simulate this structure. This model allows heterogeneous investors to make investment decisions with restrictions including wealth, market trading mechanism, and risk management. Investors’ demands and order submissions are endogenously determined. Our model successfully reproduces several key features of the Chinese financial markets including spot-futures basis distribution, bid-ask spread distribution, volatility clustering, and long memory in absolute returns. Our model can be applied in cross market risk control, market mechanism design, and arbitrage strategies analysis.

Suggested Citation

  • Hai-Chuan Xu & Wei Zhang & Xiong Xiong & Wei-Xing Zhou, 2014. "An Agent-Based Computational Model for China’s Stock Market and Stock Index Futures Market," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, April.
    • Handle: RePEc:hin:jnlmpe:563912
    DOI: 10.1155/2014/563912
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    1. Evangelos Drimbetas & Nikolaos Sariannidis & Nicos Porfiris, 2007. "The effect of derivatives trading on volatility of the underlying asset: evidence from the Greek stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 17(2), pages 139-148.
    2. Westerhoff Frank H., 2008. "The Use of Agent-Based Financial Market Models to Test the Effectiveness of Regulatory Policies," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(2-3), pages 195-227, April.
    3. 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.
    4. Rittler, Daniel, 2012. "Price discovery and volatility spillovers in the European Union emissions trading scheme: A high-frequency analysis," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 774-785.
    5. Sabrina Ecca & Michele Marchesi & Alessio Setzu, 2008. "Modeling and Simulation of an Artificial Stock Option Market," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 37-53, September.
    6. Cumming, Douglas & Johan, Sofia & Li, Dan, 2011. "Exchange trading rules and stock market liquidity," Journal of Financial Economics, Elsevier, vol. 99(3), pages 651-671, March.
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    Cited by:

    1. Gao-Feng Gu & Xiong Xiong & Hai-Chuan Xu & Wei Zhang & Yongjie Zhang & Wei Chen & Wei-Xing Zhou, 2021. "An empirical behavioral order-driven model with price limit rules," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.

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