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Market Dynamics and Stock Price Volatility

Author

Listed:
  • J. Barkley Rosser, Jr.
  • Honggang Li

Abstract

This paper presents a possible explanation for some of the empirical properties of asset returns within a heterogeneous-agents framework. The model turns out, even if we assume the input fundamental value follows a simple Gaussian distribution lacking both fat tails and volatility dependence, these features can show up in the time series of asset returns. In this mode, the profit comparison and switching between heterogeneous agents play key roles through a focus on moving averages. This builds a connection between endogenous market performance and the emergence of stylized facts

Suggested Citation

  • J. Barkley Rosser, Jr. & Honggang Li, 2004. "Market Dynamics and Stock Price Volatility," Computing in Economics and Finance 2004 91, Society for Computational Economics.
    • Handle: RePEc:sce:scecf4:91
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    Citations

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    Cited by:

    1. Frank H. Westerhoff, 2009. "Exchange Rate Dynamics: A Nonlinear Survey," Chapters, in: J. Barkley Rosser Jr. (ed.), Handbook of Research on Complexity, chapter 11, Edward Elgar Publishing.
    2. Witte, Björn-Christopher, 2011. "Removing systematic patterns in returns in a financial market model by artificially intelligent traders," BERG Working Paper Series 82, Bamberg University, Bamberg Economic Research Group.
    3. Zhong, Guang-Yan & Li, Jiang-Cheng & Jiang, George J. & Li, Hai-Feng & Tao, Hui-Ming, 2018. "The time delay restraining the herd behavior with Bayesian approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 335-346.
    4. Biondi, Yuri & Giannoccolo, Pierpaolo & Galam, Serge, 2012. "Formation of share market prices under heterogeneous beliefs and common knowledge," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5532-5545.
    5. Shu-Heng Chan & Shu G. Wang, 2010. "Emergent Complexity in Agent-Based Computational Economics," ASSRU Discussion Papers 1017, ASSRU - Algorithmic Social Science Research Unit.
    6. Dong, Yang & Wen, Shu-hui & Hu, Xiao-bing & Li, Jiang-Cheng, 2020. "Stochastic resonance of drawdown risk in energy market prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    7. Melecký, Jan & Sergyeyev, Artur, 2008. "A simple finite-difference stock market model involving intrinsic value," Chaos, Solitons & Fractals, Elsevier, vol. 38(3), pages 769-777.
    8. Westerhoff, Frank, 2009. "A simple agent-based financial market model: Direct interactions and comparisons of trading profits," BERG Working Paper Series 61, Bamberg University, Bamberg Economic Research Group.
    9. João Nicolau, 0. "A discrete and a continuous-time model based on a technical trading rule," Journal of Financial Econometrics, Oxford University Press, vol. 5(2), pages 266-284.
    10. Shu‐Heng Chen & Shu G. Wang, 2011. "Emergent Complexity In Agent‐Based Computational Economics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(3), pages 527-546, July.

    More about this item

    Keywords

    financial markets; econophysics; computational methods in statistica physics;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    Statistics

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