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Contrarians and Volatility Clustering

Author

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  • E.R. Grannan
  • G.H. Swindle
Abstract
We intoduce a new origin of volatility clustering in econonmic time series gererated by systems of interacting adaptive agents. Each agent is assigned a random subset of a fixed collection of predictors. At every time step each agent generates an action based upon its assigned predictors. Some agents are contrarians---i.e. they act at variance with the natural action suggested by a predictor. Agents that perform poorly are replaced. At each time step the signal value is generated soley by the cumulative actions of the agents on the current history of the time series. We observe numerically that under the dynamics induced by the removal of poor performers, even when contrarians are introduced at a very low density, the system evolves to a state in which contrarians comprise nearly half of the population. Furthermore, the time series generated by these systems exhibits volatility clustering. Elimination of either the contrarian behavior or the removal of poor players precludes volatility clustering.

Suggested Citation

  • E.R. Grannan & G.H. Swindle, 1994. "Contrarians and Volatility Clustering," Working Papers 94-03-010, Santa Fe Institute.
    • Handle: RePEc:wop:safiwp:94-03-010
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    Cited by:

    1. Youssefmir, Michael & Huberman, Bernardo A., 1997. "Clustered volatility in multiagent dynamics," Journal of Economic Behavior & Organization, Elsevier, vol. 32(1), pages 101-118, January.
    2. Alfarano, Simone & Lux, Thomas & Wagner, Friedrich, 2008. "Time variation of higher moments in a financial market with heterogeneous agents: An analytical approach," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 101-136, January.
    3. W. Brian Arthur & John H. Holland & Blake LeBaron & Richard Palmer & Paul Taylor, 1996. "Asset Pricing Under Endogenous Expectation in an Artificial Stock Market," Working Papers 96-12-093, Santa Fe Institute.
    4. Elena Green & Daniel M. Heffernan, 2019. "An Agent-Based Model to Explain the Emergence of Stylised Facts in Log Returns," Papers 1901.05053, arXiv.org.

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