Contrarians and Volatility Clustering
E. R. Grannan
Department of Physics, University of California,
Irvine, CA 92717, USA
G. H. Swindle
Department of Statistics and Applied Probability,
University of California, Santa Barbara, CA 93106, USA
Abstract
We introduce a new origin of volatility clustering in economic time series generated 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, that is, 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 solely 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.
