In the framework of small-scale agent-based financial market models, the paper starts out from the concept of structural stochastic volatility, which derives from different noise levels in the demand of fundamentalists and chartists and the time-varying market shares of the two groups. It advances several different specifications of the endogenous switching between the trading strategies and then estimates these models by the method of simulated moments (MSM), where the choice of the moments reflects the basic stylized facts of the daily returns of a stock market index. In addition to the standard version of MSM with a quadratic loss function, we also take into account how often a great number of Monte Carlo simulation runs happen to yield moments that are all contained within their empirical confidence intervals. The model contest along these lines reveals a strong role for a (tamed) herding component. The quantitative performance of the winner model is so good that it may provide a standard for future research.JEL classification: D84; G12; G14; G15.

We develop a model in which boundedly rational agents apply technical and fundamental analysis to identify trading signals in two different speculative markets. Whether an agent trades and, if so, in which market with which strategy depends on profit considerations. As it turns out, an ongoing evolutionary competition between the trading strategies causes complex price dynamics which closely resembles the behavior of actual speculative prices. Moreover, we find that if the agents have to pay a transaction tax in one market, price variability decreases in this market but increases in the other market. However, the imposition of a uniform tax on all transactions stabilizes both markets. Our results suggest that if regulators of a market introduce a transaction tax, other markets are likely to follow. r

Opinion dynamics, Sociophysics, Ising model,

SummaryModels with heterogeneous interacting agents have proven to be quite successful in the past. For instance, such models are able to mimic the dynamics of financial markets quite well. The goal of our paper is to explore whether this approach may offer new insights into the working of certain regulatory policies such as transaction taxes, central bank interventions and trading halts. Although this strand of research is rather novel, we argue that agent-based models may be used as artificial laboratories to improve our understanding of how regulatory policy tools function.

We incorporate the behaviour of tax evasion into the standard two-dimensional Ising model and augment it by providing policy-makers with the opportunity to curb tax evasion via an appropriate enforcement mechanism. We discuss different network structures in which tax evasion may vary greatly over time if no measures of control are taken. Furthermore, we show that even minimal enforcement levels may help to alleviate this problem substantially.

We develop a behavioral commodity market model with consumers, producers and heterogeneous speculators to characterize the nature of commodity price fluctuations and to explore the effectiveness of price stabilization schemes. Within our model, we analyze how nonlinear interactions between market participants can create either bull or bear markets, or irregular price fluctuations between bull and bear markets through a (global) homoclinic bifurcation. Both the imposition of a bottoming price level (to support producers) or a topping price level (to protect consumers) can eliminate such homoclinic bifurcations and hence reduce market price volatility. However, simple policy rules, such as price limiters, may have unexpected consequences in a complex environment: a minimum price level decreases the average price while a maximum price limit increases the average price. In addition, price limiters influence the price dynamics in an intricate way and may cause volatility clustering. r

This paper proposes a simple chartist-fundamentalist model in which we allow for nonlinear time variation in chartists' extrapolation rate. Estimation of the model using monthly data for the major currencies vis-a-vis the US dollar shows that the model is significant in-sample and that it has out-of-sample predictive power for some of the currencies. We investigate the power of tests of the random walk model to detect predictability against the alternative of the proposed model. We find that the evidence of short-term unpredictability and the long-term predictability are consistent with our model

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