Commodity markets, price limiters and speculative price dynamics

By Xue-zhong He, Frank H. Westerhoff. 2005

133 citations recorded by the source corpus.

Abstract 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.

Subfields: Computer Networks and Communications, Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/d3276e2c564a454da31b6787e53c885d1d3c5fc2

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In Computer Networks and Communications

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Citations in the corpus

A simple model of a speculative housing market (2012, methodology): “A related rule selection scenario is used, for instance, in He and Westerhoff (2005) to understand the cyclical behavior of commodity prices.”
On the complicated price dynamics of a simple one-dimensional discontinuous financial market model with heterogeneous interacting traders (2010, methodology): “Moreover, similar formulations for the behavior of chartists have been used, for instance, by Brock and Hommes (1998) and He and Westerhoff (2005).”
Heterogeneous expectations, exchange rate dynamics and predictability (2007, unknown): “The work of Meese and Rogoff (1983) showed that structural exchange rate models were not able to beat the simple random walk in out-of-sample prediction.”