Staggered updating in an artificial financial market

Authored by Christophre Georges

Date Published: 2008-09

DOI: 10.1016/j.jedc.2007.11.002

Sponsors: United States National Science Foundation (NSF)

Platforms: C++

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

We consider an environment in which traders search for trading opportunities and update their forecast rules at random intervals by OLS. The staggering of this updating process across traders allows differences in opinion to persist over time, generating nontrivial price dynamics. The nature of these dynamics is sensitive to the degree of overparameterization of forecast rules relative to market fundamentals. (C) 2008 Elsevier B.V. All rights reserved.

Tags

Agent-based modeling Learning Expectations