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Learning Dynamics in an Artificial Currency Market

Author

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  • Christophre Georges

Abstract

This paper considers the behavior of the exchange rate in a very simple artificial currency market with two currencies and artificial agents who evolve their forecast rules over time via a genetic algorithm. I consider two simple forecast rules, one linear and the other non-linear. Under the first rule, learning tends to be rapid and complete. Under the second, learning can generate persistent exchange rate dynamics.

Suggested Citation

  • Christophre Georges, 2001. "Learning Dynamics in an Artificial Currency Market," Computing in Economics and Finance 2001 31, Society for Computational Economics.
    • Handle: RePEc:sce:scecf1:31
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    Cited by:

    1. Simone Alfarano & Thomas Lux, 2007. "A Minimal Noise Trader Model with Realistic Time Series Properties," Springer Books, in: Gilles Teyssière & Alan P. Kirman (ed.), Long Memory in Economics, pages 345-361, Springer.
    2. E. Samanidou & E. Zschischang & D. Stauffer & T. Lux, 2001. "Microscopic Models of Financial Markets," Papers cond-mat/0110354, arXiv.org.

    More about this item

    Keywords

    Learning; Genetic Algorithm; Currency;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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