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Genetic learning as an explanation of stylized facts of foreign exchange markets

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  • Lux, Thomas
  • Schornstein, Sascha

Abstract

This paper revisits the Kareken-Wallace model of exchange rate formation in a two-country overlapping generations world. Following the seminal paper by Arifovic (Journal of Political Economy, 104, 1996, 510 – 541) we investigate a dynamic version of the model in which agents? decision rules are updated using genetic algorithms. Our main interest is in whether the equilibrium dynamics resulting from this learning process helps to explain the main stylized facts of free-floating exchange rates (unit roots in levels together with fat tails in returns and volatility clustering). Our time series analysis of simulated data indicates that for particular parameterizations, the characteristics of the exchange rate dynamics are, in fact, very similar to those of empirical data. The similarity appears to be quite insensitive with respect to some of the ingredients of the GA algorithm (i.e. utility-based versus rank-based or tournament selection, binary or real coding). However, appearance or not of realistic time series characteristics depends crucially on the mutation probability (which should be low) and the number of agents (not more than about 1000). With a larger population, this collective learning dynamics looses its realistic appearance and instead exhibits regular periodic oscillations of the agents? choice variables. --

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Bibliographic Info

Paper provided by Christian-Albrechts-University of Kiel, Department of Economics in its series Economics Working Papers with number |aEconomics working paper / Christian-Albrechts-Universität Kiel, Department of Economics |x2003,12.

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Date of creation: 2003
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Handle: RePEc:zbw:cauewp:1122

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Keywords: learning ; genetic algorithms ; exchange rate dynamics;

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  1. Shu-Heng Chen & Thomas Lux & Michele Marchesi, 1999. "Testing for Non-Linear Structure in an Artificial Financial Market," Discussion Paper Serie B, University of Bonn, Germany 447, University of Bonn, Germany.
  2. Kirman Alan & Teyssière Gilles, 2002. "Microeconomic Models for Long Memory in the Volatility of Financial Time Series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, De Gruyter, vol. 5(4), pages 1-23, January.
  3. Blume, Lawrence & Easley, David, 1992. "Evolution and market behavior," Journal of Economic Theory, Elsevier, Elsevier, vol. 58(1), pages 9-40, October.
  4. Brock, W.A. & Hommes, C.H. & Wagener, F.O.O., 2001. "Evolutionary Dynamics in Financial Markets With Many Trader Types," CeNDEF Working Papers 01-01, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  5. Bilson, John F O, 1981. "The "Speculative Efficiency" Hypothesis," The Journal of Business, University of Chicago Press, University of Chicago Press, vol. 54(3), pages 435-51, July.
  6. Hens, Thorsten & Schenk-Hoppe, Klaus Reiner, 2005. "Evolutionary stability of portfolio rules in incomplete markets," Journal of Mathematical Economics, Elsevier, vol. 41(1-2), pages 43-66, February.
  7. Xue-Zhong (Tony) He & Carl Chiarella, 2001. "Asset Price and Wealth Dynamics under Heterogeneous Expectations," CeNDEF Workshop Papers, January 2001, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance 5A.2, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
  8. Brock, W.A. & Hommes, C.H., 1996. "A Rational Route to Randomness," Working papers, Wisconsin Madison - Social Systems 9530r, Wisconsin Madison - Social Systems.
  9. Arifovic, Jasmina & Gencay, Ramazan, 2000. "Statistical properties of genetic learning in a model of exchange rate," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 24(5-7), pages 981-1005, June.
  10. Arifovic, Jasmina, 1996. "The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 104(3), pages 510-41, June.
  11. Giulia Iori, 2000. "A microsimulation of traders activity in the stock market: the role of heterogeneity, agents' interactions and trade frictions," Finance, EconWPA 0004007, EconWPA.
  12. Egenter, E. & Lux, T. & Stauffer, D., 1999. "Finite-size effects in Monte Carlo simulations of two stock market models," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 268(1), pages 250-256.
  13. Chia-Hsuan Yeh, Shu-Heng Chen, 2001. "The Influence of Market Size in an Artificial Stock Market: The Approach Based on Genetic Programming," Computing in Economics and Finance 2001, Society for Computational Economics 74, Society for Computational Economics.
  14. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, Elsevier, vol. 14(1-2), pages 3-24, February.
  15. Lux, T. & M. Marchesi, . "Volatility Clustering in Financial Markets: A Micro-Simulation of Interacting Agents," Discussion Paper Serie B, University of Bonn, Germany 437, University of Bonn, Germany, revised Jul 1998.
  16. Damien Challet & Matteo Marsili, 2002. "Criticality and finite size effects in a simple realistic model of stock market," Papers cond-mat/0210549, arXiv.org, revised Dec 2002.
  17. Gaunersdorfer, A. & Hommes, C.H., 2005. "A nonlinear structural model for volatility clustering," CeNDEF Working Papers 05-02, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
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