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Modeling Exchange Rate Behavior with a Genetic Algorithm

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  • C. Lawrenz
  • F. Westerhoff

Abstract

Motivated by empirical evidence, we construct a model whereheterogeneous, boundedly-rational market participants rely on a mix of technical and fundamental trading rules. The rules are applied according to a weighting scheme. Traders evaluate and update their mix of rules by genetic algorithm learning. Even for fundamental shocks with a low probability, the interaction between the traders produces a complex behavior of exchange rates. Our model simultaneously produces several stylized facts like high volatility, unit roots in the exchange rates, a fuzzy relationship between news and exchange-rate movements, cointegration between the exchange rate and its fundamental value, fat tails for returns, a declining kurtosis under time aggregation, weak evidence of mean reversion, and strong evidence of clustering in both volatility and trading volume. Copyright Kluwer Academic Publishers 2003

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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 21 (2003)
Issue (Month): 3 (June)
Pages: 209-229

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Handle: RePEc:kap:compec:v:21:y:2003:i:3:p:209-229

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Web page: http://www.springerlink.com/link.asp?id=100248
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Keywords: exchange rate theory; technical and fundamental trading rules; genetic algorithm;

References

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Cited by:
  1. Navarro-Barrientos, Jesús Emeterio & Cantero-Álvarez, Rubén & Matias Rodrigues, João F. & Schweitzer, Frank, 2008. "Investments in random environments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(8), pages 2035-2046.
  2. Floortje Alkemade & Han Poutré & Hans Amman, 2006. "Robust Evolutionary Algorithm Design for Socio-economic Simulation," Computational Economics, Society for Computational Economics, vol. 28(4), pages 355-370, November.

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