Statistical properties of genetic learning in a model of exchange rate

We study statistical properties of the time series of the exchange rate data generated in the environment where agents update their savings and portfolio decisions using the genetic algorithm. The genetic algorithm adaptation takes place within an overlapping generations model with two currencies and the free-trade, flexible exchange rate system. The theoretical model implies a constant exchange rate under the perfect foresight assumption. Under the genetic algorithm learning, the model's equilibrium dynamics is not constant but exhibits bounded oscillations. The time series analysis of the data indicates that the dynamics of the exchange rate returns is chaotic. Out-of-equilibrium inequality of rates of return on two currencies prompts the genetic algorithm agents to take advantage of the arbitrage opportunities by increasing the amount of the currency with higher rate of return in their portfolios. This profit seeking results in chaotic patterns of the exchange rate series. (C) 2000 Elsevier Science B.V. All rights reserved.