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

This paper revisits the Kareken-Wallace model of exchange rate formation. Following the seminal paper by Arifovic (1996) we investigate a dynamic version of the model in which agents´ decision rules are updated using genetic algorithms. Time series analysis of simulated data indicates that for particular parameterizations, the characteristics of the exchange rate dynamics are very similar to those of empirical data. The similarity appears to be quite insensitive with respect to the ingredients of the GA algorithm. 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).