This paper compares two competing approaches to model foreign exchange market participants' behavior: statistical learning and fitness learning. These learning mechanisms are applied to a set of predictors: chartist and fundamentalist rules. We examine which of the learning approaches is best in terms of replicating the exchange rate dynamics within the framework of a standard asset pricing model. We find that both learning methods reveal the fundamental value of the exchange rate in the equilibrium but only fitness learning creates the disconnection phenomenon and only statistical learning replicates volatility clustering. None of the mechanisms is able to produce a unit root process but both of them generate non-normally distributed returns.

Adaptive learning, Bounded rationality, Exchange rates
Foreign Exchange (jel F31), International Finance Forecasting and Simulation (jel F37)
dx.doi.org/10.1016/j.jimonfin.2012.03.001, hdl.handle.net/1765/38142
ERIM Top-Core Articles
Journal of International Money and Finance: theoretical and empirical research in international economics and finance
Erasmus Research Institute of Management

de Grauwe, P, & Markiewicz, A. (2013). Learning to forecast the exchange rate: Two competing approaches. Journal of International Money and Finance: theoretical and empirical research in international economics and finance, 32(1), 42–76. doi:10.1016/j.jimonfin.2012.03.001