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Learning to forecast the exchange rate: two competing approaches

de Grauwe, Paul and Markiewicz, Agnieszka (2013) Learning to forecast the exchange rate: two competing approaches. Journal of International Money and Finance, 32. pp. 42-76. ISSN 0261-5606

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Identification Number: 10.1016/j.jimonfin.2012.03.001


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.

Item Type: Article
Official URL:
Additional Information: © 2012 Elsevier Ltd
Divisions: European Institute
Subjects: H Social Sciences > HB Economic Theory
H Social Sciences > HG Finance
JEL classification: F - International Economics > F3 - International Finance > F31 - Foreign Exchange
F - International Economics > F3 - International Finance > F37 - International Finance Forecasting and Simulation
Date Deposited: 05 Oct 2012 15:23
Last Modified: 20 Aug 2021 02:59

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