Learning to forecast the exchange rate: Two competing approaches

Paul De Grauwe and Agnieszka Markiewicz ()

Journal of International Money and Finance, 2013, vol. 32, issue C, 42-76

Abstract: 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.

Keywords: Exchange rates; Adaptive learning; Bounded rationality (search for similar items in EconPapers)
JEL-codes: F31 F37 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0261560612000605
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Learning to Forecast the Exchange Rate: Two Competing Approaches (2006)
Working Paper: Learning to Forecast the Exchange Rate: Two Competing Approaches (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jimfin:v:32:y:2013:i:c:p:42-76

DOI: 10.1016/j.jimonfin.2012.03.001

Access Statistics for this article

Journal of International Money and Finance is currently edited by J. R. Lothian

More articles in Journal of International Money and Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().