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dc.contributor.authorChakraborty, Avik, 1975-
dc.date.accessioned2005-03-22T22:28:43Z
dc.date.available2005-03-22T22:28:43Z
dc.date.issued2004-10-01
dc.identifier.urihttp://hdl.handle.net/1794/657
dc.description36 p.en
dc.description.abstractThe Forward Premium Puzzle is one of the most prominent empirical anomalies in international finance. The forward premium predicts exchange rate depreciation but typically with the opposite sign and smaller magnitude than specified by rational expectations, a result also considered to indicate inefficiency in the foreign exchange market. This paper proposes a resolution of the puzzle based on recursive least squares learning applied to a simple model of exchange rate determination. The key assumption is that risk neutral agents are not blessed with rational expectations and do not have perfect knowledge about the market. Agents learn about the parameters underlying the stochastic process generating the exchange rate using constant gain recursive least squares. When exchange rate data are generated from the model and the empirical tests are performed, for plausible parameter values the results replicate the anomaly along with other observed empirical features of the forward and spot exchange rate data.en
dc.format.extent364631 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherUniversity of Oregon, Dept of Economicsen
dc.relation.ispartofseriesUniversity of Oregon Economics Department Working Papers;2005-4
dc.subjectSpot exchange rateen
dc.subjectForward rateen
dc.subjectConstant gainen
dc.subjectRecursive least squares learningen
dc.titleLearning, the Forward Premium Puzzle and Market Efficiencyen
dc.typeWorking Paperen


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