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Can perpetual learning explain the forward-premium puzzle?

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  • Chakraborty, Avik
  • Evans, George W.

Abstract

Under rational expectations and risk neutrality the linear projection of exchange-rate change on the forward premium has a unit coefficient. However, empirical estimates of this coefficient are significantly less than one and often negative. We show that replacing rational expectations by discounted least-squares (or "perpetual") learning generates a negative bias that becomes strongest when the fundamentals are strongly persistent, i.e. close to a random walk. Perpetual learning can explain the forward-premium puzzle while simultaneously replicating other features of the data, including positive serial correlation of the forward premium and disappearance of the anomaly in other forms of the test.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Monetary Economics.

Volume (Year): 55 (2008)
Issue (Month): 3 (April)
Pages: 477-490

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Handle: RePEc:eee:moneco:v:55:y:2008:i:3:p:477-490

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Web page: http://www.elsevier.com/locate/inca/505566

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