Can perpetual learning explain the forward-premium puzzle?

• 成果类型: Article
• 署名作者: Chakraborty, Avik; Evans, George W.
• 署名单位: University of Oregon; University of Tennessee System; University of Tennessee Knoxville
• 刊物名称: JOURNAL OF MONETARY ECONOMICS
• ISSN/ISSBN: 0304-3932
• DOI: 10.1016/j.jmoneco.2008.03.002
• 发表日期: 2008
• 页码: 477-490
• 关键词: Learning; Exchange rates; forward premium; expectations

摘要

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. (C) 2008 Elsevier B.V. All rights reserved.