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Exchange Rate Predictability

Authors: Barbara Rossi;

Exchange Rate Predictability

Abstract

The main goal of this article is to provide an answer to the question: does anything forecast exchange rates, and if so, which variables? It is well known that exchange rate fluctuations are very difficult to predict using economic models, and that a random walk forecasts exchange rates better than any economic model (the Meese and Rogoff puzzle). However, the recent literature has identified a series of fundamentals/methodologies that claim to have resolved the puzzle. This article provides a critical review of the recent literature on exchange rate forecasting and illustrates the new methodologies and fundamentals that have been recently proposed in an up-to-date, thorough empirical analysis. Overall, our analysis of the literature and the data suggests that the answer to the question: “Are exchange rates predictable?” is, “It depends”—on the choice of predictor, forecast horizon, sample period, model, and forecast evaluation method. Predictability is most apparent when one or more of the following hold: the predictors are Taylor rule or net foreign assets, the model is linear, and a small number of parameters are estimated. The toughest benchmark is the random walk without drift. (JEL C53, F31, F37, E43, E52)

Keywords

exchange rates, forecasting, instability, forecast evaluation, forecasting, exchange rates, instability, Exchange Rates, Forecasting, Instability, Forecast Evaluation., Macroeconomics and International Economics, Exchange Rates; Forecast Evaluation; Forecasting; Instability, forecast evaluation., jel: jel:C53, jel: jel:F31, jel: jel:E43, jel: jel:E52, jel: jel:F37, jel: jel:F3, jel: jel:C5

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    454
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
454
Top 0.1%
Top 1%
Top 1%
Green
hybrid