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Cited by 649

The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.

Exchange Rate Predictability

Open access
Rossi, Barbara·2013·Journal of Economic Literature

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)

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Metadata

Authors
Rossi, B.
Venue
Journal of Economic Literature
Published
2013-12-01
Volume
51
Issue
4
Pages
1063-1119
ISSN
0022-0515 (ISSN-L)
OpenAlex
W3121853541
Cited by
649
References
90
FWCI
96.75
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Exchange Rate Predictability. https://app.cochat.ai/papers/10.1257/jel.51.4.1063
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Rossi, B. (2013). Exchange Rate Predictability. Journal of Economic Literature. https://doi.org/10.1257/jel.51.4.1063

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.