Exchange Rate Predictability

By Barbara Rossi. 2013

555 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/784376747ab1f173e9b09404d43aadb4434eadea

Citations in the corpus

• Exchange Rates and Commodity Prices : Measuring Causality at Multiple Horizons (2015, unknown): “…features of foreign exchange markets: exchange rates can help predict economic fundamentals including commodity prices; see, forexample, Meese and Rogoff (1983), Engel and West (2005), Cheung, Chinn and Pascual (2005), Rogoff and Stavrakeva (2008), Chen, Rogoff and Rossi (2010), and Rossi (2013).”
• On the sources of uncertainty in exchange rate predictability (2018, unknown): “While this signals the presence of instabilities, attempts to account for them, for example by considering regressions with time-varying coefficients, have not yet produced overwhelming results (Rossi, 2013).”
• Macroeconomic forecast accuracy in a data‐rich environment (2019, unknown): “(Rossi 2013) has reviewed the recent literature on exchange rates forecasting and concluded that some exchange rates may be predictable at short horizons.”
• Exchange rate prediction redux: New models, new data, new currencies (2019, unknown): “While older models have been ably reviewed (Engel, 2014; Rossi, 2013), we believe that a systematic examination of these newer empirical models is due, for a number of reasons.”
• The Scapegoat Theory of Exchange Rates: The First Tests (2014, unknown): “Moreover, at this point the driftless random walk benchmark is also86 added to the horse race since the random walk is the most common benchmark in the FX forecasting87 literature (see Rossi, 2013, and the references therein).”
• A Time Series is Worth 64 Words: Long-term Forecasting with Transformers (2022, unknown): “Rossi (2013) argues that the toughest benchmark for exchange-rate forecasting is a random walk without drift.”
• Exchange rate predictability in a changing world (2014, unknown): “Engel et al. (2008), Molodtsova and Papell (2009), and Rossi (2013) find that empirical exchange rate models conditioned on information sets from Taylor rules outperform the random walk benchmark in out-ofsample forecasting, particularly at short-horizons.”
• Forecasting: theory and practice (2020, unknown): “The reason is that following the seminal paper of Meese and Rogoff (1983), the common wisdom is that macroeconomic models cannot outperform the random walk in exchange rate forecasting (see Rossi, 2013, for a survey).”