Exchange Rate Predictability
Barbara Rossi
February 14, 2013
Abstract
The main goal of this article is to provide an answer to the question: “Does any-
thing forecast exchange rates, and if so, which variables?”. It is well known that
exchange rate ‡uctuations are very di¢ cult to predict using economic models, and
that a random walk forecasts exchange rates better than any economic model (the
Meese and Rogo¤ puzzle). However, the recent literature has identi…ed a series of fun-
damentals/method ologies 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 ans wer to the question: "Are exchange rates predictable?" is,
"It depends" –on the choice of predictor, forecas t horizon, sample period, model, and
forecast evaluation method. Predictability is most app arent when one or more of the
following hold: the predictors are Taylor rule or net foreign assets, the model is lin-
ear, and a small number of parameters are es timated. Th e toughest benchmark is the
random walk without drift.
Keywords: Exchange Rates, Forecasting, Instability, Forecast Evaluation
Acknowledgments: I thank D. Ferraro for assistance in collecting the data used
in this paper, Janet Currie and four anonymous referees for their generous and useful
comments, and Greg Ganics for assistance in proofreading.
J.E.L. Codes: F3, C5
Barbara Rossi (ICREA-UPF, Barcelona GSE, CREI). Mailing address: Universitat Pompeu Fabra-
CREI, Carrer Ramon Trias Fargas, 25-27, Mercè Rodoreda bldg., 08005 Barcelona SPAIN. Tel.: +34-93-
542-1655; e-mail: barbara.rossi@upf.edu
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1 Introduction
The objective of this article is to o¤er a critical survey of the literature on predicting exchange
rates in the last ten years. Since Meese and Rogo¤ (1983a,b, 1988), it has been well known
that exchange rates are very di¢ cult to predict using economic models; in particular, a
simple, a-theoretical model such as the random walk is frequently found to generate better
exchange rate forecasts than economic models. The latter …nding is known as "the Meese
and Rogo¤ puzzle". It is important to note that Meese and Rogo¤’s (1983a,b) …nding that
the random walk provides the best prediction of exchange rates should not be interpreted as
a validation of the e¢ cient market hypothesis. The e¢ cient market hypothesis states that, in
the absence of risk premia or when time variation in risk premia tends to be small relative to
variation in fundamental pricing factors, bilateral exchange rates are the market’s best guess
of the relative, fundamental value of two currencies based on all available information at that
time. The e¢ cient market hypothesis does not mean that exchange rates are unrelated to
economic fundamentals, nor that exchange rates should ‡uctuate randomly around their past
values. Hence the puzzle. However, the recent literature has identi…ed new macroeconomic
and …nancial predictors that claim to forecast exchange rates. The goal of this article is to
review both traditional as well as newly proposed exchange rate predictors and evaluate their
ability to forecast exchange rates. The main goal is to provide an answer to the questions:
“Are exchange rates predictable? And, if so, which predictors are the most useful to forecast
exchange rates?”.
When trying to answer these questions, a series of complications arise. First, a wide
variety of predictors, models, estimation methods, measures of predictive content as well as
evaluation tests have been used in the literature. Thus, researchers attempting to forecast
exchange rates need to make several choices, such as: Which predictors to use? Which
forecast horizon to predict? Which model to estimate? Which data frequency? Which
sample? One of the goals of this paper is to provide guidance to researchers on navigating
the existing literature as well as to provide a reliable overview of established …ndings that
can be helpful in making these choices. Second, existing papers rely on di¤erent predictors,
tests, samples or databases; it is possible that such predictors might have lost their forecasting
ability, or may not be robust to other databases or samples. In addition, while a predictor
might be successful according to a metric/test, it may not b e so according to a di¤erent
one. We therefore perform a thorough empirical evaluation of the success of the predictors
identi…ed in the literature using the most recent techniques and databases. Thus, our article
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starts with a critical overview of existing predictors and the empirical stylized facts identi…ed
in the literature, with particular emphasis on the last ten years. Then, we illustrate the
existing empirical evidence using the most up-to-date data and evaluation techniques in
order to answer the question: “Does anything forecast exchange rates?”.
This article should be of interest for several audiences. Economists and researchers in
academia will …nd that the literature review and the empirical investigation provide guidance
to navigate the literature, and will be useful for their work. Practitioners and forecasters
at Central Banks and private businesses will also be interested in knowing which predictors,
models and methodologies successfully predict exchange rates. Policymakers, for whom
successful policy decisions crucially depend on successful forecasts, should also be interested
in our assessment on where the literature stands. Finally, newspapers’frequent discussions
of exchange rate forecasting suggest this literature review would b e useful beyond academia
and policy circles.
More in detail, why are exchange rate forecasts useful for Central Banks and policymak-
ers? Wieland and Wolters (2011) provide a detailed review on how forecasts are used in
policymaking. Typically, forecasts are used to project the consequences of particular pol-
icy measures for policymakers’targets. According to Greenspan (1994, p. 241), "implicit
in any monetary policy action or inaction is an expectation of how the future will unfold,
that is, a forecast". Wieland and Wolters (2011) provide empirical evidence that Central
Bank policies in the US and Europe are described by interest rate rules, where interest rates
respond to forecasts of in‡ation and economic activity, rather than outcomes. Not only eco-
nomic policy relies on macroeconomic forecasts: the path of the p olicy may directly a¤ect
the forecasts ("projections") of macroeconomic aggregates. In the US, for example, prior
to each Federal Open Market Committee meeting,
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the Federal Reserve sta¤ produces fore-
casts of several macroeconomic aggregates at horizons up to two years as a basis for their
discussions. The variables forecasted by the sta¤ include exchange rates, which in‡uence
current account projections as well as US real GDP growth, eventually. As pointed out in
Edge et al. (2010), among others, the Federal Reserve sta¤ forecast is derived from data, a
variety of models and forecasting techniques, as well as expert judgment. Typically, these
forecasts are conditioned on a speci…c future time path for the federal funds rate, the main
instrument of monetary policy. The p olicy scenarios considered by the Federal Reserve sta¤
may also include dollar depreciation/appreciation scenarios (i.e. scenarios in which the dollar
appreciates or depreciates more than in the baseline forecasts, where typically it is assumed
1
The Federal Open Market Committee is the meeting where US monetary policy is decided.
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constant, according to the random walk model). Policy decisions are then taken on the basis
of what the policymaker deems the most likely scenario. Exchange rate projections are also
especially important for Central Banks of countries that are heavy importers/exporters of
commodities. For example, one of the models used at the Bank of Canada is Amano and
van Norden’s (1995), where real exchange rates depend on terms of trade (see Coletti and
Murchison, 2002).
At the same time, it should be noted that this review has an empirical, "reduced-form"
focus. There are several reasons behind this choice. First, the majority of the empirical
work in this area is done with a reduced-form approach; second, while there are theoretical
structural models of exchange rate determination, typically they are too stylized to be liter-
ally taken to the data and successfully used for forecasting exchange rates. Moreover, fully
developed structural models typically do not …t exchange rate data well, not to mention
forecast them. Thus, while this article will sketch several "theoretical" models of exchange
rate determination, this discussion is mainly provided to motivate the choice of economic
predictors that have been considered in the literature. Furthermore, throughout the paper
we focus on monthly and quarterly frequencies, as they are the ones of interest to economists;
we will not consider very high frequency data analyses that are instead mostly of interest to
risk management and …nance. Finally, there is a large literature on in-sample estimation of
exchange rate models. In-sample …t does not necessarily guarantee out-of-sample forecast
success, as we will discuss. Thus, in this overview we will mainly focus on out-of-sample
forecasts, although we will provide some discussion of in-sample …t. Note that, typically,
real exchange rates are …tted in-sample, while nominal ones are forecasted out-of-sample;
therefore, we will focus on the latter.
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Overall, our analysis of the literature and the data suggests that the answer to the
question: "Are exchange rate predictable?" is, "it depends". In fact, 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 and net foreign assets fundamentals, the model is linear, and a small number
of parameters are estimated. The toughest benchmark is the random walk without drift.
There is some instability over samples for all models, and there is no systematic pattern
across models in terms of which horizons or which sample periods the models predict best.
Among the negative …ndings on which the literature has reached a consensus, typically, PPP
and monetary models have no success at short (less than 2-3 years) horizons.
2
See Rogo¤ (1996) for a review of in-sample …t of real exchange rate models.
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More in detail, we draw …ve general conclusions.
First, the degree of success in forecasting exchange rates out-of-sample does depend
on the choice of the predictor. Although there is disagreement in the literature, overall
the empirical evidence is not favorable to traditional economic predictors (such as interest
rates, prices, output and money).
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Instead, Taylor-rule fundamentals and net foreign asset
positions have promising out-of-sample forecasting ability for exchange rates. The consensus
in the literature is that the latter fundamentals have more out-of-sample predictive content
than traditional fundamentals; the disagreement in the literature is in the degree to which
they can resolve the Meese and Rogo¤ puzzle.
Second, overall, among the model speci…cations considered in the literature, the most
successful are linear ones.
4
Typically, in single-equation linear models, the predictor choice
matters more than the model speci…cation itself.
5
Third, data transformations (such as de-trending, …ltering and seasonal adjustment) may
substantially a¤ect predictive ability, and may explain di¤erences in results across studies.
6
Another important factor that, for some fundamentals, may a¤ect predictive ability is the
use of real-time rather than revised data.
7
For a given model and predictor, predictive ability
seems also to depend on the choice of the country. On the other hand, the frequency of the
data does not seem to a¤ect predictability.
Fourth, empirical results vary with the benchmark model, the sample period, forecast
evaluation method and the forecast horizon. The random walk consistently provides the
toughest benchmark. Di¤erent models vary in terms of which sample periods and forecast
horizons work best, with no apparent overall pattern.
Finally, on the one hand, our empirical analysis con…rms several …ndings in the lit-
erature: while several predictors display in-sample predictive ability for future exchange
rates, only Taylor-rules display consistently signi…cant out-of-sample forecasting ability at
short horizons; and panel monetary models display some forecasting ability at long horizons.
Furthermore, our analysis reveals instabilities in the models’forecasting performance: the
predictability of fundamentals varies not only across countries, models and predictors, but
3
Except p oss ibly for monetary fundamentals at long horizons and interest rates at short horizons.
4
Among them, error correction models (either single-equation or panel) are successful at long horizons,
although there is disagreement among researchers regarding the degree of robustness of the result.
5
For example, whether the researcher uses contemporaneous, realized or lagged fundamentals.
6
For example, pred ictability of the monetary ECM model is much weaker or completely disappears after
estimating the cointegrating parameters.
7
This is a concern for monetary fundamentals but less of a concern for Taylor-rule fundamentals.
5
