Long-Horizon Exchange Rate Expectations: Kremens, Martin & Varela (2025)

What this is. The paper’s core results, the SDF-based UIP identity it uses, and the main regression specifications: enough to know what it found and how, without reading all 30 pages. To replicate or extend it, read the full source at doi:10.1111/jofi.13504.

TL;DR

Using monthly Consensus Economics surveys of financial professionals (six high-income currencies against the dollar, December 1994 to March 2019), the paper shows that two-year-ahead exchange rate expectations successfully predict realized currency appreciation both in and out of sample, with slope coefficients statistically close to one and R-squared values around 16-19%. Survey forecasts beat the random walk benchmark of Meese and Rogoff (1983) out of sample, and are the strongest univariate predictor, outperforming the quanto-implied risk premium (QRP) of Kremens and Martin (2019), the real exchange rate predictor emphasized by Dahlquist and Penasse (2022), VIX, and capital flow proxies including the current account variable linked to Gabaix and Maggiori (2015). Three macro-finance variables (QRP, RER, current account-to-GDP) together explain most of the cross-currency and time-series variation in survey expectations, with no residual “secret sauce” from forecasters beyond these observables. The paper also confirms the finding of Nagel and Xu (2023) that short-horizon (one- and three-month) survey forecasts have near-zero predictive power, but documents that long-horizon forecasts predict short-run realizations while short-horizon forecasts do not. The factor loadings on the Dollar and Carry factors of Lustig, Roussanov and Verdelhan (2011, 2014) are included as alternative predictor variables but explain substantially less variation than survey expectations.

Core results

Magnitudes and significance are as reported; **/*** = 5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Survey expectations are the best univariate predictor of 24-month realized currency excess returns, with R-squared exceeding all competing variables in the post-GFC sample	Table II, p. 3708	Survey R-squared = 15.7%; runner-up QRP = 11.6%; RER = 10.4%; VIX = 8.5%; beta-HML = 7.2%; IRD = 1.7%; beta-dollar = 0.9%; CA/GDP = 0.0%
R2	Survey coefficients are close to one and statistically significant in panel regressions of realized currency appreciation on survey excess return expectations, controlling for interest rate differentials	Table I Panel A cols (2)-(3), p. 3704	SXR coefficient = 0.726-0.837 (standard errors 0.212-0.251); R-squared rises from 3.1% (IRD only) to 16.9-19.2%
R3	Survey forecasts beat the random walk out of sample at the 24-month horizon, both dollar-based and dollar-neutral, with bootstrapped p-values below 5%	Table III cols (1)-(2), p. 3709	Dollar-based R-squared-OOS = 19.15%; dollar-neutral = 14.99%; bootstrapped p-value = 8.81% / 3.37% respectively
R4	QRP, RER, and CA-to-GDP together explain over half the variation in survey expectations in the post-GFC sample; the full multivariate specification raises R-squared to 53.6%	Table IV col (7), p. 3711	R-squared = 53.6% (col 7, all variables); trivariate (QRP+RER+CA/GDP only, col 8) R-squared = 52.8%; QRP coefficient = 3.056*** (0.239), RER = -1.763** (0.678), CA/GDP = -1.274** (0.386) in col (7)
R5	No “secret sauce”: the residual component of survey expectations not explained by QRP, RER, CA/GDP has no predictive power for realized returns	Table V col (3), p. 3713	Coefficient on epsilon(SXR) = 0.177 (SE 0.232), statistically indistinguishable from zero; fitted value coefficient = 1.414 (0.832)
R6	Short-horizon survey forecasts (1- and 3-month) have near-zero predictive power for short-run realized returns; the pattern reverses at 12- and 24-month horizons	Table VIII Panel A cols (1)-(4), p. 3718	SXR* coefficients at 1M = 0.088 (0.067), 3M = 0.093 (0.102), 12M = 0.237 (0.215), 24M = 0.726 (0.212); R-squared rises from 0.011 to 0.157
R7	Long-horizon forecasts predict short-run realizations, but short-horizon forecasts do not predict long-run realizations	Table VIII Panel A cols (5)-(10), p. 3718	24M forecasts predicting 1M RXR*: coefficient = 1.548 (0.857), R-squared = 0.018; 1M forecasts predicting 24M RXR*: coefficient = 0.007 (0.009), R-squared = 0.019
R8	Forward expectations (3-to-24 month) predict short-run realizations; spot short-horizon forecasts do not	Table IX col (5), p. 3719	Coefficient on 3-to-24-month forward expectation predicting 3-month RCA = 0.188 (0.087), significant; coefficient on 3-month spot forecast = -0.062 (0.086), near zero

Overall (paper’s conclusion). Long-horizon survey expectations of financial professionals are broadly rational at the two-year horizon: their slope coefficient is close to one and they outperform the random walk. Three macro-finance variables (QRP, RER, CA/GDP) explain most of their variation, consistent with both risk-based and intermediary-constraints views of exchange rate determination. There is no residual forecaster information (“secret sauce”) beyond these observables. The finding that long-horizon models outperform short-horizon models at forecasting short-run outcomes presents a puzzle the paper leaves open.

Theory / model

The paper has no structural model. It grounds its empirical analysis in the standard no-arbitrage SDF identity and uses it to motivate the predictor variables. The fundamental asset pricing equation for any h-period gross dollar return $R_{t+h}$ is (p. 3700, eq. 1-2):

$$\mathbb{E}_t(M_{t+h} R_{t+h}) = 1 \tag{1}$$ $$\mathbb{E}_t R_{t+h} - R^{\$}_{f,t,h} = R^{\$}_{f,t,h} \, \text{cov}_t(-M_{t+h}, R_{t+h}) \tag{2}$$

where $M_{t+h}$ is the h-period SDF and $R^{\$}_{f,t,h}$ is the U.S. riskless rate. For a currency trade (convert USD to currency $i$, invest at the foreign riskless rate $R^i_{f,t,h}$, convert back), the return is $R_{t+h} = R^i_{f,t,h} e_{i,t+h}/e_{i,t}$ where $e_{i,t}$ is the nominal exchange rate. Substituting into equation (2) gives the UIP identity (p. 3700, eq. 3):

$$\mathbb{E}_t \frac{e_{i,t+h}}{e_{i,t}} - 1 = \underbrace{\frac{R^{\$}_{f,t,h}}{R^i_{f,t,h}} - 1}_{\text{UIP}} + \underbrace{R^{\$}_{f,t,h} \, \text{cov}_t\!\left(-M_{t+h}, \frac{e_{i,t+h}}{e_{i,t}}\right)}_{\text{residual / risk premium}} \tag{3}$$

The residual in (3) is the currency risk premium. When the marginal investor has log utility and holds the S&P 500 (so $M_{t+h} = 1/R_{t+h}$), the residual vanishes and expected appreciation equals the risk-neutral covariance term QRP (eq. 13, p. 3706):

$$\mathbb{E}_t \frac{e_{i,t+h}}{e_{i,t}} - \frac{R^{\$}_{f,t,h}}{R^i_{f,t,h}} = \frac{1}{R^{\$}_{f,t,h}} \, \text{cov}^{\mathbb{Q}}_t\!\!\left(\frac{e_{i,t+h}}{e_{i,t}}, R_{t+h}\right) \tag{13}$$

This motivates QRP as an observable proxy for the currency risk premium (and for survey expectations under rational expectations). RER and CA/GDP enter as additional predictors through their empirical association with either risk factors or intermediary balance-sheet constraints.

Key definitions. The paper defines the interest rate differential (IRD), realized currency appreciation (RCA), and survey-based currency appreciation (SCA) at horizon h as (pp. 3700-3701, eqs. 4-7):

$$\text{IRD}_{i,t,h} = \frac{R^{\$}_{f,t,h}}{R^i_{f,t,h}} - 1, \qquad \text{RCA}_{i,t,h} = \frac{e_{i,t+h}}{e_{i,t}} - 1, \qquad \text{SCA}_{i,t,h} = \tilde{\mathbb{E}}_t \frac{e_{i,t+h}}{e_{i,t}} - 1 \tag{4-7}$$

where $\tilde{\mathbb{E}}$ denotes the survey consensus (simple average across respondents). Currency excess returns are $\text{RXR}_{i,t,h} = \text{RCA}_{i,t,h} - \text{IRD}_{i,t,h}$ and survey excess return expectations are $\text{SXR}_{i,t,h} = \text{SCA}_{i,t,h} - \text{IRD}_{i,t,h}$ (eqs. 6-8, p. 3701).

The quanto-implied risk premium is constructed from quotes on conventional and quanto forwards on the S&P 500 (eq. 12, p. 3706):

$$\text{QRP}_{i,t,h} = \frac{Q_{i,t,h} - F_t}{R^i_{f,t,h} P_t} = \frac{1}{R^{\$}_{f,t,h}} \, \text{cov}^{\mathbb{Q}}_t\!\!\left(\frac{e_{i,t+h}}{e_{i,t}}, R_{t+h}\right) \tag{12}$$

where $Q_{i,t,h}$ is the quanto forward price and $F_t$ is the conventional S&P 500 forward price.

Method

The paper applies standard panel regression methods to test predictability of exchange rates and to decompose what drives survey expectations. There is no new estimator. The method builds on panel-regression and time-series-forecasting.

Baseline regression. The in-sample predictability test adds survey excess return expectations to the standard UIP regression (eqs. 9-10, p. 3701):

$$\text{RCA}_{i,t,h} = \alpha_h + \gamma_1 \text{SXR}_{i,t,h} + \gamma_2 \text{IRD}_{i,t,h} + \varepsilon_{i,t,h} \tag{9}$$ $$\text{RXR}_{i,t,h} = \alpha_h + \gamma_1 \text{SXR}_{i,t,h} + \gamma_2 \text{IRD}_{i,t,h} + \varepsilon_{i,t,h} \tag{10}$$

Under UIP, $\gamma_1 = 0$ and $\gamma_2 = 1$. The paper assesses success by whether $\hat{\gamma}_1$ is positive, economically close to one, and statistically significant. Specifications with currency and time fixed effects are also estimated. Standard errors use a nonparametric block-bootstrap (Footnote 7, p. 3703) building on the approach of Hansen and Hodrick (1980), with blocks of length equal to the forecasting horizon and randomized cross-sectional width to account for overlapping observations and cross-sectional correlation; bootstrapped standard errors are typically 10th/90th percentiles of 10,000 resamples.

Out-of-sample test. The out-of-sample R-squared following Goyal and Welch (2008) is (eq. 14, p. 3708):

$$R^2_{\text{OS}} = 1 - \frac{\sum_i \sum_t (\varepsilon^S_{i,t,t+h})^2}{\sum_i \sum_t (\varepsilon^C_{i,t,t+h})^2} \tag{14}$$

where the numerator is the survey forecast error and the denominator is the competitor model error (random walk or QRP). A dollar-neutral variant $\bar{R}^2_{\text{OS}}$ computes errors relative to currency j for each pair (i, j) to net out dollar appreciation effects (eq. 15, p. 3709).

What informs expectations. To test what drives survey expectations, the paper regresses survey excess returns (SXR) on contemporaneous macro-finance variables (eq. 16, p. 3710):

$$\text{SXR}_{i,t,h} = \alpha_h + \gamma_1 X_{i,t} + \gamma_2 \text{IRD}_{i,t,h} + \varepsilon_{i,t,h} \tag{16}$$

where $X_{i,t}$ includes QRP, RER, VIX, CA/GDP, $\beta^{\$}$, and $\beta^{HML}$. Standard errors are clustered by time and currency. All predictor variables are standardized to unit standard deviation.

Horizon analysis. The paper annualizes variables by $\text{SCA}^*_{i,t,h} = (12/h)\text{SCA}_{i,t,h}$ to compare across horizons $h \in \{1, 3, 12, 24\}$ months (eq. 18, p. 3717). Forward expectations between horizons h and H are defined as (eq. 19, p. 3718):

$$\text{sca}^{h,H}_{i,t} = \log\!\left(\frac{1 + \text{SCA}_{i,t,H}}{1 + \text{SCA}_{i,t,h}}\right) = \text{sca}_{i,t,H} - \text{sca}_{i,t,h} \tag{19}$$

Empirical specifications

In-sample predictability (R1-R2). Panel regressions using equations (9) and (10) at the 24-month horizon, post-GFC sample (December 2009 to March 2019, realizations until March 2021) with N = 672 observations (six currencies times approximately 112 months). The key specification is column (2) of Table I (p. 3704): SXR coefficient = 0.726 (SE 0.212), R-squared = 16.9% for RCA; similar results hold with currency and time fixed effects. Panel B extends to the full sample from December 1994, yielding N = 1,340 and similar coefficients.

Alternative predictors (R1). Univariate regressions of realized RXR on each alternative predictor variable separately (Table II, p. 3708). QRP data available only from December 2009 (Markit). Survey expectations achieve R-squared = 15.7% in the post-GFC sample, more than one-third higher than QRP (11.6%) and more than 50% higher than RER (10.4%).

Out-of-sample performance (R3). Surveys do not require estimated parameters so the out-of-sample test simply compares survey forecast errors to those of the random walk (RCA = 0) and QRP benchmark. Bootstrapped p-values are computed from the same block-bootstrap procedure as in-sample (Table III, p. 3709).

Decomposing survey expectations (R4-R5). Table IV (p. 3711) regresses SXR on QRP, RER, VIX, CA/GDP, $\beta^{\$}$, and $\beta^{HML}$, with standard errors clustered by time and currency. The trivariate specification (QRP, RER, CA/GDP) explains R-squared = 52.8% of variation in survey expectations. Table V (p. 3713) then tests whether the residual $\varepsilon(\text{SXR})$ from this regression predicts realized RCA: the coefficient is 0.177 with SE = 0.232, confirming no secret sauce.

Horizon comparisons (R6-R8). Table VIII (p. 3718) runs equation (10) separately for $h \in \{1, 3, 12, 24\}$ months and also uses long-horizon forecasts ($\text{SXR}^*_{24}$) to predict short-run realizations and vice versa. Table IX (p. 3719) decomposes 24-month expectations into three-month spot forecasts plus forward expectations from month 3 to month 12 and 12 to 24, regressing 24-month log realizations on these components.

Datasets used

Dataset	Role in paper	Wiki page
Consensus Economics surveys	Monthly consensus forecasts of exchange rates at 1-, 3-, 12-, 24-month horizons; the primary SCA/SXR series	no page yet
Markit quanto forwards on S&P 500	Construction of QRP (risk-neutral covariance between FX and equity); 24-month quotes, December 2009 onward	no page yet
Reuters forward exchange rates	Forward discounts / interest rate differentials (IRD) by horizon; used interchangeably with IRD under CIP	no page yet
IMF International Financial Statistics (IFS)	Current account balance and capital inflows, both scaled by GDP	IMF IFS
BIS real exchange rate	RER predictor variable	BIS EER
FRED (VIX)	30-day S&P implied volatility index as global risk perception proxy	FRED
Lane-Milesi-Ferretti (2018)	Net foreign asset positions-to-GDP for robustness	no page yet

Sample: six high-income currencies (AUD, CAD, EUR, GBP, JPY, KRW) against USD. Baseline sample: December 2009 to March 2019 (realizations until March 2021), N = 672. Full sample: December 1994 to March 2019, N = 1,340.

When to read the full paper

Read the original if you are: testing the rationality of professional FX forecasters at different horizons; evaluating survey expectations as predictors against model-based alternatives; studying what macro-finance variables drive FX risk premia and expectations; or extending the QRP framework of Kremens and Martin (2019) to a broader set of predictors. The Internet Appendix (Appendix S1) contains data-source details, pre-GFC subsample results, robustness to additional currencies and specifications, and currency-specific slope estimates.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(6). This distillation was extracted by an LLM on 2026-06-03 and is not human-verified or independently reproduced. The paper is paywalled; extract-only redistribution applies.

Kremens, Lukas, Ian W. R. Martin, and Liliana Varela. “Long-Horizon Exchange Rate Expectations.” The Journal of Finance 80, no. 6 (December 2025): 3695-3724. DOI: 10.1111/jofi.13504. Copyright 2025 the American Finance Association. Paywalled. This page is an extract by the Institute for Automated Research: core results summarized; not a substitute for the original.