The Dollar during the Great Recession: Stavrakeva & Tang (2026)

What this is. The paper’s core results, the partial-equilibrium model of the information channel, and the high-frequency local projection method: enough to understand what was found and why, without reading all 40 pages. To replicate or extend the results, read the full source at the original.

TL;DR

During the Great Recession (December 2008 to September 2012), U.S. forward guidance (FG) easings caused the dollar to persistently appreciate against both advanced-economy and emerging-market currencies, three to four weeks after each FOMC announcement. This is the opposite of the conventional wisdom that monetary easing depreciates the domestic currency. The appreciation coincided with the release of detailed FOMC meeting minutes and was driven entirely by FG, not QE. Outside the Great Recession, FG easings caused equity prices to fall (information effect); during the Great Recession, the pattern reversed and equity prices rose in response to FG easings, with the switch occurring about three to four weeks post-announcement. The paper builds a partial-equilibrium model showing this occurs when FG has a dominant information effect: calendar-based FG signals economic weakness, triggering a flight to safety, raising investor risk aversion, and lowering expected U.S. inflation relative to foreign inflation. Cross-sectional heterogeneity confirms the mechanism: the dollar appreciated most against currencies that are poor hedges (those that tend to depreciate when the U.S. economy contracts), consistent with a flight-to-safety story.

Core results

Magnitudes and significance are as reported; */**/*** = 10%/5%/1%. Locators point into the source PDF. Coefficients in Tables I-III are multiplied by 100 for presentation.

#	Result	Locator	Magnitude
R1	During the Great Recession, a positive U.S. FG surprise (unexpected easing) led to a persistent and large dollar appreciation against advanced-economy currencies (opposite to pre- and post-GR periods)	Table I, p. 980; Figure 1, p. 979	Non-QE GR coefficient at horizon 50 days: -37.68*** (SE 11.89); pre-GR coefficient at same horizon: 2.26 (0.00 R2); post-GR: 8.11
R2	The sign reversal against emerging-market currencies mirrors the advanced-economy result, with a similarly large eventual depreciation against USD	Table II, p. 981; Figure 1, p. 979	Non-QE GR coefficient at horizon 50 days: -34.27*** (SE 10.18); pre-GR: 3.12; post-GR: 13.99*
R3	The sign reversal is driven entirely by FG, not QE: using Swanson (2021) LSAP and FG factors, FG carries the appreciation and LSAP does not	Table III, p. 983; Figure 2, p. 982	FG coefficient for advanced economies at 90 days: -0.72 (0.72 SE); LSAP coefficient: 1.07* (0.53 SE). FG dominates at intermediate horizons (40-80 days): e.g. day 40, FG: -0.85* (0.47), LSAP: 0.43 (0.25)
R4	The dollar appreciation is larger for currencies that are worse hedges (higher covariance with U.S. SDF proxies), consistent with the flight-to-safety channel	Figure 3, p. 985; Figure 4, p. 986	A one-standard-deviation worse hedge quality is associated with about 10 percentage points larger dollar appreciation at 90 days; statistically significant for SDF-covariance measures
R5	FG easings during the GR caused equity prices to rise (S&P 500 and MSCI World peak ~30 pp for all announcements and ~100 pp for non-QE surprises at ~40-60 days post-announcement); outside GR, easing led to equity price declines (information effect)	Figure 5, p. 987; Figure 6, p. 988	S&P 500 non-QE GR: positive response peaking near 100 pp at ~40-60 days; all-announcement GR: positive response peaking near 30 pp; conventional negative near-term response of close to 10 pp reversed after the minutes release; pre- and post-GR coefficients negative (easing → equity falls)
R6	The paper interprets the response of its risk-aversion proxies (VIX and the Bekaert-Engstrom-Xu index) to FG easings during the GR as increased risk aversion, three to four weeks post-announcement	Figure 7, p. 1001	Peak effect at ~60 days: estimated log VIX response is nearly -50 pp and log BEX risk aversion index about -25 pp (coefficient negative and statistically significant for non-QE surprises); the paper reads this negative estimated response, through its flight-to-safety mechanism, as increased risk aversion (p. 1001, and pp. 973-974)
R7	The structural break in exchange rates is driven by currency risk premia and inflation expectations, not the nominal interest rate component	Figure 8, p. 1004; §III.B, p. 1002	Interest rate component response is small and conventional in sign across all subsamples; currency premia and inflation residual carries the sign reversal during GR (Figure 8, Panel B)
R8	The structural break in equity prices is driven by equity risk premia and dividend growth expectations, not the nominal rate component	Figure 9, p. 1005; §III.B, p. 1002	Interest rate component of equity price response is small and pushes in the conventional direction; equity risk premia and dividend growth component carries the large positive response (equity prices rising in response to FG easings) during GR, while it is the dominant channel for the negative responses outside GR

Overall (paper’s conclusion). The Great Recession was a period where the information content of U.S. forward guidance dominated the direct interest-rate channel: investors interpreted FG promises of low rates as bad news about growth, raising risk aversion and triggering a flight to safety. This generated dollar appreciation in response to policy easings, contrary to standard macroeconomic predictions. This contrasts with Rogers, Scotti, and Wright (2018), who find conventional-sign dollar responses to unconventional policy at longer horizons using a monthly VAR with sign restrictions that rule out the information channel by assumption. Equity prices also rose in response to FG easings during this period (reversing the pre- and post-GR pattern where easings led to equity price declines due to the information effect). The finding implies that models of exchange rates and asset prices need to incorporate the information channel of monetary policy, especially during periods of high fundamental uncertainty and calendar-based forward guidance.

Theory / model

The paper proposes a partial-equilibrium daily model of the U.S. economy and a marginal investor SDF. The model is purposefully stripped down to illustrate how the information channel can qualitatively reconcile the empirical facts. Building on the information-channel model of monetary policy signaling in Tang (2015), it adds a partial-equilibrium extension with exchange rates and stock prices. The signaling model of monetary policy with heterogeneous agents in Melosi (2017) co-inspires the theoretical framework, and the forward guidance with heterogeneous beliefs in Andrade et al. (2019) informs the information-effect model structure.

U.S. macroeconomic block (pp. 989-990, eqs. 3-5). Log inflation and real output follow:

$$\pi^{us}_t = \alpha \, y^{us}_t \tag{3}$$ $$y^{us}_t = -v(i^{us}_t - \pi^{us}_t) + \varepsilon^{y,us}_t \tag{4}$$

with a Taylor rule:

$$i^{us}_t = \phi^y y^{us}_t + \phi^\pi \pi^{us}_t + \varepsilon^{mp,us}_t$$

where $\varepsilon^{y,us}_t \sim N(0, \sigma^2_y)$ is the demand shock and $\varepsilon^{mp,us}_t \sim N(0, \sigma^2_{mp})$ is the monetary policy shock. Solving the system (eq. 5, p. 990):

$$\begin{aligned} y^{us}_t &= \frac{\varepsilon^{y,us}_t - v\,\varepsilon^{mp,us}_t}{1+v\kappa} \\ \pi^{us}_t &= \frac{\alpha(\varepsilon^{y,us}_t - v\,\varepsilon^{mp,us}_t)}{1+v\kappa} \\ r^{us}_t &= \frac{\kappa\,\varepsilon^{y,us}_t + \varepsilon^{mp,us}_t}{1+v\kappa} \end{aligned}$$

where $\kappa = \phi^y + \alpha(\phi^\pi - 1)$.

Investor SDF and pricing conditions (pp. 990-991, eqs. 6-7). The real log SDF is $\text{sdf}_{t,t+1} = \ln(\beta) - \rho_t \Delta c_{t+1} - c_t \Delta \rho_t$ with CRRA preferences. The exchange rate pricing condition is:

$$E_t\!\left[ \text{SDF}_{t,t+1} \cdot e^{-\pi_{t+1}} \cdot \left((1+i^{us}_t) - \frac{S_{t+1}}{S_t}(1+i^i_t)\right) \right] = 0 \tag{6}$$

and the equity pricing condition:

$$E_t\!\left[ \text{SDF}_{t,t+1} \cdot \left(e^{r^{eq}_{t+1}} - (1+i^{us}_t)\right) \right] = 0 \tag{7}$$

Currency risk premium (p. 991, eqs. 8-10). The expected excess return from being long the U.S. bond and short currency $i$:

$$\lambda_t = E_t[\Delta s_{t+1}] - \tilde{i}_t = \frac{\sigma^2_s}{2} + \sigma_{\pi,s} + \rho_t \, \sigma_{c,s} \tag{10}$$

Risk aversion is countercyclical (eq. 12, p. 991):

$$\rho_t = \rho_y \, y^{us}_t, \quad \rho_y < 0 \tag{12}$$

so a negative demand shock raises risk aversion, consistent with habit formation and intermediary asset pricing.

Information channel mechanics (pp. 992-995, eqs. 14-16). The central bank receives a private signal $\tilde{\varepsilon}^{y,us,CB}_{t+1} = \varepsilon^{y,us}_{t+1} + \hat{\varepsilon}_{t+1}$ about future demand. Investors initially believe the signal is less precise (market perception variance inflated by $q^{CB}_{t+l} = \hat{q}_{CB} > 0$ for $l < l^m$, where $l^m$ is the date of the FOMC minutes release). At the FG announcement, investors apply Bayes’ rule:

$$E_{t+l}[y^{us}_{t+h}] - E_t[y^{us}_{t+h}] = K_{t+l} \cdot a_{t+1}, \quad 1 \leq l < h \tag{15}$$

where $K_{t+l}$ (eq. 16, p. 994) is:

$$K_{t+l} = \frac{(\kappa+\alpha)\,\sigma^2_y/\sigma^2_{mp} - \left((\sigma^2_{\varepsilon,CB}+q^{CB}_{t+l})/\sigma^2_y + 1\right)v\eta}{(\kappa+\alpha)^2\,\sigma^2_y/\sigma^2_{mp} + \left((\sigma^2_{\varepsilon,CB}+q^{CB}_{t+l})/\sigma^2_y + 1\right)\eta^2} \tag{16}$$

When $\sigma^2_y/\sigma^2_{mp}$ is large (high fundamental uncertainty relative to policy uncertainty), $K_{t+l} > 0$: a negative FG surprise (lower rates promised) causes investors to revise down expected future GDP, raising risk aversion and triggering the flight-to-safety channel. The bias $q^{CB} > 0$ makes $K < 0$ on the FOMC day itself but turns positive once minutes are released (at $l = l^m$, $q^{CB} = 0$), generating the delayed reversal in estimated impulse responses.

Exchange rate decomposition (pp. 995-997, eqs. 17-21). The exchange rate change is decomposed into:

$$\Delta s_{t+l,t} = \psi^{EH}_{t+l,t} + \psi^{\lambda}_{t+l,t} + \psi^{LR}_{t+l,t}$$

• $\psi^{EH}_{t+l,t}$: interest rate differential component
• $\psi^{\lambda}_{t+l,t}$: currency risk premium component
• $\psi^{LR}_{t+l,t}$: long-run inflation / real exchange rate component

The derivative of the currency risk premium component with respect to the FG announcement (eq. 20, p. 996):

$$\frac{d\,\psi^{\lambda}_{t+l,t}}{d\,a_{t+1}} = -\rho_y \, \sigma_{c,s} \, K_{t+l} \tag{20}$$

Since $\rho_y < 0$ and $K_{t+l} > 0$ when the information channel dominates, this derivative is positive (dollar appreciates) for currencies where $\sigma_{c,s} < 0$ (currencies that are poor hedges: they tend to depreciate when U.S. consumption/output falls). The interest rate component is unconditionally positive and independent of $K_{t+l}$, so it pushes in the conventional direction; the structural break requires the flight-to-safety and inflation channels to dominate.

Method

The estimation strategy is the lag-augmented local projection (LALP) of Montiel Olea and Plagborg-Moller (2021), applied to high-frequency monetary policy surprises. The high-frequency monetary policy identification using interest-rate futures, and the “information effect” framing, follows Nakamura and Steinsson (2018). The approach builds on event-study identification and panel-regression for the cross-currency specification, and uses affine-term-structure models disciplined by survey forecasts for the channel decomposition.

Monetary policy surprise (p. 977). The surprise $mp_\tau$ is the change in Eurodollar futures expiring three quarters hence (ED4) over a one-hour window (15 minutes before to 45 minutes after) around FOMC announcements and QE announcements made outside regular FOMC meetings. ED4 captures unconventional policy during the ZLB period (Swanson (2021)).

Baseline impulse response regression (eq. 1, p. 977):

$$\bar{s}_{\tau+n} - \bar{s}_{\tau-1} = \alpha_n + \beta^{\Delta s}_n \, mp_\tau + \gamma_n(\bar{s}_{\tau-1} - \bar{s}_{\tau-2}) + \text{error}_{\tau,n} \tag{1}$$

• $\bar{s}_t = \frac{1}{K}\sum_{k=1}^K s_{k,t}$: average log exchange rate across $K$ currencies (units of currency $k$ per USD)
• $\beta^{\Delta s}_n$: impulse response at horizon $n$ ($n = 0,\ldots,90$ days)
• Sample: full sample with pre-GR, GR, and post-GR subsamples estimated separately
• SE: Newey-West, lag length set to the maximum number of overlapping FOMC dates within the estimation window for each horizon

Cross-currency heterogeneity regression (eq. 2, p. 984):

$$s_{k,\tau+n} - s_{k,\tau-1} = \alpha_{k,n} + \beta^{\Delta s}_n \, mp_\tau + \beta^{\text{hedge}}_n \cdot \text{hedge}_k \cdot mp_\tau + \gamma_{n,h}(s_{k,\tau-1} - s_{k,\tau-2}) + \text{error}_{\tau,n} \tag{2}$$

• $\text{hedge}_k$: currency $k$‘s hedging quality (covariance of exchange rate change with the SDF proxy: log S&P 500, log intermediary capital ratio, or U.S. minus country $k$ average interest rate differential), standardized to unit variance
• $\beta^{\text{hedge}}_n$: measures how the response to tightening varies with hedge quality
• Fixed effects: currency-specific intercept $\alpha_{k,n}$
• Sample: GR non-QE subsample (19 dates)

Channel decomposition (eqs. 23-24, p. 1002). The estimated $\beta^{\Delta s}_n$ is decomposed by replacing the dependent variable with each exchange rate change component estimated from an affine term structure VAR disciplined by Blue Chip Financial Forecasts survey data:

$$\beta^{\Delta s}_n = \beta^{\psi^{EH}}_n + \beta^{\psi^{\lambda}+\psi^{LR}}_n \tag{23}$$

and analogously for equity prices:

$$\beta^{\Delta p^{eq}}_n = \beta^{\psi^{EH,eq}}_n + \beta^{\psi^{\lambda,eq}+\psi^{D,eq}}_n \tag{24}$$

FG vs QE separation (pp. 977-978). Two complementary approaches: (i) restrict to FOMC announcement dates without QE announcements (19 of 33 GR dates); (ii) use the Swanson (2021) principal-component-identified FG and LSAP factors with sign restrictions.

Empirical specifications

All specifications use daily frequency data. The main sample for exchange rates is 1990 through 2019 (173 pre-GR observations, 33 GR observations, 60 post-GR observations). The GR subsample spans December 16, 2008 through September 13, 2012.

Exchange rate outcome (R1, R2). Baseline regression (eq. 1) using average log exchange rate changes against 9 advanced-economy currencies (AUD, CAD, CHF, EUR/DEM pre-1999, GBP, JPY, NOK, NZD, SEK) and 15 emerging-market currencies (BRL, CLP, COP, CZK, ILS, INR, ISK, KRW, MXN, PHP, RUB, SGD, THB, TRY, ZAR). Pegged-regime observations excluded. Newey-West SE with horizon-specific lags. Fixed effects: date-of-announcement intercept $\alpha_n$.

FG vs. LSAP factor comparison (R3). Same dependent variable, but monetary policy surprise replaced by the (negated) LSAP factor and the FG factor from Swanson (2021). Sample: July 5, 1991 through June 19, 2019 with GR subsample December 16, 2008 through September 13, 2012. 30 GR observation dates for each factor (Table III, p. 983).

Cross-currency heterogeneity (R4). Panel regression (eq. 2) on GR non-QE subsample (19 dates), with currency-specific fixed effect $\alpha_{k,n}$ and interaction $\beta^{\text{hedge}}_n$. Three hedge quality proxies used separately (Figure 3, p. 985; Figure 4, p. 986).

Risk aversion outcome (R6). Dependent variable replaced by log VIX or log Bekaert-Engstrom-Xu (2022) risk aversion index. Otherwise same regression as eq. 1. Full sample 1990-2019 (Figure 7, p. 1001).

Channel decomposition (R7, R8). Affine term structure VAR (three-factor, discipline by Blue Chip Financial Forecasts) for advanced economies to extract interest rate component. Residual is the currency risk premium and inflation component (exchange rates) or the equity risk premium and dividend growth component (equity prices). Results in Figures 8-9, pp. 1004-1005. Robustness: measurement error in interest rate expectations proxied and found to push against the main findings (footnote 33, p. 1003).

Uncertainty measures (R3, §III.C). Jurado-Ludvigson-Ng (2015) 12-month macroeconomic uncertainty, GDP forecast dispersion from Blue Chip Financial Forecasts (25th-75th percentile range), and Baker-Bloom-Davis (2016) monetary policy uncertainty index, all standardized 1990-2019 (Table IV, p. 1006). GR subsample means significantly higher for macro uncertainty and GDP dispersion; monetary policy uncertainty declines slightly, supporting the high-$\sigma^2_y / \sigma^2_{mp}$ interpretation.

Datasets used

Dataset	Role in paper	Wiki page
Eurodollar futures (ED4), daily, 15-min window around FOMC	Monetary policy surprise measure; identification instrument	no page yet
Daily bilateral nominal exchange rates (24 currencies, 1990-2019)	Primary outcome variable (log changes against USD)	no page yet
Swanson (2021) FG and LSAP factors	Alternative surprise decomposition separating forward guidance from QE	no page yet
S&P 500 and MSCI World daily total return indices	Equity price outcome variable	no page yet
VIX (CBOE Volatility Index), daily	Risk aversion proxy; additional testable implication	VIX
Bekaert, Engstrom, and Xu (2022) risk aversion index, daily	Risk aversion proxy (alternative to VIX)	no page yet
Blue Chip Financial Forecasts survey (GDP forecasts)	Discipline affine-term-structure VAR; measure GDP forecast dispersion	[no page yet]
Jurado, Ludvigson, and Ng (2015) macro uncertainty index	Measure macroeconomic uncertainty; explain why GR was special (Table IV)	no page yet
Baker, Bloom, and Davis (2016) monetary policy uncertainty index	Measure monetary policy uncertainty; complement to JLN in Table IV	no page yet
He, Kelly, and Manela (2017) intermediary capital ratio	Proxy for marginal investor SDF (alternative hedge quality measure)	no page yet

Sample: 1990 through 2019 for most series; GR subsample December 16, 2008 through September 13, 2012 (33 FOMC-adjacent observations). Pegged-regime observations excluded per Ilzetzki, Reinhart, and Rogoff (2022) classification.

When to read the full paper

Use the original if you are: decomposing the channels through which FG shocks transmit to asset prices (the Internet Appendix contains the affine term structure VAR, formal derivations, and robustness tables); extending the model to binding-ZLB environments or non-U.S. central banks; building on the cross-currency hedging heterogeneity result (Figures 3-4 and the supporting regressions); or evaluating whether the information channel was also operative outside the Dec 2008 to Sep 2012 window. The locators above point to the exact tables and figures.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 81(2). This distillation was extracted by an LLM on 2026-06-01 and is not human-verified or independently reproduced. The paper is paywalled; no PDF is hosted here. Extract only.

Stavrakeva, Vania, and Jenny Tang. “The Dollar during the Great Recession: The Information Channel of U.S. Monetary Policy and the ‘Flight to Safety’.” The Journal of Finance 81, no. 2 (April 2026): 971-1010. DOI: 10.1111/jofi.70025. (c) 2026 the American Finance Association. All rights reserved. Paywalled; this page is an extract-only distillation.