Abstract
What distinguishes foreign exchange interventions that are stabilizing from those that are manipulative? In the current no-official-agreed-upon rules environment any country that intervenes and builds up bilateral trade surpluses opens itself to charges of currency manipulation. Emerging market countries are especially susceptible because many of them rely on exchange rate stabilization policies to offset external shocks and facilitate trade. This paper proposes an approach to setting international exchange rate policy rules that discourage currency manipulation as well as spurious allegations of manipulation. It examines how intervention operations work, and demonstrates how counterfactual matching techniques can be used to test for causal links between intervention policies and exchange rate movements.
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Notes
The US Department of the Treasury press release designating China as a “currency manipulator” is dated August 5, 2019 and is available at: https://home.treasury.gov/news/press-releases/sm751 This designation was dropped in January 2020 as part of a Phase One trade agreement between the US and China.
The US Department of Commerce modified 19 CFR Part 351 effective 6 April 2020 to address subsidies resulting from currency undervaluation: https://www.federalregister.gov/documents/2020/02/04/2020-02097/modification-of-regulations-regarding-benefit-and-specificity-in-countervailing-duty-proceedings
An example is the following widely cited statement by the Brazilian Finance Minister Guido Mantega in the fall of 2010: “The world is in an international currency war as governments manipulate their currencies to improve their export competitiveness.” This statement followed the Fed’s announcement that it would begin a second round of quantitative easing (QE2). Many emerging-market currencies, including the Brazilian real, dramatically appreciated in the wake of the Fed’s QE2 announcement. Bernanke (2017) analyzes and dismisses the EM case against the US for dollar manipulation based on the view that the “expenditure augmenting” effects of QE offset the “expenditure –switching” effects. Blanchard (2017) argues that EMs should use capital restrictions in the face of QE spillover effects.
A critical requirement for the SCM approach is country-level heterogeneity. If all EMs responded to the US policy shifts by intervening in the same way, it would not be possible to create experimental controls.
Another potential remedy proposed by Bergsten and Gagnon (2017) would allow countries to neutralize currency manipulation by intervening with equal and opposite reserve assets purchases.
Dominguez (1993) describes the potential reasons for the IMF’s evolution away from rules enforcement in the context of a game theoretic model.
The 2007 Decision on Bilateral Surveillance over Members’ Policies is available at: https://www.imf.org/en/News/Articles/2015/09/28/04/53/pn0769#decision.
See the IMF’s Assessing Reserve Adequacy webpage: https://www.imf.org/external/datamapper/datasets/ARA and the IMF’s External Balance Assessment webpage: https://www.imf.org/external/np/res/eba/data.htm.
Bergsten and Gagnon (2017) criticize the IMF’s reluctance to set current account norms and explicitly name currency manipulators.
The Omnibus Trade and Competitiveness Act of 1988 requires the US Secretary of the Treasury to provide biannual reports on the international economic and exchange rate policies of the major trading partners of the United States. Under Section 3004 of the Act, reports must consider whether any foreign economy manipulates its rate of exchange against the US dollar to prevent effective balance of payments adjustments or to gain unfair competitive advantage in international trade. The Trade Facilitation and Trade Enforcement Act of 2015 formalized the US approach by requiring Treasury to monitor trading partners if they meet an explicit set of criteria.
Countries also have other available policy tools to influence exchange rates, but as Bernanke (2017) argues, monetary and fiscal policies should not be considered manipulative in the same way as reserve accumulation because these policies impact domestic employment and price stability irrespective of external impacts.
In fixed exchange rate regimes intervention is required to counter market pressure, but many countries that allow their currencies to float nonetheless intervene for various reasons. A commonly stated rationale for intervening is to stabilize exchange rates during “disorderly market conditions.” Countries also intervene to rebalance or build their foreign exchange reserve holdings for precautionary reasons.
The term “intervention” is defined as net official purchases or sales of foreign assets that result in a change in a country’s foreign reserve stocks. In some countries, it is the central bank that both decides to intervene and purchases the foreign assets, while in other countries (especially the advanced economies) the decision to intervene is made by the Treasury or Finance Ministry and the implementation of the policy is done by the central bank. If intervention operations are unsterilized, they change the monetary base and impact exchange rates in the same way as open market operations do. The intervention operations considered in this paper are sterilized, which results in changes in foreign reserve stocks, but no change in the monetary base.
Any model that allows for deviations in interest parity or goods market arbitrage provides a role for sterilized interventions (and capital controls) to influence the exchange rate and mitigate financial distortions. In portfolio balance models parity deviations arise from the perception that domestic and foreign-currency denominated assets are imperfect substitutes (Dominguez and Frankel 1993). Gabaix and Maggiori (2015) focus on an intermediation friction. Chang (2018) focuses on a credit supply constraint, specifically the net credit position of the central bank with domestic banks (sales of official reserves allows the central bank to increase the supply of credit to banks). Fanelli and Straub (2019) focus on a capital constraint; they derive a small open economy model with limited capital mobility where interventions are part of the optimal planning policy.
Ghosh et al. (2017a, b) explore additional motives for manipulation. They develop a model in which mercantilist countries have incentives to intervene in order to maintain undervalued exchange rates if there are increasing returns to scale in their export sectors. Hassan et al. (2016) examine currency manipulation in the context of large country effects. Cabezas and De Gregorio (2019) examine mercantilist, terms of trade, and speculation-deterrent motives.
Although theory suggests that the adjustment scenario is symmetric for current account deficits, Bergsten and Gagnon (2017) emphasize that interventions to maintain over-valued currencies are less likely to be effective as those against under-valued currencies, especially when governments are willing to intervene forcefully by accumulating foreign reserve stocks.
Fratzscher et al. (2019) use daily data from 33 advanced and emerging market countries to examine impacts of intervention from 1995 to 2011. Dominguez (2003, 2006) uses daily G3 (US, Japan and Germany) data to examine the impacts of intervention in the 1980s and 1990s and also finds evidence that operations were effective.
Sandri (2019) examines the profitability of Brazilian FX swaps. Domanski et al. (2016) examine EM interventions. Chamon et al. (2019) examine interventions by inflation–targeters in Latin America. Dominguez (2014) examines interventions by non-eurozone European countries, Dominguez et al. (2013) examine the impacts of foreign reserve sales by the Czech National Bank, and Dominguez (2003) examines the Fed’s intraday intervention tactics.
Daude et al (2016) among others make this point.
A related literature distinguishes between de jure and de facto exchange rate regimes (Levy Yeyati and Sturzenegger (2005), Klein and Shambaugh (2008) and Ilzetzki et al. (2017)) and the reasons self-described floaters still intervene (Levy Yayati et al. 2013). Obstfeld et al. (2017) find evidence that exchange rate regimes matter because the transmission of global financial shocks is larger for fixed rate economics. Goldberg and Krogstrup (2018) explicitly measure the transmission of global shocks using a new version of an “exchange rate pressure index.”
Mishra et al. (2014), Sahay et al (2014), Ahmed et al (2015), Clark et al. (2016) and Chari et al. (2020) all focus on the impacts of changes in Federal Reserve policy on EM capital flows in the aftermath of the global financial crisis. Fratzscher (2012) and Forbes and Warnock (2012) examine a broader set of external and local shocks.
Magud et al. (2011) summarize the findings from studies of country experiences with capital controls before the global financial crisis.
According to the sixth edition of the IMF Balance of Payments Manual (BPM6), a country’s reserve assets refer to: “those external assets that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be foreign currency assets and assets that actually exist.” (Chapter 6, 6.64; page 111).
Few countries provide detailed accounts of the individual assets (or their currency denomination) in their foreign reserve portfolios. Some central banks provide general information regarding their reserve management strategies, which are often published in annual reports. For example, De Gregorio (2011) describes the motives for reserve accumulation in emerging economies with a special focus on the Chilean approach. Dominguez et al. (2012) use the limited information provided in the SDDS template to distinguish active from passive reserve accumulation.
The selection of countries is largely driven by data availability, but also efforts to include countries across a range of continents, exchange rate histories, and policy activism. The sample includes: Argentina, Brazil, Chile, China, Colombia, Czech Republic, Hungary, India, Indonesia, Israel, Korea, Malaysia, Mexico, Philippines, Poland, Romania, Russia, South Africa, Thailand and Turkey. This paper’s data appendix provides detailed information on the sources for each of the variables used in the tables and empirical analysis.
The IMF’s Reserve Adequacy webpage includes visualization tools to view measures by country and across alternative metrics of reserve adequacy: https://www.imf.org/external/datamapper/ARA/index.html.
China is considered to have high reserve adequacy, and indeed holds the largest stock of reserves, though starting in 2014 reserves have not covered 20% of broad money.
None of the countries listed in Table 1 were contemporaneously labeled as manipulators. The assessments in Table 1 are based on 2019 US Treasury criteria, which have loosened in recent years. Prior to 2019, China was designated by the US as a manipulator in 1992, 1993 and 1994. Korea and Taiwan were the first two countries designated by the US as manipulators in 1988 and 1989. Taiwan was also designated in 1992. No other countries have been designated as manipulators by the US. The US Treasury introduced the monitoring list in 2016 when China, Japan, Korea, Taiwan, and Germany were named.
Bergsten and Gagnon (2017) use different criteria but also name China, Korea, Israel, Malaysia, Russia and Thailand as currency manipulators over the period 2003-2013 (Table 4.1, page 72).
De Gregorio (2014) suggests that official flows are often the first movers, and that private capital flows react to the interventions. It is unfortunately not possible to disentangle the timing of private versus official flows using quarterly data.
Turkey’s daily intervention data are available from Fred at: https://fred.stlouisfed.org/series/TRINTDEXR.
However, even if the underlying motivation for many reserve changes is not to directly influence currency values, models in which intervention operations can influence exchange rates, including portfolio balance models, are agnostic about intention; in these models any change in the stock of foreign reserve assets can impact exchange rates.
The US has long advocated for public reporting of intervention data. The USMCA includes commitments to publish interventions and Korea began reporting these data in March 2020 as part of a currency side-deal to the KORUS free trade agreement.
The synthetic control approach is one of a number of methods that uses a counterfactual to compare against a treatment effect. Propensity score matching is an alternative approach that selects controls based on observed characteristics; an example of this approach in the context of evaluating intervention is Fatum and Hutchison (2010). The synthetic control approach focuses instead on the outcome variable; the counterfactual is constructed by selecting a weighted average of the outcome variable from a group of controls that are similar to the treated outcome variable in the pre-treatment period. In this paper’s context, the outcome variable is the change in the exchange rate, the treatment countries are those that intervened in the foreign exchange market, and the control countries are other EMs that did not intervene. The counterfactual exchange rate is constructed to have parallel pretreatment trends. Any difference between exchange rate changes in the treated country and the synthetic control in the post-intervention period is then interpreted as a treatment effect.
The Fed’s quantitative easing programs to purchase large volumes of assets were announced in three steps (known as QE1, QE2 and QE3). In November 2008, the Fed announced the first program, QE1, which involved purchases of housing agency debt and agency mortgage-backed securities (MBS) of up to $600 billion. On March 2009, the Fed expanded its purchases of agency-related securities and began to purchase longer-term Treasury securities. In November 2010, the Fed announced the second program, QE2, which involved purchases of $600 billion long-dated Treasury securities. In September 2011, the Fed announced a new program that involved purchasing $400 billion of long-term treasury bonds by selling short-term treasury bonds. This (operation twist style) program was further extended in June 2012 until the end of the year. In September 2012, the last round of quantitative easing was announced, QE3, which consisted of an open-ended commitment to purchase $40 billion of mortgage backed securities per month. In December 2012, this program was expanded further by adding the purchase of $45 billion of long-term treasury bonds per month. In May 2013 the Fed announced it would begin slowing (tapering) its QE3 asset purchases (it did not actually begin tapering until December 2013). Quantitative easing officially ended in October 2014.
There are a number of different synthetic control methods, this paper uses the original Abadie et al. (2010) formulation where the optimal weight vector W is chosen through the minimization of the square root of the following criterion: (X1 − XoW)’ V (Xi− XoW) where V is a k × k symmetric and positive semi-definite matrix (k is the number of explanatory variables) and V is chosen to minimize the mean square prediction error in the pre-event period. An important restriction is that the sum of the weights assigned to the synthetic control equal one, which implies that the exchange rate for the treated and control countries follow parallel paths over time in the absence of treatment. Alternative methods allow for less restrictive parallel trends and provide methods to evaluate the significance of post-treatment results using placebo tests.
The monetary differentials are measured as differences between each EM’s policy rate and the US policy rate. The US policy rate is the Federal Funds rate prior to the first quarter of 2009 and the shadow fed funds rate constructed by Wu and Xia (2016) between 2009Q2 through the end of 2015. Growth differentials are measured as percent changes in EM GDP relative to the average analogous growth rate of the advanced economies. Market volatility is captured by the country-specific EMBI Global spread and net private capital inflows are measured as shares of GDP. Global market volatility is measured using the change in the VIX, and the quarter on quarter change in the commodity price index constructed by the IMF. In some cases the maximum likelihood optimization to determine the W matrix failed to converge when all five of these explanatory variables were included; in those cases dropping one of the explanatory variables (typically the change in VIX or the commodity index) led to convergence.
It is possible that the (size and persistence) criteria used to designate “interveners” in Table 2 will miss countries that aggressively intervened immediately after the QE2 or Taper Tantrum announcements but then ended operations. Correctly allocating countries into the treated and control categories is critical to the interpretation of results and is an important weakness of the SCM approach.
Chile pre-announced reserve accumulation programs that were in place in 2008 and 2011, and auctioned forex swaps in 2008 and 2009 (Pincheira 2013); Colombia and Mexico use rules-based auctions that were triggered when daily foreign exchange rate volatility exceeded pre-established thresholds (both countries discontinued these programs in 2016). Brazil put in place a rules-based intervention program in response to the Taper Tantrum that involved preannounced swap and repo operations (Chamon et al. 2017).
The outcome variable used in the estimation is the log change in the exchange rate, thereby avoiding the well-known stationarity issues with levels.
The synthetic control figures were produced in Stata using the Synth module; detailed information about the method and code is available at: https://web.stanford.edu/~ jhain/synthpage.html.
The explanatory variables in the regression specification are included in part to control for the use of other policy tools to influence the exchange rate in reaction to the Fed shocks. Interest rate changes are directly included, but changes in capital controls are not because quarterly changes in these restrictions are not available for many of the countries in this study. The interpretation of the treatment effects of intervention therefore should include a potential omitted capital control effect.
It was not possible to create a good synthetic twin for a number of the “intervener” countries after QE2, including Chile, Hungary, Israel, Malaysia, Philippines, Poland, Romania and Thailand. In each case the control country exchange rates were not similar enough to the interveners (they did not follow parallel paths) in the year prior to the QE2 announcement. In most of these cases the post-QE2 actual exchange rate was also less appreciated than the estimated synthetic twin, but the poor pre-event matching makes these experiments hard to interpret.
Chamon et al. (2017) document that it was not until August 2013 that the Central Bank of Brazil responded to the Fed announcement with a large scale intervention program involving FX swaps; but they do find evidence for significant strengthening of the BRL immediately after the intervention program was announced, using a similar synthetic control approach. Brazil did not meet the “intervener” criteria used here (net reserve sales in 6 of 12 months and the sum of monthly increases equal to at least 2% of GDP between 2013Q2 and 2014Q2).
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Acknowledgements
A version of this paper was prepared for the IMF’s Current Policy Challenges Facing Emerging Markets conference, held on July 24–25, 2019 in Santiago, Chile. I thank John Clark for valuable discussions and sharing his quarterly data. I am also grateful for helpful comments from my discussant, Eduardo Levy Yeyati, suggestions from Laura Alfaro, Enrique Mendoza, Jose De Gregorio and other participants at the conference, two anonymous referees, as well as participants at the NBER-CCER conference in Beijing, the NBER India in the Global Economy conference in Neemrana, and the Center on Finance, Law, and Policy workshop at the University of Michigan. Alex Serwer, Amy Turner, and Rachel Yang provided excellent research assistance. All remaining errors are my own.
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Data Appendix
Data Appendix
20 EM countries: Argentina, Brazil, Chile, China, Colombia, Czech Republic, Hungary, India, Indonesia, Israel, Korea, Malaysia, Mexico, Philippines, Poland, Romania, Russia, South Africa, Thailand, Turkey
Net Private Capital Flows: IMF BOP statistics (net FDI + net portfolio inflows + net other inflows – IMF net lending – other official exceptional financing)
Capital Controls: Pasricha et al. (2018) covers 18 EMs quarterly from 2001 to 2015; provides changes in the number of capital flow management policies to measure the time-varying intensity of restrictions (missing Czech Rep, Hungary, Israel, Poland and Romania).
IMF IFS Quarterly Data: bilateral nominal exchange rate (against dollar, end of period), current account balance (in dollars), exports (in dollars), imports (in dollars), GDP (domestic currency). Quarterly GDP data for China and South Africa are from FRED.
EM long term yields: FRED and Bloomberg
EM credit growth: BIS (change in total credit to the non-financial private sector)
EM Global Spread: JP Morgan (country-specific EMBI Global Spreads)
EM Output growth: Haver (real GDP growth, year on year)
Commodity Price Index: FRED (IMF index of commodity prices)
EM Policy Rates: Haver (Central Bank policy rates)
VIX: FRED (implied volatility of S&P Options)
Fed Policy: Effective Federal Funds rate, Shadow Federal Funds Rate: using Xia and Wu (2015) method, Fed Balance Sheet (total assets of Federal Reserve Banks), US 10-year Treasury yield (FRED)
Reserve Data: monthly SDDS foreign exchange reserves, except China prior to 2015.
Exchange Rate Regime Classification data: Ilzetzki et al. (2017)
Annual GDP and Current Account data from WEO database
Annual EBA data (IMF) https://www.imf.org/external/np/res/eba/data.htm
