Abstract
This paper explores the direct effects and spillovers of unconventional monetary and exchange rate policies. We find that official purchases of foreign assets have a large positive effect on a country’s current account that diminishes considerably as capital mobility rises. There is an important additional effect through the lagged stock of official assets. Official purchases of domestic assets, or quantitative easing (QE), appear to have no significant effect on a country’s current account when capital mobility is high, but there is a modest positive impact when capital mobility is low. The effects of purchases of foreign assets spill over to other countries in proportion to their degree of international financial integration. We also find that increases in US bond yields are associated with increases in foreign bond yields and in stock prices, as well as with depreciations of foreign currencies, but that all of these effects are smaller on days of US unconventional monetary policy announcements. We develop a theoretical model that is broadly consistent with our empirical results and that highlights the potential usefulness of domestic unconventional policies as responses to the effects of foreign policies of a similar type.
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Notes
Under perfect substitution, asset purchases can have a temporary effect to the extent that they provide a signal of a future monetary action. We find little evidence of subsequent changes in monetary policy consistent with this channel.
See Bayoumi et al. (2015) for a detailed discussion.
MOB is lagged in all regressions, including in interaction terms, even when the interacted variable is not lagged.
The dominant form of official flows is purchases of foreign exchange reserves. However, public-sector borrowing in foreign currency counts as a negative official flow. Foreign asset purchases by sovereign wealth funds (SWFs) also count as official financial flows. We exclude countries with significant SWFs for which data do not allow the construction of comprehensive official flows.
The adjusted fiscal balance is the residual from a regression of the fiscal balance on the level and growth rate of the output gap.
The set of instruments also includes their interaction with MOB since both NOF and its interaction with MOB need to be instrumented.
Changes in energy prices alter revenues to be allocated to SWFs in some countries, but we control for this effect on the right-hand side through the net energy exports term. Our results, explored further in Section 2E, show that net energy exports are strongly correlated with the current account only in countries that actively save energy revenues abroad.
The adjusted QE variable is the residual from a regression of the change in central bank domestic assets on the level and change of the output gap, the change in nominal GDP, and NOF.
The measure of financial integration used in creating SPILL differs from MOB in that it is based on observed transactions and reflects financial market depth, whereas MOB is based solely on legal restrictions.
In 2013, the ratio of world NOF to world GDP was about 1%. Note also that around 90% of observations of the financial integration measure take values less than twice the median value.
Using average standard errors is valid on the assumption of perfect correlation between the two coefficients. To the extent that coefficients are less than perfectly correlated, the true standard error would be smaller. Thus, our significance levels are conservative.
The coefficient on the QE interaction with MOB is not significant in either the baseline or weighted regressions. However, dropping this term from the regressions yielded a much smaller (0.07–0.10) and insignificant coefficient on QE.
We find weak evidence for an extra spillover of global NOF to the US current account. An additional spillover term that equals global NOF divided by global GDP for the United States and zero for all other countries has a moderately sized negative coefficient that is not quite significant at the 10 % level.
We use the IMF’s de facto exchange rate regime classification, but the results are robust to using the Reinhart-Rogoff classification or the Aizenman et al. (2015) rolling measure of exchange rate volatility.
One reason we did not focus on the monetary base as a measure of QE is that it is correlated with NOF in countries in which foreign assets are an important tool of monetary policy. The cyclically adjusted growth rate of the monetary base is the residual of a regression of the growth rate of the monetary base on the level and change in the output gap, the growth rate of nominal GDP, and NOF.
An observation that would be highly influential if we had not dropped it from the regression is Kuwait 1991, immediately after the Gulf War. Kuwait drew down its SWF assets by nearly $40 billion in 1991 (174% of trend GDP) to rebuild the country. These official flows clearly were exogenous to any exchange rate pressures. They depressed the current account by a comparable magnitude. This observation was dropped from our regressions because the difference in scale from other observations would have raised the issue of potential nonlinear effects that would be difficult to address in our regression model.
Similar to Azerbaijan, Nigeria has a managed exchange rate and a low level of capital mobility.
Policy rate data are from Haver Analytics.
Non-reserves flows picked up (not all of the assets bought by the central bank were foreign exchange reserves) so that the fitted value of NOF increased.
We dropped the observations with a negative effect to be symmetric. Dropping only observations that have the largest positive effect would lower the estimated coefficient by construction.
On a scale from 0 to 1, with 1 representing highest mobility, in 2010 Algeria’s mobility index was 0.16, Saudi Arabia’s was 0.70, and Norway’s and Yemen’s were 1.00.
In 2010, Indonesia had a mobility index of 0.70; Colombia had a mobility index of 0.41; and Venezuela had a mobility index of 0.12.
First-order autocorrelation of the residuals in our baseline regression is around 0.7.
Bartolini et al. (2008) find that these two economic announcements have the biggest impact on asset prices.
Another possibility, common in past years but not during this sample, is that higher bond yields reflect worries about higher inflation and the need for tighter monetary policy to fight inflation.
To prevent identification problems, we use FOMC and economic data dummy variables in separate regressions. In addition, for all assets and all countries, the β1 coefficient shown in Tables 5 to 7 is that from a restricted regression setting with no dummy variable (β2 = 0). The estimated β1 coefficient is virtually unchanged in the specifications with dummies.
Londono and Zhou (2016) highlight the common component in currency appreciation rates and the relatively low average correlation between the Japanese yen with several other currencies.
Because most country characteristics are available only at a low frequency, we use the average values of country-specific characteristics over our sample period.
We do observe other episodes of significant central bank acquisitions of domestic assets which are a form of QE that would be expected to have similar expansionary effects because they take risky assets onto the balance sheet of the central bank and off private balance sheets. However, these episodes are more frequent in emerging markets with less open capital markets.
Private domestic flows between home and abroad would respond to the same relative returns with the same signs as foreign flows.
Blanchard et al. (2015) use a model of imperfect asset substitution that is similarly based on flows.
We assume that foreign central banks buy short-term bonds, but it is straightforward to analyze shocks to inflows into long-term bonds (UFBL).
Shocks to UFBL can be fully offset by a combination of NOF and QE.
Stock price movements reflect a combination of changes in expected future income and the discount rate applied to future income. The discount rate, in turn, is affected by changes in bond yields as well as changes in risk premiums. We assume that the income effect dominates; otherwise, good news about the US economy—which also raises foreign bond yields—would not raise foreign stock prices.
Tables 11-13 show that a policy of fixing the exchange rate using NOF yields an outcome in which shocks to the foreign short-term interest rate have no effect on any variable. This generalizes to a flexible exchange rate framework in which NOF responds to RSF by just enough to stabilize the exchange rate and all other variables.
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Appendix: Data Sources and Definitions
Appendix: Data Sources and Definitions
Sources: IMF, Balance of Payments Statistics version 6 (BOP); IMF, International Financial Statistics (IFS); IMF, Monetary and Financial Statistics; IMF, World Economic Outlook (WEO); United Nations, World Population Prospects 2010 (UN); World Bank, World Development Indicators (WDI); World Bank, Worldwide Governance Indicators (WGI); Norway, Norges Bank; and Singapore, Ministry of Finance. Data on capital controls and exchange rate volatility are from Aizenman et al. (2015). Daily financial data are from Bloomberg.
1.1 Annual Regressions
CAX: The BOP current account balance minus BOP net investment income.
NPFX: BOP net financial account flows minus BOP net investment income minus NOF.
NOF: Based on the BOP data, NOF is the sum of reserves flows and net portfolio investment and other investment flows for central bank and general government except that portfolio liability flows are set at zero for advanced economies because they do not borrow significantly in foreign currency. Nonreserve flows data for Norway are from the Norges Bank website for the Norwegian Pension Fund (Global). Debt forgiveness is removed from NOF but not NOA.
NOA: Based on the IIP data, NOA is defined as the stock version of NOF. Missing values of liabilities are filled in from the World Bank’s external debt data. In countries in which less data is available for the stock than the flow variable, we use perpetual inventory to project NOA backwards. For Norway, NOA is the sum of reserves and Pension Fund (Global) assets.
GDP: Nominal GDP in US dollars and in local currency, and real GDP, are from WEO.
MOB: Capital controls index available at Aizenman et al. (2015).
QE: Central bank domestic assets. Source: IMF Monetary and Financial Statistics (MFS) and International Financial Statistics (IFS).
SPILL: Global Financial Integration multiplied with the sum of NOF across countries and divided by the sum of trend GDP across countries.
Relative PPP GDP Per Capita: WEO (relative to US level). We set this as missing before 1996 for European transition economies.
Aging: 10-year forward change in ratio of elderly to working age population. Historical elderly ratios through 2010 are from WDI. Ratios for 2020 and 2020 are from UN and are interpolated and extrapolated in order to create 10-year changes for 2001–15.
Growth: 5-year moving average of growth rate of real GDP based on WEO. We corrected an error in Malta real GDP using IFS data. We set real GDP growth as missing for European transition economies before 1996.
Net Energy Exports: Difference between energy production and consumption in tons of oil equivalent (WDI), converted into dollars using Brent oil price (IFS) assuming 7.33 barrels per ton.
Fiscal Balance: General government balance in percent of GDP (WEO) is cyclically adjusted as the residual in a panel regression of the fiscal balance on the level and change of the GDP gap with no country or year effects. The GDP gap is the difference between log real GDP and its 11-year centered moving average using WEO forecasts for 2015–18. A missing value for South Africa in 2005 is interpolated.
Global Financial Integration: Defined as the ratio of the absolute values of BOP private financial account transactions divided by the sum of the absolute values of financial and current account transactions.
Non-reserve Flows: NOF minus reserve assets flow divided by trend GDP.
Crisis: A dummy that takes the value 1 if the respective country experienced a financial or currency crisis in the previous three years. Source: Laeven and Valencia (2012).
Trade Openness: Exports of goods and services plus imports of goods and services divided by trend GDP.
Exchange rate regime: IMF de facto (coarse) index from www.carmenreinhart.com and Aizenman et al. (2015) rolling measure of ER volatility.
Scaling by trend GDP: When scaling data by GDP, we use the 11-year centered moving average of nominal GDP in US dollars (WEO), including forecast data through 2018.
1.2 Daily Regressions
(Data are from Bloomberg unless otherwise noted.)
Currency flexibility: Sample period daily standard deviation of the exchange rate with respect to the US dollar.
Exchange rate: The log of the local exchange rate (in dollars per unit of non US currency.)
(Exports-to-US)/GDP: Sample average ratio of exports to the United States to domestic GDP based on annual regression data.
FOMC Dummy (D_(US,t)): D takes the value 0 on most days and 1 on days on which the FOMC released either a policy statement, policy minutes, or there was a monetary policy speech by the FOMC Chair. (Constructed by authors)
Econ Dummy (D_(US,t)): D takes the value 1 on days when data were released on US nonfarm payrolls or the ISM purchasing managers’ index, and 0 otherwise. (Constructed by authors)
MOB: The sample average of MOB from the annual regression data.
Bank assets to GDP: Average from 2008 to 12 from Helgi Library (http://www.helgilibrary.com/indicators/index/bank-assets-as-of-gdp).
Sovereign bond yield: Daily yield of 10-year constant maturity sovereign local-currency bond.
Sovereign risk: Sample-period average sovereign bond yields.
Stock price: Log of the daily local stock market price index.
US sovereign yield: Daily yield of 10-year constant maturity US Treasury bond.
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Gagnon, J.E., Bayoumi, T., Londono, J.M. et al. Direct and Spillover Effects of Unconventional Monetary and Exchange Rate Policies. Open Econ Rev 28, 191–232 (2017). https://doi.org/10.1007/s11079-017-9437-0
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DOI: https://doi.org/10.1007/s11079-017-9437-0