Monetary Surprises and Global Financial Flows: A Case Study of Latin America

Abstract

This article examines the effect of Federal Reserve announcements on global financial flows to Latin America since the Global Financial Crisis. The Federal Reserve announcements are classified using daily measures of expectations from a shadow rate term structure model as easing (unexpected), tightening (unexpected), easing (expected), and tightening (expected). This classification is then used for an event study on daily global financial flows classified by asset class (debt, equity), currency (all currencies, hard currency, local currency), and region (Latin America, Brazil, and Mexico). The results suggest easing (unexpected) and tightening (unexpected) announcements cause debt outflows but have no effect on equity flows to Latin America. Local currency debt flows to Latin America are more sensitive than the hard currency debt flows and Brazil is the country in Latin America that responds most to these announcements.

JEL Classification: F32, G14, G15, N26

Keywords

International financial flows unconventional monetary policy Latin America portfolio flows financial stability emerging markets

1 Introduction

This article examines the effects of Federal Reserve announcements since the Global Financial Crisis (GFC) on portfolio flows to Latin America. The combination of unconventional monetary policies by the Federal Reserve and other central banks in advanced economies as well as new banking regulations after the GFC has coincided with an increase in market-based funding to emerging market economies in Latin America. The search for yield among international investors and the global interconnectedness of emerging economies with the advanced economies has generated massive portfolio inflows from advanced to emerging economies. While capital flows provide benefits in terms of financing for emerging economies, they also present vulnerabilities in the form of asset price misalignment, macroeconomic distortions, and increased financial volatility. As the Federal Reserve has started its policy normalization from the zero lower bound these capital flows have become more volatile and uncertain for emerging market economies. These spillover effects of unconventional monetary policy are of particular concern to central banks and policymakers in Latin America.

Movements in foreign portfolio flows to Latin America since the 2008 GFC can be explained by a combination of factors including Federal Reserve unconventional monetary policy. One relevant example of the global spillover effects of Federal Reserve unconventional monetary policy occurred when Federal Reserve Chairman Ben Bernanke eventually hinted at ending unconventional monetary policy, in May and June of 2013, and the markets revised their expectations of future Federal Reserve rate hikes. This event, later termed the “taper tantrum”, led to revisions in emerging market asset prices and portfolio outflows from emerging markets and Latin America. In Brazil, policymakers have responded to capital flow volatility with a combination of capital controls and foreign exchange intervention. In Mexico, policymakers have not used currency intervention, nor have they used capital controls.¹ Nonetheless, the Bank of Mexico adjusted its policy rate in step with the Federal Reserve to stem movements in capital flows and to stabilize the exchange rate between the dollar and the peso. The authorities in Mexico have the greatest tolerance for volatility and, while concerned about exchange rate overshooting, show little inclination to intervene in foreign exchange markets (Wheatley, 2016).

This article addresses several related research questions. What are the effects of Federal Reserve announcements on portfolio equity and debt flows to Latin America since the GFC? Do these effects differ for debt flows and equity flows? Do these effects differ for debt flows in hard currency and in local currency? Do these effects differ for Brazil and Mexico? To what degree can these effects be explained by changes in global uncertainty, market liquidity, and economic fundamentals.

This article answers these research questions in two parts. First, the article classifies all of the Federal Reserve announcements since the GFC using daily measures of interest rate expectations. This classification allows the announcements to be classified as easing (unexpected), tightening (unexpected), easing (expected), and tightening (expected). Second, the article uses the announcement classification to conduct an event study to examine the effect of announcements on daily frequency data for equity flows and debt flows for funds dedicated to investing in Latin America, Brazil, and Mexico. Brazil and Mexico receive the largest fraction of portfolio flows in Latin America and have globally domiciled funds dedicated to investing in their countries. Robustness checks include using intraday data on interest rate expectations to classify announcements and controlling for daily frequency measures of global risk aversion as measured by the VIX, market liquidity in the US Treasury market as measured by Hu et al. (2013a), commodity prices, and country fundamentals. This article contributes to the literature by using market expectations to classify the Federal Reserve announcements and by explaining their effects on portfolio equity and debt flows to Latin America. The empirical results indicate that easing (unexpected) and tightening (unexpected) Federal Reserve announcements cause debt outflows but have no effect on equity flows to Latin America. Local currency debt flows to Latin America are more sensitive than the hard currency debt flows and Brazil is the country in Latin America that responds most to these announcements.

This article proceeds in the following manner. Section 2 motivates the article by explaining its relation to the literature on classifying announcements at the zero lower bound, the effect of monetary policy on international portfolio flows, and financial stability in Latin America. Section 3 presents the daily data used to classify the Federal Reserve announcement days and the portfolio flow data from Emerging Portfolio Funds Research (EPFR) Global. Section 4 explains the methodology used to classify Federal Reserve announcements from October 8, 2008 until October 29, 2014 and to estimate the effect of Federal Reserve announcements on portfolio flows to Latin America. Section 5 presents the results from the Federal Reserve announcement classification and from the event study on the effects of Federal Reserve announcements on portfolio flows to Latin America. Section 6 conducts a robustness check by including liquidity measures, uncertainty measures, commodity prices, and country fundamentals into the analysis. Section 7 concludes with policy implications and future research.

2 Related Literature

This article relates to the unconventional monetary policy literature by classifying all regularly scheduled Federal Reserve announcements using daily data on interest rate expectations. During times when monetary policy is conducted using the federal funds rate, Federal Reserve announcements are classified using 30-day federal fund futures contracts (Bernanke & Kuttner, 2005; Gürkaynak et al., 2007; Kuttner, 2001). However, when interest rates hit the zero lower bound the Federal Reserve used unconventional policies (Christensen & Rudebusch, 2013; D’Amico et al., 2013; Gagnon et al., 2011; Krishnamurthy Vissing-Jorgensen, 2013; Walsh, 2014) and these short-term measures of expectations do not capture these effects. Furthermore, long-term measures of these asset prices suffer from term premia and liquidity issues (Christensen & Kwan, 2014; Gürkaynak et al., 2007).² For these reasons, this article classifies announcements using daily frequency short-rate expectations from a shadow rate term structure model developed by Christensen and Rudebusch (2014) which overcomes the liquidity and term premia issues from using federal fund futures contracts and Eurodollar futures contracts at longer horizons and zero lower bound issues when using standard term structure models (Christensen & Rudebusch, 2013, 2014; Kim & Wright, 2005; Krippner, 2015; Lombardi & Zhu, 2018; Piazzesi & Swanson, 2008). This article also conducts a robustness check using intraday changes in US bond yields the 15 minutes before until 105 minutes after to classify Federal Reserve announcements (Rogers et al., 2014; Rogers, Scotti, & Wright, 2014).

This article also relates to the literature on the global effects of unconventional monetary by examining the international spillovers on daily frequency flows of globally domiciled and regulated mutual funds and exchange-traded funds (ETFs). Several empirical studies have examined the global effects of conventional and unconventional US monetary policy (Berge & Cao, 2014; Gilchrist, Yue, & Zakrajsek, 2019; McCauley, McGuire, & Sushko, 2015; Rey, 2013; Rogers et al., 2014), the response of emerging market asset prices to US monetary policy (Bowman, Londono, & Sapriza, 2014; Moore et al., 2013; Rogers et al., 2014), and the effect of tapering news on emerging financial markets (Aizenman, Binici, & Hutchison, 2014; Eichengreen & Gupta, 2013). Other papers have studied the effect of monetary policy on portfolio flows to emerging market economies using quarterly IMF balance of payments data (Ahmed & Zlate, 2015; Lim et al., 2014)³ as well as daily, weekly, and monthly frequency portfolio flow data from EPFR Global (Curcuru, Rosenblum, & Scotti, 2015; Dahlhaus & Vasishtha, 2014; Fratzscher, Duca, & Straub, 2013; Koepke, 2014; Rai & Suchanek, 2014).⁴ This article builds upon work by Fischer (2016), which found that Federal Reserve announcements had the greatest effect on portfolio debt flows to Latin America of all the emerging market regions (Asia excluding Japan, Europe Middle East and Africa (EMEA), Latin America, and Global Emerging Market (Global EM)) and examines the effects of announcements on both portfolio equity and debt flows to Latin America, Brazil, and Mexico.

This article also contributes to the literature on the financial stability of the Latin America region by examining portfolio flows to Latin America as a region and to Brazil and Mexico. The financial crises that occurred in Latin America in the 1990s prompted the monetary authorities in this region to move from a system of fixed exchange rates to ones with their own independent monetary policy (BIS, 2015; Edwards, 2016; Mishkin & Savastano, 2000). Although Brazil and Mexico have been successful in their transitions from fixed exchange rates to inflation targeting monetary regimes (Bernanke, 2013; DePooter, Robitaille, Walker, & Zdinak, 2014) the GFC and monetary policies in advanced economies have had spillovers to Latin America. These spillovers have shown up in the form of capital flows (Ahmed & Zlate, 2015; Christensen, Fischer, & Shultz, 2019; Curcuru et al., 2015; Fratzscher et al., 2013), increased credit in banking systems in Mexico (Morais, Peydro, & Ruiz, 2015), and the implementation of capital controls in Brazil (Forbes, Fratzscher, Kostka, & Straub, 2012; Jinjarak, Noy, & Zheng, 2013). Other research has examined the behavior of foreign and domestic mutual funds to Mexico (Xiao, 2015; Zhou, Han, & Xiao, 2014) as well as bond flows and liquidity to Mexico (Christensen et al., 2019).

Figure 1.

Source: Board of Governors of the Federal Reserve System.

3 Data

This article uses all the Federal Reserve announcement dates between October 8, 2008 and October 29, 2014. These announcements include all of the regularly scheduled Federal Reserve Open Market Committee (FOMC) announcement days and a few important announcements related to large scale asset purchases that were not part of the regularly scheduled FOMC announcement days. All of the FOMC announcement days are made publicly available and were obtained from the Federal Reserve Board of Governors website.⁵ Any additional days were taken from Rogers et al. (2014) to examine the effect of Federal Reserve announcements on asset prices. However, unlike Rogers et al. (2014) which include announcement days until early 2014, this article includes FOMC announcement days until the end of large-scale asset purchases in October 2014. In total, there are 54 announcements of which 10 were Tuesday announcements, 41 were Wednesday announcements, 1 was a Thursday announcement, and 2 were Friday announcements.

This article uses daily interest rate expectations to classify these Federal Reserve announcements. This article uses the daily 2-year short-rate expectations from a shadow rate Arbitrage-Free Nelson–Siegel model developed by Christensen and Rudebusch (2013) that assumes interest rates have a lower bound of zero (Figure 2).⁶ Term structure models are widely used by financial market practitioners and central banks to examine the dynamic evolution of the yield curve using observed prices and estimating the slope, level, and curvature of the yield curve. The Nelson and Siegel (1987) term structure model is the most widely used, as it provides good yield curve fit for a cross-section of yields (Kim and Wright, 2005).

Figure 2.

Source: Bloomberg and Christensen & Rudebusch (2013).

This article uses daily frequency portfolio equity and debt flow data collected and distributed by EPFR Global. Headquartered in Cambridge, MA, EPFR Global was founded in 1995 and tracks regulated mutual fund and ETF flows that it collects from its direct relationships with fund managers and administrators. EPFR Global then uses this information to produce indicators for fund flows, country allocations, sector allocations, and industry allocations, and together with an allocation data series EPFR Global is able to estimate the flow data for country flows, sector flows, and industry flows. EPFR Global reports this data at the daily, weekly, and monthly frequencies.⁷ EPFR Global currently tracks around 15,000 funds with investments across 130 countries and that cover US$23.5 trillion worth of globally domiciled funds primarily domiciled in the USA and Europe. Of the US$23.5 trillion of assets covered, approximately US$16.2 trillion are from funds domiciled in the USA and US$5.6 trillion in Europe.⁸ The data covers 93 countries for equity flows, 100 countries for debt flows, and regional flows.

The flow data provided by EPFR Global is widely used among market participants and economic policymakers because of its timely release and its high frequency but has only recently been used by academic researchers.⁹ The daily frequency flows are made available at 5 pm EST for the previous day, the weekly fund flows data are made available at 5 pm EST each Thursday for the previous 7 days, and the monthly data is reported at 5 pm EST on the 23rd for the previous month. EPFR Global provides historical data for equity flows since January 1995 (monthly), October 2000 (weekly), and May 2007 (daily) and debt flows since January 2004 (monthly), April 2004 (weekly) and May 2007 (daily). The fund flows data includes daily flows in US dollars, cumulative flows in US dollars, daily percentage change in flows, daily percentage change in cumulative flows, total net assets, valuation change due to the exchange rate, net asset value percentage change, and the percentage change in cumulative net asset value. As shown in Online Supplementary Appendix Table B2 and B3, almost all of the funds that report at the weekly frequency also report at the daily frequency. However, not all funds that report at the monthly frequency also report at the daily and weekly frequencies.

The flow data from EPFR Global and flow data from IMF Balance of Payments differ in several ways. The IMF Balance of Payments data tracks cross-border capital flows but is only available on a quarterly basis and with a significant lag. Debt flows in the Balance of Payments are located in the financial account under portfolio investments and under liabilities. This portfolio liabilities line in the Balance of Payments covers all the cross-border debt held by nonresidents in that particular country. EPFR Global data is available at a much higher frequency than IMF Balance of Payments data and is released on a timely basis but covers a slightly different type of flows. The flow data provided by EPFR Global includes investment by residents and nonresidents whereas the Balance of Payments data separates the debt flows by residency. EPFR Global data tracks fund flows that are domiciled globally but the vast majority of them are in the United States and Europe. In addition, EPFR Global portfolio flow data accounts for approximately 60 percent of total portfolio flows into emerging market funds. The EPFR Global data only tracks regulated managed funds and so does not track hedge funds, proprietary trading desks, foreign insurance companies investing in excess cash, and wealthy individuals and individual companies unless they invest in regulated managed funds. Miao and Pant (2012) found that the debt and equity data released by EPFR Global data closely matches quarterly IMF data on debt and equities that are released at 3- to 6-month lags. They conclude that because 80 percent of the funds in the EPFR Global are US domiciled these investors can be considered foreign investors in emerging markets. Nonetheless, the EPFR Global data and IMF Balance of Payments data are different in the sense that the Balance of Payments data by definition captures the transactions between residents and nonresidents whereas fund flows cover inflows in and out of mutual funds and ETFs.

Figure 3.

Source: EPFR Global.

This article uses the daily frequency EPFR Global data for debt and equity fund flows for Latin America, Brazil, and Mexico.¹⁰ The cumulative debt and equity flows to Latin America, shown in Figures 3 and 4, both show gradual inflows starting in 2008 but equity flows start to decline starting in 2011 and debt flows decline starting in 2013.¹¹ The decline in debt flows shown in Figure 3 starting in 2013 is evident in the local currency debt flows but not in the hard currency debt flows and mixed currency debt flows to Latin America.¹² Figure 5 shows the rapid decline in local currency debt flows to Brazil after 2013 while hard currency debt flows remained around the same and shows that hard and local currency debt flows to Mexico remained small throughout the sample.¹³ The equity flows to Brazil and Mexico, shown in Figure 6, are not separated by currency and indicate a decline for Mexico and Brazil in 2013. Previous papers using EPFR Global data on portfolio flows to Latin America by Forbes et al. (2012) using monthly EPFR Global data for bonds and equities between January 2006 and July 2011 found that the imposition or relaxation of controls in Brazil leads to reallocation of portfolio shares to Russia, India, and China. Jinjarak et al. (2013) used weekly EPFR Global data for Brazil from December 2007 until December 2011 to create a synthetic control and measure how capital controls impact the inflow surge. More recently, Xiao (2015) compared domestic and foreign mutual funds in Mexico and found that foreign mutual funds to Mexico respond to global financial conditions and engage in more herding than domestic funds, that debt funds are more sensitive than equity funds, and that domestic funds mitigate domestic market stress.

Figure 4.

Source: EPFR Global.

Figure 5.

Source: EPFR Global.

Figure 6.

Source: EPFR Global.

The robustness checks include using intraday monetary surprises to classify announcements and controls for volatility, liquidity, commodity prices, and fundamentals. The intraday monetary surprises are constructed using the first principal component of the change in future yields for 2-, 5-, 10-, and 30-year Treasury futures in the 15 minutes before and 105 minutes after a Federal Reserve announcement (Curcuru et al., 2015; Rogers et al., 2014, 2015). The volatility data is the VIX, which measures the implied volatility of the S&P 500 index options calculated by the Chicago Board Options Exchange (CBOE) that measures the stock market’s expectations of stock market volatility over the next 30-day period. The liquidity measure tracks the US Treasury market and was developed by Hu, Pan, and Wang (2013a) based on the spread between seasoned and recently issued comparable Treasury securities, and weekly average trading volume in the secondary market for Treasury Inflation-Protected Securities (TIPS) as reported by the Federal Reserve Bank of New York (Hu, Pan, & Wang, 2013b). The commodity price is the West Texas Instruments (WTI) Cushing crude oil price, which is the most commonly used benchmark for global oil prices. WTI Cushing oil price measures the price of crude at Cushing, OK, and trades in pipeline lots of 1,000–5,000 barrels a day for delivery between the 25th of one month to the 25th of the next month. A second commodity price used is the Bloomberg Commodity Index, which is a diversified group of commodities that relies on liquidity data and US dollar weighted production data to determine the weights for commodities. The fundamentals data includes the J. P. Morgan EMBI and the MSCI equity index for Latin America, Brazil, and Mexico.

4 Methodology

This section describes the methodology used for estimating the effect of changes to US monetary expectations on Latin American debt and equity flows in two parts. First, this section explains the methodology used for classifying the Federal Reserve announcements as easing (unexpected), tightening (unexpected), easing (expected), and tightening (expected) using changes to monetary expectations measured by the shadow rate model. Second, this section explains the methodology for estimating the effects of Federal Reserve announcements on portfolio flows to Latin America and to Brazil and Mexico.¹⁴

4.1 Classifying Federal Reserve Announcements

Federal Reserve announcements are classified by measuring the changes in interest rate expectations around announcement days. As described in the previous section these market expectations of the future short rate are measured using daily measures from a shadow rate term structure model. This measure of market expectations of the future short rate can change even when the actual short-term policy rate remains unchanged. This measure of expectations is used to classify Federal Reserve announcements into one of the following four categories: easing (unexpected), tightening (unexpected), easing (expected), and tightening (expected). An announcement is unclassified if the measure of expectations does not change on the announcement day.

The Federal Reserve announcements between October 8, 2008 and October 29, 2014 are classified in the following manner. First, the daily expectations are converted into a daily percentage change in expectations. This daily percentage change measure is then converted into positive values by taking the absolute value of all the daily percentage change observations. Second, the mean change in the absolute value of all the daily percentage changes is calculated to find the average level of daily change in expectations over the entire sample period. Third, each of the Federal Reserve announcements is classified by comparing the percentage change in expectations on that day relative to mean absolute value of the change in that measure of expectations on all the other days in the sample period. If the change in expectations on an announcement day is above an average change in expectations on all the other days in the sample period, then it is unexpected. Conversely, if the change expectations on an announcement day are less than an average change in expectations on all other days, then it is expected. The announcements are also classified as easing if the change in expectations is negative and tightening if the change in expectations is positive.

4.2 Effects on Portfolio Flows to Latin America, Brazil, and Mexico

The event study methodology used to estimate the effect of Federal Reserve announcements on portfolio flows uses the announcements classified by the shadow rate model together with the EPFR Global data. The following equation is the specification used to understand the effect of announcements on portfolio flows to Latin America.

F l o w s i_{j r t} = β_{0} + β_{1} (S h a d o w R a t e A n n o u n c e m e n t s_{k t}) + ε i_{j r t}

(1)

The Flows variable on the left-hand side of Equation (1) is the data on daily frequency flows to Latin America from EPFR Global. This variable is classified by asset class subscript i for whether these portfolio fund flows are for debt flows or equity flows. The subscript j denotes whether debt flows are all currencies, hard currencies, local currencies, or mixed currencies. The subscript r specifies whether the investment focus is Latin America, Brazil, or Mexico. Finally, the subscript t denotes the day of the announcement to indicate the precise day for the flows around that announcement day. On the right-hand side of Equation (1) is the Shadow Rate Announcements variable that is categorized using expectations from the shadow rate model. These announcements are denoted by subscript k to denote announcements as easing (unexpected), tightening (unexpected), easing (expected), and tightening (expected). Furthermore, the subscript t denotes the announcement day.

This event study methodology is used to test the differences in the 7-day average flows before and after each of the four Federal Reserve announcement classifications.¹⁵ All four announcement classifications k are analyzed for both equity and debt i, currencies j, and regions r. Recall, the Federal Reserve announcements were classified using the daily expectations from the shadow rate model. Therefore, to study the effect of easing (unexpected) announcements on all currency j debt flows i to Latin America r we examine only the debt flows that occur around the 7 days before and after those 14 announcements. The first step in this event study methodology is to check that each Federal Reserve announcement occurs at time t equals 0. Next, a dummy variable for 7 days after each of the announcements after each of the 14 easing (unexpected) announcements. Then, an ordinary least squares regression is used to examine the difference in the average flows before and after all of the easing (unexpected) announcements. This difference estimate for the effect of easing (unexpected) announcements on debt flows to Latin America is reported in the upper left of Figure 8 with the average flows in each day represented by the blue dots. This same event study methodology is repeated for tightening (unexpected), easing (expected), and tightening (expected) Federal Reserve announcements. The results for each event study are standardized to the mean and standard deviation to compare the coefficients and significance across each of the announcement classifications.

The same event study methodology is used to explore the effects of all four announcement classifications on debt flows and equity flows to Latin America, Brazil, and Mexico. Latin America flows are analyzed separately for debt and equity flows. Latin America debt flows are further separated into all currencies, hard currencies, local currencies, and mixed currencies while the equity flows to Latin America are studied as all currencies. Brazil flows are studied separately for debt flows, separated into hard currency and local currency debt flows, as well were for equity flows. Mexico flows are analyzed only for equity flows due to data limitations for debt flows to Mexico.

F l o w s i_{j r t} = β_{0} + β_{1} (S h a d o w R a t e A n n o u n c e m e n t s_{k t}) + C o n t r o l_{m t} + ε i_{j r t}

(2)

The robustness checks include an intraday monetary surprise measure to classify Federal Reserve announcements and using additional control variables in the event study. The intraday monetary surprise measure is used to classify announcements as easing if the expectations went down and tightening if the interest rate expectations went up. The Flows variable and the Shadow Rate Announcements variables in Equation (2) are the same as those variables in Equation (1). The Control variables are introduced into the regression Equation (2) to make sure that the changes in flows before and after Federal Reserve are not the result of changes in other domestic or international factors. These controls are denoted by subscript m at time t to specify VIX, market liquidity in the US Treasury market, commodity prices, as well as J. P. Morgan EMBI and MSCI equity indices. These results from these robustness checks are reported in Appendix Tables B7–B16.

5 Results

The results are separated into a section for classifying Federal Reserve announcements using expectations from a shadow rate model and a section for the effects of Federal Reserve announcements on portfolio flows to Latin America. The results for the effects of Federal Reserve announcements on portfolio flows to Latin America are grouped into a subsection for overall Latin America flows and into a subsection for Brazil and Mexico.

5.1 Classifying Federal Reserve Announcements

The shadow rate model classifies all of the Federal Reserve announcements from October 8, 2008 to October 29, 2014. Table 1 and Figure 7 show that the shadow rate model classifies the 54 announcements during this time period as 14 easing (unexpected) events, 9 tightening (unexpected) events, 16 easing (expected) events, and 15 tightening (expected) events.

Table 1.
Daily Classification of Federal Reserve Announcements

Shadow Rate Model

Easing (unexpected) 14

Tightening (unexpected) 9

Easing (expected) 16

Tightening (expected) 15

Unclassified 0

Total 54

	Shadow Rate Model
Easing (unexpected)	14
Tightening (unexpected)	9
Easing (expected)	16
Tightening (expected)	15
Unclassified	0
Total	54

Source: The authors.

The mean absolute value change in expectations for the shadow rate model is 5.40 percent with a standard deviation of 10.25 percent. The minimum change in expectations was 0 percent and the maximum change in expectation was 191.49 percent. A significance test of the daily percentage change in shadow rate model measure of expectations on the announcement day relative to the previous 7 days is shown in Appendix Figure B3.

Figure 7.

Source: The authors.

5.2 Effects on Portfolio Flows to Latin America, Brazil, and Mexico

This section explains the event study results of Federal Reserve announcements on portfolio flows to Latin America, Brazil, and Mexico. The main result from this analysis is that both easing (unexpected) and tightening (unexpected) announcements have a statistically significant effect on debt flows but not for equity flows to Latin America. Both easing (unexpected) and tightening (unexpected) announcements cause Latin America all currency debt outflows and local currency debt outflows but have no effect on hard currency debt flows. Similarly, the announcements affect debt flows but not equity flows for Brazil and Mexico. Finally, easing (unexpected) and tightening (unexpected) announcements cause local currency debt outflows but have no effect on hard currency debt flows to Brazil.

5.2.1 Portfolio Flows to Latin America

The main result, as seen in Figure 8, is that both easing (unexpected) and tightening (unexpected) announcements have a statistically significant effect on portfolio debt flows to Latin America within a 7-day event window for all currencies. The easing (unexpected) announcements reduce all debt flows to Latin America by 0.32 standard deviations or US$11.01 million less per week in flows the week after than the week before an easing (unexpected) Federal Reserve announcement. The tightening (unexpected) announcements reduce all debt flows to Latin America by 0.38 standard deviations or US$12.54 million less per week in flows the week after than the week before a tightening (unexpected) Federal Reserve announcement.

An analysis of debt flows to Latin America by currency indicates that there is no effect for hard currency debt flows but there is an effect for local currency debt flows and mixed currency debt flows. As seen in Appendix Figure B5, the easing (unexpected) announcements reduce local currency debt flows to Latin America by 0.34 standard deviations or US$11.66 million less per week in flows the week after than the week before an easing (unexpected) Federal Reserve announcement. The tightening (unexpected) announcements reduce local currency debt flows to Latin America by 0.41 standard deviations or US$12.64 less per week in flows the week after than the week before a tightening (unexpected) Federal Reserve announcement. In Appendix Figure B6, easing (unexpected) announcements reduce mixed currency debt flows to Latin America by 0.29 standard deviations or US$100,000 less flows the week after from the week before an easing (unexpected) Federal Reserve announcement.

Figure 8.

Source: The authors.

Tightening (unexpected) announcements do not affect mixed currency debt flows.

As shown in Figure 9, the event study results indicate the Federal Reserve announcements do not have a statistically significant effect on equity flows to Latin America within the 7 days before and 7 days after event windows. This no result for equity flows to Latin America is in contrast to the statistically significant results for easing (unexpected) and tightening (unexpected) results for debt flows to Latin America.

5.2.2 Portfolio Flows to Brazil and Mexico

The same event study methodology is used to analyze the debt flows and equity flows to Brazil and equity flows to Mexico. The debt flows to Brazil are in hard currency and local currency, and the hard currency debt flows to Mexico are too small for empirical analysis.

The equity flows to Brazil and Mexico are not classified by currency.

The event study results indicate that Federal Reserve announcements have an effect on debt flows to Brazil and inconclusive effects on debt flows to Mexico. As seen in Figures 10 and 11, the easing (unexpected) announcements have an effect on local currency debt flows to Brazil but do not have an effect on hard currency debt flows. The easing (unexpected) announcements reduce local currency debt flows to Brazil by 0.30 standard deviations or US$5.44 million less per week in flows the week after than the week before an easing (unexpected) Federal Reserve announcement. As seen in Figure 10, the tightening (unexpected), easing (expected), and tightening (expected) announcements do not have a statistically significant effect on hard currency debt flows to Brazil. Data limitations prevent an estimation of the effect of Federal Reserve announcements on debt flows to Mexico.

The Federal Reserve announcements do not affect equity flows to Brazil or Mexico. As seen in Figure 12, the Federal Reserve announcements do not affect equity flows to Brazil within the 7 days before and after a Federal Reserve announcement. Similarly, as seen in Figure 13, the equity flows to Mexico do not respond to Federal Reserve announcements.

Figure 9.

Source: The authors.

Figure 10.

Source: The authors.

Figure 11.

Source: The authors.

Figure 12.

Source: The authors.

Figure 13.

Source: The authors.

As mentioned in the “Introduction” section, Brazil and Mexico used different sets of policy response since the GFC. While authorities in Brazil have used capital controls and foreign exchange intervention the authorities in Mexico have followed a more market driven approach and have not intervened in financial markets. In these empirical results, we see that the easing (unexpected) Federal Reserve announcements cause local currency debt outflows to Brazil and had no effect on hard currency debt flows to Brazil. We also observe that the Federal Reserve announcements have no effect on equity flows to Brazil or Mexico.

6 Robustness

The robustness checks include an announcement classification using intraday monetary surprises as well as controls for volatility, liquidity, global commodity prices, and fundamentals in the regressions. The first robustness check compares the announcement classification using intraday monetary surprises. The second set of checks introduce control variables into the regressions for uncertainty measured by the VIX, liquidity in the US Treasury market, oil and commodity prices measured from Bloomberg, and fundamentals measured by J. P. Morgan EMBI and the MSCI equity index.

6.1 Intraday Monetary Surprise Announcement Classification

In the first robustness check the Federal Reserve announcements are classified using intraday monetary surprises instead of daily changes in interest rate expectations. Recall, this article used daily frequency expectations from a shadow rate term structure model to classify the Federal Reserve announcements as easing (unexpected), tightening (unexpected), easing (expected), and tightening (expected). However, this classification could be biased if other events occur on Federal Reserve announcement days that systematically influence the market expectations. Intraday monetary surprises are calculated by taking the first principal component of the change in future yields for 2-, 5-, 10-, and 30-year Treasury futures in the 15 minutes before and 105 minutes after a Federal Reserve announcement (Curcuru et al., 2015; Rogers et al., 2014, 2015).

The intraday monetary surprises are used to classify announcements where there is a positive interest rate change as a tightening announcement, announcements where there is a negative interest rate change as an easing announcement, and announcements where there is no change as unclassified. As seen in Table 2 the intraday data on monetary surprises classifies 27 announcements as easing, 25 announcements as tightening, and leaves 2 announcements unclassified. Appendix Table B4 shows a complete list and comparison of all 54 Federal Reserve announcements from October 2008 until October 2014 using the shadow rate model and the intraday monetary surprises. The intraday monetary surprise announcement classification matches the daily shadow rate model classification for most of the Federal Reserve announcements during this period. The only other differences between the intraday announcement classification and the shadow rate model announcement classification are that the intraday classification leaves two announcements unclassified and the intraday classification cannot be used to determine if it is expected or unexpected.

Table 2.
Intraday Classification of Federal Reserve Announcements

Easing 27

Tightening 25

Unclassified 2

Total 54

Source: Rogers et al. (2014).

6.2 Control Variables

Another set of robustness checks introduces control variables into regression specification (1) to see whether this changes the statistical significance for the coefficient for the shadow rate announcements. For both the debt flows and the equity flows this includes uncertainty as measured by the VIX, market liquidity in the US Treasury market as measured by Hu et al. (2013a), and commodity prices from Bloomberg are included in the regression specifications. For the debt flow regressions, a control for the J. P. Morgan EMBI is included as a proxy for fundamentals and for the equity flow regressions a control for the MSCI as proxy for fundamentals.

6.2.1 VIX

Previous literature has shown that global risk aversion, measured by the VIX, may explain portfolio flows to emerging markets (Ahmed & Zlate, 2015; Ananchotikul & Zhang, 2014; Koepke, 2014; Nier, Sedik, & Mondino, 2014; Rey, 2013). When global risk aversion is high, for example, global investors are more likely to invest into “safe” assets such as US treasuries and less likely to invest in emerging markets. The VIX measures the implied volatility of the S&P 500 index options calculated by the CBOE and measures the stock market’s expectations of stock market volatility over the next 30-day period. The results from this robustness check, shown in Appendix Table B5 and Table B6, indicate that adding the VIX to the specification does not change the results for portfolio flows to Latin America or to Brazil and Mexico.

6.2.2 Liquidity

A measure of market liquidity developed by Hu et al. (2013a) is included as a control variable to explain global financial flows to Latin America. This market liquidity measure, available at daily frequency, captures the spread between seasoned and recently issued comparable Treasury securities and weekly average trading volume in the secondary market for TIPS as reported by the Federal Reserve Bank of New York. When this market is illiquid there is a shortage of arbitrage capital and tightening of liquidity in the overall market. This liquidity measure has been shown to capture major financial events such as the 1987 stock market crash, the near collapse of LTCM, 9/11, the GM credit crisis, and the fall of Bear Sterns and Lehman Brothers. When this measure is low, this suggests that there is sufficient arbitrage capital. Including a market liquidity variable tests to see that the Federal Reserve announcements are driving these portfolio flows to Latin America despite changes in overall market liquidity. The results from this robustness check, in Appendix Tables B8 and B13, indicate this measure does not change the results for portfolio flows to Latin America, Brazil, or Mexico.

6.2.3 Commodity Prices

The oil price measured by WTI is included as a control variable that may explain portfolio flows to Latin America. The results from this robustness check, shown in Appendix Tables B9 and B14, indicate that oil price does not change the results for portfolio flows to Latin America, Brazil or Mexico. The Bloomberg Commodity Index, is added as a control variable that may explain portfolio flows to Latin America. The result from this robustness check, shown in Appendix Tables B10 and B15 indicate that adding commodity prices to the specification does not change the results for portfolio flows to Latin America, Brazil, or Mexico.

6.2.4 Fundamentals

The debt market fundamentals, measured by the J. P. Morgan EMBI Global, is used as a control variable to explain portfolio debt flows to Latin America. The EMBI Global covers 32 countries and is the most comprehensive emerging markets debt benchmark covering US dollar denominated Brady bonds, Eurobonds, traded loans, and local market debt instruments issued by sovereign and quasi-sovereign entities. Instead of selecting countries according to a sovereign-credit rating level, this index defines emerging markets with a combination of the World Bank defined per capita income brackets and each country’s debt-restructuring history. The EMBI Global only considers emerging markets issues denominated in US dollars with a minimum current face outstanding of US$500 million and at least 2.5 years to maturity but relaxes some of the EMBI+ limits on secondary market trading.¹⁶ The exact ticker symbol for EMBI Global Latin America is JPMGLAT, for EMBI Global Brazil is JPMGBRA, and for EMBI Global Mexico is JPMGMEX.

The equity market fundamentals, proxied by the MSCI EM Latin America Index with Bloomberg ticker symbol MXLA, is used as a control variable to explain portfolio equity flows to Latin America. This variable is a free-float weighted index that captures large and mid-cap representation across five emerging market countries in Latin America: Brazil, Chile, Colombia, Mexico, and Peru. The index covers 85 percent of the free float adjusted market capitalization in each country. The MSCI Brazil Index, with Bloomberg ticker symbol MXBR Index, measures the performance of 61 large and mid-cap Brazilian companies and covers approximately 85 percent of the equity market in Brazil. The MSCI Mexico Index, with Bloomberg ticker symbol MXMX Index, tracks 27 large and mid-cap companies in Mexico and covers 85 percent of the equity market in Mexico.

The results from including the EMBI and the MSCI in the regression (1) as a robustness check, shown in Appendix Tables B11 and B16, indicate these variables do not change the results. The only exception is that both the easing (unexpected) and tightening (unexpected) announcements become statistically significant for the specification that includes the EMBI a control variable whereas only the easing (unexpected) announcements were statistically significant without the EMBI.

7 Conclusion

This article examined the effects of Federal Reserve announcements on portfolio flows to Latin America since the GFC. First, the article classified all the Federal Reserve announcements from October 8, 2008 until October 29, 2014 as either easing (unexpected), tightening (unexpected), easing (expected), or tightening (expected). Second, this Federal Reserve announcement classification using the shadow rate model was used for an event study on debt flows and equity flows to Latin America as well as to Brazil and Mexico. The results showed that both easing (unexpected) and tightening (unexpected) announcements cause debt outflows from Latin America but only had an effect on local currency and not hard currency debt flows. These announcements did not have a statistically significant effect on equity flows. The easing (unexpected) Federal Reserve announcements had an effect on local currency debt flows to Brazil but had no effect on hard currency debt flows to Brazil or to equity flows to Brazil. There was no effect of Federal Reserve announcements on equity flows to Mexico and data limitations for debt flows to Mexico made it impossible to estimate the effect of announcements on these flows using EPFR Global data.¹⁷ These results were robust to controlling for uncertainty measured by the VIX, liquidity measured from lack of arbitrage in the US Treasury debt market, oil and commodity prices, and fundamentals measured by the J. P. Morgan EMBI bond index and the MSCI equity index.

These results have tentative policy implications for both the Federal Reserve and for the authorities in Latin America. The article showed that Federal Reserve unexpected announcements, regardless of whether they were classified as easing or tightening, led to portfolio outflows from Latin America. This suggests that Federal Reserve communication that reduces the surprise component to markets may help reduce the volatility associated with portfolio outflows from Latin America. Furthermore, because the volatility showed up in the debt flows but not the equity flows this suggests Latin American may want to support local bond market development or to consider policies that support markets when the Federal Reserve makes announcements. The differences in policies towards foreign exchange intervention and capital controls in Brazil and in Mexico may play a role. Future research could explore the behavior of these foreign mutual fund and ETF investors as the Federal Reserve moves away from the zero lower bound and the implications this may have for the financial stability policies of the Bank of Mexico and Bank of Brazil.

Footnotes

Acknowledgements

I thank Jens H.E. Christensen for kindly providing me with the daily estimations from the AFNS and the B-AFNS model, John H. Rogers for his monetary policy surprise series, and Simon Ringrose for EPFR Global data. I also thank Michael M. Hutchison, Jens H.E. Christensen, Robin Koepke, and seminar participants at Banco de Mexico for comments and suggestions. I also thank participants at the 2017 IBEFA meetings in San Diego, CA and my discussant Victoria Nuguer. The views in this paper are solely the responsibility of the author and should not be interpreted as reflecting the views of the Federal Reserve Bank of San Francisco or the Federal Reserve System.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Notes

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Monetary Surprises and Global Financial Flows: A Case Study of Latin America

Abstract

Keywords

1 Introduction

2 Related Literature

4.1 Classifying Federal Reserve Announcements

4.2 Effects on Portfolio Flows to Latin America, Brazil, and Mexico

5.1 Classifying Federal Reserve Announcements

Table 1. Daily Classification of Federal Reserve Announcements Shadow Rate Model Easing (unexpected) 14 Tightening (unexpected) 9 Easing (expected) 16 Tightening (expected) 15 Unclassified 0 Total 54

5.2.1 Portfolio Flows to Latin America

6.1 Intraday Monetary Surprise Announcement Classification

Table 2. Intraday Classification of Federal Reserve Announcements Easing 27 Tightening 25 Unclassified 2 Total 54

6.2.1 VIX

6.2.2 Liquidity

6.2.3 Commodity Prices

6.2.4 Fundamentals

7 Conclusion

Footnotes

Acknowledgements

Declaration of Conflicting Interests

Funding

Notes

References

Table 1.
Daily Classification of Federal Reserve Announcements

Shadow Rate Model

Easing (unexpected) 14

Tightening (unexpected) 9

Easing (expected) 16

Tightening (expected) 15

Unclassified 0

Total 54

Table 2.
Intraday Classification of Federal Reserve Announcements

Easing 27

Tightening 25

Unclassified 2

Total 54