Quantitative Easing and its Effects on Emerging Market Economy Currencies

A THESIS

Presented to

The Faculty of the Department of Economics and Business

The Colorado College

In Partial Fulfillment of the Requirements for the Degree

Bachelor of Arts

By

Alan Hurbi

May 2016

Quantitative Easing and its Effects on Emerging Market Economy Currencies

Alan Hurbi

May 2016

Economics

Abstract

This paper examines the effects of the Federal Reserve’s (Fed) quantitative easing (QE) program on emerging market currencies. Through the purchase of long-term securities, the Federal Reserve drove down long-term interest rates in the United States. As a result, capital flows into emerging market economies (EMEs) increased drastically. Investors in search of higher yields pulled money out of the U.S and invested heavily in emerging markets economies. The model in this paper incorporates the change in Fed balance sheet assets and EME country specific macroeconomic variables to determine if EME currencies appreciated because of quantitative easing. The time period covered is 2008 – 2014 and the countries of focus are Brazil, Mexico, Korea, South Africa, Turkey, and Indonesia. Data was collected from the International Monetary Fund (IMF), Organization for Economic Co-operation and Development (OECD), the Federal Reserve, and various central banks. This study could not determine with statistical significance that quantitative easing caused EME currencies to appreciate, but it did find certain macroeconomic variables played a statistically significant role in determining EME currency appreciation. KEYWORDS: (Quantitative easing, Federal Reserve, Emerging market economies, Currencies) JEL CODES: (E44, E43, G21)

ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS

AlanHurbi_______________________________Signature

TABLEOFCONTENTSABSTRACT……………………………………………………………………………………………………

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1INTRODUCTION………………………………………………………………………………………. 1 2LITERATUREREVIEW……………………………………………………………………………... 52.1CapitalFlows....................................................................................................................... 52.2MacroeconomicVariables..............................................................................................

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3DATA……………………………………………………………………………………………………….4METHODOLOGY……………………………………………………………………………………….5RESULTS………………………………………………………………………………………………….6CONCLUSION……………………………………………………………………………………………7WORKSCITED…………………………………………………………………………………………..

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1

Introduction

The United States Federal Reserve conducts monetary policy to promote national

economic goals and to influence the cost and availability of money. Prior to the financial

crisis of 2008, the Federal Reserve mainly used the federal funds rate to implement

monetary policy. This is the interest rate at which very creditworthy depository

institutions can borrow and lend overnight funds to each other and is considered a

benchmark for all other interest rates in the U.S. economy. By setting new target federal

funds rates, the Federal Reserve signals its expectations for future economic conditions.

The Federal Open Market Committee (FOMC) consists of twelve members from

different Federal Reserve banks and meets eight scheduled times per year to determine

monetary policy and assess risks to stable economic growth and long-run price stability.

During these meetings, the FOMC sets a target federal funds rate based on current

economic conditions then purchases or sells short-term treasury securities to reach the

target rate. Selling short-term securities raises interest rates and decreases the monetary

base. When interest rates are high, consumers have less disposable income and must cut

back on spending. This means fewer loans for banks and less income for businesses,

contracting the economy. Contrarily, buying short-term securities lowers interest rates.

In this scenario, borrowing money is cheap and consumers are more likely to make large

purchases such as houses or cars. In theory, the economy should expand and credit

should be easily accessible.

When financial markets collapsed in 2008, the Fed pursued expansionary

monetary policy to loosen credit conditions. The federal funds rate was cut from 5.25% in

August 2007 to .25% in December 2008 to increase the money supply in the U.S.

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economy. The fed funds rate was at its zero lower bound and the Federal Reserve had

exhausted its power to pursue conventional monetary policy. If the fed funds rate were

to become negative, lenders would rather hang onto cash then give to borrowers.

When facing turbulent financial conditions and an interest rate zero lower bound,

central banks can utilize unconventional monetary policy to stabilize the economy.

Quantitative easing (QE) is a monetary policy tool where central banks purchase long-

term securities with electronically created cash. This method allows central banks to

vastly increase the money supply without having to increase the monetary base. Under

QE, when a security is purchased by the central bank, the asset side of their balance sheet

increases and the seller of the security is expected to take the new money and make more

loans, increasing investment in the economy. The end goal is to lower long-term interest

rates by decreasing bond yields and increasing access to credit.

The United States held three rounds of quantitative easing between November

2008 and September 2014 called QE1, QE2, and QE3. The intended goal of U.S.

quantitative easing was to lower mortgage rates and take pressure off the average

homeowner. In QE 1, the Fed purchased $1.25 trillion in mortgage-backed securities and

$300 billion in 10 and 30-year treasury bonds. Thirty year mortgage rates responded by

falling under 5% (Ihrig and Meade, 2015). During QE2, the Fed purchased $600 billion

of long-term treasury bonds and mortgage rates declined to nearly 4%. In QE3, the Fed

tapered its $85 billion per month long term bond purchase program to $30 billion per

month, then ended the program. In all, the Fed spent $3.5 trillion on long-term bonds and

mortgage-backed securities.

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While the U.S. experienced stabilized growth and promising economic conditions,

investors pulled billions of dollars out of the bond market in search of higher yield

investments. The money that left the United States was invested in emerging market

economies (EMEs). The transfer of wealth from the U.S. to EMEs occurred through

three main channels. Chen et al. (2011) and Lavinge et al. (2014) provide a summary of

the three transmission channels, as noted below:

1.) The portfolio balance channel: Quantitative easing requires massive purchases

of long-term assets and mortgage-backed securities. The compressing of the term

premium lowers the yield of these assets and increases the demand for substitute assets.

Private investors turned to EMEs because, since the 2000’s, they experienced strong

growth, relatively stable socio-political environments, and higher yields.

2.) The signaling channel: By undertaking quantitative easing, the Federal

Reserve is committing to keeping interest rates low for a period longer than expected,

lowering the risk neutral component of bond yields. This creates larger interest rate

differentials between the U.S. and EME’s, causing capital flows into EMEs.

3.) The exchange rate channel: The appreciation of an EME currency against the

U.S dollar causes each dollar an EME earns to be worth more. Similarly, fluctuations in

exchange rates can create sizable gains or losses for emerging market economies firms

with large foreign currency assets or liabilities. A depreciation of the U.S. dollar occurs

when capital flows out of the U.S. and into emerging market economies. The U.S

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experiences a loss of purchasing power resulting in a decreased demand for foreign

produced goods and services. This effect is amplified in the smaller open economies that

exist in many EME countries.

This paper seeks to determine if the Federal Reserve’s quantitative easing

program caused emerging market economy currencies to appreciate. While U.S. demand

for foreign goods decreased due to loss of purchasing power, the massive capital inflows

to EME’s may have offset this detrimental effect. Between 2000 and 2013 annual gross

capital inflows to developing countries grew by an order of magnitude to $1.8 trillion

annualized (Lim et al, 2014). Furthermore, from 2009 to 2013 cumulative gross financial

inflows to the developing world grew from $192 billion to $598 billion (Lim et al, 2014).

Most academics analyzing this topic utilize an event-study approach on the Federal

Reserve’s QE announcements and its effects on emerging market economy currencies.

Event-study analyses provide insight into how EMEs respond to the Fed’s monetary

policy, but they fail to assess the cumulative impact of quantitative easing on emerging

market economy currencies. This study will utilize data gathered from the Federal

Reserve Economic Database (FRED) to determine how EME exchange rates are affected

by long-term treasury note yields and various other country specific macro-economic

variables.

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Literature Review

Over the last fifteen years, the Bank of Japan, the Bank of England, the European

Central Bank, and the Federal Reserve implemented quantitative easing to combat

financial crises (Fawley and Neely, 2013). While developed countries used QE as a

financial antidote, the global ramifications of unconventional monetary policy are less

understood. It is widely agreed that quantitative easing helped developed countries stem

the effects of the global financial crisis, but unconventional monetary policy’s interaction

with emerging market economies is more dynamic, volatile, and difficult to predict.

Capital Flows

The Federal Reserve’s quantitative easing program rerouted global financial flows

by injecting trillions of dollars in the U.S economy. Broner et al. (2014) analyzes the

behavior of international capital flows during the 2007 – 2008 financial crisis and finds in

comparison to net capital flow, gross capital flows are larger and more volatile. The IMF

(2011), through a panel data study, found loose monetary policy in developed countries

to be a crucial determinant of capital flows to EMEs. Regardless of the source, major

capital inflows into an economy result in an appreciation of the real exchange rate,

increased stock valuation, stronger economic growth, and accumulation of international

reserves (Reinhart et al, 1993). When capital outflows occur, the opposite effect occurs.

Investors interpret the increased capital flows to emerging market economies as a signal

of future stability, higher than normal yields, and declining risk. Unfortunately, the cause

of the capital flows can be ambiguous. Ahmed and Zlate (2013) found that new capital

flows to EMEs were determined by growth differentials, policy differentials, and global

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risk aversion. If higher than normal yields exist due to unfavorable conditions,

speculators can create financial bubbles to earn quick profits. Fratzscher (2011) noted

that emerging market economies experienced rapid growth followed by subsequent

economic downturns when speculative investors unexpectedly liquidated their emerging

market assets.

The declining long-term bond yields in the United States aided the flow of capital

to EMEs. Speculators continued investing without hesitation, and little was done by the

United States or EMEs to protect developing economies. Although unknown to the

public, QE1, QE2, and QE3 all had finite end dates. When the Federal Reserve

announced the final stage of QE3, EMEs experienced massive capital flight. In an event

study of five emerging market economies, Taoufik Bouraoui (2015) predicted that the

announcement of the tapering of QE3 generated “an increase in interest rates and bond

yields, plummeting stock markets, currency depreciation, and deterioration in the balance

of payments.”

Macroeconomic Variables

While EME’s leaders voiced concerns about the volatile influx of capital at the

beginning of quantitative easing, the tapering and eventual ending of the QE program had

larger economic implications. Academics have come to different conclusions about

whether country specific macroeconomic variables played a role in shielding EMEs from

capital flight. In a regression analysis, Eichengreen and Gupta (2014) examined bilateral

exchange rates between April and August 2013 and macroeconomic fundamentals such

as current account deficit, real GDP growth, inflation, and foreign reserves. They found

that macroeconomic fundamentals played a statistically insignificant role in determining

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exchange rate depreciation. Instead, the size and connectedness of the emerging market

determined exchange rate depreciation because investors were more easily able to

rebalance their portfolios.

In contrast, Aizenman et al (2014) used a fixed effects model to examine capital

flows to 26 EMEs and found that robust fundamentals such as current accounts surpluses,

high international reserves, and low external debt increased countries vulnerability to

volatile capital flows (Aizenman and Hutchinson, 2014). In a similar study, Mishra et al

(2014) studied the effect of quantitative easing on 21 EME’s exchange rates and found

that macroeconomic variables played a significant role in determining EME’s exposure to

volatile capital flows. In particular, countries with higher current account balances, lower

inflation, higher reserves, and better fiscal position experienced smaller currency

depreciation when quantitative easing was tapered. While the exact cause of volatile

capital flows and currency fluctuations in emerging markets is difficult to pinpoint,

academics agree that quantitative easing had significant effect on emerging market

economy currencies.

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Data

This study uses data from six emerging market economies including Brazil,

Korea, Mexico, South Africa, Turkey, and Indonesia. As noted in other studies, these six

countries became heavily dependent on foreign investment to finance growth. Because

of this, they experienced the largest currency fluctuations during quantitative easing.

The dependent variable in this study is the percent change in foreign exchange

rate (local currency/US$). The data are quarterly and cover the period from October 1,

2008 to December 31, 2014. These are the beginning and end dates for quantitative

easing. The independent variables considered are consumer price index, GDP growth,

reserves/GDP, current account/GDP, local interest rates, and the asset side of the Federal

Reserve balance sheet. Each variable has 144 observations creating a balanced panel data

set. Although only six emerging market economies are used in the study, many more

were considered but adequate data could not be found for all independent variables

leading to their omission. The six countries used produced reliable and trustworthy data

for the time period considered. In addition, all the countries use a floating exchange rate.

Free market forces in the foreign exchange market allow for a more unbiased analysis.

All of the data except current account/GDP was gathered from the Federal

Reserve FRED database. This database collects its own data and pulls data from the

IMF, World Bank, OECD, and various central banks. All variables are differenced to

represent percent changed and are lagged by one quarter. Differencing allows the data to

be compared across different units of measurement. The data was lagged by one quarter

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because the Federal Reserve’s actions take time to materialize in emerging market

economies.

Methodology

My model focuses on how emerging market currencies were affected by the Fed’s

decision to increase the asset side of their balance sheet through quantitative easing.

It was assumed that extraneous factors changed the socio-political environments of the

emerging market economies during the time period studied; therefore, to analyze the

balanced data set, a GLS random effects regression was used. To ensure this assumption

was correct, I ran both a fixed effects and random effects model and compared them. The

only difference was that in the fixed effect model the β-value on the CPI and EME

interest rate variable were negative instead of positive. Since neither of these variables

were statistically significant, I ran a Hausman test to determine which regression was a

better fit:

H0= random effects model appropriate.

H1= fixed effects model appropriate.

Since the Prob>chi2 = 0.9472, the null hypothesis was not rejected and a random

effects model was used. Also, the CHI(2)= .0001 further confirming a random effects

model.

I ran a GLS random effects model in STATA to analyze the effects of my

independent variables on my dependent variable. I looked at the results of the Wald test

to see if the null hypothesis: β1, β2, β3…..βk = 0 was to be rejected. My hypothesis was:

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H0= Increasing fed balance sheet leads to an appreciation of EME currency and negative β H1= Increasing fed balance sheet leads to a depreciation of EME currency and a positive β

Since the LR chi2 =45.46 which was greater than the critical value, I reject my

null hypothesis.

(1) LOCAL_CURRENCY/US$=β0+ β1(CPI) + β2(GDPGROWTH)+β3(RESERVE_GDP) + β4(CRTACT_GDP)+ β5(EME_INTEREST)+β6(FED_ASSETS)

The dependent variable is a measure of the local currency to one U.S. dollar. The

goal of this model is to analyze what variables either insulated or exposed emerging

market economy currencies to exchange rate fluctuations.

The variable CPI measures raw headline inflation. It is calculated by measuring

the change in cost of a fixed basket of goods over a predetermined time period. Since

real GDP growth rate corrects for inflation, CPI is used to account for any currency that

experienced inflation or deflation. Shangil et al (2015), Rai and Suchanek (2014), and

Dalhaus and Vasishtha (2014) used this variable in their analyses. This study anticipates

the β-value to be positive. As an EME currency experiences inflation, the less valuable

the EME currency will be on the foreign exchange market. If deflation is present, the

EME currency will become more valuable on the foreign exchange market.

The variable GDPGROWTH is stated in U.S. dollars and measures the economic

productivity of an emerging market economy. A strong GDP growth rate is correlated

with small exchange rate depreciation. Lim et al (2014), Rai and Suchanek (2014), and

Shagil et al (2015) used this explanatory variable. This study anticipates the

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GDPGROWTH variable to have a negative β-value. As an economy grows, its currency

will become stronger, and the EME currency will appreciate against the U.S. dollar.

The variable RESERVES_GDP accounts for the amount of foreign reserves,

minus gold, that a central bank holds as a portion of GDP. This study anticipates that the

RESERVES_GDP variable will have a negative β-value because higher foreign reserves

increase confidence in the monetary system and exchange rate policies of a government.

A central government with abundant reserves can intervene in the foreign exchange

market to stabilize their currency. Aizenman et al (2014) and Mishra et al (2014) used

this variable in their analysis. This study anticipates that the RESERVES_GDP variable

will have a negative β-value.

The variable CURRENTACCT_GDP measures the trade surplus or deficit of a

country as a percentage of GDP. The currency exchange rate has a significant effect on

the trade balance via the current account. An overvalued currency makes exports less

competitive and imports cheaper, widening the current account deficit or lowering the

surplus. An undervalued currency makes imports more expensive and boosts the export

industry, lowering the current account deficit or strengthening the surplus. While a case

can be made for either a positive or negative β-value, this study anticipates the β-value to

be negative because an increase in exports stimulates the EME economy, appreciating the

currency. Aizenman et al (2014), Eichengreen and Gupta (2013), Mishra et al (2014), and

Rai and Suchanek (2014) used this variable in their analysis.

The variable EME_INTEREST measures the difference between the emerging

market overnight bank loan rate and the federal funds rate. This statistic helps explain

how capital moved from the U.S. to emerging markets. As the Fed kept the federal funds

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rate low, investors moved capital to emerging markets in search of higher yields, leading

this study to anticipate a positive β-value. Eichengreen and Gupta (2013) and Dalhaus

and Vasishtha (2014) used this variable in their analysis.

The variable FED_ASSETS measures the asset side of the Federal Reserves

balance sheet during quantitative easing. During the six years of quantitative easing, the

Federal Reserve ballooned its assets by $3.4 trillion. This variable is a proxy for the

lowering of long-term interest rates in the United States. This is an extremely important

variable for the study and I anticipate the β-value to be negative. As the Fed lowered

interest rates, emerging market economies experienced currency appreciation.

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Results

Table I – Descriptive statistics of random GLS regression

FX Coef Std. error

z P>|z| (95% Conf. Interval)

CPI 0.0438313 .4232916 0.10 .918 -.785805 0.8734976

GDPGROWTH -.8614765 .3022950 -2.85 .004 -1.453964 -.2689892

RESERVE_GDP -7.578239 1.257521 -6.03 0.00 -10.04293 -5.113543

CURRENTACCT_ GDP

-.0171317 .3188047 -0.05 .957 -.6419775 6.0771410

EME_INTEREST .00677580 .1409231 0.05 .962 -.2694284 .28298

FED_ASSETS -14.55514 9.997279 -1.46 .145 -34.14944 5.03917

_cons .02058560 .0101012 2.04 .042 .0007876 .0403835

Sigma_u 0 Sigma_e 0.4485697

rho 0 R-sq: within =.2399 Observations = 144

between =.6356 Wald chi2(6) = 45.46 overall =.2491 Prob>chi2 = 0.000

Since the FED_ASSETS variable was statistically insignificant with a P value =

.145, the amount of long-term securities purchased by the Federal Reserve does not

determine how much an emerging market currency appreciated. The FED_ASSETS β =

-14.55514, this implies that the emerging market economy currencies appreciated when

the Federal Reserve purchased long-term securities, but due to low statistical

significance, the amount of EME currency appreciation cannot be determined.

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Figure I – % change EME currency to 1 US dollar

As figure I depicts, all of the emerging market currencies in the sample size

experienced a 1-2% percent appreciation during the first stage of quantitative easing

(QE1). From 2008 – 2014, or QE1 to QE3, the currencies experienced relatively similar

depreciation patterns. There was not a linear trend to currency exchange rates; therefore,

the model could not produce a statistically significant linear fit. After the initial EME

currency appreciation of QE1, EME currencies slowly depreciated. Regardless,

throughout the course of the Federal Reserve quantitative easing program, emerging

market currencies did experience an overall appreciation.

There were two independent variables in the model that were statistically

significant and predicted the amount of appreciation an emerging market economy

currency experienced. The first variable, GDPGROWTH, predicted with 95%

confidence that a 1 unit increase in GDP growth led to .86 currency appreciation. This

relationship agrees with existing literature on the subject. Countries that used the influx

-1

-0.5

0

0.5

1

1.5

210/1/08

3/1/09

8/1/09

1/1/10

6/1/10

11/1/10

4/1/11

9/1/11

2/1/12

7/1/12

12/1/12

5/1/13

10/1/13

3/1/14

8/1/14

Indonesia

Turkey

SouthAfrica

Mexico

Korea

Brazil

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of foreign capital to grow and expand their economies were less affected by capital flight

that occurred when quantitative easing ended.

Table 2 - Cumulative Growth from 2008 - 2014

Country Brazil Korea Mexico South Africa

Turkey Indonesia

FX appreciation

52.12% 6.90% 34.72% 43.77% 68.94% 32.68%

GDP Growth

-2.02% -.59% .75% .79% 1.43% -.39%

The chart above depicts the overall EME currency change and GDP growth from

2008 to 2014. With the exception of Brazil, larger GDP growth was associated with a

larger currency appreciation and smaller GDP growth was associated with smaller

currency appreciation. Turkey experienced a 68.94% currency appreciation and a

cumulative GDP growth rate of 1.43%. Korea, Mexico, and South Africa had the

smallest currency appreciations and experienced negative GDP growth. Without steady

GDP growth, it is expected that these countries would not experience a large currency

appreciation.

The second variable that predicted with statistical significance how much a

country’s currency would appreciate over the course of quantitative easing was

RESERVE_GDP. This variable measures what amount of foreign reserves a country

holds in relation to its overall GDP. The model predicts with 99% confidence that a 1

unit increase in reserves/GDP will appreciate an emerging market currency by 7.57. As

an emerging market government holds more foreign currency in their banks, foreign

dollars will become less desirable, appreciating the emerging market currency.

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Table 3 - Cumulative Growth from 2008 - 2014

Country Brazil Korea Mexico South Africa

Turkey Indonesia

FX appreciation 52.12% 6.90% 34.72% 43.77% 68.94% 32.68%

Reserves/GDP 4.46% 3.88% 4.18% 1.42% 1.19% 1.15%

Table 3 depicts the cumulative appreciation of each country’s currency and the

cumulative change in reserves/GDP. In this scenario, Brazil experienced the largest

influx of foreign reserves and the largest exchange rate appreciation. Although Brazil

had small GDP growth during quantitative easing, it’s stockpiling of foreign reserves led

to a larger currency appreciation. Turkey’s reserves/GDP did not increase much over

quantitative easing, but its currency appreciation can be explained by their large

economic growth.

The model had three statistically insignificant variables, which were CPI,

CURRENCTACCOUNT_GDP, and EME_INTEREST. Current academic research on

this subject is divided on whether macroeconomic variables play a significant role in

determining currency appreciation. This analysis contradicts Lim et al (2014) and

Eichengreen and Gupta (2014). These researchers found CPI, emerging market interest

rates, and current account/GDP to play a statistically significant role in determining

emerging market currency appreciation. This study has similar findings to Mishra et al.

(2014) and Eizenman et al (2014). Their panel data analysis finds that both GDP growth

and reserve/GDP are significant variables. Larger GDP growth and high reserves

increase a country’s currency appreciation. It is widely agreed that the Federal Reserve’s

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large scale purchasing of long-term securities caused massive capital inflow into

emerging markets, this study was unable to statically pinpoint the effect this had on

currency exchange rates.

Conclusion

This study provides interesting results pertaining to the relationship between the

Federal Reserve’s quantitative easing and emerging market economy currencies. Similar

to previous literature, the effect of macroeconomic variables on emerging market

currencies played a significant role in determining currency appreciation. While only

GDP growth and reserves/GDP were statistically significant, this is not unexpected.

Other academic literature found contradicting results on the importance of

macroeconomic variables in determining EME currency changes. This study focused on

six countries over the entire span of quantitative easing. Other literature analyzed

specific QE time periods, typically QE1, QE2, QE3, or QE tapering. Furthermore, other

studies used anywhere from 5 – 80 emerging market economies. This can explain the

incongruence between this study’s results and other literature.

During quantitative easing the Federal Reserve was not always transparent with

time frames and quantity of long-term securities to be purchased. As such, capital flows

changed drastically over the time period considered. During QE 1, emerging market

economies experienced massive inflows, but QE 3 and QE tapering caused large capital

outflows. This explains why the Federal Reserve’s purchasing of long-term securities

could not predict EME currency appreciation. While the Fed continued to purchase

securities throughout the duration of QE, this in no way determined the behavior of

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investors. Instead, the start and end dates of QE played the largest role in explaining

investor behavior and capital flows, which determined exchange rates.

One aspect that this study did not cover was whether emerging market economies

pursued foreign exchange intervention or other monetary policies to combat the capital

inflows caused by quantitative easing. While monetary policies pursued by EMEs would

have been built into the exchange rate data, it was not directly studied or controlled for.

In further studies, this would be an interesting topic to cover. Since financial turmoil in

the developed world is a global phenomenon, analyzing what effect EME monetary

policy had on exchange rates could provide excellent policy advice for future crises.

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