NEW TOOLS FOR A NEW ERA:
AN ANALYSIS OF THE FEDERAL RESERVE’S INFLUENCE ON EMERGING
MARKET INTEREST RATES UNDER VARYING RISK REGIMES
A Thesis
Presented to the faculty of the Department of Economics
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
Economics
by
Jacob Nathanael Tuttle
SUMMER
2013
NEW TOOLS FOR A NEW ERA:
AN ANALYSIS OF THE FEDERAL RESERVE’S INFLUENCE ON EMERGING
MARKET INTEREST RATES UNDER VARYING RISK REGIMES
A Thesis
by
Jacob Nathanael Tuttle
Approved by:
__________________________________, Committee Chair
Kristin A. Van Gaasbeck, Ph.D.
__________________________________, Second Reader
Ta-Chen Wang, Ph.D.
____________________________
Date
ii
Student: Jacob Nathanael Tuttle
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
__________________________, Graduate Coordinator
Kristin Kiesel, Ph.D.
Department of Economics
iii
___________________
Date
Abstract
of
NEW TOOLS FOR A NEW ERA:
AN ANALYSIS OF THE FEDERAL RESERVE’S INFLUENCE ON EMERGING
MARKET INTEREST RATES UNDER VARYING RISK REGIMES
by
Jacob Nathanael Tuttle
Abstract: The 2007-2009 financial crisis rendered the Federal Reserve’s primary policy
tool, the federal funds rate, ineffective once it reached its lower bound. This gave rise to
unconventional monetary policy now known as quantitative easing. This new tool
allowed emerging markets to obtain record low interest rates on debt financing but also
influenced the direction of their local monetary policy. This thesis explores the impact of
Federal Reserve policy on emerging market interest rates using weekly data from January
2000 through April 2012. We utilize basic interest rate parity theory as the primary
transmission mechanism. We proxy Fed policy after late 2008 by utilizing the week-onweek growth of the Fed’s balance sheet. In addition, we analyze the effectiveness of
capital controls in limiting the influence of these external effects on domestic interest rate
and examine the role global risk aversion plays in this process. We find that capital
controls provide some buffers to emerging markets but the effect varies depending on the
period of analysis, as does the effect of risk sentiment. The net effect of the quantitative
iv
easing is downward pressure on local interest rates; those with capital controls in place
partially mitigate this effect.
_______________________, Committee Chair
Kristin A. Van Gaasbeck, Ph.D.
____________________________
Date
v
ACKNOWLEDGEMENTS
There are a number of people who have played pivotal roles in both my life and
professional development whom I must take a brief moment to recognize. I would like to
first thank Dr. Van Gaasbeck and Dr. Wang for their support and encouragement during
this thesis. I very much appreciate their thoughtful comments and willingness to assist me
in developing this thesis during what would normally be their summer break. During my
time at CSUS, both of these professors provided me valuable opportunities and inspired
me to push forward in economics. I must also thank all my previous professors from the
Department of Economics who have also provided invaluable guidance, frustration (the
true sign that one is an economics major) and support. I wish to also thank my family and
friends for supporting me while I worked long weeks and studied long hours; without you
I would have found this journey immeasurably more difficult. I especially thank my
mother and father who showed my siblings and I what one can achieve with hard work
and perseverance. I thank my professional mentor, Mike Rosborough, for providing
meaningful work that inspired the contents of this thesis. Lastly, I thank my beautiful
girlfriend Stephanie for her love, support, input and patience while I locked myself away
to write this thesis.
vi
TABLE OF CONTENTS
Page
Acknowledgements ..................................................................................................... vi
List of Tables .............................................................................................................. ix
List of Figures ............................................................................................................... x
Chapter
1. INTRODUCTION ..................................................................................................1
1.1 A Changing Landscape .............................................................................. 1
1.2 An Overview of the Analysis ......................................................................6
2. LITERATURE REVIEW ..................................................................................... 10
2.1 A Review of the Federal Reserve’s Impact on Emerging Markets ......... 10
2.2 Capital Controls ....................................................................................... 16
2.3 The Importance of Risk Sentiment .......................................................... 21
2.4 Credit Risk ............................................................................................... 25
3. ECONOMIC MODEL .......................................................................................... 28
3.1 A Simple Model of Interest Rate Parity................................................... 28
4. METHODOLOGY AND DATA .......................................................................... 33
4.1 Methodology ............................................................................................ 33
4.2 Data Overview ......................................................................................... 34
4.3 Dependent Variable Description .............................................................. 37
4.4 Independent Variables Descriptions ........................................................ 39
vii
4.5 Preliminary Data Analysis ....................................................................... 49
5. RESULTS ............................................................................................................. 54
5.1 Preparation for Fixed Effects Panel Estimation ....................................... 54
5.2 Pre-Lehman Period Using Seven Emerging Markets .............................. 57
5.3 Pre-Lehman Period Expansion of the Cross Section ............................... 65
5.4 Expansion of the Time Series .................................................................. 72
5.5 The Post-Lehman Period with Thirteen Emerging Markets .................... 80
5.6 Robustness of the Empirical Findings ..................................................... 86
6. CONCLUSIONS................................................................................................... 89
6.1 Summary of Research and Findings ........................................................ 89
6.2 Caveats to the Analysis ............................................................................ 92
6.3 Future Extensions..................................................................................... 93
Appendix A. Descriptive Statistics for Control Variables .......................................... 98
Appendix B. Regression Results for Control Variables ........................................... 100
References ................................................................................................................. 102
viii
LIST OF TABLES
Tables
Page
Table 1 Variable Predictions and Definitions .............................................................. 50
Table 2 Descriptive Statistics for Entity-Constant Variables by Sub-Period ............... 51
Table 3 Descriptive Statistics for Time- and Entity-Varying Variables ...................... 52
Table 4 Latin America Pre-Lehman Sample Replication Fixed Effects Results ......... 58
Table 5 Asia Pre-Lehman Sample Replication Fixed Effects Results ......................... 59
Table 6 Pre-Lehman Expanded Cross-Section Fixed Effects Results ......................... 66
Table 7 Full Period Fixed Effects Results ................................................................... 74
Table 8 Post-Lehman Period Fixed Effects Results for 13 Emerging Markets ........... 82
ix
LIST OF FIGURES
Figures
Page
Figure 1a S&P 500 April-June 2013.............................................................................. 2
Figure 1b S&P 500 Before and After Bernanke's Speech ............................................. 3
Figure 2 JP Morgan EMBI Sovereign Spreads .............................................................. 4
Figure 3 Implied Market Volatility as Measured by the VIX ........................................ 7
Figure 4 Mexico's Three-Month Deposit Rate Estimation .......................................... 39
Figure 5 Federal Funds Rate Versus the Effective Federal Funds Rate ...................... 41
Figure 6 Federal Reserve Policy (January 2000 - June 2013) ..................................... 42
x
1
CHAPTER 1
INTRODUCTION
1.1 A Changing Landscape
In the aftermath of the deepest recession in the United States since the Great
Depression,1 policymakers faced the daunting task of reviving the economy from its
disparaging state. Fiscal policy quickly became constrained given lower growth and
revenues from automatic stabilizers and a Congress that could not come to consensus on
the best course of action, which left monetary policy to do the heavy lifting. All eyes
were on the Federal Reserve (the Fed) on June 19, 2013 as financial markets eagerly
awaited the official word from the Fed: would its asset purchasing program continue?
After nearly four years of so-called “quantitative easing” (also referred to as QE2),
Chairman Bernanke had hinted that the program could soon end in response to a question
received from the Joint Economic Committee on May 22. Figure 1a depicts the volatility
seen in financial markets (modeled by S&P 500) during the April-June 2013 period;
violent moves ensued as uncertainty over the future path of liquidity took the forefront.
As the next Federal Reserve meeting approached, the market began to build hope that
liquidity would remain and market conditions would normalize (Associated Press, 2013).
Figure 1b depicts this build up and the subsequent sell off that followed the
announcement that a tapering of the asset program was indeed on the Fed’s agenda. A
Wall Street Journal article published earlier this year noted that debt and equity security
The Great Recession refers to the U.S. recession from 2007-2009. The “Great Financial Crisis” also
describes the global turmoil during this period beyond the United States.
2
The Fed, as well as the Bank of Japan, European Central Bank and the Bank of England have all utilized
some form of quantitative easing. For a discussion of the various programs, see Fawley and Neely (2013).
1
2
Source: Source: Bloomberg, Standard and Poors
holders would see massive losses upon the removal of this program; just a hint at a mild
tapering caused such an enormous clamor (Arends, 2013). Although the Fed continues to
balance its objectives of stable growth (and low unemployment) and low inflation, in the
aftermath of the crisis is has clearly focused more on the latter given that such asset
expansions put upward pressure on inflation. Thus, an ending of this program means that
not only will some liquidity dry up but also suggests that growth is now self-supporting
and inflation pressures begin building. The United States and the world abroad now face
the difficult task of weaning off the policies that helped sustain hints of growth
throughout the turbulent period.
The impact of Chairman Bernanke’s comments was not limited to the United
States market. Indeed, the resulting market frenzy resulted in large market moves around
the globe, and in particular, emerging markets saw the tight spreads they had enjoyed
3
Source: Bloomberg, Standard and Poors
through much of the second half of 2012 drastically widen (see Figure 2).3 Although the
selloff was massive, the signs clearly indicated that perhaps conditions were a little too
favorable given the deterioration in the world’s “safe” credit (the U.S.) and historically
low international interest rates. For instance, dollar-denominated Mexico government
bonds were priced within 100 basis points of similar U.S. Treasury bonds prior to the
selloff, implying that the two securities had very similar risk associated with them. In
addition, the heavy risk appetite (which developed in response to investors’ desire for
higher yielding securities) prior to this event gave rise to new issuers of international
bonds. Rwanda took advantage of the to the market in April 2013 with its first dollar
denominated bond and was able to tap international debt markets at a yield of 6.875%
The term “spreads” is defined as the nominal yield of an emerging market bond less a similar “risk-free”
asset, typically U.S. Treasury bond or local currency government bond
3
4
Source: Bloomberg, JP Morgan
(quite low for a ‘B’ rated country); their government found an investor base ten times as
large as what it was seeking (Klien, 2013). A potential change in gears by the Fed implies
a halt to the easy access to international investors, slowing of the robust inflows to
emerging markets and higher default risk as debtors find interest rates less
accommodating. Aside from the impact of future funding needs, this means that emerging
markets (and those deemed as “higher risk”) are subject to outflows as investors pull back
their funds and invest in safer assets, putting downward pressure on the local currency
and upward pressure on local rates. This effect is exacerbated if foreign funds that flowed
into the economy only resulted because of high interest and high-risk appetite; a reversal
in appetite means this “hot money” is at risk to be pulled back out.4 Thus, the results of
The term “hot money” refers to inflows resulting from high interest rate differentials between countries,
creating arbitrage opportunities. See McKinnon and Liu (2013) for a recent discussion.
4
5
such outflows can be devastating for emerging markets.
As rates in the United States edged lower from loose monetary policy, emerging
markets looked for ways to protect their economy from the resulting inflows. China,
Taiwan and Brazil are among many emerging markets to arm themselves with capital
controls as a means of ensuring stability in the event of a reversal of those flows (Reilly,
2010). Brazil recently removed its 6% Tax on Financial Operations (IOF)5 for foreign
investors as a means of bringing inflows back to the country and strengthen its weakening
currency. Perhaps this was unwise a potential unwinding of quantitative easing may bring
unwanted outflows to the country. Other countries such as China and India restrict the
inflow of foreign capital via heavy regulations and limit the amount of funds that are able
to enter the market. Out of over 180 countries covered in the International Monetary
Fund’s (IMF’s) Annual Report on Exchange Arrangements and Exchange Restrictions
(AREAER), 147 have controls on capital market securities, 124 have controls on money
market instruments and many countries have other forms of capital controls. Despite their
appeal as an additional policy tool, empirical research has been unable to find that these
measures significantly shield the economy from external shocks; in fact, some work, such
as Edwards (2012) and Romero-Avila (2009), suggests that liberalizing capital controls
can actually be beneficial for emerging markets. The great diversity, intricacy and
complexness of capital controls in and of themselves make disentangling the underlying
relationship with variables such as growth and interest rates quite difficult and thus, the
lack of significance may be attributed to specification issues within the data.
5
A tax implemented on fixed income investments by the government in response to the crisis.
6
Even countries with sufficient capital controls in place are vulnerable to swings in
risk appetite from financial markets; the movement in emerging market spreads following
Bernanke’s comments illustrates this point. A lower level of risk tolerance across
investors implies that those assets with the most credit risk (i.e. default risk) are
vulnerable to a potential selloff. The onset of the Great Recession (2007-2009) brought
about a heavy “risk-off” environment that left bond yields wide and investors in a state of
panic to protect their assets. The mounting debt and stagnant economies during this
period brought monetary policy to the forefront to assist in catalyzing the recovery. As
Figure 3 illustrates, the announcement of quantitative easing in late 2008 helped to relax
investors’ concerns about market conditions and gradually volatility subsided until the
program ended. Each time the program ended, volatility picked up almost instantly
afterwards, consistent with the premise that the Fed’s implementation of these programs
filtered down to investors’ appetite for risk. Interestingly, each successive quantitative
easing program appears to have a weaker and weaker impact on market volatility. Does
this volatility extend to emerging markets? More specifically, does it affect their local
interest rates?
1.2 An Overview of the Analysis
This study analyzes how certificate of deposit (CD) rates respond to changes in
U.S. monetary policy for a collection of 13 emerging markets and builds on the existing
literature in a number of facets. First, we adopt the framework of Edwards (2012) and
expand the sample to include a broader range of emerging markets. Second, we extend
the time horizon to carry through the financial crisis up to month-end of April 2012 in
7
Notes: Quantative easing periods (QE) are defined from the date of announcement to the end of the
purchases. Jackson Hole refers to the period between speeches that signaled to the market that quantiative
easing may resume in the near future.
Source: Bloomberg, Federal Reserve
order to assess a potential change in the behavior of local rates over the period. Third, we
utilize a measure of market volatility in the model to determine if the change in risk
sentiment among investors affects domestic interest rates and if capital controls help to
mitigate that effect. Fourth, we include measure of the Fed’s balance sheet since this
became a key policy tool once interest rates were at near zero levels. Finally, we use of
an alternative measure of credit risk in order to expand the sample and test the robustness
of the standard measure in the literature. We rely on panel regression techniques using
entity-fixed effects and Driscoll and Kraay (1998) standard errors to correct for
heteroskedasticity, serial correlation and cross-sectional dependence issues within the
data.
The results of this study broadly corroborate the findings of Hsing (2003),
8
Edwards (2012), Glick and Hutchison (2011), Miniane and Rogers (2007) and Edwards
and Rigobon (2009), and finds that the results extend to emerging markets beyond those
included in his study. As was the case for the Latin American countries in his analysis,
interest rate shocks from the U.S. only partially transmit to emerging markets. In
addition, we find capital controls are helpful in mitigating the effects of country-specific
risk, global risk contagion and quantitative easing policies but economies with capital
controls, on average, have higher deposit rates. Capital controls do not prove to be
effective means of insulating domestic deposit rates from the effect of expected currency
depreciation in the post-Lehman period and worsen this effect in other periods. The
caveat here, as in all studies utilizing capital control measures, is that there is still not a
precise way to account for capital mobility; one must keep this in mind when interpreting
the results. This study also finds that interest rate parity held, on average, over the longer
periods of the sample (12 and 8 years). In addition, domestic factors appear to play an
important role in domestic deposit rates as observed by the typically positive effect of
inflation and negative effect of GDP. Global risk sentiment has a varying effect on
domestic interest rates depending on the period. Prior to the Lehman Brothers crisis,
those prone to upward pressure by heightened global risk were markets with tighter
controls; post-Lehman this was the opposite case. Perhaps the most important addition to
the literature, however, is the finding that quantitative easing programs put downward
pressure on domestic interest rates in emerging markets. Since the federal funds rate
became an ineffective policy tool for the Fed once it reached its zero bound, accounting
for this new tool is essential for the model. The results of the analysis are stable to a
9
number of robustness checks.
This thesis proceeds in the following manner. Chapter 2 provides a detailed
overview of the existing literature on a broad range of topics with respect to emerging
markets. We discuss the Federal Reserve’s impact on emerging markets and motivate the
importance of considering further tools beyond the federal funds rate. We also explore
important topics relevant to the model used in this thesis including capital controls, global
risk and credit risk (also called country-specific risk). Chapter 3 describes a modified
model of interest rate parity that assists in properly specifying the empirical analysis.
Chapter 4 details the specific data utilized in the study and provides an overview of the
methodology. Chapter 5 summarizes the important findings from four different
subsamples and sub-periods across the timeframe spanning January 2000 through April
2012. In addition, we provide a brief section discussing three robustness checks
conducted for the various models. Chapter 6 concludes the thesis with a summary of the
important findings of the study and a number of directions future research should
consider.
10
CHAPTER 2
LITERATURE REVIEW
2.1 A Review of the Federal Reserve’s Impact on Emerging Markets
The literature examining the effect of both international interest rates and U.S.
monetary policy on emerging markets is quite expansive and show a general consensus
that there is indeed a relationship between them. Conover, Jensen and Johnson (2002)
analyzes the addition of emerging market equities to an investor’s developed market
equity portfolio under different monetary policy regimes. A key finding is that when U.S.
monetary policy is more restrictive, emerging market stocks perform stronger, the reverse
being true as well.6 This highlights the importance of the Federal Reserve to investors
and consequently to the potential for outflows to those markets. A change from a
contractionary to expansionary stance by the Fed may affect the attractiveness of
emerging markets via a reduction in the risk premium (i.e. spreads narrow) and may
result in a selloff of equities. Ince and Ozlale (2006) conducts an event study analysis to
determine if surprise policy moves have an effect on the risk perception of emerging
markets but find little evidence to support the argument, save for weak evidence for
surprise expansionary moves. This contrasts with the findings of Özatay, Özmen and
Şahinbeyoğlu (2009) which examines determinants of emerging market risk premiums
from 1998 to 2006. They utilize a panel error-correction model and find that changes in
the federal funds rate and macroeconomic news events in the U.S. heavily influence risk
The authors define periods of “tight” monetary policy as those that follow an increase in the discount rate
and periods of “easy” monetary policy as just the opposite. The authors note this as an adequate indicator
for monetary policy due to its infrequent changes, ease of interpretation and general success in prior
monetary events.
6
11
premia. However, the general state of the U.S. economy was an important factor in
determining the size and magnitude of these variables (particularly U.S. news).
Hayo, Kutan and Neuenkirch (2012) analyzes a variety of signals from the Fed
including policy rate decisions, speeches, monetary policy reports and testimony on
emerging market equity returns using a generalized autoregressive conditional
heteroskedasticity (GARCH) model; the data span 1998-2009 and thus cover a portion of
the crisis period. They find that surprise changes in the target federal funds rate do in fact
affect emerging market equity returns, suggesting that these sudden moves by the Fed
served as signals as to the direction of the economic environment. For instance, a sudden
cut may be indicative of weaker U.S. economic performance ahead. In addition, they find
that informal policy communication is nearly as important as official announcements.
Interestingly, Fed communication played a larger role during the financial crisis as well.
Note that while equity returns are not necessarily indicative of a heightened risk
premium, the signal from the Fed has implications for how investors view the world
economy. As Kaminsky and Reinhart (2004) posits, emerging markets experience capital
inflows on a pro-cyclical basis, meaning that when conditions are viewed favorably,
inflows result and when conditions are less-favorable, capital flows outward. This
fluctuation has supposed implications for both currency and domestic interest rates. For
instance, a massive capital outflow puts downward pressure on the currency and upward
pressure on domestic interest rates as a result of higher perceived risk. In addition, Hsing
(2003) shows that the federal funds rate has been hugely influential on the certificate of
deposit rate, local Treasury-bill (T-bill) rate and the cost of funds rate in Mexico, further
12
suggesting the importance of the Federal Reserve for the financial economy of emerging
countries.
Given this exposure to global risk appetite, research has explored several
mechanisms that may have acted as either a shield or a catalyst in terms of the
transmission of interest rate shocks, such as the choice of exchange rate regime. Frankel,
Schmukler and Serven (2004) examines how international interest rates affect domestic
rates under different exchange rate choices for both developed and developing nations
over the 30 year period spanning 1970-1999. The topic is motivated via a discussion of
the advantages and disadvantages between fixed and floating exchange rates, noting that
at the heart of the debate is the desire for independent monetary policy. Basic economic
theory suggests that a floating exchange rate allows for such independence and superior
protection from international interest rate shocks; however, the research is not robust to
this outcome. For all but two countries in the sample, the results indicate that regardless
of interest rate regime, interest rate shocks fully transmit in the long run.7 However, in the
short run, countries with a floating rate regime showed a slower transition to the long-run
equilibrium, suggesting that there is some policy independence in the short run. This
result differs with the findings of Shambaugh (2004) which shows that those with fixed
exchange rates give up some degree of monetary independence as compared to those with
floating regimes. This result is consistent with economic theory, which states that open
economies face an impossible trinity between fixed exchange rates, monetary
7
Germany and Japan are the only nations that showed evidence of independence from international interest
rates.
13
independence and free capital flows. Hoffmann (2007) further reinforces the importance
of regime choice in his analysis of 42 developing nations and their ability to withstand
external shocks to world gross domestic product (GDP) and world interest rates. The
results of a panel vector autoregression (VAR) confirm that floating rates were better able
to absorb external shocks (as measured by the volatility in GDP). Di Giovanni and
Shambaugh (2008) expands on the literature by showing that interest rates in major
developed nations negatively affect GDP in foreign nations and that this result varies
under different exchange rate regimes. Those with fixed exchange rates tended to be
more sensitive to these interest rate changes. In general, research suggests that there
appears to be some central bank independence, albeit it may be temporary, for emerging
markets with floating exchange rate regimes and this may allow them to better deal with
external shocks, such as federal funds rate alterations.
As Edwards (2012) notes, emerging markets have been moving from fixed to
floating exchange rate regimes, giving the more recent literature a chance to assess the
effect of global factors on domestic rates without a focus on regime choice. In his piece,
Edwards looks at a sample of seven emerging markets (four from Latin America, three
from Asia) from 2000-2008 and analyzes the effect the federal funds rate had on these
floating rate countries (without distinction between expected and surprise moves). Due to
the general limitation in the periodicity of macroeconomic data, the majority of studies in
this area rely on monthly, quarterly or annual data. Edwards (2012) explores weekly data,
using local three-month certificate of deposit rates as his dependent variable. A key
variable of interest in his study is a proxy for the degree of capital openness (further
14
explored in Section 2.2). His dynamic panel regression models show that capital controls
were not effective in cushioning the effect of changes in the federal funds rate during this
period. In addition, he finds the adjustment process to the new equilibrium was much
slower in Asia than in Latin America. This finding contradicts the results of Edwards
(2010) in which Asian countries with high capital mobility had a swifter transition to
equilibrium than other countries. The difference is attributable to the fact that Edwards
(2010) used a simpler methodology in measuring capital mobility and focuses on interest
rate differentials as opposed to the level deposit rate of the emerging market. Edwards
(2012) serves as the framework for the analysis contained in this thesis in order to expand
on and investigate the potential effects that capital controls have on floating rate
emerging markets, and in particular, how they assist in cushioning external shocks from
the Fed. This thesis uses similar weekly data and panel estimation, but examines a
broader set of countries over a longer sample period.
An important shortcoming of the analysis in Edwards (2012), however, is his
focus on the period prior to the 2007-2009 financial crisis. Given that the effects of this
crisis were felt around the world and that it coerced the Fed (and central banks from
many nations) to utilize new, unconventional policy tools, it is useful to study the
potential changes in the behavior in emerging markets during and after the financial
crisis. For instance, the Federal Reserve has maintained its policy rate to nearly zero for
over four years; from a statistical standpoint, this means there is little variation of the
instrument and makes it more difficult to find its direct impact on emerging markets.
However, the Fed adopted new tools at the onset of the Great Recession, such as
15
operation twist in 2011 and various episodes of quantitative easing.8 In fact, Raj (2013)
notes that between December 2007 and March 2009, the Fed introduced 16 different
initiatives in order to reinvigorate the economy. He generally finds that these new tools
helped in narrowing credit spreads, in particularly on securities with shorter maturities.
Baumeister and Benati (2012) found that the quantitative easing measures of both
England and the U.S. had significantly positive effects for both inflation and growth,
helping to avoid a Great Depression-like scenario. Morgan (2010) classifies the
unconventional tools adopted by central banks into three categories: commitment effect
(keeping interest rates low for a given amount of time), quantitative easing and credit
easing.9 He finds no clear impact of quantitative easing on bond yields but one must keep
in mind that his piece was published before QE2 was announced in November 2010
while quantitative easing was still in its infancy. The literature is still notably limited with
regard to this new topic and in particular, its relation to emerging markets. While Morgan
(2010) provides an overview of how such tools could be useful for application in
emerging markets as a means to free up credit blockages, he does not detail the potential
externalities that current quantitative easing policies offer emerging markets. Fratzscher,
Lo Duca, and Straub (2012) provides one of few studies to analyze the impact of QE
policies on emerging markets. During QE1, the authors find that investors rebalanced
Operation Twist describes the Fed’s attempt to shift or “twist” the yield curve in order to push long term
interest rates down; they did this by purchasing long-term Treasury bonds and selling short-term treasury
securities.
9
Note that the definition of credit easing in Morgan (2010) (also termed qualitative easing in his analysis)
is more in line with the general term “quantitative easing” often used by the Fed. Credit easing refers to the
outright purchase of government bonds by the central bank and quantitative easing refers to current account
balance targeting.
8
16
their portfolios and sold off exposure in emerging markets and replaced it with U.S.
securities; just the opposite was the case for QE2. These results are indicative of the panic
during the initial QE phase when investors looked for safe investments, while the reversal
during QE2 reflects the improving attitude towards these credits and a calming of global
risk aversion. In general, the literature lacks clarity and robustness for the effect these
policies have on emerging markets. One of the goals of this thesis is to provide a glimpse
into the post-Lehman era and determine if indeed these policies have affected the
domestic interest rates of developing economies.
2.2 Capital Controls
An important and popular topic in the literature analyzes whether the use of
capital controls has been helpful for emerging markets in absorbing external shocks.
Ostry, Ghosh, Chamon, and Qureshi (2011) provides an excellent overview of the
potential motivation for implementing capital controls. Restrictions on capital mobility
are intended to assist in limiting macroeconomic volatility and/or prevent financial
crises.10 However, these controls are not costless for the domestic economy; it can make
financing more difficult for firms. As the authors note, the literature finds widely varied
results when examining the impact of capital controls on inflows. Miniane and Rogers
(2007) studies a collection of 26 countries, analyzing the effect of U.S. monetary policy
shocks on both interest rates and exchange rates from 1975-1998 (though the authors note
the results are robust through 2004 if euro area countries are excluded from the sample).
10
Johnston and Tamirisa (1998) provide a number of stylized facts that reinforce these proposed
motivations. Their analysis suggests balance of payments, prudential, macroeconomic concerns, market
evolution and other factors lead the decision to implement capital controls.
17
The results from both panel and VAR techniques suggest that capital controls were not
effective as external shock absorbers. In the short run, stricter capital controls resulted in
a smaller deprecation of the currency, but this result only holds if the exchange rate
regime and degree of dollarization are not controlled for in the regression. Edwards and
Rigobon (2009) finds evidence that tighter capital controls helped to bolster the Chilean
economy by depreciating the currency in the 1990s and rendered them less sensitive to
global shocks. Given that this is a country-specific study, this may not be robust across
emerging markets. Glick and Hutchison (2011) studies how well capital controls
bolstered economies during currency crisis from 1975-2004 using a probit model with
random effects. The study yielded two very interesting findings. First, at no point in the
sample were capital controls effective in protecting a country from currency crises.
Second, the authors suggest that de jure measures of capital controls should account for a
depreciation effect in that investors will find loopholes to avoid the constraint; the longer
a policy in place, the longer investors have to find these loopholes. To account for this
effect, the authors test a traditional de jure measure, that of Chinn and Ito (2006), and an
augmented version that accounts for the amount of time since the last policy change (the
“duration-adjusted measure”). Even utilizing this other measure did not change the
results. However, they find this measure to be a stronger predictor of the onset of a
currency crisis; those with looser controls and freer currencies were less prone to such
events. Romero-Avila (2009) examines the issue of capital controls from a slightly
different perspective. In this study of the EU-15, he analyzes the effect of liberalizing
capital controls (and interest rate restrictions) from 1960-2001 via panel regression with
18
country-specific fixed effects. His results suggest that indeed this liberalization
contributed positively to growth, potentially through an efficiency channel with resources
now available to flow to their best uses. Ostry, Ghosh, Chamon, and Qureshi (2012)
offers an important finding from the pre-crisis era that capital controls appear to help
reduce the amount of foreign currency debt on bank balance sheets. This key finding
suggests that capital controls may bolster the financial economy from capital flight
episodes with a smaller presence of foreign capital in the banking system. Pasricha
(2012) examines recent trends for capital flow restrictions in emerging markets and finds
that these countries gradually lifted restrictions prior to the financial crisis but began to
tighten again in the recent term. In addition, she notes that these countries had other
measures of controlling inflows at their disposal, but resorted to capital restrictions,
perhaps out of convenience.11 The concluding suggestion made by Ostry et al. (2011)
suggests that policy makers should make an accurate assessment of the costs and benefits
of capital controls and explore the other mechanisms at their disposal.
A few potential factors may be causing this lack of robustness with regard to the
effectiveness of capital controls in emerging markets. In particular, they are difficult to
quantitatively measure and compare across countries, they may be being misspecified in
economic analysis and controls are sometimes applied under other fiscal or monetary
policies, which makes it further difficult to isolate the effects of the specific control
(Ostry et al., 2011). In other words, researchers may not be measuring what they want to
The author notes the IMF’s criterion to determine whether capital controls are a nation’s last resort to
foreign inflows. Three conditions must be jointly satisfied to suggest the need for capital restrictions:
monetary policy and fiscal are unable to ease an overheating economy, the exchange rate is adequately
valued (i.e. not undervalued) and international reserves are greater than prudential levels.
11
19
measure with existing capital control indices. For example, as Glick and Hutchison
(2011) suggests, researchers commonly utilize a de jure measure of capital controls but
this does not take into account the intensity of those controls, only their existence. Few
studies implore de facto measures, as these data are often very difficult to obtain,
especially on a higher frequency. A number of different methods have been explored in
an effort to find the optimal measure of a nation’s capital mobility. Many studies use data
from the IMF’s AREAER including Edwards (2012), Chinn and Ito (2006), Miniane
(2004), Quinn (2003) and Johnston and Tamirisa (1998). In 1996, The IMF greatly
expanded their annual report to include greater levels of granularity for capital controls
by creating thirteen categories of capital controls as compared to the previous single
classification. Miniane (2004) uses the AREAER to extend this index back to 1983 in
order to obtain the benefits of the disaggregated data. Quinn (2003) also uses information
from the AREAER to create a simple index from 0 to 14 that measures the degree of
controls in an economy. An often-cited index in the literature is the Chinn-Ito index,
which was developed and utilized in 2006 as a response to the difficulty in measuring the
extent of capital controls around the world.12 This index also uses information from the
AREAER to generate a measure of capital openness and does so for a sample of 181
countries from 1970 through 2011; the authors continually update the data. The index
takes on values ranging from -2.66 to 2.66 with a mean at zero, a higher number
indicating great capital mobility. Although the ideal measure of capital controls would
proxy for the level of intensity, the authors suggest that the level of extensity serves as a
12
For details on the construction of this index, see Chinn and Ito (2008).
20
sufficient proxy for this. Edwards (2007) uses three different measures of capital
mobility: a more de facto version that uses the sum of external assets and liabilities as a
share of GDP, the index created by Miniane (2004), and a third by combining two
existing data sets and then making country-specific adjustments. Using these measures,
he does find that greater capital mobility increases the likelihood for capital outflows (as
modeled with random effect probit models). Edwards (2012) utilizes a modified version
of the capital mobility index prepared by the Fraser Institute, which also uses the
AREAER to construct its values. The base values of the index are determined by the ratio
of the number of capital controls not in effect to the total number of capital controls
available in the index (13 in all). Edwards (2012) improves the Fraser Institute’s index in
two ways. First, he extends the index so that it covers a weekly frequency by adjusting
the index values on the actual week the change occurred. Second, he also makes countryspecific changes in order to have greater variation and enhance the index (though he does
not disclose the details of these adjustments). His subsequent analysis of capital controls
showed that restricting capital did not enhance the protection of domestic interest rates in
emerging economies. Quinn, Schindler and Toyoda (2011) reviews many of the popular
indices created to measure capital controls over time. They conclude that there is still no
consensus as to the best means of measuring capital mobility; the choice of instrument
will depend on the research being conducted. While a variety of capital control measures
were considered, the analysis presented in this thesis relies on a similar technique to
Edwards (2012), but does not attempt to adjust the index values based on country-specific
values to avoid specification issues.
21
2.3 The Importance of Risk Sentiment
An environment in which investors fear for the safety and profitability of their
financial capital puts emerging markets at risk for financial contagion. The recent
announcement of a potential tapering off quantitative easing provides a clear example of
the effect risk sentiment has on emerging markets. Thus, this issue may have some
importance in a model of domestic interest rates in which the goal is to observe their
behavior in response to changes in foreign interest rates (in this case, the federal funds
rate). Garcia-Herrero and Ortiz (2006) examines the effect that risk aversion has on
sovereign spreads for a selection of eight Latin American countries. Using U.S. Baa-rated
corporate spreads as measure of risk they find that risk aversion was positively and
significantly related to emerging market bond spreads; the results are robust to other
measures of global risk appetite. The study spans May 1994 through June 2006 and also
examines the behavior of spreads before and after the Enron scandal; the authors find that
global risk aversion had an even strong relationship with sovereign spreads following this
event.
Unsal and Caceres (2011) studies Asian country spreads during the 2007-2009
financial crisis using a contagion measure as a key explanatory variable. They separated
the timeframe into three periods. In the onset of the crisis (October 2008 through March
2009), contagion played a large role in the spike in Asian sovereign spreads and also note
that highly rated bonds benefited from the environment. During the second phase (April
2009 through September 2009) risk contagion subsided and spreads normalized. The final
phase lasted through 2010 where they find that contagion had a minimal impact on
22
sovereign spreads as the crisis wound down. This suggests that the risk environment is an
important component of sovereign interest rates in that the market appetite for holding
capital in emerging markets quickly evaporates when there are concerns on a broader
scale beyond country-specific risk.
Jaramillo and Weber (2012) studies the effect that fiscal variables have on
domestic bond yields under different risk environments in emerging economies; they also
find that the level of global risk aversion is an important driver of sovereign yields. In
addition, they find that in low risk averse environments, inflation and real GDP
expectations are important drivers of domestic bond yields; in periods of high-risk
aversion, fiscal debt and deficit indicators become highly important. Forbes and Warnock
(2012) analyzes sudden surges and stops of capital flows for a diverse collection of
countries from 1985-2010. They find that global risk sentiment is an extremely important
predictor of both surges and stops of capital flows. During periods of high-risk aversion,
countries were more susceptible to outflows of foreign capital and more likely to
experience inflows during low-risk aversion periods; this relationship reverse for
domestically owned capital. Calderon and Kubota (2013) reinforce these findings. They
study this same phenomenon from 1975 to 2010 and note that heightened risk aversion
increased the likelihood of outflows and declining risk aversion reduced the likelihood of
outflow-driven stops. The literature overwhelmingly reinforces the idea that a shift in risk
sentiment can have detrimental effects on the financial markets in emerging countries and
thus is a reasonable measure to include in analyzing local interest rates.
Several methods have evolved in the literature for properly measuring global risk
23
appetite; Coudert and Gex (2008) describes several of the primary instruments commonly
used for empirical analysis.13 Global Risk Aversion Indices (GRAIs) assume that as risk
aversion rises, the least risky assets should observe a disproportionate increase in risk
premia compared to the market in general. In practice this means assessing the
correlations between asset price changes and their corresponding volatility as risk-averse
sentiment rises. This technique is explored in Coudert and Gex (2008) and Unsal and
Caceres (2011). A second technique evaluates and estimates common factors of risk
premia, which is typically estimated using principal component analysis; the authors
found this the most relevant method for their analysis of risk indicators as predictors of
stock market and currency crises. A third type of risk indicator are those developed by
financial institutions such as JP Morgan, State Street and SG Capital which are based on
proprietary information on prices and volumes; these do not garner much attention in the
literature. The fourth and most common proxy cited in the literature is the Chicago Board
Options Exchange Volatility Index, also called the VIX. This instrument measures the
expected volatility of the S&P 500 over the next 30-day period and thus is a forwardlooking index.14 Several studies have utilized this metric as a gauge of global risk
sentiment (or gauge of fear as it has been called) including Garcia-Herrero and Ortiz
(2006), Forbes and Warnock (2012), Jaramillo and Weber (2012), Habib and Stracca
(2012), Özatay, Özmen and Şahinbeyoğlu (2009) and De Bock and Carvalho Filho
(2013), among others. De Bock and Carvalho Filho (2013) studies how currencies behave
13
14
Illing and Aaron (2005) also provide an extensive but straightforward overview of risk aversion metrics.
For a detailed history on the development and measurement of the VIX, see Whaley (2009).
24
during risk-off environments. They motivate the use of the VIX to proxy these
environments because the variable is measured at a high frequency and in real time
(intraday data are available), is not directly related to foreign exchange markets and has
historically performed well in recording these turbulent periods. In addition, the VIX is
noted as a fear gauge for both financial and emerging markets as well (Sarwar, 2012). As
Illing and Aaron (2005) finds, risk aversion indices do not all tell the same story;
although one may expect the various indicators to provide similar signals, there is not a
uniform convergence and one must be cautious when interpreting the results. Habib and
Stracca (2012), however, notes that the VIX as a not only a common variable in the
literature but also is highly correlated with various manifestations of global risk and risk
aversion and thus is an appropriate gauge for the purposes of this thesis.
This thesis makes another important contribution to the literature on risk and
capital controls in that the methodology used here considers how measures of global risk
interact with capital controls. Theories of interest rate parity and risk premia suggest that
a heightened risk environment have potentially large implications for domestic interest
rates (and potentially for the exchange rate) in emerging markets. Though the literature
has shown mixed results on the effectiveness of capital controls (though generally finds a
lack of significance), the relationship between capital controls and risk environment
remains little explored. The pass-through of interest rate changes in large foreign nations
such as the U.S. may hold even when emerging nations have strong capital controls,
however, that relationship could break down in periods of market stress. By interacting
global risk and capital mobility measures, one identifies the marginal effect of limiting
25
the movement of capital in different risk environments. A limitation of this study and of
capital control research in general, is that there is no precise way of measuring capital
control intensity (as discussed in Section 2.2). Thus, the results of this thesis provide just
a glimpse at the potential effect that this relationship may have for local rates; results
must be interpreted with this caveat in mind.
2.4 Credit Risk
A crucial variable to control for when analyzing the potential impact of external
factors on the domestic economy is one’s country-specific risk, or credit risk in this
regard. Özatay, Özmen and Şahinbeyoğlu (2009) notes that JP Morgan’s Emerging
Market Bond Index (EMBI) is a standard measure of credit risk for emerging market
sovereigns. The spread version of the index compares the yield on of emerging market
sovereign bonds against “risk-free” assets such as a U.S. Treasury security. While this
standardized, high frequency measure of risk is attractive there may be a suitable
alternative in credit default swaps (CDSs). While the idea of buying insurance to guard
against risk is not a new idea, CDS contracts are relatively new instruments in financial
markets. Investors seeking to have protection against the potential default of their
counterparty can purchase a CDS contract for a premium; the investor taking the other
side of the contract gains the value of the premium and as long as a default does not
occur, one makes a profit. In the market, CDS spreads are represent the price of the
contract; a higher value indicates higher risk, similar to how the EMBI reads. The
literature notes several potential advantages of CDS spreads as a measure of credit risk as
opposed to the use of bond yields. Ammer and Cal (2011) shows that CDS spreads tend
26
to move ahead of the bond market, which suggests that CDS spreads may be a stronger
measure of the instantaneous reaction of investors to credit quality changes. Zhu (2006)
finds that CDS and bond spreads are equivalent in the long run but deviate from one
another in the short term. The difference is largely attributed to CDS spreads being more
sensitive to changes in credit conditions, which may be the cause of CDS spreads moving
ahead of bond spreads. Similarly, Norden and Weber (2009) suggests that CDS spreads
contribute more to the price discovery process than bonds. Blanco, Brennan and Marsh
(2005) reinforces this finding but also notes that the reason bonds and CDS spreads
deviate from parity values is due to imperfections in the specification of the contract and
measurement errors in credit spreads. Longstaff, Mithal and Neis (2005) decomposes the
components of corporate CDS spreads into two parts: a default component and a nondefault component. Their analysis suggests that default risk is the primary driver of
spreads and that the non-default component can be attributed to both issue-specific
liquidity and overall market liquidity.
One issue with using the EMBI as a measure of credit risk is that it is limited for
certain countries that may not have been in the index before a certain period (i.e.
Indonesia) and may have fallen out at a later date (i.e. Korea). We estimate some of these
missing values for the empirical analysis; however, having fully accurate measures limits
the specification issues related to estimating variable data. CDS contracts for emerging
markets generally became available to the market in the mid-2000s. As a further
robustness check for the empirical analysis, we use CDS spreads in place of the EMBI for
the post-Lehman period analysis; this also allows the use of two additional emerging
27
markets into the sample. This particular portion of the empirical analysis expands on the
literature discussing the parity between CDS spreads and bond spreads. Given that the
existing body of research generally finds CDS spreads as a stronger measure of credit
risk, the use of this variable may also provide a stronger specification of the models
examined.
28
CHAPTER 3
ECONOMIC MODEL
3.1 A Simple Model of Interest Rate Parity
The transmission of foreign interest rates to a domestic economy is appropriately
modeled via the theory of interest rate parity. In its simplest form, interest rate parity
assumes perfect capital mobility and posits that interest rate differentials between two
countries should approximately equal the domestic currency’s expected rate of
depreciation. Assuming risk neutrality, a relatively straightforward interest rate parity
condition obtained from the results of a dynamic stochastic general equilibrium model in
Monacelli (2005):
(1)
𝑓
𝑖𝑡 − 𝑖𝑡 = 𝐸𝑡 {∆𝑒𝑡+1 },
𝑓
where it is the nominal interest rate in the domestic economy, 𝑖𝑡 is the nominal interest
rate of the foreign economy (in this case the federal funds rate) and 𝐸𝑡 {∆𝑒𝑡+1 } is the
expected depreciation rate of the domestic currency.
Aslan and Korap (2010) provides a brief but extensive overview of the literature
surrounding the theory of uncovered interest rate parity and finds that empirical research
largely struggles to find evidence that this theory holds; however, the theory remains a
popularly researched area of economics. Perhaps this is because the idea that investors
will arbitrage away any potential opportunities available in the market is logical and
relatively straightforward to apply in an empirical framework. Consider for instance, if
the Fed were to increase the federal funds rate, the now higher rates attract foreign capital
29
into the U.S. economy as investors seek to take advantage of higher returns. The outflow
from emerging markets causes downward pressure on the value of their currency and in
order to keep the relative attractiveness, one solution is to increase interest rates in the
domestic economy. The model proposed in Edwards (2012) exploits this potential
relationship and modifies the basic interest rate parity equation to account for imperfect
capital mobility (allowing for the testing of capital controls). His work and the work
presented in this thesis serve as a test of interest rate parity while controlling for other
possible instruments the nation might use to prevent a full interest rate pass-through (i.e.
capital controls). Although his analysis utilizes a panel error-correction model, his model
is easily adaptable for the purpose of this thesis and requires only a mild modification of
the methodology in order to further relax the assumption of risk neutrality.
This basic equation of interest rate parity requires a slight transformation as all
countries in the sample did not have free capital mobility and violate a key assumption of
the model. Edwards (2012) suggests a simple modification of equation (1) to allow for
capital restrictions:
(2)
𝑓
𝑖𝑡 − (1 − 𝑇)𝑖𝑡 + 𝑇 = 𝐸𝑡 {∆𝑒𝑡+1 },
where T represents a tax on outflows from the domestic economy to the foreign nation.
This equation suggests that the tax on foreign outflows causes a wider interest rate
differential between countries with which its size is dependent on the extensity of capital
controls. Note that capital controls are complex in practice and that such controls in
emerging markets are not easily quantifiable and typically have varying intensity. For
example, some emerging markets use government-issued permits to restrict foreign
30
participation in domestic financial markets. In addition, some countries are difficult for
investors to access due to issues in the settlement process, which may further distort the
pass-through effect.15 In the simple model above, capital controls characterized as a cost
or tax. This tax creates a wedge between the domestic and foreign interest rate so that the
interest rate differential may not equal the expected rate of depreciation. Edwards (2012)
also makes an additional adjustment to equation (2) to allow for imperfect substitution of
securities between the domestic and foreign countries. He notes that the pass-through
effect would be incomplete even with freely mobilized capital and posits the following
equation:
(3)
𝑓
𝑖𝑡 − 𝛽𝑖𝑡 + 𝛾 = 𝐸𝑡 {∆𝑒𝑡+1 }
0 ≤ β ≤ 1,
where β captures both the extensity of capital controls and imperfect substitution between
securities. In order to specifically examine the extent which capital controls play a role in
local interest rates, Edwards (2012) further modifies equation (3) to allow for a more
explicit specification:
(4)
𝑓
𝑖̃𝑡 = 𝛼0 + 𝛼1 𝑖𝑡 + 𝛼2 𝛿𝑡 + 𝛼3 𝜌𝑡 + 𝜔𝑡 ,
where 𝑖̃𝑡 represents the equilibrium domestic equilibrium rate, 𝛿𝑡 is the expected
depreciation in the currency, 𝜌𝑡 is the credit risk premium of the nation and 𝜔𝑡 is the
error term. In theory, if markets are fully mobile and have no capital controls in place
(and the risk environment is constant over the period of analysis), then 𝛼0 is equal to zero
and the remaining coefficients should be equal to one.
15
Note the pass-through effect discussed here is referring to the equilibration that arises when there are
significant disparities between foreign and domestic interest rates. A full pass-through effect occurs here
when an emerging economy’s interest rate adjusts by the same amount that the Fed’s policy rate changed.
31
One crucial issue remains, however; equation (4) assumes risk-neutrality, which
this thesis is quite likely to violate (especially given that the period covered spans through
the 2007-2009 financial crisis and the European Debt Crisis). It may be the case that
different risk environments affect the transmission of foreign interest rates to those in the
local economy. Controlling for varying risk sentiment not only allows for stronger
modeling of the interest rate transmission mechanism but also allows for analysis of the
strength of its effect on domestic interest rates. This relationship can be modeled by
explicitly including a measure of global risk in equation (4). Given that capital controls
are, by design, thought to protect against inflows and outflows of capital, and that shocks
to global risk can result in large capital movements, then it is useful to also allow for an
interaction between these two terms. These modifications result in the following
equation:
(5)
𝑓
𝑖̃𝑡 = 𝛼0 + 𝛼1 𝑖𝑡 + 𝛼2 𝛿𝑡 + 𝛼3 𝜌𝑡 + 𝛼4 𝑚𝑡 + 𝛼5 𝑔𝑡 + 𝛼6 (𝑚𝑡 ∗ 𝑔𝑡 ) + 𝜔𝑖 ,
where 𝑚𝑡 is the capital mobility indicator and 𝑔𝑡 is the global risk indicator. Indeed, the
literature generally finds that capital controls are not an effective means to protect
unwanted capital movements. However, the inclusion of the interaction between global
risk and capital mobility allows for the possibility that capital controls are effective under
global stress scenarios. In other words, by limiting the mobility of capital movement in or
out of a country, an economy will be better protected under market stress scenarios
simply because investors are unable to pull their funds out. This is a central question to
this thesis.
As this thesis covers the recent period of unconventional monetary policy, the
32
effect the Fed has on emerging markets may be more difficult to discern. Once interest
rates hit the zero-bound in late 2008, the Fed’s official policy rate has not changed. Using
asset purchases as an alternative, the Fed hoped to avoid losing its influence over the
markets, as occurred in Japan, and allow monetary policy to assist in the recovery. Thus,
it is important to include this new monetary policy instrument in them model. Equation
(6) adds a measure of Fed asset purchases to Equation (5) in order to capture its effect on
emerging market interest rates:
(6)
𝑓
𝑖̃𝑡 = 𝛼0 + 𝛼1 𝑖𝑡 + 𝛼2 𝛿𝑡 + 𝛼3 𝜌𝑡 + 𝛼4 𝑚𝑡 + 𝛼5 𝑔𝑡 + 𝛼6 (𝑚𝑡 ∗ 𝑔𝑡 ) + 𝛼7 𝐹𝑡 + 𝜔𝑖 ,
where 𝐹𝑡 measures the size of the Fed’s balance sheet at time t (we measure this as the
week-on-week growth of Fed assets). While the federal funds rate does not vary over this
period, we include it in the model during the full sample period for completeness, as it is
necessary to have both of the Fed’s key tools it used over both sub-periods. The effect of
quantitative easing on emerging markets is still a growing area of research in the field;
this study uniquely studies the pre-crisis and post crisis eras (as well as the combination
of these periods) and how the influence of the Fed on the market has changed.
33
CHAPTER 4
METHODOLOGY AND DATA
4.1 Methodology
This analysis of emerging market local interest rates relies on longitudinal data
collected primarily from Bloomberg (unless otherwise specified). The model described in
the previous chapter is easily adopted for panel data by allowing equation (6) to account
for entity-specific variation that is fixed over the sample period (fixed effects). The
general model is specified as follows:
(7)
𝑓
𝑖̃𝑖,𝑡 = 𝛼𝑖 + 𝛼1 𝑖𝑡 + 𝛼2 𝛿𝑖,𝑡 + 𝛼3 𝜌𝑖,𝑡 + 𝛼4 𝑚𝑖,𝑡 + 𝛼5 𝑔𝑡 + 𝛼6 (𝑚𝑖,𝑡 ∗ 𝑔𝑡 ) + 𝛼7 𝐹𝑡 +
+𝜔𝑖 ,
where 𝛼𝑖 is the country-specific intercept (i.e. the entity-fixed effect) and the remaining
variables are defined as in equation (6). Note, that time-fixed effects are not appropriate
for this model, since key variables utilized in the analysis vary across time, but not across
entities, such as the global risk indicator and the federal funds rate. In order to limit
omitted variable bias, the empirical analysis also includes country specific controls for
growth, inflation, government debt and government balances as well as global controls
for various commodity prices.
The fixed-effect panel estimation employed here uses Driscoll and Kraay (1998)
standard errors (where their use is feasible), which account for potential serial correlation
and heteroskedasticity, and are robust to cross-sectional dependence.16 Cross-sectional
16
Driscoll and Kraay (1998) standard errors are obtainable using a specially written program in Stata. See
Hoechle (2007) for details on this program.
34
dependence in the error term results in macroeconomic panels because of financial
integration between countries; this interdependence between nations becomes part of the
error term (De Hoyos and Sarafidis, 2006). Driscoll and Kraay (1998) demonstrates that
the failure to account for spatial dependence leads to poorly estimated standard errors
(though consistent parameters); they use nonparametric techniques and transform the
orthogonality conditions to create a robust covariance matrix estimator. Using Monte
Carlo simulations, they find that their method yields more robust standard errors than
other traditional measures such as standard OLS standard errors, White heteroskedasticity
consistent standard errors and Newey-West heteroskedasticity and autocorrelation
consistent (HACs) standard errors when cross-sectional dependence is present. Although
the initial use of these standard errors did not allow for inclusion of fixed effects,
Vogelsang (2012) shows that fixed-effects do not bias the results and thus, are
appropriate for use in this empirical analysis. However, these standard error estimates are
only valid when cross-sectional dependence is present and thus we test the data for this
prior to estimation.
4.2 Data Overview
The choice of the individual countries for inclusion in the analysis is central for
empirical estimation. Edwards (2012) uses a sample of just seven emerging markets, all
of which have floating exchange rates and generally used inflation targeting frameworks
over the sample period; these countries include Brazil, Chile, Colombia, Mexico,
35
Indonesia, South Korea and the Philippines.17 This framework is appropriate as the
literature suggests that a nation’s exchange rate regime may play an important role in
protecting its economy from external shocks. However, using this criteria, there are other
emerging markets that may merit inclusion in the sample beyond those seven nations and
there is room for expansion. We utilize a systematic method for choosing emerging
markets in order to avoid introducing bias into the sample. First, we omit countries that
JP Morgan’s EMBI Global does not consider emerging markets. This bond index
measures spreads and returns of a broad range of emerging market countries and is
widely used in empirical literature as a means to measure a sovereign’s country-specific
risk.18 Next, we examine the IMF’s AREAER and include countries that primarily relied
on either a managed or an independent float over the sample period.19 Lastly, we omit
countries lacking data on key variables such as the CD rate and EMBI.20 This leaves 13
countries, six of which are new relative to the sample of Edwards (2012); the new
countries are Peru, Poland, Romania, South Africa, Thailand and Turkey.
The data span the period from January 1, 2000 through April 27, 2012. The start
date of the period is chosen in order to avoid the complications of the pre-Euro era and to
encompass the sample chosen in Edwards (2012). The end date is chosen based on the
availability of data from the AREAER. The IMF releases each edition of the annual
17
Note that some countries in the sample of Edwards (2012) briefly fell under the classification of
“monetary aggregate targeting” according to the AREAER. These were brief periods and did not reflect a
move from floating to fixed exchange rate regimes; thus, they do not introduce bias by their inclusion in the
regressions.
18
See Section 4.4.2 presented later in this chapter for more information.
19
See Chapter 2 for a discussion of the AREAER and its use in this field.
20
Countries removed from the sample include Uruguay, Ghana, Zambia, Jamaica, Guatemala, Sri Lanka,
Serbia and Mongolia.
36
publication with data corresponding to the previous year so that, for instance, the 2005
report is updated for data through December 31, 2004. The most recent editions have
included data through the first few months of the year, such as the 2012 edition, which is
updated through April 30, 2012. Since we use the AREAER to classify both the sample
of countries for inclusion and the capital controls in place, the sample is appropriately
limited to the latest available data. In general, the data are weekly frequency, with
exception to macroeconomic data that are available less frequently such as GDP and
inflation figures. Weekly frequency is appropriate for analysis of emerging market
interest rates because it allows the researcher to better disentangle the underlying
relationships within the data. For instance, looking at deposit rates over a monthly or
longer period may overlook important intra-month variation such as short-lived shocks
that dissipate by month end. Frequently, empirical researchers utilize annual or quarterly
data because of limitations of data availability (capital mobility, GDP, inflation) or
difficulty obtaining the data from proprietary sources. Similarly, daily or intraday data
may be too noisy to exhibit meaningful trends, thus weekly lends itself as an appropriate
periodicity. The observations used in this study are simply the last reported value of a
variable as of the Friday of that week’s market close.21
There are three distinct periods of interest for this analysis (pre-Lehman, postLehman and the full sample). In the initial model, we focus on the sample analyzed by
Edwards (2012) which spans January 2000 through the week before the fall of Lehman
21
Note some observations do not have data reported on all Fridays or have brief periods without reported
observations. We assume that these missing periods are equal to the last reported value (for Friday’s this
may be the prior Thursday), as would be the most current pricing in the market available.
37
Brothers; we refer to this timeframe as the pre-Lehman period. The second period focuses
on the period from just after the Lehman collapse through April 2012 in order to assess a
potential structural change after the crisis in 2008; we refer to this timeframe as the postLehman period. Lastly, we focus on the full period from 2000-2012 to see how this
compares to the results of the two sub-periods. Dissecting these periods allows one to
determine a potential structural break in the data in the aftermath of the crisis. In an
analysis of sovereign risk pricing before and during the European debt crisis, Beirne and
Fratzscher (2013) shows that the drivers of CDS spreads and bond yields changed from
the pre-crisis period. In fact, the authors find that fundamentals became a key component
of sovereign risk pricing in the crisis period, suggesting that prior to the crisis, the market
was not fully pricing in the actual credit risk that investors faced. If this is indeed the
case, then it may also hold true for the recent financial crisis and filter through to
domestic interest rates. Thus, the analysis of the pre-Lehman period may hold
substantially different results than the post-period and justifies the use of subsamples.
4.3 Dependent Variable Description
The choice of dependent variable is difficult in that the rate must provide a fair
representation of interest rates of the domestic economy; a common rate used in the
literature is the 3-month certificate of deposit rate. Frankel, Schmukler and Serven (2004)
notes that money market rates are a stronger measure of domestic rates as deposit rates
tend to be more rigid and are subjected to greater administrative controls. This rigidness
may pose an issue in the estimation of the model in that this analysis relies on the
instantaneous impact of federal funds rate changes; any stickiness in deposit rates may
38
result in insignificant coefficients. However, the drawback of using money market rates is
that they are not widely available and at a weekly frequency. The 3-month CD rate is a
preferred measure of interest rates as it is a money market instrument itself and is
typically available at daily frequencies across countries. To that end, we utilize local
market three-month certificate of deposit rates as the dependent variable for this study,
following Edwards (2012). While the majority of the sample has full data for this
variable, Thailand and Romania have incomplete observations. However, this does not
pose a problem for model estimation since these countries are only added for the postLehman period (where the sample is complete).
CD data are available for nearly all of the remaining 13 countries in the sample
with exception to Mexico, which stopped reporting this data in late 2006. In order to have
a more complete set of data and to provide observations for the post-Lehman period, we
estimate the missing observations using a similar technique to Edwards (2012). He
regresses the variable of interest on another related variable for periods where data for
both are available; we utilize the resulting regression estimates in the model once the
deposit data are no longer available. Since the three-month Mexican peso swap rate has a
correlation of 94% with the three-month certificate of deposit rate and is a money market
instrument, this indicates the swap rate is a suitable instrument for estimation. The
regression uses only data when the two securities are available together (2000-2006) and,
as Figure 4 illustrates, appears to be a sufficient, though imperfect, proxy for deposit
rates. This relationship, however, assumes the relationship is stable over the entire period,
which may not be the case given the crisis in the post-Lehman era. We only use the
39
Notes: Shaded areas represent quantitative easing periods. Federal Reserve balance sheets are in real
terms; adjusted to June 2013 price levels according to CPI.
Source: Bloomberg, Federal Reserve, Author’s Calculation
estimated values once the official deposit rate data are no longer reported. Annual deposit
data from the World Bank suggest that these are fair estimates.
4.4 Independent Variables Descriptions
4.2.1 U.S. Monetary Policy Stance
The federal funds rate, the rate that U.S. domestic banks borrow from other banks,
is the primary measure of the Fed’s monetary policy stance. The Federal Open Market
Committee (FOMC) of the Fed meets eight times during the year to decide on its
direction for monetary policy and votes on whether to increase or decrease this rate.
During the period that follows the meeting, Treasury securities are bought and sold from
the Fed’s holdings in order to maintain that rate. This means that the official federal funds
rate target is constant between meetings, if not longer; the current target range of 0-0.25%
40
has remained unchanged since December 2008. From an empirical standpoint, the lack of
variation makes the task of teasing out a significant and meaningful relationship between
other regressors difficult.
For this reason, researchers use the effective federal funds rate to measure the
stance of U.S. monetary policy. This rate is a volume-weighted average of interest rates
charged by brokers (Federal Reserve, 2013). Figure 5 shows that the effective rate
strongly tracks the official policy rate, as one would expect. This rate is also a convenient
measure in that it represents a direct proxy of the true effectiveness of Fed policy in
practice. For instance, if the Fed has its policy rate set at 1% but the effective rate is
closer to 0.5%, then the policy rate has not been fully incorporated into market pricing;
this indicates that policy is less effective, making the transmission of changes in the
federal funds rate less efficient. In addition, with a flat federal funds rate in the recent
term, the effective rate provides additional variation, as seen in figure 5. Thus, the
effective rate is an appropriate and useful proxy for the purposes of this thesis. If interest
rate parity theory holds, the coefficient on this variable should be positive and close to
one for a full pass through.
As discussed in Chapter 2, quantitative easing has become an important tool for
the Federal Reserve. With the federal funds rate sufficiently bounded between zero and
one-quarter of a percent for nearly the entire post-Lehman period, discerning the effect of
fed policy by the federal funds rate alone may not be sufficient. Thus, accounting for
quantitative easing may prove essential in explaining the influence that the Fed has on
emerging market interest rates. The Fed facilitated these programs via asset purchases in
41
Source: Bloomberg, Federal Reserve
order to create liquidity in the market and in some cases, keep the long end of the yield
curve especially depressed (in order to assist with the U.S. housing market recovery).
Since these purchases will appear as assets on their balance sheet, measuring the size of
the Fed’s balance sheet over time provides a means of capturing quantitative easing
empirically. We use the total aggregate level of assets across the Federal Reserve system.
These data are available at a weekly frequency and released on Thursdays with updates
through the prior day. Although most data in this thesis are collected as of the last value
observed in a given week, this brief lag is unlikely to be problematic for analysis as this
gives the best estimate of the Fed’s activity during the week. Nonetheless, this caveat
must be noted when interpreting the results. Figure 6 illustrates both the federal funds
rate and the Fed’s balance sheet over time with periods of quantitative easing highlighted
as well. Not surprisingly, the Fed’s assets skyrocketed in late 2008 as the federal funds
42
Source: Bloomberg, Author’s Calculations
rate neared its bottom threshold. Note the initial 2008 spike in assets was not from
quantitative easing itself but from other asset purchasing programs the Fed launched in
order to rescue depository institutions, large financial institutions (such as AIG) and
government agencies such as Freddie Mac (Federal Reserve Bank of St. Louis, 2013).
While this may not have been official quantitative easing, this demonstrates the Fed’s use
of its balance sheet as a tool to prevent a deepening crisis. We utilize the growth in the
Fed’s balance sheet (calculated using logged differences) as the primary estimate for
quantitative easing in order to avoid stationarity issues. We also employ an alternative
measure by using a binary variable that takes a value of one during periods of
quantitative easing and a value of zero otherwise. Because of the great volatility going on
during these periods and the simplicity of a binary variable, we also utilize an interaction
between these two measures to discern the effect of balance sheet growth during periods
43
of quantitative easing on emerging market interest rates. We expect quantitative easing to
have a negative effect on emerging market interest rates because a higher level of asset
purchases by the Fed keeps rates lower in the U.S. and is an incentive for these markets
to keep rates low to prevent excessive capital inflows.
4.4.2 Country-Specific Credit Risk
Another key variable used in the study is the measure of credit or country-specific
risk, which measures a country’s perceived risk of default. JP Morgan’s EMBI is a
common measure of this in empirical research (as discussed in Chapter 2) given its broad
scope, simplicity of application and success in modeling country-specific risk. The sheer
complexity in devising a method to weight different bond issues between countries with
different characteristics makes the index a desirable find for researchers. Diez and
Phinney (2012) provides a thorough discussion of the three different versions of this
index: the EMBI Global, the EMBI+ and the EMBI Diversified. The latter two indices
are more limited versions of the EMBI Global; they put constraints on market liquidity
(EMBI+) and limit the weights of certain countries (EMBI Diversified). The EMBI
Global is the broadest of JP Morgan’s indices and considers emerging markets based on
per capita GDP and debt-restructuring history, only bonds issued with a minimum face
value of $US500 million are considered in the index. The securities contained within
each of the three indices are denominated in hard currency (i.e. U.S. dollar-denominated
debt) and do not include debt denominated in an emerging market’s local currency (i.e.
Mexico debt denominated in pesos). This makes the indices particularly attractive
because capturing external debt dynamics removes potential confounding of local market
44
dynamics as those in the local market are likely to be less concerned about the risk of
default. For instance, spreads of local currency corporate bonds would be measured
against the risk-free Treasury securities of their own government (although foreign
investors can and do play roles in local-currency markets); this is not the same
interpretation foreign investors have when examining these markets. Since there is no
unified definition as to the classification of a country as an emerging market, a broader
definition is preferred to have a representative sample, and thus we utilize the EMBI
Global (in bond spreads form) as the primary measure of credit risk.
The EMBI index is available at a daily frequency (with a one day lag), but due to
the movement of countries in and out of the index, some entities have limited
observations. Edwards (2012) corrects for this in the case of Korea by running a simple
regression of the EMBI index on CDS spreads when both data are available (as described
in estimating the missing observations of Mexico’s deposit rate). Indonesia has a similar
issue but the data missing for the EMBI are in the early period of the sample (May 2004
and prior) where the CDS data are unavailable. We estimate Korea’s EMBI using the
same approach as Edwards (2012) for the observations after April 2004 and leave the
observations missing for Indonesia. However, for the post-Lehman period, CDS data are
available for all countries in the sample without missing observations. Thus, this provides
an opportunity to test the relative equivalence of CDS and EMBI data given the
arguments in favor of the former’s usefulness in measuring credit risk. In addition, since
we estimated Korea’s EMBI data during this period, the CDS data are a stronger
reflection of credit risk, as they are non-derived. Properly accounting for country-specific
45
risk is essential for the model as the foreign investors with their capital in the domestic
market are likely sensitive to developments in that market. This would inhibit the
equilibrium process proposed by interest rate parity theory in that deposit rates may be
affected as a result of this change; thus it is essential to include in the model. We expect
both measures of credit risk to yield positive coefficients given that increased default risk
means financial institutions may have to increase deposit rates to prevent a deposit flight.
4.4.3 Exchange Rate Risk
The expected depreciation of the domestic country’s currency is an important
variable in the model as it is central to interest rate parity theory. Edwards (2012)
provides a straightforward method for calculating this rate by differencing the threemonth non-deliverable forward rate of a country’s currency (logged) from the current
value of the spot rate (logged) and annualizing this differential by multiplying by four;
both variables are available on a daily basis. This worked well for his sample but not all
currencies have non-deliverable forward rates because their currencies are deliverable
including Romania, South Africa, Thailand and Turkey; for these countries we use the
three-month deliverable forward rate in place of the non-deliverable forward rate. Note
that Romania is missing data prior to 2004, which cannot be estimated and is left missing
in the panel. Forward rates are also missing for Indonesia prior to March 2001 and both
Chile and Peru prior to mid-July 2000. Indonesia’s rates can be determined by adding the
forward points to the spot rate, which results in an estimated forward rate; however, since
Chile and Peru’s rates are indeterminable and cannot be estimated, they are left blank for
this period. We believe the expected rate of depreciation to be positive and relatively
46
close in magnitude to the coefficient on the effective federal funds rate, consistent with
interest rate parity theory.
4.4.4 Capital Controls
Capital controls are perhaps the most difficult variable in the study to measure as
there is a lack of uniformity of controls between countries, which makes calculating a
quantitative value particularly elusive and especially at a high frequency such as this
study. Edwards (2012) provides a transformation of an annual index created by the Fraser
Institute. The index data take on values from zero to ten with a higher number implying
greater capital mobility. Edwards (2012) modifies their index by adjusting the values at
the time a change in capital mobility occurred (i.e. instead of an annual number for the
year, the number can vary according to regulation changes during the year). He uses
sources beyond the AREAER for this adjustment, making judgment calls on when
something restricts or eases capital mobility; Edwards does not detail the specific
methods used to make these adjustments in his analysis. This method may introduce
some unintentional bias in the sample due to specification errors with the variable.
We adopt a method more in line with Edwards (2010), using the calculation
methodology of the Fraser Institute capital mobility index and making only a slight
modification. If an index value changes in the following year, we use the AREAER to
identify the date the change occurred and manually adjust the values from the week of
that change through the remainder of the year. This is a more systematic approach but
still results in some countries having little to no variation over the sample period. This is
a general problem with capital control measures and is not easily correctable without
47
using ad hoc judgments as to what constitutes a change in capital control. As a robustness
check, we also utilize the Chinn-Ito. Both the created index and the Chinn-Ito measures
are capital mobility measures, so larger values are indicative of higher mobility. Properly
accounting for capital mobility is essential for the model as this could stand as a barrier to
prevent foreigners from pulling out their capital in the domestic market. An omission of
this variable in the model would imply that capital freely moves between internationally,
which is certainly not a realistic assumption as we noted in Chapter 3. While the
specification of capital controls is not ideal, it does allow for differentiation beyond an
entity-fixed effect in the model since these policies generally changed over the time. If
capital controls are able to limit capital inflows from becoming excessive as interest rate
differentials widen, then the coefficient on this variable will be positive.
4.4.5 Global Market Risk
Global risk is an important component of this study as it allows the model to
account for the degree of risk-aversion in financial markets during the different periods of
analysis. We utilize the VIX as it is widely used as an indicator of global risk in the
literature (see Chapter 2 for this discussion). The VIX is a forward-looking instrument as
it measures the market’s expected volatility over the 30 days that follow; its
interpretation, however, can be misleading as it is measured on an annualized basis. To
determine the expected volatility over that 30-day period, the value of the VIX is divided
by the square root of 12. A VIX value of 10%, for instance, implies the S&P 500 will
change by 2.89% (increasing or decreasing) over the next 30-day period. Since this is a
scalar transformation, this does not need to be applied to the VIX data for empirical
48
analysis. In general, a high (low) level of volatility is indicative of a risk-off (risk-on)
period in that higher (lower) volatility pushes investors to reposition their portfolios to
safer (riskier) assets. Incorporating a global risk appetite measure into the interest parity
model allows for the relaxation of the risk-neutrality assumption. Emerging markets are
often compared alongside the high-yield corporate market, which certainly indicates that
investors do not see investments in these sovereigns as risk-neutral. For emerging
markets, this means highly volatile periods may lead to capital outflows and thus, we
expect a positive relationship between the VIX and deposit rates. The interaction between
the VIX and capital mobility, however, may also yield a positive sign showing that
capital controls help to protect deposit flights during riskier periods.
4.4.6 Other Explanatory Variables
Several controls, though not the focus of the study, are needed in order to
minimize bias resulting from the omission of variables related to the error term. Since
commodities are commonly a crucial source of export income for emerging markets, we
include three different commodity proxies for energy, agricultural products and industrial
metals. For each category, we obtain JP Morgan price index values from Bloomberg. We
also include two measures of the macroeconomy for each country, namely year-on-year
real GDP growth and year-on-year inflation. Inflation is available at a monthly frequency
and GDP is available on a quarterly basis; both are held constant in between releases and
are obtained via Bloomberg. These are likely to play important roles in the model as GDP
proxies the business cycle and inflation allows nominal interest rates to increase as a
result of rising prices, as standard economic theory would suggest. Note that Indonesia’s
49
real GDP growth was not available for the first two quarters of the sample and are left
empty in the panel dataset.
Lastly, we include three different fiscal indicators including general government
debt, the primary budget balance and the current account balance, each measured as a
share of GDP. We obtain the former two instruments from Fitch Ratings, which are
available at an annual frequency; we obtain the latter via Bloomberg, which is available
at a quarterly frequency. Note that we hold each of these variables constant between
observations. The current account measures the net inflows of capital into a country but
primarily serves its purpose here as a trade proxy and as a signal of information about the
general direction of capital flows (though it may be netted out). Economic theory
suggests that higher government debt and deficits crow out private investment by pushing
up interest rates, suggesting these as relevant variables for the model. These factors serve
as proxies for domestic policies and help to limit potential omitted variable bias in the
model; the international scene may influence domestic deposit rates but it is important
not to ignore potential sources of confounding within the domestic market. Table 1
provides a summary of the variables discussed in this section and their expected signs.
4.5 Preliminary Data Analysis
Tables 2 and 3 highlight descriptive statistics for the primary variables of interest
for this study. We split these into two tables in order to highlight different features of the
data. Table 2 analyzes variables that are constant across entity but varying over time and
50
Table 1 - Variable Predictions and Definitions
Measurement
Source
Expected Sign
Certificate of Deposit Rate
Percentage
Bloomberg
N/A
Yes - Levels
Effective Federal Funds Rate
Percentage
Bloomberg
(+)
Assumed - Levels
EMBI Global
Basis points
Bloomberg
(+)
Yes - Levels
CDS Spread
Basis points
Bloomberg
(+)
Yes - Levels
Expected Depreciation
Percentage
Bloomberg
(+)
Yes - Levels
Capital Mobility
Index value
IMF/Fraser Institute
(0/+)
No - Little variation
Volatility Index
Percentage
Bloomberg
(+)
Yes - Logged
$USD
Bloomberg
(-)
Yes - Logged Diff.
Variables of Interest
Federal Reserve Balance Sheet
Stationary?
Measurement
Source
Expected Sign
Stationary?
Agricultural Commodity Index
$USD
Bloomberg/JP Morgan
(+)
Yes - Logged Diff.
Energy Commodity Index
$USD
Bloomberg/JP Morgan
(+)
Yes - Logged Diff.
Metals Commodity Index
$USD
Bloomberg/JP Morgan
(+)
Yes - Logged Diff.
Gross Domestic Product
Annualized growth rate
Bloomberg
(+)
Yes - YoY Growth
Inflation
Annualized growth rate
Bloomberg
(+)
Yes - YoY Growth
Primary Budget Balance
Controls
Annualized growth rate
Fitch Ratings
(-)
No - Little variation
Government Debt
As a share of GDP
Fitch Ratings
(+)
No - Little variation
Current Account Balance
As a share of GDP
Bloomberg
(-)
No - Little variation
separates them according to the different periods of interest.22 There are 643 weeks over
the entire sample with approximately 70% covering the pre-crisis period. During the preLehman period, there is little difference between effective and official federal funds rates;
this breaks down during the post-Lehman period where the official rate is now nearly
twice the effective rate. Note the substantial difference between the Fed’s assets before
and after the Lehman crisis. During the eight years of the sample prior to the crisis, the
Fed’s balance sheet did not even double while it nearly tripled from the beginning to the
end of the post-Lehman period (both in real terms). Interestingly, the VIX has much
greater volatility in the post-Lehman period with a standard deviation twice what it was
in the initial period. We log the VIX in the empirical analysis in order
to eliminate the right skewness in the distribution. Table 3 summarizes the variables that
vary across both time and entity and are disaggregated by country. First note that the
22
See Appendix A for control variable descriptive statistics.
51
Table 2 - Descriptive Statistics for Entity-Constant Variables by Sub-Period
Minimum Maximum
Mean
Median
St. Dev.
Pre-Lehman Period (454 Observations)
Official Federal Funds Rate (%)
1.00
6.50
3.35
3.26
1.84
Effective Federal Funds Rate (%)
0.96
6.86
3.35
3.06
1.84
Volatility Index (%)
10.02
42.66
19.69
18.99
6.63
Federal Reserve Balance Sheet ($bln)
768.16
1,002.34
906.90
937.16
69.04
Post-Lehman Period (189 Observations)
Official Federal Funds Rate (%)
0.25
2.00
0.33
0.25
0.30
Effective Federal Funds Rate (%)
0.04
1.48
0.17
0.15
0.17
Volatility Index (%)
14.47
79.13
27.65
23.95
12.39
Federal Reserve Balance Sheet ($bln)
1,042.28
2,957.62
2,490.72
2,450.40
323.27
Full Period (643 Observations)
Official Federal Funds Rate (%)
0.25
6.50
2.46
1.75
2.08
Effective Federal Funds Rate (%)
0.04
6.86
2.41
1.74
2.12
Volatility Index (%)
10.02
79.13
22.03
20.13
9.44
Federal Reserve Balance Sheet ($bln)
768.16
2,957.62
1,372.44
959.44
745.24
Notes: Federal Reserve balance sheets are in real terms; adjusted to June 2013 price levels according to CPI.
maximum amount of observations in the sample is 8,359 observations (13 countries by
643 weeks); the table reveals a number of interesting characteristics about the data.
Deposit rates between countries vary greatly with low average rates in Chile and quite
high average rates in Brazil and Turkey; the sample average is certainly skewed to the
right at 8.48%. It is these higher rates of deposit that make emerging market desirable for
an investor to place capital. Most countries have complete data except Romania and
Thailand, but these countries only enter the sample in the post-Lehman period and it is
not problematic.
The EMBI data vary widely between the countries. Interestingly, Brazil and
Turkey have the highest average EMBI spread in the dataset but have recently come in to
much tighter levels, both now investment grade credits. We note the appeal of using CDS
spreads as an alternative to the EMBI here, as with missing data in countries like
Romania (just nine observations) and Thailand, CDS spreads offer a full set of
observations in the post-Lehman period. At first glance, these two series do not look
CD Rate (% )
Mean
Standard Deviation
Observations
EMBI (Bps)
Mean
Standard Deviation
Observations
CDS Spreads (Bps)
Mean
Standard Deviation
Observations
Expected Depreciation (% )
Mean
Standard Deviation
Observations
Capital Mobility (Index)
Mean
Standard Deviation
Observations
Chinn-Ito Index
Mean
Standard Deviation
Observations
Brazil
Chile
2.38
2.34
643
Colombia
7.55
2.64
643
Indonesia
10.33
3.57
643
Korea
4.37
1.25
643
Mexico
3.68
1.98
643
Peru
5.52
3.50
643
Philippines
6.92
3.22
643
Poland
7.18
4.70
643
Romania
8.59
3.24
398
8.98
2.27
643
Thailand
3.77
2.27
576
Turkey
25.22
17.48
643
8.48
8.28
8045
Sample
402.39 205.54
292.16 260.34
6800
603
5.65
7.73
8038
2.92
2.60
8359
0.34
1.18
8359
20.07
15.84
643
2.21
0.45
643
-0.74
0.59
643
92.92
64.16
526
1.41
2.08
643
1.54
0.00
643
-0.48
0.50
643
211.58 145.25
162.84 86.82
604
498
6.32
2.56
643
0.77
0.00
643
-1.17
0.00
643
5.48
4.16
378
6.03
2.45
643
1.36
1.36
643
79.82
77.92
602
4.23
4.01
643
1.65
0.66
643
-0.14
0.45
643
143.78 186.62 287.16
89.08 102.54 145.14
526
445
551
4.83
4.98
643
0.77
0.00
643
-0.17
0.48
643
1.61
2.84
615
8.37
0.25
643
2.44
0.00
643
5.53
2.81
643
1.67
0.29
643
0.95
0.39
643
89.59
81.72
531
0.96
2.30
643
3.94
2.83
643
0.13
0.37
643
245.65 232.88
161.30 141.45
395
484
7.25
3.77
643
1.54
0.00
643
0.99
0.38
643
4.36
3.57
643
0.95
0.77
643
-0.35
0.65
643
70.31
55.37
484
0.58
6.82
615
4.79
2.30
643
1.69
0.93
643
470.44
656.52
551
10.41
4.99
643
3.68
1.52
643
-0.07
0.53
643
505.41 143.45 372.19 294.52 130.35 235.85 338.23 358.04 147.51 408.95 209.57 103.87 422.39 278.66
396.90 65.38 206.28 159.91 57.16 94.92 191.79 147.18 82.92 23.16 111.13 47.61 236.67 219.54
7177
643
325
643
9
643
643
643
643
643
413
643
643
643
15.29
4.46
641
South Africa
Table 3 - Descriptive Statistics for Time- and Entity-Varying Variables (Full Sample)
52
53
related. An ordered ranking of both series, for instance, yields different rankings (though
both have Brazil with the most credit risk over the period). However, one must keep in
mind that the CDS data do not enter the sample for most countries until 2004 and these
averages are capturing different ranges of data. Table 3 also illustrates the relationship
between higher deposit rates and the expected rate of depreciation suggest by interest rate
parity theory. Notice how Turkey and Brazil have the largest expected rates of
depreciation and largest deposit rates while Chile has just the opposite.
The degree of capital controls varies greatly among developing countries; Peru,
Chile and Romania have notably more open markets (on average). The Philippines, South
Africa, Indonesia and Thailand have no variation over the period meaning there were
little changes in capital mobility. The Chinn-Ito index is also included in the table to
illustrate the differences between the two mobility measures. An ordered ranking of these
countries by capital mobility by either index yields similar though different results. For
instance, Indonesia and Mexico have greater capital mobility according to the Chinn-Ito
measure as compared to the derived measure. Overall, the indices have a 75% correlation
between them and it is not immediately clear which is the stronger measure. We utilize
the Chinn-Ito index in place of the derived measure as a robustness test for the various
model specifications presented in Chapter 5.
54
CHAPTER 5
RESULTS
5.1 Preparation for Fixed Effects Panel Estimation
Prior to the use of Driscoll and Kraay (1998) standard errors, the data must first
be tested for cross-sectional dependence as noted in Hoechle (2007); standard error
estimates in models without cross-sectional dependence may not be valid. De Hoyos and
Sarafidis (2006) discuss three potential methods to conduct this test using techniques
proposed by Friedman (1937), Frees (1995) and Pesaran (2004). 23 The former rely on
nonparametric distributions to calculate test statistics but are only suitable for strongly
balanced panel data. Pesaran’s technique examines the residuals of a particular regression
and determines the degree of pairwise correlation while allowing panels to be
unbalanced. An issue with Pesaran’s technique is that it requires N>T which is not the
case for this dataset, although this statistic shows evidence of cross-sectional dependence
for several model specifications presented in this thesis. Baum (2001) provides another
method for calculating the existence of cross-sectional dependence by using a BreuschPagan Lagrange Multiplier test of independence; this tests the null hypothesis that
residuals are not correlated across entities. The p-value for this statistic is included in
each table of the results to validate the use of Driscoll and Kraay (1998) standard errors.24
Note that the only models we are not able to adjust for cross-sectional dependence are
certain specifications of the Latin America and Asia subsamples. When calculating the
23
24
De Hoyos and Sarafidis (2006) provide a program in Stata, which easily runs these diagnostics.
We also utilize Pesaran’s (2004) technique as a secondary test for cross-sectional dependence.
55
standard error adjustment for cross-sectional dependence, the appropriate lag length is
selected using the Newey-West plug-in procedure as discussed in Hoechle (2007).25 In
cases where cross-sectional dependence is not evident, clustered standard errors (by
country) are used instead which allows for robust standard errors in the presence of
autocorrelation and heteroskedasticity; p-values for these issues are also reported in the
regression results using methods prescribed by Wooldridge (2002) and Green (2000). We
conduct a Hausman test after each regression in order to confirm that a fixed effect model
is the most appropriate for the analysis. With exception to one case, we find the fixed
effects model to be more efficient than random effects and thus we do not report the
resulting statistic.26
The issue of stationarity has become a salient point of discussion in longitudinal
data models. Breitung and Pesaran (2005) discuss various methods of testing panel data
for unit roots and note recent interest in the literature to control for the effects of crosssectional dependence in empirical models. In order to detect the existence of a unit root
we combine the techniques of Maddala and Wu (1999) via the Fisher test and Pesaran
(2007) via the cross-sectiontionally oriented ADF test (CADF). The former is a standard
stationarity test that is robust for long time series panels and for unbalanced panel data; it
tests individual panels and combines the p-values for one test-statistic. The latter relaxes
the assumption of cross-sectional independence of the Fisher test by incorporating both
25
𝑇
2
This plug-in is computed by the following equation: 𝑚(𝑇) = 𝑓𝑙𝑜𝑜𝑟[4( )9 ]. Stata automatically
100
calculates the appropriate lag length when utilizing Driscoll and Kraay (1998) standard errors.
26
The single case occurs during the second model specification of the aggregated Edwards (2012) sample;
since this is only an efficiency concern, this particular model is not the final specification, and the
assumptions of random effects are unlikely to be valid, fixed effects are used.
56
level and lagged averages of the cross-sections and averages the values of t-statistics.
Both tests have a null hypothesis that all panels contain a unit root against the alternative
that at least one panel is stationary.
One issue with detecting roots in this study is that many of the data simply have
very little variation within weekly time series; when a change does occur, the data appear
to move suddenly with a semi-permanent new path. For instance, the current account
balance, debt and deficit statistics have a fixed data point for each year of the sample and
move only at the beginning of each year; all of these variables test positive for unit roots
with a p-value of 1.000 using Pesaran’s test. In addition, many of the time series are fixed
across panels such as the federal funds rate, the VIX, the commodity controls and Fed
balance sheet size; these also result in a p-value of 1.000 using Pesaran’s test. In fact,
both the entity-invariant and infrequently varying data result in the same test-statistic of
17.627. Using the Fisher method and a few alternate specifications of the variables, we
construct the variables as follows. The EMBI and CDS measures are found to be
stationary in level form (marginally so in the case of the EMBI). The Fed balance sheet
size and commodity indices are stationary in logged differenced form while GDP,
inflation and deposit rates are stationary and do not require a transformation. Capital
mobility and the three fiscal measures test positive for a unit root. Since each has little
variation and a transformation has no meaningful interpretation in the model, these are
left unaltered. The effective federal funds rate tests positive for a unit root but recent
evidence suggests, however, that the federal funds rate is in fact a stationary process, at
least through April 2008 (Bec and Bassil, 2009). We maintain this variable in its level
57
form in order to preserve the interpretation of the variable with regard to interest rate
parity theory under the assumption of stationarity for the Pre-Lehman period (January
2000-September 12, 2008) and compare the results with the findings of Edwards (2012).
We confirm stationarity in the Post-Lehman period (September 19, 2008 – April 27,
2012) via the Fisher test. Table 1 summarizes these modifications.
5.2 Pre-Lehman Period Using Seven Emerging Markets
The initial specification of the model replicates the sample of Edwards (2012)
with respect to entity and time period selection. The sample includes Brazil, Chile,
Colombia, Mexico, Indonesia, Korea and the Philippines from 2000 through the second
week of September 2008 (i.e. the week prior to the collapse of Lehman brothers).
Edwards intentionally chose this period in order to avoid the potential contagion of the
financial crisis. Later model specifications used in this thesis expand the time series
through the crisis to test the robustness of the model and the potential of a structural
break following this event. Although this is not an endogenously determined break, the
intent is to extend the literature and provide a potential direction for future work. Initially,
we analyze the countries according to region (Latin America and Asia) to enable a
comparison with the results of Edwards (2012); in Section 5.3, we aggregate the seven
countries into one model as a first step towards expanding the cross-section. Tables 4 and
5 present the results of the regressions for the Latin America and Asia subsamples, each
with five model specifications.
The initial model tests the basic relationship between deposit rates, the federal
58
Table 4 - Latin America Pre-Lehman Sample Replication Fixed Effects Results
Specification
(1)
(2)
(3)
(4)
Standard Error Type
DK
DK
DK
DK
Effective Federal Funds Rate
0.353***
0.541***
0.513***
0.458***
(0.052)
(0.071)
(0.077)
(0.078)
EMBI
0.007***
0.006***
0.002**
0.003***
(0.001)
(0.001)
(0.001)
(0.001)
Expected Depreciation
0.185***
0.197***
0.207***
0.209***
(0.049)
(0.044)
(0.042)
(0.039)
Capital Mobility
0.0388
0.251*
0.214
0.196
(0.070)
(0.135)
(0.140)
(0.139)
EFFR*Capital Mobility
--0.059**
-0.059
-0.044
(-0.026)
(-0.031)
(-0.029)
EMBI*Capital Mobility
-0.0003
0.001***
0.0004**
(0.000)
(0.000)
(0.000)
Depreciation*Capital Mobility
--0.003
-0.020
-0.021*
(-0.013)
(-0.013)
(-0.012)
Inflation
--0.393***
0.441***
(0.054)
(0.057)
GDP
---0.319***
-0.309***
(-0.070)
(-0.066)
VIX (Logged)
----1.374***
(-0.250)
VIX*Capital Mobility
----Constant
Observations
Number of Countries
R-Squared (Within)
Modified Wald-Test (P-Value)
Wooldridge AC Test (P-Value)
Breusch-Pagan LM Test (P-Value)
F-Statistic
Maximum Number of Lags
3.421***
(0.592)
1,786
4
0.559
(0.000)
(0.026)
(0.000)
77.3
5
2.811***
(0.504)
1,786
4
0.570
(0.000)
(0.023)
(0.000)
57.6
5
4.115***
(0.965)
1,784
4
0.709
(0.000)
(0.029)
(0.001)
98.1
5
7.227***
(1.141)
1,784
4
0.724
(0.000)
(0.029)
(0.000)
78.3
5
(5)
HAC
0.462*
(0.183)
0.001
(0.001)
0.219**
(0.041)
2.445***
(0.245)
-0.058
(-0.044)
0.001**
(0.000)
-0.026
(-0.017)
0.454**
(0.116)
-0.308**
(-0.090)
0.864***
(0.088)
-0.780***
(-0.106)
1.662
(2.866)
1,784
4
0.759
(0.000)
(0.028)
(0.208)
109.0
5
Notes: Standard errors reported in parentheses. Significance levels: *** p<0.01, ** p<0.05, * p<0.1
Modified Wald test follows methodology of Green (2000); null hypothesis of homoskedasticity. Wooldridge
autocorrelation test follows methodology of Wooldridge (2002); null hypothesis of no autocorrelation. BreuschPagan LM Test follows methodology of Breusch and Pagan (1980). DK defines use of Driscoll and Kraay (1998)
standard error adjustment; HAC defines use of heteroskedasticity and autocorrelation-adjusted standard errors.
funds rate, country-specific risk, expected currency depreciation and capital mobility.
The coefficient on the effective funds rate is positive and statistically significant in Latin
America. Since the coefficient is not equal to one (as tested via a Wald test), this suggests
59
Table 5 - Asia Pre-Lehman Sample Replication Fixed Effects Results
Specification
(1)
(2)
(3)
(4)
Standard Error Type
DK
DK
DK
HAC
Effective Federal Funds Rate
0.650***
0.766***
0.724***
0.740***
(0.048)
(0.071)
(0.015)
(0.010)
EMBI
0.003***
0.006***
0.008**
0.008*
(0.001)
(0.001)
(0.002)
(0.002)
Expected Depreciation
0.397***
0.401***
0.413**
0.408**
(0.039)
(0.053)
(0.060)
(0.054)
Capital Mobility
0.128***
0.972***
1.115**
1.157**
(0.043)
(0.136)
(0.184)
(0.166)
EFFR*Capital Mobility
--0.123***
-0.130**
-0.129**
(-0.021)
(-0.014)
(-0.014)
EMBI*Capital Mobility
--0.004***
-0.005
-0.005
(-0.001)
(-0.002)
(-0.002)
Depreciation*Capital Mobility
--0.006
-0.047
-0.044
(-0.027)
(-0.051)
(-0.049)
Inflation
--0.206
0.216
(0.116)
(0.116)
GDP
--0.0692
0.0628
(0.085)
(0.075)
VIX (Logged)
---0.340
(0.336)
VIX*Capital Mobility
----Constant
Observations
Number of Countries
R-Squared (Within)
Modified Wald-Test (P-Value)
Wooldridge AC Test (P-Value)
Breusch-Pagan LM Test (P-Value)
F-Statistic
Maximum Number of Lags
2.424***
(0.291)
1,132
3
0.735
(0.000)
(0.002)
(0.000)
72.7
5
1.739***
(0.273)
1,132
3
0.747
(0.000)
(0.002)
(0.000)
61.6
5
-0.864
(-0.804)
1,130
3
0.792
(0.000)
(0.002)
(0.032)
42.4
5
-2.317
(-1.918)
1,130
3
0.793
(0.000)
(0.002)
(0.231)
---
(5)
HAC
0.715***
(0.021)
0.006*
(0.003)
0.406**
(0.055)
0.353
(0.518)
-0.109*
(-0.030)
-0.006
(-0.002)
-0.041
(-0.052)
0.210
(0.120)
0.0577
(0.082)
-0.111
(-0.524)
0.271
(0.205)
-0.685
(-2.549)
1,130
3
0.795
(0.000)
(0.002)
(0.440)
---
Notes: Standard errors reported in parentheses. Significance levels: *** p<0.01, ** p<0.05, * p<0.1
Modified Wald test follows methodology of Green (2000); null hypothesis of homoskedasticity. Wooldridge
autocorrelation test follows methodology of Wooldridge (2002); null hypothesis of no autocorrelation. Breusch-Pagan
LM Test follows methodology of Breusch and Pagan (1980). DK defines use of Driscoll and Kraay (1998) standard
error adjustment; HAC defines use of heteroskedasticity and autocorrelation-adjusted standard errors.
that interest rate shocks do not fully transmit to this region (as is the case throughout all
models examined in this section); the same effect is found in Asia although the
coefficient is nearly twice the size. Both regions show positive and statistically
60
significant coefficients for the EMBI and expected currency depreciation. Note that the
latter variable has a coefficient about half the size of the coefficient on the effective funds
rate in both regions as well. This shows that depreciation is not completely mitigating the
difference in international interest rates with the United States and the basic theory of
interest rate parity does not hold. As discussed in Chapter 3, however, that model
assumes freely mobile capital. The inclusion of capital mobility in the model allows for
the relaxation of this assumption; this variable is not statistically significant in Latin
America but is in Asia. The positive sign indicates that as capital controls are relaxed the
country generally sees higher interest rates. Though an issue with this specification is that
it does not suggest what transmission mechanism capital controls might be utilizing.
Specification (2) expands on the previous analysis by allowing interaction terms
between capital mobility and the other regressors; in doing so, we are better able to
establish the channel through which capital controls are affecting domestic interest rates
as shown in Edwards (2012). This altered specification results in positive statistically
significant coefficients on capital mobility for both countries; both coefficients show a
larger size than in their initial specifications. Both countries also share a negative and
statistically significant coefficient on the interaction between the effective federal funds
rate and capital mobility. The negative sign illustrates that greater capital mobility
actually helps to mitigate the effects of interest rate changes in foreign nations. This is
consistent with the finding in Edwards (2012) that capital controls not only are
ineffective in protecting the economy from interest rate shocks, they enlarge the effect of
the interest rate pass-through. Edwards (2012) posits that this may be the case because
61
countries with greater capital mobility tend to have lower inflation rates and thus are less
sensitive to interest rate shocks from abroad.
A shortcoming of his model, however, is that Edwards (2012) does not explicitly
control for inflation, which may be an important regressor; we address this in the next
specification. With regard to the interaction between the EMBI and capital mobility, only
Asia’s term is statistically significant and negative. This finding shows that with freer
movement of capital, concerns of country-specific risk are less important to the market;
this may be explained by the fact that capital controls can prevent investors from pulling
their money out of the country (this is part of what they are designed to do) and thus
require higher deposit rates to attract foreign capital. However, this effect is not robust in
other specifications. In both countries, the coefficient on the effective federal funds rate is
larger and remains statistically significant near this new level in the remaining
specifications. The results indicate that interest rate parity theory is able to explain some
of the pass-through effect but not entirely, suggesting risk parity may not hold during this
period. In general, the results of the initial two specifications reinforce the findings of
Edwards (2012); the remaining specifications seek to enhance the model by controlling
for additional factors.
Without the use of time-fixed effects, the model is greater exposed to estimation
issues caused by omitted variable bias. In Tables 4 and 5, specification (3) introduces
control variables into the model by including commodity prices, GDP, inflation and fiscal
factors to help mitigate this bias; for brevity only the GDP and inflation terms are
62
included in the results table.27 The inclusion of these controls in the model greatly
enhances the fit of the model for both Latin America and Asia as illustrated by higher
within R-squared values. Both regions see the coefficient on the effective federal funds
rate shrink marginally, although it remains positively statistically significant as do the
EMBI and expected depreciation coefficients. Capital mobility remains statistically
significant for Asia, as does the negative coefficient on the interaction between capital
mobility and the effective federal funds rate. The coefficient on the interaction between
the EMBI and capital mobility, however, becomes statistically insignificant and remains
so for the remainder of the specifications. Interestingly, this same interaction becomes
statistically significant for Latin America and is positive for the remaining specifications,
suggesting that capital controls are helpful in preventing outflows when country-specific
risk concerns increase.
Unfortunately, the capital mobility measure is not able to distinguish between
different types of controls in place; it may be the case that for Latin America there are
policies in place to limit international investors’ ability to rapidly withdraw their deposits
(deposit flight). Latin America has two particularly notable significant terms: inflation
and GDP. Both are statistically significant with the former having a positive sign and the
latter having a negative sign; these effects remain through the remaining specifications.
One would expect higher inflation to push up nominal interest rates as higher price levels
increase the opportunity cost of holding money. With regard to growth, interest rates in
an economy tend to rise during favorable periods but perhaps this negative effect on
27
See Appendix B for the remainder of the control variable results.
63
deposit interest rates is capturing the effect of increased consumer savings. For instance,
if the economy is growing robustly, there is an incentive to save funds in order to spend
them when growth becomes less favorable (countercyclical spending). This would
incentivize banks to reduce the premium on holding money with high demand for CDs.
Note the effect of inflation on deposit rates is stronger than GDP’s effect, so the net effect
between a growing economy with higher inflation, is higher rates.
Specifications (4) and (5) introduce global risk sentiment to the model and relax
the risk-neutrality assumption of risk parity. One could argue that the EMBI also relaxed
this assumption but Edwards (2012) uses this term more as a means to allow for the
(likely) possibility that securities in emerging markets and developed markets are not
perfect substitutes for one another. That notwithstanding, the addition of a global risk
measure allows the model into incorporate risk beyond country-specific risk. Adding the
VIX variable into the regression generally results in mild changes to the coefficients of
other variables for both regions. We observe, however, a notable change for Latin
America with the interaction between the expected depreciation and capital mobility
becoming negative and statistically significant; this effect disappears in the remaining
specifications. The VIX is not a statistically significant regressor in Asia, but is for Latin
America with a large negative coefficient. This indicates that as global risk increases,
deposit rates fall; perhaps this is also evidence of savings behavior during rocky periods
as suggested with a negative GDP coefficient.
In order to better discern the effect that capital controls have on global risk,
specification (5) adds an interaction between the two variables. This addition proves
64
important for Latin America as its formerly negative coefficient on the VIX becomes
positive and the new interaction term has a negative coefficient (both significant at the
1% level). These coefficients illustrate that heightened global risk, on average, will
increase deposit rates but markets with freer capital movement can better avoid the shock.
Since the VIX is a non-zero number, the inclusion of the interaction term makes the
coefficient on capital mobility much higher than in previous specifications and now
significant. For Asia, adding in the interaction term renders the capital control coefficient
insignificant. The lack of significance with the two new terms suggests that their markets
are generally unaffected by changes in global risk sentiment, at least as measured by the
VIX. Through these specifications, however, the coefficients on the effective federal
funds rate and the expected depreciation term remain positively statistically significant at
generally stable levels; risk parity remains unable to fully explain the transmission
process.
The general findings during the Pre-Lehman period for these seven countries
suggest that interest parity theory is able to explain only a portion of the variation in
deposit rates. Arbitrage opportunities still exist since currency depreciation is not eroding
the gains from interest rate differentials and thus, pure interest rate parity does not hold.
In addition, the results of this section broadly reinforce those found in Edwards (2012). In
his analysis, he found Latin America to be less affected by changes in the federal funds
rate than the Asian economies. In fact, he notes that about half of the rate change
transmits to Latin America and full effect transmits to Asia. Although the magnitude of
the coefficients between these studies differs, they both offer similar conclusions.
65
Accounting for capital controls was important in the model as noted by positive and
statistically significant coefficients amongst both regions. However, testing two relatively
small subsamples makes it difficult to make inferences about the broader population of
emerging markets. This is especially true for Asia given that we had just three countries
with substantially different economies. In addition, we note to the reader that non-entity
variables such as the VIX and federal funds rate may capture similar variation as the
entity-fixed effects, potentially confounding the coefficients. This is an inherent issue
with this type of data when implementing across several panels as opposed to a single
entity over time. In the section that follows, we expand the cross-section for the preLehman period to examine the robustness of the results presented here.
5.3 Pre-Lehman Period Expansion of the Cross Section
Using the final specification presented in the Section 5.2, we gradually extend the
cross-section by including the following countries: Peru, Poland, South Africa and
Turkey. This allows for a better determination of the external validity of the analysis with
respect to the results of Edwards (2012) and the analysis presented in Section 5.2. We
select these countries because they utilized floating exchange rates, are classified as
emerging markets (by JP Morgan) and have appropriate data availability. We focus only
on the pre-Lehman period during this section before expanding the time series through
the post-Lehman period; Table 6 presents the results for this broader sample of countries.
Specifications (1) through (4) show the gradual expansion of the cross-section and
specifications (5) and (6) perform two robustness checks before expanding the sample
through the crisis.
66
Table 6 - Pre-Lehman Expanded Cross-Section Fixed Effects Results
Specification
(1)
(2)
(3)
(4)
(5)
(6)
Standard Error Type
DK
DK
DK
DK
DK
DK
Effective Federal Funds Rate
0.616***
0.277***
0.628***
0.530***
0.518***
0.585***
(-0.066)
(-0.051)
(-0.071)
(-0.053)
(-0.053)
(-0.061)
EMBI
0.002**
0.003***
0.003***
0.003***
-0.006***
(-0.001)
(-0.001)
(-0.001)
(-0.001)
(-0.001)
Expected Depreciation
0.277***
0.215***
0.545***
0.564***
0.540***
0.404***
(-0.041)
(-0.042)
(-0.041)
(-0.037)
(-0.041)
(-0.062)
Capital Mobility
2.251***
0.308*
0.099
-0.041
-0.716**
-(-0.284)
(-0.160)
(-0.239)
(-0.232)
(-0.290)
EFFR*Capital Mobility
-0.095***
0.080***
0.023
0.039***
0.028*
-(-0.024)
(-0.01)
(-0.016)
(-0.014)
(-0.017)
EMBI*Capital Mobility
0.0008***
0.0006***
0.0007***
0.0008***
--(0.000)
(-0.000)
(0.000)
(0.000)
Depreciation*Capital Mobility
-0.035***
-0.008
-0.062***
-0.060***
-0.044***
-(-0.012)
(-0.009)
(-0.016)
(-0.016)
(-0.014)
Inflation
0.356***
0.402***
0.320***
0.331***
0.357***
0.333***
(-0.036)
(-0.036)
(-0.039)
(-0.040)
(-0.044)
(-0.045)
GDP
-0.124**
-0.050
-0.017
-0.006
-0.090
-0.023
(-0.049)
(-0.040)
(-0.054)
(-0.052)
(-0.058)
(-0.056)
VIX (Logged)
1.093***
0.034
1.066***
0.306
0.591***
0.290
(-0.233)
(-0.203)
(-0.322)
(-0.267)
(-0.219)
(-0.244)
VIX*Capital Mobility
-0.654***
-0.203***
-0.065
-0.041
0.243**
-(-0.078)
(-0.052)
(-0.084)
(-0.079)
(-0.094)
Constant
-1.763
4.458***
-5.794***
-2.290*
-1.738**
-2.253
(-1.285)
(-0.921)
(-1.868)
(-1.357)
(-0.796)
(-1.390)
Observations
2,914
3,340
4,246
4,699
4,903
4,699
Number of Countries
7
8
10
11
11
11
R-Squared (Within)
0.722
0.715
0.865
0.859
0.838
0.852
Modified Wald-Test (P-Value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
Wooldridge AC Test (P-Value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
Breusch-Pagan LM Test (P-Value)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
F-Statistic
135.9
77.5
197.3
222.9
264.8
211.6
Maximum Number of Lags
5
5
5
5
5
5
Notes: Standard errors reported in parentheses. Significance levels: *** p<0.01, ** p<0.05, * p<0.1
Modified Wald test follows methodology of Green (2000); null hypothesis of homoskedasticity. Wooldridge
autocorrelation test follows methodology of Wooldridge (2002); null hypothesis of no autocorrelation. Breusch-Pagan
LM Test follows methodology of Breusch and Pagan (1980). DK defines use of Driscoll and Kraay (1998) standard
error adjustment; HAC defines use of heteroskedasticity and autocorrelation-adjusted standard errors.
The first specification combines the two subsamples from Latin America and Asia
that we analyzed previously. As these were the countries examined in Edwards (2012),
this is a logical starting point. The results show that the effective federal funds rate,
EMBI and expected depreciation maintain their respective strengths and signs in the
model (all positive coefficients). The sign on the capital mobility term looks similar to
67
that of the final two specifications of the Latin America subsample; this result shows that
capital controls help to limit interest rate shocks from abroad. The coefficients on the
capital mobility interactions with the effective federal funds rate and the EMBI are
statistically significant with the former being negative and the latter being positive.
Interestingly, the capital control interaction with expected depreciation is negative and
statistically significant suggesting that capital controls make the effect of expected
depreciation on domestic deposit rates stronger; this is consistent with the Edwards and
Rigobon (2009). This is indicates that those involved in the market are increasingly more
sensitive to a change in the expected rate of depreciation as capital controls tighten. The
VIX and its interaction term also have similar signs and significance levels as seen in the
Latin America subsample. The coefficient on the VIX generally remains positive and its
interaction with capital mobility negative through most specifications presented in this
section. Together this suggests that in the pre-Lehman period, global risk pushed spreads
up in emerging markets (perhaps because of their then weaker economies) and the
existence of capital controls further strengthened this effect. Domestic fundamentals are
statistically significant variables with a positive sign on inflation and a negative sign on
GDP (as was the case in the Latin America subsample). The results of this first
specification are generally in line with what we observed in Section 5.2 with regard to
coefficient signs and significance.
Specification (2) in Table 6 adds Peru to the model; the initial standout statistic is
the lower positive (but statistically significant) coefficient on the federal funds rate. This
results from the interaction between capital mobility and the effective federal funds rate.
68
Since the capital mobility term has no variation during the pre-Lehman for Peru, this
reduces the magnitude of the coefficient on capital mobility and subsequently diminishes
the magnitude of the coefficient on the federal funds rate. Despite these changes, both
terms remain statistically significant in the model. Interestingly, the interaction between
capital mobility and the federal funds rate turns positive because of this country’s
addition to the model, suggesting that capital controls help limit the effect of interest rate
shocks. This effect remains significant even if we add all 11 countries to the model. The
same is true for the interaction between the EMBI and capital mobility terms. The VIX
loses significance and power in this specification but the interaction with capital mobility
remains negative and statistically significant. Domestic inflation continues to play an
important role in the model though GDP loses its significance (and does so through the
remainder of this section). Despite some larger changes in magnitude, the general picture
remains consistent with the previous model.
Specification (3) adds European countries to the model (Turkey and Poland) and
specification (4) adds in South Africa to complete the aggregated model. In both
specifications, the coefficient on the effective federal fund rate is higher than in
specification (2) reinforcing the notion that this coefficient was skewed due to Peru’s lack
of variation in capital mobility over the period. The EMBI retains its positive and
statistically significant sign throughout both models, which further suggests higher credit
risk leads to higher deposit rates. The coefficient on the VIX is statistically significant in
specification (3) but not in (4); its interaction term with capital mobility loses
significance in both models. It is possible that a portion on the variation in VIX is
69
incorporated in the information contained in the EMBI as sovereign spreads tend to rise
in response to global risk aversion; this idea is tested in specification (5). The coefficient
on expected currency depreciation retains significance but also gains considerable
magnitude in both models, particularly so in specification (4). In fact, after conducting a
Wald test for equality of the coefficients between expected depreciation and the federal
funds rate, we are unable to reject the null. This suggests that interest rate parity exists
without a full pass-through as the sample expands to a greater number of emerging
markets. This finding is in line with existing literature that interest rate parity holds over
longer time horizons.
As noted above, the inclusion of both the VIX and the EMBI could render one
variable statistically insignificant if they both explain similar variation. Although the
magnitude in spreads varies significantly between emerging markets, there is certainly a
tendency to increase during risk-off episodes (i.e. higher volatility as measured by the
VIX). However, the VIX caries more explicit information about the state of global
markets as opposed to country-specific risk, so the inclusion of both variables is relevant
for the model. In specification (5), we omit the EMBI and its interaction from the
regression and examine the stability of the remaining variable coefficients and in
particular, to observe changes in the VIX measures. The majority of the variables
maintain significance, magnitude and direction with a few notable exceptions. First, note
that the capital mobility measure becomes statistically significant and is now negative. In
addition, both the VIX and its interaction with the capital mobility measure are both
positive and significant. While the VIX generally showed a positive sign, its interaction
70
has typically been negative. In the absence of a measure of country-specific risk, it
appears that the VIX becomes important in explaining this portion of the variation. In
addition, without the interaction between capital mobility and the EMBI, the interaction
between the VIX and capital mobility now captures this variation. Given that the
interaction between the EMBI and capital mobility was positive and statistically
significant, it is less of a surprise to see the VIX’s interaction term now positive as well.
This finding illustrates that the model could provide additional value if one could find a
measure of global risk that is not correlated with the variation of country-specific risk
measures.
As observed in specification (2) in Table 6 with regard to capital mobility, a
variable’s lack of variation can be problematic in establishing its importance in a model.
Specification (6) makes an additional robustness check before expanding the sample
through the post-Lehman era by removing capital mobility measures and its related
interactions from the regression. The results show that inflation, the effective federal
funds rate, the EMBI and currency depreciation are robust to the various model
specifications. The VIX loses significance in this version of the model but this may be a
reflection of the importance of its interaction with capital mobility. Interestingly, note
that despite the removal of five variables, the fit remains relatively strong as
demonstrated by the within R-squared. Of course, the caveat to a relatively simple test
such as this is that the model is subject to greater omitted variable bias from the removal
of regressors that are correlated with the dependent variable. However, this specification
71
demonstrates that though capital controls may be crudely measured, they do not appear to
be causing issues within the model once we expand the sample.
This section illustrates the general robustness of the effects that the federal funds
rate, expected depreciation, country-specific risk and capital mobility have on deposit
rates in emerging markets. Indeed, examining the various interaction terms with the
capital mobility measure throughout the specifications, one observes several terms that
are consistently significant to the model. For instance, the interaction with the EMBI
illustrates that as country-risk rises, capital controls dampens the pressure of interest rate
hikes. Interest rate parity did not hold when we used smaller sample sizes but once
expanded to eleven countries, the theory held. We examine this idea further in Section
5.4 as we expand the sample through a longer time horizon. In addition, the results
generally indicate that during this period global risk was typically associated with higher
deposit rates in emerging markets; capital mobility appears to weaken the effects of this
risk. However, these effects are difficult to ascertain due to multicollinearity with the
EMBI. However, as noted previously, emerging markets were considerably weaker in the
pre-Lehman era and the upward pressure on interest rates under heightened global risk is
likely a reflection of investor concerns for these markets during this period.
Although we do not report the control variables’ results in the text, government
debt and the current account balance both had positive and statistically significant
coefficients in several specifications. The coefficient on debt is as expected while in the
case of the current account, a negative coefficient is somewhat surprising. However, one
must not forget that in emerging markets, it is common to have significant repatriation
72
flows, which could feasibly put upward pressure on domestic deposit rates through
greater deposit demand. Additionally, the negative coefficient on the interaction term
between expected depreciation and capital mobility suggests that those with more open
markets are less affected by changes in currency expectations. This result is consistent
with the findings in the literature. For instance, Glick and Hutchison (2011) finds that
countries with freer capital mobility are less subject to currency crises, and Edwards and
Rigobon (2009) shows that tighter capital controls increase the depreciation of a countries
currency. Thus, the results of this section generally point to robust conclusions from
Section 5.2. However, in this section, we find that by aggregating the countries (instead
of examining by small samples of a region), we are able to make general inferences about
emerging markets as a whole over the pre-Lehman period. The following section extends
this analysis through the crisis to determine if these effects remain in the post-Lehman
period.
5.4 Expansion of the Time Series
This section makes an important contribution to the literature by examining
whether the results observed during the pre-Lehman are consistent with those in the postLehman era. We focus on the same 11 countries that we analyzed in Section 5.3 since
Romania and Thailand lack important data from the pre-Lehman era. The data span the
period from January 2000 through April 2012, a total of 643 weeks. The various
specifications in this section augment the model with additional variables relevant to the
period. We include a binary measure that takes on the value of one during quantitative
easing periods and zero otherwise. In addition, we include a binary variable for the Post-
73
Lehman period that takes on a value of one the week of the Lehman Brother’s default and
zero otherwise. Importantly, we implement the variable measuring Fed balance sheet
growth in the model to account for monetary policy in the later period of the sample. The
model also utilizes various interaction terms between these variables and other regressors.
Table 7 summarizes the results of this section.
Specification (1) simply extends the initial model from Section 5.3 to the full time
period of the sample and adds in Fed balance sheet growth. The results generally are in
line with the pre-Lehman period. The effective federal funds rate, EMBI, expected
depreciation and inflation retain statistical significance and direction. Note, however, that
the coefficient on expected depreciation is now slightly larger than the coefficient on the
effective federal funds rate. We conduct a Wald test that examines whether the difference
between these variables is statistically different from zero and are unable to reject the null
of equivalence; this was also the case in specification (4) of Section 5.3. This is evidence
that over this longer time horizon, interest rate parity appears to hold, although not all of
an interest rate shock is transmitted to domestic interest rates in emerging economies (as
the coefficients on these variables are not equal to one when examined via a Wald test).
Also note that the federal funds rate retains a relatively large coefficient at 0.568 despite
having little variation in the post-Lehman period; perhaps this is its long-term
relationship for emerging markets. Both of the capital mobility interactions between the
EMBI and expected depreciation also retain their respective signs and significance. The
individual capital mobility term now has a negative and statistically significant negative
sign, which is consistent throughout the specifications in this section (and with the
74
Specification
Standard Error Type
Effective Federal Funds Rate
Table 7 - Full Period Fixed Effects Results
(1)
(2)
(3)
(4)
DK
DK
DK
DK
0.568***
0.585***
0.585***
0.584***
(-0.045)
(-0.060)
(-0.060)
(-0.060)
0.003***
0.003***
0.003***
0.003***
(-0.001)
(-0.001)
(-0.001)
(-0.001)
0.619***
0.621***
0.622***
0.622***
(-0.036)
(-0.038)
(-0.038)
(-0.038)
-0.578**
-0.582**
-0.581**
-0.554**
(-0.232)
(-0.235)
(-0.234)
(-0.232)
-0.009
-0.009
-0.009
-0.009
(-0.015)
(-0.015)
(-0.015)
(-0.015)
0.0008***
0.0008***
0.0008***
0.0008***
(0.000)
(0.000)
(0.000)
(0.000)
-0.062***
-0.063***
-0.063***
-0.063***
(-0.013)
(-0.013)
(-0.013)
(-0.013)
0.347***
0.346***
0.346***
0.346***
(-0.039)
(-0.040)
(-0.040)
(-0.040)
0.002
0.003
0.002
0.002
(-0.035)
(-0.035)
(-0.035)
(-0.035)
-0.652**
-0.688**
-0.711**
-0.686**
(-0.264)
(-0.277)
(-0.281)
(-0.275)
0.151**
0.151**
0.150**
0.140*
(-0.074)
(-0.074)
(-0.074)
(-0.073)
3.793
3.649
4.317*
0.407
(-2.304)
(-2.231)
(-2.396)
(-1.793)
---0.108
-0.105
(-0.115)
(-0.115)
---4.614
-4.864
(-4.626)
(-4.553)
-0.148
0.217
0.213
(-0.260)
(-0.276)
(-0.276)
-----
Post-Lehman*Cap Mobility
--
--
--
--
(5)
DK
0.552***
(-0.058)
0.004***
(-0.001)
0.599***
(-0.038)
-0.549**
(-0.226)
0.0014
(-0.015)
0.0006***
(0.000)
-0.056***
(-0.013)
0.337***
(-0.040)
-0.038
(-0.035)
-0.235
(-0.241)
0.128*
(-0.072)
2.163
(-1.776)
-0.0416
(-0.117)
-8.379*
(-4.372)
1.533***
(-0.412)
-0.006***
(-0.001)
--
Bal Sheet Growth*Capital Mobility
--
--
--
1.760*
(-1.052)
6,778
11
0.872
(0.000)
(0.000)
(0.000)
193.6
6
1.695
(-1.077)
6,778
11
0.872
(0.000)
(0.000)
(0.000)
350.7
6
1.781
(-1.087)
6,778
11
0.872
(0.000)
(0.000)
(0.000)
312.9
6
1.397***
(-0.354)
1.728
(-1.081)
6,778
11
0.872
(0.000)
(0.000)
(0.000)
321.9
6
1.309***
(-0.297)
0.927
(-1.012)
6,778
11
0.876
(0.000)
(0.000)
(0.000)
337.5
6
EMBI
Expected Depreciation
Capital Mobility
EFFR*Capital Mobility
EMBI*Capital Mobility
Depreciation*Capital Mobility
Inflation
GDP
VIX (Logged)
VIX*Capital Mobility
Fed Balance Sheet Growth
Quantitative Easing (Binary)
QE*Fed Balance Sheet Growth
Post-Lehman Period (Binary)
Post-Lehman*EMBI
Constant
Observations
Number of Countries
R-Squared (Within)
Modified Wald-Test (P-Value)
Wooldridge AC Test (P-Value)
Breusch-Pagan LM Test (P-Value)
F-Statistic
Maximum Number of Lags
(6)
DK
0.450***
(-0.053)
0.003***
(-0.001)
0.599***
(-0.038)
-0.453**
(-0.190)
0.045***
(-0.016)
0.0009***
(0.000)
-0.058***
(-0.012)
0.343***
(-0.040)
-0.022
(-0.036)
0.086
(-0.236)
0.004
(-0.068)
2.790
(-1.983)
-0.0505
(-0.116)
-7.951*
(-4.296)
0.332
(-0.430)
-0.005***
(-0.001)
0.360***
(-0.078)
1.024***
(-0.253)
0.449
(-0.971)
6,778
11
0.877
(0.000)
(0.000)
(0.000)
386.8
6
Notes: Standard errors reported in parentheses. Significance levels: *** p<0.01, ** p<0.05, * p<0.1
Modified Wald test follows methodology of Green (2000); null hypothesis of homoskedasticity. Wooldridge
autocorrelation test follows methodology of Wooldridge (2002); null hypothesis of no autocorrelation. Breusch-Pagan
LM Test follows methodology of Breusch and Pagan (1980). DK defines use of Driscoll and Kraay (1998) standard
error adjustment; HAC defines use of heteroskedasticity and autocorrelation-adjusted standard errors.
75
literature). This further reinforces the argument that capital controls do not sufficiently
protect an economy from international capital flows, at least not outright.
The coefficient on Fed balance sheet growth is insignificant with a positive sign;
this may be a result of its average effect over the entire period since this was not Fed’s
primary policy tool during that time.28 We test this idea in later specifications. The VIX
coefficient is negative and statistically significant which implies that, on average, higher
global risk pushed down deposit rates. As discussed in section 5.2 when this effect was
briefly found for Latin America, this may be capturing increased savings behavior in the
domestic economy during periods of global market turmoil, which puts downward
pressure on interest rates. Its interaction term with capital mobility has a positive sign,
which suggests the upward pressure global risk aversion puts on interest rates is mitigated
when capital controls are in place. This contrasts with the results of Sections 5.2 and 5.3
in which global risk put upward pressure on rates and capital controls made this effect
worse. This difference may be explained by the macroeconomic improvement of
emerging markets in the later portion of the period. We further analyze this idea in
Section 5.5. An issue with this particular specification of the model is the assumption of
equality between the two periods; the remaining specifications relax this idea and add
credence to the idea that a structural break may be occurring between these periods.
Specification (2) adds the binary variable for the post-Lehman period to the
regression as a first step in establishing differences between the two sub-periods. The
28
Note that we tested the measure of Fed balance sheet growth as a regressor in Sections 5.2 and 5.3 but it
was not statistically significant. This lack of significance over the pre-Lehman era may be dominating the
results of this variable during the full sample period.
76
coefficient on this binary variable is positive but insignificant while the coefficients on
the other variables remain broadly stable from the previous specification. On its own, this
suggests there is a negligible outright difference between deposit rates in the two periods.
This test is relatively simple as it only looks for explicit difference between deposit rates
and does not consider the potential that other variables in the regression may have
different effects on deposit rates between the two periods.
With the model extending through the post-Lehman era, we are able to add
additional indicators that may prove vital for this particular period. Specification (3) adds
a binary variable for quantitative easing periods, as well as an interaction between this
term and Fed balance sheet growth to the model. The results show, once again, that the
coefficients on the primary variables remain stable. However, the coefficient on the Fed
balance sheet growth term becomes statistically significant with a positive sign. This
implies that as the Fed purchased assets over the period, this generally put upward
pressure on deposits in emerging markets; a 1% increase in assets is associated with a 4.3
percentage points increase in deposit rates of emerging markets. Perhaps this is because
as the U.S. economy was in a generally expansive period from 2000-2008, interest rates
rose significantly in the economy (notably, three-month CDs). Over this same period, the
Fed also tightened monetary policy and its balance sheet slowly grew over the period;
this would imply that in emerging markets, higher deposit rates resulted in order to keep
deposits from leaving their domestic economies. Elsewhere in the model, the effects of
quantitative easing and its interaction with Fed balance sheet growth are statistically
insignificant but have the expected negative signs. Further analysis is needed to better
77
disentangle the underlying relationships during the crisis period.
In specification (4), we augment the model with an interaction term between
capital mobility and Fed balance sheet growth. This is an important variable to add as it
allows the researcher to see if capital controls provided any buffer from the effects of
asset purchases by the Fed over the sample period. As seen in Table 7, the coefficient on
the interaction term is statistically significant and positive, although the Fed’s balance
sheet growth coefficient is statistically insignificant. Together, this suggests that
emerging markets with capital controls in place are less affected by the Fed’s purchasing
activities. Consider a country with completely mobile capital (i.e. a score of 10); this
would suggest that a 1% increase in the Fed’s balance sheet would lead to a 14% increase
in local deposit rates (all else equal). This effect is persistent throughout the remaining
specifications in this section. The caveat with this result is that it accounts for the entire
sample period and the result may differ when examining the post-Lehman result; it could
potentially be stronger since asset purchases became the Fed’s primary policy tool.
Elsewhere in the regression, the quantitative easing binary and its interaction with Fed
balance sheet growth remain statistically insignificant and the other coefficients in the
model retain their signs, significance levels and magnitudes.
The final two specifications shown in Table 7 add various interaction terms with
the post-Lehman binary variable to the model in order to better understand how the
78
behavior of these variables may have changed in response to the crisis.29 Specification (5)
adds an interaction term between the post-Lehman period and the EMBI, which yields a
number of interesting results. First, the post-Lehman period binary variable becomes
positive and significant, though its interaction with the EMBI has a negative (and
statistically significant) sign. Since no country in the sample had an EMBI spread of less
than 95 basis points, the large coefficient on the post-Lehman binary indicator is greatly
mitigated when accounting for this interaction. Second, Fed balance sheet growth
strengthens but remains insignificant; its interaction with capital mobility, however,
remains statistically significant with little change in the magnitude. Third, the VIX
becomes statistically insignificant in the model but its interaction with capital mobility
remains positive, further evidence that capital controls assist in avoiding contagion from
global risk swings. Fourth, although the binary indicator for quantitative easing periods is
statistically insignificant, its interaction with Fed balance sheet growth becomes
statistically significant and has a large negative sign. This result is quite important as it
implies that during periods of quantitative easing, the net effect of these policies
generally puts downward pressure on emerging market deposit rates. We further explore
this finding in the Section 5.5 in which we focus exclusively on the Post-Lehman period.
Finally, in specification (6), we add an interaction between the post-Lehman
binary variable and the capital mobility term, which is positive and statistically
significant; higher capital controls thus, are generally related to countries with lower
29
We test an interaction between the post-Lehman era binary variable and the effective federal funds rate
but the term is insignificant. The same is true for an interaction between the VIX and the post-Lehman era.
We do not report the results of either here.
79
interest rates in the post-Lehman period. Interestingly, this pushes the coefficient on the
effective federal funds rate lower but the interaction between the federal funds rate and
capital mobility becomes positive and statistically significant; together, the net impact on
interest rates is similar as in prior regressions since few countries had a capital mobility
value of zero. This larger net effect is further reinforced by the coefficient on expected
depreciation since it saw no change from the previous specification of the model and still
suggests interest rate parity is holding over the period. The interaction between the VIX
and capital mobility becomes statistically insignificant as a result of the new term’s
addition but this is not too surprising given that these two interaction terms have a fair
degree of correlation (0.51). Elsewhere in the model, the coefficients remain stable with
only marginal changes in magnitude.
The broad results from the 2000-2012 period point to, on average, a relatively
stable longer-term relationship between the effective federal funds rate and the expected
depreciation of the domestic currency, evidence that interest rate parity may hold over
longer periods. This finding is in line with Aslan and Korap (2010) and the literature in
general. Note, however, that a full interest rate pass-through does not occur because of
the many other factors at work in the model (i.e. risk sentiment, capital controls and
domestic fundamentals). We find that global risk aversion generally puts downward
pressure on deposit rates with capital controls helping to mitigate the upward pressure;
the net effect greatly depends on the degree of capital mobility in an emerging market.
This section also demonstrated that quantitative easing put downward pressure on
emerging market deposit rates whilst the effective federal funds rate still remained
80
positive and statistically significant (both in line with theory). These effects were
partially offset in countries with capital controls in place. However, the federal funds rate
captures an average effect from the two periods, which motivates further exploration of
the post-Lehman period by itself to determine how the effect may have changed. In
addition, the interaction terms between the post-Lehman period binary indicator and other
variables of interest show that there may be important differences between the two subperiods analyzed. In order to confirm this difference, we perform a Chow test on the
model, which suggests that a structural break has indeed occurred. This evidence is
sufficient to motivate a brief overview of the post-Lehman period.
5.5 The Post-Lehman Period with Thirteen Emerging Markets
In this final section of the empirical analysis, we expand the cross-section to
include all 13 countries of the sample and focus only on the post-crisis period. As shown
in Section 5.4, there is a statistically significant difference between the coefficients of the
variables during the two different periods of analysis. This motivates a deeper
investigation into the post-Lehman period to better understand these differences. The
exact period of the analysis spans the week of September 19, 2008 through April 2012. In
order to utilize the 13 countries without significant data losses, we replace the EMBI with
CDS spreads. This makes adding Romania and Thailand to the sample feasible.30 This
section also demonstrates the near equivalence of these two measures for the purposes of
this model; two of the model specifications utilize EMBI spreads for the 11 countries
with complete data and we compare these with the results using the CDS spreads for all
30
See Chapter 2 for a discussion of previous research between bond yields and CDS spreads.
81
13 countries. Note that interest rate parity is less of a concern for this section due to the
lack of variation in the federal funds rate. In other words, we do not have an outside
interest rate shock to test the transmission. However, the analysis allows one to better
observe how the channels of Fed policy changed between periods. In developing this
model, we test the effective federal funds rate as a regressor and find it to be insignificant
(and thus are not included in the final regression specifications). This presents a chance to
test Fed balance sheet growth as the post-Lehman proxy for Federal Reserve policy.
Table 8 presents the results of this section.
Specifications (1) and (2) utilize a basic form of the model without the use of the
VIX and quantitative easing terms. Note the only difference between these two models is
the use of CDS spreads as a substitute for the EMBI in specification (2). This allows for a
direct comparison between the two measures of country-risk by observing the differing
signs and magnitudes between their coefficients and the stability of the other variable’s
coefficients. The initial results are strikingly similar between the two specifications; the
CDS and the EMBI have the same coefficient, which is positive and statistically
significant for both variables. The Fed balance sheet growth variable is statistically
insignificant but its interaction term with capital mobility has a positive and statistically
significant term, as was the case in Section 5.4. This finding suggests that in general,
greater asset purchases by the Fed put upward pressure on interest rates; this is the
reverse of what central bankers in emerging markets have stated. However, we have not
specifically focused on quantities easing periods, which we test in a later specification.
Both models also show evidence that capital controls help to limit the effect of country-
82
Table 8 - Post-Lehman Period Fixed Effects Results
Specification
(1)
(2)
Standard Error Type
DK
DK
EMBI
0.003***
-(-0.001)
CDS
-0.003***
(-0.001)
Capital Mobility
-0.226**
-0.143
(-0.095)
(-0.089)
EMBI*Capital Mobility
0.0005***
-(-0.000)
CDS*Capital Mobility
-0.0003***
(-0.000)
Expected Depreciation
0.199***
0.181***
(-0.050)
(-0.050)
Depreciation*Capital Mobility
-0.012
0.006
(-0.008)
(-0.006)
Inflation
0.243***
0.278***
(-0.056)
(-0.059)
GDP
-0.115***
-0.121***
(-0.019)
(-0.012)
Fed Balance Sheet Growth
2.192
2.01
(-2.912)
(-2.857)
Bal Sheet Growth*Capital Mobility
0.898***
1.294***
(-0.190)
(-0.190)
VIX (Logged)
--VIX*Capital Mobility
--
--
Quantitative Easing (Binary)
--
--
QE*Fed Balance Sheet Growth
--
--
QE*VIX
--
--
Constant
10.45***
(-0.901)
2,079
11
0.657
(0.000)
(0.000)
(0.000)
123.2
4
10.51***
(-0.774)
2,457
13
0.682
(0.000)
(0.001)
(0.000)
219.3
4
Observations
Number of Countries
R-Squared (Within)
Modified Wald-Test (P-Value)
Wooldridge AC Test (P-Value)
Breusch-Pagan LM Test (P-Value)
F-Statistic
Maximum Number of Lags
for 13 Emerging Markets
(3)
(4)
(5)
DK
DK
DK
--0.005***
(-0.001)
0.005***
0.006***
-(-0.001)
(-0.001)
-0.725***
-0.732***
-0.364*
(-0.207)
(-0.210)
(-0.201)
--0.0005**
(-0.000)
-0.0002
-0.0002
-(-0.000)
(-0.000)
0.186***
0.183***
0.204***
(-0.048)
(-0.049)
(-0.048)
0.005
0.007
-0.012
(-0.006)
(-0.006)
(-0.008)
0.271***
0.247***
0.211***
(-0.058)
(-0.052)
(-0.051)
-0.123***
-0.117***
-0.115***
(-0.012)
(-0.011)
(-0.019)
2.955
8.967***
7.953***
(-3.230)
(-1.624)
(-1.630)
0.994***
0.919***
0.787***
(-0.220)
(-0.211)
(-0.209)
-0.803***
-1.019***
-0.762***
(-0.210)
(-0.262)
(-0.258)
0.205***
0.200***
0.0445
(-0.057)
(-0.057)
(-0.060)
--0.267
-0.32
(-0.777)
(-0.768)
--16.48***
-14.47***
(-3.361)
(-3.725)
-0.0891
0.0813
(-0.255)
(-0.252)
13.15***
13.81***
13.16***
(-1.333)
(-1.543)
(-1.598)
2,457
2,457
2,079
13
13
11
0.686
0.696
0.669
(0.000)
(0.000)
(0.000)
(0.001)
(0.001)
(0.000)
(0.000)
(0.000)
(0.000)
192.9
216.5
155.8
4
4
4
Notes: Standard errors reported in parentheses. Significance levels: *** p<0.01, ** p<0.05, * p<0.1
Modified Wald test follows methodology of Green (2000); null hypothesis of homoskedasticity. Wooldridge
autocorrelation test follows methodology of Wooldridge (2002); null hypothesis of no autocorrelation. BreuschPagan LM Test follows methodology of Breusch and Pagan (1980). DK defines use of Driscoll and Kraay (1998)
standard error adjustment; HAC defines use of heteroskedasticity and autocorrelation-adjusted standard errors.
83
specific risk shocks to domestic interest rates, though the effect is larger for the EMBI.
This is consistent with respect to previous subsamples. The capital mobility term itself,
however, has a statistically significant negative sign (though only for the EMBI
specification), which indicates that restrictive capital mobility policies generally are not
favorable for deposit rates.
Inflation maintains its positive relationship in these models and GDP is negative
and statistically significant as well. The idea that GDP has a negative relationship with
deposit rates is actually somewhat intuitive if one assumes savings behavior is stronger
during difficult times (as discussed in Section 5.2). One may also rationalize its negative
effect on deposit rates as a result of banks needing additional capital during sluggish
periods and paying a higher rate. The expected depreciation variable retains the statistical
significance and positive sign, but its interaction with capital mobility is no longer
significant. This suggests that these controls are less effective in protecting domestic rates
from the effects of changes in currency expectations in the wake of the crisis. In general,
the results of these two models show the general equivalence between the CDS and the
EMBI and allow for the expansion of the cross-section to the remaining two countries of
the sample.
We add the global risk measure and its interaction term to the model in
specification (3). The addition of these terms previous results—a higher VIX is
associated with lower deposit rates and its interaction with capital mobility having the
opposite effect. Much like the negative sign of GDP, one could consider the effect of the
VIX term on deposit rates partially resulting from increased savings during turbulent
84
economic times. High global risk aversion may induce consumers to save more, thus a
higher VIX would push deposit rates down due to the higher demand. However, the
positive interaction term suggests that the VIX also puts upward pressure on deposit rates
for those with capital that is more mobile. In all, since most economies in the sample
have neither fully mobile nor immobile capital, the full impact on interest rates will vary
substantially. Consider, for instance, a country with a capital mobility score of four. In
this specification, having this score erodes the effects of the heightened global risk.
Further research into the specific capital controls in place would be considerably useful
here to determine if capital limitations are dominating this effect. Few other variables
appear to have significant changes to their coefficients. The marginal significance of the
interaction term between CDS and capital mobility is no longer present and the capital
mobility indicator grows strongly more negative; the latter results because of the
inclusion of the interaction between the VIX and capital mobility. The term measuring
Fed balance sheet growth remains statistically insignificant but grows in size as its
interaction with capital mobility remains statistically significant with a marginally
smaller coefficient (in absolute terms). Perhaps the most interesting find with regard to
these results is the relative stability of the coefficients when adding two new countries
into the regression; the data are largely consistent and suggests that there may be some
external validity to the results.
The final two specifications implement the quantitative easing measures we have
discussed throughout this thesis; we utilize the EMBI in specification (5) in order to
illustrate the robustness of the CDS results. Despite the addition of three new terms to the
85
regression, the results remain stable and comparable between the regressions using the
EMBI and CDS measures, though with two notable differences. The VIX maintains its
statistical significance and negative sign for both models but its interaction with capital
mobility is only statistically significant in specification (4). However, the VIX has a
higher correlation with the EMBI than with CDS spreads and this might be rendering the
coefficient on the interaction term insignificant. The second difference results from the
EMBI interaction with capital mobility maintaining its sign and significance while the
same interaction with CDS spreads is not significant. Given that the effect is quite
marginal, the two additional countries to the CDS model may be resulting in this
insignificance.
The additional terms in this model provide very intriguing results and reinforce
the findings found in the full sample analysis of the previous section. Fed balance sheet
growth is generally associated with upward pressure on interest rates (as by the now
statistically significant coefficients in Table 8); however, during periods of quantitative
easing, this effect reverses and overtakes the typical positive effect; this result is
observable in both specifications. This suggests that perhaps the complaints of central
bankers in emerging markets have validity to them in that excessive asset purchases by
the Fed have made it difficult for them to control domestic rates. However, since we have
not explicitly controlled for central bank actions, the results here may also suggest that
central banks cut rates in response to Fed policies. Using either the CDS or EMBI sample
changes only the magnitude of this effect but the general conclusion holds. We also find
that countries with greater capital mobility were exposed to higher interest rates, resulting
86
from the Fed expanding its balance sheet. This means that greater capital mobility
enables, countries to mitigate some of the effects of asset purchases during quantitative
easing periods as well. Regardless of direction, the Fed has a clear effect on emerging
markets; future work will need to explore how this relationship has evolved further.
The results provided in this section are merely a glimpse into the vastly changed
world in the aftermath of the 2007-2009 financial crisis. Once the federal funds rate
reached its lower bound, the Fed no longer had the control over monetary policy that it
once did, leading it to find new unconventional measures. Quantitative easing put
downward pressure on deposit rates in emerging markets as the Fed continued to expand
its balance sheet and keep monetary policy relatively loose, even though the federal funds
rate target remained unchanged. In addition, higher volatility generally put downward
pressure on rates; however, those countries with more mobile capital saw this effect
mitigated; the net impact on interest rates is highly dependent on the level of capital
restrictions in an economy. In general, we find mixed results concerning the effects of
capital controls on deposit rates. Countries with more mobile capital appear to have lower
deposit rates but in the post-Lehman era it did not help mitigate the effects of currency
depreciation. Yet capital controls also help limit potential rising rates from market
volatility, country-specific risk and the effects of quantitative easing. As is commonplace
in the literature, the caveat to this result is that a more precise measure of capital controls
is needed to truly declare these findings robust.
5.6 Robustness of Empirical Findings
This section provides three basic robustness checks to the results of the empirical
87
analysis. We employ an alternative capital mobility measure, which is often used in the
literature, to examine how well the results using the derived index compare to one that is
well established. In addition, we use time-fixed effects to test the stability of the
coefficients that vary across time and entities. Lastly, we utilize the official federal funds
rate in lieu of the effective federal funds rate to ensure we are appropriately measuring
Fed policy.
First, we utilize the Chinn-Ito index31 as an alternative to the derived index that in
the various model specifications employed. We restrict this robustness check only for the
specifications in which 11-13 countries are included in the model as slight differences
between the indices can have significant results with a small sample of entities. After
conducting the test, there are very few differences between the two regressions in each of
the specifications. The magnitudes may differ but one would expect this given that the
value of this index is measured from a range of -2.66 to 2.66 as opposed to the 0-10 index
used in the thesis. Accordingly, the general results hold and are robust to this alternative
index. The largest difference when using the Chinn-Ito index is observed during the full
sample period in which the VIX has a negative instead of a positive sign but this effect
disappeared once we focus on the post-Lehman era. In addition, given that there is a
structural break, one should not place too much value on this differential result.
Interestingly, the post-Lehman era analysis show a nearly identical within R-squared
values and the coefficients on the VIX and Fed balance sheet measures, in particular, are
quite robust as well; we do not observe any meaningful differences in terms of
31
See Chapter 2 for more information on this index alternative.
88
significance or direction. Overall, these results suggest the derived measure of capital
mobility is capturing similar variation as the Chinn-Ito index and is an appropriate
measure given existing indices.
This study is unable to implement time-fixed effects due to the existence of
several entity-invariant variables such as the effective federal funds rate, the VIX and Fed
balance sheet growth. Thus, we employ a relatively straightforward robustness check, and
remove all of these entity-invariant regressors and implement time-fixed effects,
monitoring the behavior of the remaining coefficients now that omitted variable bias is
less of an issue. The results here also suggest the conclusions of this analysis are robust
since the majority of the variable signs remain the same, with exception to the capital
mobility interaction terms in larger samples. Interestingly, depreciation and inflation
coefficients tended to be the most similar to one another in each of the regressions. The
small deviations point to the potential of omitted variable bias without time-fixed effects,
however, the bias appears to be small.
Although the effective federal funds rate and the official rate are highly
correlated, we also run the empirical analyses with the official rate in order to ensure the
effects are robust and a fair representation of the Fed’s policies. For all models, the
results are essentially unchanged when using the official rate in place of the effective
rate. Only small differences occur in the magnitudes of coefficients during the full sample
period since the effective federal funds rate had greater variation in the post-Lehman era.
Thus, the results remain robust when we use official federal funds rate in the regression
as opposed to the effective rate.
89
CHAPTER 6
CONCLUSIONS
6.1 Summary of Research and Findings
This thesis examines the impact of Federal Reserve policy on emerging market
deposit rates and its changes since the 2007-2009 financial crisis. In particular, we aim to
empirically evaluate the effect of quantitative easing on emerging markets deposit rates.
We analyze this relationship for 13 emerging markets from different regions over the
period from January 2000 through April 2012. We split the empirical work into three
sub-periods including the period preceding the fall of Lehman Brothers, the period after
this and the full sample period. This is done in order to disentangle the relationships
between the variables and how they might have changed in response to the global crisis.
In addition to this baseline analysis, we study the roles that capital controls, global risk
sentiment, expected domestic currency depreciation and country-specific risk play in the
transmission of interest rate shocks from the Federal Reserve; we utilize the theory of
interest rate parity as a mechanism for this transmission. The empirical analyses use fixed
effect regression with Driscoll and Kraay (1998) standard errors to adjust the estimates
for autocorrelation, heteroskedasticity and cross-sectional dependence.
The results show that the federal funds rate has indeed influenced interest rates in
emerging economies. We find that interest rate parity exists over longer periods, in the
case of this study, over 8- and 12-year periods. In addition, we find that interest rate
shocks from the U.S. do not fully transmit to emerging economies; the aggregate results
suggest that roughly 50-60% of an interest rate change will be reflected in emerging
90
market deposit rates. Nonetheless, the Fed’s influence and use of the federal funds rate
was prominent throughout the pre-Lehman and full period analyses.
Overall, we find that capital controls provide mixed effects with regard to
protection from external interest rate shocks. During the pre-Lehman and full sample
periods, capital controls were helpful in shielding interest rates from heightened countryspecific risk but this effect did not generally hold in the post-Lehman period. Capital
controls marginally shielded deposit rates from shocks to the federal funds rate in both
the pre-Lehman and full periods, though the result was not consistent across model
specifications. We also find that capital controls helped to shield the effects of rising
interest rates from Fed asset purchases. In addition, we find capital controls tend to
worsen the effect of changes in the expected rate of depreciation for the domestic
currency in all but the post-Lehman period. The models generally suggest that the mere
existence of capital controls in an emerging economy is associated with higher deposit
rates, certainly the opposite effect of their intention. However, higher deposit rates may
also be a characteristic of those economies who implement stricter capital controls; as
Edwards (2012) noted, higher inflation is often associated with more tightly controlled
markets.
The results of the various model specifications suggest that heightened global risk
has a varying effect on interest rates in emerging markets depending on the timeframe of
the analysis and the degree of capital mobility. During the pre-Lehman era, rising risk
aversion in the markets is associated with higher deposit rates, but this effect is mitigated
in markets with higher capital mobility. This finding may be indicative of the state of
91
emerging markets during this period in that they were still in early phases of
development. The markets may have been much more speculative of countries with
capital controls in place. The findings of both the full period and post-Lehman era are just
the opposite of those found for the pre-Lehman period. The results of these models
suggest that heightened global risk put downward pressure on emerging market deposit
rates. However, the interaction between global risk and capital mobility generally puts
upward pressure on interest rates. The negative effect of the VIX on interest rates might
reflect consumers’ savings behavior; during turbulent times in financial markets, this
might push deposit rates down. The existence of capital controls exacerbates this effect.
Those with capital controls in place see this effect the largest and those without have a
great susceptibility to the sentiment of foreign investors. Regardless of the time period
one examines, the degree of capital mobility clearly plays an important role in
determining just how much global risk aversion affects emerging market interest rates.
Further research is warranted to better disentangle the effects of global risk and countryspecific risk.
One of the most intriguing findings, however, is the effect of quantitative easing
on interest rates in the post-Lehman era. We find that during these particular periods, the
Fed’s asset purchases put substantial downward pressure on emerging market interest
rates. This contrasted with the finding that generally Fed asset purchases put upward
pressure on emerging market interest rates; those with capital controls were less
susceptible to those effects. As was the case of global risk aversion, the net effect of Fed
asset purchases depends on the degree of capital mobility in an economy. These are
92
important discoveries that researchers must take into account in order to properly model
the Fed’s impact on emerging markets. In addition, the results also signal the likelihood
that interest rates will rise as the asset purchases begin to taper off which may be
concerning for economies in need of capital.
6.2 Caveats to the Analysis
There are several caveats to this analysis, which provide an excellent lead-in to
the directions which future research much take. First, the capital control measure used in
this study is somewhat crudely constructed, as are most studies; external validity is
questionable until stronger, more detailed measures are obtained. Second, this study only
examines countries with floating exchange rate regimes and does not consider those with
other regimes. Examining markets with other exchange rate regimes could greatly alter
the results; perhaps, capital controls have different characteristics and effects for
emerging markets with varying regimes. Third, the sample is still quite limited with only
13 countries involved due to data availability. As the data becomes available, however,
this shortcoming will be overcome. Fourth, the correlation between the VIX and the
EMBI may have caused the effects of one or the other variable to become insignificant.
Deriving a measure of global risk that is uncorrelated with country-specific risk would
likely improve the results. Fifth, one cannot help but wonder if the countries included in
the model are truly representative of the population. For instance, the Asia subsample
analysis discussed in Chapter 5 found results that greatly differed from Latin America
subsample in terms of significance and coefficient magnitudes. As we aggregated the
samples, the coefficients generally behaved more stably but we must consider the
93
possibility that the results of the analysis are sensitive to the choice of the countries
included in the analysis. Sixth, we assumed an exogenous break date, which may not
have been optimal; determining the appropriate date endogenously would render the
results more robust. Seventh, the panel estimation with fixed effects was suitable in
determining the long-run coefficients; the shortcoming of this, however, is that we lose
information about deviations from the long-run equilibriums. Models that are more
dynamic will likely provide more detailed and consistent results. Eighth, using weekly
data allows for stronger granularity in the results but having a large degree of nonvarying data is not desirable. Finding better variable proxies and controls would provide
stronger coefficient estimates. Ninth, the use of non-entity varying data (such as the
federal funds rate, the VIX or Fed balance sheet growth) makes it possible that their
coefficient estimates are capturing fixed effects variation. This is an issue with panel
studies of this nature; instrumental variables would help to remedy this issue. Finally, we
must note that using a panel analysis with financial and macroeconomic data makes
endogeneity a potential issue. For instance, it is possible that capital controls increase as a
means of controlling deposit rates; this needs further examination in the literature. Given
these issues, researchers have much more to explore in this field.
6.3 Future Extensions
This thesis provides robustness checks to Edwards (2012) but also provides
several insights into future research questions. The global economy has been greatly
altered as a result of the 2007-2009 financial crisis. For instance, emerging markets have
proven to be large contributors to global growth in the wake of the crisis as developed
94
world suffered from severe recessions. Going forward, it is of great interest for
economists to further analyze mechanisms that enable emerging markets to remain on or
find their optimal growth paths. There a number of ways in which the research can
further expand in this field of empirical analysis.
We must emphasize the importance of finding better measures of capital controls,
particularly those at a higher frequency to enable a more precise estimation of their
effects. For instance, the optimal measure of capital controls attempts to capture the
intensity of capital controls as opposed to the extensity typically found in the existing
literature. This type of index would give a much more holistic picture as to the true
restrictiveness of a market. As an example, if an emerging market has a sufficiently high
degree of capital controls but has weak enforcement, the de jure measures popularly used
in the literature will overstate the true effect of capital controls. In addition, these controls
take many forms that are difficult to quantify; developing several different measures of
the type and intensity of capital controls would provide a great deal more color on the
topic. However, either of these ideas would require a sufficiently large amount of time
and resources as countries tend to have wide disparities with the policies between them.
Nonetheless, this is a widely known shortcoming in the literature.
There are additional variables that researchers should consider including in
models such as this thesis explored. For instance, acquiring data detailing the level of
foreign deposits in a nation’s banking system would give a stronger picture of the true
impact foreigners have on both emerging market financial systems and their economies in
general. Additionally, interacting this variable with a measure of foreign interest rates,
95
such as the federal funds rate, would provide a more precise means of measuring the
effect of foreign investors on emerging markets.
As noted in section 6.2, both this thesis and Edwards (2012) focus exclusively on
countries with floating exchange rate regimes. However, in doing so, we may be missing
valuable insights with regard to those countries who utilize other regimes or who are
already in the process of transitioning to a floating rate regime. Applying the framework
of this thesis for a sample of countries with fixed exchange rate regimes, for instance,
may yield very different results.
Recently, the Fed has expanded unconventional monetary policy beyond asset
purchases. As Hayo, Kutan and Neuenkirch (2012) demonstrates, the Fed’s use of its
communication tools has been an important indicator of investors (particularly those in
emerging markets). Future work should consider incorporating both quantitative easing
and a quantified measure of communication items from the Fed.
Future research should also seek to include measures of global risk with minimal
correlation with country-risk measures such as the EMBI. One way to do this would be to
utilize an instrumental variables approach to separate the effects; however, finding
appropriate instruments is often difficult. Nonetheless, this would help to corroborate the
results found in this study.
The use of a panel VAR may provide valuable insights by allowing the researcher
to relax assumptions about the exogeneity of the regressors and allow variables to have
lagged components. It may be the case that Fed policy has a lag of several weeks or
months before filtering down to emerging markets. This would be an interesting model
96
for new research in this area to explore.
Lastly, we noted the issues of assuming a known exogenous break date
throughout the text and further suggest research identify break dates endogenously. This
would help to detect the exact timing of the break and allow for the identification of other
unknown breaks. For instance, during the period studied in this thesis, the Argentina
default occurred in the early 2000s, which adversely affected Brazil through its export
linkage; this could result in a structural break occurring for this country, confounding
estimation results. In addition, Perron (1989) notes that the majority of macroeconomic
time series do not have unit roots but are stationary around deterministic trends; the only
two shocks that have had persistent effects are the stock market crash of 1929 and the oil
shock in 1973. If this is indeed the case, research must further explore whether the 20072009 financial crisis has left a permanent impact on the world or if it is only a temporary
moment of distress. If the former is true, this will truly influence how researchers model
emerging markets between these two periods.
Though the period that followed the Lehman Brothers crisis was quite turbulent,
many emerging markets found solace in a (Fed-induced) low interest rate environment.
This enabled many of them to secure financing that would have otherwise been either
very expensive or unattainable. This study clearly demonstrates that quantitative easing
and Fed policy in general have effects beyond U.S. market and that capital controls have
mixed effects on emerging market interest rates. The results suggest that capital controls
offer some benefits for emerging markets such as mitigating increases in interest rates
from depreciation and country-specific risk concerns (though these effects varied by
97
period). In addition, Fed balance sheet growth has less impact on economies with tighter
capital controls. The downside is that capital controls are associated with higher interest
rates. As the Fed looks to wind down asset purchases, policy makers in emerging markets
must be cognizant of the effects on their markets. If they were not before, the reaction of
global financial markets in May and June 2013 might just give them the epiphany. While
capital controls may be an appealing quick fix for an economy, they offer a mixed bag of
benefits and costs; emerging markets must tread carefully as global markets move beyond
the effects of the financial crisis.
98
APPENDIX A
DESCRIPTIVE STATISTICS FOR CONTROL VARIABLES
Descriptive Statistics for Entity-Constant Control Variables by Sub-Period
Minimum Maximum
Mean
Median
Pre-Lehman Period (454 Observations)
Agricultural Commodities Index
93.04
245.37
126.98
116.61
Energy Commodity Index
136.67
794.48
322.08
275.19
Industrial Metals Index
84.72
320.39
164.19
134.00
Post-Lehman Period (189 Observations)
Agricultural Commodities Index
129.26
260.01
190.50
179.63
Energy Commodity Index
260.59
556.57
417.10
412.65
Industrial Metals Index
121.54
290.24
217.43
224.38
Full Period (643 Observations)
Agricultural Commodities Index
93.04
260.01
145.65
127.40
Energy Commodity Index
136.67
794.48
350.01
372.83
Industrial Metals Index
84.72
320.39
179.84
156.44
Notes: All indices reported in real terms; adjusted to June 2013 price levels according to CPI.
St. Dev.
31.54
138.20
73.40
35.06
66.37
43.93
43.59
129.01
70.40
Gross Domestic Product
Mean
Standard Deviation
Observations
Inflation
Mean
Standard Deviation
Observations
Primary Budget Balance
Mean
Standard Deviation
Observations
Government Debt
Mean
Standard Deviation
Observations
Current Account Balance
Mean
Standard Deviation
Observations
1.95
2.59
643
-4.38
1.42
643
-6.90
3.29
643
-3.12
2.58
643
3.08
3.25
643
-4.14
2.96
643
-1.05
3.66
8359
-1.05
2.03
643
0.56
2.26
643
-1.00
1.93
643
-1.31
0.92
643
42.26 48.82 33.85 36.05 34.66 53.24 46.77 21.81 35.65 33.09 51.65 38.78
3.92 21.48 2.06 3.58 9.59 9.49 5.74 7.21 5.28 4.71 13.74 15.55
643 8359
643
643
643
643
643
643
643
643
643
643
9.39
3.78
643
56.83
3.01
643
2.37
1.09
643
1.17
2.59
8359
2.86
1.79
643
-0.12
2.33
643
1.83
2.95
643
-2.08
2.55
643
-2.11
1.60
643
2.47
1.57
643
1.57
1.79
643
0.12
0.81
643
2.91
1.38
643
1.48
1.27
643
0.85
1.29
643
2.38
3.40
643
3.07
0.61
643
2.01
1.84
643
6.58
8.48
8359
20.19
19.72
643
2.65
2.07
643
5.85
2.90
643
14.37
12.91
643
3.51
2.46
643
4.76
1.97
643
2.60
1.70
643
4.94
1.67
643
3.17
0.92
643
7.88
3.95
643
5.64
2.01
643
3.32
2.26
643
6.69
2.90
643
-1.74
1.11
643
4.07
3.27
8333
Brazil
4.69
5.93
643
Chile
4.01
3.83
643
Colombia
3.57
1.94
643
Indonesia
3.73
4.27
643
Korea
3.94
2.00
643
Mexico
4.71
1.83
643
Peru
5.62
3.51
643
Philippines
0.56
1.33
643
Poland
4.50
2.87
643
Romania
5.37
1.10
617
Thailand
4.25
2.05
643
Turkey
4.43
2.56
643
Sample
3.56
2.72
643
South Africa
Descriptive Statistics for Time- and Entity-Varying Conntrol Variables (Full Sample)
99
100
APPENDIX B
REGRESSION RESULTS FOR CONTROL VARIABLES
Latin America Pre-Lehman Sample Replication Control Variables Results
Specification
(1)
(2)
(3)
(4)
Standard Error Type
DK
DK
DK
DK
Metals Index (Growth)
--0.573
-1.554*
(1.115)
(-0.904)
Energy Index (Growth)
--0.316
0.421
(0.850)
(0.746)
Agriculture Index (Growth)
---0.421
-0.138
(1.414)
(1.273)
Government Debt
---0.024
-0.010
(-0.026)
(-0.025)
Primary Balance
---0.039
-0.090
(-0.078)
(-0.077)
Current Account Balance
--0.110
0.025
(0.077)
(0.078)
(5)
HAC
-1.879
(-1.271)
0.468
(0.524)
0.008
(2.074)
-0.027
(-0.08)
-0.174
(-0.101)
-0.067
(0.177)
Asia Pre-Lehman Sample Replication Control Variables Results
Specification
(1)
(2)
(3)
(4)
Standard Error Type
DK
DK
DK
HAC
Metals Index (Growth)
--0.603
1.028
(1.016)
(0.583)
Energy Index (Growth)
---1.866
-1.867
(-1.351)
(-1.333)
Agriculture Index (Growth)
--3.014*
2.908*
(0.843)
(0.963)
Government Debt
--0.0177
0.0287
(0.009)
(0.010)
Primary Balance
--0.0404
0.0421
(0.106)
(0.088)
Current Account Balance
--0.081
0.098
(0.093)
(0.077)
(5)
HAC
1.053
(0.576)
-1.974
(-1.338)
2.792*
(0.927)
0.0253
(0.012)
0.0307
(0.088)
0.084
(0.074)
Pre-Lehman Expanded Cross-Section Control Variables Results
(1)
(2)
(3)
(4)
(5)
DK
DK
DK
DK
DK
-0.458
-0.659
0.402
-0.218
0.637
(-0.635)
(-0.628)
(-1.046)
(-0.870)
(-0.949)
Energy Index (Growth)
-0.063
0.243
-0.538
-0.435
-0.213
(-0.576)
(-0.559)
(-0.995)
(-0.845)
(-0.690)
Agriculture Index (Growth)
1.191
0.46
0.675
0.912
0.289
(0.884)
(-0.855)
(1.479)
(1.381)
(1.154)
Government Debt
0.019
-0.044***
0.109***
0.080***
0.084***
(-0.018)
(-0.014)
(-0.033)
(-0.029)
(-0.019)
Primary Balance
-0.116**
-0.228***
-0.021
-0.017
-0.093
(-0.050)
(-0.048)
(-0.086)
(-0.071)
(-0.061)
Current Account Balance
0.034
-0.116***
0.432***
0.262***
0.237***
(-0.043)
(-0.039)
(-0.073)
(-0.045)
(-0.045)
(6)
DK
-0.497
(-0.970)
-0.453
(-0.896)
1.353
(1.451)
0.068***
(-0.027)
0.077
(-0.077)
0.221***
(-0.049)
Specification
Standard Error Type
Metals Index (Growth)
101
Specification
Standard Error Type
Metals Index (Growth)
Energy Index (Growth)
Agriculture Index (Growth)
Government Debt
Primary Balance
Current Account Balance
Specification
Standard Error Type
Metals Index (Growth)
Energy Index (Growth)
Agriculture Index (Growth)
Government Debt
Primary Balance
Current Account Balance
Full Period Control Variables Results
(1)
(2)
(3)
DK
DK
DK
-0.381
-0.396
-0.344
(-0.783)
(-0.792)
(-0.784)
-0.564
-0.523
-0.450
(-0.628)
(-0.641)
(-0.639)
0.888
0.858
0.846
(-1.097)
(-1.108)
(-1.109)
0.041**
0.043**
0.042**
(-0.019)
(-0.019)
(-0.019)
0.088*
0.096*
0.092*
(-0.050)
(-0.053)
(-0.054)
0.263***
0.263***
0.265***
(-0.039)
(-0.040)
(-0.040)
(4)
DK
-0.352
(-0.781)
-0.437
(-0.642)
0.863
(-1.103)
0.042**
(-0.019)
0.091*
(-0.054)
0.265***
(-0.040)
Post-Lehman Period Control Varibles Results
(1)
(2)
(3)
DK
DK
DK
-0.112
-0.401
-0.498
(-0.911)
(-0.796)
(-0.797)
-1.015
-0.899
-1.014
(-0.883)
(-0.845)
(-0.86)
1.085
0.757
0.851
(-1.023)
(-0.913)
(-0.915)
-0.178***
-0.199***
-0.208***
(-0.019)
(-0.014)
(-0.016)
0.215***
0.089
0.112*
(-0.077)
(-0.060)
(-0.063)
0.246***
0.101***
0.121***
(-0.042)
(-0.031)
(-0.033)
(5)
DK
-0.398
(-0.688)
-0.950
(-0.640)
0.828
(-1.138)
0.032*
(-0.019)
0.149***
(-0.057)
0.260***
(-0.040)
(4)
DK
-1.053
(-0.734)
-0.478
(-0.771)
1.183
(-0.951)
-0.205***
(-0.017)
0.102
(-0.067)
0.123***
(-0.031)
(6)
DK
-0.411
(-0.688)
-0.873
(-0.642)
0.756
(-1.142)
0.038**
(-0.019)
0.114**
(-0.058)
0.255***
(-0.041)
(5)
DK
-0.933
(-0.839)
-0.700
(-0.881)
1.352
(-1.107)
-0.190***
(-0.022)
0.202**
(-0.082)
0.257***
(-0.044)
102
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