Tuttle Thesis

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

ii
A Thesis
by
Approved by:
__________________________________, Committee Chair
Kristin A. Van Gaasbeck, Ph.D.
__________________________________, Second Reader
Ta-Chen Wang, Ph.D.
____________________________
Date

iii
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. Date
Department of Economics

iv
Abstract
of
by
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-on-
week 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

v
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

vi
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.

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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

viii
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

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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

x
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

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
1
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).

2
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
Source: Source: Bloomberg, Standard and Poors

3
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%
3
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
Source: Bloomberg, Standard and Poors

4
(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
4
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.
Source: Bloomberg, JP Morgan

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
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),
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

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
6
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.

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
8
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.

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
11
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.

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 country-
specific 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 forward-
looking 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
Illing and Aaron (2005) also provide an extensive but straightforward overview of risk aversion metrics.
14
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 non-
default 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) 𝑖 𝑡 − 𝑖 𝑡
𝑓
= 𝐸𝑡{∆𝑒𝑡+1},
where it is the nominal interest rate in the domestic economy, 𝑖 𝑡
𝑓
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) 𝑖 𝑡 − (1 − 𝑇)𝑖 𝑡
𝑓
+ 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) 𝑖 𝑡 − 𝛽𝑖 𝑡
𝑓
+ 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) 𝑖̃ 𝑡 = 𝛼0 + 𝛼1 𝑖 𝑡
𝑓
+ 𝛼2 𝛿𝑡 + 𝛼3 𝜌𝑡 + 𝜔 𝑡,
where 𝑖̃ 𝑡 represents the equilibrium domestic equilibrium rate, 𝛿𝑡 is the expected
depreciation in the currency, 𝜌𝑡 is the credit risk premium of the nation and 𝜔 𝑡 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) 𝑖̃ 𝑡 = 𝛼0 + 𝛼1 𝑖 𝑡
𝑓
+ 𝛼2 𝛿𝑡 + 𝛼3 𝜌𝑡 + 𝛼4 𝑚 𝑡 + 𝛼5 𝑔𝑡 + 𝛼6(𝑚 𝑡 ∗ 𝑔𝑡) + 𝜔𝑖,
where 𝑚 𝑡 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) 𝑖̃ 𝑡 = 𝛼0 + 𝛼1 𝑖 𝑡
𝑓
+ 𝛼2 𝛿𝑡 + 𝛼3 𝜌𝑡 + 𝛼4 𝑚 𝑡 + 𝛼5 𝑔𝑡 + 𝛼6(𝑚 𝑡 ∗ 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) 𝑖̃𝑖,𝑡 = 𝛼𝑖 + 𝛼1 𝑖 𝑡
𝑓
+ 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, post-
Lehman 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 post-
Lehman 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 post-
Lehman 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
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%
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

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
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
Source: Bloomberg, Author’s Calculations

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 three-
month 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
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 pre-
Lehman 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.
Variables of Interest Measurement Source ExpectedSign Stationary?
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 Indexvalue IMF/Fraser Institute (0/+) No - Little variation
Volatility Index Percentage Bloomberg (+) Yes - Logged
Federal Reserve Balance Sheet $USD Bloomberg (-) Yes - Logged Diff.
Controls Measurement Source ExpectedSign 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 - YoYGrowth
Inflation Annualized growth rate Bloomberg (+) Yes - YoYGrowth
Primary Budget Balance 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
Table 1 - Variable Predictions andDefinitions

51
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
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 ($bln) 768.16 1,002.34 906.90 937.16 69.04
Post-Lehman Period(189 Observations)
Federal Reserve Balance Sheet ($bln) 1,042.28 2,957.62 2,490.72 2,450.40 323.27
Full Period(643 Observations)
Federal Reserve Balance Sheet ($bln) 768.16 2,957.62 1,372.44 959.44 745.24
Table 2 - Descriptive Statistics for Entity-Constant Variables by Sub-Period
Notes: Federal Reserve balance sheets are in real terms; adjusted to June 2013 price levels according to CPI.

52
Brazil
Chile
Colombia
Indonesia
Korea
Mexico
Peru
Philippines
Poland
Romania
SouthAfrica
Thailand
Turkey
Sample
CDRate(%)
Mean15.292.387.5510.334.373.685.526.927.188.598.983.7725.228.48
StandardDeviation4.462.342.643.571.251.983.503.224.703.242.272.2717.488.28
Observations6416436436436436436436436433986435766438045
EMBI(Bps)
Mean505.41143.45372.19294.52130.35235.85338.23358.04147.51408.95209.57103.87422.39278.66
Observations64364364341364364364364364396433256437177
CDSSpreads(Bps)
Mean470.4470.31245.65232.8889.59143.78186.62287.1679.82211.58145.2592.92402.39205.54
Observations5514844843955315514455266024986045266036800
ExpectedDepreciation(%)
Mean10.410.584.367.250.965.531.614.834.235.486.321.4120.075.65
Observations6436156436436436436156436433786436436438038
CapitalMobility(Index)
Mean3.684.790.951.543.941.678.370.771.656.030.771.542.212.92
Observations6436436436436436436436436436436436436438359
Chinn-ItoIndex
Mean-0.071.69-0.350.990.130.952.44-0.17-0.141.36-1.17-0.48-0.740.34
Observations6436436436436436436436436436436436436438359
Table3-DescriptiveStatisticsforTime-andEntity-VaryingVariables(FullSample)

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.

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