[2005] three essays in international finance

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THREE ESSAYS IN INTERNATIONAL FINANCE

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate School of

The Ohio State University

By

Rodolfo Martell, M.A.

The Ohio State University

2005

Dissertation Committee: Approved by

Professor Ren M. Stulz, Adviser

Professor G. Andrew Karolyi _________________

AdviserProfessor Bernadette A. Minton Graduate Program in Business Administration


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ABSTRACT

Recent research in international finance focuses on the extent to which markets

are integrated across countries, how shocks propagate from one country to another and

how firms in foreign countries react to country level shocks. This dissertation provides

empirical evidence on the degree of integration in international bond markets, on the

propagation of extreme shocks between cross-listed shares and domestic markets and on

the dispersion in capital market reactions across firms to sovereign rating changes.

In the first dissertation essay, I study the determinants of credit spread changes of

individual U.S. dollar denominated bonds domestic and foreign sovereign using

fundamentals specified by structural models. Credit spreads are important determinants

of the cost of debt for all issuers and are fully determined by credit risk in structural

models. I construct a new dataset of domestic corporate and sovereign U.S. dollar bonds,

which I use to find that changes in spreads not explained by fundamentals have two large

common components that are distinct for each type of debt I study. Using a vector

autoregressive (VAR) model, I find that domestic spreads are related to the lagged first

component of sovereign spreads. Consequently, even though there is no

contemporaneous common component in bond spreads, there seems to be a common

component when focusing on the dynamics of these spreads. Traditional macro liquidity


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variables are related to the common components found in domestic and sovereign

spread changes. My findings suggest possible explanations for the common component

documented by previous research in domestic debt spreads. My research shows that, after

taking into account the dynamics of the common components in credit spreads across

debt types, the cost of debt for firms and countries depends to some extent on shocks that

affect all types of debt.

The second dissertation essay studies the extreme linkages between Latin

American equities and the US stock market using tools from Extreme Value Theory

(EVT). Bivariate extreme value measures are applied on six different country pairs

between the U.S. S&P500 Index and each of the following countries: Argentina, Brazil,

Chile, Colombia, Mexico and Venezuela. I find evidence of: a) asymmetric behavior in

the left and right tails of the joint marginal extreme distributions, and b) differences in

extreme correlations for different instruments (investing in ADRs vs. investing directly in

the local stock markets) when no difference was to be expected. There is also evidence of

a structural change in the correlations for the Mexican case before and after the 1995

Mexican crisis.

The third dissertation essay studies the effect of sovereign credit rating changes

issued by Standard and Poors and Moodys on the cross section of domestically traded

stocks. I first establish, consistent with earlier literature that analyzed similar phenomena

in the U.S. (e.g. Holthausen and Leftwich, 1986; Goh and Ederington, 1993), that local

stock markets react only to news of sovereign credit rating downgrades. Cumulative

abnormal returns of stock indices also show that investors react only to rating

announcements made by Standard & Poors and not to those by Moodys. I then study the


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cross sectional variation of the abnormal returns of individual firms associated with

sovereign credit rating changes. I find that larger firms experience larger stock price

drops after a sovereign credit downgrade. Also, firms located in more developed

emerging countries experience smaller stock price reductions following sovereign credit

downgrades. Finally, I document that firms that had access to international capital

markets experience larger abnormal returns than firms that do not have access to

international financial markets.


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Dedicated to my family


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VITA

July 9, 1972.................Born Puebla, Puebla, Mexico

1996............................Bachelor of Arts in Economics, Udla-Puebla, Mexico

1996 1999 ...............Analyst,BancrecerPetroleos Mexicanos

2000............................Master of Arts in Economics, Ohio State University

PUBLICATIONS

Research Publication

1. R. Martell and R. Stulz, Equity Market Liberalizations as Country IPOs.American Economic Review, 93(2), 97, (2003)

FIELDS OF STUDY

Major Field: Business Administration

Concentration: Finance


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TABLE OF CONTENTS

Abstract ............................................................................................................................... ii

Dedication ........................................................................................................................... v

Acknowledgments.............................................................................................................. vi

Vita.................................................................................................................................... vii

List of tables....................................................................................................................... xi

List of figures................................................................................................................... xiii

Chapter 1: Introduction....................................................................................................... 1

Chapter 2: Understanding common factors in domestic and international bond spreads... 6

2.1. Introduction.............................................................................................................. 62.2. Debt spreads of sovereign bonds ........................................................................... 10

2.2.1. Sovereign debt literature. ................................................................................ 112.2.2. Implications of the literature and proxies used to test them. .......................... 14

2.2.2.1 Bond-specific variables............................................................................. 152.2.2.2. Country-specific variables ....................................................................... 152.2.2.3. U.S. interest rate term structure. .............................................................. 16

2.2.3. Data description .............................................................................................. 162.2.4. A model for sovereign spreads ....................................................................... 202.3. Debt spreads of domestic bonds ............................................................................ 22

2.3.1. Domestic debt literature.................................................................................. 232.3.2. Theoretical determinants of domestic debt spreads ........................................ 25

2.3.2.1. Bond specific variables ............................................................................ 252.3.2.2. Firm specific variables............................................................................. 25


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2.3.2.3 U.S. interest rate term structure ............................................................ 262.3.3. Data description .............................................................................................. 262.3.4. A model for domestic debt spreads................................................................. 28

2.4. Analyzing the common factor................................................................................ 292.4.1. Establishing the existence of common factors................................................ 292.4.2. Explanatory power of the extracted components............................................ 33

2.5. Looking into the information content of the common factors ............................... 342.5.1. Lead-lag relations............................................................................................ 34

2.6. Conclusions and future work ................................................................................. 39

Chapter 3. Latin American and U.S. equities return linkages: An extreme value approach

........................................................................................................................................... 41

3.1 Introduction............................................................................................................. 413.2. Literature review.................................................................................................... 44

3.2.1. The univariate case ......................................................................................... 473.2.2. The bivariate case ........................................................................................... 50

3.3. Data........................................................................................................................ 513.4 A small test for the Mexican pairs .......................................................................... 553.5. Concluding remarks............................................................................................... 55

Chapter 4. The effect of sovereign credit rating changes on emerging stock markets ..... 58

4.1. Introduction............................................................................................................ 584.2. Literature review.................................................................................................... 654.3. The effect of sovereign rating changes on stock market indices ........................... 71

4.3.1. Data ................................................................................................................. 724.3.2. Methodology ................................................................................................... 744.3.3. Discussion of index level results..................................................................... 75

4.4. Impact of sovereign rating changes at the firm level ............................................. 79

4.5. Conclusions............................................................................................................ 86

Chapter 5: Conclusions ..................................................................................................... 88

Bibliography ..................................................................................................................... 91


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Appendix A. A comparison of sovereign bond coverage on Datastream and the NAIC . 99

Appendix B. Tables ........................................................................................................ 103

Appendix C. Figures ....................................................................................................... 134


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LIST OF TABLES

Table 1. Expected signs on explanatory variables for sovereign sample ....................... 104

Table 2. Summary statistics for sovereign sample.......................................................... 105

Table 3. Sovereign spreads fixed effect regressions....................................................... 106

Table 4. Expected signs on explanatory variables for domestic sample......................... 107

Table 5. Summary statistics for domestic sample........................................................... 108

Table 6. Domestic spreads fixed effect regressions........................................................ 109

Table 7. Correlation structure of residuals...................................................................... 110

Table 8. Principal component analysis of residuals........................................................ 112

Table 9. Sovereign and domestic regressions including the common factors ................ 114

Table 10. Vector autoregression model with exogenous variables................................. 115

Table 11. Summary statistics.......................................................................................... 116

Table 12. Extreme correlations using different number of tail exceedances.................. 117

Table 13. Sovereign rating changes by Standard & Poor's............................................. 118

Table 14. Sovereign rating changes by Moody's ............................................................ 119

Table 16. Stock index results using Moody's ratings...................................................... 121

Table 17. Stock index results using initial ratings .......................................................... 122

Table 18. First ratings for Argentina............................................................................... 123

Table 19. Stock market reaction to the first rating by either agency .............................. 124


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Table 20. Cumulative Abnormal Returns (CAR) for stocks with international financing

......................................................................................................................................... 125

Table 21. Cumulative Abnormal Returns (CAR) for all stocks following a sovereign

rating downgrade ............................................................................................................ 126

Table 22. Cumulative Abnormal Returns (CAR) for all stocks following a sovereign

rating upgrade ................................................................................................................. 129

Table 23. Countries included in this comparison............................................................ 132

Table 24. Coverage for sovereign bonds on Datastream and Warga databases. ............ 133


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LIST OF FIGURES

Figure 1. First common component ................................................................................ 135

Figure 2. Second common component............................................................................ 135

Figure 3. Q-Q Plots for the left tail and the right tail of the dollar return of the Mexican

equity index..................................................................................................................... 136

Figure 4. Q-Q Plots for the left tail and the right tail of the dollar return of the Mexican

ADR equally weighted portfolio..................................................................................... 137

Figure 5. Q-Q Plots for the left tail and the right tail of the dollar return of the S&P 500

equity index..................................................................................................................... 138

Figure 6. Excess mean graphs for the left tail and the right tail of the dollar return of the

Mexican equity index...................................................................................................... 139


Mexican ADR equally weighted portfolio...................................................................... 140


S&P 500 equity index ..................................................................................................... 141

Figure 9. Correlation between S&P and the Mexican stock market index and correlation

between S&P and Mexican ADRs.................................................................................. 142

Figure 10. Correlation between S&P and the Chilean stock market index and correlation

between S&P and Chilean ADRs ................................................................................... 142


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Figure 11. Correlation between S&P and the Venezuelan stock market index and

correlation between S&P and Venezuelan ADRs........................................................... 143

Figure 12. Correlation between S&P and the Colombian stock market index and

correlation between S&P and Colombian ADRs............................................................ 143

Figure 13. Correlation between S&P and the Brazilian stock market index and correlation

between S&P and Brazilian ADRs ................................................................................. 144

Figure 14. Correlation between S&P and the Argentinean stock market index and

correlation between S&P and Argentinean ADRs.......................................................... 144


between S&P and Mexican ADRs before the 1995 Mexican crisis ............................... 145


between S&P and Mexican ADRs after the 1995 Mexican crisis .................................. 145

Figure 17. Sovereign Downgrades (S&P) ...................................................................... 146

Figure 18. Sovereign Upgrades (S&P) ........................................................................... 146

Figure 21. Sovereign Downgrades (Moodys)................................................................ 147

Figure 20. Sovereign Upgrades (Moodys) .................................................................... 147


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CHAPTER 1

INTRODUCTION

The last twenty years have witnessed large reductions in regulations and barriers

that prevented financial integration across countries. As markets slowly became more

integrated, brand new fields for financial research opened up. Not only could we study if

foreign markets behaved in a similar way to U.S. markets, but we also could study issues

surrounding the integration of U.S. and international financial markets. The first

dissertation essay investigates whether common factors that explain credit debt spread

changes for domestic and sovereign debt after taking into account fundamentals are

related, and then proceeds to analyze the determinants of these common factors. This

dissertation essay looks at two groups of assets that had previously been studied only

separately. It focuses on credit spread changes of U.S domestic bonds and sovereign

bonds, making it the first paper to bring together these two groups of individual bonds to

study their joint dynamics.

Previous research in spread changes of U.S. domestic bonds identified a common

component unrelated to credit risk in the time-series and cross-section of the unexplained

portion of the spreads (Collin-Dufresne, Goldstein and Martin, 2001; Huang and Huang,

2003). If the U.S. and overseas market for dollar-denominated credit-risky bonds is


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integrated, the information present in the unexplained portion of U.S. dollar sovereign

debt spread changes should be related to unexplained portion of U.S, domestic bonds

spread changes. This especially should be the case if that common component can be

explained by liquidity shocks, since such shocks are pervasive across markets (Chen,

Lesmond, and Wei, 2002; Chordia, Sarkar, and Subrahmanyam, 2003; Kamara, 1994).

Existing research investigates separately the existence of common components in

changes in credit spreads for domestic credit-risky debt (Collin-Dufresne, Goldstein and

Martin, 2001) and dollar-denominated sovereign debt (Scherer and Avellaneda, 2000;

Westphalen, 2003). The contribution of this dissertation essay is to study the relation

between the common components identified in domestic debt and the common

components found in sovereign credit spreads.

To conduct this analysis, a new dataset comprised of all domestic industrial and

U.S. dollar-denominated sovereign debt is constructed. This dataset contains data for 233

non-callable, non-puttable bonds issued by 37 emerging countries and 3097 domestic

corporate bonds issued by 649 different companies that traded between January 1990 and

January 2003. Results obtained help to discriminate between competing explanations for

the common component previously documented for domestic debt, and also might

suggest new explanations.

I find strong evidence of the existence of two common factors unrelated to credit

risk in debt spread changes of U.S. denominated sovereign debt and in the debt spread

changes of domestic bonds. While principal component analysis shows no evidence of

contemporaneous correlation between the two domestic and the two sovereign factors, a

vector autoregressive (VAR) model shows that domestic spread changes are related to the


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lagged sovereign spreads first principal component. Finally, I find that all four common

factors are related to the flows of money going into equity and bond funds, and the

second common component of each group is related to the net borrowed reserves from

the Federal Reserve, a macroeconomic measure of liquidity.

The second dissertation essay analyses the extent of the financial and economic

integration between Latin American countries and the United States by focusing on the

behavior of linkages between financial assets using a statistical technique known as

Extreme Value Theory (EVT). EVT is the study of outliers or extremal events. Since

large movements in returns are usually characteristic of financial crisis and since these

large movements can be considered outliers, the use of EVT seems to be warranted. This

approach has several advantages. First among these are the well-known results on

asymptotic behavior of the distribution of very high quantiles. Second, no assumptions

are needed about the true underlying distribution that generated data in the first place.

Since financial contagion usually occurs during periods of very high distress, it seems to

be best analyzed using techniques that focus on the tails of a distribution function. (Bae,

Karolyi and Stulz, 2003)

The financial assets I analyze are American Depositary Receipts (ADRs) and their

domestic counterparts in Latin America. Latin American firms can cross-list their shares

in the U.S. via ADR programs, and at the end of 2001 there were 1,322 non-U.S. firms

with sponsored programs, including 623 trading on American stock exchanges with a

total trading volume of $752 billion.

This chapter documents evidence of the asymmetric transmission of shocks from

U.S. stock markets into domestic markets. It builds on the work of Longin (1996) and


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Longin and Solnik (2001) applying EVT in finance by examining the linkage between

financial assets available to U.S. investors looking for international exposure before and

after main events such as the 1995 Mexican crisis. It also adds to the growing literature

on financial contagion by employing a statistical technique more appropriate than

current approaches based on elliptic distributions for the often temporary, but large,

movements in prices.

The third dissertation essay studies the effect of sovereign credit rating changes

on the cross-section of locally-traded firms. A sovereign credit rating reflects the rating

agencys opinion on the ability and willingness of sovereign governments to service their

outstanding financial obligations and it reflects macroeconomic factors related to political

and financial stability. Sovereign credit ratings have large effects that spread to firms

located within their borders, and changes to these ratings constitute country-wide shocks

that can have sizable effects on the terms under which firms obtain financing and the

overall cost of capital.

This chapter contributes to the existing literature by extending our understanding

of how much information sovereign rating changes convey to individual stocks within

domestic markets. Specifically, I investigate if and why a country rating matters for firms

within a country. I show that sovereign rating changes affect the terms on which a

domestic firm can get credit, creating an exogenous change in the cost of capital. I divide

the results into two parts: I first present the effect of rating changes at the aggregate level

using national stock indices, and then proceed to study the effect of those sovereign

rating changes on the individual firms located within those countries. Index level results

are consistent with the extant literature on the effect of credit-rating changes on U.S.


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firms. I do find evidence of a significant negative stock price reaction to sovereign rating

downgrades while I find no evidence of a stock price reaction to sovereign rating

upgrades. Further, I document that local stock markets react only to news of sovereign

rating downgrades issued by Standard & Poors.

To conduct this analysis I collected all sovereign rating changes issued by

Standard and Poors (S&P) and Moodys on 29 emerging countries from 1986 until 2003.

I study the stock price reaction to 136 downgrades (81 from S&P and 55 from Moodys)

and 100 upgrades (57 and 43 from S&P and Moodys respectively). I also collect

information on 1281 individual firms located in 29 emerging countries. After computing

abnormal returns for each firm, cross-sectional regressions of those abnormal returns are

run on firm-specific characteristics and country-specific variables. I document how the

size and wealth of the country where a firm is domiciled are related to the extent to which

that a firm will be affected by a sovereign-rating change. More importantly, I find that

previous access to international capital markets is an important determinant of the extent

to which a firm is affected by a sovereign credit rating change.


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CHAPTER 2

UNDERSTANDING COMMON FACTORS IN DOMESTIC AND

INTERNATIONAL BOND SPREADS

2.1. Introduction.

In this chapter I analyze the determinants of credit spread changes of individual

U.S domestic and sovereign bonds. Previous research has focused on one type of bonds at

a time, making this paper the first one to bring together the credit spreads on these two

types of debt to study their joint dynamics. If the market for dollar-denominated credit-

risky bonds is integrated, we can expect credit and non-credit related shocks to affect all

bonds, i.e. the information present in the time series cross-section of the unexplained

portion of U.S. dollar sovereign debt spread changes should be related to the common

component unrelated to credit risk identified by previous research in spread changes of

U.S. domestic bonds (Collin-Dufresne, Goldstein and Martin, 2001; Huang and Huang,

2003). This should especially be the case if that common component can be explained by

liquidity shocks, since such shocks are pervasive across markets (Chen, Lesmond, and

Wei, 2002; Chordia, Sarkar, and Subrahmanyam, 2003; Kamara, 1994). In this chapter, I

investigate whether common factors that explain credit spread changes for domestic and

sovereign debt after taking into account fundamentals are related and analyze the

determinants of these common factors.


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Existing research investigates separately the existence of common components in

changes in credit spreads for domestic credit-risky debt and dollar-denominated

sovereign debt. Scherer and Avellaneda (2000) identify the existence of two common

factors for sovereign debt spread changes. Westphalen (2003) finds evidence of a

common factor for sovereign debt spread changes of bonds denominated in several

currencies after controlling for country risk proxies. Research on changes in domestic

bond credit spreads by Collin-Dufresne, Goldstein and Martin (2001) finds one common

component after controlling for fundamentals. The relation between these common

components has not been examined in the literature.

I extend the research on common components present in bond spreads by

examining whether the information in the dynamics of U.S. dollar denominated sovereign

debt spreads is associated with the common component found in U.S. corporate bond

spreads. Specifically, I estimate different models of spread changes for each type of

bonds domestic and sovereign because these two groups vary in their source of credit

risk. Using principal component analysis for each debt type, I extract common factors

from the unexplained portion of credit spread changes from these models. I investigate

whether the common factors in U.S. dollar denominated sovereign debt are related to the

common factors present in U.S. corporate debt spread changes using both regressions

explaining contemporaneous changes in spreads and a dynamic model of changes in

spreads. Finally, I attempt to provide an economic interpretation for the relations I

uncover.

To conduct this analysis, I construct a new dataset that is comprised of all

domestic industrial and U.S. dollar-denominated sovereign debt. This dataset contains


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data for 233 non-callable, non-puttable bonds issued by 37 emerging countries and 3097

domestic corporate bonds issued by 649 different companies that traded between January

1990 and January 2003. This dataset is different from the ones used by earlier studies in

at least three ways. First, extant bond studies that use Datastream bond data do not

include dead issues, i.e., bonds that have matured or were retired, while I include them

to avoid a survivorship bias. Second, the Fixed Income Database used in some other

studies has a limited coverage of high-yield issues since it mainly covers investment-

grade bonds (Huang and Kong, 2003). I do not have this problem because my dataset

contains data for the complete universe of bonds covered by Datastream.

1

Finally, this

dataset covers a longer time period than any previous study.

My results help to discriminate between competing explanations for the common

component previously documented for domestic debt, and also suggest new explanations.

I find strong evidence of the existence of two common factors unrelated to credit risk in

debt spread changes of U.S. denominated sovereign debt and in the debt spread changes

of domestic bonds. While principal component analysis shows no evidence of

contemporaneous correlation between the two domestic and the two sovereign factors, a

vector autoregressive (VAR) model shows that domestic spread changes are related to the

lagged sovereign spread first common component. Finally, I find that all four common

factors are related to the flows of money going into equity and bond funds, as measured

by the Investment Company Institute (ICI), while only the second common component of

1 Informal conversations with Datastreams customer service revealed that several large banks, includingLehman Brothers, were among their providers for bond data. Since Lehman Brothers was the provider forthe FISD, we feel confident Datastreams data includes what is covered in the FISD and has broadercoverage of high-yield bonds because of the additional data providers. A comparison between FISD andDatastream sovereign bond data can be found in Annex A.


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each group is related to the net borrowed reserves form the Federal Reserve, a

macroeconomic measure of liquidity.

This chapter is the first one to bring together these two types of credit-risky

dollar-denominated debt to study the joint dynamics of the common factors in their credit

spreads. The results I obtain improve our understanding of the determinants of the cost of

debt for foreign countries and for domestic firms. For example, my results suggest that

the cost of debt for foreign countries and domestic firms is not only a function of their

own creditworthiness but also depends on shocks that affect the price of all debt.

Further, these results help us understand better the extent to which the sovereign

and domestic corporate bond markets are integrated. In a fully integrated dollar debt

market, we would expect the relation between domestic corporate credit spreads and

sovereign credit spreads to be contemporaneous. Further research should investigate

whether the lack of a contemporaneous relation is due to differences in liquidity and

infrequent trading or if this reflects a market inefficiency.

Finally, the lack of a relation between the common components of domestic

corporate credit spread changes and sovereign credit spread changes suggests that the

cost of debt for emerging markets depends mostly on country and emerging-market

specific considerations. This is surprising in light of a considerable literature that

emphasizes the impact of developed country developments for capital flows into

emerging markets (Calvo, Leiderman, and Reinhart, 1993; Chuhan, Claessens, and

Mamingi, 1998). Further investigation of the robustness of my results might shed greater

insight into this issue.


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This chapter proceeds as follows. Section II describes the literature, sample,

variables and methodology used to model credit spread changes for sovereign bonds.

Section III does the same for credit spread changes for domestic corporate bonds. I

investigate, using a variety of techniques, the existence and nature of the factors affecting

debt spread changes in section IV. Section V analyzes the dynamics of the common

factors and investigates whether liquidity and/or demand related variables are related to

them. Section VI concludes.

2.2

Debt spreads of sovereign bonds

In order to examine whether a common factor is associated with the variation in

U.S. domestic corporate and U.S. dollar denominated sovereign spreads, the unexplained

variation in each spread (i.e. residuals) must be calculated. My choice of variables to

compute the credit risk portion of debt spread changes is based on the determinants of

bond spread changes specified by structural models. For sovereign bond spreads, I expect

bond-specific characteristics to be associated with bond spreads. Additionally, I expect

bond spreads to be related to macro or country-specific factors as well as systematic

factors. In this section, I review the relevant literature on U.S. dollar denominated

sovereign bond spreads (section 2.1), and then discuss the testable implications of the

extant literature and describe the proxies that are used to test the hypotheses derived from

it (section 2.2). I describe the sovereign bond sample next (section 2.3), present a model

to estimate debt spreads, discuss the results, and explain the computation of residuals

(section 2.4).


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2.2.1 Sovereign debt literature.

The international debt market changed dramatically in the past 25 years. In the

1980s bank loans were the principal instrument of this market. By the end of that decade,

reckless lending and borrowing caused outstanding debt balances to skyrocket to

unsustainable levels. The crushing pressure of debt payments forced several emerging

market countries to the verge of default. To avoid the ripple effects of such a default on

the worlds financial system which was still recovering from the 1987 stock market

crash-- the U.S. government helped put in place a plan that would allow these countries

to orderly restructure their debt schedule. The Brady plan, formulated in 1989 by then

Secretary of the Treasury Nicholas Brady in association with the World Bank and IMF,

called for the issuance of sovereign bonds to replace the loans of commercial banks.2

Brady bonds opened a vast and untapped market for emerging market countries hungry

for U.S. dollars to help finance their growth, commercial deficits or simply to cover

current expenses. Bank loans, while still an important component in sovereign debt

balances, gave way to sovereign bonds as the principal financing instrument for emerging

countries in the 1990s. Bonds were clearly preferred for several reasons, for instance the

dispersion of creditors and the existence of a market where these bonds could be actively

traded, which provided investors with a transparent benchmark measure of country risk.

2These bonds were coupon bearing (fixed, floating or hybrid), long maturity (ten to thirty years) issued in

registered or bearer form, whose principal and part of the interest were guaranteed by collateral of U.S.Treasury bonds and other high grade securities. Some of them included special recovery rights (warrants)that could be detached and traded separately. This last characteristic made the computations of yields forthese bonds especially tricky.


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It was previously mentioned in this chapter I take a structural approach to the

modeling of debt spreads. It is important to mention, though, that sovereign debt is

different from corporate debt. One of the most important characteristics of any debt

contract is the guarantee provided by the legal framework to creditors that allows them,

in the case of default, to take possession of collateral and/or to liquidate the defaulting

debtors assets. There is no enforceable bankruptcy code for sovereign bonds, making it

effectively impossible for a creditor to successfully pursue a claim on a defaulting

countrys assets. Acknowledging the endogenous default decision that countries face in

this framework, Gibson and Sundaresan (1999) present a model in which creditors can

impose trade sanctions and capture some fraction of the defaulting countrys exports, and

Westphalen (2002) extends their model to include rescheduling in the form of a bond

exchange. Finally, Westphalen (2003) applies a methodology used to study corporate

credit spread changes (Collin-Dusfrene et.al. 2001) to a sample of sovereign bonds issued

in different foreign currencies.4

So far, research on sovereign debt spreads has focused more on how spreads are

determined at issue than on the study of the dynamics of the cross-section. There are two

reasons for this. First, thin trading in many of these bonds produces relatively fewer

sovereign bond transactions data. As a result, some data vendors resort to provide matrix

prices (e.g. Bloomberg), which are not useful for research purposes.5

Second, in the early

1990s, when the market for sovereign debt was in its infancy, countries started by issuing

4 Another approach to the study of sovereign spreads has been implemented through the use of models based on an exogenously specified intensity process, known as reduced-form models. Merrick (2000)studies the implied recovery rations in Argentinean and Russian bonds. Pags (2001) fits the joint Liborstructure and discount Brady bond prices to a reduced-form model using a two factor affine-yield model.Duffie, Pedersen and Singleton (2003) conduct an analysis of Russian debt.5 Actual quotes and/or transaction prices are available from different providers in the Bloomberg terminalthrough an additional subscription service.


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few bonds. As their credibility improved, reinforced by the implementation of structural

reforms in their economies, and investors got acquainted with this new supply of bonds,

sovereign issuers increased the number and amount of debt offerings. Therefore, it took

some years for this market to be sufficiently diverse and liquid enough to allow the

construction of a data panel suitable for research purposes. Today, the sovereign debt

market is more developed we have more bonds with longer time series each one- and

there are better and more alternatives to obtain bond data several information services

provide access now to observed pricing data, although some remain very expensive.

2.2.2 Implications of the literature and proxies used to test them.

Structural models of sovereign debt have identified macroeconomic variables that

affect sovereign debt spreads.6 Based in part on previous literature, I put together three

groups of variables that should capture most of the debt spread variation. The first group

contains bond-specific variables, i.e., variables that vary within bond issues, e.g. years to

maturity. The second group contains variables that vary from country to country but are

the same for all bonds from a given country (country-specific variables). The third group

contains variables that are the same for all bonds in the sovereign sample, and try to

capture changes in the U.S. interest rate term structure.

6 One problem with most empirical work exploring the relation between macroeconomic variables and debtspreads is that they conduct static analysis, i.e., only study the cross-section of spreads at one point in time,usually at issuance. For instance, GDP growth has been theoretically and empirically shown to havesignificant explanatory power over issue level spreads. This is not useful in this context since most of thedata used in this paper is released monthly, quarterly or even annually in some countries.


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2.2.2.1 Bond-specific variables.

The bond-specific variable used is years to maturity. By definition, a bonds life

to maturity duration measures how long an investor has to wait before getting their

money back. Sovereign bonds pay (relatively) large coupons and therefore a large

proportion of the cash flows are paid throughout the life of these bonds, thus we have to

consider the possibility that years to maturity could be an overstated proxy of a bonds

average life.

2.2.2.2 Country-specific variables.

These variables are chosen to capture a measure of a countrys distance-to-

default, i.e., a countrys ability (and/or willingness, depending on the model of reference)

to keep servicing its debt. Following Eaton and Gersovitz (1981), Bulow and Rogoff

(1989), Krugman (1985, 1989), Gibson and Sundaresan (1999) and Westphalen (2002), I

collect data on exports and total debt outstanding to construct a debt-to-exports ratio.

Borrowing from Krugman and Rotembergs (1991) speculative currency attacks model, I

use international reserves to construct a debt-to-reserves ratio as an alternate proxy of

distance-default. Westphalen (2003) uses a political risk measure which I also use here. I

also collect the Standard and Poors (S&P) ratings history for each country. I use the

monthly volatility of local stock returns as a proxy for volatility in a countrys wealth or

value. This measure is also used because it is a good proxy for local risk. One direct

testable implication of this is that spreads should increase with volatility. Finally, the

local stock market return in U.S. dollars is included.


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2.2.2.3 U.S. interest rate term structure.

Since all the bonds in my dataset are denominated in U.S. dollars, I care about

factors that affect the U.S. yield curve term structure. From Litterman and Scheinkman

(1991) we know that the U.S. yield curve level and slope are important explanatory

factors of the term structure. Further, in this framework, if a countrys wealth follows a

stochastic process analogue to a firms value process, the risk neutral drift will be

positively related to the risk-free rate. An increase (decrease) in the risk-free rate should

increase (decrease) the countrys wealth over time, making default less (more) likely to

happen. Since an upward-sloping yield curve slope is, according to the expectations

hypothesis theory of the term structure,7

predicting higher interest rates in the near future,

I expect this slope to have some effect on spreads today. Also, a positively sloped interest

rate term structure is perceived as signaling increased economic activity in the near

future.

Table 1 presents the predicted correlation signs between the variables previously

mentioned and debt spreads.

2.2.3 Data description.

I collect monthly data on all U.S. dollar denominated bonds with Datastream

coverage. Datastreams yields are calculated using average market maker prices provided

by the International Securities Market Association (ISMA). I am able to identify 5270

live and 3451 dead bonds8 issued by foreigners that traded between January 1990 and

7 Bodie, Kane and Marcus (1999), pp. 446.8 One important feature of Datastreams coverage of bonds is that only live issues (i.e., issues that arecurrently trading) appear in their bond lists. Therefore, to make sure I had all available data, I conducted a


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January 2003. I eliminate from the dataset all bonds that were callable and/or puttable at

borrowers option, all that had an early redemption feature and/or were extendible at the

bond holders option, and all that were not issued by a sovereign entity.9

This leaves my

dataset with 181 live and 52 dead bonds. Also, I eliminate all observations with less than

one year to maturity because, as these bonds approach their maturity date, they are less

traded, which in turn dries up their liquidity and distorts prices and yields. 10 After all

these adjustments, I come up with a sample that contains 9,275 monthly observations

from 233 bonds issued by 37 different countries, which did trade between January 1990

and January 2003.

For each bond, I collect the monthly redemption yield (datatype 4 in Datastream).

I also collect the monthly U.S. Treasury yield curve. Then, I compute debt spreads as the

difference between the redemption yield of the sovereign bond and the value of a linear

interpolation of the U.S. Treasury yield curve to obtain the yield of a U.S. instrument

with identical maturity as the bond being analyzed.11

I collect years to maturity time

series for each bond. As proxies for the U.S. Treasury yield curves level and slope, I

collect monthly annualized yields for the on-the-run two and ten year Treasury notes.12

country by country search of U.S. dollar denominated bonds in the Dead bonds (not trading anymorebecause they were retired or they matured) section of the Datastream Extranet web site.9 I am not using Brady bonds in my analysis because their characteristics are inherently different fromregular sovereign bonds. The existence of collateral as well as the existence of value recovery rightsattached to Brady bonds makes them a class on their own. Further, the tendency is for sovereigns to retirepar and discount Brady bonds, so that movements in Brady bonds prices might be reflecting low volume

and thin trading problems and not changes associated with the underlying value of the issuer and the overallliquidity of the market For instance, Mexicos Ministry of Finance and Public Credit announced on April 7,2003 that it was calling US$3,839 million of its dollar-denominated Series A and B Brady Par Bonds,which were the last outstanding series of Mexican Brady Bonds denominated in dollars.10 Sarig and Warga (1989). This effect is even more pervasive when considering that liquidity was not greatin the first place.11 I also collected the monthly U.S. Treasury yield curve using CMT (constant maturity treasuries) tocalculate spreads and our results are insensitive to the choice of U.S. benchmark curve.12 The use of CMT yields for those maturities did not affect our results at all.


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Monthly exports data expressed in nominal U.S. dollars come from the IMFs

International Financial Statistics. Debt outstanding and foreign reserves data are obtained

from the joint BIS-IMF-OECD-World Bank statistics on external debt. Quarterly data on

the total amount outstanding of bank loans, of debt securities issued abroad and of Brady

bonds is obtained from this source, as well as monthly data on the amount of international

reserve assets, excluding gold.13 One shortcoming of this database is that not all series are

available on a quarterly basis and there are some gaps in the data, especially in the earlier

1990s.

The Economist Intelligence Unit (EIR) started publishing in March 1997 a

measure of country risk for emerging markets. It measures political, economic policy,

economic structure, currency, sovereign debt and banking sector risks. This index can be

used as a guide for the general risk of a specific country. It provides help in assessing the

risk of investing in the financial markets of those economies as well as the risks involved

in direct investment. The values are derived from measuring the risk associated with four

aspects of the country political risk, economic risk, economic structure risk and liquidity

risk.14

To get a measure of monthly local wealth volatility, I use an equity volatility

measure as proxy. Ideally, I wanted to use MSCI country indices, since they are

calculated for each country using the same methodology. However, MSCI country

indices were not available daily going back to the early 1990s for many of the countries

13 All figures are expressed in current U.S. dollars.14 The overall risk rating is measured on a scale from 1 to 100 where 1 denotes the least risk and 100 themost risk possible. For example, in December 2002, the value of the index was 78 for Argentina, 63 forBrazil and 48 for Mexico.


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included in this paper. Therefore, I used Datastream local equity indices. For more than

half of the countries in the sample (twenty one), I collect daily data for the local

Datastream equity index. For eight additional countries, I collect daily data from their

own local equity indices. For the remaining countries Datastreams world total return

index was used. To correct for differences in the scales of the indices the coefficient of

variation (sample standard deviation over sample mean) was computed.

I also collect the available history of Standard & Poors (S&P) country ratings

from Bloomberg, and follow Eom, Helwege and Huang (2003) for translating S&P

ratings into numerical values, where a rating of AAA has a value of 1, AA+ a value of 2

and so on.

Table 2 has summary statistics for the sovereign sample. Observations are

grouped in five different categories according to their S&P rating. It is evident from panel

A that all groups display a high degree of non-normality. Also, as expected, spreads

increase as we move down in ratings. The mean debt spread in the overall sample is 483

basis points, the maximum spread is 3939 basis points and the minimum is 1.9 basis

points. Interestingly, the standard deviation also increases as the rating deteriorates. Over

the sample period, the standard deviation is on the order of 25.3 to 809 basis points.

There is evidence of extreme movements in each group as the 90% and 10% values are

away from the mean by several times the standard deviation.

Panel B has the mean values, by group and for the overall sample, of some

country specific variables. Debt-to-reserves, debt-to-exports and political risk all increase

in value as move down in rating to signal a worsening of a countrys situation. I expect


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these variables to have on average higher values as we move form high to low ratings,

and that is precisely what I find.

2.2.4 A model for sovereign spreads.

I estimate the following equation for each bond observation in the sample:

Spreadi,t= Constant + 1*Debt to foreign reserves ratioi,t+ 2*Country risk

measurei,t+ 3*U.S. Treasury yield curve level,t+ 4*U.S. Treasury yield curve (1)

slope,t+ 5*Local volatilityi,t-1 + 6*Local returnt-1 + 7*Years to maturityi,t+ i,t

Following earlier research, I estimate regressions on debt spread changes.15

To

estimate this equation, I decided to use an OLS model with Newey-West adjusted

errors.16

A priori, I expect the coefficients to have the signs described in Table 1. Table 3

shows the results of estimating equation (1) in four different rating groups. These groups

are similar to those presented in Table 2, except that the first and second groups from that

table were grouped together in Table 3.

The model seems to have a good fit, as measured by R-squared measures, which

range from 19% to 30%. For brevity, I will discuss only the results for the overall sample.

The debt-to-reserves ratio and the political risk measure both have a positive coefficient

15

Some previous research has been conducted on spread levels, for instance, Houweling et. al. (2002).Cantor and Packer (1996) and Eichengreen and Moody (1998) run regressions on the log of the yieldspread.16 I experimented with several other methodologies. I estimated equation 1 using OLS fixed effects,grouping our sample by bond, by country, and by region. I also estimated FGLS (Feasible GeneralizedLeast Squares), OLS with panel corrected standard errors and OLS with Huber/White standard errorcorrection. All methodologies produced quantitatively and qualitatively similar results; however resultswere more consistent using OLS coefficients with Newey-West adjusted errors. Results obtained with othermethods are not reported in this paper and are available from the author.


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(as expected) and are highly significant. Two lags of the political risk variable were

included to account for the possibility of autocorrelation in this variable. These variables

measure the ability to service debt and the overall political and economic environment of

the issuer. An increase in political risk would signal higher instability and/or the

possibility of expropriation and therefore should be associated with a higher spread. An

increase in the debt-to-reserves ratio could be caused by an increase in the nominal debt

amount or a decrease in international reserves, both of which should be associated with a

higher spread. I also find that the coefficient estimates when using debt-to-exports in

place of debt-to-reserves are not significant and have the wrong sign, so they are not

reported.

The coefficient associated to the U.S. Treasury yield curve level is negative and

highly significant. Previous work had obtained insignificant positive coefficients (Cline

and Barnes, 1997; Min, 1998; and Kamin and Von Kleist, 1999), and significant negative

coefficients (Eichengreen and Mody, 1998). One interpretation of these negative

coefficients is that, as interest rates go up, low rated countries find it less convenient to

issue debt. Also, most structural models predict a negative relation because higher

interest rates increase the drift of the process followed by the firms (in this case,

countrys) value.17 A higher firm (country) value should be associated with a smaller

spread and hence the negative sign.

The coefficient associated with the U.S. Treasury slope term is always positive

and significant, however, this is unexpected. Following the expectations theory of interest

rates, a positively sloped yield curve signals higher future rates, which should be

17 Longstaff and Schwartz (1995).


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associated with smaller spreads. Two reasons for this effect were previously mentioned.

On one hand, we could expect the average quality of sovereign issuers to increase

because low rated countries decide not to issue debt and this increase in overall quality

puts downward pressure on spreads. On the other hand, higher rates will mechanically

increase the distance to default in most Merton-based structural models which would also

lead to a decrease in spreads.

Local volatility is positive and highly significant, as expected. The local stock

return has the expected (negative) sign and also is significant. The coefficient on changes

of years to maturity is negative and not significant. I interpret this coefficient as evidence

of the existence of a survivorship bias in which only relatively better countries make it to

issue longer term debt, as explained by Helwege and Turner (1999) for the domestic case.

It may be the case that investors think that in the case of a default, short term maturities

are more risky than long term maturities since countries will usually default first on

issues with closer maturities, making short term issues riskier. The lack of consistent

cross-default clauses in some countries allows them to default or re-schedule debt

payments selectively. Finally, for a country facing financial difficulties, a longer time

horizon will provide the necessary time and maneuvering room to enact reforms and

measures that will allow the country to return to fiscal stability, effectively making longer

term debt less risky.

2.3 Debt spreads of domestic bonds.In this section, I review the relevant literature on U.S. dollar denominated

domestic bond spreads (section 3.1). Then I discuss the variables used in the computation


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of domestic spreads (section 3.2), and I describe the characteristics of the domestic bond

sample (section 3.3). I then proceed to estimate domestic debt spreads, discuss the results

and compute residuals (section 3.4).

2.3.1 Domestic debt literature.

The first structural model of risky debt is by Merton (1974). In this paper, Merton

used an option pricing approach to include systematic and idiosyncratic risk in the

calculation of the value of a put option on the firms value.18

In Mertons model a firm

defaults on its debt when its assets are not enough to cover its outstanding obligations.

Default occurs when the firms value crosses from above a given threshold. The initial

model allowed for default only at maturity and was extended by Black and Cox (1976) to

allow for earlier default. Another extension was introduced by Longstaff and Schwartz

(1995) by incorporating stochastic interest rates. Strategic default was introduced in

models by Anderson and Sundaresan (1996) and Mella-Barral and Perraudin (1997).

Modeling endogenous corporate default was introduced by Leland (1994) and Leland and

Toft (1996). As these models need a fair amount of abstraction to achieve tractability, it

is not surprising that they prove to be difficult to implement and then almost always with

disappointing results (see Eom, Helwege and Huang (2003) for a review of the problems

and limitations faced by structural models).

This lack of results motivated some researchers to try another approach, using

reduced-form models, or intensity-based models. These models ignore firm-specific

18 Specifically, Mertons (1974) model states that a risky zero-coupon bond has the same payoff structureas a risk-free bond plus being short a put option on the firms value with a strike price equal to the facevalue of the debt.


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fundamentals and do not explicitly model the processes followed by the firms leverage

and/or value. Reduced-form models assume an unpredictable default process governed by

an exogenous hazard rate. For instance, Duffie and Singleton (1997) use a generic point

process and Lando (1998) uses a Cox process. Through extensive calibration, reduced

form models generally produced better results at explaining and forecasting yield spreads

than structural models.

More recently, Elton, Gruber, Agrawal and Mann (2001) tried to explain

corporate spreads using explanatory factors that included the probability of default, the

loss given default, and the difference in tax regimes. Collin-Dufresne et. al. (2001) tried

to explain changes in the credit risk portion of corporate spreads using data on spot rates,

reference yield curve slope, firms leverage and volatility, estimates for jumps in the

firms value and a proxy for the general business climate. Both papers, the former being

more of a reduced-form approach and the latter using a variables specified by a structural

framework, find similar results in that their models left a large portion of the cross-

sectional time variation of spreads unexplained, and further, they find that a single

common unknown factor could explain up to 75% of the residual variation. Huang and

Huang (2003) calibrate several classes of structural models to be consistent with the

recent history of observed defaults. They find that different models could generate the

wide range of credit spreads observed in the recent past, and further they provide some

evidence about the predictive power of such models.


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2.3.2 Theoretical determinants of domestic debt spreads.

Structural models of domestic debt have identified variables that affect debt

spreads. In a manner consistent with the previous section, I put together three lists of

variables that should capture most of the debt spread variation. As with the sovereign

case, the first list also contains bond-specific variables, i.e., variables that vary within

bond issues, e.g. years to maturity. The second list contains variables that vary from firm

to firm but are the same for all bonds issued by firm (firm-specific variables). The third

list contains variables that are the same for all bonds in the domestic sample, and try to

capture changes in the U.S. interest rate term structure as well as changes in the U.S.

economic climate.

2.3.2.1 Bond-specific variables.

The bond-specific variable is years to maturity. The same arguments from section

2.2.1 apply here.

2.3.2.2Firm-specific variables.I choose two firm-specific variables following the basic spirit of Mertons model

as presented in Stulz (2003). The first variable, leverage, has been used in previous

research as a successful proxy of a firms financial health. The second variable is the

volatility of a firms equity. A priori I expect a negative relation between each of these

two variables and debt spreads, since an increase on any of them would make default

more likely.


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Debt spreads are computed in the same way as for the sovereign sample. Years to

maturity data is collected for each bond. The measures of the U.S. Treasury yield curves

level and slope are the same as the ones used in the previous section. The proxy for the

U.S. economic climate is the S&P 500 total return index from Datastream.

To compute the leverage ratio I collect book value of debt from COMPUSTAT

(items 45 and 51) and the market value of equity from CRSP. Leverage ratios are then

computed, following earlier literature, as:

)(

)(

ValueMarketEquityDebtofValueBook

DebtofValueBook

+

Table 5 shows descriptive statistics for the domestic sample. Although it would be

desirable to classify this sample also by rating, however, Datastreams coverage of

ratings is sketchy at best. In light of that problem, I decided to classify the data according

to leverage. As can be seen from the leverage columns of panel A, credit spreads increase

as firms become more levered. Further, the standard deviation of credit spreads also

increases with leverage. Data on the No leverage data column refers to firms that are

either private or are not covered by COMPUSTAT. The presence of heavy tails in each

category is evident from the dispersion observed in the max, min, 10% and 90% values,

where the 10% and 90% values are several standard deviations away from the mean.


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2.3.4 A model for domestic debt spreads.

I estimated the following equation for domestic bonds:

Spreadi,t= Constant + 1*Leverage ratioi,t+ 2*Stock return volatilityi,t+

3*U.S. Treasury yield curve levelt+ 4*U.S. Treasury yield curve slopet+ (2)

5*S&P index returnt-1 + 6*Years to maturityi,t+ i,t

The results from these regressions are reported on Table 6. I estimated equation

(2) for each leverage group and for the overall sample. Clearly, the model performs better

in the highly leveraged group, as evidenced by the higher R-squared value. This result is

consistent with previous studies which find that structural models perform better for

longer maturity, lower rated sub-groups. As with the sovereign case, I will only go over

the overall sample results. On the coefficient of changes in years to maturity, the negative

but insignificant coefficient is consistent with previous results of Helwege and Turner

(1999) and with the basic Merton (1974) model predictions, as described in Stulz (2003),

for conservative levels of debt. The coefficients of lagged leverage and stock return

volatility contemporaneous and lagged- are positive and strongly significant. The sign of

the U.S. yield curve level is also as expected and similar to the results obtained by earlier

studies.

In contrast to the sovereign sample, nothing conclusive can be said about the sign

and significance of the coefficient estimated for changes of the U.S. Treasury slope is

significantly positive in every specification. Finally, the S&P index return is significant

and with a negative sign, just as predicted by the theory.


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2.4. Analyzing the common factor.

As previously mentioned, the goal of this chapter is to investigate whether the

common factor identified in domestic credit spread changes also is present in sovereign

debt spread changes. In this section, I establish the existence of a common factor in both

the residuals from the regressions on sovereign and domestic debt spread changes.

2.4.1 Establishing the existence of common factors.

In order to investigate whether common factors are present in the unexplained

variation in spreads, I use principal components analysis. This is a statistical technique

for data reduction whose objective is to find unit-length linear combinations of the

original variables that capture the maximum variance. I apply principal component

analysis to the residuals obtained from the regressions discussed in previous sections to

verify whether the unexplained variation is truly noise or whether there is evidence of a

common factor driving this unexplained portion of the variance of credit spread

changes.19

The first problem faced when applying principal components analysis is how to

organize unbalanced panels in the most efficient form. Research in this area conducted by

Boivin and Ng (2003) shows that more data is not always better when conducting this

type of factor analysis. In fact, in their forecast exercise they show that factors extracted

from as few as 40 variables could be more informative than factors extracted from all 147

series in their setup. Basically, their result obtains because of large cross-correlation in

19 The serial correlation of the residuals from the regressions for sovereign yield changes is -0.1513 with pvalue of 0.2290 and 0.0068 with p value of 0.9345 for domestic yield changes.


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errors and of small variability of the common components. Sadly, until today there is no

guide as to what data should be included in a principal component analysis or what is the

optimal number of series to include in this exercise. Recent work from Scherer and

Avellaneda (2000) applies principal component analysis to eight variables only,

effectively using one or two bonds per country in their study. While this might be a

solution for the sovereign case where it is easier to identify benchmark bonds for each

issuer, this is not feasible in the domestic sample. This sample has bonds from 649

different firms, and in many cases there are tens of bonds outstanding from a given firm.

Also, applying principal component analysis to all the bonds in the domestic sample is

not a good idea, since that would most certainly only increase the amount of statistical

noise while adding very little or no new information at all.

I decide to follow the approach implemented by Collin-Dufresne et. al. (2001) and

create groups or bins of data to efficiently summarize the information content of the

residuals. I divide each sample (sovereign and domestic) in three maturity categories and

three leverage (debt-to-reserves, in the sovereign case) categories, creating a total of nine

bins in each sample. Then, each observation is assigned to a bin. I estimate again

equations (1) (for the sovereign bins) and (2) (for the domestic bins), compute the

residuals, and calculate averages across residuals for each bin. Table 7 shows the

correlation structure for the average domestic residuals (panel A), the average sovereign

residuals (panel B) and for all averages domestic and sovereign (panel C). The average

correlation for the sovereign sample is 0.75, and 0.87 for the domestic sample. To

investigate whether the relatively high correlations found in panel A and C are caused by

a common component, I proceed to conduct principal component analysis.


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Table 8 shows the results of applying this eigenvalue decomposition to the bins

constructed earlier. Panel A shows strong evidence of the existence of a common factor

in sovereign spreads. The first common factor explains 76.09% of the variation, as shown

by the proportion of the first eigenvalue. The second common component explains the

remaining variance that is orthogonal to the first common component. It is difficult to

interpret the second component because its eigenvalue is well below the value of the first

eigenvalue and is much closer to the third eigenvalue. However, if this is to be interpreted

as evidence of a second common factor, it would explain an additional 20.37%.

According to Scherer and Avellaneda (2000), a number between 65% and 80% for the

first common component would be considered as indicative of strong co-movement

characterized by a high correlation in the spread changes. My results are consistent with

their result obtained with spreads computed from Brady issues for selected countries

in which they found evidence of two common factors driving most of the variation in

spread changes.

Panel B of Table 8 also shows strong evidence of the existence of a common

factor to all domestic spreads. The first common component explains 86.23% of the

variance. There is weak evidence on the existence of a second component which explains

an additional 8.53% of the variance. The existence of a first common factor that explains

such a large portion of the variance is consistent with previous research, e.g., Collin-

Dufresne et. al. (2001). The existence of a second common factor has not been

documented for domestic debt before, but this could be due to the fact that I am using a

larger dataset and that I am looking at a longer time period that earlier studies.


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Finally, panel C has the results of looking at the common components of both

groups of bonds, sovereign and domestic. Interestingly, I find no evidence suggestive of

the existence of a common factor to both groups of bonds. The first common factor

explains 42.06% of the residual variance of spread changes, while the second factor

explains an additional 33.12%. As mentioned before, Scherer and Avellaneda (2000)

consider a value of 65% for the first common component as the lower boundary for a

weak coupling, or correlation, between spread changes. The result I obtain is puzzling

because if the market for dollar-denominated credit-risky bonds is integrated, and if the

common components I find can be explained by liquidity shocks, then such shocks

should be pervasive across markets (Chen, Lesmond, and Wei, 2002; Chordia, Sarkar,

and Subrahmanyam, 2003; Kamara, 1994). According to panel C, this is not what is

happening.

In order to shed more light on the issue of whether the common factor identified

in both samples is indeed the same in both groups, I extract the first and second common

components of each sample to compare them. These common factors are plotted in figure

1. The interpretation of the units in the y-axis is as follows. To compute the principal

components, I analyzed the correlation matrix. This is equivalent to all the variables to

having mean 0 and standard deviation 1. Thus, the common factors are expressed in

terms of these standardized variables. Loosely speaking, the units on the y-axis in both

figures can be interpreted as percentage points.

The pattern seems to suggest a lead-lag relation between the first factor from the

domestic sample and the first factor extracted from the sovereign sample. Figure 2 shows

the second common component extracted from both samples. The figure seems to suggest


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a weak contemporaneous relation. These issues will be investigated further in the next

sections.

2.4.2 Explanatory power of the extracted components.

In this section, I examine whether these common factors have explanatory power

over the cross-section of debt spreads for the other type of debt. I estimate again

equations (1) and (2) including in each equation the two common components extracted

from the other group, i.e., I include the factors extracted from the sovereign sample into

the domestic sample regression and vice versa. Results are shown on Table 9.

First, I will talk about the sovereign regression when the domestic common

components are included. Looking at rating categories, the explanatory power of the

equation for the lower rated group (B- to C) increases, as measured by the increases of R-

squared statistic from 22% to 31%. This sub-sample is the one with the least number of

observations. There is, however, no gain in explanatory power in the overall sample.

Further, in every case only the contemporaneous value of the first component from the

domestic sample is significant. In the case of the second component extracted from the

domestic sample, only the lagged value of the second factor is significant for all sub-

groups.

The domestic sample has strikingly different results. In this case, I included in the

domestic spread changes equation both common components extracted from the

sovereign sample. The explanatory power of the overall equation is increased almost by

30%, from an R-square value of 0.09 to 0.12. The only significant common component

coefficient is the contemporaneous effect of the first common component from the


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sovereign sample. The explanatory power in most domestic sub-samples increases by a

similar percentage as the R-squared value of the overall sample. Overall, I interpret these

results as evidence of the existence of a relation between the first common component

extracted from the sovereign spread changes and domestic debt spread changes. The

dynamics of the relation between the common components extracted from each type of

debt is investigated in the next section.

2.5. Looking into the information content of the common factors.

The principal component analysis conducted neither provides information on the

dynamics of the factors identified nor provides an economic interpretation of them. In

this section I investigate the contemporaneous and inter-temporal relation between factors

and also investigate whether these factors might be capturing liquidity and/or

supply/demand shocks.

2.5.1 Lead-lag relations.

The picture shown in figure 1 suggests the possibility of an intertemporal relation

between the first factor extracted from the sovereign sample and the first factor extracted

from the domestic sample. Using a vector-autoregression approach, I investigate the

possibility of one of these markets acting as an early signal for potential problems that

can affect the bond market in general. Previous work like Joutz and Maxwell (2002) and

Cifarelli and Paladino (2002) have applied VAR procedures in a credit spread framework

to study the relation between credit spreads from different countries. More recently,

Longstaff, Mithal and Neis (2003) applied a VAR framework to study the relation


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between bond and credit derivatives markets. To explore the lead-lag relation between

the sovereign and the domestic factors, the following simple vector-autoregression

specification is used:

1

1

11

1

11 =

=

++++=k

j

tjtj

k

j

jtjt XFacDomFacSovaFacSov

2

1

22

1

22 =

=

++++=k

j

tjtj

k

j

jtjt XFacDomFacSovaFacDom

Table 10 shows the results for the simple case when k is equal to two. Both the

Akaike Information and Schwartz criteria suggest that a VAR system of two lags is

warranted by the data. I first run the VAR model without exogenous variables to have an

initial idea of the lead-lag structure. For brevity, I only report the R-squared value for

each equation and also because the basic lead-lag relation is unchanged when the

exogenous variables are included. I then run the VAR model with exogenous variables.

These exogenous variables are chosen to capture liquidity and supply/demand effects.

Most previous studies dealing with credit spreads specifically abstain from liquidity

effects because of the lack of consensus on how to measure and model liquidity premium

affecting spreads (Chen, Lesmond and Wei, 2003). Longstaff, Mithal and Neis (2003)

study the consistency of the price of credit risk between the bond and derivatives

markets. They find that the implied cost of credit is higher in the bond market than in the

credit derivative market, and advance a possible explanation for this based on the

existence of a liquidity component in debt spreads. Their measure for this liquidity


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premium is the difference between the price of credit risk in the bond and credit

derivative markets.20

Since all the bonds in the sample are denominated in U.S. dollars and they all

trade in U.S. financial markets, I am interested in variables that measure the overall

liquidity in these markets. I use two general measures of liquidity. The first proxy is the

difference in yield between the on-the-run21 thirty year U.S. Treasury bond and the most

recent off-the-run bond is computed. Off-the-run bonds are bonds that whilst not being

the most recently issued in a certain maturity range, are very similar to the on-the-run

issue in all respects. Therefore, any differences in prices and therefore in yields- is

usually considered to be due to liquidity. As liquidity dries up, this difference is expected

to decrease.

The second proxy for general liquidity in the market is the net borrowed reserves

from the Federal Reserve,22

which is considered a measure of the monetary stance. A

loose monetary policy usually implies an increase in liquidity via the decrease of credit

constraints. Harvey and Huang (2002) showed that the Federal Reserve, through its

ability of changing the money supply, impacts the trading of bonds and currencies.

Following Chordia, Sarkar and Subrahmanyan (2003) I define net borrowed reserves as

total borrowing minus extended credit minus excess reserves, divided by total reserves.

Since borrowed reserves represent the amount that banks are short to satisfy the Feds

requirements, a lower value of this measure indicates looser monetary conditions.

20 Collin-Dufresne et. al. (2001) point that Chakravarty and Sarkar (1999), Hotchkiss and Ronen (1999) andSchultz (1999) found evidence of the existence of relatively high transaction costs and low volume in bondmarkets, and therefore, Collin-Dufresne et. al. (2001) interpret these results as evidence of a liquiditypremium.21 An on-the-run bond is the most recently issued (and typically the most liquid) government bond with agiven maturity.22 See Chordia, Sarkar and Subrahmanyan (2003).


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To capture possible supply/demand shocks I collect data from the Investment

Company Institute (ICI) on the monthly flows into mutual funds. ICIs statistics are

collected from approximately 8,300 mutual funds, and are divided in flows into equity

funds and bond funds. These measures could potentially capture changes in investors

attitudes towards risk or any other supply/demand shocks unrelated to overall market

liquidity.23

Table 10 reports the results of the VAR model with the exogenous variables. It

seems that the sovereign factors have explanatory power over the domestic factors but

not the other way around. I will discuss each one of the four equations in the VAR model,

starting with the first common domestic factor. The second lag of the first sovereign

factor is significant in the regression for the first domestic factor. The flows to stocks and

flows to funds variables are negative and significant. This equation is the one with the

smallest gain in R-squared when including the exogenous variables.

The first sovereign factor seems to be slightly autoregressive from the barely

significant coefficient for its own first lag. Coefficients for the domestic factor lags are

not significant, while all the coefficients for the exogenous variables are highly

significant. It seems as if this factor is capturing both liquidity and demand shocks as

implied by the coefficients associated with the exogenous variables. This equation also

has the highest increase in explanatory power, since the R-squared increased from 0.05 to

0.37 when the exogenous variables are added to the specification.

23 Of course, we have to consider the possibility of endogeneity in our variables, since for instance, achange in the Feds stance could make certain markets more attractive an

[2005] three essays in international finance

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