three essays in international finance

variables are related to the common components found in domestic and sovereign spread changes.. The effect of sovereign credit rating changes on emerging stock markets .... Existing rese

THREE ESSAYS IN INTERNATIONAL FINANCE

The Ohio State University

2005

Professor René M Stulz, Adviser

Professor G Andrew Karolyi _

Adviser Professor Bernadette A Minton Graduate Program in Business Administration

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

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 Poor’s and Moody’s 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 & Poor’s and not to those by Moody’s I then study the

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

Dedicated to my family

ACKNOWLEDGMENTS

I wish to thank my adviser, René Stulz, for intellectual support, encouragement, and enthusiasm which made this dissertation possible, and for his patience in correcting both my stylistic and methodological errors

I thank Andrew Karolyi for stimulating discussions, guidance, and encouragement, not only with this dissertation but throughout my graduate studies

I am grateful to Bernadette Minton for discussing with me various aspects of this thesis, and for her insightful feedback

I also wish to thank Mike Cooper, Craig Doidge, Jean Helwege, Francis Longstaff, and seminar participants at Drexel University, Fordham University, Ohio State University, Purdue University, Queen’s University, and University of Virginia for helpful comments and suggestions

VITA

July 9, 1972 Born – Puebla, Puebla, Mexico

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

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 6

2.2 Debt spreads of sovereign bonds 10

2.2.1 Sovereign debt literature 11

2.2.2 Implications of the literature and proxies used to test them 14

2.2.2.1 Bond-specific variables 15

2.2.2.2 Country-specific variables 15

2.2.2.3 U.S interest rate term structure 16

2.2.3 Data description 16

2.2.4 A model for sovereign spreads 20

2.3 Debt spreads of domestic bonds 22

2.3.1 Domestic debt literature 23

2.3.2 Theoretical determinants of domestic debt spreads 25

2.3.2.1 Bond specific variables 25

2.3.2.2 Firm specific variables 25

2.3.2.3 U.S interest rate term structure 26

2.3.3 Data description 26

2.3.4 A model for domestic debt spreads 28

2.4 Analyzing the common factor 29

2.4.1 Establishing the existence of common factors 29

2.4.2 Explanatory power of the extracted components 33

2.5 Looking into the information content of the common factors 34

2.5.1 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 41

3.2 Literature review 44

3.2.1 The univariate case 47

3.2.2 The bivariate case 50

3.3 Data 51

3.4 A small test for the Mexican pairs 55

3.5 Concluding remarks 55

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

4.1 Introduction 58

4.2 Literature review 65

4.3 The effect of sovereign rating changes on stock market indices 71

4.3.1 Data 72

4.3.2 Methodology 74

4.3.3 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

Appendix A A comparison of sovereign bond coverage on Datastream and the NAIC 99 Appendix B Tables 103 Appendix C Figures 134

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

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

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 Figure 7 Excess mean graphs for the left tail and the right tail of the dollar return of the Mexican ADR equally weighted portfolio 140 Figure 8 Excess mean graphs for the left tail and the right tail of the dollar return of the 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

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

Figure 15 Correlation between S&P and the Mexican stock market index and correlation between S&P and Mexican ADRs before the 1995 Mexican crisis 145

Figure 16 Correlation between S&P and the Mexican stock market index and correlation 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 (Moody’s) 147

Figure 20 Sovereign Upgrades (Moody’s) 147

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

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

lagged sovereign spread’s 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

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 agency’s 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

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 & Poor’s

To conduct this analysis I collected all sovereign rating changes issued by Standard and Poor’s (S&P) and Moody’s on 29 emerging countries from 1986 until 2003

I study the stock price reaction to 136 downgrades (81 from S&P and 55 from Moody’s) and 100 upgrades (57 and 43 from S&P and Moody’s 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

CHAPTER 2

UNDERSTANDING COMMON FACTORS IN DOMESTIC AND

INTERNATIONAL BOND SPREADS

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

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 Datastream’s customer service revealed that several large banks, including Lehman Brothers, were among their providers for bond data Since Lehman Brothers was the provider for the FISD, we feel confident Datastream’s data includes what is covered in the FISD and has broader

coverage of high-yield bonds because of the additional data providers A comparison between FISD and Datastream sovereign bond data can be found in Annex A

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

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)

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 world’s 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.2Brady 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

2

These 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 for these bonds especially tricky.

The sovereign spread, or credit spread, computed now from bond yields, continued to be such a benchmark measure of country risk.3

Starting in the 1980s, the determinants of sovereign debt spreads has been studied

by Eaton and Gersovitz (1981), where governments trade off the cost of paying debt versus reputation costs or exclusion from capital markets, and Bulow and Rogoff (1989), who provide rational explanations for international lending and model the costs of debt repudiation as direct sanctions Edwards (1984) analyzed the macroeconomic determinants of the debt spread measured as the difference between the interest rate charged to a particular country and LIBOR (London InterBank Offered Rate) Hernández-Trillo (1995) uses a measure openness, unexpected shocks to GDP, international reserves and the risk free rate to explain the probability of default in sovereign loans The international episodes of financial contagion experienced in the second half of the 1990s attracted even more attention to this area, as researchers started

to devote more time to the study of periods of increased co-movements among international financial markets For instance, Cantor and Packer (1996) and Eichengreen and Moody (1998) study the determinants of bond spreads at the issue level, finding that agency ratings include most of the information existing in macroeconomic variables More recently, Scherer and Avellaneda (2000), Joutz and Maxwell (2002) and Cifarelli and Paladino (2002) study selected series from several emerging markets using principal component analysis and vector-autoregressions

3 The credit spread is often referred to as yield spread, debt spread or simply spread These terms are used interchangeably in this paper

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 debtor’s 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 country’s 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 country’s 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 Pagès (2001) fits the joint Libor structure 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 terminal through an additional subscription service

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 debt spreads 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 have significant explanatory power over issue level spreads This is not useful in this context since most of the data used in this paper is released monthly, quarterly or even annually in some countries

2.2.2.1 Bond-specific variables

The bond-specific variable used is years to maturity By definition, a bond’s 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 bond’s average life

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 country’s wealth follows a stochastic process analogue to a firm’s 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 country’s 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 Datastream’s 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 Datastream’s coverage of bonds is that only ‘live’ issues (i.e., issues that are currently trading) appear in their bond lists Therefore, to make sure I had all available data, I conducted a

January 2003 I eliminate from the dataset all bonds that were callable and/or puttable at borrower’s option, all that had an early redemption feature and/or were extendible at the bond holder’s 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 curve’s level and slope, I collect monthly annualized yields for the on-the-run two and ten year Treasury notes.12

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 great

in the first place

11 I also collected the monthly U.S Treasury yield curve using CMT (constant maturity treasuries) to calculate 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

Monthly exports data expressed in nominal U.S dollars come from the IMF’s 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 the most risk possible For example, in December 2002, the value of the index was 78 for Argentina, 63 for Brazil and 48 for Mexico

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 Datastream’s 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 & Poor’s (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 country’s situation I expect

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:

∆Spread i,t = Constant + β 1 *∆Debt to foreign reserves ratio i,t + β 2 *∆Country risk

measure i,t + β 3 *∆U.S Treasury yield curve level ,t + β 4 *∆U.S Treasury yield curve (1)

slope ,t + β 5 *∆Local volatility i,t-1 + β 6 *Local return t-1 + β 7 *∆Years to maturity i,t + ε 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 yield spread

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 Generalized Least Squares), OLS with panel corrected standard errors and OLS with Huber/White standard error correction All methodologies produced quantitatively and qualitatively similar results; however results were more consistent using OLS coefficients with Newey-West adjusted errors Results obtained with other methods are not reported in this paper and are available from the author

(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 firm’s (in this case, country’s) 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)

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

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 firm’s value.18 In Merton’s model a firm defaults on its debt when its assets are not enough to cover its outstanding obligations Default occurs when the firm’s 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, Merton’s (1974) model states that a risky zero-coupon bond has the same payoff structure

as a risk-free bond plus being short a put option on the firm’s value with a strike price equal to the face value of the debt

fundamentals and do not explicitly model the processes followed by the firm’s 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 firm’s 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

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

2.3.2.2 Firm-specific variables

I choose two firm-specific variables following the basic spirit of Merton’s model

as presented in Stulz (2003) The first variable, leverage, has been used in previous research as a successful proxy of a firm’s financial health The second variable is the volatility of a firm’s 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

2.3.2.3 U.S interest rate term structure

The domestic sample is denominated in U.S dollars Therefore, I care about factors that affect the U.S yield curve term structure Similar to the sovereign case, I use the U.S yield curve level and slope as explanatory factors of the term structure (Litterman and Scheinkman, 1991) The arguments used in section 2.2.3 also apply here Since I collect data only on bonds issued by U.S industrial firms, I assume their exposure

to the economic cycle is better captured by the S&P 500 index and therefore I collect monthly returns for this index Table 4 presents the predicted relations between the variables previously mentioned and debt spreads

2.3.3 Data description

The domestic sample contains all U.S denominated bonds issued by industrial domestic firms Applying the same selection criteria as those for the sovereign sample, I end up with 2,493 live and 604 dead bonds issued by 649 different firms during the January 1990 – January 2003 period for a total of 71,831 usable observations This sample differs from previous studies in at least three aspects First, it covers a larger time period than previous research Second, I collect data for the entire universe of bonds issued in U.S dollars by domestic industrial firms, not only for those traded by any specific group of investors Third, the Fixed Income Database used in earlier studies like Collin-Dufresne et al (2001) mainly covers investment grade bonds, and so results obtained for high-yield bonds using that database might not be representative (Huang and Kong, 2003)