#predictability in emerging sovereign debt markets

8/8/2019 #Predictability in Emerging Sovereign Debt Markets

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Predictability in Emerging Sovereign Debt Markets

Gergana Jostova

George Washington UniversityDepartment of Finance

Lisner Hall 540J, 2023 G Street, Washington, DC 20052

Tel: (202) 994-7478, Email: [email protected]

Webpage: home.gwu.edu/jostova

First draft: July 12, 2000This revision: October 28, 2003

I thank seminar participants at Boston College, the European Finance Association meeting 2001, theMidwest Finance Association meeting 2002, the Eastern Finance Association meeting 2003, the Washington

Area Finance Association meeting 2002, JPMorgan, and Deutsche Bank. The study has benefited from thecomments of Alexander Philipov, Pierluigi Balduzzi, Alan Marcus, Wayne Ferson, Doron Avramov, CesareRobotti, Robert Savickas, Evan Gatev, Eric Jacquier, Edith Hotchkiss, Edward Kane, and an anonymousreferee.


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Predictability in Emerging Sovereign Debt Markets

Abstract

This paper finds strong evidence of predictability in Brady bonds, the most liquid

emerging debt market, by implementing a new model for credit spreads. Predictability

is economically and statistically significant and robust to various considerations. Active

management provides US investors in emerging markets with double the buy-and-hold

returns at lower risk and the equivalent of free options on Brady bonds. Our analysis

suggests that predictability is primarily driven by credit spread deviations from funda-

mentals, rather than time-varying risk or risk premia. We believe this inefficiency is the

result of the restrictions of a non-transparent, institutionally dominated, dealer market

and the lack of a well developed derivatives market for emerging country credit risk.


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1. Introduction

Brady bonds1 are the primary financing vehicle of emerging market countries, currently rep-

resenting 80% of their total government external debt2. Brady debt now totals over $100

billion. Brady bonds are also the primary choice of diversification into emerging markets for

US mutual, pension, and endowment funds. These bonds are the single most traded emerging

market debt instrument, with transaction volume of $1 trillion in 1993 and $2.7 trillion in

1996 (see Hassan (2001)). The sharp increase in Brady bond turnover, relative to the mod-

est increase in their amount outstanding, suggests a large increase in their liquidity. The

most liquid and popular Brady bonds are issued by the four largest emerging market debtors,

Argentina, Brazil, Mexico, and Venezuela, which account for 75% of the market. The high

liquidity of these countries Brady bonds is underscored by a typical bid-ask spread of $0.25

(0.4%), although large trades can get even better terms (see Cumby and Pastine (2001) and

Claessens and Pennacchi (1996)). As a result, Brady debt provides an easy and inexpensive

way for US investors to diversify into emerging debt markets.

Since their creation in 1990, Brady bonds have generated an average annual return

of 43% by 2001. However, emerging countries have also been ridden by frequent financial

crises - the Peso (December 1994), the Asian (October 1997), the Russian (August 1998),

and Brazilian (January 1999) crises - leading to sudden declines in emerging debt portfolios.

Understanding credit risk and the ability to time changes in credit fundamentals is essential in

emerging debt markets. The combination of high returns, high liquidity, and frequent turmoil

makes active management all the more attractive.

In this respect, the contribution of this study is twofold. First, the paper proposes a

general two-stage model for credit risk, and develops a formulation of the model that cap-

tures the dynamics of credit spreads in emerging debt markets. Second, the study presents

strong evidence of predictability in the Brady bond market and supports it with a varietyof out-of-sample tests. Predictability in emerging sovereign debt markets has not yet been

investigated. So far, predictable variation in equity and bond returns has been studied in

1Named after the Nicholas Brady plan of 1989 to restructure the troubled Mexican loans. Earlier loanswere swapped with Brady bonds with lower coupons and shorter maturity by collateralizing the face value ofthe bond with US treasuries. The swap transformed Mexicos government loans into tradable and liquid debtinstruments. Argentina, Brazil, and Venezuela followed suit and all issued Brady bonds in the early 1990s.Currently 17 emerging market countries have Brady debt.

2Source: JPMorgan: Introducing the Emerging Market Bond Index Plus,http://www2.jpmorgan.com/MarketDataInd/EMBI/embi.html.

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developed markets3, as well as in emerging market equity returns4. Studies of emerging debt

markets have focused on the pricing and issuance of Brady debt (see Claessens and Pennac-

chi (1996), Cumby and Pastine (2001), Duffie, Pedersen, and Singleton (2003), Eichengreen

and Mody (1998)), or on identifying fundamental determinants of debt prices (Boehmer and

Megginson (1990)).

We study the predictable component in the changes of the credit spread index, called

the Emerging Market Bond Index5 (EMBI) spread6, of the four biggest issuers: Argentina,

Brazil, Mexico, and Venezuela. Brady bonds trade on dealer markets, in which dealers trade

on spreads rather than prices. JPMorgan, one major dealer in the Brady market, derives

the credit spread implied in the price of each Brady bond, of each countrys EMBI index

(a total return index of the countrys most liquid Brady bonds), and of the World EMBI

index. Predictability of credit spread changes translates into predictability of Brady bond

excess returns over US treasuries7. In this study, the models formulation is based on credit

spreads, but its predictive power is evaluated out-of-sample based on both realized credit

spread changes as well as actual holding-period returns to a US investor after accounting for

transaction costs.

The proposed model for credit spreads has two stages. The first stage describes the long-

term equilibrium relation between a countrys credit spread level and local macroeconomicfactors. The financial intuition behind the long-term equilibrium is that the spread implied

in market prices is a premium for holding defaultable sovereign instruments. This premium

depends on the intrinsic credit risk of the government, which is a function of the economic

conditions in the emerging country. Under the assumption of market rationality, there should

be a stable relation between the level of spreads observed on the market and the intrinsic

credit risk of the government, as proxied by the local macroeconomic factors, despite the well-

known instability of emerging market conditions. The stability of this relationship provides

important information in predicting the direction of future credit spread changes.

The second stage relates the short-term dynamics of spread changes to global instru-

ments as well as to the deviation of the spread level from its long-term equilibrium (derived

3See Avramov and Chordia (2003), Avramov (2002), Ang and Bekaert (2001), Bekaert and Hodrick (1992),Ferson (1989), Ferson and Harvey (1993, 1999), Harvey (1991), Ilmanen (1995), and Lewellen (1999).

4See Bekaert (1995), Bekaert and Harvey (1995), and Harvey (1993, 1995).5The EMBI spread of a country is the spread above the US treasury spot curve that sets the total market

value of all Brady bonds equal to their discounted payments. (More details in the Data section.)6Credit spreads are the direct counterpart of equity excess returns and are a standard focus in the literature

on defaultable bonds.7

A change in the spread causes a change in the relative discount factors that translate the debts promisedcash flows back to the present, and hence in a change in the bond price. As a result, predicting the directionof the spread change is equivalent to forecasting the sign of the Brady bond excess return over US treasuries.

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in stage one). The results show that this deviation from fundamentals is the most important

instrument in predicting subsequent spread changes. The inclusion of this deviation can be

viewed as an innovation to traditional single-equation predictive models. The importance

of this innovation is assessed out-of-sample using a broad set of tools for testing both the

statistical and economic significance of the observed predictability.

We take special effort to show that the documented predictability is not spurious or

unexploitable. Predictability is evaluated out-of-sample using three independent tests. The

first test is based on realized holding period returns. An active Brady-bond strategy based on

the models predictions allows US investors to double the returns from a buy-and-hold strategy,

while taking less risk and accounting for transaction costs. Previous studies (e.g. Ilmanen

(1995) and Harvey (1991, 1993, 1995)) document only slight predictability in developed and

emerging equity markets. We also show the superior performance of the active strategy is

robust to the timing of the initial investment. Second, we apply Mertons (1981) equilibrium

test for market-timing value, which is based on the number of correct out-of-sample directional

forecasts of credit spread changes. Mertons test shows that the model adds significant value

to US investors and provides them with the equivalent of free options on Brady bond indexes.

Finally, to check the robustness of the results to the relatively small size of the out-of-sample

window, we perform Henriksson and Mertons (1981) nonparametric small-sample market-timing test, which specifies a sufficient number of correct predictions necessary to reject the

null of no predictability for a particular sample size (the smaller the sample size, the harder

it is to reject no predictability). The null hypothesis of no predictability is rejected at the

1% significance level in Brady markets. The last two out-of-sample tests, both based on the

number of correct predictions, serve to eliminate concerns that the results are driven by a few

lucky periods. All three tests agree that the spreads deviation from fundamentals is the

instrument that drives all predictability.

Predictability is robust to various considerations. Concerns about spurious predictiveregression and biased t-statistics (see Nelson and Kim (1993)) or biased slope coefficients (see

Stambaugh (1999)) are confined to in-sample results. Under the null hypothesis of no pre-

dictability, spurious regressions would not bias out-of-sample results and would not produce

superior returns out-of-sample (see Ferson, Sarkissian, and Simin (2003) and Lo and MacKin-

lay (1996)). A robust estimator is used to correct for data non-stationarity and cross-country

correlations (contagion) and simulations are conducted to assess possible small sample biases.

Transaction costs are accounted for by buying at the ask and selling at the bid price when

rebalancing. Liquidity is not an issue as Brady bonds are the most liquid emerging debtmarket and the active strategy involves only monthly rebalancing.

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The asset pricing literature has documented time-series return predictability (see e.g.

Fama and French (1988,1989) and Keim and Stambaugh (1986)). This predictability has been

attributed to time-varying risk, market price of risk, and market inefficiency (see Avramov and

Chordia (2003) and Ferson and Harvey (1991, 1999)). The predictability documented in this

paper is primarily due to informational inefficiency as it is driven by the spreads deviation

from its fundamental value (90% of which is determined by country-specific factors). We show

that global equity and bond instruments do not provide any out-of-sample predictability in

the Brady market.

We believe this informational inefficiency is the result of the characteristics of the Brady

market. First, Brady markets are dominated by large institutional investors following con-

strained investment policies, which slows down the process of price adjustment. There is

an absence of arbitrageurs and unrestricted investors8 due to the scarcity of derivatives and

the large transaction lots. Second, unlike Treasury markets which are also dominated by

large institutional investors, Brady markets lack the completeness provided by fully devel-

oped derivatives markets which would allow the separate pricing of credit risk. Third, Brady

bonds trade in non-transparent dealer markets which are generally associated with lower in-

formational efficiency. Section 6 discusses these sources of predictability in detail.

We focus on Brady bonds rather than other emerging market debt instruments, because(1) they are by far the most liquid and largest emerging debt market, and (2) they have

significantly longer history than other emerging market bond indexes. While investors in

emerging debt markets have three options: domestic bonds, Eurobonds, and Brady bonds,

the Brady bond market is by far the most liquid and largest market of all (Solnik (2000, p.

369)) as the issue size of Brady bonds is quite large9. If one is to make a case for active

management, it is more reasonable to go with more liquid instruments. The typical Brady

bond bid-ask spread of $0.25 is very low relative to that of Eurobonds and more so than

that of domestic bonds issued by emerging countries. Second, the history of the Brady bondindexes is longer than that of Eurobonds, most of which were issued after 1995, allowing us to

study the statistical property of these indexes over a longer period of time. In addition, the

correlation between the excess returns of Brady bonds and other emerging market instruments

is almost perfect to make the analysis applicable to other instruments or to allow investors to

take advantage of their relative mispricing10.

8Unrestricted investors here refer to speculators who can take unrestricted positions in the asset theyconsider mispriced.

9Unlike Brady bonds, whose issue size is very large, the typical Eurobond is $100 million or less. The set

of actively traded Eurobonds is very limited. Source: JPMorgan: Introducing the Emerging Market BondIndex Plus, http://www2.jpmorgan.com/MarketDataInd/EMBI/embi.html.

10The analysis of emerging market domestic, Eurobonds, and Brady bond indexes (provided by JPMorgan)

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The remaining of the paper is organized as follows. Section 2 motivates the two-stage

model for credit spreads. Section 3 describes the data. Section 4 suggests a robust estimation

methodology. Section 5 presents the results from economic and statistical tests of out-of-

sample predictability. Section 6 discusses the causes for the existence of the documented

predictability and section 7 concludes the paper. Appendix A provides methodological details

on the SUCCR11 estimator used in the estimation of the first stage of the model and Appendix

B presents simulation results on the SUCCR small-sample bias reduction.

2. The model

Credit spreads reflect the market estimate of the credit quality of risky bonds, an unobservable

intrinsic characteristic. The intrinsic credit quality of bonds is driven by the debtors true

capacity to service its debt. In the case of sovereign debt, this capacity depends on the

economic, fiscal, and financial conditions in the emerging country, which can be proxied by

a set of properly motivated macroeconomic and financial indicators. Under the assumption

of market rationality, the market-determined credit spread should not deviate significantly

from the intrinsic credit risk of the local government. Hence, market rationality dictates that

credit spreads will converge to their long-term equilibrium levels commanded by the true creditquality of the debtor. In the short-term, however, spreads may deviate from their fundamental

value due to investors sentiment, market momentum, or institutional factors.

When a long-term equilibrium exists, future credit spread changes depend on the cur-

rent deviation of the spread from its long-term equilibrium level with respect to the local

macroeconomic factors. Statistically, long-term equilibrium is represented by cointegration12.

Engle and Granger (1987) prove that omitting deviations from long-term equilibrium when

predicting changes of cointegrated variables results in model misspecification, because current

deviations from equilibrium contain information about future changes beyond that provided

by the levels or differences in the variables. Following Engle and Grangers analysis, standard

predictive models, conditioning returns (or changes in prices) on a set of exogenous instru-

ments, will be misspecified if there exists a long-term equilibrium between the level of prices

and fundamental factors.

The existence of a long-term equilibrium between credit spreads and fundamental fac-

reveals that the correlation between their monthly spread changes range from 0.8 to 0.9, implying the excessreturns for the three bond types should be similar before transaction costs.

11

Seemingly Unrelated Canonically Cointegrating Regressions, Park and Ogaki (1991).12Cointegration implies that although individual series may experience permanent shocks, these shocks will

affect all variables in a way that preserves their long-term equilibrium. (See Data section.)

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tors in Brady bond markets (this assumption is tested in the Data section) motivates the

formulation of a two-stage model for credit spreads:

Spreadi,t = BiFi,t + i,t (1)

Spreadi,t+1 = E,iSpreadEMBI,t + W,iMSCIt + ii,t + ei,t+1 (2)where

E(i,t) = 0;E(2i,t) =

2i ;E(i,tj,t) = ij

Et1(i,t) = f(i,t1, i,t2,...) (if a long-term equilibrium exists)

E(ei,t) = 0;E(e2i,t) =

2i ;E(ei,tej,t) = 0

and the variables are defined as:

Spreadi,t = country is credit spread above the US treasury spot curve

Fi,t = vector of country is local macroeconomic factors

Bi = vector of country is spread sensitivities to local factors

i,t = spread deviation from long-term equilibrium in country i

SpreadEMBI,t = change in the World EMBI spread index

MSCIt = change in the Morgan Stanley Capital International world equity index

i,t = estimate ofSpreadi,ts deviation from its long-term equilibriumE,i ,W,i = sensitivity to the respective instrument

i = speed of adjustment to long-term equilibrium

2i = variance of the deviation from long-term equilibrium

ij = covariance between spread deviations in countries i and j

2i = unexplained variance of country is spread changes

The proposed two-stage model has a strong link to existing credit risk models. The first

stage relates to credit scoring models and links the level of spread observed on the market to

the equilibrium spread level commanded by the intrinsic credit risk of the Brady bond. The

second stage relates to predictive models with two important adjustments: (1) expected bond

excess returns are represented by expected changes in credit spreads. This representation

is motivated by the fact that bond excess returns are driven by changes in credit quality or

liquidity13. In the case of the highly liquid Brady bonds, changes in credit quality is the major

factor driving excess returns. However, it is the change in spreads, rather than returns per

se, that are directly related to the first stage of the model. (2) The deviation from long-term

equilibrium from the first stage is a new instrument included in the predictive stage.

13Since we are dealing with excess returns, the effect of interest rate changes cancels out.

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2.1. Determinants of long-term equilibrium credit spread levels

Our choice of factors (summarized in Table 1), used to proxy for the intrinsic credit risk inemerging countries, is motivated by whether the factors reflect on the expected default proba-

bility of the government and/or the recovery rate in case of default. The factors considered for

each country are the Local Equity Index, Consumer Price Index (CPI), Real Exchange Rate

Index, Short-term Interest Rates, Money Supply, Unemployment, and Gross Domestic Product

(GDP). Our selection draws on economic theory, and previous studies on credit spreads or

asset returns.

The Local Equity Index is used as a proxy for the wealth of the economy. This variable

also reflects on capital gains, tax revenues, and the ability of the government to service its

debt. The inclusion is supported by Barnhill et al. (2000), Bookstaber and Jacob (1986),

Ramaswami (1991), and Shane (1994) who document the co-movement between high-yield

bonds14 and equity indexes. As in Ferson (1989), the CPI is included to control for the

inflationary component of the stock index. By the purchasing power parity, the CPI should

also have an affect on exchange rates, imports, exports, and the balance of payments of a

country, which in turn affect the countrys funds available to service its debt. Real Exchange

Rates have an impact on the countrys terms of trade and current account and appreciating

exchange rates have been the precursor of financial crises in emerging markets, including the

recent crisis in Argentina. Real exchange rates have been found to be a significant factor

in pricing international assets in studies by Adler and Dumas (1983), Dumas and Solnik

(1995), and Sercu (1980). Local Short-Term Interest Rates reflect local intertemporal rates of

substitution. High interest rates often indicate a large local debt as the government increases

its demand for loanable funds. Domestic debt represents an extra burden on the government

in servicing its external debt. High interest rates further lead to underinvestments which

hamper the future growth of the economy and reduce the governments debt service capacity.

Fama and Schwert (1977) and Ferson (1989) find evidence that short-term interest rates are

an important factor in pricing US long-term bonds and equities. The Money Supply variable

reflects on the governments monetary policy and discipline. A growing money supply is often

the result of a large fiscal deficit (monetizing the deficit), which leads to hyperinflation and

ultimately to real economic crises. Fama (1981), Geske and Roll (1983), and Patelis (1997)

find money supply to be a significant variable in explaining asset returns. Unemployment

reflects the real productive capacity of the economy and Shiller (1984) finds unemployment to

be a significant business cycle indicator. GDP measures the governments ability to generate

cash flows and service its debt. Since prices are discounted expected future cash flows, Fama

14A BBB+/Baa1 rating or below defines Emerging Markets in the context of external debt markets.

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(1990) argues that such a production measure should be significant in explaining yields and

returns.

2.2. Predictors of short-term credit spread changes

In the predictive second stage of the model, spread changes are conditioned on lagged global

instruments, as well as on the spreads deviation from long-term equilibrium, i,t1, derivedin the first stage of the model.

The inclusion of this deviation is an innovation of this study, prompted by the presence

of a long-term equilibrium between credit spreads and underlying macroeconomic fundamen-

tals. Any deviation from the long-term equilibrium expressed in eq. (1) is temporary by

definition. The equilibrium error, it, should, therefore, be stationary and mean-reverting,

even if the spreads and economic factors experience persistent shocks. Ignoring deviations

from long-term equilibrium biases the expectation of future spread changes, because, while

the expected equilibrium error (E(i,t)) is zero unconditionally, its conditional expectation

(Et1(i,t)) is different from zero and affects subsequent changes in spreads. The deviation

from long-term equilibrium levels carries information beyond that contained in the differenced

factors or spreads (see Engle and Granger (1987)). For example, if spreads on the market are

below the equilibrium commanded by the level of the underlying factors, they will correct

partially up as the long-term equilibrium relation prevents the spread and factor levels from

drifting too far apart. The degree and speed of correction is measured by i (the coefficient

of i,t1), which should be between -1 and 0, reflecting a partial adjustment to long-termequilibrium. The deviation instrument will be significant in predicting spread changes under

two conditions: (1) the long-term equilibrium relation in the first stage exists and is correctly

identified, and (2) the market fails to react instantaneously to temporary misalignment in

credit spread levels.The remaining instruments in the predictive stage are global equity and bond instru-

ments, included to test whether time-varying risk or risk premia can capture some of the

predictability in Brady markets. The first instrument is the lagged change in the World

Emerging Market Bond Index (World EMBI) spread, SpreadEMBI,t1. One can think of the

first instrument as representing global Brady-bond excess returns, thus capturing the return

of the asset class on which the study focuses. The second instrument is the change in the

Morgan Stanley Capital International (MSCI) equity index, MSCI, which is a global total

return equity index. Its inclusion follows Ilmanen (1995) and Harvey (1995) who find globalinstruments to have some predictive power in developed and emerging equity markets.

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3. Data

The study uses data from the four largest emerging market debtors: Argentina, Brazil, Mexico,

and Venezuela, accounting each for about 25%, 20%, 20%, and 10% of total emerging market

Brady debt, respectively. All series consist of end-of-month observations from April 1993

to February 2001. Although some EMBI indexes date back to December 1990, when the

World EMBI index was constructed, the econometric methodology of the study requires equal

observations for all countries and limits the sample to the shortest history of Argentinas

Brady debt.

3.1. EMBI spreads and total return indexes

All EMBI spread and total return series are provided by JPMorgan. The company derives

the spread of each Brady bond and computes the EMBI spread and total return indexes for

each country with Brady bonds. These indexes are continuously provided on Bloomberg by

JPMorgan and are used directly by dealers and investors to compare sovereign instruments.

We use the country EMBI spread index in our analysis.

The credit spread of a Brady bond is defined as the spread above the US treasury spot

curve that sets the current market price of the bond equal to its discounted payments:

P0 =t=1

Ct(1 + rt)t

+T

t=+1

Ct(1 + rt + Spread)t

+Face

(1 + rT)T(3)

where P0 is the current price of the Brady bond, T is the maturity, Ct is a cash flow scheduled

for period t, and rt is the US treasury spot rate for delivery at time t. The final payment is

discounted at the US treasury bill rate as the face value of Brady bonds is guaranteed by the

US government through the Nicolas Brady plan of 1989. This final payment does not carry

credit risk and does not provide a default premium. The first payments are also discounted

at the US treasury spot rates when the Brady plan provides for a rolling interest-rate guarantee

for the immediate payments on the debt. The purpose of the spread is to provide a single

measure of the pure sovereign default risk of Brady instruments.

JPMorgan also computes each countrys spread, Spreadi,t, which is the weighted average

spread of all Brady bonds that meet certain size and liquidity requirements, as well as the

World EMBI spread index, SpreadEMBI,t, of all emerging market countries with Brady debt.

The four countries EMBI spreads are highly correlated with the World EMBI andamong themselves. Table 2 presents the sample correlations among the EMBI spreads of

the four countries and the World EMBI spread. This high level of cross-country correlation

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(ranging from 74% to 90%) suggests that spreads may have common trends and shocks and

should be examined together rather than country by country. Figure 1 shows that spreads

have experienced periods of high volatility especially around the Peso (December 1994), Asian

(November 1997), and Russian (August 1998) crises, as well as during the Brazilian devalu-

ation (January/February 1999). Descriptive statistics of spread levels and spread changes of

the four countries are provided in Tables 3 and 4, respectively.

3.2. Macroeconomic factors

The monthly economic variables used are country-specific indicators, that are publicly avail-

able and can be obtained from the IMF and IIF databases (the sources used are summarized

in Table 1). The financial series - local stock index, local interest rates, and exchange rates -

are available continuously for all countries. The economic series - unemployment, GDP, CPI,

money supply - are available on a monthly basis, but are reported with a two- to three-week

lag. To make them contemporaneous with the EMBI spreads in terms of information arrival,

we lag all economic series by one month. For example, Septembers consumer price index for

Argentina is reported in October, while Octobers sovereign spread is known the same day in

October. Those two variables are contemporaneous in the sense that they become public in

the same month. It is important to make sure that all predictions are based on information

that is publicly available when the active strategy is implemented. Descriptive statistics for

all macroeconomic indicators are presented in Table 5.

3.3. Diagnostic tests

The EMBI spreads and all macroeconomic variables in the long-term equation are nonsta-

tionary and integrated of order one according to the Augmented-Dickey-Fuller (ADF) test 15.

This result is not surprising for the level of macroeconomic variables. The intuition behind the

nonstationarity of the EMBI spreads (especially given the short history of the Brady market)

lies in the very nature of emerging markets. EMBI spreads capture the economic conditions

of these countries, which are evolving, volatile, and often unpredictable. Regressions of non-

stationary variables would produce spurious results unless some equilibrium relation ties the

series together. Such a relation is modeled through cointegration. Intuitively, cointegration

implies that, although the individual economic variables may experience permanent exogenous

shocks, such shocks affect all variables in a way that preserves the equilibrium among them.

15Results from the ADF tests are available from the authors upon request.

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In the context of this paper, cointegration in the long-term equilibrium stage is essential

to estimating the dynamics of the predictive stage. Engle and Granger (1987) show that in

the presence of cointegration, the level of the macroeconomic factors contains information

beyond that contained in the first differences of the variables. Deviations from the long-term

equilibrium levels of the variables are useful in predicting subsequent changes in EMBI spreads

(the predictive stage).

Johansens (1988) multivariate procedure is used to test for cointegration. We find at

least two cointegrating equations between the spreads and macroeconomic variables in each

country16. The specific features of our econometric methodology, however, require that there

exist a single cointegrating equation in each country (see Appendix A). Therefore, when-

ever more than one cointegrating relation is found for a country, we reduce the number of

macroeconomic factors until a single long-term equilibrium relation among the spread and the

remaining factors is obtained. There is no significant loss of information because the removed

variables are stationary combination of the remaining variables in the set. The final set of

variables used in each country long-term dynamics stage are presented in Table 6.

4. Estimation Methodology

With the structure of the model and characteristics of the data in mind, this section motivates

the use of a robust and efficient estimation methodology for each stage. The parameters

of the long-term equilibrium stage are estimated using Park and Ogakis (1991) Seemingly

Unrelated Canonical Cointegrating Regression (SUCCR). This method makes full use of the

nonstationarity and cross-country correlation in our data and produces efficient and unbiased

estimates. The parameters of the short-term dynamics equation are estimated using standard

OLS methodology.

4.1. Long-term equilibrium stage: the SUCCR methodology

The data used in this study have two dimensions, the parameters of which cannot be appropri-

ately estimated using methods traditionally used in the finance literature. In the time-series

dimension, all series have unit roots and are cointegrated within each country. OLS estimates

would be superconsistent, but their limiting distributions would be biased and inefficient17.

16Cointegration results are available from the authors and are omitted here to preserve space.

17Park and Ogaki (1991), Park (1992) and Pedroni (1996) show that OLS estimates of the coefficients ofnonstationary regressors are inefficient, and their distributions are asymptotically biased and contain nuisanceparameters. The bias in the asymptotic distribution of the OLS (or GLS) estimates is due to the fact that all

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Fully modified OLS procedures18 would produce correct hypothesis tests in the presence of unit

roots but would not be suitable for the cross-country correlation in spreads and residuals. Zell-

ners (1962) Seemingly Unrelated Regression (SUR) would account for the cross-correlation,

but would not correct for the bias due to nonstationarity in the time-series. One methodology

that accounts for both the time-series and cross-sectional properties of our data is Park and

Ogakis (1991) Seemingly Unrelated Canonical Cointegrating Regression (SUCCR).

The SUCCR methodology is an optimal statistical procedure for a system of poten-

tially correlated cointegrating regressions. The econometric details on the SUCCR estimator

are presented in Appendix A. Next, we provide briefly the intuition, advantages, and gen-

eral structure of the SUCCR estimator in the context of traditional least-square estimators.

Consider a general panel structure represented by:

y = X+ u (4)

where y = (y1, ...,y

n),yi = (yi1,...yiT)

, i = 1...n, is a vector of all dependent variables

stacked, X = block-diagonal(Xi),Xi = (xi1,...xiT) is a block-diagonal matrix with the re-

gressors of country i in the ith block, = (1, ...,

n) is a vector of stacked cointegrated

vectors, and u = (u1,...,u

n),ui = (ui1, ...uiT)

is a the vector of stacked residuals.

The SUCCR estimator is the modified system GLS estimator using the longrun co-variance matrix19, thus adjusting for autocorrelation in the errors, while also canonically

transforming all variables to eliminate the bias due to the presence of unit roots:

SUCCR = (X(1 I)1X)1X(1 I)1y (5)All notations are defined in Appendix A.

The SUCCR estimator generalizes Parks (1992) CCR (Canonical Cointegrating Re-

gressions) estimator,

CCR = (X

X)1Xy, by using system information in the same way

as the SUR estimator, SUR = (X(1I)1X)1X(1I)1y, generalizes the usual OLSestimator, OLS = (XX)1Xy, for stationary panels. However, the SUCCR estimator usesthe adjusted longrun variance of the errors, , rather than the shortrun variance, , used

by SUR. The longrun variance, , accounts for the autocorrelation in residuals, while the

canonically adjusted longrun variance, , further corrects for the presence of unit roots in

the regressors.

sample moments converge to random matrices when the variables are nonstationary and cointegrated ratherthan to constant matrices for which traditional techniques are designed.

18Phillips (1988, 1991), Johansen (1988, 1989), Phillips and Hansen (1990), Park (1992), and Stock and

Watson (1991) provide corrections for the asymptotic bias of OLS. The problems of nuisance parameters areaddressed by Phillips and Durlauf (1986) and Park and Phillips (1988, 1989). These techniques are designedfor the estimation of a single equation, rather than a system of correlated equations as in the present study.

19See Hamilton (1994), p. 248, for Newey-West adjustment for autocorrelated errors.

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In summary, the advantages of SUCCR over traditional methods, such as SUR and OLS,

can be represented in the following adjustments. First, the SUCCR procedure corrects for

the asymptotic bias introduced by the unit roots by modifying the regressors and covariance

matrices. It utilizes the presence of unit roots in the entire system, not just equation by

equation. Second, the SUCCR estimator accounts for the cross-correlation across equations

(or countries) by using the system-wide covariance structure of the errors. Therefore, the

SUCCR estimator in (5) improves on OLS, SUR, and CCR by correcting simultaneously for the

problems of nonstationary and cross-correlated panel data. The SUCCR methodology allows

for stationary regressors in addition to the nonstationary ones used here. If all regressors

are stationary, the SUCCR procedure reduces to SUR. Appendix B documents simulation

results of the finite sample improvement (in both bias and mean square errors) of the SUCCR

estimator over OLS, GLS, SUR, and CCR. Our results show that the SUCCR estimator has

the lowest small-sample bias among all methods and this bias falls rapidly as the sample size

increases.

4.2. Short-term dynamics stage

The second stage models the dynamics of credit spread changes. All instruments in this stage

are stationary:

Spreadi,t+1 = E,iSpreadEMBI,t + W,iMSCIt + ii,t + ei,t+1 (6)Since spreads are unit-root, their first differences are stationary. The error correction compo-

nent, t, is stationary due to the existence of a cointegrating relation. All remaining instru-ments are stationary first differences of unit-root variables. Given stationarity, this stage can

be estimated using OLS or GLS methods (the two methods are later compared).

5. Results

The in-sample results show that the long-term equilibrium relation is strong in all countries

and local fundamentals capture essentially all of the variation in credit spread levels. The

out-of-sample results demonstrate predictability in Brady markets is significant and valuable

to US investors. Predictability is driven by the spreads deviation from equilibrium.

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5.1. In-sample results

5.1.1. Long-term dynamics

Results from the SUCCR estimation, presented in Table 6, show that the long-term equilib-

rium between each countrys credit spreads and local macroeconomic indicators is significant

and strong. All estimates of the cointegrating vectors are significant and have the expected

sign. The high adjusted R2 in each country (89% to 93%) show that most of the variation in

credit spreads is captured by local factors.

Table 7 compares alternative estimates of the long-run equilibrium parameters for each

country. The SUR and MSUR estimates have the biggest small sample bias. They areoften quite different from the SUCCR or OLS estimates and sometimes have the wrong sign.

Although, both the OLS and SCCR small sample biases are relatively small in panels with 95

time-series observations, our simulations (Appendix B) indicate that the SUCCR improvement

becomes more significant as the sample size increases to 150 monthly observations, while OLS

and SUR show no reduction in small-sample bias (see Tables 13 and 14).

Figures 2 and 3 illustrate the spread deviations from equilibrium estimated with OLS

and SUCCR, respectively. Both the SUCCR and OLS estimated errors are stationary due to

the cointegration among spreads and macroeconomic factors, but the SUCCR errors are less

auto- and cross-correlated than the OLS series. These deviations are used as instruments in

the predictive stage of the model.

5.1.2. Short-term dynamics

Table 8 shows the in-sample estimates of the second, predictive, stage of the model. The OLS

and GLS coefficients, showing the sensitivity of spread changes to the instruments, have the

same sign, but are different in size. In both cases the coefficient measuring the speed of spreadreversion to fundamental value, , is the most significant instrument - |t statistic| = 6.23

(for OLS) and |tstatistic| = 4.83 (for GLS). The negative sign shows that spreads do indeed

revert to their equilibrium level. The estimated speed of reversion, , is higher when estimated

with OLS (-0.26), suggesting that about one fourth of any credit spread misalignment is

corrected within the next month. The GLS estimate (-0.14) implies a slower mean-reversion.

The coefficients on the remaining two instruments, traditional instruments, measure

the in-sample significance of global factors. The equity instrument is insignificant at the 95%

confidence level, while the bond instrument is significant but not as much as the deviation

from equilibrium.

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The second stage of the model was also estimated allowing for country-specific sensitiv-

ities to the instruments. We find, however, that the restriction of common coefficients, cannot

be rejected and only those results are reported in the paper. These findings suggest that the

speed of correction of any spread misalignment, and hence informational inefficiency, is the

same across countries.

5.2. Out-of-sample performance

Several out-of-sample tests are performed to assess the robustness of predictability. First,

to evaluate its economic significance, we compare the realized returns of an active strategy

based on the predicted spread changes to a riskier buy-and-hold strategy. Second, we apply

Mertons (1981) market-timing test to estimate the value-added of Brady bond predictability

to a US investor. This test, which is based on the number of correct directional forecasts,

ensures that results are not driven by the size of the returns in a few lucky periods. Third,

Henriksson and Merton (1981) nonparametric test evaluates the small sample robustness of

the market-timing results.

The out-of-sample period is 35 months long - from April 1998 to February 2001. We

estimate the model using an extended window methodology. The model parameters are es-

timated based on data from period 1 to period t, and a one-step-ahead forecast is made for

each individual country spread change in t + 1. With 35 months and 4 countries, we make

35 4 = 140 individual out-of-sample predictions about the direction of each credit spread

change. This period includes the Russian and Brazilian crises. We allow for the maximum

out-of-sample testing window given the limited time-series observations (95 months in total).

Extending the testing period further leaves insufficient observations to estimate the in-sample

parameters of the model (Park and Ogaki (1991) show that the SUCCR method performs

well in samples larger than 60 time-series observations).

5.2.1. Profitability of predictability

An active strategy is used to evaluate the economic significance of predictability based on

realized holding period returns during the out-of-sample period. For each country, the active

strategy is a 0-1 strategy which switches between Brady bonds and US treasury bills based

on the models one-step-ahead forecast of the credit spread change:

Active = 100% EMBIi,t ifE(Spreadi,t+1|t) 0100% cash(T-bills) ifE(Spreadi,t+1|t) > 0 for i = 1, ...,4 andt = 0,...,w 1.15


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where w is the length of the out-of-sample testing window (35 months in this case) and t is

the information available at time t. For each $100 invested in emerging markets, we assign

$25 to each country. It is 100% of each $25 in this particular country that is actively managed

based on country is forecast.

The benchmark relative to which we evaluate the active strategy is a passive strategy

equally weighted in the four Latin-American countries, i.e.:

Passive = 100% EMBIit for i = 1, ...,4 and t = 0,...,w 1.

The logical question is why 100%. The goal is to assure that the passive strategy is at least

as risky as the active one. The active investor never holds more exposure to Brady bondsthan the passive investor. In fact, the risk of the passive strategy is a limiting case of that of

the active20. Any superior returns from the active strategy are, therefore, risk-adjusted. Note

that the benchmark strategy does not imply that the investor holds only Brady bonds, nor

do we suggest that investors should change their global asset allocation. The issue here is, for

any amount allocated to Brady bonds, whether active management adds value. Transaction

costs are incorporated in the active strategy returns, as we are buying at the ask and selling

at the bid when rebalancing. We use a total return EMBI index, which includes capital gains

and distributions, to calculate the holding period return on the two strategies.

Table 9 summarizes the cumulative performance of the passive and active strategies

over the 35-month period out-of-sample testing window. Alternative sets of instruments are

examined to evaluate their relative predictive value. The equilibrium deviation estimated with

SUCCR, set (2), has the highest predictive power. It alone generates compounded returns

of 19.5% per year and a Sharpe ratio of 0.63, more than double the return of the riskier

passive investment (over 9% per year with Sharpe ratio of 0.18). This instrument shows

the combined predictive power of the two-stage model and the SUCCR estimator. The OLS

estimated correction, set (3), shows the value of the first stage, separating the effect of the

SUCCR procedure (an annual return of 15.3% and a Sharpe ratio of 0.41). Both estimates

confirm that the predictive power of the spread deviation is economically significant. The

global equity and bond instruments do not provide any predictive power, suggesting that

predictability is unlikely to be due to variations in risk premia. Their predictions generate an

annual return of 2.8% and a Sharpe ratio of 0.07, largely underperforming the buy-and-hold

strategy. Moreover, they reduce by 2.4% per year the market-timing returns of the deviation

instrument (compare set (1) to set (2)).

20The active strategy will be as risky as the benchmark passive strategy only if for all periods and allcountries the model predicts a negative spread change. This is a possible, but unlikely scenario.

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Figure 4 further illustrates the relative performance of the active strategy with alter-

native instruments. The deviation from equilibrium is clearly the most important instrument

and generates the most abnormal profits. An active investor using the SUCCR errors as in-

struments would have been able to generate almost 70% return on his Brady-bond investment

over a 35-month period, while a buy-and-hold investor would have generated less than 30%.

The evolution of the incremental wealth due to the use of the SUCCR rather than OLS is

illustrated in Figure 5. The pre-May 1999 slope is steeper than the one following, suggesting

that initially active management based on SUCCR generates wealth much faster than OLS

and then slows down in the second half of the sample. While the almost monotonic upward

slope in the graph suggests that SUCCR makes more correct directional forecasts than OLS

in up and down markets, its outperformance is more dramatic in the first half, when spreads

are more volatile (see Figure 1). SUCCR does better in periods of more volatile markets

as it accounts for contagion and it is exactly when active management is most important in

emerging markets.

The biggest gain from the active strategy comes from timing the Russian crisis cor-

rectly. Fama (1998) warns that using cumulative returns could artificially augment the out-

performance of a model as a single large positive or negative return will be compounded in

subsequent periods, even with no additional abnormal performance. As a result, Fama recom-mends using average, rather than cumulative returns. The second column of Table 9 addresses

Famas concern and presents simple average returns (with no monthly compounding) of the

active and passive strategies. The results do not change. The two-stage model, coupled with

the SUCCR methodology, generates the highest average returns of 19.8% per year over the

out-of-sample period.

Figure 6 addresses concerns that the difference between the passive and active strate-

gies is driven by the choice of a particular starting point of investment. The figure illustrates

the relative performance of the strategies for different starting points. Each subsequent pointrepresents the cumulative wealth at the end of February 2001 assuming the initial $1 invest-

ment is shifted by a month (the X-axis represents the month of initial investment). The active

strategy using SUCCR always dominates regardless of the starting point. However, the gap

between the active and passive strategies narrows due to a shortening holding period and,

more importantly, to the fact that after August 1999 Brady markets have mostly gone up.

Given the way the strategies are defined (p. 15-16), correct market timing in up-markets

makes the active and passive strategies identical, both invested 100% in emerging markets.

Thus, in up-markets a successful active strategy does not pull ahead as in down markets. Wetherefore turn to our next test, which focuses on the models ability to consistently generate

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correct predictions about the direction of market prices.

5.2.2. The value of market-timing: Mertons test

The ensure that our findings are not driven by a few lucky periods, we use a second market-

timing test, which is based on the number of correct predictions of up or down markets.

The tests depends only on the number of times the sign (not the size) of returns is correctly

predicted.

Merton (1981) develops an equilibrium theory for the value of market-timing skills and

shows an isomorphic correspondence between successful market-timing and free options on

the market. This correspondence is independent of investors preferences and prior probability

distributions and is based only on the managers ability to make one of two possible predictions:

a risky asset will either outperform or underperform the risk-free investment. This fits well

the specifics of this study as the active strategy is based on positive or negative spread change

forecasts (i.e., forecasts of negative or positive excess returns over US treasuries).

Merton demonstrates that a necessary and sufficient condition for market timing to be

valuable is that there be a significant number of correct forecasts in down and in up markets21,

i.e. the conditional probabilities, p1 and p2, to satisfy:

p1 + p2 > 1 (7)

where p1 (p2) is the probability of correctly forecasting down (up) markets22. The larger p1+p2,

the more valuable the forecast information is, as (p1 + p2 1) represents the percentage of a

free option that a market-timing model provides by correctly predicting market movements.

The results for the 140 (35 months 4 countries) out-of-sample directional forecasts

of credit spread changes are reported in Table 10. In all four countries, the sum of the two

conditional probabilities exceeds one. By Mertons argument, this is a necessary and suffi

cientcondition for our market-timing model to have positive value. This sum is 1.35 on average

for the four countries, implying that the model provides on average 35% of a free option on

the Brady bond market. The results in Table 10 are due to the following three instruments:

SpreadEMBI,t, MSCIt, and SUCCR,t.Table 11 compares the value added of alternative sets of instruments. Using the equi-

librium deviation as an instrument produces the highest market-timing value. Global instru-

21Merton shows that an unconditional probability of providing a correct forecast of more than 50% of the

times does not prove market-timing ability.22In terms of the present model, due to the inverse relation between price and spread, p1 is the probabilityof a correct forecast when actual spreads increase, while p2 is the probability of correctly forecasting a decreasein spreads.

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ments (SpreadEMBI,t, MSCIt) do not provide out-of-sample predictability in emerging

debt markets (Mertons combined probabilities are 1.00 and 1.02, respectively). The use of

Park and Ogakis robust SUCCR estimator is partially responsible for the higher conditional

probability of timing the market correctly (Mertons probability is 1.28 using SUCCR,t alonevs. 1.22 when using the OLS estimate OLS,t).5.2.3. Nonparametric small-sample tests of market-timing

Given the unfortunately limited history of Brady markets, we evaluate the robustness of the

previous results to the sample size of this study. Henriksson and Merton (1981), henceforth

HM, derive small sample nonparametric tests for the significance of market timing. Thesetests are independent of the distribution of security returns and provide a critical number

of correct predictions in up and down markets necessary to reject the null hypothesis of no

predictability for a given out-of-sample testing window. The smaller the sample size, the

higher the necessary percentage of correct predictions needed to reject the null.

Given the null hypothesis of no predictability derived in Mertons (1981) study, i.e.

H0 : p1 + p2 = 1 (8)

HM show that the critical number of correct forecasts, x(c), for rejecting the null of no

predictability is the solution to the following equation:

n1x=x

N1x

N2

n x

/

N

n

= 1 c (9)

where N1(N2) number of observations where Spreadsit 0 (Spreadsit < 0), N

N1 + N2 = total number of observations, n1(n2) number of (un)successful predictions,

given Spreadsit 0 (Spreadsit < 0), n n1 + n2 = number of times the forecast is

Spreadsit 0, c chosen confidence level, and n1 min(N1, n) upper bound on correct

predictions ofSpreadsit 0. The null of no predictability will be rejected if the number

of correct forecasts in up markets is higher than the test critical value (n1 x(c)) for the

desired confidence level (1 c) and given sample size (N).

Table 12 presents the results from HMs market-timing test. Global instruments do

not provide enough correct predictions to reject the null hypothesis of no predictability (p-

value of 46%). When the SUCCR estimate of the deviation, SUCCR, is used as a predictor,no predictability can be rejected at the 99% confidence level, while the OLS estimates allow

rejection with 97% confidence. HMs test shows that our findings are robust to the size of ourout-of-sample window and confirms our previous findings that predictability in emerging debt

markets is genuine and driven by the credit spreads deviation from its long-term equilibrium.

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6. What is the source of predictability?

Our model reveals Brady bond spreads do not adjust instantaneously to new information.

We believe this is due to a combination of characteristics of the Brady market. It is a non-

transparent dealer market, dominated by large institutional investors with regulatory and

investment policy restrictions. We believe the lack of unrestricted investors and arbitrageurs,

arising from the large transaction lots and the lack of fully developed derivatives markets, are

key features that differentiate the Brady market from more informationally efficient dealer

markets such as the US treasury market.

Brady bond markets are dominated by large institutional investors, such as mutual,

endowment, and pension funds, due to a minimum transaction size of $2 million 23. Although

these investors actively manage their emerging market investments, they broadly follow a

benchmark, relatively over- or underweighting their exposure to a specific country (generally

with a limit of20% of the countrys benchmark weight24). Due to active management, credit

spreads experience price pressure when emerging country fundamentals change. Our results

show that over a period of a few months the credit spreads implied in market prices do change

to reflect the changing fundamentals. Yet it takes longer for the market to clear and spreads

to adjust as institutional money managers cannot drastically rebalance their portfolios in and

out of those countries, thus allowing for short-term predictability.

Large players also dominate US Treasury bond markets, where trades are usually in $1

million lots. Yet these markets are functioning efficiently. Inefficiencies in the Treasury bill

market are small, because they can be arbitraged (or quasi-arbitraged) away (see Rendleman

and Carabini (1979)). US interest rate derivatives are highly liquid, exchange traded, and

inexpensive. Moreover, the ability to strip coupon bonds into multiple zero coupon bonds

facilitates the pricing of Treasuries. Therefore no-arbitrage conditions prevent the Treasury

market from being informationally ineffi

cient. Derivatives play a key role in improving thepricing of risk by providing price discovery for the cash market.

Derivatives on emerging country sovereign credit exist but their market is still illiquid

due to the lack of a secondary derivatives market and to the illiquidity of the repo market

for Brady debt. Credit derivatives25 could offer efficiency gains in the Brady bond market by

enabling the separate pricing of credit risk. While the global credit derivatives market has

23Source: Emerging Market Trade Association, http://www.emta.org/emarkets/.24For example, if the countrys benchmark weight is 10%, the managers weight should be between 8% and

12%. This information is based on interviews with institutional money managers.25Credit derivatives separate the credit risk from an underlying and enable investors to gain or reduce

exposure to credit risk.

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grown from under $250 billion in 1997 to over $1.5 trillion in 2001, the emerging credit deriva-

tives market took off in late 1997. Despite their rapid growth, emerging market derivatives

currently account for only 1% of global derivatives26. More importantly, the absence of a sec-

ondary market (emerging market credit derivatives are issued over-the-counter by banks like

DeutscheBank and JPMorgan), their lack of liquidity, and the need for hedging using the repo

market, are reducing the potential efficiency gains derived from their introduction. Credit

derivatives are constrained by the illiquidity of the Brady repo market because long default

swap positions are hedged by short positions in bonds. As a result, derivative premiums are

still quite expensive due to the hedging risks incurred by protection sellers. In addition, the

default swap27, the derivative accounting for 85% of notionals, is a default-triggered derivative

whose valuation has been shown by Chen and Sopranzetti (2003) to have little correlation with

changes in credit spreads, thus offering poor hedging potential in the case of no default.

The illiquidity of emerging market derivatives is exacerbated by their limited use by

investors due to regulatory and investment policy restrictions. A 1998 survey by Levich,

Hayt, and Ripston (1999) of derivatives usage among US institutional investors shows that

only 46% of institutions are allowed to use derivatives and only 27% actually use them due

to excessive capital requirement28 or investment policy restrictions. More importantly, while

more than 83% of institutions are allowed to use US interest rate derivatives, only 40% areallowed to use emerging market bond derivatives and only 20% actually use them. Where

derivatives are used, the positions are small relative to total assets (the mode being 1% of

total assets). Further, the principal reasons for using derivatives are risk reduction (55%) and

asset allocation (26%), rather than market timing (15%).

The lack of pre- and post-trade transparency further reduces the speed of price discov-

ery in the Brady market. Brady bonds trade in over-the-counter markets composed of brokers,

dealers, and investors worldwide, linked informally through a network of broker screens. Deal-

ers bids and offers are anonymous. Actual trading is conducted orally through inter-dealerbrokers. The identity of the brokers counterparties are not revealed even after the trade29.

Research shows that while such lack of transparency typically leads to higher liquidity (as

traders are unwilling to reveal their intentions and market makers can more easily dispose

of large inventories), it is generally associated with less informative prices (see Bloomfield

26Source: International Monetary Fund, Selected Topic: The Role of Financial Derivatives in EmergingMarkets, Global Financial Stability Report, Market Developments and Issues, December 2002.

27A credit default swap is a financial contract under which the protection buyer pays a periodic fee in returnfor a payment by the protection seller contingent on the occurrence of default.

28

Current bank regulations require that banks hedging positions via credit swaps reserve capital againstboth the loan and the derivative contract, rather than netting the position. Source: DerivativesStrategy.com.29Source: Emerging Market Trade Association, http://www.emta.org/emarkets/.

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and OHara (1999), Gemmill (1996), OHara (2003), Porter and Weaver (1998), and Simaan,

Weaver, and Whitcomb (2003)).

7. Conclusion

Using a new two-stage model for credit spreads, we present evidence of significant predictabil-

ity in the largest, most accessible, and liquid emerging debt market. An active strategy based

on this model provides Brady-bond investors with returns twice as large as those of a riskier

buy-and-hold strategy. Mertons (1981) and Henriksson and Mertons (1981) statistical tests,

based on the relative number of correct forecasts, confirm that the market-timing profits inthis market are economically and statistically significant. The observed predictability provides

US investors with the equivalent of free options on Brady bond indexes.

The results suggest that the two-stage model captures well the credit risk structure

of emerging sovereign debt markets. The strong long-term equilibrium relation between the

level of credit spreads (default premiums) and local macroeconomic conditions (fundamental

risk) in emerging debt markets suggests market rationality as the information gets fully re-

flected in market prices. Local factors explain 90% of the variation in credit spreads, leaving

little to global factors and residual variance. These findings are consistent with Erb, Harvey,

and Viskanta (2000), Cumby and Pastine (2001), and Claessens and Pennacchi (1996) who

view emerging market volatility as largely idiosyncratic. Yet prices and spreads fail to react

instantaneously to new information, giving rise to the documented predictability.

We find that global instruments do not have genuine out-of-sample predictive power,

suggesting that time-varying risk or time-varying risk-premia are unlikely explanations for

the documented predictability. The predictability can be attributed to the use of the spreads

deviation from its long-term equilibrium as an instrument, implying market inefficiencies. We

believe that the absence of unrestricted investors and arbitrageurs, due to the large transaction

lots and the lack of fully developed derivatives markets, coupled with the lack of pre- and post-

trade transparency, are key features that differentiate the Brady market from other more

informationally efficient bond markets with large transactions sizes such as the US treasury

market.

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A. The SUCCR methodology

The appendix presents the details of the SUCCR procedure using Park and Ogakis (1991)

original notation.

The system of regressions is given by:

y1t = x

1t1 + u1t...

yit = x

iti + uit (A1)...

ynt = x

ntn + unt

where i = 1,...,n is the cross-sectional dimension, while t = 1,...,T is the time-series dimen-

sion. yit is a scalar and the dependent variable in each regression. Each xit is an si 1 vector

of regressors, and si is the number of regressors in regression i. Each it is an si 1 vector

of sensitivities to the regressors. uit is the error term of regression i and is assumed to be

stationary.

The system can also be rewritten in matrix format as:

y = X+ u (A2)

where for i = 1,...,n.

yi = (yi1, ...yiT) (A3)

Xi = (xi1,...xiT) (A4)

ui = (ui1,...uiT) (A5)

and

y = (y1, ...,y

n) (A6)

= (1,...,

n) (A7)

u = (u1, ...,u

n) (A8)

X = block-diagonal(Xi) (A9)

All {xit} are integrated processes of order one, which can assume deterministic trends.

Park and Ogaki specify the regressors in the following three different ways:

M(a) : xit = x0

it (A10)

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M(b) : xit = ipit + x0it (A11)

M(c) : xit = (p

it,q

it),qit = ipit + x0it (A12)

M(d) : xit = pit (A13)

where {pit} is a general deterministic trend and {x0it} is a purely stochastic integrated process.

With M(a), each regression represents cointegration represents cointegration in the sense

of Engle and Granger 87. Since {xit} do not have any deterministic components, neither would

{yit} for the relation in eq. (A1) to hold.

Under M(b), both {xit} and {yit} contain the deterministic trend {pit}. Park and Ogaki

show that the ith relation in eq. (A1) in this case is stronger than with M(a).When {xit}s dynamics are described by M(c), {yit} may or may not have a deterministic

component. The inclusion of {pit} in the regression effectively detrends both series. The

cointegrating relation is between the stochastic components of{yit} and {xit} only, which the

authors call stochastic cointegration.

The SUCCR methodology is general enough to allow stationary regressors as in M(d)

in addition to regressors as in M(a)-M(c). When all {xit} are as in M(d), the procedure is

reduced to the usual SUR. In the present paper, all our regressors are nonstationary, hence

case M(d) is does not apply.

The long-run relations in eq. (A1) with any specification M(a)-M(c) are testable through

tests of cointegration.

The vector of stationary processes driving the system is defined as {wit}, where:

wt = (u

t,x0

t ) (A14)

where ut = (u1t,...,unt) and x0t = (x

01t, ...,x

0nt)

. This process has a covariance structure

given by:

= limT

1

TE

Tt=1

wt

Tt=1

wt

=

11 1221 22

(A15)where the partition is made conformable with that of{wt} in (A14). is the longrun variance

of{wt}. The usual, shortrun, variance of{w} is given by:

= limT

1

T

Tt=1

E(wtw

t) =

11 1221 22

(A16)

with partition similar to that of

. When {wt} is a martingale diff

erence sequence

=

.Note, that the author sometimes denote the variance of the errors in eq. (A1), 11 and 11,

as 0 and 0.

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The longrun variance30:

= + + (A17)

where

=q

a=1

1

a

q + 1

wtaw

t (A18)

where q is the number of lags considered in the autocorrelation.

Park and Ogaki assume 22 > 0 which implies neither redundant nor cointegrated

variables in {xt}. The requirement is that there exist a single cointegrating relation among

the unit root regressors and the dependent variable in the model. Park and Ogaki (1991)

argue that having more than one cointegrating equation (say k cointegrated vectors) will not

add information to the system, as some (k 1) variables are redundant in the sense that they

are stationary combinations of the remaining factors. Having more than one cointegrating

vectors in eq. (A1) prevents the coefficient vector i from being uniquely determined.

To comply with this SUCCR requirement, we first establish the number of cointegrating

vector per country (say k)and then exclude (k1) of macroeconomic factors. We keep the ones

that produce the lowest AIC value. One can use some other criterion for exclusion without

significantly changing the estimation results.

Park (1992) provides a way to efficiently estimate an equation with integrated regressors.The estimator is called Canonically cointegrating regressions, or CCR, represented by:

CCR = (XX)1Xy (A19)This statistical procedure adjust for the asymptotic bias introduced by the unit roots by

modifying the regressors:

yit = yit i12

122x

0t

ii1wt (A20)

xit

= xit i1wt

where i12

is the i-th row of12,

i = limT

1

T

Tt=1

E(xitw

t) (A21)

while the other variables are defined before. A consistent estimate of can be obtained as:

= 2 + 2 (A22)

where 1 + 2 and 1 + 2 where the partition is made conformable with wt =

(ut,x0t ). i are the columns of corresponding to the regressors x0

it in x0t .

30See Hamilton (1994) for Newey-West adjustment for autocorrelated errors.

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The cointegrating relations in each regression in (A1) continue to hold as all transfor-

mation in A20 involve only stationary terms (hence the term canonically cointegrated). The

regressors are transformed in such a way that the usual least squares procedure yields both

efficient estimates and chi-square tests on the coefficients.

The SUCCR system can be rewritten as:

y1t = x

1t1 + u

1t

...

yit = x

iti + u

it (A23)..

.ynt = x

ntn + u

nt

where

uit = uit i12

122x

0t (A24)

such that

limT

1

T

Tt=1

E(xtu

t ) = 0 (A25)

which implies that the SUCCR errors are asymptotically independent of the regressors. From

(A24), we have

ut = ut 121

22x0

t (A26)

Defining y, X, and u similarly to (A2), we can rewrite the SUCCR model in matrix

format:

y = X+ u (A27)

Park and Ogaki (1991) extend the CCR methodology which was developed for a single

cointegrating regression. The SUCCR estimator is the modified system GLS estimator using

the longrun variance of the SUCCR errors {ut}.:

= 11 1212221 (A28)

Park and Ogaki (1991) SUCCR estimator is given explicitly as:

SUCCR = (X(1 I)1X)1X(1 I)1y (A29)The SUCCR estimator generalizes Parks (1992) CCR estimator by using system infor-

mation in the same was as SUR

SUR = (X(1 I)1X)1X(1 I)1y (A30)26


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generalizes the usual OLS estimator

OLS = (XX)1Xy (A31)for stationary panels.

Yet the authors show that the usual GLS estimator can increase the asymptotic bias

of OLS in a general SUCR system as in A27, as the usual GLS (or SUR) uses the shortrun

variance of the errors, 0 , not the longrun variance, 0. The shortrun variance does not

correct for autocorrelation in the errors in (A1).

As the CCR estimator as in (A19), the SUCCR methodology in (A29) corrects for biases

and nuisance parameters introduced by using OLS or SUR in a SUCR system as in (A1). Itdoes so using the same stationary adjustment to the variables in the model as in (A20).

The SUCCR estimator in (A29) improves OLS, SUR, and CCR by correcting all prob-

lems of a nonstationary cross-correlated panel.

Tests can be conducted on a general hypothesis:

H0 = () (A32)

where is assumed to be continuously differentiable with first derivative evaluated at thetrue value of. With q restrictions under H0, the test statistic is the same as in the standard

SUR, except that the longrun variance is used:

(SUCCR)((X(1 I)X)1)1(SUCCR) D 2q (A33)For a simple t-test on the coefficient, (SUCCR) = diag(SUCCR), hence = I, the

above can be simplified to:

diag(SUCCR)(I(X(1 I)X)1I)1diag(SUCCR)= diag(SUCCR)(I(X(1 I)X)1I)1diag(SUCCR)= diag(SUCCR)((X(1 I)X)1)1diag(SUCCR)= diag(SUCCR)(X(1 I)X)diag(SUCCR) D 2q (A34)

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B. SUCCR simulation results

A Monte Carlo simulation exercise is performed to assess the relative merit of the SUCCR

estimation methodology in small samples. I simulate a multivariate error-correction process

with parameters similar to the ones estimated for the variables in this study. The cointegrating

vector is then estimated using the five estimation techniques mentioned in the paper: OLS,

SUCCR, SUR, SCCR, and MSUR. The goal of this simulation is to analyze the improvement

in small sample bias of SUCCR over the remaining methods. I simulate series with 95 and

150 time periods to assess the sensitivity of the SUCCR methodology to changes in the length

of the estimation window. The results show that SUCCR produces the smallest small sample

bias relative to the four other methods. The SUR and MSUR estimates exhibit the maximum

bias. The SUCCR improvement becomes even more apparent as the sample size increases

from 95 to 150 time periods.

The simulation results reported in Tables 13 and 14 are based on a system consisting of

four equations (four countries) and four explanatory variables, Xit, each. The cointegrating

equations for each country are all standardized to be i = (i1,i2, i3,i4) = 1, where

i = 1, 2, 3, 4.

An error-correction process is simulated such that yitXit

= A (t1) + Yit

Xit

(B1)where yit is a scalar and the dependent variable in our model, Xit is a 4 1 vector of ex-

planatory variables in each country, t1 = (1,t1, 2,t1, 3,t1, 4,t1), a 4 1 vector, rep-

resents the deviation from the equilibrium cointegrating equation for each country i, i.e.

i,t1 = (yi,t1 iXi,t1), i is the 41 cointegrating vector of each country i.Yit

Xit N(0,)

and

A =

Y4

0 0 0

0 Y4 0 0

0 0 Y4 0

0 0 0 Y4

X1 0 0 0

0 X1 0 0

0 0 X1

0

0 0 0 X1

, =

R1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0 0 0 0 R3 R2 R2 R2

0 0 0 0 R2 R3 R2 R2

0 0 0 0 R2 R2 R3 R2

0 0 0 0 R2 R2 R2 R3

(B2)

where Yi , a scalar, is the error correction coefficient of the dependent variable of country i.

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In the current simulation exercise, we set Y1 = Y2 =

Y3 =

Y4 = 0.2,0.1, or 0.2. We try

different specification to analyze the sensitivity of the SUCCR estimation to changes in the

process parameters. Xi , a 4 1 vector, is the error correction coefficient of the dependent

variable of country i. As in Park and Ogaki (1991), we set X1 = X

2 = X

3 = X

4 =

(0.2, 0.2, 0.2, 0.2). 0 = (0, 0, 0, 0). The blocks of the covariance matrix are as follows:

R1 =

1 1 1 1

1 1 1 1

1 1 1 1

1 1 1 1

, R2 =

2 2 2 2

2 2 2 2

2 2 2 2

2 2 2 2

, R3 =

1 3 3 3

3 1 3 3

3 3 1 3

3 3 3 1

(B3)

The covariance matrix assumes the error components are standard normal random

variables and the following correlations among the variables:

0 = the correlation among changes in the dependent and independent variables of country

i, yit and Xit,

1 = the correlation among changes in the dependent variables yit and yjt for i = j;

2 = the correlation among changes in the independent variables across countries, Xit and

Xjt for i = j;

3 = the correlation among changes in the independent within a country, Xkit and X

jit;

The results in Tables XIII and XIV are based on 2,000 iterations.

Note that as the sample size increases from 95 to 150 time periods, the SUCCR small

sample bias becomes noticeably smaller than the remaining methods. SCCR is the next best

method to use for a cointegrated and cross-country correlated multivariate process. The SUR

method which utilizes the system information without correcting for cointegration does worstof all methods. OLS performs relatively well, especially with 95 series observations, but lags

behind SUCCR in the 150 sample size experiment.

Increasing the sample size from 95 to 150 observations does not significantly improve

the bias problem in the OLS, SUR, and MSUR methods. In some cases the bias even in-

creases. With the SUCCR and SCCR techniques, however, the improvement is significant

and always positive. Park and Ogakis (1991) simulation results find even larger improvement

with sample size of 300 time-series observations. These results are promising and confirm the

initial expectation that the value of the SUCCR methodology should increase with time and

should find more applications in other financial areas as well.

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#predictability in emerging sovereign debt markets

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