JSTOR

I. Introduction

Brady bonds1 are the primary financing vehicle of emerging market countries, representing 80% of their total government external debt. As of 2001, Brady debt totals over $100 billion. Brady bonds are also the primary choice of diversification into emerging markets for U.S. 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 turnover of Brady bonds, relative to the modest 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 Claessens and Pennacchi 1996; Cumby and Pastine 2001). As a result, Brady debt provides an easy and inexpensive way for U.S. 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 the Brazilian (January 1999) crises—leading to sudden declines in emerging debt portfolios. Understanding credit risk and the ability to time changes in credit fundamentals are 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, this study makes two contributions. First, the paper proposes a general two‐stage model for credit risk and develops a formulation of the model that captures 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 variety of 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 developed markets (see Ferson 1989; Bekaert and Hodrick 1992; Ferson and Harvey 1993; Ilmanen 1995; Lewellen 1999; Avramov 2002; Ang and Bekaert 2004, 1999; Avramov and Chordia 2005) as well as in emerging market equity returns (see Harvey 1991, 1995; Bekaert 1995; Bekaert and Harvey 1995). Studies of emerging debt markets have focused on the pricing and issuance of Brady debt (see Claessens and Pennacchi 1996; Eichengreen and Mody 1998; Cumby and Pastine 2001; Duffie, Pedersen, and Singleton 2003) or on identifying fundamental determinants of debt prices (Boehmer and Megginson 1990).

I study the predictable component in the changes of the credit spread index, called the emerging market bond index2 (EMBI) spread, of the four largest 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 country’s EMBI (a total return index of the country’s most liquid Brady bonds), and of the world EMBI. Predictability of changes in the credit spread translates into predictability of Brady bond excess returns over U.S. Treasuries.3 In this study, the model’s formulation is based on credit spreads, but its predictive power is evaluated out of sample on the basis of both realized credit spread changes and actual holding period returns to a U.S. 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 country’s credit spread level and local macroeconomic factors. 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 instruments as well as to the deviation of the spread level from its long‐term equilibrium (derived in stage 1). 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.

I 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 model’s predictions allows U.S. investors to double the returns from a buy‐and‐hold strategy, while taking less risk and accounting for transaction costs. Previous studies (e.g., Harvey 1991, 1995; Ilmanen 1995) document only slight predictability in developed and emerging equity markets. I also show that the superior performance of the active strategy is robust to the timing of the initial investment. Second, I apply Merton’s (1981) equilibrium test for the value of market timing, which is based on the number of correct out‐of‐sample directional forecasts of credit spread changes. Merton’s test shows that the model adds significant value to U.S. 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, I perform Henriksson and Merton’s (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” draws. All three tests agree that the spread’s deviation from fundamentals is the instrument that drives all predictability.

Predictability is robust to various considerations. Concerns about spurious predictive regression 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 predictability, spurious regressions would not bias out‐of‐sample results and would not produce superior returns out of sample (see Lo and MacKinlay 1996; Ferson, Sarkissian, and Simin 2003). A robust estimator is used to correct for data nonstationarity 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 price and selling at the bid price when rebalancing. Liquidity is not an issue since Brady bonds are the most liquid emerging debt market, and the active strategy involves only monthly rebalancing.

The asset‐pricing literature has documented time‐series return predictability (see, e.g., Keim and Stambaugh 1986; Fama and French 1988, 1989). This predictability has been attributed to time‐varying risk, market price of risk, and market inefficiency (see Ferson and Harvey 1991, 1999; Avramov and Chordia 2005). The predictability documented in this paper is primarily due to informational inefficiency since it is driven by the spread’s deviation from its fundamental value (90% of which is determined by country‐specific factors). I show that global equity and bond instruments do not provide any out‐of‐sample predictability in the Brady market.

I believe that this informational inefficiency is the result of the characteristics of the Brady market. First, Brady markets are dominated by large institutional investors following constrained investment policies, which slows down the process of price adjustment. There is an absence of arbitrageurs and unrestricted investors4 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 developed derivatives markets, which would allow the separate pricing of credit risk. Third, Brady bonds trade in nontransparent dealer markets that are generally associated with lower informational efficiency. Section VI discusses these sources of predictability in detail.

I 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 a 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, 369) since the issue size of Brady bonds is quite large.5 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 bond indexes is longer than that of Eurobonds, most of which were issued after 1995, allowing one 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 mispricing.6

The remainder of the paper is organized as follows. Section II presents the two‐stage model for credit spreads. Section III describes the data. Section IV suggests a robust estimation methodology. Section V presents the results from economic and statistical tests of out‐of‐sample predictability. Section VI discusses the causes for the existence of the documented predictability, and Section VII concludes the paper. Appendix A provides methodological details on Park and Ogaki’s (1991) seemingly unrelated canonically cointegrating regressions (SUCCR) 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.

II. 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 debtor’s 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 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 credit quality of the debtor. In the short term, however, spreads may deviate from their fundamental value because of investors’ sentiment, market momentum, or institutional factors.

When a long‐term equilibrium exists, future credit spread changes depend on the current deviation of the spread from its long‐term equilibrium level with respect to the local macroeconomic factors. Statistically, long‐term equilibrium is represented by cointegration.7 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 Granger’s analysis, standard predictive models, conditioning returns (or changes in prices) on a set of exogenous instruments, 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 factors in Brady bond markets (this assumption is tested in the data section) motivates the formulation of a two‐stage model for credit spreads: and where ; ; ; (if a long‐term equilibrium exists); ; ; ; and the variables are defined as follows: Spread_i,t is country i's spread above the U.S. Treasury spot curve; is a vector of country i's local macroeconomic factors; is a vector of country i's spread sensitivities to local factors; is the spread deviation from long‐term equilibrium in country i; is the change in the world EMBI spread; is the change in the Morgan Stanley Capital International world equity index; is the estimate of Spread_i,t's deviation from its long‐term equilibrium; , is the sensitivity to the respective instrument; α_i is the speed of adjustment to long‐term equilibrium in country i; is the variance of the deviation from long‐term equilibrium in country i; σ_ij is the covariance between spread deviations in countries i and j; and is the unexplained variance of country i's 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 liquidity.8 In the case of the highly liquid Brady bonds, changes in credit quality are the major factor driving excess returns. However, the changes in spreads, rather than returns per se, 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.

A. Determinants of Long‐Term Equilibrium Credit Spread Levels

My choice of factors (summarized in table 1), used to proxy for the intrinsic credit risk in emerging countries, is motivated by whether the factors reflect on the expected default probability 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). My selection draws on economic theory and previous studies on credit spreads or asset returns.

Table 1 Local Macroeconomic Factors in Long‐Term Equilibrium Stage of the Model

Open New Window

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 Bookstaber and Jacob (1986), Ramaswami (1991), Shane (1994), and Barnhill, Joutz, and Maxwell (2000), who document the comovement between high‐yield bonds9 and equity indexes. As in Ferson (1989), the CPI is included to control for the inflationary component of the stock index. By purchasing power parity, the CPI should also have an effect on exchange rates, imports, exports, and the balance of payments of a country, which in turn affect the country’s funds available to service its debt. Real exchange rates have an impact on the country’s 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 Sercu (1980), Adler and Dumas (1983), and Dumas and Solnik (1995). 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 government’s debt service capacity. Fama and Schwert (1977) and Ferson (1989) find evidence that short‐term interest rates are an important factor in pricing U.S. long‐term bonds and equities. The money supply variable reflects on the government’s 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 government’s ability to generate cash flows and service its debt. Since prices are discounted expected future cash flows, Fama (1990) argues that such a production measure should be significant in explaining yields and returns.

III. 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%, respectively, of total emerging market Brady debt. All series consist of end‐of‐month observations from April 1993 to February 2001. Although some EMBIs 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 Argentina’s Brady debt.

A. 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. I use the country EMBI spread in this analysis.

The credit spread of a Brady bond is defined as the spread above the U.S. Treasury spot curve that sets the current market price of the bond equal to its discounted payments: where P₀ is the current price of the Brady bond, T is the maturity, C_t is a cash flow scheduled for period t, and r_t is the U.S. Treasury spot rate for delivery at time t. The final payment is discounted at the U.S. Treasury bill rate since the face value of Brady bonds is guaranteed by the U.S. 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 τ payments are also discounted at the U.S. Treasury spot rates when the Brady plan provides for a rolling interest rate guarantee for the immediate τ 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 country’s spread, Spread_i,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, Spread_EMBI,t, of all emerging market countries with Brady debt.

The four countries’ EMBI spreads are highly correlated with the world EMBI and among 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 (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 devaluation (January/February 1999). Descriptive statistics of spread levels and spread changes of the four countries are provided in tables 3 and 4, respectively.

Table 2 Sample Correlations among Sovereign Spread Levels

Open New Window

(48 KB)

Fig. 1.— History of EMBI Spreads for Argentina, Brazil, Mexico, and Venezuela The figure presents the history of EMBI spreads in basis points. The sample is limited by the history of Argentina, which issued Brady debt in April 1993. The period covers the Mexican peso crisis (December 1994), the Asian crisis (October 1997), the Russian crisis (August 1998), and the Brazilian devaluation (January/February 1999).

Open New Window

Table 3 Descriptive Statistics of EMBI Spreads

Open New Window

Table 4 Descriptive Statistics of EMBI Spread Changes

Open New Window

B. Macroeconomic Factors

The monthly economic variables used are country‐specific indicators, which are publicly available and can be obtained from the International Monetary Fund (IMF) and Institute of International Finance (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, and 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, I lag all economic series by one month. For example, September’s CPI for Argentina is reported in October, and October’s 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.

Table 5 Descriptive Statistics of Macroeconomic Factors in the Long‐Term Equilibrium

Open New Window

C. Diagnostic Tests

The EMBI spreads and all macroeconomic variables in the long‐term equation are nonstationary and integrated of order 1 according to the augmented Dickey‐Fuller test. 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 nonstationary 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.

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).

Johansen’s (1988) multivariate procedure is used to test for cointegration. I find at least two cointegrating equations between the spreads and macroeconomic variables in each country. The specific features of the econometric methodology, however, require that there exist a single cointegrating equation in each country (see App. A). Therefore, whenever more than one cointegrating relation is found for a country, I 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 a stationary combination of the remaining variables in the set. The final set of variables used in each country at the long‐term dynamics stage are presented in table 6.

Table 6 SUCCR Estimates of the Long‐Term Equilibrium Stage

Open New Window

IV. 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 Ogaki’s (1991) seemingly unrelated canonical cointegrating regression (SUCCR). This method makes full use of the nonstationarity and cross‐country correlation in the data and produces efficient and unbiased estimates. The parameters of the short‐term dynamics equation are estimated using standard ordinary least squares (OLS) methodology.

V. Results

The in‐sample results show that the long‐term equilibrium relation is strong in all countries, and local fundamentals capture essentially all the variation in credit spread levels. The out‐of‐sample results demonstrate that predictability in Brady markets is significant and valuable to U.S. investors. Predictability is driven by the spread’s deviation from equilibrium.

A. In‐Sample Results

1.Long‐Term Dynamics Results from the SUCCR estimation, presented in table 6, show that the long‐term equilibrium between each country’s 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%–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 modified SUR (MSUR) estimates have the biggest small‐sample bias. They are often quite different from the SUCCR or OLS estimates and sometimes have the wrong sign. Although both the OLS and system CCR (SCCR) small‐sample biases are relatively small in panels with 95 time‐series observations, my simulations (App. B) indicate that the SUCCR improvement becomes more significant as the sample size increases to 150 monthly observations, whereas OLS and SUR show no reduction in small‐sample bias (see tables B1 and B2 below).

Table 7 Comparison of Alternative Estimates of the Long‐Term Equilibrium

Open New Window

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 because of the cointegration among spreads and macroeconomic factors, but the SUCCR errors are less autocorrelated and cross‐correlated than the OLS series. These deviations are used as instruments in the predictive stage of the model.

(49 KB)

Fig. 2.— Credit spread deviations from their long‐term equilibrium for Argentina, Brazil, Mexico, and Venezuela estimated with OLS (in basis points). The figure presents the deviations from the first stage of the model, which are used as instruments in predicting spread changes in the second stage.

Open New Window

(55 KB)

Fig. 3.— Credit spread deviations from their long‐term equilibrium for Argentina, Brazil, Mexico, and Venezuela estimated with SUCCR (in basis points). The figure presents the deviations from the first stage of the model. These deviations are used as instruments in predicting spread changes in the second stage of the model.

Open New Window

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 spread reversion to fundamental value, α, is the most significant instrument—|t‐statistic| = 6.23 (for OLS) and |t‐statistic| = 4.83 (for GLS). The negative sign shows that spreads do indeed revert to their equilibrium level. The estimated speed of reversion, α, 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.

Table 8 Estimates of the Parameters

Open New Window

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, whereas the bond instrument is significant but not as much as the deviation from equilibrium.

The second stage of the model was also estimated allowing for country‐specific sensitivities to the instruments. I 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.

B. Out‐of‐Sample Performance

Several out‐of‐sample tests are performed to assess the robustness of predictability. First, to evaluate its economic significance, I compare the realized returns of an active strategy based on the predicted spread changes to a riskier buy‐and‐hold strategy. Second, I apply Merton’s (1981) market‐timing test to estimate the value‐added of Brady bond predictability to a U.S. 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's (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. I estimate the model using an extended window methodology. The model parameters are estimated on the basis of data from period 1 to period t, and a one‐step‐ahead forecast is made for each individual country spread change in . With 35 months and four countries, I make individual out‐of‐sample predictions about the direction of each credit spread change. This period includes the Russian and Brazilian crises. I 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).

1.Profitability of Predictability An active strategy is used to evaluate the economic significance of predictability on the basis of realized holding period returns during the out‐of‐sample period. For each country, the active strategy is a 0–1 strategy that switches between Brady bonds and U.S. Treasury bills on the basis of the model’s one‐step‐ahead forecast of the credit spread change: where w is the length of the out‐of‐sample testing window (35 months in this case), and Φ_t is the information available at time t. For each $100 invested in emerging markets, I assign $25 to each country. It is 100% of each $25 in this particular country that is actively managed on the basis of country i’s forecast.

The benchmark relative to which I evaluate the active strategy is a passive strategy equally weighted in the four Latin American countries, that is, 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 bonds than the passive investor. In fact, the risk of the passive strategy is a limiting case of that of the active.13 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 I 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, since we are buying at the ask and selling at the bid when rebalancing. I use a total return EMBI, 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 a 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).

Table 9 Out‐of‐Sample Realized Returns of Active vs. Passive Strategy

Open New Window

Figure 4 further illustrates the relative performance of the active strategy with alternative 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 instruments would have been able to generate almost a 70% return on his Brady bond investment over a 35‐month period, whereas 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 fig. 1). SUCCR does better in periods of more volatile markets since it accounts for contagion and it is exactly when active management is most important in emerging markets.

(40 KB)

Fig. 4.— Relative performance of active strategies based on the model predictions with alternative sets of instruments. All active strategies involve no short selling and are based on one‐step‐ahead forecasts of the model. The passive strategy is a 100% buy‐and‐hold investment in Brady bonds, equally weighted among Argentina, Brazil, Mexico, and Venezuela (the passive strategy is riskier than all active ones). Notation: SUCCR correction uses instruments ; OLS correction uses instruments ; and traditional instruments uses instruments .

Open New Window

(28 KB)

Fig. 5.— Incremental wealth process of the active strategy based on SUCCR estimates relative to that based on OLS estimates. The figure illustrates the incremental wealth due to using SUCCR in estimating the long‐term equilibrium stage of the model relative to the wealth process based on OLS estimates, i.e., . Both active strategies involve no short selling and are based on one‐step‐ahead forecasts of the model. Notation: SUCCR uses instruments , and OLS correction uses instruments .

Open New Window

The biggest gain from the active strategy comes from timing the Russian crisis correctly. Fama (1998) warns that using cumulative returns could artificially augment the outperformance of a model since a single large positive or negative return will be compounded in subsequent periods, even with no additional abnormal performance. As a result, Fama recommends using average rather than cumulative returns. Column 2 of table 9 addresses Fama’s 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 strategies 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 point represents the cumulative wealth at the end of February 2001 assuming that the initial $1 investment 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 as a result of a shortening holding period and, more important, the fact that after August 1999 Brady markets have mostly gone up. Given the way the strategies are defined (Sec. VB1), 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. I therefore turn to the next test, which focuses on the model’s ability to consistently generate correct predictions about the direction of market prices.

(54 KB)

Fig. 6.— Relative performance of the active and passive strategies when the timing of the initial investment changes. The figure illustrates the final wealth on February 28, 2001, resulting from investing $1 in the months shown on the X‐axis under the different investment strategies. All active strategies involve no short selling and are based on one‐step‐ahead forecasts of the model. Notation: SUCCR correction uses instruments ; OLS correction uses instruments ; and traditional instruments uses instruments .

Open New Window

2.The Value of Market Timing: Merton’s Test The ensure that my findings are not driven by a few “lucky” periods, I use a second market‐timing test, which is based on the number of correct predictions of up or down markets. The test 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 manager’s 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 since the active strategy is based on positive or negative spread change forecasts (i.e., forecasts of negative or positive excess returns over U.S. 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 markets,14 that is, the conditional probabilities, p₁ and p₂, to satisfy where p₁ (p₂) is the probability of correctly forecasting down (up) markets.15 The larger , the more valuable the forecast information is, since 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 × four 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 Merton’s argument, this is a necessary and sufficient condition for my 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: ΔSpread_EMBI,t, ΔMSCI_t, and .

Table 10 Sample Probabilities of Significant Market‐Timing Value

Open New Window

Table 11 compares the value‐added of alternative sets of instruments. Using the equilibrium deviation as an instrument produces the highest market‐timing value. Global instruments (ΔSpread_EMBI,t and ΔMSCI_t) do not provide out‐of‐sample predictability in emerging debt markets (Merton’s combined probabilities are 1.00 and 1.02, respectively). The use of Park and Ogaki’s robust SUCCR estimator is partially responsible for the higher conditional probability of timing the market correctly (Merton’s probability is 1.28 using alone vs. 1.22 when using the OLS estimate ).

Table 11 Sample Probabilities of Significant Market‐Timing Value Using Alternative Sets of Instruments

Open New Window

3.Nonparametric Small‐Sample Tests of Market Timing Given the unfortunately limited history of Brady markets, I evaluate the robustness of the previous results to the sample size of this study. Henriksson and Merton (1981) derive small‐sample nonparametric tests for the significance of market timing. These tests 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 proposed in Merton’s (1981) study, that is, Henriksson and Merton show that the critical number of correct forecasts, , for rejecting the null of no predictability is the solution to the following equation: where N₁ (N₂) is the number of observations; ( ); is the total number of observations; n₁ (n₂) is the number of (un)successful predictions, given ( ); is the number of times the forecast is ; c is the chosen confidence level; and is the upper bound on correct predictions of . The null of no predictability will be rejected if the number of correct forecasts in up markets is higher than the test critical value ( ) for the desired confidence level ( ) and given sample size (N).

Table 12 presents the results from Henriksson and Merton’s 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, , is used as a predictor, no predictability can be rejected at the 99% confidence level; the OLS estimates allow rejection with 97% confidence. Henriksson and Merton’s test shows that the findings are robust to the size of the out‐of‐sample window and confirms the previous findings that predictability in emerging debt markets is genuine and driven by the credit spread’s deviation from its long‐term equilibrium.

Table 12 Results from Small‐Sample Tests for Significant Market‐Timing Value Using Alternative Sets of Instruments

Open New Window

VI. What Is the Source of Predictability?

My model reveals that Brady bond spreads do not adjust instantaneously to new information. I believe that this is due to a combination of characteristics of the Brady market. It is a nontransparent dealer market, dominated by large institutional investors with regulatory and investment policy restrictions. I believe that the lack of unrestricted investors and arbitrageurs, arising from the large transaction lots and the lack of fully developed derivatives markets, is a key feature that differentiates the Brady market from more informationally efficient dealer markets such as the U.S. Treasury market.

Brady bond markets are dominated by large institutional investors, such as mutual, endowment, and pension funds, because of a minimum transaction size of $2 million. 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 of ±20% of the country’s benchmark weight16). Owing to active management, credit spreads experience price pressure when emerging country fundamentals change. My 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 since institutional money managers cannot drastically rebalance their portfolios in and out of those countries, thus allowing for short‐term predictability.

Large players also dominate U.S. Treasury bond markets, where trades are usually in lots of $1 million. 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). U.S. 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 Treasury bonds. Therefore, no‐arbitrage conditions prevent the Treasury market from being informationally inefficient. Derivatives play a key role in improving the pricing of risk by providing price discovery for the cash market.

Derivatives on emerging country sovereign credit exist, but their market is still illiquid because of the lack of a secondary derivatives market and the illiquidity of the repo market for Brady debt. Credit derivatives17 could offer efficiency gains in the Brady bond market by enabling the separate pricing of credit risk. While the global credit derivatives market has grown from under $250 billion in 1997 to over $1.5 trillion in 2001, the emerging credit derivatives market took off in late 1997. Despite their rapid growth, emerging market derivatives currently account for only 1% of global derivatives (source: International Monetary Fund 2002). More importantly, the absence of a secondary market (emerging market credit derivatives are issued over the counter by banks such as 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 because of the hedging risks incurred by protection sellers. In addition, the default swap,18 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 U.S. institutional investors shows that only 46% of institutions are allowed to use derivatives, and only 27% actually use them because of excessive capital requirements19 or investment policy restrictions. More important, while more than 83% of institutions are allowed to use U.S. interest rate derivatives, only 40% are allowed 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 posttrade transparency further reduces the speed of price discovery 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. Dealers’ bids and offers are anonymous. Actual trading is conducted orally through interdealer brokers. The identities of the broker’s counterparties are not revealed even after the trade (source: Emerging Market Trade Association). 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 Gemmill 1996; Porter and Weaver 1998; Bloomfield and O’Hara 1999; O’Hara 2003; Simaan, Weaver, and Whitcomb 2003).

VII. Conclusion

Using a new two‐stage model for credit spreads, I present evidence of significant predictability in the largest, most accessible, and most 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. Henriksson and Merton’s (1981) and Merton’s (1981) statistical tests, based on the relative number of correct forecasts, confirm that the market‐timing profits in this market are economically and statistically significant. The observed predictability provides U.S. 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 reflected 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 those of Claessens and Pennacchi (1996), Erb, Harvey, and Viskanta (2000), and Cumby and Pastine (2001), 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.

I 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 spread’s deviation from its long‐term equilibrium as an instrument, implying market inefficiencies. I believe that the absence of unrestricted investors and arbitrageurs, due to the large transaction lots and the lack of fully developed derivatives markets, and the lack of pre‐ and posttrade transparency are key features that differentiate the Brady market from other more informationally efficient bond markets with large transactions sizes such as the U.S. Treasury market.