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Emanuele Baldacci (chief economist at SACE, the Italian export credit company) was a visiting scholar in the Fiscal Affairs Department during August 2008. We would like to thank Julio Escolano, Manmohan Kumar, Ashoka Mody and Jari Stehn for helpful comments and suggestions on an earlier version, and Annette Kyobe and Sukhmani Bedi for research assistance. The usual disclaimer applies.
The EMBIG index was introduced in January 1998 and includes countries rated BBB+ or lower by Standard & Poor’s. The countries included are both middle-income and low-income economies that have undergone at least an episode of debt restructuring. The EMBIG has rapidly become a global market benchmark for emerging economies’ credit risk assessment.
CDS spreads can be viewed as the marginal cost of debt, while the EMBIG sub-index for a country is more representative of the average cost of traded debt. According to Singh and Andritzky (2005), CDS spreads are a leading indicator of borrowing costs during crisis periods, while the average cost is a better proxy for the sovereign risk in the absence of default.
The loan value and its duration, however, are not reported to influence country spreads significantly.
These authors also find that private sector bond issuers pay a higher yield than sovereign counterparts (on average almost 30 percent higher). Cavallo and Valenzuela (2007) also show a significant link between sovereign and private bond spreads using firm-level quarterly data. They report that sovereign risk pass-through to private spreads is less than proportional.
Eichengreen and Mody (1998) also find that market sentiment plays a key role in determining country risk premiums on launch spreads once selection bias is removed from estimation.
The link between credit rating and macroeconomic variables is found to be significant in many studies. Cantor and Packard (1996) show that ratings account for over 90 percent of spread variation and that macroeconomic policy tends to be linked to better ratings as the authorities change their stance to earn better assessments from the financial markets. Afonso (2002) and Afonso, Gomes, and Rother (2007) show that economic growth and inflation are important variables underlying country ratings.
In a recent paper by Gonzales-Hermosillo (2008), common global factors tend to become more important for credit spread variation in periods of financial distress. Dailami, Masson, and Padou (2005) also point to nonlinear effects of common factors on spreads depending on countries’ vulnerability conditions.
Rocha and Garcia (2006) propose a structural model to estimate the term structure of sovereign spread. They calibrate the model for a typical emerging market economy and estimate the implied default probability for a sample of countries. In their model, real exchange rate movements are a trigger for defaults and determine the link between exchange rate depreciation and country risk. In particular, a currency depreciation can exacerbate fiscal disequilibria when the economy has an open capital account, but a relatively limited external revenue income base.
Gibson and Sudersen (1999) also show the role that exports can have as collateral in a theoretical model of debt.
The considerations made in this section are general and can be applied both to a sovereign bond issue as well as a banking sector loan to a government entity. The comparative empirical literature shows that similar factors affect the level of spread charged on either debt instruments (Edwards, 1984).
Generalizing this simple framework to a multi-period model would complicate the formal treatment but will not change the main conclusion of the model.
This method weighs the original risk variables according to the factor loadings of the first principal component of the data matrix that accounts for most of the original data dispersion. Optimal weights are derived from the procedure to maximize the data variance explained by this linear combination of the original data. Weights are a function of the correlation between the original data and the latent variable associated with the principal component. Varimax rotation of the solution has been used to maximize the explanatory power of original variables and facilitate the result interpretation. The index is strongly correlated as a result with the political violence, expropriation and transfer risk variables that are used in the political risk index literature (Ferrari and Rolfini, 2008). Compared to the latter, though, our index has the desirable property of choosing weighting factors endogenously in a way that the index accounts for most of the data dispersion.
The countries included in the sample are: Argentina, Bolivia, Brazil, Chile, China, Colombia, Chech Republic, Ecuador, Egypt, Hungary, India, Indonesia, Jordan, Lebanon, Malaysia, Mexico, Nigeria, Pakistan, Peru, Philippines, Poland, Russia, South Africa, South Korea, Thailand, Turkey, Ukraine, Uruguay, Venezuela, and Vietnam.
A 20 percent increase in the index is equivalent to moving from the bottom quintile to the medium-risk range of the country distribution (a change typically associated with strong reform implementation and institutional progress taking many years).
We conducted a Hausman test, which rejected the fixed effects model at 1 percent significance level.
We also tried the specification by including current revenue and current spending and we found both insignificant, implying that cuts in current spending or higher revenues have no impact on spreads. It is our premise that inclusion of the primary surplus already takes into account for changes in these variables.