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We are grateful to Bernardin Akitoby, Emre Alper, Reza Baqir, Serhan Cevik, Carlo Cottarelli, Julio Escolano, Enrique Flores, Atish Ghosh, Alejandro Guerson, Laura Jaramillo, Todd Mattina, Geremia Palomba, Christine Richmond, Alex Segura-Ubiergo, Abdelhak Senhadji, Suchanan Tambunlertchai and participants at the IMF FAD Seminar for their insights and comments, and Malin Hu and Nancy Tinoza for excellent research assistance.
Indeed, the pricing of sovereign bonds have been found to be related to credit ratings. In a pioneering study, Cantor and Packer (1996) investigate the determinants of credit ratings and their link with sovereign spreads, using cross-sectional data for 49 countries. Their results show that credit ratings are mostly explained by a set of six macroeconomic variables: per capita income, GDP growth, inflation, external debt, level of economic development, and default history. Eichengreen and Mody (1998) find that countries with higher credit ratings have lower spreads. However, Gonzalez-Rosada and Levy-Yeyati (2008) and Cavallo et al. (2008) have questioned the result on the basis of endogeneity of rating changes to sovereign spreads.
To be included in the EMBIG index, countries have to satisfy one of the following criteria: (i) be classified as low or middle per capita income by the World Bank; (ii) regardless of their World Bank-defined income, have restructured external or local debt in the past 10 years. For a given bond to be included in the instrument, they have to have a face value of over US$500 million, with maturity of more than two years and six months, and verifiable daily prices and cash flows.
The data on emerging market economies covered the following countries: Argentina, Brazil, Bulgaria, Chile, China, Colombia, Ecuador, Egypt, Hungary, Indonesia, Kazakhstan, Malaysia, Mexico, Pakistan, Panama, Peru, Philippines, Poland, Russia, Serbia, Tunisia, Turkey, Ukraine, Uruguay, Venezuela, and Vietnam.
Using semi-annual averages of the spreads does not alter the results. This timing was adopted to make spreads as close as possible to the data release date.
Money market data was the most comprehensive data available, both time and country-wise. Data for domestic bonds returns either from Bloomberg or DataStream were not available for a long-enough period of time (typically, they started after 2000) and for a large set of countries (typically covering only about 10 emerging markets). When money market data was lacking or of a short-horizon, which was the case for 5 countries, we have instead used the central bank discount rate.
Other variables that are not systematically controlled for and are often used in the literature were found insignificant. These include the Fed Fund rate, the overall fiscal balance, the terms of trade, and measures of fiscal institutions. The presentation aims to be parsimonious and focused on the variables that have been found most relevant in explaining spreads in our estimation so these results were not shown and are available upon request.
An early application of this approach was suggested by Giavazzi and Favero (2004) for Brazil, using time-series econometrics, who show that Brazilian EMBI spreads react non-linearly to global risk aversion, with the non-linearity linked to the level of fiscal fundamentals.
The Fiscal Monitor uses cyclically adjusted primary balances instead of primary balance and a gradual adjustment for about 10 years to the targeted debt stabilizing primary balance followed by a 10-year period of constant primary balance at the debt stabilizing level.
The primary gap (pb*-pb) was unsuccessfully tested as a transition variables as the estimation did not converge.