between ratings and market-implied yields fell and the ability of ratings to predict default deteriorated. Alsakka and Gwilym (2012) analyze the sovereign watch and outlook signals from Moody’s, Standard and Poor’s and Fitch. They demonstrate that each agency’s actions indicate or imply different policies. For instance, Standard and Poor’s has more emphasis on short-term accuracy while Moody’s actions are consistent with greater stability.

With regard to the impact of ratings, Kim and Wu (2008), analyze how sovereign credit ratings history provided by independent rating agencies affects domestic financial sector development and international capital inflows to emerging countries. They find strong indication that sovereign credit ratings affect financial intermediary sector developments and capital flows. Afonso, Furceri and Gomes (2012) use EU sovereign bond yields and CDS spreads daily data to carry out an event study analysis on the reaction of government yield spreads before and after announcements from rating agencies. Their results demonstrate significant responses of government bond yield spreads to changes in rating notations and outlook, particularly in the case of negative announcements. Alsakka and Gwilym (2012a) analyze how the foreign exchange markets reacted to sovereign credit events prior to (20002006) and during the crisis (2006-2010). They find that rating agencies’ signals do affect the own country exchange rate and identify strong spillover effects to other countries’ exchange rates in that region.

# 3. Characteristics and numerical analytics of sovereign ratings

Sovereign ratings are assigned by various agencies, but the three prominent ones are Standard and Poor’s, Moody’s and Fitch. The broad rating scales are similar, allowing for the classification of around 20 ratings with each divided into three outlooks, namely, “positive,” “stable,” and “negative.” These outlooks indicate, respectively, the possibilities of an upgrade, remaining at the same rating, or having a lower rating. Systems of converting the alphabetical ratings into numerical scores have been proposed in papers such as Dadush and Dasgupta (2001) and Gaillard (2009). We build on this system but introduce an innocuous cardinal change (Sen 1977; Basu 1983) of multiplying each number by 3, as done in Basu et al. (2012).<sup>2</sup> This modification allows for a richer analysis.<sup>3</sup> Since in addition to the grades, the agencies give three outlook classifications, positive, stable, and negative for each grade, we refine the conversion system further by subdividing each grade across the three outlook classes and assigning a numerical score corresponding to each. Furthermore, following Ratha, De and Mohaptra (2011), better scores are assigned smaller numbers since the scores for the lower-than-default category are indeterminate. The resulting scoring system is described in Table 1.

> 2 The measure proposed in Basu et al. (2012) is a non-cardinal, positive monotone transformation of the measure developed in this paper.

> 3 It should be recognized that rankings could be done in many different ways, satisfying different axiomatic properties, with the additional hazard of there being sets of reasonable axioms which are together inconsistent (see, for example, Pattanaik and Xu, 2007). We use a very simple form here but there is scope for developing this further in different ways.

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