Abstract
In this paper we examine the relevance of changes in sovereign credit rating for the borrowing cost of EU countries. Our results indicate that discretionary credit rating announcements are only of limited economic importance for the borrowing cost of these countries. It seems that rating agencies do not reveal important new information to financial markets, in addition to that already contained in the underlying fundamentals. Hence, given the sentiment in financial markets, the borrowing cost of a country can only be reduced by improving macroeconomic and fiscal fundamentals.
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Notes
This includes the following countries: Croatia, Austria, Belgium, Bulgaria, Czech Republic, Denmark, Estonia, Finland, France, Italy, Latvia, Lithuania, Hungary, Germany, Netherlands, Poland, Romania, Slovenia, Slovakia, Sweden, Spain and the UK. We excluded those EU countries for which data on comparable CDS spreads were not available.
In this paper CDS spreads have been used as a proxy variable of the country risk premium due to the unavailability of a comparable series of bond yield spreads for all analyzed countries. However, the correlation between CDS spreads and bond spreads is very high both at daily frequency and at lower frequencies. In addition, Kunovac (2013) has shown that results on the determinants of CDS spreads and spreads of generic bond yields are equal to a great extent. Moreover, in theory CDS and bond spreads should have very similar dynamics. Indeed, suppose that i is the yield of a 1-year bond, r is the yield of an equivalent non-risky instrument, and cds is the pertaining credit risk insurance premium for the bond. Then the purchase of the insured portfolio that consists of this bond and insurance in the form of CDS is approximately equal to the purchase of a risk-free bond and the following holds: \(i-cds=r\). From this it follows that \(cds=i-r\), which means that CDS and bond spreads are in theory equivalent. In practice there are a number of reasons why CDS and bond spreads diverge (De Wit 2006) but generally there is a high correlation between them.
The measure of risk aversion included in this analysis is the volatility index on European stock exchange indices (Euro Stoxx 50 Volatility Index). This volatility indicator, as well as stock indices, and 5-year CDS spreads for each country are obtained from Bloomberg database. The following stock market indices are included: BEL 20 Index, Sofix, PX, DAX, TALSE Index, IBEX, CAC 40 Index, CROBEX, FTSE MIB Index, KAX, RIGSE Index, VILSE Index, BUX, AEX, AT Index, WIG Index, PSI 20 Index, BET Index, SBITOP Index, SKSM Index, HEX, SBX and FTSE.
For clarity, only the results for the change in the rating on the day of the announcement and for the first 2 days after the announcement are shown. We also estimated a model with a greater number of lags, but further lags were not statistically significant. In addition to rating changes, we estimated a model with outlook changes and this variable (and lags of this variable) was not statistically significant.
All variables, except the economic freedom index, are downloaded form the Eurostat database. All regressors enter the equations as 1-year moving averages in order to remove short-run cyclical fluctuations.
We are using the overall score of the economic freedom index published on annual basis by the Heritage Foundation. This index is capturing institutional and political factors, and it is based on 12 quantitative and qualitative indicators, grouped into four broad categories: rule of law, government size, regulatory efficiency and market openness. More information about this index can be found on the Heritage Foundation website.
It is noteworthy that rating agencies base their decisions, to some extent, on the basis of expectations (forecasts) of macroeconomic fundamentals. In order to verify whether the link between a country’s rating and fundamentals is different for the case of observed fundamentals in comparison to the case of forecasted fundamentals, we estimated these relationships for both cases on a narrower set of European countries for which a long enough series of forecasts was available. In doing this, we used the macroeconomic projections of the European Commission. The results obtained for the model with forecasted fundamentals are in line with the results obtained for the model with observed fundamentals.
The results remain virtually unaffected if period averages are used.
The estimated parameters for the said alternative specifications for the average rating are shown in Table A1 in the electronic supplementary material.
Figures for individual agencies are shown in figures A1, A2 and A3 in the electronic supplementary material.
The Economic Policy Uncertainty Index is developed by Baker et al. (2015). It measures policy-related economic uncertainty and it is constructed as an average of three subindices: (i) newspaper index covering economic policy-related topics, (ii) index measuring tax code provisions expiring in future years and (iii) index capturing disagreement among economic forecasters.
For instance, Butler and Fauver (2006) provide a detailed analysis about the effect of political indicators on Institutional Investor Ratings by using cross-sectional data on a heterogeneous set of 96 countries including both emerging and developed economies.
We have also tried specification expanded by the common factor of European CDS spreads as an additional control variable which approximates the spillover index. However, the problem is that the mentioned variable also contains information on the common fundamentals of analyzed countries leading to a significant correlation between the mentioned spillover index and the rating implied by fundamentals used here. It is also noteworthy that the objective of this paper is to examine the relative importance of the discretionary actions of rating agencies and fundamentals, while the impact of spillover and contagion on the borrowing cost is analyzed in more detail in Kunovac (2013). Therefore, in this paper we will use the volatility index as the only control variable.
In order to save space, the graphs are depicting the contribution of the overestimation indicator only. Moreover, in the paper we are showing results for the average rating only, while results for individual agencies are shown in figures A4, A5 and A6 in the electronic supplementary material.
Other specifications of rating categories have been estimated as well, and the estimated boundaries between the speculative and investment categories remained almost unchanged in relation to the division suggested here.
In addition to EU member states, the following countries were included: Australia, Brazil, Chile, Canada, Indonesia, Israel, Japan, South Korea, Kazakhstan, Mexico, Malaysia, Norway, Panama, Peru, the Philippines, Qatar, Russia, Thailand, Turkey and South African Republic.
Similar results about the lagged reaction of credit rating agencies were found in Reinhart (2002).
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Acknowledgements
The authors would like to thank the anonymous referee, Ivan Zilic, Velimir Sonje, Marina Tkalec, as well as Nauro Campos, Ricardo Lago and other participants of the 22nd Dubrovnik Economic Conference and the 7th Croatian National Bank Economic Workshop. The opinions expressed in this paper are those of the authors and do not necessarily represent the official views of the Croatian National Bank.
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Kunovac, D., Ravnik, R. Are Sovereign Credit Ratings Overrated?. Comp Econ Stud 59, 210–242 (2017). https://doi.org/10.1057/s41294-017-0024-6
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DOI: https://doi.org/10.1057/s41294-017-0024-6