Ebook: Rating Change Probabilities: An Empirical Analysis of Sovereign Ratings : An Empirical Analysis of Sovereign Ratings

This study analyzes the determinants of rating changes and the variables' marginal effects on rating change probabilities. Based on the results, it presents transition matrices by computing transition probabilities. Furthermore, this study analyzes subsamples of the data set, conditional on the business cycle and the economic strength of a country, by using interaction effects. The Author of this study thereby verifies whether or how the transition matrices change by including interaction effects. He applies a latent variable approach, using an ordered probit model, to calculate the effects of different variables on the probabilities of rating changes. Auszug aus dem Text Text Sample: Chapter 3, Agencies’ rating assessment: Rating agencies emphasize that ratings are based on both quantitative and qualitative elements. The qualitative approach is supposed to add additional information which is not incorporated in the quantitative data. Rating agencies have multiple instruments to disclose obligors’ creditworthiness to investors. Besides rating, also outlook and review serve as additional sources of information for investors. The outlook usually precedes rating reviews and serves as an indicator as to the direction of the credit assessment in the medium term. There are four categories ranging from positive, negative, stable and developing (Moody’s, 2012a). ‘A review indicates that a rating is under consideration for a change in the near term’ ranging from upgrade (UPG), downgrade (DNG) to uncertain direction (UNC). This section explains the basic rating definitions and introduces the agencies’ methodology to construct rating transition matrices. 3.1, Rating Definitions and Methodology: ‘Sovereign debt ratings are forward-looking qualitative measures of the probability of default, given in the form of a code’ (Afonso et al., 2009). There are both short- and long-term ratings. In this Bachelor thesis I will discuss only long-term ratings because they are better known and because they allow a broader ranking of letter rating categories. There are 9 coarse rating categories and 21 fine rating categories for long-term ratings, whereas there are only four short term ratings which are P-1, P-2, P-3 and not prime. Table 1 shows the order of Moody’s ratings with the respective evaluation of a corresponding country’s credit standing (Moody’s, 2012a). Next to Moody’s credit codes, I also added the codes that I use for my analysis, in order to be able to calculate with them. Moody’s have a three-stage process to determine a sovereign rating (Moody’s, 2008). In the first step, they assess the country’s economic resiliency. In the second step, they assess the government’s financial robustness. Finally, in the third step they determine the rating within the fine rating category. The first step is described by two factors. The first factor is the economic strength, which is given by the quantitative values of GDP per capita and the volatility of GDP. The second factor is the institutional strength of the country, such as ‘property right, transparency, the efficiency and predictability of government action, the degree of consensus on the key goals of political action’ (Moody’s, 2008). The second step is again described by two factors. The first factor is the financial strength of the government, such as the sort of debt and the government’s ability to ‘raise taxes, cut spending, sell assets, obtain foreign currency,…’(Moody’s, 2008). The second factor is the sensibility to event risk. This factor accounts for the country’s ability to resist to the ‘occurrence of adverse economic, financial or political events’ (Moody’s, 2008). Due to the last two mentioned steps, Moody’s have placed the country in one of the nine coarse rating categories. The third step defines the fine category from the 21 ratings by ‘adjusting the degree of resiliency to the degree of financial robustness’. 3.2, Rating Transition Matrices: According to Moody’s, a rating change represents a ‘variation in the intrinsic relative position of issuers and their obligations’ due to ‘alteration in creditworthiness, or that the previous rating did not fully reflect the quality of the bond as now seen’ (Moody’s, 2012c). Even though long-term ratings are supposed to reflect the country’s long-term rating standing, there are cases, in which Moody’s change ist rating assessment annually or quarterly. This especially happens in times of economic turmoils like the current sovereign debt crisis in Europe. The big three rating agencies S & P, Moody’s and Fitch create one and five year credit transition matrices. Credit transition matrices describe migration probabilities of firms and countries to change from any initial credit standing to any terminal rating in a future time. Moody’s make use of survival or duration modeling, called ‘multiple destination proportional hazards model’ to calculate the transition probabilities (Moody’s, 2011). Duration models simulate how long the rating stays the same and whether there will be an upgrade, a downgrade or a default. The model uses both macroeconomic and issuer specific variables to determine the transition probabilities. Moody’s have identified two macroeconomic factors to have general predictive power for the creditworthiness of countries, which are the unemployment rate forecast and the forecast of the high yield spread over Treasuries. The unemployment rate is meant to describe the ‘macroeconomic health’. The high yield spread over Treasuries is meant to incorporate the ‘market’s perception of credit quality and hence credit availability’. Additionally the credit transition matrix takes into account today’s and historic issuer-specific elements: ‘the current rating, whether the issuer was upgraded or downgraded into ist current rating, how long the issuer has maintained ist current rating, how long the issuer has consecutively maintained any credit rating, and the issuers current outlook or watchlist status’ (Moody’s, 2011). Biographische Informationen Alex Bergen, B.Sc. was born in 1989 in Jarowoje, Russia. He studied Economics at the University of Mannheim for the Bachelor program and graduated in 2012. Within his Bachelor studies, he completed an Erasmus semester at the Bilkent University in Ankara, Turkey. For his Bachelor thesis he was awarded with the first price by the DZ Bank Group in the category of Bachelor theses. In the year following, he worked at big German banks in the departments of Corporate Finance and Asset Management. After that practical experience, he started his Master program of Finance at the Frankfurt School of Finance & Management which he will finish with Finance, M.Sc. in 2015.