The relationship between sovereign credit rating and trends of macroeconomic indicators

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The sovereign credit rating provides information about the creditworthiness of a country and thereby serves as a tool for investors in order to make right decisions concerning financial assets worth investments. Thus, determination of a sovereign credit rating is a highly complex and challenging activity. Specialized agencies are involved in rating assessment. So, it’s essential to analyze the efficiency of their work and seek out easily accessible tools for generating assessments of such ratings. The objective of this article is to find out whether sovereign credit rating can be reliably estimated using trends of selected macroeconomic indicators, despite the fact that sovereign credit rating is most likely influenced by non-economic factors. This can be used for strategic considerations at national and multinational levels. The relationships between sovereign credit rating and the trends of macroeconomic indicators were examined using statistical methods, linear multiple regression analysis, cumulative correlation coefficient, and multicollinearity test. The data source used is comprised of selected World Bank indicators meeting the conditions of completeness and representativeness. The data set has shown a cumulative correlation coefficient value greater than 95%, however at 100% multicollinearity. This is followed by the gradual elimination of indicators, but even this did not allow achieving acceptable values. So, the conclusion is that rating levels are not explainable solely by the trends of economic indicators, but other influences, e.g. political. However, the fact that the statistical model yielded acceptable results for five and fewer indicators allowed a regression equation to be found that gives good estimates of a country’s rating. This allows, for example, predicting of ratings relatively easy by forecasting the development of selected macroeconomic indicators.

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    • Figure 1. Complete x graph evaluating multiple regression and correlation analysis
    • Figure 2. Trends of cumulative characteristics when gradually eliminating indicators
    • Figure 3. Comparison of estimated and real average credit rating marks
    • Table 1. Non-linear assignment of numeric values to credit rating marks
    • Table 2. Comparison of arithmetic means and median when aggregating credit rating marks
    • Table 3. Regression coefficients for the 17 indicators
    • Table 4. Correlation matrix for 17 indicators
    • Table 5. Trends of cumulative characteristics when gradually eliminating indicators from 17 to 2