Моделирование суверенного кредитного риска с учетом экологической эффективности

Abstract

This study contributes to the evaluation of the role of the eco-efficiency factor in explaining sovereign credit risk. The purpose of the study is to develop a model of sovereign credit risk taking into account the environmental efficiency factor. The sample includes the following countries: Argentina, Belgium, China, China, Egypt, Germany, Greece, Nigeria, Pakistan, Portugal, Spain. 58 % of the world’s countries are categorized as investment grade, indicating that more than half of the countries have high and very high solvency. Using the multiple regression method, the factors affecting the sovereign credit rating are identified: GDP per capita, annual change in inflation, international reserves, private sector credit, total public debt, and environmental performance. The results show a strong positive relationship between credit rating and GDP per capita, private sector lending volume, indicating that countries with better GDP per capita have lower credit risk. To prove the relationship between environmental and credit risk, two multiple regression models were constructed: without and with the environmental factor. The results show that including the environmental factor in modeling a country’s credit rating is appropriate because it improves the quality of the model. According to the two comparison criteria, the model including the environmental factor had better results; the coefficient of determination was larger and the approximation error was smaller. Countries have different environmental situations and different degrees of environmental policy management. There is a relationship between environmental and credit risk of countries, but the environmental situation of a country is not the only determinant factor affecting credit risk. If a country has a high environmental performance index value, it may have an average sovereign rating and vice versa. The scientific usefulness of the study lies in the fact that the constructed model can be used by investors, creditors, rating objects to calculate their own values of sovereign credit ratings.