Impact of the COVID-19 outbreak on credit ratings: Application of the through-the-cycle approach

Abstract

The objective of this study was to analyze how the COVID-19 crisis has affected the determinants and predictability of the domestic credit rating issued by Fitch Ratings in Argentina. Additionally, it aims to evaluate the effects of credit rating agencies using the through-the-cycle method. Given the subjective nature of credit rating categorization, researchers have developed models for explaining and predicting credit ratings. This subjectivity is significant during economic events. Therefore, it is important to investigate whether the factors that determine and predict credit ratings remained consistent before and during the COVID-19 crisis. This paper contributes significantly to understanding how the application of the through-the-cycle method affects the determinants and predictability of credit ratings in economic crises. Theapplication of the through-the-cycle method by credit rating agencies as a criterion during the COVID-19 crisis resulted in a breakdown of the usual correlation between determinants and credit rating. Understanding whether variables are permanent or transitory components is crucial for investors and borrowers to anticipate credit rating changes during economic downturns. The dependent variables are the long-term domestic credit rating categories. The independent variables are derived from the Fitch Ratings credit rating methodology and the literature, which includes quantitative and qualitative variables. The statistical methods used are ordinal logistic regression, generalized ordinal logistic regression, and support vector machines. The COVID-19 crisis was considered a transitory event due to the application of the through-the-cycle approach by rating agencies. During the pandemic, specific determinants of credit ratings are not considered due to their transitory nature. The study identifies interest coverage ratio and competitive position as transitory components. This approach led to less predictability but a more stable credit rating.