Forecasting Sovereign Credit Risk Amidst a Political Crisis: A Machine Learning and Deep Learning Approach

The purpose of this paper is to forecast the sovereign credit risk for Egypt, Morocco, and Saudi Arabia during political crises. Our approach uses machine learning models (Linear Regression, Ridge Regression, Lasso Regression, XGBoost, and Kernel Ridge) and deep learning models (RNN, LSTM, BiLSTM, and GRU) to predict CDS-based implied default probabilities. We compare the predictive accuracy of the tested models with the results showing that Linear Regression outperforms all other techniques, while deep learning architectures, such as RNN and GRU, demonstrate a competitive performance. To validate the sovereign credit risk prediction, we use the forecasted implied default probability from the Linear Regression model to determine the corresponding forecasted implied rating according to the Thomson Reuters StarMine Sovereign Risk model. The results reveal significant differences in the perceived creditworthiness of Egypt, Morocco, and Saudi Arabia, reflecting each country’s economic fundamentals and their ability to manage global shocks, particularly those related to the Russo-Ukrainian war. Specifically, Egypt is perceived as the most vulnerable, Morocco occupies an intermediate position, and Saudi Arabia is seen as having a low credit risk. This study provides valuable managerial insights by enhancing tools for the sovereign credit risk analysis, offering reliable decision-making in volatile global markets. The alignment between forecasted ratings and default probabilities underscores the practical relevance of the results, guiding stakeholders in effectively managing credit risks amidst economic uncertainty.