This study sought to ascertain a machine learning algorithm capable of predicting crises in the African stock market with the highest accuracy. Seven different machine-learning algorithms were employed on historical stock prices of the eight stock markets, three main sentiment indicators, and the exchange rate of the respective countries’ currencies against the US dollar, each spanning from 1 May 2007 to 1 April 2023. It was revealed that extreme gradient boosting (XGBoost) emerged as the most effective way of predicting crises. Historical stock prices and exchange rates were found to be the most important features, exerting strong influences on stock market crises. Regarding the sentiment front, investors’ perceptions of possible volatility on the S&P 500 (Chicago Board Options Exchange (CBOE) VIX) and the Daily News Sentiment Index were identified as influential predictors. The study advances an understanding of market sentiment and emphasizes the importance of employing advanced computational techniques for risk management and market stability.

This paper studies the volatility of electricity spot prices in the Nordic market (Sweden, Finland, Denmark, and Norway) under regime switching. Utilizing Markov-switching GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, we provide strong evidence of nonlinear regime shifts in the volatility dynamics of these prices. Using in-sample criteria, we find that regime-switching models have lower AIC (Akaike information criterion) than single-regime GARCH models. In addition, out-of-sample forecasts indicate that regime-switching GARCH models have superior Value-at-Risk (VaR) prediction ability relative to single-regime models, which is directly pertinent to risk management. These findings highlight the importance of incorporating regime shifts into volatility models for accurately assessing and mitigating risks associated with electricity price fluctuations in deregulated markets.

Based on trade data from 2005 to 2020, this study investigates the driving forces behind China’s grain virtual water (VW) import trade, with a particular focus on the role of the Belt and Road Initiative (BRI). By incorporating economic distance (ED) and institutional distance (ID) into the gravity model framework and applying a high-dimensional fixed-effects Poisson pseudo-maximum likelihood estimation method, the study offers new empirical insights. The results indicate that ED is negatively associated with virtual water trade (VWT) in grains, while ID exhibits an inverted U-shaped relationship with VWT. Furthermore, the BRI significantly moderates the effects of ED and ID, weakening their influence on VWT. Additionally, the initiative demonstrates a clear trade creation effect, promoting increased VW imports. These findings contribute to a deeper understanding of the mechanisms shaping VWT and offer valuable policy guidance for enhancing international cooperation under the BRI framework.