Mineral policy and sustainable development goals: Volatility forecasting in the Global South's minerals market

This paper aims to evaluate the volatility of precious metals, specifically Palladium, Gold, and Platinum, within the context of the global minerals market. The research focuses on understanding the price dynamics of these metals and their implications for sustainable development, particularly in the Global South. The study employs a comprehensive approach, utilizing advanced machine learning and deep learning models such as GRU, Huber, Lasso, LSTM, Random Forest, Ridge Regression, SVM, ANN, and XGBoost. These models are assessed based on their forecasting accuracy for different time horizons, using metrics such as RMSE and MAPE. The findings reveal that the ANN, XGBoost, and LSTM models exhibit robust performance in forecasting the volatility of precious metals across various time horizons. The research highlights the unique volatility patterns of each metal and underscores the effectiveness of machine learning techniques in capturing these dynamics. The study acknowledges limitations such as the exclusion of macroeconomic and geopolitical factors in the forecasting models. Future research is suggested to integrate these factors to enhance forecasting accuracy. The study's findings are pivotal for investors, policymakers, and market regulators, especially in the context of the Global South and sustainable development. The research offers valuable insights for risk management strategies, investment planning, and policy formulation aimed at promoting market stability and sustainable economic growth. The study emphasizes the importance of selecting appropriate forecasting models based on specific time horizons and market requirements.