In this study, the dynamic volatility spillovers among emerging markets, Bitcoin, and commodities are analyzed using Diebold and Yilmaz's spillover framework. As a by-product, a total volatility spillover index among an emerging markets index, Bitcoin, gold, and oil prices is forecast using traditional methods, machine learning, and deep learning, providing a method for anticipating turbulent periods. The results support the importance of volatility in oil prices, uncertainty about U.S. economic policy, and the stability of the sovereign bonds market for the dynamics of volatility spillovers, validating the ability of machine and deep learning approaches to predict those spillovers.
