This paper examines the complex relationship between geopolitical risk events and cryptocurrency market volatility using high-frequency transaction data from 2018 to 2024, with particular focus on implications for emerging economies including African financial markets. We develop a novel artificial intelligence framework that integrates natural language processing of geopolitical news with high-frequency trading interval analysis to predict cross-market spillover effects. Our model identifies structural changes in cryptocurrency fund flows following major geopolitical events and quantifies the sensitivity of different cryptocurrency classes (traditional, green, and stablecoins) to these shocks. Using a comprehensive dataset of 1.2 billion transactions across 15 major cryptocurrencies and a geopolitical event database of 327 significant incidents, we demonstrate that our AI-enhanced approach outperforms traditional econometric models in capturing non-linear risk transmission dynamics. The model achieves a 55.8% improvement in predictive accuracy over standard GARCH models during periods of heightened geopolitical tension. Furthermore, we find asymmetric responses across cryptocurrency categories, with stablecoins exhibiting increased resilience through significantly lower volatility increases (22.3%) compared to traditional cryptocurrencies (89.4%) during geopolitical shocks. These findings have important implications for hedging strategies, portfolio diversification, and financial stability in increasingly interconnected global markets, particularly for emerging economies where cryptocurrency adoption is rapidly expanding and regulatory frameworks are still evolving.

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Keywords

Cryptocurrency volatility, geopolitical risk, artificial intelligence, high-frequency trading, market spillovers, emerging markets, financial stability