Clustering asset markets based on volatility connectedness to political news
利用自然语言处理构建政治新闻指数，通过VAR模型测量国际资产市场波动率与该指数的关联性，并基于此关联性将市场聚类为八类，揭示市场对政治动态的敏感度差异。
To assess similarities in international asset markets’ responses to political news, we construct a political news index using advanced natural language processing. We then examine how the volatility across international asset markets is connected to the development of our political news index by measuring the daily directional connectedness using a VAR-based framework. Finally, we apply an unsupervised algorithm to cluster markets based on their volatility connectedness to political news. Our analysis reveals eight distinct clusters that reflect the markets’ sensitivities to political dynamics. This data-driven analysis offers insights into the influence of political developments on market volatility.