The financial turmoil surrounding the Great Recession called for unprecedented intervention by Central Banks: unconventional policies affected various areas in the economy, including stock market volatility. In order to evaluate such effects, by including Markov Switching dynamics within a recent Multiplicative Error Model, we propose a model–based classification of the dates of a Central Bank's announcements to distinguish the cases where the announcement implies an increase or a decrease in volatility, or no effect. In detail, we propose two smoothed probability–based classification methods, obtained as a by–product of the model estimation, which provide very similar results to those coming from a classical k–means clustering procedure. The application on four Eurozone market volatility series shows a successful classification of 144 European Central Bank announcements.

大萧条前后的金融动荡要求中央银行进行前所未有的干预：非常规政策影响了经济的各个领域，包括股票市场的动荡。为了评估这种影响，通过将马尔可夫切换动力学纳入最近的乘性误差模型，我们提出了一种基于模型的中央银行公告日期分类，以区分公告暗示波动性增加或减少的情况，或没有效果。详细地，我们提出了两种平滑的基于概率的分类方法，它们是模型估计的副产品，其提供的结果与经典k均值的结果非常相似聚类过程。四个欧元区市场波动系列的应用显示成功分类了144个欧洲中央银行公告。