On Classifying the Effects of Policy Announcements on Volatility

TL;DR

This paper introduces a model-based classification method using Markov Switching dynamics within a Multiplicative Error Model to analyze the effects of Central Bank policy announcements on stock market volatility, successfully classifying 144 ECB announcements.

Contribution

It develops a novel classification approach combining Markov Switching models and probability smoothing to assess policy impacts on volatility.

Findings

Effective classification of ECB announcements into impact categories

Method aligns with traditional clustering results

Applicable to multiple Eurozone market series

Abstract

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.