Sentiment dynamics and volatility: A study based on GARCH-MIDAS and machine learning

Abstract

This work investigates the relationship between investor sentiment and volatility of stock indexes. A sentiment proxy is constructed via a machine learning approach from the consumer confidence indexes of four countries. Granger causality tests highlight the influence of sentiment on volatility. This impact is quantified via GARCH-MIDAS models that, retaining variables in their sampling frequency, allow the estimation of the long-run volatility without information loss. Sentiment is finally used to predict long-run volatility. Thus, further insights into the relationship between investor sentiment and return volatility are provided, helping investors to stabilize the former and contain its effect on market uncertainty.