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

Luigi Riso and Gianmarco Vacca

Finance Research Letters, 2024, vol. 62, issue PB

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.

Keywords: Investor sentiment; Noise trading; Stock market volatility; MIDAS; Best Path Algorithm (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:62:y:2024:i:pb:s1544612324002083

DOI: 10.1016/j.frl.2024.105178

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