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

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  • Riso, Luigi
  • Vacca, Gianmarco

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.

Suggested Citation

  • Riso, Luigi & Vacca, Gianmarco, 2024. "Sentiment dynamics and volatility: A study based on GARCH-MIDAS and machine learning," Finance Research Letters, Elsevier, vol. 62(PB).
    • Handle: RePEc:eee:finlet:v:62:y:2024:i:pb:s1544612324002083
    DOI: 10.1016/j.frl.2024.105178
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