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Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit

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  • Guidolin, Massimo
  • Pedio, Manuela

Abstract

Using data on international, on-line media coverage and tone of the Brexit referendum, we test whether it is media coverage or tone to provide the largest forecasting performance improvements in the prediction of the conditional variance of weekly FTSE 100 stock returns. We find that versions of standard symmetric and asymmetric Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models augmented to include media coverage and especially media tone scores outperform traditional GARCH models both in- and out-of-sample.

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  • Guidolin, Massimo & Pedio, Manuela, 2021. "Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit," Finance Research Letters, Elsevier, vol. 42(C).
    • Handle: RePEc:eee:finlet:v:42:y:2021:i:c:s1544612321000246
    DOI: 10.1016/j.frl.2021.101943
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    4. Hartvig, Áron Dénes & Pap, Áron & Pálos, Péter, 2023. "EU Climate Change News Index: Forecasting EU ETS prices with online news," Finance Research Letters, Elsevier, vol. 54(C).

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    More about this item

    Keywords

    Attention; Sentiment; Text Mining; Forecasting; Conditional Variance; GARCH model; Brexit;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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