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

Massimo Guidolin and Manuela Pedio ()

Finance Research Letters, 2021, vol. 42, issue C

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.

Keywords: Attention; Sentiment; Text Mining; Forecasting; Conditional Variance; GARCH model; Brexit (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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Working Paper: Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:42:y:2021:i:c:s1544612321000246

DOI: 10.1016/j.frl.2021.101943

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