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

Massimo Guidolin and Manuela Pedio ()

No 20145, BAFFI CAREFIN Working Papers from BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy

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)
JEL-codes: C53 C58 G17 (search for similar items in EconPapers)
Pages: 34
Date: 2020
New Economics Papers: this item is included in nep-big, nep-for, nep-ore and nep-rmg
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Journal Article: Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit (2021)
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