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

34 Pages Posted: 15 Jul 2020 Last revised: 5 Jan 2021

See all articles by Massimo Guidolin

Massimo Guidolin

Bocconi University - Department of Finance

Manuela Pedio

University of Bristol; Bocconi University - CAREFIN - Centre for Applied Research in Finance

Date Written: July 1, 2020

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 outperforme traditional GARCH models both in-and-out-of-sample.

Keywords: Tention, Sentiment, Text Mining, Forecasting, Conditional Variance, GARCH Model, Brexit

JEL Classification: C53, C58, G17

Suggested Citation

Guidolin, Massimo and Pedio, Manuela, Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit (July 1, 2020). BAFFI CAREFIN Centre Research Paper No. 2020-145, Available at SSRN: https://ssrn.com/abstract=3650975 or http://dx.doi.org/10.2139/ssrn.3650975

Massimo Guidolin (Contact Author)

Bocconi University - Department of Finance ( email )

Via Roentgen 1
Milano, MI 20136
Italy

Manuela Pedio

University of Bristol ( email )

University of Bristol,
Senate House, Tyndall Avenue
Bristol, BS8 ITH
United Kingdom

Bocconi University - CAREFIN - Centre for Applied Research in Finance ( email )

Via Sarfatti, 25
Milan, 20136
Italy

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