Media attention vs. sentiment as drivers of conditional volatility predictions : an application to Brexit

by Massimo Guidolin and Manuela Pedio

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

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Year of publication:	[2020]
Authors:	Guidolin, Massimo ; Pedio, Manuela
Publisher:	Milano, Italy : BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Università Bocconi
Subject:	Attention \| Sentiment \| Text Mining \| Forecasting \| Conditional Variance \| GARCH model \| Brexit \| Volatilität \| Volatility \| ARCH-Modell \| ARCH model \| Prognoseverfahren \| Forecasting model \| Großbritannien \| United Kingdom \| EU-Staaten \| EU countries \| Anlageverhalten \| Behavioural finance \| Data Mining \| Data mining \| Schätzung \| Estimation \| Kapitaleinkommen \| Capital income \| Social Web \| Social web