Template-type: ReDIF-Paper 1.0
Author-Name: Massimo Guidolin
Author-Name: Manuela Pedio
Title: Media Attention vs. Sentiment as Drivers of Conditional Volatility Predictions: An Application to Brexit
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.
Classification-JEL: C53, C58, G17
Keywords:Attention, Sentiment, Text Mining, Forecasting, Conditional Variance, GARCH model, Brexit
Length: 34
Number: 20145
Creation-Date: 2020
File-URL: https://repec.unibocconi.it/baffic/baf/papers/cbafwp20145.pdf
File-Format: application/pdf
File-Size: 679
Handle: RePEc:baf:cbafwp:cbafwp20145