• Medientyp: E-Book
  • Titel: Media attention vs. sentiment as drivers of conditional volatility predictions : an application to Brexit
  • Beteiligte: Guidolin, Massimo [VerfasserIn]; Pedio, Manuela [VerfasserIn]
  • Erschienen: Milano, Italy: BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Università Bocconi, [2020]
  • Erschienen in: Working paper series ; 145
  • Umfang: 1 Online-Ressource (circa 34 Seiten); Illustrationen
  • Sprache: Englisch
  • DOI: 10.2139/ssrn.3650975
  • Identifikator:
  • Schlagwörter: Attention ; Sentiment ; Text Mining ; Forecasting ; Conditional Variance ; GARCH model ; Brexit ; Graue Literatur
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: 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
  • Zugangsstatus: Freier Zugang