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Forecasting volatility with Google Search Volume: A GARCH, EGARCH, and MIDAS approach on the Swedish stock market
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In this thesis we study the dynamics of volatility of the OMXS30 and five of its listed stocks and retail investor attention, measured by a Google Search Volume index (GSV). We extend the basic GARCH, EGARCH, and MIDAS models by including GSV as a proxy for investors’ attention to the Swedish stock market, GSV acting as relative measures of the number of search queries that have been searched for in Google in a given week. Forecasts are evaluated with realized variance, accuracy is measured with the Root mean square error (RMSE), and the Diebold-Mariano test is applied. The main contribution from this thesis is introducing GSV into GARCH, EGARCH and MIDAS models for volatility prediction on the Swedish stock market. We find some minor improvements in forecasting accuracy, the index OMXS30 gaining bigger improvement in forecasting accuracy from including GSV than the stocks do.

Place, publisher, year, edition, pages
2017.
Keyword [en]
realized variance, forecasting, Google Search Volume, investor attention, GARCH, EGARCH, MIDAS
National Category
Social Sciences
Identifiers
URN: urn:nbn:se:uu:diva-324418OAI: oai:DiVA.org:uu-324418DiVA: diva2:1109991
Subject / course
Statistics
Supervisors
Examiners
Available from: 2017-06-15 Created: 2017-06-15 Last updated: 2017-06-15Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf