Importance of macroeconomic variables for variance prediction: a GARCH-MIDAS approach

Research output: Contribution to specialist publication or newspaperSpecialist publication article

Abstract

This paper applies the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long-term components of the return variance. A principal component analysis is used to incorporate the information contained in various variables. Our results show that including low-frequency macroeconomic information in the GARCH-MIDAS model improves the prediction ability of the model, particularly for the long-term variance component. Moreover, the GARCH-MIDAS model augmented with the first principal component outperforms all other specifications, indicating that the constructed principal component can be considered a good proxy of the business cycle.

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Authors
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Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Probability Theory and Statistics

Keywords

  • Mixed data sampling, Long-term variance component, Macroeconomic variables, Principal component, Variance prediction
Original languageEnglish
Pages600-612
Volume32
Issue number7
Specialist publication or newspaperJournal of Forecasting
PublisherJohn Wiley & Sons
Publication statusAccepted/In press - 2013
Publication categoryPopular science