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

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Author

Summary, in English

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.

Publishing year

2013

Language

English

Pages

600-612

Publication/Series

Journal of Forecasting

Volume

32

Issue

7

Document type

Journal article

Publisher

John Wiley & Sons Inc., John Wiley & Sons Inc.

Topic

  • Probability Theory and Statistics

Keywords

  • Mixed data sampling
  • Long-term variance component
  • Macroeconomic variables
  • Principal component
  • Variance prediction

Status

Inpress

ISBN/ISSN/Other

  • ISSN: 1099-131X