Hossein Asgharian
Professor
Importance of macroeconomic variables for variance prediction: a GARCH-MIDAS approach
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.
Department/s
- Department of Economics
- Department of Statistics
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