Importance of macroeconomic variables for variance prediction: a GARCH-MIDAS approach
(2013) In Journal of Forecasting 32(7). p.600-612- 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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3172522
- author
- Asgharian, Hossein LU ; HOU, Ai Jun LU and Javed, Farrukh LU
- organization
- publishing date
- 2013
- type
- Contribution to specialist publication or newspaper
- publication status
- in press
- subject
- keywords
- Mixed data sampling, Long-term variance component, Macroeconomic variables, Principal component, Variance prediction
- categories
- Popular Science
- in
- Journal of Forecasting
- volume
- 32
- issue
- 7
- pages
- 600 - 612
- publisher
- John Wiley & Sons Inc.
- ISSN
- 1099-131X
- language
- English
- LU publication?
- yes
- id
- 0e5dd582-44f0-4d8b-a1c5-555d98ec6736 (old id 3172522)
- date added to LUP
- 2016-04-01 10:23:08
- date last changed
- 2021-11-15 11:36:27
@misc{0e5dd582-44f0-4d8b-a1c5-555d98ec6736, 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.}}, author = {{Asgharian, Hossein and HOU, Ai Jun and Javed, Farrukh}}, issn = {{1099-131X}}, keywords = {{Mixed data sampling; Long-term variance component; Macroeconomic variables; Principal component; Variance prediction}}, language = {{eng}}, number = {{7}}, pages = {{600--612}}, publisher = {{John Wiley & Sons Inc.}}, series = {{Journal of Forecasting}}, title = {{Importance of macroeconomic variables for variance prediction: a GARCH-MIDAS approach}}, volume = {{32}}, year = {{2013}}, }