The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: 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 different 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 as a good proxy of the business cycle. Copyright (c)... (More)
- 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 different 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 as a good proxy of the business cycle. Copyright (c) 2013 John Wiley & Sons, Ltd. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4157996
- author
- Asgharian, Hossein LU ; Hou, Ai Jun and Javed, Farrukh LU
- organization
- publishing date
- 2013
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Mixed data sampling, long-term variance component, macroeconomic, variables, principal component, variance prediction
- in
- Journal of Forecasting
- volume
- 32
- issue
- 7
- pages
- 600 - 612
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- wos:000326065800003
- scopus:84887062530
- ISSN
- 1099-131X
- DOI
- 10.1002/for.2256
- language
- English
- LU publication?
- yes
- id
- 2f3286fc-7539-40fb-9010-9d40166eba91 (old id 4157996)
- date added to LUP
- 2016-04-01 10:47:07
- date last changed
- 2022-04-28 01:06:44
@article{2f3286fc-7539-40fb-9010-9d40166eba91,
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 different 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 as a good proxy of the business cycle. Copyright (c) 2013 John Wiley & Sons, Ltd.}},
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 = {{The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH-MIDAS Approach}},
url = {{http://dx.doi.org/10.1002/for.2256}},
doi = {{10.1002/for.2256}},
volume = {{32}},
year = {{2013}},
}