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

Asgharian, Hossein LU ; HOU, Ai Jun LU and Javed, Farrukh LU (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.
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author
; and
organization
publishing date
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}},
}