{"controller"=>"catalog", "action"=>"show", "id"=>"246900581"}
  • EN
  • DA

Danish NationalResearch Database

  • Search Publications & Researchers
  • Open Access Indicator
  • Publications
  • Researchers
Example Finds records
water{} containing the word "water".
water supplies"{}" containing the phrase "water supplies".
author:"Doe, John"author:"{}" containing the prase "Doe, John" in the author field.
title:IEEEtitle:{} containing the word "IEEE" in the title field.
Need more help? Advanced search tutorial
  • Selected (0)
  • History

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

    • Save to Mendeley
    • Export to BibTeX
    • Export to RIS
    • Email citation
Authors:
  • Asgharian, Hossein ;
    Close
    Lund University
  • Hou, Ai Jun ;
    Close
    Department of Business and Economics, Faculty of Business and Social Sciences, SDU
  • Javed, Farrukh
    Close
    Lund University
DOI:
10.1002/for.2256
Abstract:
This paper aims to examine the role of macroeconomic variables in forecasting the return volatility of the US stock market. We apply 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. We investigate several alternative models and use a large group of economic variables. A principal component analysis is used to incorporate the information contained in different variables. Our results show that including low-frequency macroeconomic information into 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.
Type:
Journal article
Language:
English
Published in:
Journal of Forecasting, 2013, Vol 32, Issue 7, p. 600-612
Keywords:
GARCH-MIDAS, long-term variance component, macroeconomic variables, principal component, variance prediction
Main Research Area:
Social science
Publication Status:
Published
Review type:
Peer Review
Submission year:
2013
Scientific Level:
Scientific
ID:
246900581

Full text access

  • Openaccess University of Southern Denmark
  • Doi Get publisher edition via DOI resolver
Checking for on-site access...

On-site access

At institution

  • University southern denmark

Metrics

Feedback

Sitemap

  • Search
    • Statistics
    • Tutorial
    • Data
    • FAQ
    • Contact
  • Open Access
    • Overview
    • Development
    • FAQ
    • Contact
  • About
    • Institutions
    • Release History
    • Cookies and privacy policy

Copyright © 1998–2018.

Fivu en