- Authors:
- 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
Checking for on-site access...
