Asgharian, Hossein2; Hou, Ai Jun3; Javed, Farrukh2
1 Department of Business and Economics, Faculty of Business and Social Sciences, SDU2 Lund University3 Department of Business and Economics, Faculty of Business and Social Sciences, SDU
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.
Journal of Forecasting, 2013, Vol 32, Issue 7, p. 600-612
GARCH-MIDAS, long-term variance component, macroeconomic variables, principal component, variance prediction