Importance of the macroeconomic variables for variance prediction A GARCH-MIDAS approach
AbstractThis 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.
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Bibliographic InfoPaper provided by Knut Wicksell Centre for Financial Studies, Lund University in its series Knut Wicksell Working Paper Series with number 2013/4.
Length: 30 pages
Date of creation: 24 Feb 2013
Date of revision:
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Mixed data sampling; long-term variance component; macroeconomic variables; principal component; variance prediction.;
Find related papers by JEL classification:
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
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