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Hossein Asgharian. Photo.

Hossein Asgharian

Professor

Hossein Asgharian. Photo.

The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH-MIDAS Approach

Author

  • Hossein Asgharian
  • Ai Jun Hou
  • Farrukh Javed

Summary, in English

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 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. Copyright (c) 2013 John Wiley & Sons, Ltd.

Department/s

  • Department of Economics
  • Department of Statistics

Publishing year

2013

Language

English

Pages

600-612

Publication/Series

Journal of Forecasting

Volume

32

Issue

7

Document type

Journal article

Publisher

John Wiley & Sons Inc.

Topic

  • Probability Theory and Statistics
  • Economics

Keywords

  • Mixed data sampling
  • long-term variance component
  • macroeconomic
  • variables
  • principal component
  • variance prediction

Status

Published

ISBN/ISSN/Other

  • ISSN: 1099-131X