Regularization for stationary multivariate time series
AbstractThe complexity of multivariate time series models increases dramatically when the number of component series increases. This is a phenomenon observed in both low- and high-frequency financial data analysis. In this paper, we develop a regularization framework for multivariate time series models based on the penalized likelihood method. We show that, under certain conditions, the regularized estimators are sparse-consistent and satisfy an asymptotic normality. This framework provides a theoretical foundation for addressing the curse of dimensionality in multivariate econometric models. We illustrate the utility of our method by developing a sparse version of the full-factor multivariate GARCH model. We successfully apply this model to simulated data as well as the minute returns of the Dow Jones industrial average component stocks.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Quantitative Finance.
Volume (Year): 12 (2012)
Issue (Month): 4 (January)
Contact details of provider:
Web page: http://www.tandfonline.com/RQUF20
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty).
If references are entirely missing, you can add them using this form.